Debugging in MATLAB: A Barrier to Engineer Ambition

Analog design is still too often driven by iteration and SPICE sweeps. But there is a more structured way to design. This training introduces a gm/ID + Python-based workflow that makes analog design faster, more systematic, and more predictable. What you’ll learn: ✔ gm/ID-based design methodology ✔ Python-driven transistor sizing using LUTs ✔ Trade-off analysis: gain, speed, power, area ✔ Practical OTA design workflows This is about moving from iterative design → to data-driven engineering. 📅 8–29 June 2026 🎥 Recordings included 👉 Register: https://lnkd.in/eFpZapqz #AnalogDesign #ICDesign #Python #Semiconductors #Engineering

Analog design is still too often driven by iteration and SPICE sweeps. But there is a more structured way to design. This training introduces a gm/ID + Python-based workflow that makes analog design faster, more systematic, and more predictable. What you’ll learn: ✔ gm/ID-based design methodology ✔ Python-driven transistor sizing using LUTs ✔ Trade-off analysis: gain, speed, power, area ✔ Practical OTA design workflows This is about moving from iterative design → to data-driven engineering. 📅 8–29 June 2026 🎥 Recordings included 👉 Register: https://lnkd.in/eFpZapqz #AnalogDesign #ICDesign #Python #Semiconductors #Engineering

Analog design is still too often driven by iteration and SPICE sweeps. But there is a more structured way to design. This training introduces a gm/ID + Python-based workflow that makes analog design faster, more systematic, and more predictable. What you’ll learn: ✔ gm/ID-based design methodology ✔ Python-driven transistor sizing using LUTs ✔ Trade-off analysis: gain, speed, power, area ✔ Practical OTA design workflows This is about moving from iterative design → to data-driven engineering. 📅 8–29 June 2026 🎥 Recordings included 👉 Register: https://lnkd.in/eEqVkxke #AnalogDesign #ICDesign #Python #Semiconductors #Engineering

A while back, I worked on an object counting problem—specifically counting vehicles moving on both sides of a lane. At the time, the implementation was in Python, but I always wanted to push it further and rebuild the entire pipeline in C++ with optimized detection and tracking. Last week, I finally completed this as a side project. The system now: • Uses a YOLO model deployed via TensorRT for high-performance inference • Integrates tracking for consistent ID assignment • Implements line-crossing logic for accurate vehicle counting (avoiding double counts) This shift to C++ + TensorRT significantly improves performance and makes the system more suitable for real-time deployment. Next steps: • Improve post-processing and counting robustness • Benchmark performance across different hardware setups • Explore scaling to multi-stream scenarios Would love to hear thoughts from others working on similar real-time vision systems. looking for some good application to work on. #ComputerVision #YOLO #TensorRT #ObjectDetection  #CPlusPlus #RealTimeAI #ObjectTracking  #VideoAnalytics #AIEngineering #BuildInPublic

2D Elastic Chain Contact Simulation Yes, it looks simple. Developing the tool took 4 hours (repo script 1 hour, 3 hour bug solving). Building the simulation parameters took 3 hours. It handles contact, stiffness, damping, and numerical stability without breaking. P.S.: No commercial engineering tools were used. Just: Engineering fundamentals Python AI-assisted code development

🚀 Day 42 | 100 Days of Coding Challenge #DrGViswanathanChallenge 📘 Problem Solved: Maximal Rectangle (LeetCode) 🔧 Approach Used (Histogram + Monotonic Stack): • Treated each row as a histogram • Built height array where consecutive '1's increase height • For every row, applied Largest Rectangle in Histogram logic • Used stack to calculate max area efficiently 📌 Key Idea: Convert the 2D matrix problem into a series of histogram problems and solve each using stack. ⏳ Complexity: Time: O(n × m) Space: O(m) 🧠 Key Learning: Breaking a complex 2D problem into simpler 1D problems can make it much easier to solve. 💡 Optimization Insight: Using a monotonic stack helps compute largest rectangle in histogram in O(n), which makes the overall solution efficient. 📂 Topics Covered: Stacks, Monotonic Stack, Dynamic Programming, Arrays 📊 Example: Input: [["1","0","1","0","0"], ["1","0","1","1","1"], ["1","1","1","1","1"], ["1","0","0","1","0"]] Output: 6 🔥 One of the toughest and most rewarding problems so far! #100DaysOfCode #DSA #CPP #LeetCode #CodingJourney #CompetitiveProgramming

