Category: Engineering

  • Engineering Question

    Problem 1

    Consider the system described by the following transfer function:

    begin{equation*}

    frac{Y(s)}{R(s)} = frac{6}{(s+1)^2(s+4)}.

    end{equation*}

    begin{enumerate}[label=alph*.]

    item textbf{Find the differential equation of the system.}

    item textbf{Derive a state-space representation of the system.}

    end{enumerate}

    Problem 2

    For the system represented by:

    [

    A = begin{bmatrix} -3 & 1 \ -1 & 2 end{bmatrix},quad

    B= begin{bmatrix}1\1end{bmatrix},quad

    C= begin{bmatrix}1 & 0end{bmatrix},quad D=[0]

    ]

    Find the transfer function of the system.

    Problem 3

    Consider the following block diagram representation of a feedback control system as shown in Figure~ref{fig:prob3}:

    begin{enumerate}[label=alph*.]

    item textbf{Determine the transfer function of the system.}

    item textbf{Derive the state-space representation, $K=5$, $alpha=0.5$.}

    item textbf{Confirm the transfer function using the state-space approach.}

    end{enumerate}

    Problem 4

    Consider the system described by:

    begin{equation*}

    ddot{y} + dot{y} + tfrac{1}{2}y = tfrac{1}{2}u

    end{equation*}

    begin{enumerate}[label=alph*.]

    item textbf{Find the state transition matrix $Phi(t)$.}

    item textbf{Find the homogeneous state vector.}

    item textbf{Find the homogeneous output response.}

    item textbf{Find the transfer function of the system.}

    item textbf{Find the forced state vector.}

    item textbf{Find the forced output response.}

    item textbf{Find the complete output response of the system.}

    end{enumerate}

    Problem 5

    Consider the following electrical circuit shown in Figure~2:

    begin{enumerate}[label=alph*.]

    item textbf{Obtain the transfer function $E_o(s)/E_i(s)$.}

    item textbf{Find the state-space representation of the system (i.e., the matrices A, B, C, and D).}

    end{enumerate}

    Problem 6

    Consider the following state-space system:

    [

    dot{x}(t) =

    begin{bmatrix}

    -1 & 0 & 1 \

    1 & -2 & 0 \

    0 & 0 & -3

    end{bmatrix}

    x(t)

    +

    begin{bmatrix}

    0 \

    0 \

    1

    end{bmatrix}

    u(t),

    quad

    y(t) =

    begin{bmatrix}

    1 & 1 & 0

    end{bmatrix}

    x(t).

    ]

    subsection*{(a) State transition matrix $Phi(t)$}

    subsection*{(b) Homogeneous output response with $x(0) = [1 ;; 0 ;; 1]^T$}

    subsection*{(c) Transfer function}

    Problem 7

    Verify if the following matrices can be state transition matrices $Phi(t)$.

    subsection*{(a)}

    [

    Phi(t) =

    begin{bmatrix}

    -e^{-t} & 0 \

    0 & 1-e^{-t}

    end{bmatrix}.

    ]

    subsection*{(b)}

    [

    Phi(t) =

    begin{bmatrix}

    1-e^{-t} & 0 \

    1 & e^{-t}

    end{bmatrix}.

    ]

    subsection*{(c)}

    [

    Phi(t) =

    begin{bmatrix}

    1 & 0 \

    1-e^{-t} & e^{-t}

    end{bmatrix}.

    ]

    subsection*{(d)}

    [

    Phi(t) =

    begin{bmatrix}

    e^{-2t} & te^{-2t} & tfrac{t^2}{2}e^{-2t} \

    0 & e^{-2t} & te^{-2t} \

    0 & 0 & e^{-2t}

    end{bmatrix}.

    ]

    Problem 8

    Consider the system with the following closed-loop transfer function:

    [

    frac{Y(s)}{R(s)} = frac{K}{0.5s^5+7s^4+3s^3+42s^2 + 4s +56}.

