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mimo_singleris

minicps

MiniCPS: a framework for Cyber-Physical Systems real-time simulation, built on top of mininet

minimalpositioncalibration

Liao Wu, Xiangdong Yang, Ken Chen, Hongliang Ren. A minimal POE-based model for robotic kinematic calibration with only position measurements. IEEE Transactions on Automation Science and Engineering. 2015, 12(2): 758-763.

minmax-adv

Code for "Adversarial Attack Generation Empowered by Min-Max Optimization", NeurIPS 2021

minmax-mtsp

A Reinforcement Learning Approach for Optimizing Multiple Traveling Salesman Problems over Graphs

mirrorproxpc

mit-6.0002-introduction-to-computational-thinking-and-data-science

https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/index.htm

mixed-monotonic

Source code for "Mixed Monotonic Programming for Fast Global Optimization" by Bho Matthiesen, Christoph Hellings, Eduard A. Jorswieck and Wolfgang Utschick, submitted to IEEE Transactions on Signal Processing.

ml-face-recognition-system

A draft machine learning based Face Recognition System works for 2018 Hackathon ArcSoft（虹软）task.

ml-phy5g-beam-selection

ml5g-phy-channelestimation-icarus

This repository contains the source codes and the description of the proposed solution to the challenge ML5H-PHY [channel estimation] by the team ICARUS.

ml_for_channel_estimation

mm-hypothesis

Code repository for "Heuristic Framework for Testing the Multi-Manifold Hypothesis".

mm_dist_lapl

mmsr

The code for NeurIPS 2020 paper: Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion.

mmtc-noma

mmv-amp-algorithm-for-massive-connectivity-with-massive-mimo

This code is for paper: L. Liu and W. Yu, "Massive connectivity with massive MIMO-Part I: Device activity detection and channel estimation," IEEE Trans. Signal Process., vol. 66, no. 11, pp. 2933-2946, Jun. 2018. and L. Liu and W. Yu, "Massive connectivity with massive MIMO-Part II: Achievable rate characterization," IEEE Trans. Signal Process., vol. 66, no. 11, pp. 2947-2959, Jun. 2018.

mmv-amp-with-side-information

This code is for paper: [On Massive IoT Connectivity with Temporally-Correlated User Activity](https://arxiv.org/pdf/2101.11344.pdf).

mmwave-localization-learning

🎯 ML-based positioning method from mmWave transmissions - with high accuracy and energy efficiency

mmwave-mu-mimo

The project represents the main code for the proposed cross-layer Dynamic sub-array scheduling for 5G applications, in collaboration with Mathworks inc.

mmwave-tensor-channel-estimation

Reference: ''Z. Zhou, J. Fang, L. Yang, H. Li, Z. Chen and R. S. Blum, "Low-Rank Tensor Decomposition-Aided Channel Estimation for Millimeter Wave MIMO-OFDM Systems," in IEEE Journal on Selected Areas in Communications, vol. 35, no. 7, pp. 1524-1538, July 2017.''

mmwavebeamforming

Comparison of Hybrid Beamforming Precoding Algorithms using Millimeter Waves

mmwavecellfree

This repository contain main codes for the paper "Self-Organizing Cell-Free mmWave Wireless Network: Hybrid Analog-Digital Beamforming and DRL-Based Modeling"

mobile-aloha

Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

mobile-ar-in-edge-computing-client

mobility-drones

MATLAB codes for the paper "Performance Characterization of Canonical Mobility Models in Drone Cellular Networks".

modanet

In the paper, a multi-task deep convolutional neural network, namely MoDANet, is proposed to perform modulation classification and DOA estimation simultaneously. In particular, the network architecture is designed with multiple residual modules, which tackle the vanishing gradient problem. The multi-task learning (MTL) efficiency of MoDANet was evaluated with different variants of Y-shaped connection and fine-tuning some hyper-parameters of the deep network. As a result, MoDANet with one shared residual module using more filters, larger filter size, and longer signal length can achieve better performance of modulation classification and DOA estimation, but those might result in higher computational complexity. Therefore, choosing these parameters to attain a good trade-off between accuracy and computational cost is important, especially for resource-constrained devices. The network is investigated with two typical propagation channel models, including Pedestrian A and Vehicular A, to show the affect of those channels on the efficiency of the network. Remarkably, our work is the first DL-based MTL model to handle two unrelated tasks of modulation classification and DOA estimation. Please cite the papar as:

mog-dun

PyTorch code for JSTSP2021 paper "Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network""

momix

a branch and bound method for multi-objective mixed integer convex optimization problems presented in the paper "SOLVING MULTIOBJECTIVE MIXED INTEGER CONVEX OPTIMIZATION PROBLEMS" by Marianna De Santis, Gabriele Eichfelder, Julia Niebling and Stefan Rocktaeschel - SIAM J. O PTIM . Vol. 30, No. 4, pp. 3122--3145, 2020

monocular-obstacle-avoidance

The code for the paper "Towards Monocular Vision based Obstacle Avoidance through Deep Reinforcement Learning"