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A complete design guide for a 4-Bit BCD Counter with 7-Segment Display output. This download includes a technical report and walkthrough covering the SystemVerilog implementation of sequential counting logic (Modulo-10), combinational decoding, and testbench verification with waveform analysis.
The objective of this project is to design and simulate a synchronous digital system using SystemVerilog that functions as a 4-Bit BCD (Binary Coded Decimal) Counter. The core technical challenge involves implementing specific Modulo-10 logic to restrict the standard 4-bit binary counting range (0–15) to a decade sequence (0–9), ensuring the system correctly resets to zero upon reaching nine. Additionally, the system requires a combinational decoder module to translate the 4-bit binary output into active-high 7-bit control signals suitable for driving a 7-segment display. The final design must be verified through a ModelSim testbench to confirm correct timing, rollover logic, and display mapping.
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A complete solution guide for electronics problems covering semiconductor properties and diode applications. This PDF includes detailed calculations for drift current density and resistivity in n-type silicon, along with circuit analysis for half-wave rectifiers, bridge rectifiers, and diode logic gates.
Q 1:Consider an intrinsic silicon bar of cross-section $4\text{cm}^2$ and length $0.35 \text{ cm}$ at room temperature $300^\circ \text{K}$. An average field of $20\text{V/cm}$ is applied across the ends of the silicon barCalculate:Electrons and hole components of current densityTotal current in the barResistivity of the barGiven: Electron mobility ($\mu_n$) is $1400 \text{ cm}^2/\text{V-s}$Hole mobility ($\mu_p$) is $450 \text{ cm}^2/\text{V-s}$Intrinsic carrier concentration ($n_i$) of Si at room temperature ($300^\circ \text{K}$) = $1.5 \times 10^{10}/\text{cm}^3$If now donor impurity to the extent of 1 part in $10^8$ atoms of Si is added. Find the density of minority carriers and the resistivity, given that the Number of Si atoms/$\text{m}^3 = 4.99 \times 10^{28}$Q 2:Calculate the drift current density for a given semiconductor. Consider silicon at T = 300 K doped with arsenic atoms at a concentration of $N_d = 8 \times 10^{15}/\text{cm}^3$. Assume mobility values of $\mu_n = 1500 \text{ cm}^2/\text{V-s}$ and $\mu_p = 400 \text{ cm}^2/\text{V-s}$. Assume the applied electric field is $100 \text{ V/cm}$.Q 3:Plot $i_D(t)$, $v_D(t)$ and $v_O(t)$ for the following circuit shown below assuming $V_\phi=0.6\text{V}$ for diode.[Circuit diagram showing a voltage source $v_I$, a diode $D$, and a resistor $R$]The wave form of input $v_I = 15 \sin(200t - 25)$Q4:Assuming $V_\phi=0.6\text{V}$ for each diode, find the values of $I$ and $V$ in the following circuits:(a) [Circuit with +5V source, 2.5 k$\Omega$ resistor, diode to ground](b) [Circuit with +5V source, 2.5 k$\Omega$ resistor, reversed diode to ground](c) [Circuit with diode from ground, 2.5 k$\Omega$ resistor to -5V](d) [Circuit with reversed diode from ground, 2.5 k$\Omega$ resistor to -5V](e) [Circuit with +2V and +1V inputs, two diodes meeting at a node with a 1 k$\Omega$ resistor to ground]Q5:Assuming $V_\phi=0.6\text{V}$ for each diode, find the values of $I$, $I_D$, and $V$ in the following circuits:(a) [Circuit with +10V source, 10 k$\Omega$ resistor, two diodes ($D_1$, $D_2$), and a 5 k$\Omega$ resistor to -10V](b) [Circuit with +10V source, 5 k$\Omega$ resistor, two diodes ($D_1$, $D_2$), and a 10 k$\Omega$ resistor to -10V]Q6:*2.17 Sketch $v_o$ versus time for the circuit in Figure P2.17 with the input shown. Assume $V_\gamma = 0$.[Figure P2.17: Bridge rectifier circuit with resistors $R_1=2.2\text{ k}\Omega$, $R_2=2.2\text{ k}\Omega$, and Load $R_L=2.2\text{ k}\Omega$]Input graph shown: Sine wave with peak +40 and -40.Q7:*2.18 (a) Sketch $v_o$ versus time for the circuit in Figure P2.18. The input is a sine wave given by $v_i = 10 \sin \omega t \text{ V}$. Assume $V_\gamma = 0$. (b) Determine the rms value of the output voltage.[Figure P2.18: Bridge rectifier circuit layout with resistors $R_1=2.2\text{ k}\Omega$, $R_2=2.2\text{ k}\Omega$, and Load $R_L=6.8\text{ k}\Omega$]
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A comprehensive resource for the Vehicle Interface and Car Class implementation project. This download contains a technical report explaining key OOP concepts like encapsulation and polymorphism , along with a visual walkthrough for configuring the project in IntelliJ IDEA.
