2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)最新文献_第6页

Towards reliable intelligent occupancy detection for smart building applications 面向智能建筑应用的可靠智能占用检测

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946831

Christopher Luppe, Amir Shabani

引用次数: 13

Millimeter-wave CMOS PLL using a push-push oscillator 毫米波CMOS锁相环采用推推式振荡器

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946791

Sanjeev Jain, N. Zhang, L. Belostotski

引用次数: 5

Accurate UWB and IMU based indoor localization for autonomous robots 基于超宽带和IMU的自主机器人室内精确定位

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946751

Alvin Marquez, Brinda Tank, S. K. Meghani, Sabbir Ahmed, K. Tepe

引用次数: 17

Second-order adjoint sensitivities: An alternative formulation 二阶伴随灵敏度:另一种表述

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946611

M. Bakr, A. Elsherbeni, V. Demir

引用次数: 0

Noise power estimation for broadcasting OFDM systems 广播OFDM系统的噪声功率估计

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946746

A. Khan, V. Jeoti, M. Rehman, Muhammad Taha Jilani

{"title":"Noise power estimation for broadcasting OFDM systems","authors":"A. Khan, V. Jeoti, M. Rehman, Muhammad Taha Jilani","doi":"10.1109/CCECE.2017.7946746","DOIUrl":"https://doi.org/10.1109/CCECE.2017.7946746","url":null,"abstract":"The estimation of the noise power is a core issue in wireless communication systems. In broadcasting, every OFDM frame starts with a preamble symbol, which facilitates the noise power estimation. However, the performance of preamble-based noise estimation schemes worsens in fast-changing environments and cannot efficiently track the noise variation well. In order to track the noise variation symbol to symbol, pilot-based schemes would be more fruitful. Accordingly, in this paper, we propose a novel pilot-based noise power estimation scheme for OFDM systems that estimate the noise power for each data symbol. It makes use of the pilot subcarriers reserved for channel estimation in the data symbol. The proposed algorithm computes the circular correlation between received signal and comb type sub-pilot subsequence twice to generate the noise power. Compared with the conventional preamble-based scheme, the proposed pilot-based noise algorithm generates near-ideal accuracy at different values of signal-to-noise ratio (SNR). Without the additional overheads of the preamble, the scheme offers the significant saving of computation over a conventional time domain noise estimator.","PeriodicalId":238720,"journal":{"name":"2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115604742","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: 5

Fuzzy logic controller for hybrid renewable energy system with multiple types of storage 多存储类型混合可再生能源系统的模糊控制器

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946738

Majed Althubaiti, Michael Bernard, P. Musílek

{"title":"Fuzzy logic controller for hybrid renewable energy system with multiple types of storage","authors":"Majed Althubaiti, Michael Bernard, P. Musílek","doi":"10.1109/CCECE.2017.7946738","DOIUrl":"https://doi.org/10.1109/CCECE.2017.7946738","url":null,"abstract":"The objective of this work is to design a scheme to control the power flow of a hybrid renewable energy system with multiple renewable energy sources (solar energy and wind energy) and multiple energy storage systems. The use of energy storage is necessary due to the intermittency of the renewable energy sources and the consequent peak power shift between the sources and the load. In addition, the use of the energy storage can increase the overall system reliability and stability. In this work, batteries are used as the primary energy storage system for short to medium storage term, while hydrogen fuel cell is used as the long-term energy storage. A supervisory control system is designed to handle various changes in power supply and power demand by managing power intermittency, power peak shaving, and long-term energy storage. Since both power supply and demand are not fully predictable and they have time variant nonlinear behavior, computational intelligence is introduced to solve this issue and provide suitable control algorithm. This type of control, based on fuzzy logic, combines knowledge of the system and control. The proposed methodology can be optimized such that the system is able to adapt to its working environment and deliver best results under given circumstances. For more realistic simulation results, the designed model utilizes real solar power data collected from the University of Queensland in Australia, while real wind power and load data was collected by the Alberta Electric System Operator in Alberta, Canada. The model of the system is designed and simulated in MATLAB SimPowerSystems™ to verify the effectiveness of the proposed scheme.","PeriodicalId":238720,"journal":{"name":"2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114188938","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: 20

Detection of unknown applications in smartphones: A signal processing perspective 智能手机中未知应用的检测:信号处理视角

