Name: Md Shamim Hussain
Type: User
Company: Rensselaer Polytechnic Institute
Bio: Md Shamim Hussain is a Ph.D. student in Computer Science at Rensselaer Polytechnic Institute, NY. He got his B.Sc. and M.Sc. in EEE from BUET, Dhaka.
Location: Troy, New York, USA
Md Shamim Hussain's Projects
Investigation in 4x Super-resolution by Deep Convolutional Neural Networks
8 bit MSAP Microprocessor
Simulation of ASK (Amplitude Shift Keying) and PSK (Phase Shift Keying) modulation and demodulation in Labcenter Proteus.
Asynchronous ADMM for Consensus Optimization
Coarse Grained Classification of the Audioset Dataset into Speech, Music and Noise Classes
Assigning Speech Music and Noise Labels to Audioset
A Simple Conditional Variation Autoencoder
Edge-Augmented Graph Transformer
Edge-Augmented Graph Transformer
This project implements the fft algorithm on the Spartan 3E FPGA (Papillio One Platform) which in turn is used as a spectrum analyzer with some added hardware (e.g. ADC).
Investigation into Generative Neural Networks.
Nearest neighbor tight-binding estimation of band structures of bulk and nano-ribbon (Armchair and Zigzag) graphene.
This is the official implementation for "Do Transformers Really Perform Bad for Graph Representation?".
Band diagrams of a simple 1D crystal obtained from Kronig-Penney model
Determination the poles of Auto-Regressive Systems in Noise and poles and zeros of Auto-Regressive Moving Average system by SGD in Frequency Domain.
Evaluation of the classification performance (Speech, Music, and Noise) of 1D (WaveNet) and 2D (MobileNet) CNN and RNN (GRU) on the MUSAN corpus.
Solution of higher order polynomial equation for real roots and determination of their multiplicities by the Newton-Raphson method.
A Parallel Implementation of The Apriori Algorithm on AiMOS Supercomputer Using CUDA and MPI
An Implementation of Restricted Boltzmann Machine with Pytorch
My Personal Website
This is a single channel speech dereverberation method based on DOI: 10.1109/TSA.2005.858066; implemented in MATLAB
Stochastically Subsampled Self-Attention (SSA)
Triplet Graph Transformer
Fork of One Transformer Can Understand Both 2D & 3D Molecular Data