Unit 6 Individual Assignment Your final submission of the homework assignment must be submitted via Brightspace by the posted due date and time. The Assignment will be locked at the due date/time. Please contact me well ahead of the deadline if you have extenuating circumstances. You must upload an Excel spreadsheet for your assignment, with formulas showing all calculations. If you wish, you may submit, in addition, a Word document or PDF with accompanying text. Please name your file “Lastname, Firstname Unit 6”. For this final unit, the assignment drills down from analyzing the company, as a whole, to analyzing specific initiatives. Problem 1 A company has a 7.4% WACC and is considering two investments with the following net cash flows: Yr 0 Yr 1 Yr 2 Yr 3 Yr 4 Yr 5 Yr 6 Yr 7 Project A -$320 -$385 -$178 -$5 $452 $555 $888 -$100 Project B -$520 $115 $135 $155 $175 $195 $215 $0 a. What is each project’s NPV? b. What is each project’s IRR? c. If you had to choose one of these investments, which would you select, and why? Problem 2 The Greener Green Company is considering purchasing additional equipment that would have an initial cost of $400,000. They estimate it would add $215,000 to pre-tax revenues and variable operating expense (before taking account of depreciation) per year of 37%, for the first 4 years. In Year 5, they will cease production, and therefore will have no additional revenues or ongoing operating costs, but will incur special shutdown costs of $45,000 (aside from depreciation). The packaging machine will be depreciated on a straight-line basis, over 5 years, and will have no salvage value (ignore the MACRS depreciation methodology for this problem.) Assuming a 25% marginal tax rate, and a 8.5% WACC, calculate the NPV of this investment. Do you recommend this project? Why? Good luck!
Econ 136 Final Spring 2024 1. True or false. (25 points, 5 points each) Are the following statements true or false? Explain your answer in no more than two sentences. You will be graded on your explanation. (i) Historically, low dividend-price ratios of the S&P 500 have predicted high (positive) subsequent price growth and essentially no subsequent change in dividends. (ii) The following fact violates the semi-strong form of the efficient markets hypothesis: prices of companies tend to increase a few days before public announcement of good news. (iii) When the risk-free rate increases, the optimal portfolio share of risky assets for a mean- variance investor declines. (iv) In a CAPM equilibrium, since investors are compensated for holding risk, two securities with the same standard deviation should have the same expected return. (v) Consider a European call and a European put for the same non-dividend-paying stock, same expiration T, and same strike X, where X = F and F is the forward price of the underlying for delivery date T. Under no arbitrage, the two options have the same price today. 2. Welfare efects of risk. (20 points, 5 points each) Consider an economy where CAPM holds, the risk-free rate is Rf = 2%, and the return of the market portfolio has expectation E[Rm] = 10% and standard deviation 40%. (a) Draw the capital allocation line. What is the optimal portfolio of a mean-variance investor with risk aversion A = 2? What is the mean and standard deviation of this portfolio? Show this portfolio in the figure. What is the value of this investor’s mean- variance utility function if he invests in this optimal portfolio (i.e., what is E[Rp] - (A/2)Var(Rp))? (b) What is the optimal portfolio of an investor with risk aversion A = 4? What is its mean and standard deviation? Show this portfolio in the figure (from part (a)) as well. What is the value of this investor’s mean-variance utility function when investing optimally? (c) Now suppose that due to a reduction in uncertainty, the standard deviation of the mar- ket return falls to 20%, while other parameters are unchanged. Draw the new capital allocation line (in a new figure). What are the new optimal portfolios of the two in- vestors? What are these portfolios’ means and standard deviations? Show them in the figure. Which investor’s portfolio share of risky assets changes by more after the change in the standard deviation of the market return? Why? (d) What are the values of the two investors’ utility functions now, given their new optimal investments? Which investor’s utility increases by more after the change in the standard deviation of the market return? Why? Comment on this statement: “Reductions in risk are most beneficial to conservative investors who are highly sensitive to fluctuations in their wealth.” 3. Capital budgeting. (30 points, 5 points each) Consider an economy where the risk-free rate is Rf = 4%, the expected return on the market portfolio is E[Rm] = 12%, and the standard deviation of the return on the market portfolio is 20%. The covariance between the return on ABC stock and the return on the market portfolio is 0.06. All of this data refers to annual returns. Suppose that ABC stock pays a dividend of $10 per share next year, and dividends are expected to grow at a rate of 2% per year. Recall that under the Gordon Growth Model (GGM), we can write the price of a stock at any time t as Pt = R−g/Dt+1 where R is the discount rate and g is the growth rate of dividends. (a) ABC’s manager argues that according to the Gordon Growth Model (GGM) his shares should sell for a price of $10/(.04 - .02) = $500. Explain why this valuation is inappro- priate. (b) Assuming that CAPM holds, compute ABC’s beta with respect to the market portfolio, and the expected rate of return of ABC stock. (c) Given your answer to (b), what price does the GGM imply for a share of ABC stock? (d) It turns out that the market price of ABC is $50, which is diferent from what you computed in (c) [if not, you made a mistake!]. However, you realize that ABC has only narrowly avoided bankruptcy last year, and has a very high book-to-market ratio. Is the fact that ABC has a lower price than what’s predicted by part (c) consistent with what you know about the expected return of stocks with high book-to-market ratios? Why? (e) Now you want to apply a more sophisticated asset pricing model than CAPM to price ABC. Let HML = RH -RL denote the excess return of value stocks over growth stocks, and SMB = RS - RB the excess return of small stocks over big stocks. Suppose that ABC has a beta of 1.5 with respect to HML, and a beta of zero with respect to SMB. The beta of ABC with respect to the market portfolio is still what you computed in part (b). If the expected excess return of value stocks over growth stocks is E[HML] = 4%, what should be the expected return of ABC according to the Fama-French model? Is it higher or lower than the expected return you computed in (b)? Why? (f) Using the expected return you computed in part (e), what should the price of a share of ABC stock be according to the GGM? Does your answer justify the market price of $50? 4. Options. (25 points, 5 points each) Consider an economy in three periods, t = 0, t = 1 and t = 2. Suppose that a stock index behaves as follows: the initial index value at time t = 0 is 100, and each period the index either rises by 15 or falls by 5 with equal probability (so for example at t = 2, the highest possible index value is 100+15+15=130). The index does not pay dividends during these two periods. The riskfree rate of return each period is Rf = 0%. Now consider a European call option on the index, with expiration date t = 2, and strike price X = 100. (a) Draw the event tree for this economy. For each node in period t = 1 and t = 2, write St, the current price of the index. For each node at t = 2, write the payof of the call option. (b) Consider the node where the stock price has gone up to 115 in period 1. Construct a portfolio at this node that replicates the payof of the option in both possible states at t = 2. Specifically, assume that at this node, you purchase x shares of the index and y shares of the riskfree asset. Solve for x and y from the assumption that this is a replicating portfolio. What is the price of this portfolio at t = 1 (in the event when the stock price is S1 = 115)? What is the price of the option, C1? (c) Following a similar procedure as in (b), now solve for the price of the option at t = 1 in the event when the stock price is S1 = 95. (d) Now go back to period t = 0. To compute C0, construct a portfolio of the index and the riskfree asset that pays C1 in period t = 1 (that is, the number you obtained in (b) if the price goes up in period 1, and the number you obtained in (c) otherwise). What is the price of this portfolio? What is the price of the call option? (e) Suppose that a European put option on the index with expiration t = 2 and strike price X = 100 is traded at a price of P0 = 5.425. Is there an arbitrage opportunity in this economy? If yes, construct a portfolio of the put, the call, the index and the riskfree asset to exploit it. If not, why? 5. Spot-forward parity. (15 points, 5 points each) Use the following notation: Symbol Description T t S ST K f F r time when forward contract matures (years) current time (years), where t ∈ [0, T] price of stock underlying forward contract at time t price of stock underlying forward contract at time T delivery price of the forward contract value of a long forward contract at time t forward price at time t risk-free rate of interest per annum (cts comp) (a) For a non-dividend paying stock, construct two portfolios at time t, that have the same payof at time T, and use the law of one price to establish the following relationship: f = S — K · e-r(T-t) (b) Noting that when a forward contract is initiated, the forward price equals the delivery price, which is chosen so that the value of the contract is zero, show that: F = S · er(T-t) (c) At maturity, a forward contract pays of ST — K. By risk-neutral valuation we can write f = e-r(T-t)ERN[ST — K] where ERN[·] indicates we are taking the expectation using the risk-neutral probabilities. Recall that in a“risk-neutral world”(i.e., when the objective probabilities and the risk- neutral probabilities coincide) the expected return of all risky assets is r. Use this fact to carefully derive the relationship from part (a).
MAT244H1F Final Report Fall 2024/2025 The Setup The city of Oronto is concerned about its future. Having watched other cities around the country deal with high rent prices and a fluctuating population, they decided to get ahead of the game. They commissioned a study on the relationship between rent and population in their city from the prestigious Housing Institute of Greater Oronto (HIGO). HIGO came up with the following model: P 0 (t) = P(t) · (10 − P(t)) − R(t) R 0 (t) = 0.5 · R(t) where • t is measured in decades from the year 2024. • P(t) is the population in millions at decade t. • R(t) is the average price of a rental unit at decade t in units of thousands of dollars (per month). HIGO based their model on the following assumptions: (A1) The city land has a natural carrying capacity (i.e., a maximum number of people that can sustainably live within the city limits). (A2) People are attracted by people and will move to the city if many other people already live in the city. (A3) People will move away from the city if the rent is too high. (A4) Rent increases with inflation. For your information, the current population of Oronto is 5 million people and the average rent is $2500 per month. The Task Your job is two-fold. You aim to • inform. the city council of the consequences/predictions of HIGO’s model for the city of Oronto; and, • propose legislation (affecting rent) so that the population and rent of Oronto eventually stabilizes. Rules 1. Make reasonable assumptions. You will be exploring HIGO’s model and proposing your own, modified, model based upon your proposed legislation. As the city of Oronto is fictional, you are allowed a great deal of freedom to make assumptions and proposals. However, your assumptions must be reasonable. Tips on reasonability: • Don’t assume the city has an infinite budget. For example, proposing that the city subsidize all rents to $0 is not reasonable (nor would any proposal that results in rents ending up at a steady state of $0). • The population should not stabilize at 0. If a city starts out at 5 million people, a city council will not be interested in a plan that sees the city disappear or nearly disappear. • Your forecast won’t work for centuries, so make sure your argument doesn’t depend on a rent-population relationship holding 500 years from now. You may create assumptions on city budgets or federal assistance to the city, etc. as long as the numbers you propose are feasible (e.g., you shouldn’t require a national budget that is 10× that of the US). 2. Legislation can only affect rent. You are proposing legislation to the city council to stabilize the rent and population of Oronto. Your proposal for legislation can only affect rent. For example, you cannot propose legislation where people are forbidden from moving away from the city, etc.. 3. Consider your audience. You are making a proposal to a city council. If your proposal is to compute the arctan of rent multiplied by the cubed root of population and then take the coefficients of the decimal expansion of that number and use them to create a Taylor series which computes the optimal rent at decade t, the city council won’t understand and won’t consider your proposal. Keep things as simple as possible so the council can understand. The Report You will be investigating the HIGO model and proposing your own modification of said model. Normally, one would prepare a long technical report and an executive summary. However, you will only be turning in a short technical report and an executive summary. Your final report will consist of three parts. All parts must be typed, though you may hand-annotate figures or insert equations by hand. 1. Background Assumptions (1 page). In one page, you should outline the most important background assumptions needed to understand your report and execu-tive summary. For example, if you assume that Oronto is a fiscally conservative city, or that the council is divided among those who want the largest city possible, and those who prefer a smaller population, those assumptions go here. Note: you must still list your assumptions where relevant elsewhere in your exec-utive summary/technical report. The purpose of this page is for you to give the marker the necessary context to understand your writeups. 2. Executive Summary (2 pages). You are to write a 2 page executive summary. Your audience is the Oronto city council. Your executive summary must • Explain your findings about what the (unmodified) HIGO model predicts • Explain your proposed legislation and the predicted consequences of your legislation • Address trade-offs the city may need to make to meet its desired goals Your executive summary should follow the guidelines of a good summary (e.g., as outlined by USC). It may be single or double spaced, but it should be readable (e.g., do not squish the margins to try to fit in more words). 3. Technical Report (2 pages). You are to write a technical report. Your technical report must be at most 2 pages, excluding figures. Including figures, your technical report may be no more than 5 pages. Your figures may be interspersed among the text of your report or they may be listed at the end of your report (it is preferable that you intersperse your figures). Your audience is fellow MAT244 students. Your goal with the technical report is to explain how you analyzed the HIGO model, what your proposed legislation is, and how your proposed legislation affects/modifies the HIGO model. Your technical report must • Include relevant assumptions • Explain how you analyzed the HIGO model • Explain your proposed legislation • Show a system of equations that models the city rent/population if your legislation were to be adopted • Include figures/diagrams/equations/etc. to support your conclusions Your technical report should be an independently readable document. Since the technical report is quite short, it does not need to address issues like the pros/cons of your legislation (that will show up in the executive summary). However, it is not a place for “scratch work”. For example, your technical report would not walk through every equation you solved when finding an affine approximation/finding eigenvalues/etc., but it may include overviews like, “After finding an affine ap-proximation about the equilibrium solution, we found the coefficient matrix has eigenvalues 2 and 7, suggesting that the equilibrium is unstable.” You are given a lot of freedom on this project. Have fun and be creative! Through this project, we are looking to see you demonstrate that you can analyze a system of differential equations using the tools that we’ve learned this semester and that you can modify a differential-equations based model and analyze the result. If you demonstrate that in a well-written way, that’s exactly what we’re looking for! Submitting Submit on Gradescope. Since your reports will be typed, we are not using a template-based submission.
