xiaogaogaoxiao,github

This repository contains the implementation of the paper "Spectrum Anomaly Detection for Optical Network Monitoring using Deep Unsupervised Learning" published in the IEEE Communication Letters.

202104-sufficient

An implementation of the sufficient descent Riemannian conjugate gradient methods

2d-3d-point-set-registration-based-on-global-rotation-search

# 2D-3D Point Set Registration Based on Global Rotation Search # Copyright (C) 2018 Yinlong [email protected] # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # Any publications resulting from the use of this code should cite the # following paper: # Yinlong Liu, Yuan Dong, Zhijian Song and Manning Wang, "2D 3D Point Set Registration Based on Global Rotation_Search", IEEE Transactions on Image Processing (TIP) # # #==================Note================= # #1. First step->open demo_rot.m. It is a demo of Rotation Search in SO(3) for 2D-3D point set registration. # #2. The input data is set to a real circumstance that 3D point set is far away for projection plane, and in front of camera. Our method also can be applied to unusual condition that camera is surrounded by 3D point set, only if you make some fix. # #3. RotaionSearch.m is the kernel of algorithm. You can easily extend it to SE(3) searching by grid-search, while tuning parameters depends on your tasks. # #4. Fast and Global 2D-3D point set registration without correspondence is still an open problem and need further explorations. I am very happy that if you could benefit from our code. # # # Author: Yinlong Liu # Date: 20181218 # Revision: 1.0

3sat

5gm-beam-selection

Beam-selection for mmWave MIMO using machine learning

5gm-data

Datasets and code for machine learning in 5G mmWave MIMO systems involving mobility (5GMdata)

5gnr-hetnet_model

This work demonstrates the performance feasibility of a Heterogeneous cellular Network (HetNet) setup depicting a 5G dense urban microcellular deployment scenario with a Wireless Backhaul.

a-deep-learning-based-illegal-insider-trading-detection-and-prediction-technique-in-stock-market

Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns. My research paper (IEEE Big Data 2018) on this can be found here: https://arxiv.org/pdf/1807.00939.pdf

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