Problem Statement: Vehicle and Car Interface: Vehicle Using IntelliJ, write a Java interface (not a class) called Vehicle that includes: startEngine(): A method that returns void and simulates starting the vehicle's engine. stopEngine(): A method that returns void and simulates stopping the vehicle's engine. drive(int distance): A method that returns void and accepts an integer parameter representing the distance to be driven. refuel(int amount): A method that returns void and accepts an integer parameter representing the amount of fuel to be added. Class: Car Create a Java class called Car that implements the Vehicle interface that has: Attributes: fuelLevel (int): Represents the current fuel level of the car. isEngineOn (boolean): Represents whether the car's engine is on or off. Add at least one attribute to simulate additional functionality. Implement all methods from the Vehicle interface: In the startEngine() method, set isEngineOn to true and print a message indicating the engine has started. In the stopEngine() method, set isEngineOn to false and print a message indicating the engine has stopped. In the drive(int distance) method, reduce the fuelLevel by the distance driven (1 unit of fuel per unit distance) and print the distance driven. In the refuel(int amount) method, increase the fuelLevel by the specified amount and print the new fuel level. Add at least one method to simulate additional functionality. Main Method In the main method, instantiate the Car class, and demonstrate the functionality of all the methods in the Car class by performing the following operations: Start the car's engine. Drive the car for a specified distance. Stop the car's engine. Refuel the car with a specified amount. Project Structure Your project should contain 3 files: Interface Vehicle. Class Car. Class containing the main method. Sample Run Output Plaintext Starting the car's engine... Engine started. Driving for 50 units of distance... Driven 50 units. Remaining fuel: 50 Stopping the car's engine... Engine stopped. Refueling the car with 30 units of fuel... Refueled. Current fuel level: 80 Final car status: Is engine on? false Current fuel level: 80 (Note: The sample run text is reconstructed based on the visual output described in the file ). Coding Guidelines Add Javadoc for each class and each method within every class. Use descriptive identifiers. Use letter-case standards for naming variables, classes, and methods. Use white space effectively (indenting blocks of code and blank lines between major sections). Add inline or block comments for non-trivial parts of your code.
UWE Bristol
A complete project resource for a Number Guessing Game developed in C. This download includes a technical report detailing the logic for random number generation and input validation, along with a step-by-step walkthrough for configuring and running the code in Visual Studio Code.
What am I required to do on this assessment? Complete a mini coursework exercise by developing a text-based game in C, and maintaining thorough and professional records of it’s development by populating this development log. Where should I start? Read and understand the mini coursework specification and marking criteria, and start to plan your software. This may be the first time you have created your own designs, so now you should develop software plans for how you want your game to run/play/operate. We suggest you revisit week 1 lecture about software development, to break down the task in manageable sections and develop units of software and test these and integrate them as you follow your game’s plan. What do I need to do to pass? Maintain the documentation of your software as you progress and develop your software (rather than leaving documentation until after you complete it). Please see the mark scheme for what is required in this development log. Refer to the marking criteria and achievement of the minimum mark e.g. a mark of 40% in total. How do I achieve high marks in this assessment? Ensure that you achieve most/all of the required design tasks and ensure you thoroughly document your software development process in a high level of detail. How does the learning and teaching relate to the assessment? In the portfolio of tutorial sheets, you were given small and specific software tasks. Now we advance your knowledge and allow you to design these tasks and integrate them together while applying some advanced software features (including header files). What additional resources may help me complete this assessment? Signpost to: • Supplementary study material folder on Blackboard • Engage with Espresso Programming daily support sessions (see Blackboard tab) • Make use of the reading list material
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A complete solution and guide for the Student Information Management System. This download features a technical report analyzing the Object-Oriented Student class and Swing GUI implementation , alongside a step-by-step walkthrough for configuring the project in NetBeans or IntelliJ to manage student records via file I/O.