ST309 – Exercise 6 This counts for 10% of the final assessment of the course. The marks in brackets reflect marks for each question. Please submit your solutions in a pdf file to Moodle by the noon (UK time) of Friday, 13 December 2024. Late submission entails penalties: 5 marks (out of maximum 100) will be deducted for every half-day (12 hours) within the first 24 hours after the deadline, and 5 marks will be deducted for each further 24 hours. Submissions are not accepted after 5pm on Wednesday, 18 December 2024. You should only submit your own work, and cannot use materials from the past and/or your classmates. Plagiarism is a very serious ofense that is quite easy to detect. It will result in instant failure (mark 0). This exercise is on credit card fraud detection based on a data set downloaded from Kaggle Datasets at https://www.kaggle.com/mlg-ulb/creditcardfraud Background information on the data is available at https://www.kaggle.com/mlg-ulb/creditcardfraud/home Previous attempts can be found at https://www.kaggle.com/janiobachmann/credit-fraud-dealing-with-imbalanced-datasets/versions (All those analysis was done using Python. But you should be able to follow the ideas, understand most the results. Especially some initial data exploration is easy to follow.) The dataset contains 284,807 credit card transactions in two days in September 2013 by European card- holders, of which 492 are frauds. So the data is highly unbalanced: the positive cases (i.e. frauds) account for merely 0.172% of all transactions. Due to the confidentiality issues, the original features for each transaction are masked via a linear transfor- mation. The 28 transformed features are presented as V1, V2, · · · , V28. According to the above webpage, those 28 features are the principal components of the original features. No further information on those features is provided. In addition to those 28 variables, there are 3 untransformed variables: • Time: number of seconds elapsed between each transaction and the first transaction in the dataset • Amount: the amount of the transaction • Class: binary label with value 1 for ‘fraud’ and 0 otherwise. The whole dataset has 284,807 rows and 31 columns. The task is to build up efective algorithms for detecting fraudulent credit card transactions. The data is extremely imbalanced with only 0.172% ‘positives’ (i.e. frauds). Hence the information on frauds is overwhelmed by that on true and genuine transactions. This imbalance leads the fitted models using the whole data predominately led by the information on ‘negatives’, and the signal on ‘positives’ is too weak to be picked up. To balance the information used in building classifiers, we have created a more balanced but, unfortunately, much smaller training data with 24.62% positive cases, and also a testing data set which is about equally imbalanced as the whole data set. • creditCardTrain.csv: of size 1592×31, consisting of 1200 randomly selected non-fraudulent transactions plus 392 randomly selected fraud transactions. The true positive rate is about 24.62%. • creditCardTest .csv: of size 57889 × 31, consisting of 57789 randomly selected non-fraudulent transac- tions plus 100 remaining fraud transactions. It has no overlaps with creditCardTrain .csv. The true positive rate is 0.173%. The two data sets are placed on the course Moodle page. For your information, I attach below the codes for constructing those two data sets. > library(readr); library(dplyr) > CC=read_csv("creditcard .csv") # read_csv is a much faster version of read .csv > CC1=CC[CC$Class==1,] # extract all frauds > dim(CC1) [1] 492 31 > train1=sample(1:492, 392) > CC1train=CC1[train1,] > CC1test=CC1[-train1,] > CC0=CC[CC$Class==0,] # extract all genuine transactions > dim(CC0) [1] 284315 31 > train0=sample(1:284315, 58988) > Dtrain=bind_rows(CC1train, CC0[train0[1:1200],]) # bind the rows from two data together > dim(Dtrain) [1] 1592 31 > Dtrain=arrange(Dtrain, Time) # re-arrange rows according to ascending order of Time > write.csv(Dtrain, row.names=F, "creditCardTrain.csv") > Dtest=bind_rows(CC1test, CC0[train0[1200:58988],]) > dim(Dtest) [1] 57889 31 > Dtest=arrange(Dtest, Time) > write.csv(Dtest, row.names=F, "creditCardTest.csv") > rm(CC, CC0, CC1, CC1test, CC1train, Dtest, Dtrain, train0, train1) # remove those objects Your analysis should be based on creditCardTrain .csv. creditCardTest .csv represents the true reality, and should be used only to test the performance of your models. 1. Carry out some exploratory data analysis first. You may like to address the issues such as • are there any missing values and outliers? [5 marks] • should you apply any transformations to any variables, for example, log(Amount + 1)? [10 marks] • is Time relevant to detecting frauds? [5 marks] 2. Suppose that the credit card company charges 2% fees for each transaction (deducted from the payment to payee). (a) Estimate the expected potential loss of a fraudulent transaction. [5 marks] (b) Estimate the expected profit from a genuine transaction. [5 marks] (c) Let α denote the probability that a transaction is fraud. What is the minimum value of α to declare ‘Fraud’ in order to ensure that the expected profit from a single transaction is non-negative? [5 marks] A simple illustration on how a credit card works: Suppose you purchase an item from a shop for £100 payed out of your credit card, the credit card company will pay £98 to the shop at the time. By the end of the month, you pay back ££100 to the credit card company. So the company make a profit of £2. But if the purchase was not made by you (i.e. a fraud), you will not pay anything to the credit card company. The company will make a loss of £98. 3. Let the profit matrix be non-Fraud Fraud No B −C Yes −1 0 where C and B are calculated, respectively, in 2(a) and 2(b) tomer’s unhappiness when a genuine transaction is denied. (a) Construct a decision tree for detecting frauds. (b) Find the value of the cutting-of probability, denoted by α(^), which maximizes the expected profit. [10 marks] (c) Test the performance of your decision tree on the testing data set, with the cutting-of probability 0.5 andα(^) respectively. Now you should calculate the true profits or losses according to the real amount of each transaction in the testing data sets. [10 marks] (d) Construct a logistic regression model for detecting frauds. You may use the same predictor selected in the tree model above. [10 marks] (e) Plot the ROC curves with the testing data for both the tree and the logistic regression classifiers constructed above, and compare them using the ‘area under curve’ . [15 marks] 4. In your opinion, what are the pros and cons of the above analysis? Do you have any suggestions for further improvement? [10 marks] Note. The strategy to build classifiers using a subset with a much higher positive rate was merged after some initial and less successful attempts. This learning process also re ects one important principle of data analytics: Iteration is the law of learning!
BIE6433 Computational Biomechanics of Musculoskeletal System (15 credits, Level 7) Aim The aim of this project (100% of your module mark) is to estimate the bone strength (i.e., failure load) of either the left or the right proximal femur of one elderly patient and relate the results to the likelihood of bone fracture. Outline A 73-year-old, female patient has presented in the hospital for clinical investigation of suspected osteoporosis. The patient weight is 79kg, height is 161cm and her T-score (measured by Dual-energy X-ray absorptiometry) is -1.2. As part of her clinical examination, a set of full thigh-length CT scan was collected for both the left and right femurs. The fully anonymised CT scans (VTK file) are provided to you (local ethics approval has been obtained from the Sheffield Teaching Hospitals). In addition to the routine clinical examinations, the clinician has asked an engineer to perform a quantitative finite element analysis (FEA) of the bone strength of this patient to provide additional information on their risk of femoral fracture. You need to examine the literature on osteoporotic fracture and FEA to review a range of modelling approaches for this problem and critically compare them in your report, before choosing the most suitable modelling approach (i.e., boundary constraints, loading conditions, etc.) for your assignment. Your task, as the engineer in charge, is to: a) generate a suitable FE model of the proximal femur (do NOT need to segment the entire shaft) based on the CT scans. (The computer lab tasks will guide you through the creation of a personalized proximal femur model based on CT scans that contain derived material property. See Lab materials on Blackboard.). b) simulate in the computer, a quasi-static sideways fall loading condition (see Fig. 1) using the most suitable boundary constraints to predict the bone strength for either the left or the right proximal femurs of this patient. c) compare your results with the previous literature you have examined. d) discuss the validity and limitations of your approach. Fig 1. An illustrative diagram showing a protected sideway fall conducted in a lab. Make sure that you list all modelling assumptions. Justify your choice of methodology and parameters. The computer lab handouts (available on Blackboard) are based on ITK-snap and ANSYS software packages. The recommended FE software package to use is ANSYS, either Workbench or Mechanical APDL. If you are new to FE and ANSYS, we have provided the Level 3 Fundamental FEA materials (pre-recorded lectures on Blackboard under Background knowledge) for you to catch up on the fundamentals. You can also ask us questions during the computer labs, which will be led by the lecturer and GTAs. In addition, there are some links (below) to useful online resources that demonstrate the use of ANSYS in various FE applications: https://courses.ansys.com/ https://forum.ansys.com/ https://www.ansys.com/academic/students/student-teams Structure of report Your report should have a maximum of 3000 words (excluding tables and list of references, but including figure/table captions), no appendix is allowed. You should use a minimum font size of 11 with 2 cm margins. The report should be no longer than 13 pages (excludes Cover Page and the List of References). Note that any extra pages will not be read. The report should be submitted via Turnitin by noon Wednesday 11th December UK time. The standard University penalty (refer to your student handbook) applies for late submission. If the use of unfair means is confirmed, the relevant sections will receive a 0 mark and your name will be reported to the Department, who may carry out further disciplinary actions. Your report should consist of the following main sections: introduction, a brief literature review, methods, results, and discussion. • Introduction: This should consist of 1 paragraph, it should be clear and concise, and set the scene. • Literature review: This section should provide a critical review to the range of modelling methods used in the literature, summarizing their advantages and disadvantages, and explain how they help you determine your proposed methodological approach. • Methods: This section should provide a clear and concise description of the methods used for modelling, with appropriate use of figures, tables and equations. List all assumptions (with justification) and provide references to any external parameters used. The pre-processing steps (segmentation and mesh generation) should be brief (half page to one page long) as the process has already been provided to you. Detailed description should be provided for the type of displacement and force boundary conditions used in the FE simulation. • Results: This section should clearly describe the results obtained from the FE simulations with illustrated figures and tables. Each figure and table should have informative captions. • Discussion: This section should provide a structured discussion with logical presentation of your arguments, quoting both your results and the literature for comparison on model predictions. Your discussion should demonstrate innovative thinking and critical analysis and show understanding of the wider engineering context. • References: The School does not have a specific requirement on the referencing style. Therefore, you can choose any standard referencing styles, e.g., IEEE, Harvard, etc. Learning outcomes assessed • A1. Apply fundamental laws and principles of physics and/or engineering to medical applications, some of which are at, or are informed by, the forefront of the discipline. • A2. Formulate strategies to solve complex problems in physics or engineering using a variety of experimental, analytical, design, statistical, mathematical and/or computational techniques. • A7. Demonstrate a critical awareness of the role of medical physics and/or biomedical engineering in medicine considering the technological, social and ethical aspects of the field and its development. • A8. Communicate scientific concepts to a range of audiences in a concise, accurate and informative manner, leading to the presentation of logical conclusions at a level appropriate to the audience. • A9. Manage their own learning and make selective use of a variety of resources including appropriate texts, research articles and other primary sources in their work.
