zzg1234567's Stars
Abhishek-chohan/WiFi-Positioning-and-analysis-system
It is very difficult to think of an aspect of life that has not been affected by the Internet. It does more than just connecting computers. It connects people, lives, stories, and businesses. Wireless networks are present in all the large buildings or sites, and they are anticipated to provide high-speed Internet for the connected users. This can be attained by connecting wireless routers to the Internet backbone through fast connection cables (e.g. fiber optics), or as well finding the optimal position of the router along with the location, so that the targeted area is covered with Internet access as much as possible, provided that the cost constraints of routers and the cost of their mutual interconnection are satisfied. As the placement of WI-FI routers in the network is a very intensive problem concerning connectivity and coverage. It directly affects the transmission loss, installation cost, operational complexity, wi-fi network coverage, etc. However, optimizing the location of the routers can resolve these issues and increase network performance. Therefore, using major deep-learning models this problem can be resolved. The proposed model concentrates on the optimization of the objective function in terms of the empty spaces in the location, hindrances such as concrete walls, metallic objects, etc. in the area, client coverage in the location, and the network connectivity. It is an initial step to ensure the desired network performance such as throughput, connectivity, and coverage of the network. The model also additionally bifurcates the areas into divisions based on the network coverage in each region for particular chores like messaging, streaming, gaming, etc. Furthermore, an advanced Wi-Fi analyzing system for generating different results based on the observations of the Wi-Fi router and the network it is placed in is implemented. It gives an analysis report of the Wi-Fi router. It dictates the number of users presently connected to the system with their description like IP Address, Physical Address, etc and also determines the information regarding the devices in the network range of the router. It executes signal strength testing that demonstrates the strength of the signal in the network and also performs a speed testing module that determines the upload speed and the download speed of the system using real-time graph plotting. The computational experiment, performed over a dataset of sample house maps, to indicate the optimal position of the Wi-Fi proposes that the approach can obtain great results. Consequently, the results indicate that the approach can be easily adapted for application in practice for determining the network areas based on the signal strengths in the region, in terms of the Wi-Fi router placement and analyze the wireless network, devices in the network, and the connected users. The application can be extended to provide co-ordinates for a 3D map. The model can also be paired with some hardware to increase portability.
ajnas/WiFiPS
WiFi Based Indoor Positioning System, A MVP android Application
HarisIqbal88/PlotNeuralNet
Latex code for making neural networks diagrams
XudongSong/CNNLoc-Access
SensorsCloudsServicesLab/DIY_IPS-Indoor-Positioning-System
BingJiaChen/Meta-learning-for-device-free-indoor-localization
dwijokosuroso/small_GANs
Using GANs for RSSI synthesis for fingerprint-based indoor localization
Yang-Qirui/GraFin
Reproducation of GraFin: An Applicable Graph-based Fingerprinting Approach for Robust Indoor Localization
drtiwari/Indoor-Positioning-using-WiFi-Machine-Learning-for-Industry-5.0
Wifi based Indoor Localization system utilizing Wi-Fi Fingerprinting Technique - AI/ML Approach
MateuszPogorzelski/neural_network
Bachelor Thesis: "Research and analysis of deep learning architectures and their impact on the radiolocation system effectiveness in indoor environment."; Gdansk University of Technology 2023
enacheandrei7/indoor-localization-dnn-knn-lstm
Indoor localization algorithm based on RSSI fingerprinting that utilizes DNN for zone estimation and KNN for position estimation
amritaJune01/Wi-Fi-based-Indoor-Localization
gokcegok/indoor-localization-with-rnn-based-models
guanyingc/latex_paper_writing_tips
Tips for Writing a Research Paper using LaTeX
tim7107/DL_project_Wifi-signal-Indoor-localization-using-Deep-learning-Model
Wifi signal Indoor localization using Deep learning Model
mazzorca/BLE-indoor-high-precision-localization-system-
IS2AI/tutorial_indoor_localization_WiFine
In this tutorial, we will load, preprocess a simplified version of the WiFine dataset. The data will be used to train a location prediction model based (a random forest regressor and a multilayer perceptron)
exmorse/IndoorLocalizationProject
Indoor localization using WiFi and NeuralNets for Mobile Systems course
Sachini/niloc
Neural Inertial Localization
AobingJava/JavaFamily
【Java面试+Java学习指南】 一份涵盖大部分Java程序员所需要掌握的核心知识。
akullpp/awesome-java
A curated list of awesome frameworks, libraries and software for the Java programming language.
doocs/advanced-java
😮 Core Interview Questions & Answers For Experienced Java(Backend) Developers | 互联网 Java 工程师进阶知识完全扫盲：涵盖高并发、分布式、高可用、微服务、海量数据处理等领域知识
CyC2018/CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计
hello-java-maker/JavaInterview
【Java面试+Java后端技术学习指南】：一份通向理想互联网公司的面试指南，包括 Java，技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、分布式、数据库（MySQL、Redis）、Java 项目实战等
trekhleb/javascript-algorithms
📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings
Snailclimb/JavaGuide
「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试，首选 JavaGuide！
microsoft/DeepSpeed
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Significant-Gravitas/AutoGPT
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
reworkd/AgentGPT
🤖 Assemble, configure, and deploy autonomous AI Agents in your browser.
binary-husky/gpt_academic
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, moss等。