    ]

    begin{enumerate}[label=alph*.]

    item textbf{Apply the Routh-Hurwitz criterion to study the stability of the system.}

    item textbf{Is there any pole on the $jomega$-axis? If yes, find them.}

    end{enumerate}

    Problem 9

    Determine whether the system is stable or not, and if not, determine the number of unstable poles for the following characteristic equations:

    begin{enumerate}[label=alph*.]

    item $C.E_1 = s^5+2s^4+2s^3+2s^2+3s+4$

    item $C.E_2 = s^6+s^5+3s^4+4s^3+s^2+s+1$

    end{enumerate}

  • DNN Roofline & Benchmarking (Final, corrected)

    # HW1 Part 2 DNN Roofline & Benchmarking (Final, corrected)

    #

    # Run the notebook cells sequentially in Jupyter/Colab.

    # Requirements: `torch`, `torchvision`, `timm`. Optional but recommended: `thop`, `ptflops`.

    # This notebook implements Parts 15 and includes brief discussion Markdown blocks after each part.

    #

    # %%

    # Cell 1: imports & device detection

    # %%

    # %% [markdown]

    # ## Part 1 Chip analysis & roofline plot

    # We collect peak FLOPs and memory bandwidth for a set of diverse chips including CPU, GPU, ASIC, and SoC.

    # Replace numeric values with exact datasheet numbers + citations in your report.

    # %%

    # Cell 2: CHIP TABLE (datasheet example numbers)

    # %%

    # Cell 3: Roofline plotting helper

    # %%

    # %% [markdown]

    # **Discussion Chip Analysis**

    #

    # In this comparison, high-end data center GPUs such as the NVIDIA A100 achieve the highest peak FLOPs and memory bandwidth, making them ideal for compute-intensive DNN workloads. ASICs such as Google TPU v3 are optimized for matrix operations and deliver excellent energy efficiency but are less flexible than GPUs. General-purpose CPUs (e.g., Intel Xeon Platinum 8380) have the lowest FLOPs but provide better versatility for mixed workloads. Mobile/SoC devices like Apple M2 and NVIDIA Jetson Orin NX demonstrate significantly lower absolute performance but much higher energy efficiency per watt, suitable for edge inference. Overall, GPUs dominate in raw performance, while ASICs and SoCs optimize for specialized efficiency.

    # %%

    # Cell 4: Models to analyze (>=6)

    # %%

    # %% [markdown]

    # ## Part 2 DNN Compute & Memory Analysis

    # For each selected model, compute FLOPs, parameter count, activation bytes, and operational intensity.

    # FLOPs are computed using `thop` (MACsFLOPs) if available; otherwise `ptflops` is used (converted as needed).

    # %%

    # Cell 5: FLOPs & params computation (thop preferred) and activation memory estimation

    # %%

    # %% [markdown]

    # **Discussion DNN Compute and Memory Analysis**

    #

    # The FLOPs and parameter counts vary widely across models.

    # Lightweight models such as MobileNet V2 and EfficientNet-B0 show low FLOPs and memory footprints, resulting in higher operational intensity and better suitability for mobile/edge devices.

    # In contrast, deeper networks like ResNet-50 and ViT-Base exhibit much higher FLOPs but also higher memory demands, making them more compute-bound.

    # When overlaid on the GPU roofline, lightweight models tend to be **memory-bound**, while heavy architectures approach the **compute-bound** region of the curve.

    # %%

    # Cell 6: Visualize GFLOPs, Params, and Operational Intensity

    # %%

    # Cell 7: Overlay model operational intensities on the primary chip roofline

    # %%

    # %% [markdown]

    # **Discussion DNN Compute/Memory Overlay**

    #

    # Overlaying operational intensity on the GPU roofline shows which models are memory-bound versus compute-bound on the primary GPU. Models with low operational intensity (small FLOPs per byte) land in the bandwidth-limited regime; models with large FLOPs per byte approach the compute limit.

    # %%

    # Cell 8: Benchmarking helpers and run benchmarks at batch sizes {1,64,128,256}

    # %%

    # %% [markdown]

    # **Discussion Benchmarking**

    #

    # Latency generally increases with model complexity but is not perfectly correlated with FLOPs or parameter count. For convolutional models, FLOPs often predict latency better; transformer-based models may exhibit memory- and scheduling-induced deviations. Throughput scales with batch size until memory or hardware saturation limits further gains.

    # %%

    # Cell 9: Latency vs FLOPs and Latency vs Params (annotated)

    # %%

    # %% [markdown]

    # **Discussion FLOPs vs Parameters as latency predictors**

    #

    # FLOPs sometimes align with latency for compute-bound workloads, but memory access patterns, kernel implementations, and batching affect runtime. Parameter count correlates imperfectly: high parameter models can be memory-hungry but not necessarily slow if compute is optimized. Use both metrics and measured latency to draw conclusions.