CAN201 Introduction to Networking Networking Project Contribution to Overall Marks 40% Submission Deadline of Part I 15th Nov. 2024, 23:59 Submission Deadline of Part II 13th Dec. 2024, 23:59 Type Team coursework Learning Outcome [A] [B] [C] [D] How the work should be submitted? ● SOFT COPY ONLY! ● Every team leader must submit the work through Learning Mall. Specification of Part II (20% of overall marks) This part of the networking project aims to use Mininet to create a simple SDN network topology and emulate a traffic control function through using the SDN flow entry. Assuming that the client side only knows the service running on server 1 and communicates with server 1 (without knowing the existence of the service on server 2). However, the SDN controller can manipulate (forward/redirect) the traffic without the awareness of the client. The detailed project tasks are specified as follows. For Part II, the client side program and server side program will be given. Figure 1. A simple SDN network topology Use Mininet Python library to create a Python file to build a simple SDN network topology as Fig. 1 shows. Note that Client uses IP address 10.0.1.5/24, Server1 uses IP address 10.0.1.2/24, and Server2 uses 10.0.1.3/24. Also, Client, Server1 and Server2 need to use the MAC address as Fig. 1 presents. Task 2 Program and run an SDN controller application using Ryu framework (see Task 4.1 and Task 5.1) and make sure every node (i.e., Client, Server1 and Server2) are reachable with each other. In other words, they can ‘ping ’ with each other. Notice that any flow entry (excluding the table-miss flow entry) should set an idle timeout of 5 seconds. Task 3 Apply the given socket client program (client.py) and the given socket server program (server.py) to this SDN network topology. Specifically, run server.py on both Server1 and Server2, and run client.py on Client. With that, use the socket client side on Client to send traffic to the socket server side on Server1. Notice that wait 5 seconds after ping (the idle timeout mentioned in Task 2) and then start run the client.py, which is to make sure that the flow entry caused by ICMP ping packets has been removed. Task 4 4.1. Program an SDN controller application that is able to create a flow entry after receiving the first (TCP SYN segment caused) Packet_In SDN packet (from the SDN switch to the SDN controller), then install the flow entry to the SDN switch, and then send out the Packet_Out SDN packet that contains the TCP SYN segment, whereby all the following traffic sent from Client to Server1 is forwarded to Server1. 4.2. With task 4.1, use Wireshark/Tcpdump on Client to capture the packets and then calculate the networking latency (from the first SYN segment till the last ACK segment indicating the TCP 3- way handshake is done). Task 5 5.1. Program an SDN controller application that is able to create a flow entry after receiving the first (TCP SYN segment caused) Packet_In SDN packet (from the SDN switch to the SDN controller), then install the flow entry to the SDN switch, and then send out the Packet_Out SDN packet that contains the TCP SYN segment, whereby all the following traffic sent from Client to Server1 is redirected to Server2. 5.2. With task 5.1, use Wireshark/Tcpdump on Client to capture the packets and then calculate the networking latency (from the first SYN segment till the last ACK segment indicating the TCP 3- way handshake is done). Submission: Codes: ● >= Python 3.6; ● The whole implementation includes multiple Python scripts as follows: 1) Network topology Python file (which is used to create the SDN network topology for completing Task 1). Please name it “networkTopo.py” . 2) The Ryu SDN controller Python program (for performing Task 2 and Task 4.1). Please name it “ryu_forward.py” . 3) The Ryu SDN controller Python program (for performing Task 2 and Task 5.1). Please name it “ryu_redirect.py” . Project Report: ● A cover page with your full names (pinyin for Chinese student; name on your passport for international student) and student IDs of the whole team; ● 4 ~ 6 pages (including everything such as the reference while excluding the cover page), double columns, using the IEEE conference template provided; ● PDF format, LaTeX is recommended, IEEE conference template: https://www.overleaf.com/latex/templates/ieee-conference-template/grfzhhncsfqn; ● Including: - Abstract - Introduction: project task specification (introduce some background about SDN and describe the task of this project, do not copy from this document and use your own words), challenge (identify the research/development problems you are going to address), practice relevance (come up with the potential applications with your proposal, e.g., load balance, secure traffic control, etc.), contributions (key points that you did for this coursework). - Related Work: research papers, technical reports, or similar applications that solve or facilitate network traffic redirection. - Design: the design of you solution, which should include the network system design diagram (and you need to describe it using your own words) based on Fig. 1, the workflow of your solution (in particular, the steps of creating the flow entry, installing the flow entry, etc.), the algorithm (i.e., the kernel pseudo codes of the network traffic redirection function) for the SDN controller. - Implementation: the host environment where you develop the implementation, such as the host CPU, Memory, Operating System, etc. Also, the development softwares or tools, like the IDE, the Python libraries, the SDN controller software (i.e., Ryu here), etc. Further, steps of implementation (e.g., program flow charts), programming skills (OOP, Parallel, etc.) you used, and the actual implementation of the traffic redirection function. In addition, the difficulties you met and how did you solve them. - Testing and Results: testing environment (can be more or less the same with your host implementation environment), testing steps (the steps of using the developed Python programs to complete the project tasks 1-4, including snapshots), and testing results, i.e., the networking latency comparison between the forwarding case (Task 4.2) and the redirection case (Task 5.2), and you should apply figures of bars or curves for showing average performance. - Conclusion: what you did for this project and any future work for improvement. - Acknowledgement: individual contribution percentage should be clarified here if the project is a teamwork by using this format: Student1’s name (ID) contributes XX% to the project, Student2’s name (ID) contributes XX% to the project, and Student3’s name (ID) contributes XX% to the project, etc. If there is no clarification of individual contribution, it is considered that all the individual team contributes the same percentage to the project. - Reference [IEEE format] Meanwhile, you have to follow the compulsory requirement (no tolerance): ● Only ZIP file is allowed to submit; ● The ZIP file should be named as: CAN201-CW-Part-II-Student1name-Student2name- Student3name-Student4name-Student5name ● The ZIP file includes two folders, i.e., “Codes” and “Report” . The Codes folder includes all the Python files, and the Report folder includes the report file; ● Python files are: networkTopo.py, ryu_forward.py, ryu_redirect.py; ● The report file should be named as: Report_Part_II.pdf; Allowed Python modules: os, sys, shutil, socket, struct, hashlib, math, tqdm, numpy, threading, multiprocessing, gzip, zlib, zipfile, time, mininet, ryu. Marking Criteria The following marking scheme is for the team, and every team member shall contribute to the project. Also, several specific rules should be followed: 1. Every team should use the “ACKNOWLEDGMENT” section of the IEEE template to describe the individual contribution(s) using the following format: Student1’s name (ID) contributes XX% to the project, Student2’s name (ID) contributes XX% to the project, and Student3’s name (ID) contributes XX% to the project, etc. 2. If there is no clarification about the individual contributions, it is considered that every team member in the same team has the same contribution percentage and will have the same mark of the CW project. 3. The individual contribution must be in a range: for a 5-person team, it must be 10% - 30% (15% and 30% are included); for a 4-person team, it must be 15% - 35% (15% and 35% are included). If any individual contribution percentage of a team is out of the range (e.g., a 5-person team has the contributions like: 60%, 10%, 10%, 10%, 10%), the team may go through a review by the module leader about the contribution discrepancy. 4. The algorithm for calculating individual mark as follows: a. Assuming the 5-person team’s mark is m, student1 contributes x%, student2 contributes y% and student3 contributes z%, student4 contributes u%, student5 contributes v%. b. The student who gets the most contribution will get mark m. c. Student 1’s mark will bex/max(x,y,z,u,v)*m. d. Student 2’s mark will bey/max(x,y,z,u,v)*m. e. Student 3’s mark will be z/max(x,y,z,u,v)*m. f. Student 4’s mark will be u/max(x,y,z,u,v)*m. g. Student 5’s mark will bev/max(x,y,z,u,v)*m. Report (50%) Marking Criteria Item Mark Contents (40%) Abstract 3% Introduction 5% Related Work 4% Design 8% Implementation 7% Testing and Results 7% Conclusion 3% Reference 3% Typography (5%) Report structure, style, and format 5% Writing (5%) Language 5% Marking Scheme: 1. Contents (40%) 1.1. Abstract (3%) - Good (3%) - Appropriate (1-2%) - No abstract (0%) 1.2. Introduction (5%) - Excellent (5%) - Lack of necessary parts (1%-4%) - No introduction (0%) 1.3. Related Work (4%) - Sufficient (4%) - Not enough (1%-3%) - No introduction (0%) 1.4. Design (8%) - Excellent: adequate and accurate figures and text description (8%) - Reasonable: clear figures and text description (4%-7%) - Incomplete: unclear figures and text description (1%-3%) - No design (0%) 1.5. Implementation (7%) - Excellent: sufficient details of implementation (7%) - Reasonable: clear description of implementation (4%-6%) - Incomplete: unclear description of implementation (1%-3%) - No implementation (0%) 1.6. Testing and Results (7%) - Excellent: sufficient testing description, correct experimental results using figures with clear text description and analysis (7%) - Acceptable: clear testing description, appropriate experimental results using figures with acceptable text description and analysis (3%-6%) - Incomplete: lack of testing description, experimental results with figures, or text description and analysis (1%-2%) - No testing and results (0%) 1.7. Conclusion (3%) - Excellent conclusion (3%) - Acceptable conclusion (1%-2%) - No conclusion (0%) 1.8. Reference (3%) - Excellent reference with the correct IEEE format (3%) - Incorrect or inconsistent reference format (1%-2%) - No reference (0%) 2. Typography (5%) - Beautiful and clear typography: 5% - Acceptable typography: 2%-4% - Bad typography: 0% ~ 1% 3. Writing (5%) - Accurate and concise language: 3%-5% - Unclear and confusing language: 1% ~ 2% Codes (50%) Program testing steps: 1．Forwarding case: 1.1 Run the networkTopo.py to create the SDN network topology. Check Client, Server1, and Server2 use the correct IP addresses and MAC addresses. 1.2. Run ryu_forward.py on Controller, and use Client to ping Server1’s IP address and Server2’s IP address. 1.3. Run server.py on both Server1 and Server2, and also run client.py on Client after the previous ICMP ping incurred flow entry’s idle timeout (i.e., 5 seconds). a. Show the flow table on Switch. b. Show Server1 receives the traffic sent from Client. 2．Redirection case: 2.1 Run the networkTopo.py to create the SDN network topology. Check Client, Server1, and Server2 use the correct IP addresses MAC addresses. 2.2. Run ryu_redirect.py on Controller, and use Client to ping Server1’s IP address and Server2’s IP address. 2.3. Run server.py on both Server1 and Server2, and also run client.py on Client after the previous ICMP ping incurred flow entry’s idle timeout (i.e., 5 seconds). c. Show the flow table on Switch. d. Show Server2 receives the traffic sent from Client. Marking scheme: 1. Step 1.1 and 2.1 (10%) - Complete topology with correct IP addresses: 10% - Incomplete topology or incorrect IP addresses: 3%-9% (3 MAC and 3 IP addresses, 1 for hostnames) - No networkTopo.py or not executable networkTopo.py: 0%-2% Note: if no networkTopo.py or the networkTopo.py is not executable, the marking stops here. 2. Step 1.2 (5%) - The program ryu_forward.py can work and Client can ping Server1 and Server2: 5% - If ryu_forward.py can work but Client cannot ping Server1 or Server2: 3%-4% - If no ryu_forward.py or ryu_forward.py is not executable: 0%-1% Note: if no ryu_forward.py or ryu_forward.py is not executable, no marking for Step 1.3. 3. Step 1.3 (10%) - The flow entry can be shown correctly and Server1 can receive traffic: 10% - The flow entry cannot be shown correctly or Server1 cannot receive traffic: 5% - Neither the above: 0% 4. Step 2.2 (10%) - The program ryu_redirect.py can work and Client can ping Server1 and Server2: 10% - If ryu_redirect.py can work but Client cannot ping Server1 or Server2: 6%-8% - If no ryu_redirect.py or ryu_redirect.py is not executable: 0%-3% Note: if no ryu_redirect.py or ryu_redirect.py is not executable, no marking for Step 2.3. 5. Step 2.3 (15%) - The flow entry can be shown correctly and Server2 can receive traffic: 15% - The flow entry cannot be shown correctly or Server2 cannot receive traffic: 7% - Neither the above: 0%
MATH50010 coursework 2024-25 26/11/2024 This coursework is due at 1pm on Monday 9th December. Please submit it via the turnitin link on blackboard. Your submission should contain your CID but not your name. The Task In this coursework, we will analyse the air quality in London. The dataset daqi_2023 .csv contains the daily air pollution index for greater London for every day in 2023. The first column contains the date of the measurement, and the Index column contains the index for that day. More details on the air pollution index can be found here: https://uk-air.defra.gov.uk/air-pollution/daqi?view=more-info. Importantly, an air pollution index of 1,2 or 3 is considered low. A low air pollution index means that there should not be enough pollutants in the air to cause any respiratory issues. An air pollution index of 4-6 is considered moderate which may affect those with respiratory conditions. An air pollution index of 7 or above is considered high and on such days there is a risk associated with strenuous outdoor activity. The dataset daqi_2023 .csv is available to download from blackboard. In this coursework we will model the air pollution using Markov Chains constructed from this data set. The following is a step-by-step workflow to guide you through the task. Your coursework submission should be written using RMarkdown, and compiled to a PDF for submission. All code should be commented clearly. For the highest marks, you should communicate to the marker clearly what you are trying to do, and justify any arbitrary choices. There are a total of 50 marks available for this coursework. 6/50 marks are available for an extension question, you can still get a good mark overall without attempting this question. (4 marks) Loading and exploration 1. Read the data in to R. 2. (2 marks) We want to split the data into low, moderate and high index levels. A level of 3 of below is considered low, index level of 4-6 is moderate, and an index level of 7 or above is high. Create a new variable called ‘state’ indicating whether the pollution is high (2), moderate (1) or low (0) on each day. 3. (2 marks) Calculate the proportion of days in each of the states defined above. (9 marks) A Markov Chain Model We will now model the data as a Markov Chain. 4. (3 marks) We look at the transitions between states. Count the number of pairs in each of the possible pairs of successive states. Overlaps are OK, e.g. the sequence 0100 corresponds to one (0,1) transition, one (1,0) transition and one (0,0) transition. 5. (4 marks) Assume that the high/moderate/low air pollution index forms a three-state time-homogeneous Markov chain. Use the data to estimate the transition matrix of the chain. 6. (2 marks) Write a function that simulates draws of length m from a three state Markov chain. (21 marks) Parameter Estimation 7. (4 marks) Use your function from question 6. to simulate n independent ‘years’ of daily air pollution index classifications using the transition probabilities from the data. For each of the n realizations of the chain, compute the estimates of the transition probabilities. Show that the estimators are approximately unbiased. 8. (3 marks) Calculate the variances of the estimates and comment on any differences. 9. (3 marks) Compute the correlation between your estimate of P (X1 = 1|X0 = 0) and your estimate of all other transition probabilities. Are there any other correlations you expect to be significant? We will now investigate calculating the parameters of the transition matrix via maximum likelihood. Using the Chain Rule for probabilities and the Markov Property, we can write the likelihood of observing samples x0 , x1 , . . . , xn from a Markov chain as P (X0 = x0 ) n P (Xi = xi |Xi−1 = xi−1). 10. (2 marks) Write down the log-likelihood of the data in terms of the elements of the 3 state transition matrix (these will be the parameters we want to estimate). 11. (3 marks) When computing the maximum likelihood estimate, we want to make sure we are estimating the smallest possible number of parameters. Can you reduce the number of parameters of your model? Write down the log-likelihood with the smaller number of parameters. [Hint: use properties of transition matrices] 12. (5 marks) Calculate the maximum likelihood estimates of the transition probabilities, and verify that these are similar to your estimates in question 5. (Note that you do not need to estimate the initial state distribution) 13. (1 mark) Briefly explain why estimating the initial state distribution from the data via maximum likelihood is more challenging. (14 marks) Estimating high/low air pollution events For these questions, we will use our estimated transition matrix from question 5. or 11. to estimate the probability of high or low air pollution events. You should state which estimate you are using. You should find an expression for the quantities of interest analytically then substitute in the estimated transition matrix for the transition probabilities to create your estimates. 14. (3 marks) Given that we start in a low air pollution index day, estimate the probability that the air pollution index is low for the rest of the year (i.e. every other day that year has a low air pollution index). 15. (5 marks) Estimate the expected number of days in a year that have a high air pollution level given that we start in a low air pollution day. Compare your answer to the number of high air pollution days in the dataset. 16. (6 marks, extension) Estimate the probability that after a low air pollution index day, the next low air pollution day is exactly a week later (i.e. the probability that after a low air pollution index day, the next low air pollution index day is 7 days later with no low air pollution index days inbetween). (2 marks) Conclusions 17. (2 marks) Comment on any limitations of your study and the Markov Chain model.