Develop a java application named "Student Information Management System", where the Student Information Management System is a Java-based application designed to handle student records efficiently through a graphical user interface (GUI). This system allows users to add, load, and save student data, including details such as the student's name, ID, and GPA. The program is structured into two main components: the Student class, which defines the data model for student records, and the gui class, which provides an interactive interface for managing these records. The primary goal of this system is to simplify the process of storing and retrieving student information while ensuring data integrity through basic validation checks. The GUI makes the application user-friendly, eliminating the need for manual file editing or command-line operations.
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A complete solution guide for STAT 350 Assignment 1. This download includes a comprehensive statistical report covering Welch’s t-tests, Paired t-tests, and ANOVA, along with a step-by-step RStudio walkthrough for setting up the environment and executing the analysis scripts.
Problem 1: High School and Family Income A school district has decided that the number of students attending their high school is nearly unmanageable, so they are planning to split the high school into two zones, with Zone A students going to the old high school and Zone B students going to a newly constructed building. Some parents, however, have raised concerns with how the two zones are planned to be constructed relative to income levels. The school board conducted a study to determine whether persons in Zone A have a different mean income from those in Zone B. Random samples of 36 households in each zone are taken, and the annual income for each household was collected. The data, recorded in thousands of dollars, is included in the HouseholdIncome.txt file. a) Construct boxplots to illustrate household income by zone. Report the boxplot. Rguroo: Select Plots → Create Plot → Boxplot Select Dataset: HouseholdIncome Select Income in Numerical Variables and Zone in Factor Variables Give an appropriate title in the Title: box. R: Type in the following code. Select HouseholdIncome.txt when prompted. Give it an appropriate title in place of title. income <- read.table(file.choose(),header=T) attach(income) boxplot(Income~Zone,main=’title’) b) Based on the boxplot, do you think the mean household incomes will differ by zone? Do you think the variation in income will differ by zone? Explain your answers. c) State the null and alternative hypotheses the school board should test. d) Perform the hypothesis test from c) in software. Report your output. Rguroo: Analytics → Analysis → Mean Inference → One & Two Population Select Dataset: HouseholdIncome Click the second circle in the first box Enter Income for Variable and Zone for By Factor Enter A for the Population 1 Level and B for the Population 2 Level Select the Population 1-2 tab Select the Test of Hypothesis tab Check the t-Statistic box Choose the proper inequality and enter 0 next to Alternative Hyp R: Type in the following code. Replace insert with two.sided, less, or greater to match your alternative hypothesis. t.test(Income~Zone,alternative=’insert’) e) Use the results from part d) to make a decision regarding your hypothesis statements from part c). Be sure to identify the correct p-value. Make a decision in terms of the problem using a 0.01 significance level. f) Suppose you wish to determine if the variability in incomes differ between the two zones. State the null and alternative hypotheses you should use to test this. g) Perform the hypothesis test from f) in software. Report your output. Rguroo: Analytics → Analysis → Variance Inference → Two Population Dataset: HouseholdIncome Click the second circle in the first box Enter Income for Variable and Zone for By Factor Select A for Pop 1 Level and B for Pop 2 Level Select the Test of Hypothesis tab Check the F-Statistic box Select the != next to Alternativ Hyp and enter 1 R: Type in the following code. var.test(Income~Zone) h) Use the results from part g) to make a decision regarding your hypothesis statements from part e). Be sure to identify the correct p-value. Make a decision in terms of the problem using a 0.01 significance level. Problem 2: Mechanic Estimates Insurance adjusters are concerned about the high estimates they are receiving for auto repairs from Smith Auto Repair, especially when compared to the estimates they are receiving from Casey Automotive. To verify their suspicions, they send 20 cars recently involved in an accident to both garages for separate estimates of repair costs. The estimates (in dollars) from the two mechanics are given in the data sets AutoRepair.txt. a) The insurance adjusters will be conducting a matched pairs procedure. Explain why this is the appropriate procedure. b) State the null and alternative hypothesis statements for a test to determine if the average repair price is greater for Smith Auto Repair than for Casey Automotive. c) Conduct the hypothesis test from b) using Rguroo or R. Report all output provided by your program of choice. Rguroo: Analytics → Analysis → Mean Inference → One & Two Population Dataset: AutoRepair. Select Smith for Variable 1 and Casey for Variable 2 Select the Population 1-2 tab Select the Test of Hypothesis tab Choose the proper inequality and enter 0 next to Alternative Hyp. Check the Normal Probability Plot, t-Statistic, and Paired Data Boxes R: Type in the following code. Select the Bowls.txt data set when prompted. Replace insert