    # %%

    # Cell 10: Throughput vs batch (use base=2 for log scale)

    # %%

    # Cell 11: Measured performance overlay on roofline (compute FLOPs/sec from measured latencies)

    # %%

    # %% [markdown]

    # **Discussion Hardware Utilization and Peak Performance**

    #

    # The measured GPU performance points are typically below the theoretical roofline. This gap arises from kernel launch overhead, memory access inefficiencies, and less-than-ideal use of specialized units (e.g., tensor cores). Models with low operational intensity are limited by memory bandwidth; compute-heavy models may approach the FLOP ceiling but still show headroom lost to implementation inefficiencies.

    # %%

    # Cell 12: Forward vs Backward runtime (runtime measured) and FLOPs heuristic

    # %%

    # %% [markdown]

    # **Discussion Inference vs Training**

    #

    # The backward pass consistently takes roughly 23 the time of the forward pass, matching the expected FLOPs ratio. This occurs because gradients must be computed and stored for every parameter during training. Deeper models like ResNet-50 often show larger backward-to-forward ratios due to more operations and memory traffic. Pie charts (below) break down latency/FLOPs/activation by layer type for the forward pass and estimate the backward breakdown.

    # %%

    # Cell 13: Per-layer breakdown (resnet50 example) + forward/backward pie charts

    # %%

    # %% [markdown]

    # **Discussion Per-layer breakdown and forward/backward**

    #

    # Convolutional and linear layers dominate both FLOPs and latency. The backward pass is estimated as ~2 forward FLOPs, and backward latency is distributed proportionally to forward-layer time when measured backward time is available. This provides an actionable decomposition for optimization.

    # %%

    # Cell 14: final notes + saved files

    # Part 1a Chip Roofline Plot

    # Define chip specs and plot_roofline function

    # Part 2c Operational Intensity Overlay

    # Collect model results into a list of dicts

    # (Replace the FLOPs and memory values with the ones you already computed in Part 2a/2b)

    # ————————————————-

    # Part 2a & 2b Model FLOPs, Parameters, and Memory Footprint

    # ————————————————-

    # Pick the best device available (GPU MPS CPU)

    # Example input for profiling (batch size 1, 3 channels, 224×224 image)

    # Define the models you want to analyze

    # Collect results

    # —— Bar Chart: FLOPs ——

    # —— Bar Chart: Parameters ——

    # —— Bar Chart: Memory Footprint ——

  • Literature review

    i am working on my masters project in which I created a neural network to predict house prices using the feed forward method. Attached you will find a zip file that contains my paper named literature review – Rev a. In which there are multiple examples of different types of house prediction models. These models need to be compared to feed forward neural networks and state why it is better to use a feed forward neural network. I am giving you the sources used for what I already wrote but I do not have sources for background on feed forward neural network. Please rewrite my essay and have each section be compare to feed forward neural networks

  • commercial implications of the instructed variations

    Ensure the work is relevant to the UK construction industry, including applicable contract forms, regulations, and industry practices.

    Below are few questions from other students and answers from instructors:

    1) Question from student:

    In regard to the four variations, which of the following scenarios is correct?

    1- The depth and configuration is just one variation, applied to 4 pile caps (according to the drawings) makes it 4 variation.

    2- The depth of pile cap is one variation, where the configuration of the piles are another, as for the remaining last two variations we need to make assumptions.(I lean more towards this approach).

    I appreciate everyone to share their views

    Answer from instructor:

    As the assessment scenario states, “the clients design team introduced changes to the buildings substructure and issued four variation instructions to the contractor…”, it suggests that variations in the scenario are related to the substructure. So, if you have taken an approach to assume the other two/three variations in relation to the substructure, that is sensible and appropriate.

    Please make sure you state your assumptions clearly. As long as they are sensible and justified, they will be considered appropriate within the context of the assessment.

    2) Question from student

    I assume I can relay these variations into NEC4 option C contract type, as this is the preferred method of contract for civils in the industry due to the uncertainty the civils scope entails. Would it be preferred we reference contract clauses for assessing compensation events 61-65 and payment etc etc??

    Answer from instructor:

    Yes, it will be better to refer to the relevant clauses. Please state your assumptions (e.g. Type of contract) clearly.

    3) Question from Student

    Am I right to assume that for Task 1, it’s a report with assumptions about impact on cost, time, etc. but more an explanation of the impact and that would mean for the main contractor as opposed to actual calculations, etc. For clarity, I haven’t made any assumptions about contract sum and the increase to this in terms of actual figures within the report. Am I on the right track or should I be including calculations within the report, or is it enough to reference the 5% variation more generally?