ENGM029 Power System Analysis Lab Sheet Lab 1 Before your Start • Ensure you read the instruction document ‘DIgSILENT PowerFactory Modelling Instruction’ and DIgSILENT tutorial documents ‘DIgSILENT Tutorial_Introduction’, ‘DIgSILENT Tutorial_Project’, ‘DIgSILENT Tutorial_LoadFlow’ in the ELE page; • DIgSILENT PowerFactory provides Student Licence (PF4S); you are encouraged to make direct contact with DIgSILENT to acquire one licence so you can install in your own computer and can access to and practice the simulation study remotely. Requirements on Report • A report needs to be produced to answer each question in the lab sheet. • Assessment: The lab report has a total of 100 marks. The lab report counts for 10% of your final mark. • The report should be written in a concise manner to both summarising and discussing the results. • The report should be submitted as PDF file online through ELE2 system by noon, 1st December 2023. • Avoid any form. of plagiarism. • Format to be used for the results: 1. Objective The first Power System Analysis lab aims to enhance your knowledge and skills in power system modelling and power flow calculation and analysis. At the end of the lab session, you should be able to build simple power network model, including generator, transformer, transmission line and load, and carry out power flow simulation and analyse by explaining indicative parameters such as active power, reactive power, bus voltage magnitude and phase angle. 2. Introduction In this Lab Session, you will learn how to simulate a small power transmission network and how to perform. power flow calculation in a powerful modern power system simulation software, DIgSILENT PowerFactory. By this simulation exercise, you will learn the underload and overload effects over a long transmission distance on the system operational performance, mitigation method to stablise the bus voltage, and the efficient Newton-Raphson approach and its application in simulation calculation. 3. Problem 1 _ Power Flow Problem [50 marks] Preparation Open the project file ‘Power System Analysis_Lab 1_P1.pfd’. Ensure the library contains generator type G1, tower type L6, conductor types 1Z×275 kV_ASCR Zebra 400 mm2 and 4Z×275 kV_ASCR Zebra 400 mm2 (300×300). Problem formation: A load centre is supplied by hydro-generator (11 kV terminal voltage) through a 11/275 kV step-up transformer, a 200 km long double-circuit transmission line operating at 275 kV, and a 275/33 kV step-down transformer. The configuration of the transmission system is given in the Figure Q1. The load level varies from 40 MW to 340 MW. Figure Q1 The parameters of the components are given in the following tables: Transmission Line Table 1 Double-circuit Transmission Line Parameter 00)Earth Conductor Type1Zx275 kV_ASCR Zebra 400mm2VoltageRatioConnectionPowerRatingShort-circuit %LoadlosskWNoloadcurrent%No Loadloss kWStep-uptransformerT11-211/275Yd 11200MVA165000.880Bus NumberVoltage Rating kVBus111Bus2275Bus3275Bus 433Bus typeG1Rated Apparent Power400 MVARated Voltage11 kVRated Power Factor1.
Assignment Soft-Field You should aim to spend a TOTAL of two days on the assignment. Your time will be spent performing image reconstruction and analysis of prepared data files using EIDORS, according to the guidelines given below, and writing a report in the form. of a paper. It is your responsibility to plan your time ahead during this course module. Lab space is available to you and there will be demonstrator support. The demonstrators will be instructed to offer guidance but not to do the assignment for you. The report will be in the form. of a short paper, typical of those that are submitted to international conferences – see Appendix A. Marks will be deducted for not satisfying the format. The marking template is shown in Appendix B. Please follow the exact order of the headings. This assignment is intended to encourage exploration and appreciation of the strengths and weaknesses of image reconstruction for electrical resistance tomography using EIDORS. You will be given a measurement reference file and measurement data files and will perform. image reconstruction to determine preferred values for the reconstruction parameters. At the end you report what you have done, what you expected to observe compared to what you actually observed and some conclusions – all in 7 pages! You are strongly advised to divide your time equally between using EIDORS and writing the report. In other words, roughly 1 day on exploring reconstruction with EIDORS and interpreting results and 1 day writing the report. For submission deadline, please refer to Bb. Summary of Soft Field Reconstruction so far: In the laboratory sessions you were introduced to image reconstruction for electrical resistance tomography using EIDORS running under MATLAB. This involved : · Creating a model of the measurement vessel and generating a mesh to be used for finite element modelling. · Computing the forward solution to give the boundary voltages that result when a current pattern is applied on the electrodes. Results are displayed as graphs. · Computing the inverse solution using two reconstruction algorithms, linear back projection and iterative Gauss Newton. Results are displayed as colour-coded cross-sections. Summary of useful commands : · Create the model : create_netgen_model “.geo” file · Use Netgen to create a Mesh (“.vol” file) from the model (“.geo” file) · Read in the mesh : FEM_read_model_2 · Calculate the boundary voltages : [forward_results, forward_parameters]=forward_solution(model_parameters); · Read in the measurement data : [measurement_data]=read_lct_data_interactive(forward_results); · Solve the inverse problem : [inverse_results, model_parameters] = inverse_solution(forward_results, model_parameters, forward_parameters, measurement_data); · Display the cross-section : slicer_plot_n(model_parameters, 0.025, inverse_results.sol_lbp ); The Assignment You will be allocated 2 personal measurement files corresponding to the same arrangement of 2cm diameter plastic and metal objects in water. These files are different for each student. Your two measurement files correspond to the same arrangement of materials in the vessel but one is acquired with the adjacent strategy and one with the opposite strategy. These may be “full” or “with reciprocity” strategies. You should deduce which strategy has been used by considering the data contained in the file and use the appropriate reference files for reconstructions. Information on the setup for the measurements is given at the start of the data files. This is followed by rows containing measurements of Real Voltage, Imaginary Voltage, Current and Phase for each measurement in the excitation strategy. For our purposes only the Real Voltage is used. You are to explore the quality of the resulting reconstructed images for these files under a range of conditions. Explore the number of iterations using Non-linear Gauss Newton. A range of 1 to 100 iterations is suggested. Explore the smoothing factor. A range from 1 to 1e-8 is suggested Compare the results for the adjacent and opposite strategies on your data. Your report should clearly state your strategy in exploring these parameters. For instance : “It was decided to reconstruct an image for every value for the number of iterations from 1 to 100. Starting with 1 iteration for the opposite and adjacent strategies, then 2 iterations for the adjacent and opposite strategies, and so on up to 100 iterations. Then this was repeated for the following values of the smoothing factor : 1, 1e-1, 1e-2, 1e-3, 1e-4, 1e-5, 1e-6, 1e-7, 1e-8. This strategy was selected to cover all possible values of the parameters.” Note : The above strategy is exhaustive but is not recommended as it would involve 100 x 2 x 9 = 1800 reconstructions! So, how did you decide which values to explore? It is informative to consider noise in the images. This might be evaluated by reconstructing a “noise” file compared to the reference. Select the noise files corresponding to the reference files that you have used. In an ideal world these would give perfectly uniform. images with the same value of conductivity (0.01S/m) for each pixel but, due to the inevitable noise in the measurements, this is not the case. Evaluation might involve consideration of the images or numerical analysis of the conductivity values in the array “inverse_results.sol.nlgn”. You might compare this to the noise in the measurement data. During your explorations you should consider the following : What is your best suggestion for the arrangement of objects ? (plastic/metal, position) What is your estimate of the error on the position ? Which strategy gives the best results ? (opposite/adjacent) How many iterations would you recommend ? Which smoothing factor gives the best results ? How noisy are the images ? How do you decide on answers to these issues ? All measurements have been acquired using the gold vessel as described in the laboratory script. and therefore your model .geo and the mesh .vol from the lab activity do not need to be altered. It is tempting to improve results by using a finer mesh but because of the increased computational demands this is not advisable for the present exercise. You will need to compute forward solutions (boundary voltages) according to the excitation strategy that is used. [forward_results, forward_parameters]=forward_solution(model_parameters); Do you want the ground node at the [t]op or the [b]ottom of the tank or [u]ser defined: t ….. always “t” What is the injection current in Amps? 0.001 ….. always 0.001 What is the conductivity of the background in S/m? 0.01 ….. always 0.01 How many planes of electrodes do you have? 1 ….. always 1 Do you want to use an [a]djacent or [o]pposite or [p]seudo-opposite or [c]ross-drive protocol? Depends on measurement data files a=adj; o=opp Do you want to take full [b]etween plane, [f]ull planar, planar with [r]eciprocity or [u]ser defined voltage measurements? Depends on measurement data files “f” or “r” Do you want to calculate the Jacobian matrix? [Y/N] y The data files are located in P:Assignment files and should be copied into your working directory (P:”). Load the data into MATLAB : [measurement_data]=read_lct_data_interactive(forward_results); Solve the inverse problem (compute the reconstructed cross-sectional image) [inverse_results, model_parameters] = inverse_solution(forward_results, model_parameters, forward_parameters, measurement_data); What type of smoothing do you want? 1 always “1” What smoothing weight do you want? 1 always “1” Select the Non-linear Gauss Newton algorithm (nonlin) Select the smoothing factor Select the number of iterations Points to note : · In your report you are expected to suggest conclusions regarding the relative performance of the different arrangements. In order to do this effectively you should ensure that you practice “good science”. In other words, when making comparisons, only one parameter should be different. If more than one parameter is changed then it is not possible to identify which one is responsible for any change that is observed. Therefore at the beginning you should plan your programme of “experiments” and the strategy should be described in the report. · The assignment aims to explore the quality of image reconstruction. For instance, the more ambitious students might wish to consider the noise in the reconstructed conductivities by processing the appropriate array in the workspace. · Clearly, the report should be a record of your own work and the reports will be evaluated for similarities. However, discussion with others in the class is encouraged in order to help each other to greater achievements. Note : the images that you reconstruct must be generated from the data files that you are allocated and identified at the top of this script. You can find your allocated data files from blackboard. The excitation strategies of the measured data files are described in the first line of the data files. · One aim of this assignment is to encourage you to think and work in a way that might be helpful in your dissertation and subsequently if you undertake research that ultimately may result in publications or patents. In this respect an important issue is to draw realistic and justified conclusions, not just the ones that you might expect - or feel that we might expect! It is good practice, before each experiment, to write down what you expect to happen and why, then compare it to the actual result. If it is different to your expectation, identify the difference and try to suggest the reason. To assist this process you are encouraged to be diligent in maintaining a log of your activities on the assignment. · You are to submit paper and electronic copies of the report. The electronic version should be placed in the “Software and MRI Coursework drop tray” under “Assignments” on Blackboard. Remember : You are trying to explore the effect on the results, either reconstructed images or predicted boundary voltages, due to changes in the excitation strategy, iterations or smoothing factor. Also, how do you actually decide on the quality of the results?
COM6503: 3D Computer Graphics: Assignment 1 (40%) Deadline: 3pm, Wednesday 11 December 1. Introduction The assignment will involve using modern OpenGL to render a scene. Scene graphs are required in the modelling process and animation controls are required for hierarchical models. 2. Learning outcomes After completing this assignment, you will be able to: • Use data structures and mathematics in representing and manipulating 3D objects • Produce interactive software that makes use of a graphics API Figure 1. The scene – a room on a spaceship with a view of space out of the window. The dashed blue arrows show the rotation of the spotlight on top of the small robot (robot 2) and the rotation of the globe. 3. Requirements Figure 1 shows a scene set in a room on a spaceship. The whole scene should be modelled using transformed flat planes, cubes and spheres. The view through the window is space (i.e. stars, etc). You must satisfy all the following requirements. 3.1 The room • In Figure 1, the room is made of three walls and a floor. The wall nearest to the viewer is not modelled. The room size is your choice. • The left wall has a window in it. • The back wall has a texture added to it. This texture includes a large shiny text message of a name. You should use part of your name for this, e.g. forme, I would choose ‘Steve’ . A diffuse and a specular map will be required. The specular map will make the name part appear shiny in contrast to the rest of the wall. You must create the diffuse and specular maps. These textures should be stored in files called diffuse_name.jpg and specular_name.jpg in the assets/textures folder, where your chosen name part is used in the filename so they are easy to find and check, e.g. diffuse_steve.jpg and specular_steve.jpg. • The right wall has a repeating texture added to it. Here, a picture of a bug is used (as illustrated in Figure 2, which repeats four times in one direction and three times in the other). You should make your own choice of texture. It must repeat over the wall, i.e. the wall is not a single texture image containing multiple pictures. Figure 2. A bug texture in a repeating pattern. • The floor should be textured. You choose a texture. However, it should also feature a series of thick lines in any colour you choose. This sequence of lines is the path a robot needs to follow. • A ceiling is not shown but should be added to make the scene look better. This should be textured too. You choose the texture. 3.2 The window and the view • The left wall in the room is a large window. • An outside scene of space (i.e. stars, etc) can be seen through the window. Consider how you might do this: Should the scene be a texture map pasted onto the wall to look like a fake window and a scene? Or should there be a hole in the wall for the window and a texture map is pasted onto a plane that is a certain distance outside the window (as illustrated in Figure 1)? Should the window have a frame? Or should the window be the size of the whole wall with a simple small frame. around the edges? Should a box of textures be added outside the window so a texture can be seen at all angles when looking out of the window? Or should a skybox be used that is outside the whole room rather than a separate plane with a texture map on it? • Depending on the approach you choose, how does it look when the camera moves position in the room when looking out of the window? (Is it possible to stand in the room and always see a view of space outside through the window?) • The scene outside the window should change overtime whilst the program is running,e.g. the stars and planets move. • The quality of what you produce for this part of the scene will be part of the marking. Some alternatives indicated above are more advanced than others. You must choose what to try. 3.3 The robots • There are two robots, both modelled as scene graph hierarchies. Both are made up of transformed spheres and/or cubes. • Robot 1, the dancing robot, is at the back left of the room in Figure 1. • Robot 2, the small robot, is on the track on the floor at the right side of the room in Figure 1. • Robot 1 should feature a base, three parts that form the leg and body, two arms and a head, as illustrated in Figure 1. Each of the parts can Figure 3. Some poses of robot 1, the dancing robot. articulate to make the robot dance, as illustrated in Figure 3, which shows three poses. • The head of robot 1 should be your own design. It must have a minimum of 4 pieces. Figure 4 gives a few possible designs. Again, this is a chance to show some creativity. Figure 4. Some possible head designs for robot 1. • Robot 2, the small robot, has a body, two eyes and an antenna. The antenna has a spotlight attached to it which is made of two transformed spheres, one for a small bulb and one for its casing (alternatively called its holder) – these are shaded as white and grey, respectively, in Figure 1. You can choose to vary the look of the spotlight, but it must feature a bulb and a casing for the bulb. The two together indicate which direction the spotlight is pointing in and thus where the pool of light from the spotlight should appear in the scene. • Robot 2, the small robot, continually follows the path of grey lines on the floor, with its two eyes showing the forward direction. • When robot 2 reaches a corner in the path, it changes direction to follow the new line. • The hierarchy and associated transformations are more important than the quality of the individual pieces in each of the robot hierarchies. Transformed spheres and cubes must be used. I want you to demonstrate that you understand transformations and a scene graph hierarchy. • You must texture-map both robots. You must decide on which textures to use. You cannot use the same texture(s) on each robot. • I’ll be looking for a little creativity in the animation for each robot. For example, how should robot 1 perform. its dance? How should its parts move with respect to each other? Perhaps some cartoon effects could be used, e.g. head parts appearing to enlarge or move position for parts of the dance? For robot 2, when it reaches a corner, how should it turn? Should it lean a little to one side? Or spin in some way? 3.4 Spotlight • Robot 2 has a bright white spotlight attached to the top of its antenna. The dashed lines in Figure 1 are to illustrate where this spotlight is. These dashed lines would not be seen in the real effect!! • The spotlight continuously rotates around the top of the antenna whilst robot 2 moves. Thus, different parts of the scene will be illuminated by the spotlight as robot 2 moves. (The spotlight stops rotating when robot 2 stops moving.) • This is an advanced requirement as you are responsible for working out how to implement a spotlight effect. 3.5 The globe • This is made of transformed spheres (one for the globe and one for the central axis) and a transformed cube (for the pedestal). • The globe continually rotates about the central axis. • The texture map for the globe must look like planet Earth or a cartoon version of it. You will need to find a texture to show this or draw a cartoon version of it yourself. • The stand for the globe also needs to be texture mapped. Try to be creative with this. 3.6 General illumination • The scene should be illuminated with a general world light which can be positioned anywhere in the scene. • This general world light will illuminate all parts of the scene. • When you switch off the general light (using an interface option – see the next section), the effects of the spotlight will be much clearer. • (You can include more than one general light if you wish.) • You do NOT have to do shadows. Do not worry about shadow effects. (The general world light will illuminate all polygons with a normal pointing towards it and the spotlight will illuminate all objects in the direction it is pointing in which are inside its spotlight area as there are no shadow effects to show light not reaching particular points.) 