Going from writing Python code and problem solving to working with ROS2 robotics is a big change in how you think. Your code is kept in a clean digital box in standard Python scripts. You need to make an architecture in ROS2 that can understand physical space, build systems, and the timing of hardware. I've been digging deep into the ROS2 backend for the past few days, and I finally got to see the math come to life on my screen. -> I made my first ROS2 package from the ground up. Taking the time to learn how colcon build reads package.xml and Python files to make the setup.The bash overlay finally made the "magic" of ROS2 make sense. -> Learned how nodes drop high-speed "best effort" sensor data if they think it will be delivered reliably (QoS). We also learned how to use namespaces and parameter overrides so that two identical sensors, like dual LiDARs, don't try to use the same USB ports. -> This was the big win today: turning spatial math into 3D visualization. I went from simple 2D logic to writing a full Xacro/URDF blueprint for a multi-link robotic arm with a base, three joints, a gripper, and a camera to make a complicated TF Tree. I was able to broadcast the robot using robot_state_publisher, start RViz2, and use joint_state_publisher_gui to move the arm's joints in real time after dealing with a lot of strict syntax rules and file path errors. Going from raw XML code and terminal errors to a moving 3D model makes the steep learning curve entirely worth it. #ROS2 #Robotics #RoboticsEngineering #Python

Teaching Machines to See in Black & White: Thresholding! 🔳🏁 Day 84/100 In a world of gray areas, sometimes a computer needs a clear 'Yes' or 'No'. 🏗️ For Day 84 of my #100DaysOfCode journey, I tackled Image Thresholding. This is the fundamental process used in QR code scanning and document digitization. By picking a mathematical 'threshold', we can strip away shadows and noise, leaving behind a high contrast binary image that a machine can easily interpret. Technical Highlights: 🔳 Binary Classification: Using np.where to force pixel values into a strict 0 (Black) or 255 (White) state. ⚡ Noise Reduction: Simplifying complex visual data into clean shapes the first step in Optical Character Recognition (OCR). 🧮 Vectorized Decision Making: Implementing conditional logic across entire matrices without the overhead of Python loops. 🔍 Feature Extraction: Understanding how thresholding helps self-driving cars identify lane markings on diverse road surfaces. Do check my GitHub repository here : https://lnkd.in/d9Yi9ZsC #100DaysOfCode #ComputerVision #NumPy #Python #BTech #IILM #AIML #ImageProcessing #SoftwareEngineering #LearningInPublic #WomenInTech

Day 93: Optimization > Completion 📉 Problem 3661: Maximum Walls Destroyed by Robots Today was a massive lesson in efficiency. I initially cleared all test cases with a Memoization + Binary Search approach, but at O(N²), I knew it wasn't the most optimal way to handle the constraints. I decided to dig deeper and refactor the entire logic into a cleaner Dynamic Programming solution. The Strategy Shift: • The O(N²) Trap: My first pass worked, but nested recursion with memoization can get heavy. I wanted to see if I could solve it in a single linear pass. • State Transition: I moved to a DP approach using subLeft and subRight to track the maximum walls destroyed up to each robot. • Precision Boundary Logic: By pre-calculating the left and right firing ranges for each robot using Binary Search (lowerBound/upperBound), I could transition states in O(N) time. I’m honestly not proud of my initial approach today. It felt a bit like forcing a solution rather than finding the most elegant one. Pushing myself to find the O(N) path when I already had a working "Pass" was the real challenge, but it’s where the growth happens. We go again tomorrow. 🚀 #LeetCode #Java #DynamicProgramming #BinarySearch #Algorithms #DailyCode

🚀 Extron Global Configurator: Bridge or Brain. A lot of AV systems get built where Global Configurator Pro is doing all the heavy lifting and that’s where scalability breaks. Instead of relying on GC Pro for device intelligence, build a reusable Python driver module that handles: ✅ command queueing (no flooding devices) ✅ auto reconnect logic ✅ polling + keepalive ✅ feedback parsing + state tracking ✅ consistent API functions like PowerOn(), SetInput(), MuteOn() Then connect it to Global Configurator Pro like a bridge GC Pro button press ➝ sends string like: DISPLAY:PWR_ON Python module receives it ➝ calls the real driver: display.PowerOn() This architecture turns Global Configurator into a front-end UI mapping tool, while Python becomes the reusable driver engine. #Extron #AVProgramming #ControlSystems #Python #GlobalConfigurator #AVIntegration #Automation #ProAV