2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)最新文献_第3页

Implementation of High Speed and Low Area Extended Euclidean Inversion over Ternary Fields 三元场高速低面积扩展欧几里得反演的实现

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861557

Ibrahim H. Hazmi, F. Gebali, Atef Ibrahim

引用次数: 0

Netflix Control Method Using Smartwatches and Continuous Gesture Recognition 使用智能手表和连续手势识别的Netflix控制方法

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861610

T. H. Nascimento, Fabrízzio Soares, Hugo A. D. Nascimento, Marcos Alves Vieira, T. Carvalho, W. F. D. Miranda

{"title":"Netflix Control Method Using Smartwatches and Continuous Gesture Recognition","authors":"T. H. Nascimento, Fabrízzio Soares, Hugo A. D. Nascimento, Marcos Alves Vieira, T. Carvalho, W. F. D. Miranda","doi":"10.1109/CCECE.2019.8861610","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861610","url":null,"abstract":"This work proposes and evaluates a method for interaction and control of Netflix using continuous recognition of gestures in smartwatches. This method allows the user to pause, resume, rewind, move forward, increase and decrease the volume of the Netflix player using gestures similar to traditional icons for these tasks. The recognition of gestures is done using the algorithm of continuous recognition of gestures, so it is possible to recognize a gesture before it is finalized, in this way, it is possible to perform the action quickly thus improving the feedback to the user. We have developed a prototype for smartwatches that communicates with a communication platform that receives the actions and is responsible for executing them on Netflix. When performing a gesture, the first step is to recognize it, in sequence, the gesture is sent to the interaction platform and, finally, the action related to the gesture is performed. We performed an experiment with users and a usability and experience test, the results show that the gestures proposed in the method are intuitive and that it has the potential to be used in a pleasant and satisfactory way by the users.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117118557","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: 8

Towards Natural Scene Rock Image Classification with Convolutional Neural Networks 基于卷积神经网络的自然场景岩石图像分类研究

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861885

A. Pascual, Lei Shu, Justin Szoke-Sieswerda, K. McIsaac, G. Osinski

{"title":"Towards Natural Scene Rock Image Classification with Convolutional Neural Networks","authors":"A. Pascual, Lei Shu, Justin Szoke-Sieswerda, K. McIsaac, G. Osinski","doi":"10.1109/CCECE.2019.8861885","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861885","url":null,"abstract":"Autonomous image recognition has numerous potential applications in the field of planetary science and geology. During exploration, geologists could encounter an unknown rock and instead of having to bring back the sample to the laboratory for analysis, a better approach would be to have a mobile device classify the image of a rock. As well, instead of waiting a long time for a planetary rover to send back an image to Earth for classification, its on-board computer could have a software that could automatically classify images of outcrops. In 2017, Shu et. al. used a Support Vector Machine (SVM) classification algorithm to classify uniform rock images into 9 different classes with the image features extracted autonomously. Through this method, they achieved a classification accuracy of 96.71%. Recent publications have shown that Convolutional Neural Networks (CNNs) perform better than other algorithms in classifying images of everyday objects, more specifically for the ImageNet dataset. In light of this development, this paper demonstrates the use of CNNs to classify the same set of rock images. With the addition of dataset augmentation, a 3-layer CNN is shown to have a significant improvement over Shu et. al.’s results, achieving an average accuracy of 99.60% across 10 trials on the test set. Having proven that CNNs can classify uniform and clean images of rocks, this research then tackles a more interesting and practical problem in classifying natural scene images of rocks where the images are taken during field exploration without a standardized method and specialized equipment. The task has been simplified into a binary classification problem where the images are classified into breccia and non-breccia. This research shows that a 5-layer CNN achieves 89.43% classification accuracy for this task.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"444 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125763493","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: 9

Construction of Autonomous Driving Maps employing LiDAR Odometry 基于激光雷达里程计的自动驾驶地图构建

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861901

Ismail Hamieh, Ryan Myers, Taufiq Rahman

{"title":"Construction of Autonomous Driving Maps employing LiDAR Odometry","authors":"Ismail Hamieh, Ryan Myers, Taufiq Rahman","doi":"10.1109/CCECE.2019.8861901","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861901","url":null,"abstract":"Autonomous driving typically relies on a priori maps for localization and path planning. In order to construct such maps, data from perception sensors such as light detection and ranging (LiDAR), global positioning system (GPS), inertial measurement unit (IMU), etc. are employed in simultaneous localization and mapping (SLAM) algorithms. Since LiDAR can currently provide the highest accuracy representation of the environment, generating mapping data from LiDAR odometry has observed significant interest in the literature. Furthermore, LiDAR based odometry can provide high quality mapping information in feature-rich GPS-denied areas like urban centers where ground level roads are occluded by tall buildings. This paper describes an experimental setup composed of hardware and software stacks required for realizing LiDAR based odometry generation in roadway environments. Subsequently, an open-source implementation that was reported to perform well on the widely accepted KITTI benchmarking dataset was experimentally evaluated. This experimentation was focused on the validation of LiDAR based mapping and odometry generation in a typical suburban environment. The corresponding experimental observations are presented and a number of propositions are made for further improvement.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122382420","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