3.7 User interface • A user-controlled camera should be positioned in the scene. Use the camera that was given in the tutorial material – the mouse can be used to change the direction the camera is pointing in, and the keys can be used to move about. Do not change the key mappings from the ones in the tutorial. If you change the key mappings, it will make it difficult to mark. It doesn’t matter that the camera can move and see outside the room. • It should be possible to turn the general light on and off (or, more creatively, dim it, i.e. reduce the intensity) from the interface. • It should be possible to turn the spotlight (lamp bulb) on and off (or, more creatively, dim it, i.e. reduce the intensity) from the interface. • Robot 1’s dancing animation should start when robot 2 is ‘near’ to it and stop when robot 2 is not ‘near’ to it. You decide how near it needs to be. You will need to implement a proximity test (based on distance) to do this. • You should also add buttons on the interface, so you can start and stop robot 1’s dancing animation whenever you want to, irrespective of the proximity test. This will be useful for testing purposes rather than waiting for robot 2 to traverse all the way round the room!! • It should be possible to stop and start robot 2’s movement - the spotlight stops rotating when robot 2 stops moving. You need to implement interface controls to do this. • There is no need to stop and start the globe rotation. This just continually rotates. 3.8 Animation • Some of the animations are not straightforward and you may decide not to do them, although that would affect your marks for animation. • Use Euler angles for the animation. Do not consider using quaternions, as this is beyond the requirements for this assignment. 4. Deliverables • You should submit a zip file containing a copy of your program code (and any other necessary resources,e.g. imagefiles for the textures and a readme.txt file that describes everything) via Blackboard – this can be done via the link to the assignment handout. • You should submit whatever you have done, even if you have not completed all the requirements – for example, you might have produced a model of the scene but not done the animation. If you submit nothing, you cannot receive any marks. • The program MUST compile and run from the command window on a Windows PC or the terminal window on a Mac. You should assume that the jogl environment (and paths) has already been set up (on my machine), so you do not have to include this as part of what you hand in. I won’t install ‘YetAnotherIDE’ to make your program work; I want to compile and run the program from a command (or terminal) window using the standard javac and java commands. • You must include appropriate comments to identify parts of the code that you wrote, e.g. /* I declare that this code is my own work */ /* Author */. This could be done around major chunks of code and/or at the start of a class to identify the main changes you made. • You can make use of all the code that I have given you in the tutorial material. However, use your comments to state which bits/chunks/files are new. • The body of the Blackboard submission message should state that the work you have handed in is your own in addition to the code that was supplied in the tutorial material. • The name of the main class in your program should be Spacecraft. That way it is easy for me to compile and run the program. (In previous years, where this has been ignored, I have wasted time for some handins trying to workout which was the main class to use.) • Optional: You might like to make a short video of your animation. If you do so, DO NOT include this in the handin as it will be too big for Blackboard to handle – we tried using Blackboard for this in the past and it crashed the system!! Instead, put the animation on YouTube or your personal website and give the URL of the animation in a readme.txt file. Indeed, if you are thinking of a career in the graphics/games industry, then you should be adding such animation pieces to your personal website (your digital portfolio) to show off what you are capable of. 5. Marking I will check that the program meets the requirements listed above. The program must compile and do some part of the work requested even if it is not complete. Your program will be run and exercised thoroughly. In considering the requirements, four aspects will be considered (including the quality of the work for each aspect): • (29 marks) Modelling the scene: Each robot must be a hierarchical model. Is there a spotlight model attached to robot 2’s antenna? Is there a globe? How is the room modelled? (Consider drawing scene graphs for the scene before starting to program.) • (28 marks) Texturing: Use of texture mapping in the scene, e.g. basic texture mapping, use of diffuse and specular textures, extra texturing effects, e.g. the changing view through the window. • (15 marks) Lighting and interface controls: all lights should behave correctly such that their effect is seen on the scene. All necessary interface controls, as described in the above specification, should be included. • (28 marks) Robot 1 and 2 animations. Spotlight animation. Animation for the globe. Is all the animation smooth rather than jerky?
Department of Computer Science CS917 Foundations of Computing - Maths and Stats Assignment This assignment is due at noon on Thursday 19th December, 2024. The submission is on Tabula, and should comprise scanned copies of written work. The work that you submit should be your own work and please show full working where appropriate, as this is necessary to gain full marks. Marks for each question are indicated. The total marks you can get is 100. If you have any questions then do please email meat [email protected]. 1 Discrete Mathematics 1. For each of the following formulae, find a logically equivalent formula in which Λ , =→ and ←→ do not occur: (i) :(p =→ q) [5 marks]; (ii) ((p Λ q) _r) [5 marks]; (iii) :((p Λ q) ←→ r) [5 marks]. Use the truth table to show that the proposed solution is indeed equivalent with the original one. [total 15 marks] 2. Write out paraphrases of the following, using 8, 9 and = (i) Frodo has a ring [2 marks]; (ii) Sauron does not have any rings [2 marks]; (iii) The One Ring rules all the other rings [2 marks]; (iv) The ring that Frodo has is the One Ring [2 marks]; (v) whoever wears the ring, becomes invisible (i.e., no other ordinary human can see that person) [3 marks]; (vi) Bombadil Tom can see the ring-wearer, hence he is no ordinary human (use the previous statement from (v) to formulate this one) [3 marks]. Auxiliary clauses: has(x, y): x has/is in possession of y; rules(x, y): x rules y; wears(x, y): x wears y; sees(x, y): x can see y; and ordinary(x): x is an ordinary human. [total 14 marks] 3. Write predicate logic formulae which state that the relation expressed by Rx,y has the following properties: (i) Rx,y is irreflexive [3 marks]; (ii) Rx,y is intransitive [3 marks]; (iii) Rx,y is not a partial order [3 marks]. Note that this formulation is a bit diferent from the one in the slides. To make this consistent, think about Rx,y as Rp with relation p between x and y (you can assume that both x andy are from the same set A). So you can use Rx,y as p in your formulations. Therefore, in your answer, you can use both notations, just be consistent (i.e., if you choose 1 notation, then use the same for all your answers). [total 9 marks] 4. Determine which of the following functions are injective and which are surjective (please provide explanations as well): (i) f : Z ! N, where 8n ∈ Z: f(n) = n2024 + 1 [3 marks]; (ii) g : N x N ! N, where 8(n, k) ∈ N x N: g(n, k) = 2n3k5n+k [3 marks]; (iii) h : P(N) ! P(N), where 8A ∈ P(N): h(A) = N A (recall what P(N) means) [3 marks]; (iv) k : N ! Z, where 8n ∈ N: k(n) = (-1)n [3 marks]. [total 12 marks]. 2 Statistical Analysis Question 1 has two parts, each is marked out of 5. Both Question 2 and 3 are marked out of 10. Question 4 is marked out of 20. 1. A large database is compiled from files contributed by three sources: Source A, Source B, and Source C, which account for 20%, 50%, and 30% of the total files, respectively. The percentage of empty files from each source is 4% for Source A, 2.5% for Source B, and 1.5% for Source C. If a file is selected at random and found to be empty, what is the probability that it originated from Source A? 2. A survey was conducted on a large number of individuals to record their dates of birth. Assume that the sample size is sufflciently large to treat the dates of birth as uniformly distributed across all possible days. (i) What is the smallest number of randomly selected individuals re- quired such that the probability of at least two of them sharing the same birthday exceeds 50%? Assume there are 365 days in each year. (ii) Two individuals are selected at random from those born between January 1st , 1961, and December 31th , 2000. Given that at least one of these individuals was born in a leap year, what is the probability that both individuals were born in a leap year? 3. A directory contains 6 high-priority records and 4 low-priority records. These records need to be analysed by employees, and the workload is divided among them. Four records are randomly assigned to Sam, but the priority of each record is not identifiable from the file names. Given that the first record Sam analyses is low-priority, what is the probability that all the remaining records assigned to him are of the same priority (all remaining either low-priority or high-priority)? 4. A small company operates two 72-core computers, Computer A and Com- puter B. (i) On Computer A, it is known that each core is busy 50% of the time on average. At any given time, what is the probability that 36 or more cores are busy simultaneously? (ii) On Computer B, a random sample of 8 observations is taken. The average number of busy cores in this sample is 40.5, with a sample standard deviation of 3.2 cores. One individual claims that the true mean number of busy cores is 36. Based on the sample data, is there sufflcient evidence to reject this claim at the 5% significance level?
MAT135 – Differential Calculus - Fall 2024 Written Assignment Clarified MAT135 Assignment 9 (Written Component) Due 1 December 2024, at 9pm More details 0. What’s this new version? Students expressed some confusion with Q1(a) and Q1(b), and in retrospect Q1(a) is worded in a way that really does not seem to need a theorem. This version clarifies those things. All changes are in red. 1. Deadline: The deadline to submit this assignment is strict, to the second. Assignments that are even a few seconds late will normally receive a grade of 0. Technical issues are not a valid reason to be late, so you are taking a risk if you leave uploading your solutions to the last minute. Please give yourself lots of time to upload your assignment! 2. Purpose and feedback: The purpose of the written assignments is to give you some practice in thinking about and writing solutions to mathematical problems, without anytime pressure. You will receive feedback on your writing and on your solutions. You are encouraged to take this opportunity to carefully write your solutions and think about how to best present your reasoning behind them. 3. Writing solutions: Explain all your work, show your steps as well as your reasoning. You should write in words what you are doing and why. The person reading your solution should easily be able to understand what you have done, because you explained it, in words. Submissions with little or no written explanations will not receive full marks. 4. Uploading: A handwritten assignment can be photographed or scanned. It is important that the images (the scans or photos) are clear and easy to read (not too dark, too bright, too blurry, etc.). Your submitted files are what will be marked, so if the grader(s) can’t read them or can’t make sense of what you’ve written, you will not get full marks. You may also write your solutions on a tablet, or type your solutions (LaTeX only please) as long as your solutions are clear, easy to read and follow, and are all your own work. Make sure you upload solutions to each problem to the correct place. If you upload solutions to the wrong problems, or uploadallyour solutions to the same problem, it may result in getting0on all problemsuploaded to the wrong places. 5. Grading: The assignment is out of 10 marks, with the marks for each question indicated beside it. Marks for each solution will depend on your answer, as well as the quality of your explanation of those answers. 6. Academic Integrity: The solutions that you submit to this assignment must be all your own work. By submitting this assignment you declare that: (a) Your solutions are all your own work, explained in your own words. (b) You have not copied any part of the assignment solutions from anyone or anywhere. (c) You have not let anyone else copy any part of your solutions to the assignment. (d) You have not used Generative AI (e.g., ChatGPT) for any part of the assignment. Background Several months have passed since the aliens arrived. The world’s leading scientists are jealous that the aliens continue to seek the help of MAT135 students at UTM, even after 10% of the class tried to kill Estra on Written Assignment 6. As incredible as their time here with us has been, the aliens have begun making preparations to leave Earth and head back to their home planet. Despite all their incredible technological advancements, they still have some basic problems that first-year calculus students (and only first-year calculus students) can help them solve. Problems 1. (3 points) Estra, who you’ve come to learn is the alien ship’s navigator, is planning their route and is concerned about their fuel levels for their journey home. The alien ship uses a mysterious fuel called orsh, a substance unfamiliar to humanity. Estra estimates that the regular functions of their ship while it sits at UTM (lights, communications, hydroponic fruit gar- dens, and so on) consume orsh at a rate between 4000 and 5000 litres per day. The ship’s fuel gauge says there are currently 300000 litres of orsh on board, but Estra tells you that the fuel gauge’s reading can be off by as much as 3000 litres (they’ve been meaning to get it fixed, but it’s so hard to find a good mechanic...). You may assume that the amount of orsh in the ship’s tank is a differentiable function of time. (a) (2 points) Assuming Estra’s estimates are correct, and accounting for the potential error from the orsh gauge, what is the range of possible amounts of orsh that might be left in the ship’s tank after two weeks of normal operation? State your final answer as an interval (like [a,b]). Explain your answer, and justify the use of any theorems you use. Clarification: Due to the wording of the information about orsh usage given above (it’s given as range of litres used per day, rather than a direct statement about a derivative) it is possible to interpret this question in a way that can be solved without any calculus theorems. Our original intention with this question was to mirror examples from LEC 27 (it’s virtually the same as those examples in all but its numbers.) We will accept answers of either form. (b) (1point)Estra believes thejourney to theirhomeplanetwill require no lessthan200000 litres oforsh. Again, assuming their estimate for the ship’s orsh consumption is correct (and still accounting for the gauge’s error), how many more full days can the ship remain at UTM before it no longer has enough orsh to make the journey home? State your final answer as an integer. Explain your answer, and justify the use of any theorems you use. Clarification: Everything mentioned for part (a) also applies here. In addition, we’d like to clarify that this question is asking for how many full days the aliens can stay at UTM starting from now (when the gauge reads 300000). Many students mistakenly thought we were asking for how many days after the two weeks mentioned in part (a). 2. (3 points) The aliens want to stay longer than your estimate from Q1(b) allows, so their chief scientist Genly has come up with a way of converting water into orsh. He says that by applying a great deal of pressure to water, the aliens can obtain many litres of orsh from a single litre of water, with the exact amount varying as a function of the pressure. Genly’s first version of this process produces orsh according to the following function, where G(P) is the number of litres of orsh produced by applying a pressure P > 0 (measured in kilopascals, kP) to a litre of water: G(P) = 10+(P−10)2/5P2 The aliens can apply very large amounts of pressure to the water, but need your help to determine what to do. (a) (Warm-up! Do not submit this part to Crowdmark.) Check that G(10) = 50. What are the units for this quantity? Compute the value of G for some other values of P. What do you think is happening for large values of P? (b) (1 point) Show with a limit that increasing the pressure P to very, very large levels doesn’t produce more orsh per litre of water. (c) (2 points) Find the ideal pressure Pi at which this process is most efficient (i.e,. at which it produces the largest quantity of orsh per litre of water). How much orsh is produced (per litre of water) at the pressure Pi? Clearly state your two answers at the beginning of your solution, and show your work below. Don’t forget to justify why your answer is where this maximum occurs. 3. (2 points) Genly’s first version of the process was good, but he is confident he can do better. He spendsanother day refining his ideas, and comes up with a new, adjustable version of his process that produces Gb(P) litres of orsh per litre of water, where Gb(P) = 10+(P−b)2/5P2, where b is a positive constant that Genly can manipulate with his laboratory equipment. We’ve used the name Gb above so it’s clear that different values of b make for different functions. (a) (1 point) For a (general) value of b, find the ideal pressure Pb at which this process is most efficient (i.e., at which it produces the most litres of orsh per litre of water). Clearly state your answer at the beginning of your solution, and show your work below. Don’t forget to justify why your answer is actually where this maximum occurs. Hints: Think about whether your answer should depend on b. You’ve already found P10! (b) (1 point) For a (general) value of b, let O(b) be the maximum output value of Gb. Find a formula for O(b), and show that as b increases, O(b) also increases. In other words, the maximum efficiency of this process increases as b increases. Clearly state your formula at the beginning of your solution, and show your work below. 4. (2 points) A rival alien scientist named Faxe has come up with their own conversion process for turning water into orsh. Their process is described by the function F(P) = 1+(P−2)2/P3+10. (a) (1 point) We already know that Genly’s processes have a high peakefficiency at a low pressure. If the aliens are able to apply very high pressures (much higher than the values of Pb you were thinking about in Q2 and Q3), which process would be produce more orsh per litre of water? State your answer (“Genly” or “Faxe”) at the top of your answer and, in at most two sentences and some short computations, justify your choice below. (b) (1 point) Another alien named Tibe works in Faxe’s laboratory and has experimented with Faxe’s process at high pressures. Tibe says that for very high pressures, the increase in orsh production per litre of water from Faxe’s process is directly proportional to the increase in pressure (i.e., Tibe believes that F is a line for large values of P). Faxe hears this and thinks this is at best a good approximation for what happens at high pressures. The two of them come to the MAT135 students, who have just learned about different types of asymptotes in class, for some clarification. In at most a few sentences and some computations, explain which one of Faxe or Tibe is more correct about F, and why.