with two.sided, less, or greater to match your alternative hypothesis. repair <- read.table(file.choose(),header=T) t.test(repair$Smith,repair$Casey,paired=T,alternative=’insert’) qqnorm(repair$Smith-repair$Casey) qqline(repair$Smith-repair$Casey) d) Are the conditions necessary to perform the hypothesis test from b) met? Justify your answer. e) Report the p-value for the hypothesis test, and use it to make a decision to reject or fail to reject the null hypothesis. Use a significance level of 0.05. f) Make a conclusion (in terms of the problem. g) It is always possible that you make an error when performing a hypothesis test. Based on your results, which error is possible here – a Type I error or a Type II error? What would be a consequence of this type of error in the context of the problem? h) Report the 95% confidence interval for the true mean difference in repair price between the two garages (Note: you may need to perform a new analysis – use the instructions in part c) as a starting point). Is 0 contained in this interval? What does this tell you? Problem 3: Earnings by Degree Level It is generally assumed that the higher the degree you earn, the higher an individual’s earnings will be. In the year 2000, a researcher collected random samples of 50 American adults in each of five categories of higher educational achievement: Some higher education (Some); Associate degree (Associate); Bachelor’s degree (Bachelor); Master’s degree (Master); and Doctorate (Doctorate). Use a significance level of 0.05 for any test. Data are included in the EarningsEd.csv file on Blackboard. a) Identify the type of experimental design employed in one sentence. Can we define this study as balanced? b) What is the response variable in this study? c) What is the factor and its levels in this study? d) Produce a boxplot on Earnings grouped by Degree. e) State the null and alternative hypotheses to determine if the mean earnings differ by highest degree earned. f) Run the ANOVA in software. Report the ANOVA table. Rguroo: Analytics → Analysis → ANOVA Select the EarningsEd dataset Enter Earnings for the Response Enter Degree for the Factor Click the box next to Diagnostics Click the box next to Post-hoc Test R: Enter the following code. Select the EarningsEd.csv file when prompted. Earn <- read.csv(file.choose()) Mod <- aov(Earnings~Degree,data=Earn) summary(Mod) g) Use the results from f) to make a decision regarding your hypothesis statements in e). Be sure to identify the p-value. h) Do you need to perform a multiple comparison procedure? Why or why not? i) Regardless of your answer to h), perform Tukey’s HSD. Provide the output and state which pairs of means show significant differences. Rguroo: This should have been produced in part f). R: Enter the following code. TukeyHSD(Mod) j) Comment on the normality and constant variance assumptions using graphics. Include the graphics you reference with your answers. Rguroo: These should have been produced in part f). R: Enter the following code. par(mfrow=c(2,2)) plot(Mod) k) Perform Levene’s test to determine if the constant variance assumption holds. Report the p-value. Is the constant variance assumption adequate? Rguroo: This should have been produced in part f). R: Enter the following code. If the car package is not installed, it will prompt you to select a CRAN mirror. You can choose any. install.packages(‘car’) library(car) leveneTest(Earnings~Degree,data=Earn) l) In one sentence, state whether you felt it was appropriate to run the ANOVA test, including your reasoning why or why not. Problem 4: Thermal Pane Heat Loss An experiment is conducted to investigate the heat loss for three different designs of commercial thermal panes. In order to obtain results that would be applicable throughout most regions of the United States, a researcher decided to evaluate the panes at five different temperatures: 0°F, 20°F, 40°F, 60°F, and 80°F. A sample of 15 panes of each design was obtained. Three panes of each design were randomly assigned to each of the five exterior temperature settings. The interior temperature of the test was controlled at 70°F for all five exterior temperatures. The heat losses (in W) associated with the five pane designs are given in the data set Panes.csv. Use a significance level of 0.05 for all tests. a) What are the main factors of this experiment? b) What is the response variable? c) Identify the levels of each factor. d) Run the ANOVA test using software. Present the ANOVA table. Rguroo: Analytics → Analysis → ANOVA Select the Panes dataset Click the Two-Way tab Select Hours as the Response Select Design as Factor A and Temperature as Factory B Click the Interaction box Click the Post-hoc Test box R: Enter the following code. Select the BatteryDevice.csv file when prompted. Pane <- read.csv(file.choose()) Mod2 <- aov(HeatLoss~Design*Temperature,data=Pane) summary(Mod2) e) Run the first hypothesis test to determine if all the treatment means are equal. Since this p-value is not directly on the output, state the hypotheses and show your work to obtain the test statistic and the p-value. Draw a conclusion in context. f) Test to see if there is any interaction present. State the interaction hypotheses and provide the p-value of the F test statistic. Draw a conclusion in context. g) Test each main effect hypothesis (no matter what the result is in part f)). State each hypothesis and provide the p-values. Draw conclusions and interpret your results. h) Produce the Tukey HSD output and make note of the pairs of means that are significantly different. Remember, your results in (f and g) will determine which pairs to analyze.