    Answer from instructor:

    Yes, you are right. You are not expected to present actual calculations or carry out a detailed quantitative analysis for Task 1. Your report should focus on a qualitative analysis, using reasonable assumptions and considering the 5% variation in a general sense.

    Attached Files (PDF/DOCX): Assessment Brief.pdf

    Note: Content extraction from these files is restricted, please review them manually.

  • Assignment 10 (ELEN 210)

    SEE ATTACHED.

    Attached Files (PDF/DOCX): Assignment 10 (ELEN 210).docx

    Note: Content extraction from these files is restricted, please review them manually.

  • Discussion 10 (ELEN 201)

    Pick ONE of the ODD problems from this collection “” that has not already been solved. Pretend that you are tutoring someone who has never seen these problems before and give a detailed demonstration of its solution.

    Note: Problems are selected on a first-come basis. If a classmate has already chosen a problem you had wanted to answer, please select a different problem. Our goal is to answer and discuss as many different problems as possible.

    Select Start a New Conversation and make the problem number the subject of your post.

    The answers are at the end of the document, so don’t just give an answerwe can already see what the answers are. Don’t post an explanation unless your answer matches the correct one!

    It isn’t enough to solve the problem; you need to explain your logic.

    Pasting from Word does not work. It does not preserve the math notation. Also, please do not use attachments – that just makes things harder for everyone else. Instead, type directly into the response box and use the Edit Equation tool (fx)you will find in the menu above (in the second row of tools, near the end). For help on using it, see the attached document called “Using the Equation Editor.pdf.”

    This is a moderated forum. Your posting will not be visible to the rest of the class until I approve it. Occasionally, more than one person will tackle a problem before they can see the work of others. In that case, credit will be given to all posters. Once the solution to a problem has become visible, that problem is off limits and you will need to choose a different problem in order to get credit.

    I will indicate in the grading comments if corrections need to be made. If you haven’t received credit, first double-check for my comments in the gradebook. If everything looks OK, then message me asking me to check on it.

    • If I indicate that there is a problem, you must make the necessary corrections and have your work posted in order to receive credit.
  • Lab report

    I have attached my lab manual and the code, and results I got for each task (task 1 & 2). According to them write me the following: (400 words total) – 1) Description of task 1 2)explanation of the code 3) result and discussion explain the figures and discuss result (400 words total) – 1) Description of task 2 2)explanation of the code 3) result and discussion explain the figures of the result and discuss the result (250 words total) – Write me a conclusion for the report – Mention references

    Attached Files (PDF/DOCX): Lab4.pdf

    Note: Content extraction from these files is restricted, please review them manually.

  • Critical Evaluation of Materials Science Comparing AI- Assis…

    I’m attaching the complete rubric for this assignment. I will need two peer- review articles for this. I originally chose grain structure and phase development in hydrated cement systems; however, I’m open to the other topics listed in the rubric.

    Attached Files (PDF/DOCX): material science.docx

    Note: Content extraction from these files is restricted, please review them manually.

  • Engineer Economics Personal Financing

    In this assignment, you will build a personal finance model using ProjectionLab.com to simulate your future financial life. This exercise will help you understand the real-life financial decisions you will encounter throughout your career, as well as how to plan for major life events and personal goals. A key outcome is identifying when you will achieve Financial Independence (FI)the point at which your savings and investments reach 25 times your annual living expenses (a common financial independence rule). You will also use a Monte Carlo Simulation to test the robustness of your financial plan.

    Attached Files (PDF/DOCX): Engineering Economics_ Personal Finance Model Assignment.pdf

    Note: Content extraction from these files is restricted, please review them manually.

  • P2 Final report calculations,graphs tables transfer

    I just need a transfer of important information from P1 to my P2 final report calculations for the launcher, appendix, and to place all the graphs in tables in there in the right placement, team 41, so only use the data in the team 41 sections.

    Attached Files (PDF/DOCX): P2-SolutionsGrid-Team 41.docx, IEP-P2_Team41-FinalReport (1).docx, ENGR_1523_-_P1_Report_partial (1).docx, P2-SolutionsGrid-Team 41.docx, IEP-P2_Team41-FinalReport.docx, ENGR 1523 – Report Template.docx, LeTourneau University ENGR 1812.pdf

    Note: Content extraction from these files is restricted, please review them manually.