Assessment 2 PS923 - Methods and Analysis in Behavioural Science Autumn Term 2024 (updated: 2024-12-06) • This assessment counts for 36% of your overall grade. • Submission Instructions: Submit your solution as one html or pdf document containing R code, R output, figures, and written out text (i.e., full sentences) to Tabula (Assessment 2) by 12:00 noon (midday) on Wednesday, 15th January 2025. • Please use RMarkdown to create the document. • Important: Your document should be called YOUR-STUDENT-ID_a2 (followed by the correct file ex- tension). Please also add your student ID to the top of the document. To help ensure anonymous marking, please refrain from using your name in either the document, script, or the file name. • Your text does not need to contain references (i.e., references to scientific papers). General Guidelines Please complete the following tasks. Your answers should have two separate sections for each task, one immediately after the other. In the first section, write out your answers using complete sentences, as you might for the results section of a paper. Include descriptive statistics in the text, or in tables or figures as appropriate. Tables and figures should be of publication quality (i.e., fully labelled, etc.). Integrate inferential statistics into your description of the results. Your answers might be short. Given the correctness/appropriateness of the statistical analysis, the first section will play the main role for your mark. The second section acts as an appendix; this should include the complete R code that you used and its output. Add comments (after a #) to explain what the code does. The code should show all of the commands that you used; enough for others to replicate exactly what you did (I will be copying and pasting code to run it, so make sure that works). The second section will be used to help identify the source of any mistakes. You can include figures here that you used to explore the data that you do not wish to include in the first section. For practical reports and papers you would only submit the first section in the main manuscript. For an example of such a solution, see the Assignment 1 sheet. Finally, please note that submitting AI-generated text for this assessment will be considered as plagiarism; i.e., suspected cases will be referred to the academic integrity panel. Task 1 Personalised references When applying for a job, it’s obviously highly desirable to have a relevant CV and good references. This task relates to whether, and the extent to which, the quality or style of references may influence decisions. The file candidate .csv provides simulated (i.e., fictitious) data for how potential employers (identified by uID number) perceived various job candidates (rated on a scale of 0 to 100; 0 denoting definitely wouldn’t interview, 100 being definitely would interview). Each rating was given after reading the candidate’s CV and a reference letter for the candidate. The experiment used two types of candidates; each was either applying for a managerial role (where the employer would be likely to interact regularly with the candidate in-person if they subsequently got the job) or a technical role (where they would be less likely to meet on a day-to-day basis). The reference of each candidate had a bias: each was manipulated to either be slightly positive or slightly negative. Half the references were personalised (these included a small photo of the reviewer’s face in a corner of the page, and a ‘flashy’ signature in coloured ink) whilst the other half were not (no photo, and just the printed name of the referee). Note that each employer only saw one reference (positive or negative) for any particular candidate, and each employer saw an equal number of positively and negatively biased reviews across the different items. Given that references supply information, the general expectation is that the positively biased references will tend to produce higher ratings than the negatively biased references, but is this effect similar for both personalised and non-personalised references (and for different role types)? Some might expect that the impact of the reference bias would be greater for personalised references than the nonpersonal. The focal hypothesis is that the effect of bias (on ratings) is stronger for personalised references than for nonpersonal references. The type of role (management or technical) mainly serves as a control variable but should also be considered. Please analyse the data with a repeated-measures (within-subjects) ANOVA and report the results as you would in a journal paper. If you were to run a similar study in future (i.e., with the same general aims, but with the potential for small changes in the design), is there anything particular that you would change, or specifically aim to control for? Please comment on this at the end of your report. Task 2 Short-cuts and time penalties Time is a precious commodity, so it is not surprising that many choices in life depend on perceptions of risk in relation to how much time something might take or save. This task provides simulated data (in file speed_greed.csv) on how individuals make choices when they are trying to achieve a goal in a minimal amount of time. The participants encounter choices in an online (single-player) game; they make several such choices before completing each level of the game. The options are sometimes useful shortcuts and sometimes impose time penalties. The decisions govern how much of a risk they take when choosing between shortcuts, or when avoiding time penalties. Prior to each decision, the participant learns whether they will face a shortcut option or a time-penalty (i.e., they know that they will gain or lose time on that decision, but not how much time). Having learned that there is an available shortcut, for instance, they will be given a choice between a high- variance choice (e.g., saving either 100 or 500 seconds, with equal probability), or a low-variance choice with the same average (e.g., saving either 200 or 400 seconds, again with equal probability). Thus, either option saves the same amount of time on average (300 seconds in this example) – but one option is known to have a higher variance (i.e., it is more ‘risky’). If, instead of a shortcut, they had been informed that they would receive a time penalty, then after making their choice for whether to go for the more or less risky option, they would then lose that amount of time (according to the same general scheme). Having familiarised themselves with the game (moving up, down, left, right, and how to select options when faced with choices), each participant was tasked with traversing 3 levels (L1, L2, L3). To motivate the participants, payment for their involvement was linked to the speed with which they completed the 3 levels (the faster, the better). In each level, 18 key decisions were recorded (of whether they took the high or low risk option); choices for a high variance outcome are denoted by 1; choices for the low variance outcome are denoted by 0. The file provides data for 80 participants; the data is in a wide format (one line per participant), with the intent being to have recorded a 1 or 0 for each decision; however, the recording system was not perfect; very occasionally it would not record a value for one of the choices. Each participant was assigned to one Experience group: shortcut, penalty, or mixed, which governed experiences during level 2 of the game. In levels 1 and 3, some gamble options were for shortcuts, and some were for time penalties. The options presented to a participant in level 1 was repeated in level 3 (though in a different order, to help prevent participants noticing). During level 2, the participant’s group determined whether they repeatedly faced shortcuts, time penalties or a mix of the two. The order of trials in each group was randomized separately for each participant, so each individual saw the trials in an order that was uniquely created for them. Your task is to analyse the data with an ANOVA and address the research question of whether a series of positive or negative experiences (i.e., shortcuts or penalties) has an effect on the probability of making a risky (i.e., high variance) choice. In other words, are risk preferences stable or affected by recent experiences? Please report the results as you would in a journal paper.
AY 2024/25 IOM207 Individual Case Analysis The following case describes how a PhD student, called Fangxu, did some research. You need to analyze the case and answer the questions, which are listed at the end of the case. There are five questions, and the marks allocation and the expected maximal word count for the five questions are listed below. 1 2 3 4 5 Marks allocation 10% 10% 25% 25% 30% Expected word count 150 150 350 350 450 Please follow the requirements listed below when answering the questions: 1. Please submit your answer in a Word Document (.docx). The answer sheet template is in LMO. The file should be named as [Your Student ID_Your Full Name_IOM207_Individual CW.docx]. 2. You must use appropriate in-text citations and references when answering Q3-Q5. Please cite the resources you use in an APA style. No in-text citations and references when answering Q3-Q5 means “Inadequate execution of the brief” and will induce a low score. 3. You must cite our mandatory textbook when answering Q3-Q5. No citations/references or inappropriate citations/references will cause mark deduction. 4. Your answer should mainly rely on the learning resources used in this module (e.g., lecture notes, the mandatory textbook, and optional textbooks). 5. Additional academic resources are encouraged. 6. Turnitin results will cause mark deductions only for extremely similar cases. The module leader and the examiner who marks the assignment will decide whether a suspicious case(s) constitutes academic misconduct. Turnitin system will exclude reference lists when checking the similarity rate. 7. Generative AI is not permitted for the individual assignment. 8. Please refer to the ACADEMIC INTEGRITY POLICY to avoid any academic misconduct. For example, ask for detailed written feedback from lecturers and use it on your assignment before the submission deadline, which will be regarded as COLLUSION. 9. Please indicate the word account for each question at the end of your answer. Exceeding the word limit will cause a mark deduction. Reference will not be included in the word count. 10. Your answers are expected to be written in an academic style. with references in an APA style. Case: Fangxu was a full-time PhD student. He was starting his first year of study. The previous summer, he worked as an intern in the Human Resources department at the headquarters of a large Chinese bank. During his time as an intern, staff in the HR department discussed how several staff members were unhappy about the latest round of pay rises. More specifically, some employees questioned the procedures used to decide if a staff member should be given a pay rise. This was a problem for the bank, as those members of staff who had not received a pay rise felt they had been treated unfairly. This resulted in resentment amongst these staff members. The HR business press had also identified this issue, reporting similar problems in major Chinese corporations. When Fangxu started his PhD, he began to examine the existing literature that examined the procedures used to determine pay raises and the circumstances under which staff may perceive that they have been treated unfairly. However, he was surprised to learn that very few academic papers even mentioned the topic and those that only mentioned it in relation to other concepts, such as employee turnover. Fangxu realized that the procedures used to determine pay raises and the circumstances around which employees may perceive that they have been treated unfairly represented a new area for academic research. In addition, Fangxu could not find an appropriate theory for studying this research topic. After speaking with his supervisor, Fangxu decided to study pay rise procedures and the circumstances that lead to employees' perceptions of unfairness. Fangxu and his supervisor also agreed that Grounded Theory would be the most appropriate qualitative research methodology for his research. After working as an intern, Fangxu developed a few contacts at the bank. Therefore, he approached the head of Human Resources and asked if he could study pay rise procedures. Fangxu knew that the Head of Human Resources was concerned about the issue, and after a few weeks, he heard back from her, and she agreed to give Fangxu access to the company. When Fangxu started his research, he used two forms of data collection. His primary source of data was from semi-structured interviews, where he interviewed employees who had and had not received pay raises. Additionally, staff members from the HR team who processed the applications were also interviewed. In keeping with the Grounded Theory methodology, Fangxu used ‘theoretical sampling.’ In addition, Fangxu was able to review HR documents related to pay raise applications and the official set of procedures used to determine pay raises. Questions: 1) What was the business problem that Fangxu wanted to examine? 2) What was the gap in the academic literature that provided Fangxu with his research topic? 3) What is Grounded Theory and why do you think Fangxu decided to choose Grounded Theory as his methodology? 4) Why did Fangxu use ‘theoretical sampling’ as part of his Grounded Theory research? 5) What are the advantages and disadvantages of conducting grounded theory research in the context of research that Fangxu has undertaken?