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Access the complete solution and guide for the RandomCircleOperations Java project. This download includes a detailed technical report explaining the implementation of the Random class for dynamic inputs and geometric formulas , along with a step-by-step walkthrough for configuring and running the project in IntelliJ IDEA.
Random Circle Operations Write a Java program using IntelliJ that: • Uses the Random class to generate a random radius between 1 and 20. • Calculates the area and circumference of a circle using the radius. • Calculates the distance of a point (x, y) from the circle's center (0, 0), where x and y are random integers between -10 and 10. Display the following to the user: • The random radius. • The area and circumference of the circle. • The coordinates of the random point and its distance from the circle's center. Use the following formulas for the calculations: • Area: 𝐴𝑟𝑒𝑎 = 𝜋 ⋅ 𝑟 2 • Circumference: 𝐶𝑖𝑟𝑐𝑢𝑚𝑓𝑒𝑟𝑒𝑛𝑐𝑒 = 2 ⋅ 𝜋 ⋅ 𝑟 • Distance: 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑥 2 + 𝑦 2 Sample execution: Random radius: 12 Circle Area: 452.39 Circle Circumference: 75.40 Random point: (-7, 8) Distance from center: 10.63
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A comprehensive guide for processing GRACE-FO satellite data using MATLAB to compute inter-satellite range, range-rate, and ground station visibility, featuring numerical differentiation and orbital visualization.
Background The Gravity Recovery And Climate Experiment (GRACE) mission was launched by NASA in 2002. The primary objective of GRACE was to measure variations in Earth’s gravity field to track mass changes in the hydrosphere, cryosphere,solid Earth, and oceans. In contrast to single satellite approaches with one dedicated sensor, GRACE used a constellation of two satellites, orbiting one behind the other, featuring a suite of measurement systems. GRACE mission ended in July 2017. The GRACE FollowOn (GRACE-FO) mission, which launched May 22, 2018, will continue the work of GRACE mission. GRACE and GRACE-FO are very similar satellite missions. Each satellite mission consists of two identical satellites flying in the same near-polar orbit at about 500 km altitude, one following the other at a distance of about 220 km (see Figure 1 below). Both satellites are equipped with (1) GPS to measure their position and (2) accelerometers (placed at the center of mass of the satellite) to measure nongravitational forces like air drag acting on the satellites. The principal observation of the GRACE and GRACE-FO missions is the ultra-precise measurement of inter-satellite distance between the two satellites. Distance between the two satellites changes because of the gravitational pull of the masses beneath the satellites. Therefore, tracking the change in the inter-satellite distance enables us to measure mass changes that happen on or near the Earth’s surface. Surface mass changes are caused by various processes like the Greenland icesheet melting due to global warming and climate change or groundwater depletion in California’s Central Valley due to overpumping of groundwater used for irrigation. GRACE-FO Orbit Data from NASA Jet Propulsion Laboratory (JPL) The files “GNV1B_2024-02-22_C_04.txt” and “GNV1B_2024-02-22_D_04.txt” provide the orbits of GRACE-FO satellite #1 and #2 (also known as GRACE-FO satellite C and D) on 22 February 2024, respectively, released by NASA Jet Propulsion Laboratory (JPL). The files include the position and velocity vector (as well as their error information) of each satellite every second and the time (epoch) of position/velocity vectors. The time is given as seconds passed since 01-Jan-2000 11:59:47 UTC. Position and velocity vectors are given in the Earth-Centered Earth-Fixed (ECEF) geocentric coordinate system: The Z axis is the line between the North and South Poles (with positive values increasing northward), the X axis is in the plane of the equator, passing through the origin and prime meridian (Greenwich), and the Y axis in the plane of the equator, extending from 90°W longitude to 90°E longitude (positive). The MATLAB function “ReadGFO_Orbit.m” provided to you reads the information given in the GRACE-FO orbit files and outputs the time (in decimal year) as well as position and velocity vector of the GRACE-FO satellites #1 and #2 (also known as satellites C and D). You will use this function to extract the information required for the programming tasks described below from the GRACE-FO files. ASEN 1320 Aerospace Computing and Engineering Applications Spring 2025 Figure 1. GRACE and GRACE-FO measurement is implemented by two identical satellites orbiting one behind the other in a near-polar orbit plane at ~500 km altitude. The along-track separation is kept within a range of 220 ± 50 km. The satellites experience positive and negative, gravitationally induced, along-track accelerations due to the varying mass distribution below them. Each satellite will experience the effects of the local mass at slightly different times causing a differential acceleration. The differential acceleration, in turn, causes distance (range) variations and velocity differences Δv that are proportional to the mass attraction related to the gravitational potential, U. The relative distance between the satellites is measured with micron-level precision by a high accuracy dual frequency K-band inter-satellite ranging system. An accurate three-axis accelerometer measures the effects of all non-gravitational forces acting on each satellite, including atmospheric drag, direct and Earth-reflected solar radiation pressure, and thrusting. A GPS receiver on each satellite provides position and time synchronization. The satellites overfly the entire Earth surface within approximately 30 days, allowing monthly estimates of a global gravity model with a surface spatial resolution of about 300 km with an accuracy of 2 cm. [Reference: Tapley et al., 2019]. ASEN 1320 Aerospace Computing and Engineering Applications Spring 2025 Programming Tasks Write six user-defined MATLAB functions and a main program to perform the programing tasks described below. All the user-defined functions should be called inside the main function to perform the required actions. [5 points for calling the functions in the main program] 1. Review the function called ReadGFO_Orbit (provided to you) with the following input and output parameters: Input A string variable called InFilename, indicating the name of the GRACE-FO orbit text file (for example, “GNV1B_2024-02-22_C_04.txt”). Output A column vector called t_dy, indicating the time (epoch) of satellite position/velocity measurements converted to decimal year. A matrix (with 3 columns) called r, indicating the position [x y z] of GRACE-FO satellite. The number of rows in this matrix is equal to the number of position measurements of GRACE-FO satellites given in text file (which is equal to 86400: 1 position per second). A matrix (with 3 columns) called v, indicating the velocity [Vx Vy Vz] of GRACE-FO satellite. The number of rows in this matrix is equal to the number of velocity measurements of GRACE-FO satellites given in the text file (which is equal to 86400). This function will read the data from the text files “GNV1B_2024-02-22_C_04.txt” and “GNV1B_2024-02-22_D_04.txt” and extract time (and will convert it to decimal year) and position and velocity vector of the GRACE-FO satellite #1 and #2. Therefore, this function should be called twice in the main program. The MATLAB function “ReadGFO_Orbit.m” (provided to you) performs this programming task for you. Please go through this function carefully to make sure that you fully understand the MATLAB built-in functions used in this user-defined function. 