Assistant Engineering
Resume:
SANTOSH PAUDEL
+1-208******* email: *************@*.**********.***
EDUCATION
Boise State University 2017 - present
PhD, Computer and Electrical Engineering
Expected graduation date: Dec 2020 GPA: 3.9 (till now) Tribhuvan University 2013-2015
Msc, Computer System Engineering GPA: 3.96
Udacity 2019-2019
Sensor Fusion Nanodegree
TECHNICAL STRENGTHS
Computer Languages C/C++, Python,Julia
Software & Tools Matlab, Machine Learning tools (Tensor ow, Pytorch) Operating System Windows, Linux
EXPERIENCE
Developing A Robust Signal Processing Framework for Analyzing Cybersecurity Attacks in Distributed MIMO Radar Systems: Jan 2017 - present Graduate Research Project, BSU
Analyzed the radar system performance on target detection, and location estimation based on various adversary/cyberattack models.
Designed robust detector and robust localization of target for distributed MIMO radar and developed theoretical framework for optimal solution for di erent adversary models(probabilistic and static).
Aim to develop novel robust signal processing techniques for target detection and localization for various con guration of MIMO radars in the presence of the adversary using mathematical optimization, statistical learning tools and game-theoretical frameworks. Graduate Teaching Assistant Jan 2017 - Dec 2017
Boise State University, Signal and Systems (ECE 310)
Assisted undergraduate students for the problems related to the course material.
Coordinated to the faculty member during classroom instruction and course projects. PROJECTS
Tracking multiple cars in highway using Unscented Kalman Filter:
Implemented UKF to estimate state of cars by integrating multiple sensors ( noisy Radar and Lidar data) in C++.
The position( px, py) and velocity ( vx, vy ) errors in terms of RMSE is less than [0.048, 0.062, 0.35, 0.39]
Lidar Obstacle Detection:
Detect car and trucks on a narrow street, by streaming of actual pcd data obtained from a self driving car in C++.
The detection pipeline covered ltering, segmentation, clustering, and bounding boxes.
Segmentation was done by the 3D RANSAC algorithm.
Clustering based on Euclidean clustering algorithm along with the KD-Tree. Track an object in 3D using Camera and Lidar sensor:
Keypoint detectors(HARRIS, FAST, BRISK and SIFT) and descriptors(BRIEF, ORB, FREAK, AKAZE and SIFT) was used for keypoints matching in sequaence of camera images.
Lidar 3D point cloud data was projected into bounding box of objects identi ed by CNN network(YOLO framework).
Robust 3D object tracking system was developed by computing time of collision based on keypoints matching of corresponding bounding box with lidar data cluster over time. Radar Target Generation and Detection:
FMCW waveform based radar target generation and detection for given system speci cations (Fre- quency=77GHz,Range resolution=1m, Max. range=200m,Max.velocity=70m/s,velocity resolution=3m/s).
Performed range FFT on the received signal to determine the range.
Robust 3D object tracking system was developed by computing time of collision based on Implemented CFAR on the Range Doppler Map to display the target. RELEVANT COURSES
Core Courses Other Courses
Deep Learning with Python Neural Networks
Computational Statistics Linear System
Digital Image Processing Optimization Theory and Practice Stochastic Signals and Systems Information and Coding Theory AWARDS
Dr. Homii J Baba Scholarship award from Indian government, 2013
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Merit based Scholarship award from I.O,E, Nepal, 2007
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