Assessment (non-exam) Brief Module code/name INST0007/Web Technologies Academic year 2024/25 Term 1 Assessment title Coursework: critical report of the developed website (with pre-requisite participation elements to qualify for self-assessment). Individual/group assessment Individual Section A: Core information Submission dates Pre-requisites for self-assessment of the critical report: 15/11/2024 – 0% In-class Moodle-based test (pass mark is a pre-requisite for taking part in self-assessment) Thursdays between 03/10-05/12 – 0% Submission of weekly tutorial sheets (at least 5 out of 8). Website Submission and Exhibition: 11/12/2024 – Developed website submitted on Moodle and for the Exhibition (See Task 1 and Task 2 in Part B). Critical Report (self-assessment): 08/01/2025 - 100% Critical self-assessment report of the submitted website with evidence from Task 1 and 2. (See Task 3 in Part B). Section B: Assessment Brief and Requirements Task 1. Designing and Developing the Website As part of this assessment, you are required to design, develop, and deploy a small website (or a cohesive portfolio of web pages) to showcase the knowledge and skills you have acquired over the course of this module. Your website will be part of an exhibition available to all students enrolled on the module to view and evaluate. You are strongly advised to develop a web-based CV as your portfolio. However, you may choose to develop a website showcasing personal interests, voluntary work, social/political work, or notes around academic research or scholarship. You must design and develop the website by taking into consideration user experience, accessibility, responsiveness, and relevant performance metrics (e.g. PageSpeed Insights). You may want to apply other user/automated testing methods (e.g. WAVE from WebAIM) to further improve your work prior submission. User-Centred Design and User Experience should remain focal for the developed website. You should demonstrate the stages of the design process, through wireframes and references to best practice. Responsive design and accessibility considerations should also be evident in the final submission. You are allowed to use open-source templates, or your own code developed as part of your Tutorial Sheet work, however, you should change the code and acknowledge the original author (on a separate References HTML page and in the accompanied report). Changing third party code should not be limited to content, but should include HTML and CSS changes. This submission should demonstrate your design and development skills. You have the freedom to choose the scope and structure of your personal website. There are, however, a set of required elements your website should contain. These are: · have three or more HTML pages, · have one of the pages dedicated to referencing external code used in the website, · have a navigation element consistently displayed throughout the website (i.e. all pages), · have HTML form. elements, · have consistency in look and feel across pages, · have the website published on UCL Personal Webpages server. NOTE: Please refer to the assessment criteria prior to starting the work on your website. You are advised to keep a record of your learning achievements (related to technical skills or broader understanding) throughout the duration of your work, to help you remember the details at time of reflection and self-assessment. This task is considered completed, when you: · Submit code to your final website on Moodle by the given deadline. Task 2. Hosting the website Prior to starting the work you should familiarise yourself with the web hosting available to UCL students commonly referred to as UCL Personal Webpages ( https://www.ucl.ac.uk/isd/services/websites-apps/personal-webpages) and the teaching material provided on Moodle. An example of a website available on the hosting is available here: https://www.ucl.ac.uk/~uczckst/inst0007/week03/exercise03/wk03-exercise03.html You will only be able to upload your website on the UCL Personal Webpages from UCL-managed machines (on campus), via Desktop@UCL Anywhere or using UCL’s Citrix Workspace. NOTE: You should remember that the UCL Personal pages are available to everyone on the web. You should remember not to add any content that may not be suitable for publishing widely and if necessary remove your website once the assessment mark is confirmed. This task is considered completed, when you: · Provide the URL to your deployed website to be included for an exhibition. Task 3: Self-assessment report Critically assess your own website against the given criteria and a good example of a peer’s website submitted for the exhibition. Your critical assessment must be submitted as a report using the form. provided (see Appendix 1). Your report should highlight the strengths and weaknesses of your website, including the design, development process, and evaluation as seen in the assessment criteria. Your assessment should also include a reflective assessment of your learning journey. When writing your critical self-assessment you should include reflections around: · the key stages of the project and justifications for decisions, such as user-centred design, and user experience; · the use of specific tools and technologies, such as prototyping to wireframing; · application of user-testing or usability evaluation method; · justifications for choosing specific user-testing or usability methods; · validity of the HTML, CSS, and JavaScript. code; · accessibility at design and deployment stages; · website performance such as Google PageSpeed Insights tests; · the key learning outcomes from working on the project; · key challenges that helped you learn; · key limitations and the future work that remained beyond the scope of the project. Along with critical assessment you should allocate a mark that reflects your assessment for each of the given criteria and in accordance to the given rubric. The self-awarded marks for individual criteria will form. your final mark for the module if you satisfy the pre-requisites for self-assessment and if the mark is not revised by tutor. Reports, which contain marks that do not reflect assessment with the given rubric, will be adjusted by the tutor and lead to revision of the final mark to reflect the lack of critical self-assessment. This task is considered completed, when you: · Submit your self-assessment report on Moodle by the given deadline. Notes on Marking Where pre-requisites are not met, the marks are guaranteed to be revised by the tutor. Additionally, any mark above 74 will also be subject to review and adjustment by the tutor. Where self-assessment criteria and the corresponding mark appear to be applied inaccurately, the marks will be adjusted to better reflect the performance on assessing your work critically and assigning a mark in line with the rubric and critical thought. Note: Module leader reserves the right to review and adjust ANY of the marks based on the submitted self-assessment report. Students should follow the assessment rubric closely to reduce the likelihood of mark adjustments by tutor. How to qualify for taking part in self-assessment and reduce the changes of your mark being adjusted by the tutor? All pre-requisites, as listed in Section A will need to be completed by the provided deadlines. The following chart (Figure 1) will help you navigate this assessment and minimise the risk of having your mark adjusted by the tutor. Figure 1: Navigating assessment and minimising reducing the likelihood the risk of having your mark adjusted by the tutor. Section C: Module Learning Outcomes covered in this Assignment This assignment contributes towards the achievement of the following stated module Learning Outcomes as highlighted below: · understand the basic principles of website design and development; · familiarised with technologies and related tools for prototyping, mark-up, and scripting; · understand concepts and develop skills related to user experience and accessibility; · understand concepts related to good practices of developing and evaluating websites.
Harmonic Motion Experiment Seven Physics 191 Before Lab ● Read the lab guide and attempt all theory questions ● Review error analysis concepts in the Experiment One and Experiment Two lab guides as needed ● Watch a short Crash Course video on Simple Harmonic Motion Experiment Overview This is a two-week experiment. You are expected to show all of your work for any calculations. It is best to perform any algebra symbolically. All tables and plots should include a caption, and each section needs a short introduction providing context for your work. ● Write a lab report that can be understood by a general scientific audience ● Study the properties of oscillations described by Hooke’s law ● Characterize the stifness of a spring by measuring its spring constant in diferent situations ● Observe how friction manifests in periodic motion 1 Theory Oscillations are ubiquitous in nature. The action of a heartbeat or firing of a neuron, the business cycles in economics, the operation of electrical grids, and the seismic activity of the earth are just a few examples of systems which undergo oscillation. An oscillation is the periodic (repetitive) variation of some quantity. Physical oscillation is commonly referred to as harmonic motion, a name originating from the first inves- tigations of musical harmony. Early experiments performed by Pythagoras and his students were the first to quantify the relationship between physical motion and sound. In this lab, you will study the behavior of a weight attached to a spring. Despite the simplicity of the system, the corresponding analysis extends far beyond the realm of springs (and even mechanics). 1.1 Simple Harmonic Motion In the pendulum motion lab, you were introduced to the concept of a restoring force. When stationary, the pendulum hangs in its equilibrium position. If dis- placed a small amount to the left, the gravitational force acts to pull the bob back to equilibrium. A displacement to the right does the same thing. In this example, the gravitational force behaves as a restoring force as it always acts to restore the bob to its equilibrium position. The defining characteristic of simple harmonic motion is that the force responsible for motion is a restoring force which depends linearly on the displacement of the weight from equilibrium. The spring force happens to meet both of these criteria in most physical situations. Figure 1: Successive snapshots of a weight on a spring show that the position of the weight varies sinusoidally in time (image source: Bauer & Westfall, 2014). Figure 2: The position of the weight at the min- imum, equilibrium, and maximum positions la- beled on the x-axis. Hooke’s law is the empirical description of the force exerted by a spring. Consider the spring shown in Figure 1. The weight oscillates because the spring exerts a restoring force as it extends and compresses. This force is described by Hooke’s law which, in one dimension, has the form. Fs = -kx (1) where k is a number characterizing the stifness of the spring called the spring constant and x is the displacement from equilibrium. Figure 2 shows three important positions of the weight. The x-axis is oriented vertically and pointed downward. (a) The spring is fully compressed with the weight at its minimum position, x = -A. Here, the weight experiences a force Fs = -k(-A) = kA. This amounts to a push towards equilibrium. (b) The spring is in its equilibrium position with the weight at x = 0. According to Equation 1, the spring force at this point is Fs = 0. While the spring exerts no force at this point, the weight’s inertia causes it to continue moving through the equilibrium position. (c) The spring is fully extended with the weight at its maximum position, x = A. Here, the weight experiences a force Fs = -kA. This amounts to a pull towards equilibrium. This process repeats periodically, with the weight continuously moving between -A and A. The number A is called the amplitude, which is a measure of the distance traveled in one cycle. The period is the time of one complete cycle and is typically denoted as T. The ordinary frequency is the number of cycles per second and is directly related to the period by f = T/1 (2) With a qualitative understanding of simple harmonic motion, we are in a position to develop a quantitative model of the displacement as a function of time, x(t). The first step is to identify any involved forces and use our friend Fnet = ma. Consider the simplest case in which the only force involved is the spring force, Fs = -kx. This gives the relationship Fnet = ma = -kx. (3) This equation can be used to determine the displacement as a function of time, but doing so requires some footwork. The first step is to recognize that the acceleration is the second derivative of the position, a = d2 x/dt2 . Substituting this into Equation 3 gives m dt2/d2x = -kx. (4) Taking one final step makes the relationship more clear, writing the previous equation as (5) where x is written as x(t) to explicitly show that the position is a function of time. This is an ordinary diferential equation with a form identical to that for the angle θ(t) in the pendulum motion lab. The equation above says that x(t) must be a function whose second derivative equals itself times a negative constant. The sine and cosine functions behave in this way (you should verify this for yourself). With most diferential equations, the best way to find a solution is to guess and check. We have a hunch that the solution is sinusoidal, which is further supported by the red curve traced in Figure 1. Let’s try x(t) = A cos (ω0t) (6) where ω0 is the angular frequency. In order to check ifour guess is valid, we can take derivatives of x(t) and see if Equation 5 is satisfied: The trick is to compare Equations 5 and 7, as they are both equal to the second derivative of the position: This shows that Equation 6 is a valid solution, with the added bonus of finding a relationship between the angular frequency, spring con- stant, and mass (shown in red). Thus, the angular frequency of our oscillating mass is ω0 = √ (9) There is one problem left to address. According to Equation 6, the position at t = 0 is fixed: x(0) = A cos (0) = A. If we start measuring when the weight is not exactly at its maximum value, then x(0) A. This is illustrated in Figure 3, where t = 0 corresponds to a value less than the maximum. To account for this shift in position, we introduce a number called a phase constant which is typically denoted by a lowercase Greek phi, φ . With this modification, we arrive at a general solution for the position as a function of time: x(t) = A cos (ω0t + φ). (10) With this equation, the position of the weight can be predicted at any point in time as long as long as A, ω0 , and φ are known. Figure 3: A graph of x(t) with the amplitude A and period T indicated. 1.2 Damped Harmonic Motion The motion described in the previous section is called ”simple” because the behavior does not change for diferent masses and springs. Regardless of the situation, an object in simple harmonic motion will continue to oscillate indefinitely. While this is a good approximation for many systems, a more accurate model must consider the efects of dissipative processes. Without an external energy source, all macroscopic oscillations will eventually decay as stored energy is converted into heat. This decay reduces the oscillation amplitude in a process called damping. The efects of damping can be introduced to the model by adding a damping force that resists motion. The damping force can be approximated as Fd = -bv where b called the damping coefficient and v is the Figure 4: Successive snapshots of a weight on a spring oscillating in water, where the amplitude rapidly decays (image source: Bauer & Westfall, 2014). velocity of the object. Circling back to Fnet = ma, we can follow the same procedure as in the previous section, where the total force on the mass is now Fnet = Fd + Fs ma = -bv - kx The last line gives the relationship necessary to determine the position of a damped oscillator as a function of time. By defining √ = b/2m and using the definition ω0(2) = k/m from above, the resulting diferential equation can be expressed compactly as = -2√ (11) In the last line, we have acknowledged that the velocity is the time derivative of position, v = dx/dt. This is the damped analog of Equation 5 for the simple harmonic oscillator. When damping is present, the properties of the spring, mass, and friction significantly afect the character of x(t). Under the conditions of this experiment, you will study underdamped oscillations. The equation for the position as a function of time resembles Equation 10, but now the amplitude is also a function of time, giving x(t) = A(t) cos (ωt + φ). It turns out that the amplitude decays exponentially as A(t) = A0 e-√t where A0 is the maximum amplitude. This gives the explicit form of the position for a damped oscillator, x(t) = A0 e-√t cos (ωt + φ), (12) which is the red curve shown above in Figure 4. Let’s take a closer look at the parameters in this equation: ● A0 is the maximum amplitude. This value is reached only in the first oscillation, as all subsequent oscillations have a decaying amplitude. ● √ is the damping rate, defined above as (13) where √ is a lowercase Greek gamma. The damping rate is directly related to the oscillation decay time, with units of s-1 . ● ω is the system angular frequency. This is the measurable frequency of the oscillator. It is related to the undamped angular speed, ω0 , and the damping angular speed, √ , by (14) This shows that the oscillation frequency is always reduced when compared with the undamped fre- quency, with the reduction dependent upon the damping coefficient (b) and the mass of the oscillator. Figure 5: The position of a underdamped harmonic oscillator as a function of time, with the exponential damping curve shown in light blue (image source: Bauer & Westfall, 2014). The graph in Figure 5shows the position as a function of time with the efects of damping. The sinusoidal nature is still evident, but now the curve is ”enveloped” by an exponential function which decays with time (light blue curve). The rate at which oscillations decay is extremely important to engineers in many fields. The development of car suspension, rocket engines, bridges and buildings, and audio systems are just a few examples which require careful damping rate tuning. 