Saurab Gyawali
@saurab_gyawali
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Aspiring Data Scientist 🥼📊 | Pulchowk Campus🎓 | AI ML Learner 🎯 Statistical Modeling | Predictive Analytics | Deep Learning | Python | C++ | Data Structure
Nepal
Joined August 2024
🎶 Signal Restoration Using Data Science & Interpolation – 5/n Of Turning Concepts Into Real Life Applications 🔍✨ . ✔️ Applied data science techniques (moving average, Savitzky–Golay smoothing, Ridge regression) to denoise and restore a noisy signal
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RT @Minimaldex: 🎉 GIVEAWAY! 🥳🎉 .Win an amazing Epomaker HE80 keyboard!. To enter: .1️⃣ Follow @Minimaldex & @EpomakerK .2️⃣ Like & Retwee….
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RT @saurab_gyawali: 🎶 Signal Restoration Using Data Science & Interpolation – 5/n Of Turning Concepts Into Real Life Applications 🔍✨ . ✔️ A….
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📎 Check the visuals and code 🚀. #Python #Electromagnetics #DataVisualization #Simulation #EngineeringTools #ElectricField #Matplotlib #Numpy #Physics #STEM
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I built a Python simulation that visualizes electric field vectors and potential contours for a system of point charges using numpy and matplotlib. 💯 Ideal for:.🪨 Engineering & Physics students.🪨 Educators looking for simulations
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🔌 Electric Field & Potential Visualizer Using Python⚡ - 4/n Of Turning Concepts Into Real Life Applications 🚀 . Ever wondered how point charges shape the invisible forces around us?
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🔫 Here are applications of graph colouring:. 1. Scheduling problems (e.g., exam timetabling, job assignments).2. Register allocation in compilers.3. Map colouring (e.g., political maps).5. Circuit board testing and layout
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🧠🔢 Sudoku Solver Using Graph Coloring in Python - 3/n Of Turning Concepts Into Real Life Applications 🚀 . I just built a Sudoku solver that applies graph coloring techniques from discrete mathematics .
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🛠️ Tools Used: Python | NumPy | Matplotlib 📁. 🎛️ Domain: Control Systems | Signal Processing | Stability Analysis 📡. #bodeplot #python #pythondeveloper #systemanalysis #electronics #ece #communication #engineering #matplotlib.
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Using Python's Libraries [ NumPy and Matplotlib ] , I implemented precise calculations for both magnitude and phase response over a logarithmic frequency scale enabling a clear visualization of system behavior across frequencies , by removing linear scale error .
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The transfer function under study: . H(s) = 200(s + 1) / [s² * (s² + 2s + 100) * (s + 5)]
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✈️ 📊 Bode Plot Maker - 2/n Of Turning Concepts Into Real Life Applications 🌎 . I tried to go deep into frequency domain analysis in Control Systems & Signal Analysis , I developed a Python-based tool to generate Bode plots for higher-order transfer functions.
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- Routing in transportation and networks.- Designing circuits and finite state machines.- Optimizing schedules and resources. This is not math on paper it is math that moves planes, packets and people.
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Clear, visual, and minimum-cost path. A concrete demonstration of how algorithms are used to define routing in airline systems, supply chains, logistics, and rideshare. In Discrete Mathematics, we are connecting real life directly to its applicability, and from:.
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In this project, I created a model of U.S. cities and their connected travel costs as a weighted graph and, exclusively through logic and mathematics, calculated the most affordable route from LA to NYC , no GPS, no trial and error. The outcome?.
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You can, however, do it with Discrete Mathematics : specifically Graph Theory and Dijkstra's Algorithm.
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✈️Affordable Route From Los Angeles to New York 🌎 Through Pure Mathematics ➗. How do you plan the most affordable route from Los Angeles to New York across a network of cities?. Not with Google Maps. Or brute force.
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