2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)最新文献_第2页

General Framework for Multi-Classification of EEG Signals Based on Multi-Scale Properties 基于多尺度特征的脑电信号多分类通用框架

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255822

S. Lahmiri

{"title":"General Framework for Multi-Classification of EEG Signals Based on Multi-Scale Properties","authors":"S. Lahmiri","doi":"10.1109/CCECE47787.2020.9255822","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255822","url":null,"abstract":"Numerous computer automated diagnosis (CAD) systems have been proposed to detect epilepsy in electroencephalogram (EEG) signals. The aim of this paper is to look at multi-scaling properties obtained by multi-scale analysis (MSA) as main distinctive features to simultaneously distinguish between all categories of EEG signals that compose the popular database hosted by the department of epileptology, University of Bonn, Germany. Particularly, multi-scale analysis is employed to capture long-range properties of the EEG signal at different scales used to represent its short and long variations. Then, the obtained multi-scale properties are used to train four different classifiers; namely, k-nearest neighbor (k-NN), linear discriminant analysis (LDA), naïve Bayes (NB), and the support vector machine (SVM). Experimental results based on ten-fold cross-validation method show that each single classifier achieves 100% accuracy. In this respect, multi-scale properties are found to be effective as they outperformed existing works on the same database by achieving perfect accuracy to distinguish between all five distinct EEG categories. Overall, the obtained results are promising.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129289449","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: 0

Worker Safety Considerations for Deployment of Mobile Disconnect Switches on Transmission Lines 在输电线路上部署移动断开开关的工作人员安全考虑

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255735

J. Khan, M. Armstrong, A. Moshref

引用次数: 0

Resource Allocation in CAT-M and LTE-A Coexistence: A Joint Contention Bandwidth Optimization Scheme CAT-M和LTE-A共存中的资源分配:一种联合争用带宽优化方案

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255823

Radwa A. Sultan, A. Refaey, W. Hamouda

{"title":"Resource Allocation in CAT-M and LTE-A Coexistence: A Joint Contention Bandwidth Optimization Scheme","authors":"Radwa A. Sultan, A. Refaey, W. Hamouda","doi":"10.1109/CCECE47787.2020.9255823","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255823","url":null,"abstract":"There are high expectations for IoT devices and networks concerning reliability, performance, quality, and long-term availability. Indeed, wireless connectivity is the most critical success factor for the IoT era. Recently, the cellular technologies focused on introducing new releases, like LTE Cat-M1, to provide global coverage and mobility for the IoT applications. However, the cellular spectrum is already congested, and adding new services will defiant the existing ones. Herein, the network key performance indicator (KPI) should be considered to enhance the resource management for LTE and LTE CAT M1 users. Tackling the coexistence between the aforementioned in the 1.4 Mhz band, three coexistence optimization problems are formulated. The first and the second coexistence optimization problems are formulated assuming higher IoT-traffic priority, and higher LTE-traffic priority, respectively. On the other hand, the third problem is formulated assuming that both the IoT-traffic and the LTE-traffic have the same priority. Afterward, a scheduling optimization solution algorithm is proposed using the interior point method. Finally, the performance of the proposed scheduling algorithm is evaluated via numerical analysis.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124210944","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: 1

Low-Power Low-Cost Audio Front-End for Keyword Spotting 低功耗低成本音频前端关键字定位

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255693

Daljit Josh, John-Anthony Elenis, Heman Muresan, P. Spachos, S. Gregori

引用次数: 2

Data-Driven Performance Prediction Using Gas Turbine Sensory Signals 使用燃气轮机传感信号的数据驱动性能预测

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255821

T. Ravichandran, Yuan Liu, Amar Kumar, A. Srivastava, Houman Hanachi, G. Heppler

{"title":"Data-Driven Performance Prediction Using Gas Turbine Sensory Signals","authors":"T. Ravichandran, Yuan Liu, Amar Kumar, A. Srivastava, Houman Hanachi, G. Heppler","doi":"10.1109/CCECE47787.2020.9255821","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255821","url":null,"abstract":"The performance of a gas turbine engine (GTE) deteriorates with degradation and aging. The availability of the operating data from the GTE with the capability to perform data analysis provides an opportunity to identify short-term and longterm performance deterioration and relate to more difficult to detect components degradation. In this work, a data-driven and machine learning-based predictive modeling framework has been developed for performing combined input and model selection towards generating easily interpretable, parsimonious and accurate regression models intended for gas turbine engine performance analysis. The proposed multistage predictive modeling framework incorporates the orthogonal least squares (OLS) learning and multi-criteria decision-making approach for selecting inputs and model structures in a computationally efficient manner while optimizing multiple objectives. The regression models obtained from this framework for predicting power and exhaust gas temperature (EGT) outputs using GTE operational data collected over a period of three years have demonstrated short-term and long-term performance deterioration patterns for the GTE.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121626142","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: 3