Implementation and Characterization of a Memristive Memory System 记忆记忆系统的实现与特性

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861788

David Radakovits, N. Taherinejad

引用次数: 9

Comparative Analysis of Classifiers for EMG Signals 肌电信号分类器的比较分析

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861835

Bushra Saeed, S. O. Gilani, M. Z. Rehman, Mohsin Jamil, Asim Waris, M. Khan

{"title":"Comparative Analysis of Classifiers for EMG Signals","authors":"Bushra Saeed, S. O. Gilani, M. Z. Rehman, Mohsin Jamil, Asim Waris, M. Khan","doi":"10.1109/CCECE.2019.8861835","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861835","url":null,"abstract":"Electromyographic signals have a considerable importance in robotic hand prosthesis and various biomedical applications. The analysis of these signals for pattern recognition of arm movements is helpful to facilitate the handicap individuals with upper limb impairment or paralysed individuals who are able to reinstitute innate control of hand. These signals need to be recorded from the patients hand with the help of electrodes which may be contaminated with noises or undesired signals which affects the output accuracy. To ensure the detection of data with reduced noise and to execute the optimal performance from the analysis, the signals are preprocessed. The data collected for the 52 movements from 27 different subjects is provided by NinaPro database which allows the whole research community to add more advancement to this field. The purpose of this research is to analyse the dataset from this easily accessible database for twelve finger and hand movements acquired from 27 subjects. This processed data was then tested for two different classifiers, Linear Discriminant Analysis classifier and Artificial Neural Network classifier, to examine their percent classification accuracy. The data classified with Linear Discriminant Analysis gives the mean classification accuracy of 85.41% while Artificial Neural Network classifier shows 91.14%. The results for the dataset used in this study revealed that Artificial Neural Network performs better in the classification and recognition of data for hand movements as compared to Linear Discriminant Analysis.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126766761","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: 6

Model Predictive Control Based Approach for Microgrid Energy Management 基于模型预测控制的微电网能量管理方法

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861781

I. Novickij, G. Joós

引用次数: 7

Flexible Robust Unit Commitment Considering Subhourly Wind Power Ramp Behaviors 考虑亚小时风力发电坡道行为的柔性稳健机组承诺

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861590

Bo Hu, Yuzhong Gong, C. Chung

{"title":"Flexible Robust Unit Commitment Considering Subhourly Wind Power Ramp Behaviors","authors":"Bo Hu, Yuzhong Gong, C. Chung","doi":"10.1109/CCECE.2019.8861590","DOIUrl":"https://doi.org/10.1109/CCECE.2019.8861590","url":null,"abstract":"The focus of existing studies on day ahead unit commitment (DAUC) considering wind power have mainly been on the hourly operation constraints. However, if the sub-hourly wind power variations are not carefully considered, the obtained unit commitment (UC) solutions may not be flexible enough to accommodate the sub-hourly wind variations and results in wind curtailments. To ensure the full utilization of wind power, in this paper a robust optimization-based UC model considering sub-hourly wind power variation is proposed. The objective is to provide a flexible and robust UC solution for the thermal units, which ensures sufficient ramp up and ramp down reserves for the variations of wind power in the intra-hour time frame. Firstly, a non-parametric approach based on the 2-dimensional kernel density estimation is proposed to quantify the sub-hourly wind power variability. Then, based on the quantification results, a set of ramp constraints are imposed on the robust UC model. A column and constraint generation method is applied to solve the improved UC model. The proposed model is tested and compared with conventional UC models on IEEE 39 bus test system to verify its effectiveness.","PeriodicalId":352860,"journal":{"name":"2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126217902","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

Times Series Prediction used in Treating Municipal Wastewater for Plant Irrigation 时间序列预测在植物灌溉城市污水处理中的应用

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861819

M. Mohebbian, S. Vedaei, A. Bahar, K. Wahid, A. Dinh

引用次数: 0

Wireless Sensor Networks Analysis based on MAC Protocols 基于MAC协议的无线传感器网络分析

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) Pub Date : 2019-05-05 DOI: 10.1109/CCECE.2019.8861569

Furat Al-Obaidy, Shadi Momtahen, F. Mohammadi

引用次数: 2