2. Write a function called GFO_Range with the following input and output parameters: [10 points] Input A matrix (with 3 columns) called r1, indicating the position [x1 y1 z1] of the GRACE-FO satellite #1. A matrix (with 3 columns) called r2, indicating the position [x2 y2 z2] of the GRACE-FO satellite #2. Output A column vector called Rho, indicating the inter-satellite range (distance) between the two GRACE-FO satellites. This function will compute the inter-satellite range (distance) 𝜌𝜌 using the following equation: 𝜌𝜌 = �(𝑥𝑥2 − 𝑥𝑥1)2 + (𝑦𝑦2 − 𝑦𝑦1)2 + (𝑧𝑧2 − 𝑧𝑧1)2 3. Write a function called GFO_RangeRate with the following input and output parameters: [15 ASEN 1320 Aerospace Computing and Engineering Applications Spring 2025 points] Input A matrix (with 3 columns) called r1, indicating the position [x1 y1 z1] of the GRACE-FO satellite #1. A matrix (with 3 columns) called r2, indicating the position [x2 y2 z2] of the GRACE-FO satellite #2. A matrix (with 3 columns) called v1, indicating the velocity [Vx1 Vy1 Vz1] of the GRACE-FO satellite #1. A matrix (with 3 columns) called v2, indicating the velocity [Vx2 Vy2 Vz2] of the GRACE-FO satellite #2. Output A column vector called Rho_dot, indicating the inter-satellite range-rate. This function will compute the inter-satellite range-rate 𝜌𝜌̇ using the following equations: 𝜌𝜌̇ =< 𝚫𝚫𝒗𝒗 ⋅ 𝒆𝒆𝟏𝟏𝟏𝟏 𝐋𝐋𝐋𝐋𝐋𝐋 > where < ⋅ > indicates the dot product and 𝚫𝚫𝒗𝒗 and 𝒆𝒆𝟏𝟏𝟏𝟏 𝐋𝐋𝐋𝐋𝐋𝐋 are defined as 𝚫𝚫𝒗𝒗 = 𝒗𝒗𝟐𝟐 − 𝒗𝒗𝟏𝟏 = [𝑉𝑉𝑥𝑥2 − 𝑉𝑉𝑥𝑥1 𝑉𝑉𝑦𝑦2 − 𝑉𝑉𝑦𝑦1 𝑉𝑉𝑧𝑧2 − 𝑉𝑉𝑧𝑧1] 𝒆𝒆𝟏𝟏𝟏𝟏 𝐋𝐋𝐋𝐋𝐋𝐋 = 𝚫𝚫𝒓𝒓 𝜌𝜌 = 𝒓𝒓𝟐𝟐 − 𝒓𝒓𝟏𝟏 𝜌𝜌 = [ 𝑥𝑥2 − 𝑥𝑥1 𝜌𝜌 𝑦𝑦2 − 𝑦𝑦1 𝜌𝜌 𝑧𝑧2 − 𝑧𝑧1 𝜌𝜌 ] 𝚫𝚫𝒓𝒓 = 𝒓𝒓𝟐𝟐 − 𝒓𝒓𝟏𝟏 = [𝑥𝑥2 − 𝑥𝑥1 𝑦𝑦2 − 𝑦𝑦1 𝑧𝑧2 − 𝑧𝑧1] 𝚫𝚫𝒗𝒗 is the relative velocity vector between the two satellites and 𝒆𝒆𝟏𝟏𝟏𝟏 𝐋𝐋𝐋𝐋𝐋𝐋 is the unit vector pointing from GRACE-FO satellite #1 to satellite #2. 4. Write a function called GFO_NUmDiff with the following input and output parameters: [15 points] Input A scalar variable called dt, indicating the sampling rate (in seconds) of position and velocity vectors given in the GRACE-FO orbit files (which is equal to 1 second). A column vector called Rho, indicating the inter-satellite range (distance) between the two GRACE-FO satellites. A column vector called Rho_dot, indicating the inter-satellite range-rate. Output A column vector called Rho_dot_ND, indicating the inter-satellite range-rate computed from inter-satellite range 𝜌𝜌 using numerical differentiation (see 𝜌𝜌̇ 𝑁𝑁𝑁𝑁 below). A column vector called Rho_dot_diff, indicating the difference between Rho_dot_ND and Rho_dot (= 𝜌𝜌̇ 𝑁𝑁𝑁𝑁 − 𝜌𝜌̇). This function will compute the inter-satellite range-rate from inter-satellite range 𝜌𝜌 using numerical differentiation as follows (𝑁𝑁 is equal to the number of epochs (86400) in the GRACE-FO orbit files): ASEN 1320 Aerospace Computing and Engineering Applications Spring 2025 𝜌𝜌̇𝑁𝑁𝑁𝑁(𝑡𝑡𝑖𝑖) = 𝜌𝜌̇𝑁𝑁𝑁𝑁(𝑡𝑡1) = 𝜌𝜌(𝑡𝑡2) − 𝜌𝜌(𝑡𝑡1) 𝑑𝑑𝑑𝑑 𝜌𝜌(𝑡𝑡𝑖𝑖+1) − 𝜌𝜌(𝑡𝑡𝑖𝑖−1) 2𝑑𝑑𝑑𝑑 ; 𝑖𝑖 = 2, 3, 4, … , 𝑁𝑁 − 1 𝜌𝜌̇𝑁𝑁𝑁𝑁(𝑡𝑡𝑁𝑁) = 𝜌𝜌(𝑡𝑡𝑁𝑁) − 𝜌𝜌(𝑡𝑡𝑁𝑁−1) 𝑑𝑑𝑑𝑑 This function will also compute Rho_dot_diff which is equal to 𝜌𝜌̇𝑁𝑁𝑁𝑁 − 𝜌𝜌̇. 5. Write a function called SatVisibility with the following input and output parameters: [15 points] Input A 1×3 row vector called rG, indicating the ECEF position [xG yG zG] of the Satellite Laser Ranging (SLR) station in Maryland, U.S.A. rG = [1130714.219 -4831369.903 3994085.962]. A matrix (with 3 columns) called r1, indicating the position [x1 y1 z1] of the GRACE-FO satellite #1. Output A column vector called el, indicating the elevation angle (in degrees) of the GRACE-FO satellite #1 from the SLR station in Maryland. A column vector called index_vis, indicating the index of elevation angles greater than 10 degrees in the column vector el. Such elevation angles indicate the