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946674

R. S. R. James, Abdurhman Albasir, S. Naik, M. Dabbagh, P. Dash, Marzia Zaman, N. Goel

{"title":"Detection of unknown applications in smartphones: A signal processing perspective","authors":"R. S. R. James, Abdurhman Albasir, S. Naik, M. Dabbagh, P. Dash, Marzia Zaman, N. Goel","doi":"10.1109/CCECE.2017.7946674","DOIUrl":"https://doi.org/10.1109/CCECE.2017.7946674","url":null,"abstract":"Different applications in smartphones result in different power consumption patterns. The fact that every application has been coded to perform certain tasks leads to the claim that every action on-board (whether software or hardware) will consequently have a trace in the power consumption of the smartphone. However, similar power consumption patterns are observed for the same application irrespective of the types of operating system they are executed on. Therefore, it is safe to further claim that no two applications can have similar power consumption patterns as it is highly unlikely that they have exactly the same source code. The idea behind this work is that by analyzing only the power consumption signals of a smartphone, valuable information regarding its operation can be revealed. In view of this, the authors propose a systematic and generic methodology that involves performing some signal processing techniques on the power consumption signals of different applications in smartphones to detect and separate unknown activities/applications from known ones. In this work, we present a proof of concept supported with some test cases to validate the approach. The preliminary results hold promise in detecting malware in smartphones.","PeriodicalId":238720,"journal":{"name":"2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115791105","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

Fusion of transfer learning features and its application in image classification 迁移学习特征融合及其在图像分类中的应用

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946733

Akilan Thangarajah, Q. M. J. Wu, Yimin Yang, A. Safaei

{"title":"Fusion of transfer learning features and its application in image classification","authors":"Akilan Thangarajah, Q. M. J. Wu, Yimin Yang, A. Safaei","doi":"10.1109/CCECE.2017.7946733","DOIUrl":"https://doi.org/10.1109/CCECE.2017.7946733","url":null,"abstract":"Feature fusion methods have been demonstrated to be effective for many computer vision based applications. These methods generally use multiple hand-crafted features. However, in recent days, features extracted through transfer leaning procedures have been proved to be robust than the hand-crafted features in myriad applications, such as object classification and recognition. The transfer learning is a highly appreciated strategy in deep convolutional neural networks (DCNNs) due to its multifaceted benefits. It heartens us to explore the effect of fusing multiple transfer learning features of different DCNN architectures. Thus, in this work, we extract features of image statistics by exploiting three different pre-trained DCNNs through transfer learning. Then, we transform the features into a generalized subspace through a recently introduced Autoencoder network and fuse them to form intra-class invariant feature vector that is used to train a multi-class Support Vector Machine (SVM). The experimental results on various datasets, including object and action image statistics show that the fusion of multiple transfer learning features improves classification accuracy as compared to fusion of multiple hand-crafted features and usage of single component transfer learning features.","PeriodicalId":238720,"journal":{"name":"2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123682150","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: 26

An energy management system for hybrid microgrids in remote communities 一种用于偏远社区混合微电网的能源管理系统

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946775

S. A. Helal, R. J. Najee, Maria O. Hanna, M. Shaaban, A. Osman, Mohamed S. Hassan

{"title":"An energy management system for hybrid microgrids in remote communities","authors":"S. A. Helal, R. J. Najee, Maria O. Hanna, M. Shaaban, A. Osman, Mohamed S. Hassan","doi":"10.1109/CCECE.2017.7946775","DOIUrl":"https://doi.org/10.1109/CCECE.2017.7946775","url":null,"abstract":"A new energy management system is proposed in this paper to optimally schedule different generation technologies in hybrid AC/DC microgrid for remote communities. The proposed scheme relies on a microgrid controller (MGC), which minimizes the operational costs for different distributed generation (DG) units in the islanded microgrid by sending droop characteristics parameters to the DG units to ensure optimum power sharing for each time slot. Moreover, the MGC controls the customers' appliances and water desalination units to ensure optimal utilization of the system assets with minimum operational costs while satisfying the customer requirements. In addition, the MGC sends operational parameters for the interlinking converters between the AC and DC subsystems to ensure stable operation of the microgrid by controlling power exchange between the AC and DC sides. The energy management problem is formulated as a mixed-integer non-linear programming, which is solved by the MGC. Optimal decisions are sent to local controllers and smart meters to be implemented. Simulation results on an islanded hybrid AC/DC microgrid are provided. The results demonstrate the effectiveness and robustness of the proposed approach.","PeriodicalId":238720,"journal":{"name":"2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122152835","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: 35

Design of a neural network based power system stabilizer in reduced order power system 基于神经网络的降阶电力系统稳定器设计

2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2017-04-01 DOI: 10.1109/CCECE.2017.7946634

M. Masrob, M. Rahman, G. George

引用次数: 7