A Cooperative Spectrum Sensing Architecture and Algorithm for Cloud- and Big Data-based Cognitive Radio Networks 基于云和大数据的认知无线电网络协同频谱感知体系结构与算法

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255729

Victor Balogun, O. Sarumi

{"title":"A Cooperative Spectrum Sensing Architecture and Algorithm for Cloud- and Big Data-based Cognitive Radio Networks","authors":"Victor Balogun, O. Sarumi","doi":"10.1109/CCECE47787.2020.9255729","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255729","url":null,"abstract":"Cognitive Radio Network (CRN) was designed to lessen the shortage of radio resources. The Secondary Users (SUs) can opportunistically utilize any available spectrum when the Primary Users (PUs) are inactive. Some of the challenges of CRN include the service interruption loss, complexity of processing and exchange of large amount of data, limited available memory to SUs and the non-real-time exchange of spectrum sensing data. These challenges can lead to significant degradation in the performance of a CRN. Therefore, there is a need to seek solutions that will alleviate these problems. The Cloud system incorporated with Big Data Analytics algorithm can be a potential solution. In this paper, we propose a Cloud-based Cooperative Spectrum Sensing model for CRN that allows the SUs to aggregate their individual spectrum sensing data into a cloud environment, where it can be analyzed using a proposed expanded Apache Spark algorithm incorporated with the hybridization of three machine learning methods-ensemble classifier approach that can effectively and efficiently analyze the spectrum sensing data for easy access, real-time analysis, deep insight and on-demand decision support for the SUs. In addition, the two-layer Fusion Center design proposed introduces redundancy by using the cloud as a secondary Fusion Center while still maintaining a primary land-based Fusion Center.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133886119","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

Stall Control and MPPT for a Wind Turbine, Using a Buck Converter in a Battery Storage System 在蓄电池系统中使用降压变换器的风力发电机失速控制和最大ppt

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255761

Ali Yazhari Kermani, R. Fadaeinedjad, A. Maheri, E. Mohammadi, G. Moschopoulos

引用次数: 3

Deep Reinforcement Learning Algorithm for Smart Data Compression under NOMA-Uplink Protocol NOMA-Uplink协议下智能数据压缩的深度强化学习算法

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255757

Mohamed Elsayed, A. Badawy, A. Shafie, Amr M. Mohamed, T. Khattab

{"title":"Deep Reinforcement Learning Algorithm for Smart Data Compression under NOMA-Uplink Protocol","authors":"Mohamed Elsayed, A. Badawy, A. Shafie, Amr M. Mohamed, T. Khattab","doi":"10.1109/CCECE47787.2020.9255757","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255757","url":null,"abstract":"One of the highly promising radio access strategies for enhancing performance in the next generation cellular communications is non-orthogonal multiple access (NOMA). NOMA offers a number of advantages including better spectrum efficiency. This paper focuses primarily on proposing an energy efficient system for transmitting medical data, such as electroencephalogram (EEG), collected from patients for the sake of continuous monitoring. The framework proposes the use of deep reinforcement learning (DRL) to provide smart data compression in uplink-NOMA protocol. DRL enforces the data compression ratios for the nodes in order to avoid outage constraints at any sensor node. Jointly, it optimizes the power consumption of these sensor nodes. The data compression for such sensor network is vital in order to minimize the power every sensor consumes to maximize its service lifetime. We minimize the expected distortion under practical channel realization and outage probability constraints using NOMA-uplink protocol. Meanwhile, we optimize the power efficiency of the user node in order to increase the battery lifetime.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128924435","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: 1

ANN-supervised Interface System for Microturbine Distributed Generator 微型水轮分布式发电机的人工神经网络监督接口系统

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255730

M. R. Hamouda, M. Marei, M. Nassar, M. Salama

引用次数: 0

Management Emulation of Advanced Network Backbones in Africa: 2019 Topology 非洲先进网络骨干网管理仿真:2019拓扑

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255779

J. Castillo-Velazquez, Luis-Carlos Revilla-Melo

引用次数: 3