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d2l-en

Interactive deep learning book with code, math, and discussions. Available in multi-frameworks.

daily_arxiv

Generate a list of papers with publicly available source code in the daily arxiv

data-scientist-jobs-in-uk

decentralized-energy-fabric-on-ibp20

deep-ee-opt

Source code for "A Globally Optimal Energy-Efficient Power Control Framework and its Efficient Implementation in Wireless Interference Networks" by Bho Matthiesen, Alessio Zappone, Karl-L. Besser, Eduard A. Jorswieck, and Merouane Debbah, accepted for publication in IEEE Transactions on Signal Processing.

deep-learning-power-allocation-in-massive-mimo

This is the code package related to the follow scientific article: Luca Sanguinetti, Alessio Zappone, Merouane Debbah 'Deep-Learning-Power-Allocation-in-Massive-MIMO' presented at the Asilomar Conference on Signals, Systems, and Computers, 2018. http://www.asilomarsscconf.org

deep-ml-meetups

A central repository for all my projects

deephybridbeamforming

These MATLAB scripts are prepared by A.M.E for the following paper, A. M. Elbir, "CNN-Based Precoder and Combiner Design in mmWave MIMO Systems," IEEE Communications Letters, vol. 23, no. 7, pp. 1240-1243, July 2019 please cite the above work if you use this codes, For any comments and questions please email: [email protected] This codes can also be used with slight modification for applications, such as antenna selection + multi-user hybrid beamforming and DOA estimation.

deeplearning_mimo-noma

Realization of MIMO-NOMA signal detection system based on **C. Lin et al., “A deep learning approach for MIMO-NOMA downlink signal detection,” MDPI Sensors, vol. 19, no. 11, pp. 2526, 2019.

design-of-routing-protocol-for-underwater-wireless-sensor-network

All routing protocol performs specific task in underwater wireless sensor network(UWSN). Routing is responsible for networking problems. The main design issues of the routing protocols for UWSN are Bandwidth capacity, problem related to low energy, node mobility and high propagation delays. In this project Dynamic route discovery, Cluster and depth based and Location based routing protocols are proposed. In Dynamic route discovery protocol the location of nodes, base stations and the battery powers are collected. Based on the location and the battery power route discovery process is done periodically. In order to improve the propagation delay, bandwidth and battery life time,cluster and depth based routing protocol is proposed. A cluster based network is formed to simplify the routing for forwarding the data packets. Route is discovered based on the depth of the previous sender and decides whether to forward a packet or not. Location based routing protocol is proposed to improve the delay and successful rate. A routing pipe is formed to buildconnection between source, destination and packet delivery. Estimation of neighborhood and to adjust the forwarding policy may be done using self-adoption algorithm. It may be simulated using Network Simulator(NS2) with 50 nodes and the routing efficiency may be analyzed using the parameters network throughput, packet delivery ratio and energy consumption.

divergent-association-task

The Divergent Association Task is a brief measure of creativity

dmd2018

Detection of malicious domain names using machine learning and deep learning models

downlink-and-uplink-cellular-network-analysis-using-stochastic-geometry

Modelling of Base stations using a homogeneous Poisson point process. Computation of Signal to Noise and Interference ratio (SINR) and Coverage probability for downlink and uplink scenarios

dppl

Matlab scripts for the paper "Machine Learning meets Stochastic Geometry: Determinantal Subset Selection for Wireless Networks"

dqn-powercontrol

draggan

Official Code for DragGAN (SIGGRAPH 2023)

droo

A Deep Reinforcement Learning Approach for Online Offloading in Wireless Powered Mobile Edge Computing Networks

dynamic-pso-la

Improving Learning Automata based Particle Swarm: An Optimization Algorithm

edge_inference_three-step_framework

edgecloudsim

EdgeCloudSim: An Environment for Performance Evaluation of Edge Computing Systems

edgecomputing

Network simulator for edge computing and cloud computing

edgefogsimulator

Edge-Fog cloud simulator and LPCF task assignment calculator

efficient-resource-allocations

eh_iot

Energy Harvesting For The Internet-of-Things: Measurements And Probability Models (Testbed and Analysis Code)

emotion-detection-from-text-using-pytorch-and-federated-learning

Emotion detection from text using PyTorch and Federated Learning

end-to-end-learning-of-communications-systems-without-a-channel-model

Implementation of research paper: End-to-End Learning of Communications Systems Without a Channel Model

energy-aware-routing-algorithm-for-wireless-and-selfish-delay-tolerant-network-

Routing Algorithm for selfish Delay Tolerant Network where Selfishness of node is proportional to battery available in the node

energy-efficiency-in-reinforcement-learning

Code for the paper 'Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks'

energy-efficient-cloud-sensor

:recycle: This application provides user an interface to interact with the tempreture predicted by the Artificial Neural Network.

energy-prediction-smart-cities

Comparing different algorithms for forecasting energy usage for implementation of Edge/Fog Computing systems