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On the Impact of Biceps Muscle Fatigue in Human Activity Recognition.

Author(s): Elshafei M, Costa DE, Shihab E

Nowadays, Human Activity Recognition (HAR) systems, which use wearables and smart systems, are a part of our daily life. Despite the abundance of literature in the area, little is known about the impact of muscle fatigue on these systems' performance. I...

Article GUID: 33557239
Towards Detecting Biceps Muscle Fatigue in Gym Activity Using Wearables.

Author(s): Elshafei M, Shihab E

Fatigue is a naturally occurring phenomenon during human activities, but it poses a bigger risk for injuries during physically demanding activities, such as gym activities and athletics. Several studies show that bicep muscle fatigue can lead to various inj...

Article GUID: 33498702
Finite Element Modelling of Bandgap Engineered Graphene FET with the Application in Sensing Methanethiol Biomarker.

Author(s): Singh P, Abedini Sohi P, Kahrizi M

In this work, we have designed and simulated a graphene field effect transistor (GFET) with the purpose of developing a sensitive biosensor for methanethiol, a biomarker for bacterial infections. The surface of a graphene layer is functionalized by manipula...

Article GUID: 33467459
A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects.

Author(s): Khannouz M; Glatard T;

This paper evaluates data stream classifiers from the perspective of connected devices, focusing on the use case of Human Activity Recognition. We measure both the classification performance and resource consumption (runtime, memory, and power) of five usua...

Article GUID: 33202905
Contactless Capacitive Electrocardiography Using Hybrid Flexible Printed Electrodes.

Author(s): Lessard-Tremblay M, Weeks J, Morelli L, Cowan G, Gagnon G, Zednik RJ

Traditional capacitive electrocardiogram (cECG) electrodes suffer from limited patient comfort, difficulty of disinfection and low signal-to-noise ratio in addition to the challenge of integrating them in wearables. A novel hybrid flexible cECG electrode wa...

Article GUID: 32927651
Determining the Optimal Restricted Driving Zone Using Genetic Algorithm in a Smart City.

Author(s): Azami P, Jan T, Iranmanesh S, Ameri Sianaki O, Hajiebrahimi S

Sensors (Basel). 2020 Apr 16;20(8): Authors: Azami P, Jan T, Iranmanesh S, Ameri Sianaki O, Hajiebrahimi S

Article GUID: 32316356
A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors.

Author(s): Dehghani A, Sarbishei O, Glatard T, Shihab E

Sensors (Basel). 2019 Nov 18;19(22): Authors: Dehghani A, Sarbishei O, Glatard T, Shihab E

Article GUID: 31752158
Characterization and Efficient Management of Big Data in IoT-Driven Smart City Development.

Author(s): Alsaig A, Alagar V, Chammaa Z, Shiri N

Sensors (Basel). 2019 May 28;19(11): Authors: Alsaig A, Alagar V, Chammaa Z, Shiri N

Article GUID: 31141899
A Crowdsensing Based Analytical Framework for Perceptional Degradation of OTT Web Browsing.

Author(s): Li K, Wang H, Xu X, Du Y, Liu Y, Ahmad MO

Sensors (Basel). 2018 May 15;18(5): Authors: Li K, Wang H, Xu X, Du Y, Liu Y, Ahmad MO

Article GUID: 29762493
Fast Feature-Preserving Approach to Carpal Bone Surface Denoising.

Author(s): Salim I, Hamza AB

Sensors (Basel). 2018 Jul 21;18(7): Authors: Salim I, Hamza AB

Article GUID: 30037109
Big Data-Driven Cellular Information Detection and Coverage Identification.

Author(s): Wang H, Xie S, Li K, Ahmad MO

Sensors (Basel). 2019 Feb 22;19(4): Authors: Wang H, Xie S, Li K, Ahmad MO

Article GUID: 30813353
Surface Profiling and Core Evaluation of Aluminum Honeycomb Sandwich Aircraft Panels Using Multi-Frequency Eddy Current Testing.

Author(s): Reyno T, Underhill PR, Krause TW, Marsden C, Wowk D

Sensors (Basel). 2017 Sep 14;17(9): Authors: Reyno T, Underhill PR, Krause TW, Marsden C, Wowk D

Article GUID: 28906434
Title:Big Data-Driven Cellular Information Detection and Coverage Identification.
Authors:Wang H,  Xie S,  Li K,  Ahmad MO
Link:https://www.ncbi.nlm.nih.gov/pubmed/30813353?dopt=Abstract
Category:Sensors (Basel)
PMID:30813353
Dept Affiliation: ENCS
1 College of Smart City, Beijing Union University, Beijing 100101, China. 161081210208@buu.edu.cn.
2 College of Smart City, Beijing Union University, Beijing 100101, China. 171081210207@buu.edu.cn.
3 College of Smart City, Beijing Union University, Beijing 100101, China. like@buu.edu.cn.
4 Department of Electrical and Computer Engineering, Concordia University, Montreal, QC H3G IM8, Canada. omair@ece.concordia.ca.

Description:

Big Data-Driven Cellular Information Detection and Coverage Identification.

Sensors (Basel). 2019 Feb 22;19(4):

Authors: Wang H, Xie S, Li K, Ahmad MO

Abstract

As one of the core data assets of telecom operators, base station almanac (BSA) plays an important role in the operation and maintenance of mobile networks. It is also an important source of data for the location-based service (LBS) providers. However, it is always less timely updated, nor it is accurate enough. Besides, it is not open to third parties. Conventional methods detect only the location of the base station (BS) which cannot satisfy the needs of network optimization and maintenance. Because of these drawbacks, in this paper, a big-data driven method of BSA information detection and cellular coverage identification is proposed. With the help of network-related data crowd sensed from the massive number of smartphone users in the live network, the algorithm can estimate more parameters of BSA with higher accuracy than conventional methods. The coverage capability of each cell was also identified in a granularity of small geographical grids. Computational results validate the proposed algorithm with higher performance and detection ability over the existing ones. The new method can be expected to improve the scope, accuracy, and timeliness of BSA, serving for wireless network optimization and maintenance as well as LBS service.

PMID: 30813353 [PubMed - indexed for MEDLINE]