- Design of 4x4 Sudoku Game in Proteus.
- Design of Dot Matrix Display in Proteus and Implementation in PCB.
- Power Flow Analysis of a 9 bus Systemin PSAF.
- Implementation of a step down transformer.
- Design ofa transmitter and receiver operating in high frequency region.
- Design of 8bit PC in Proteus.
Software and Programming Skills:
- Turbo C (C Programming)
- Eclipse( Java)
- Auto Cad
Project works in Graduate Courses:
Advanced Digital Signal Processing (April, 2017 at BUET)
- Estimating random channel impulse response using LMS, VSS-LMS, NLMS and RLS algorithms
- ECG and EOG canceller using VSS-LMS and an acoustic Echo canceller using RLS
- Recovery of reverberated signal using adaptive beam former and LP residual signal
- Implementation of Piseranko, MUSIC, minimum variance, Welch and AR modelling
Digital Speech Processing (Oct, 2017 at BUET)
- Music and speech classification using GMM Information and Coding Theory (April, 2017 at BUET)
- Protein similarity analysis using Kolmogorov complexity.
Digital Image Processing (April, 2018 at BUET)
- Non-cooperative iris segmentation by convolutional encoder decoder network.
Genomic Signal Processing (April, 2018 at BUET)
- Protein similarity analysis by wavelet decomposition of cellular automata images.
Computer Vision (Fall 2019 at RPI )
- Linear and non-linear estimation of camera projection matrix
- Stereo calibration and 3D reconstruction from stereo images
- Kalman and Particle filtering for object tracking and factorization method for 3D structure deduction
Pattern Recognition (Fall 2019 at RPI)
- Protein function prediction from protein sequences.
Introduction to Deep Learning (Spring 2020 at RPI)
- Image classification using multi-class logistic regression, Neural network and CNN
- Human pose estimation with spatial-temporal RNN
- Action Recognition with spatial-temporal RNN Computational Optimization (Spring 2020 at RPI)
- Robust PCA using ADMM and ALM solver
Visual Scene Graph and its Applications (Spring 2020 at RPI)
- Evaluating existing models of scene graph generation
Introduction to Probabilistic Graphical Model (Fall 2020 at RPI)
- Implementation of Belief Propagation, Approximate Inference, Structural EM and MRF
- Learning and Inference under uncertain evidence
Physical Chip Design Engineer at PrimeSilicon
- Duration: From September, 2014 to June, 2016.
- Tapeout projects - 28nm tech node 22x17.3 mm2 chip (140M GC) & 11x11 mm2 chip (57M GC)
- Software Learned – Cadence, Verilog, AtopTech Aprisa, Calibre Physical Verification, Quantus QRC Extraction, Conformal LEC, Tempus Timing Signoff Solution, Unix Environment.
- Programming Languages -- Perl, TCL/Tk
Lecturer at Dept. of EEE, University of Liberal Arts Bangladesh (ULAB)
- Duration: From September, 2017 to May, 2019.
- Courses Taught – Electrical Circuits I, Physics, Electric Machines I, Analog & Digital Communication, Control System Engineering, Microwave Engineering, Wireless Communication.
Teaching Assistant at Dept. of ECSE, Rensselaer Polytechnic Institute
- Duration: Fall 2019 & Spring 2020.
- Courses Taught – Embedded Control, Computer Components and Operations
Research Assistant at Dept. of ECSE, Rensselaer Polytechnic Institute
- Duration: From Summer 2020 to Spring 2021
- Research Project in collaboration with IBM – Developing new algorithms for scene graph generation