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Ahmad Abdullah's Projects

bpa_emtp icon bpa_emtp

Electromagnetic Transients Program (EMTP) of Bonneville Power Administration (BPA)

computer-science icon computer-science

:mortar_board: Path to a free self-taught education in Computer Science!

digital icon digital

A digital logic designer and circuit simulator.

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PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO

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WSU EE214 - Design of Logic Circuits - Fall 2020

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Project One for Electrical Engineering 234: Getting Started with Microprocessors

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Repository for EE324 at WSU

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Introduction to Digital Systems | WSU Fall 2017

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Distance Protection Zone 3 Misoperation During System Wide Cascading Events: The Problem and a Survey of Solutions

fmask icon fmask

The software called Fmask (Function of mask) is used for automated clouds, cloud shadows, and snow masking for Landsats 4-8 and Sentinel 2 images.

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Ultrafast Transmission Line Fault Detection Using a DWT Based ANN

ieee_t_and_d_2016 icon ieee_t_and_d_2016

A Wavelet Entropy Approach for Detecting Lightning Faults on Transmssion Lines

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ATPMAT: An Open Source Toolbox for Systematic Creation of EMTP Cases in ATP Using Matlab

openetran icon openetran

OpenETran can presently simulate multi-conductor power lines, insulators, surge arresters, non-linear grounds, and lightning strokes. It efficiently calculates energy and charge duty on surge arresters, and iterates to find the critical lightning current causing flashover on one or more phases. It is also suitable for use in substation insulation coordination. Capacitor switching, TRV, and other applicaitons may be added.

peci_2016 icon peci_2016

Towards a New Paradigm for Ultrafast Transmission

segment-anything icon segment-anything

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

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Code to reproduce the results in the FAIR research papers "Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples" https://arxiv.org/abs/2104.13963 and "Supervision Accelerates Pre-training in Contrastive Semi-Supervised Learning of Visual Representations" https://arxiv.org/abs/2006.10803

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