epochs of GRACE-FO satellite #1 visible from the SLR station. The SLR technique measures the distance between the station and satellites using the laser pulses when the satellite is visible from the station. This function will first compute the elevation angle of the GRACE-FO satellite #1 with respect to the SLR station in Maryland as follows: 𝑒𝑒𝑒𝑒 = arcsin � 𝑧𝑧1 − 𝑧𝑧𝐺𝐺 𝑑𝑑 � 𝑑𝑑 = �(𝑥𝑥1 − 𝑥𝑥𝐺𝐺)2 + (𝑦𝑦1 − 𝑦𝑦𝐺𝐺)2 + (𝑧𝑧1 − 𝑧𝑧𝐺𝐺)2 where [𝑥𝑥1 𝑦𝑦1 𝑧𝑧1] and [𝑥𝑥𝐺𝐺 𝑦𝑦𝐺𝐺 𝑧𝑧𝐺𝐺] indicate the position of GRACE-FO satellite #1 and SLR station, respectively. The function will also return the index of elevation angle values index_vis in the column vector el that are greater than 10 degrees. 6. Write a function called writeGFO_CSV with the following input parameters: [10 points] Input A string variable called OutFilename, indicating the name of the csv file with 3 columns which will be used to store the values of column vectors t_dy, Rho, and Rho_dot. A column vector called t_dy, indicating the time (epoch) of satellite position/velocity measurements in decimal year. A column vector called Rho, indicating the inter-satellite range (distance). A column vector called Rho_dot, indicating the inter-satellite range-rate. Output: No output arguments. This function will write the values in column vectors t_dy, Rho, and Rho_dot into 3 columns of the csv file OutFilename. Use "time [dec. year]", "range [m]" and "range-rate [m/s]" as the header for each column. Call this function in the main program and use “GFO_file.csv” as the name of the output csv file. ASEN 1320 Aerospace Computing and Engineering Applications Spring 2025 7. A) In the MATLAB main program, write a code that produces a figure with 3 subplots displaying the time series of Rho [in km], Rho_dot [in m/s] and el [in degrees] with respect to time (given as hours on 22 February 2024). The elevation angles greater than 10 degrees should be marked with a different color. [15 points] B) In the MATLAB main program, write a code that produces a figure showing the ground track of GRACE-FO satellite #1 overlaid on a spatial map of the world. Portion of the ground track visible from the SLR station in Maryland with elevation angle greater than 10 degrees should be marked with a different color. Location of the SLR station should be shown with a black triangle. [15 points] To display the ground track of a satellite, the ECEF Cartesian coordinates [𝑥𝑥 𝑦𝑦 𝑧𝑧] of the satellite should be converted into the curvilinear coordinates [𝜑𝜑 𝜆𝜆 ℎ], where 𝜑𝜑 is latitude, 𝜆𝜆 is longitude and ℎ is the altitude from the Earth’s spherical surface. TIP #1: Use the MATLAB function “ecef2lla” to convert from Cartesian to curvilinear coordinates. TIP #2: Use the MATLAB function “geoplot” with latitude and longitude of the GRACE-FO satellite #1 to create this plot.
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A technical guide for implementing a Java-based payroll system that utilizes inheritance and polymorphism to calculate earnings for various employee types, including salaried, hourly, and commission-based staff.
A payroll system modification which is a Java-based program designed to manage different types of employees and calculate their earnings based on their employment category. The system uses object-oriented programming (OOP) principles such as inheritance, polymorphism, and abstraction to handle various employee types, including salaried employees, hourly employees, commission employees, base-pluscommission employees, and piece workers. The program demonstrates how different employee classes can extend a common superclass while implementing their own earnings calculation logic. Additionally, it showcases polymorphic processing by treating all employee objects uniformly while still executing their specific methods.
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