scholarly journals RSRP difference elimination and motion state classification for fingerprint-based cellular network positioning system

2018 ◽  
Vol 71 (2) ◽  
pp. 191-203 ◽  
Author(s):  
Lin Ma ◽  
Ningdi Jin ◽  
Yongliang Zhang ◽  
Yubin Xu
Keyword(s):  
Cellular Network ◽  
Positioning System ◽  
Motion State ◽  
State Classification

scholarly journals Stereo camera visual SLAM with hierarchical masking and motion-state classification at outdoor construction sites containing large dynamic objects

2021 ◽  
Vol 35 (3-4) ◽  
pp. 228-241
Author(s):  
Runqiu Bao ◽  
Ren Komatsu ◽  
Renato Miyagusuku ◽  
Masaki Chino ◽  
Atsushi Yamashita ◽  
...  
Keyword(s):  
Visual Slam ◽  
Construction Sites ◽  
Stereo Camera ◽  
Motion State ◽  
State Classification ◽  
Dynamic Objects

scholarly journals Gated Recurrent Unit with RSSIs from Heterogeneous Network for Mobile Positioning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Junxiang Wang ◽  
Canyang Guo ◽  
Ling Wu
Keyword(s):  
Neural Network ◽  
Mobile Devices ◽  
Heterogeneous Network ◽  
Cellular Network ◽  
Location Based Services ◽  
Series Data ◽  
Positioning System ◽  
Mobile Positioning ◽  
Gated Recurrent Unit ◽  
Research Studies

Recently, research studies on Location-Based Services (LBSs) based on networks including cellular network and Wi-Fi network have gradually become popular. Received Signal Strength Indicators (RSSIs) from the network can be detected and collected by mobile devices to estimate the locations without adopting the Global Positioning System (GPS). Previous research studies utilized the RSSIs of only cellular network or only Wi-Fi network to estimate location, which leads to a two-fold predicament involving error limits of cellular network-based methods and environmental constraints of Wi-Fi network-based methods. In addition, accommodating a highly temporal dependence of RSSI series data, this paper proposed a mobile positioning system based on Gated Recurrent Unit (GRU) with RSSIs from the heterogeneous network. GRU learns the temporal correlation of RSSIs and the relationship between RSSIs and GPS coordinates to estimate the locations of mobile devices. A large number of real experiments have been carried out to verify the performance of the proposed method, and experimental results demonstrate that the proposed method has lower errors (i.e., 5.86 m and 75% of errors within 4 m) compared with Neural Network (NN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM).

Recognition of classroom student state features based on deep learning algorithms and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Hu Jingchao ◽  
Haiying Zhang
Keyword(s):  
Deep Learning ◽  
Feature Recognition ◽  
Subjective Evaluation ◽  
Recognition Algorithm ◽  
Video Data ◽  
State Recognition ◽  
Detection Model ◽  
State Classification ◽  
Intelligent Models ◽  
Model Recognition

The difficulty in class student state recognition is how to make feature judgments based on student facial expressions and movement state. At present, some intelligent models are not accurate in class student state recognition. In order to improve the model recognition effect, this study builds a two-level state detection framework based on deep learning and HMM feature recognition algorithm, and expands it as a multi-level detection model through a reasonable state classification method. In addition, this study selects continuous HMM or deep learning to reflect the dynamic generation characteristics of fatigue, and designs random human fatigue recognition experiments to complete the collection and preprocessing of EEG data, facial video data, and subjective evaluation data of classroom students. In addition to this, this study discretizes the feature indicators and builds a student state recognition model. Finally, the performance of the algorithm proposed in this paper is analyzed through experiments. The research results show that the algorithm proposed in this paper has certain advantages over the traditional algorithm in the recognition of classroom student state features.

Aplikasi Pemosisi Lokal Berbasis Android Dengan Menggunakan GPS

INTI TALAFA ◽  
2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Yaman Khaeruzzaman
Keyword(s):  
Global Positioning System ◽  
Positioning System ◽  
Database Server ◽  
Global Positioning

Seiring dengan pesatnya kemajuan teknologi saat ini, kebutuhan manusia menjadi lebih beragam, termasuk kebutuhan akan informasi. Tidak hanya media informasinya yang semakin beragam, jenis informasi yang dibutuhkan juga semakin beragam, salah satunya adalah kebutuhan informasi akan posisi kita terhadap lingkungan sekitar. Untuk memenuhi kebutuhan itu sebuah sistem pemosisi diciptakan. Sistem pemosisi yang banyak digunakan saat ini cenderung berfokus pada lingkup ruang yang besar (global) padahal, dalam lingkup ruang yang lebih kecil (lokal) sebuah sistem pemosisi juga diperlukan, seperti di ruang-ruang terbuka umum (taman atau kebun), ataupun dalam sebuah bangunan. Sistem pemosisi lokal yang ada saat ini sering kali membutuhkan infrastruktur yang mahal dalam pembangunannya. Aplikasi Pemosisi Lokal Berbasis Android dengan Menggunakan GPS ini adalah sebuah aplikasi yang dibangun untuk memenuhi kebutuhan pengguna akan informasi lokasi dan posisi mereka terhadap lingkungan di sekitarnya dalam lingkup ruang yang lebih kecil (lokal) dengan memanfaatkan perangkat GPS (Global Positioning System) yang telah tertanam dalam perangkat smartphone Android agar infrastruktur yang dibutuhkan lebih efisien. Dalam implementasinya, Aplikasi Pemosisi Lokal ini bertindak sebagai klien dengan dukungan sebuah Database Server yang berfungsi sebagai media penyimpanan data serta sumber referensi informasi yang dapat diakses melalui jaringan internet sehingga tercipta sebuah sistem yang terintegrasi secara global. Kata kunci: aplikasi, informasi, pemosisi, GPS.

Sign in / Sign up

Export Citation Format

Share Document