2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM)最新文献_第2页

A new middleware for managing heterogeneous robot in ubiquitous environments 一种在泛在环境中管理异构机器人的新型中间件

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2020-10-28 DOI: 10.1109/WINCOM50532.2020.9272477

Dimitri Marcheras, Marwane Ayaida, N. Messai, F. Valentin

引用次数: 3

A new approach for increasing K-nearest neighbors performance 一种提高k近邻性能的新方法

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2020-10-28 DOI: 10.1109/WINCOM50532.2020.9272459

Youssef Aamer, Yahya Benkaouz, M. Ouzzif, Khalid Bouragba

引用次数: 0

Lifetime-Efficient Indoor Guidance for Smart Parking 终身高效的智能停车室内引导

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2020-10-28 DOI: 10.1109/WINCOM50532.2020.9272415

Moussa Coulibaly, A. Errami, Essaid Sabir

{"title":"Lifetime-Efficient Indoor Guidance for Smart Parking","authors":"Moussa Coulibaly, A. Errami, Essaid Sabir","doi":"10.1109/WINCOM50532.2020.9272415","DOIUrl":"https://doi.org/10.1109/WINCOM50532.2020.9272415","url":null,"abstract":"In an increasingly urbanized world, road transportation plays an important role in today's economy. To reduce congestion due to parking searches, the concept of smart parking was introduced. One of the essential and interesting features of smart parking is the guidance to a vacant spot. To do so, in such parking, each spot is generally equipped with an IoT sensor. These sensors form an IoT wireless sensor network (WSN). The battery lifetime remains one of the main limitations of such a network. Here, we propose an algorithm for indoor guidance to a vacant spot based on finding a battery level balance between the parking sensors. A comparison between five parking strategies including our proposal is presented. Although still under improvement, the results of the first simulations highlight two majors points. The first concerns the extension of the first sensor fall's point. The second regards the number of sensors' battery replacement. Indeed, starting with the same initial battery level, when using our proposal, every sensor will have the same number of battery replacement when the other strategies operate more replacement. The proposed lifetime-efficient guidance allows reducing significantly the number of interventions relating to the batteries replacement of IoT parking sensors and push back the fall of the first sensor so extend the parking overall lifetime.","PeriodicalId":283907,"journal":{"name":"2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116928469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Cloth Fitting Based on Skeleton Driving and Implicit Skinning 基于骨架驱动和隐蒙皮的布料装配

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2020-10-28 DOI: 10.1109/WINCOM50532.2020.9272498

Ait Mouhou Abderrazzak, A. Saaidi, M. Benyakhlef, Abbad Khalid

引用次数: 0

Study of Cooperative MIMO Approach in Hilly Environment 丘陵环境下协同MIMO方法研究

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2020-10-28 DOI: 10.1109/WINCOM50532.2020.9272457

Teodora Kocevska, A. Hrovat, T. Javornik

引用次数: 0

A Smart Elderly Monitoring System based on Cloud 基于云的智能老年人监护系统

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2020-10-28 DOI: 10.1109/WINCOM50532.2020.9272424

Ezaz Aldahasi, Manal Mohamed Alhejazi, Eyman Abdulrahman Alyahayan

引用次数: 0

New method of compensation the out-of-focus defect introduced by the optical system of a micro-camera 一种补偿微相机光学系统失焦缺陷的新方法

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2020-10-28 DOI: 10.1109/WINCOM50532.2020.9272505

H. Ghennioui, Mohamed El-Kamili

引用次数: 0

[Copyright notice] (版权)

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2020-10-28 DOI: 10.1109/wincom50532.2020.9272430

引用次数: 0

GastroNet: Abnormalities Recognition in Gastrointestinal Tract through Endoscopic Imagery using Deep Learning Techniques GastroNet:使用深度学习技术通过内窥镜成像识别胃肠道异常

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2020-10-28 DOI: 10.1109/WINCOM50532.2020.9272456

Samira Lafraxo, Mohamed El Ansari

{"title":"GastroNet: Abnormalities Recognition in Gastrointestinal Tract through Endoscopic Imagery using Deep Learning Techniques","authors":"Samira Lafraxo, Mohamed El Ansari","doi":"10.1109/WINCOM50532.2020.9272456","DOIUrl":"https://doi.org/10.1109/WINCOM50532.2020.9272456","url":null,"abstract":"The human gastrointestinal (GI) tract may be infected by various diseases. If not detected at early stages, these abnormalities have the possibility to progress into gastric cancer, which is a common type of malignancies with yearly global cases exceeding one million. Endoscopy is a routinely used strategy for the examination of gastrointestinal tract diseases. During the examination, and due to many reasons like irregular morphologies, a huge number of frames, and exhaustion, gastrologists can miss some abnormalities. Thus, the automated classification of anomalies in endoscopic images is becoming necessary to assist medical diagnosis and reduce the cost and time of the medical process. Recent advances and high performance of deep learning techniques make it the best choice to adopt as a computer-aided-diagnosis strategy. In this paper, a novel deep learning model based deep convolutional neural network is proposed. Our model aims to automatically detect diseases from endoscopic images. The newly designed architecture is validated on the publicly available dataset KVASIR, which contains 8000 images. The results of our CNN approach compared to other well known pre-trained models showed important improvement and achieved 96.89% in terms of accuracy. The experiments demonstrated that the system can perform a high detection level without any human intervention.","PeriodicalId":283907,"journal":{"name":"2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125982999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Deep Learning Based Prediction of Signal-to-Noise Ratio (SNR) for LTE and 5G Systems 基于深度学习的LTE和5G系统信噪比预测

2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM) Pub Date : 2020-10-28 DOI: 10.1109/WINCOM50532.2020.9272470

Thi-Phuong-Nhung Ngo, B. Kelley, P. Rad

{"title":"Deep Learning Based Prediction of Signal-to-Noise Ratio (SNR) for LTE and 5G Systems","authors":"Thi-Phuong-Nhung Ngo, B. Kelley, P. Rad","doi":"10.1109/WINCOM50532.2020.9272470","DOIUrl":"https://doi.org/10.1109/WINCOM50532.2020.9272470","url":null,"abstract":"Deep learning (DL) is applied to predict signal-to-noise ratio (SNR) in de facto LTE and 5G systems in a non-data-aided (NDA) manner. Various channel conditions and impairments are considered, including modulation types, path delays, and Doppler shifts. Both time-domain and frequency-domain signal grids are evaluated as inputs for SNR prediction. A combination of convolutional neural network (CNN) and long short term memory (LSTM) - CNN-LSTM - is used as the SNR predictor. Learning both spatial and temporal features is known to improve DL prediction accuracy. Techniques employed to enhance performance are SNR range/resolution manipulation, binary prediction, and multiple input prediction. Computer simulation is conducted using MATLAB LTE, 5G, and DL toolboxes to generate OFDM signals, model fading channels with AWGN noise, and construct CNN-LSTM. Simulation results show, with off-line training, DL based prediction of SNR in LTE and 5G systems has better accuracy and latency than traditional estimation techniques. Specifically, SNR prediction for SNR range of [-4, 32] dB and resolution of 2 dB utilizing time-domain signals has an accuracy of 100%, hence normalized mean square error (NMSE) of zero, and a latency of 1 millisecond or less.","PeriodicalId":283907,"journal":{"name":"2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132820206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10