1.3 Damping Rate Estimation There is a useful method for estimating the damping rate for a decaying oscillation that only requires a graph of the position as a function of time. Take a close look at decaying amplitude of the graph in Figure 5, represented by A(t) = A0 e- t. Dimensional analysis tells us that the damping rate, √ , must have units of s-1 (the argument of an exponential function must be unitless). It is common to define a corresponding characteristic time constant (15) where τ is a lowercase Greek tau. This is analogous to the relationship between the period and the ordinary frequency, T = 1/f. With this definition, the amplitude can be expressed as A(t) = A0 e-t/τ . (16) This curve is shown to the right. Consider the value of A(t) at two diferent times: ● At t = 0, A(0) = A0 . This is the maximum amplitude in this example. ● At t = τ , A(τ ) = A0 e-τ /τ = A0 e-1 . Recalling that the number e ≈ 2.718, its inverse is e-1 = 0.368. This gives A(τ ) = 0.368A0 . In other words, the time at which the amplitude is 36.8% of its initial value is the time constant. Figure 6 Using this process, the time constant can be used in conjunction with Equation 15 to determine the damping rate. A table summarizing important quantities related to oscillation and damping times is given below. Quantity Relationship Ordinary Frequency Angular Frequency Damping Rate Time Constant f = 1/T ω = 2πf √ = b/2m τ = 1/√ 1.4 Theory Questions 1. It is important to recognize that the spring force depends on the displacement from equilibrium. A more general expression for Hooke’s law is Fs = -k△x where △x = x - x0 is the amount by which the spring is stretched. We typically define coordinates (the measurement system) such that the equilibrium position is x0 = 0 to obtain the form Fs = -kx. The diagram below shows three cases of an identical spring suspending diferent masses. The masses are stationary with displacements indicated on the x-axis. The position x = 0 corresponds to the end of the unstretched spring. a. (1 pt) A free-body diagram is a tool to help you write an equation for Fnet. Draw a free-body diagram showing the forces acting on an arbitrary mass, m. b. (1 pt) Write down an equation for net force acting on a mass. With this, solve for the acceleration, a, in terms of the spring constant k, the displacement x, the gravitational acceleration g, and the mass. c. (2 pt) What is the acceleration when the mass is at rest in the equilibrium position? Use this fact and your result from the previous question to obtain an equation that gives the displacement in terms of g , k, and m. d. (1 pt) Use your equation to predict the displacement you would measure in a lab if you hung a 1 kg mass from a spring with k = 7 N/m. Use g = 9.8 m/s2 as the gravitational acceleration. e. (1 pt) Sketch a graph of the displacement vs. mass. Indicate the three points (m, x1 ), (2m, x2 ), and (3m, x3 ) corresponding to the picture above. What can be said about the slope of the graph? f. (1 pt) How would the graph change if the positions were measured from the top of the spring instead of the position labeled x = 0? Does changing the starting point of the measurement change the slope of the graph sketched in the previous question? g. (1 pt) Does the force exerted by a spring depend on the choice of a coordinate system? 2. (1 pt) If a large weight is attached to a small spring, the spring will overstretch and deform. The deformation changes the behavior. of the spring. With this observation, do you think Hooke’s law is valid for any displacement? 3. (1 pt) Is gravity necessary for a mass on a spring to oscillate? 2 Experimental Setup In this experiment, you will use electronic equipment to measure the mechanical properties of a system com- prised of a spring and a set of masses. A digital oscilloscope is a tool that produces a real-time visualization of an input voltage. If a damped oscillating voltage is fed into an oscilloscope, the device can generate an image like that shown below, where the vertical axis is voltage and the horizontal is time. This allows for digital measurements of the amplitude or period (labeled as T Figure 7). Figure 7 1. (1 pt) For the oscillation shown in Figure 7, determine the peak amplitude A0 (no units are necessary), the period T, the ordinary frequency f, and the angular frequency W . 2. (1 pt) Does the signal in Figure 7 look like a damped or an undamped oscillation? A dynamic force transducer is an interface between the spring (a mechanical object) and the digital oscilloscope. The dynamic force transducer (DFT) has an arm that extends outward and attaches directly to the spring. When a force is exerted on the arm, such as the force from a spring, the DFT generates a voltage that is directly proportional to the applied force. DFT Operation ● The side of the DFT has two knobs: – Sensitivity should always be turned to its maximum value (clockwise). – Zero Adjust allows the output voltage to be shifted to 0 V when a force is applied to the arm. This is akin to the ”tare” setting on a digital scale. ● If you do not see a signal from the DFT, make sure the device is switched to ON. If this does not work, your TA can check the battery. Oscilloscope Operation The oscilloscope has a myriad of settings and features, but you will only need to change a few things throughout the experiment. Changing any settings on the device only efects what is observed on the screen, nothing about the input signal changes. Figure 8: The oscilloscope control panel with the relevant controls circled. Oscilloscope Setup & Adjustments ● A BNC cable connects the DFT to the oscilloscope. Ensure the the cable is attached to channel A. ● . ● Ensure that the VAR knob is pointed at ”CAL” and left alone. ● Turn on the oscilloscope with the green ”POWER” button. ● Press the ” .” ● Press the ”DIGITAL MEMORY” button (circled in green). ● Use the ”TB” control (circled in cyan) to adjust the time base. This is the scale for the horizontal axis which corresponds to time. The time on the display indicates the amount of time corresponding to the 1 cm grid on the output screen. Adjust this setting to ”0.5 s.” ● The voltage scale operates in the same way, but the changes are to the vertical axis. To adjust the voltage scale, use the leftmost control circled in red, labeled as ”A V – mV.” Set the value to ”0.1 V.” ● Note: you are encouraged to play with the time and voltage scales, choosing values which give the best presentation on the output screen. ● The ”XMAGN” button is the rightmost button circled in green. Press this button until the display shows [- - - - - - - - -] so the device continuously collects new data. This may change the TB setting. Readjust to the value to ”0.4 s.” ● Press ”GND” (circled in red) to ground the input, setting it to 0 V. Turn the ”X-POS” and ”Y-POS” knobs to center the output line on the screen. Do not touch these knobs for the rest of the experiment. This ensures consistency of values on the output screen. ● Press ”GND” again so the signal from the DFT is displayed on the output screen. As the force applied to the DFT changes, the signal on the screen will move up or down. If the line moves beyond the output screen, adjust the DFT ”Zero Adjust” knob. Figure 9 Oscilloscope Measurements The oscilloscope has horizontal and vertical cursors (lines) that allow for measurements of voltage and time intervals. The cursors are setup and moved using the ”soft-keys” which are shown in the Figure 9 (the surface of the buttons point toward the ceiling). Inspect the main screen. If there is no text at the bottom of the screen, press one of the blue soft-keys below the screen to make the text appear. If one of the soft-keys has ”RETURN” displayed above it, keep pressing it until it no longer says ”RETURN” to get back to the top menu. You should see: CURSORS SETTINGS TEXT OFF ● To display the measurement cursors, first press the ”MODE” soft-key. Toggle ”V-CURS” and ”T- CURS” to show or hide the lines. The press ”RETURN” to move to the previous options. ● In order to measure time intervals, you will use the T-cursors which display as two vertical lines. The horizontal distance between the two lines corresponds to an amount of time, which is displayed on the top of the screen. ● Use the soft-keys to move the two lines to the left or right. These cursors allow you to measure the period of oscillation. By aligning the cursors to adjacent peaks, the period can be determined. ● Instead of determining the frequency using the value displayed on the screen, calculate the value using f = 1/T. This will be more accurate than using the displayed value. 3 Static Measurement of the Spring Constant The first set of measurements will be made without the electronic devices. The multipart theory question was written to help you prepare for this experiment. Your goal is to use Hooke’s law to determine the spring constant, k, by making a series of measurements with the weight stationary. The spring will stretch by diferent amounts depending on the suspended mass. The displacements can be measured directly with a ruler or meter stick. 1. (1 pt) Write an equation that relates the mass, spring constant, and displacement (this was the subject of the first set of theory questions). 2. (1 pt) Does the spring constant depend on the mass suspended by the spring? 3. (3 pt) Describe the method you will use to make your measurements and analyze the results. Be sure to include the dependent and independent variables in your experiment. Think about a plot you can make to determine the value of k from your measurements of mass and displacement. 4. Organize a table and make your measurements. The mass of the spring can be neglected, but the hook must be included in all measurements of mass. 5. (3 pt) Generate a plot of your data using curve.fit and determine k ± δk. There are multiple ways this can be done. You are free to choose a method. Be sure to include horizontal and vertical error bars corresponding to measurement uncertainties. Show your error bar calculations. 6. (1 pt) Predict the undamped angular frequencies, ω0 , using your determined value of k for each diferent mass.
CSE 101 Introduction to Data Structures and Algorithms Winter 2024 Description: Introduction to abstract data types and basics of algorithms. Linked lists, stacks, queues, hash tables, trees, heaps, and graphs will be covered. Students will also be taught how to derive big-Oh analysis of simple algorithms. All assignments will be in C/C++. Prerequisites: CSE 12 or BME 160; CSE 13E or ECE 13 or CSE 13S; and CSE 16; and CSE 30; and MATH 11B or MATH 19B or MATH 20B or AM 11B. Lecture: TTh 3:20pm - 4:55pm Kresge 3105 Class Webpage:https://people.ucsc.edu/~ptantalo/cse101/Winter24/ Instructor: Patrick Tantalo https://users.soe.ucsc.edu/~ptantalo/ Email: [email protected] Office Hours: Wednesday: 10:00am - 12:00pm & 2:00pm - 4:00pm Zoom Link (Uses CruzID Gold) Meeting ID: 950 0400 0649 Dates: Wednesday January 10 - Wednesday March 13 Teaching Assistants: Vincent Tan ([email protected]) Engin Tekin ([email protected]) Saeed Kargar ([email protected]) Sai Venkat Malreddy ([email protected]) Akashleena Sarkar ([email protected]) Amin Karbas ([email protected]) Jacqueline Yan ([email protected]) Karthik K Bhat ([email protected]) Course Tutors: TBA LSS Large Group Tutors: Tony Umemoto ([email protected]) Xavier Thompson ([email protected]du) Required Text: Introduction to Algorithms (3rd edition) by Cormen, Leiserson, Rivest and Stein. MIT Press 2009 (ISBN 978-0-26-203384-8) Recommended Texts: Open Data Structures (pseudo-code edition) by Pat Morin.https://opendatastructures.org/ Data Abstraction & Problem Solving with C++ (6th edition) by Carrano & Henry. Pearson 2013 (ISBN 978-0-13-292372-9) Coursework: 50% Programming Assignments: Eight projects due at roughly 7-8 day intervals 15% Midterm Exam 1: Thursday, February 1 (3:20-4:25pm, lecture to follow) 15% Midterm Exam 2: Thursday, February 29 (3:20-4:25pm, lecture to follow) 20% Final Exam: Monday, March 18 (4:00-5:30pm) All scores are rounded to the nearest 10th of a percent. They will not be rounded further. No scores are curved. The following letter grade boundaries will be used to determine your grade in the class. Grading scale: A+ 99.0% - 100% A 94.0% - 98.9% A- 91.0% - 93.9% B+ 89.0% - 90.9% B 84.0% - 88.9% B- 81.0% - 83.9% C+ 79.0% - 80.9% C 70.0% - 78.9% C- 68.0% - 69.9% D+ 65.0% - 67.9% D 61.0% - 64.9% D- 59.0% - 60.9% F 0% - 58.9%
CISC2001 Lab Project Assignment Nov, 2024 1 Reminder • Deadline: Before the final exam 2 Assembly Programming Project 2.1 Task Description Design, code and test/debug an Aarch64 assembly program that performs the following: 1. Read in a line of character which contains from 1 to 100 bytes; 2. The characters/bytes represent UTF-8 encoding of Unicode Character; 3. Count the number of Unicode characters in the input stream; 4. The result of the count should return as the exit code of the program with the following code: 1 ... 2 // Read i n the string with syscall to read (fd , ∗ buffer , len ) 3 mov x0 , #0 // f d for stdin 4 ld r x1 , =buffe r // load the address of buffer to x1 5 mov x2 , #200 // number of character to be read 6 mov w8 , #63 // syscall# for read 7 s vc #0 // invoke the syscall 8 9 // after the call , the number of byte read i n should stored i n x0 10 // and the input bytes are store i n buffer 11 // This i s the part that you should work on your magic 12 13 // i . e . , count the number of Unicode character i n the buffer 14 // After that , assumed that the result was found and stored i n x1 15 mov x0 , x1 // save the result a s exit code i n x0 16 mov w8 , #93 // service # for exit 17 s vc #0 Note: Since UTF-8 is a variable-length encoding scheme, the number of Unicode characters will not be the same as the number of bytes in the input string. 2.2 Testing • To test your program, you can 1. download the test-tools.zip file in the attachment files; 2. copy the file to your emulator in the same directory as your source file, using the scp command; (path-1 means the path of test-tools.zip under emulator (windows/macOS)) 1 scp −P 8022 path−1 ubuntu@localhost :/home/ubuntu/ 3. run the following 1 unzip test −tools . zip 2 chmod +x tester . sh 3 ./ tester . sh yourprogram Where yourprogram is the name of your assembly program. For the first two instructions, you only need to run them once. After that, each time you modify your source code, you only need to run the last instruction to test your updated program. • If the unzip program is not available in your system, you need to first install it with: 1 sudo apt install unzip • Note: – The test-tools.zip is now the final version. It has all the 9 seen test cases. When grading your assignment, an additional unseen test case will be added to the test system. – For some of the test data, you can see the actual content if you open them in a text editor. Just keep in mind that there is an invisible ” ” at the end of each data file. So, the number of characters in the data should be one more than what you see inside the file. 2.3 Requirements • The project should be completed by at most two students in a group; • Write the complete information about your group members at the beginning of your source as remarks: 1 // Group Members : 2 // 1. A−B0−1357−9 Chan Tai Man 3 // 2. A−B0−2468−0 Lie Kai Ian • Submissions that fail to achieve any one of the following will receive 0 marks: – The submission must be made on or before the deadline; – The submission must be the source code file of the assembled language program; (i.e.: filename.s) – The program must be free of syntax error and must be able to be assembled and linked successfully by the test system; – The program must pass at least one of the test cases in the test system; • Marking – Please fully document your code; – Your final mark is directly proportional to the number of test cases that your program can pass; 2.4 Files to be submitted • Source code file (i.e.: filename.s); • Project report (including screenshots of experimental results.).
