shamim-hussain/parallel-apriori-with-cuda-and-mpi

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shamim-hussain/parallel-apriori-with-cuda-and-mpi

shamim-hussain

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A Parallel Implementation of The Apriori Algorithm on AiMOS Supercomputer Using CUDA and MPI

egt_pytorch

Edge-Augmented Graph Transformer

egt

Edge-Augmented Graph Transformer

speech_dereverbaration_using_lp_residual

This is a single channel speech dereverberation method based on DOI: 10.1109/TSA.2005.858066; implemented in MATLAB

tgt

Triplet Graph Transformer

musan_investigation_cnn_rnn

Evaluation of the classification performance (Speech, Music, and Noise) of 1D (WaveNet) and 2D (MobileNet) CNN and RNN (GRU) on the MUSAN corpus.

ask_psk_modem

Simulation of ASK (Amplitude Shift Keying) and PSK (Phase Shift Keying) modulation and demodulation in Labcenter Proteus.

ssa

Stochastically Subsampled Self-Attention (SSA)

async_admm_consensus

Asynchronous ADMM for Consensus Optimization

Jupyter Notebook

graphene_bulk_nanoribbon_tightbinding

Nearest neighbor tight-binding estimation of band structures of bulk and nano-ribbon (Armchair and Zigzag) graphene.

kronig_penney

Band diagrams of a simple 1D crystal obtained from Kronig-Penney model

8bit_MSAP

8 bit MSAP Microprocessor

audioset_speech_music_noise

Assigning Speech Music and Noise Labels to Audioset

Jupyter Notebook

4x_superresolution_cnn

Investigation in 4x Super-resolution by Deep Convolutional Neural Networks

audioset_coarse_grained_classification

Coarse Grained Classification of the Audioset Dataset into Speech, Music and Noise Classes

Jupyter Notebook

fpga_fft_spectrum_analyzer

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).

rbm-pytorch

An Implementation of Restricted Boltzmann Machine with Pytorch

newton_raphson_real_roots

Solution of higher order polynomial equation for real roots and determination of their multiplicities by the Newton-Raphson method.

generative_neural_networks

Investigation into Generative Neural Networks.

model_parameter_estimation_sgd

Determination the poles of Auto-Regressive Systems in Noise and poles and zeros of Auto-Regressive Moving Average system by SGD in Frequency Domain.