Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Last update: Dec 14, 2022

Overview

Deep-Learning-based-Spectrum-Sensing

Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

The files in MATLAB are used to simulate signal and extract features. Use cs_feature.m to receive QPSK signals in AWGN channels and extract cyclostationary features of different signal, and save the .mat data for training.

The files in Python are use to train deep networks. Build a dataset "dataset" containing two typs of signals, use SignalDataloader.py to load the data and use train.py to train a deep linear network, and calculate the probability of detection and the probability of falsealarm.

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GitHub Repository

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Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

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Overview

Deep-Learning-based-Spectrum-Sensing

Owner

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

An end-to-end library for editing and rendering motion of 3D characters with deep learning [SIGGRAPH 2020]

In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

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Tensorflow 2 implementations of the C-SimCLR and C-BYOL self-supervised visual representation methods from "Compressive Visual Representations" (NeurIPS 2021)

Use MATLAB to simulate the signal and extract features. Use PyTorch to build and train deep network to do spectrum sensing.

Related tags

Overview

Deep-Learning-based-Spectrum-Sensing

Owner

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

An end-to-end library for editing and rendering motion of 3D characters with deep learning [SIGGRAPH 2020]

In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

Unofficial pytorch implementation of 'Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization'

A Python implementation of active inference for Markov Decision Processes

ETMO: Evolutionary Transfer Multiobjective Optimization

Canonical Capsules: Unsupervised Capsules in Canonical Pose (NeurIPS 2021)

*ObjDetApp* deploys a pytorch model for object detection

UPSNet: A Unified Panoptic Segmentation Network

[BMVC 2021] Official PyTorch Implementation of Self-supervised learning of Image Scale and Orientation Estimation

patchmatch和patchmatchstereo算法的python实现

Create animations for the optimization trajectory of neural nets

PyTorch implementations of the paper: "Learning Independent Instance Maps for Crowd Localization"

Pytorch implementation for "Open Compound Domain Adaptation" (CVPR 2020 ORAL)

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Signals-backend - A suite of card games written in Python

The code for our paper CrossFormer: A Versatile Vision Transformer Based on Cross-scale Attention.

Tensorflow 2 implementations of the C-SimCLR and C-BYOL self-supervised visual representation methods from "Compressive Visual Representations" (NeurIPS 2021)

ObjDetApp deploys a pytorch model for object detection