Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255719
Rama Ferguson, Brody Voth, Zachary di Giovanni, Diego Felix de Almeida, Michal Aibin
The rapid changes and increase of modern and cloud-ready services “on-demand” increase the utilization of Content Delivery Networks (CDNs) to deliver service and content to end-users efficiently. In order to minimize the communication cost and the average waiting time, it is necessary to send the end-users' requests to the best available servers. In this paper, we design and implement a Honeybee algorithm that adapts quickly to possible servers' downtime to avoid communication delays. We then compare it to other algorithms available in the literature. Finally, the evaluation is performed using various scenarios with networking issues, such as single server failures or natural disasters consisting of multiple server issues.
{"title":"Honeybee Algorithm for Content Delivery Networks","authors":"Rama Ferguson, Brody Voth, Zachary di Giovanni, Diego Felix de Almeida, Michal Aibin","doi":"10.1109/CCECE47787.2020.9255719","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255719","url":null,"abstract":"The rapid changes and increase of modern and cloud-ready services “on-demand” increase the utilization of Content Delivery Networks (CDNs) to deliver service and content to end-users efficiently. In order to minimize the communication cost and the average waiting time, it is necessary to send the end-users' requests to the best available servers. In this paper, we design and implement a Honeybee algorithm that adapts quickly to possible servers' downtime to avoid communication delays. We then compare it to other algorithms available in the literature. Finally, the evaluation is performed using various scenarios with networking issues, such as single server failures or natural disasters consisting of multiple server issues.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"11 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":"131066911","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255776
Islam I. Osman, M. Shehata
Moving object detection is a challenging task in computer vision. A class agnostic model is learned to detect moving objects in a video despite their category. This is done using the proposed MODSiam that takes a single background image of the scene and the current frame as input, then the model extracts features from both inputs and merges then to output the foreground objects. A comparison of using this model with three different backbone convolutional neural networks is presented. The evaluation is done using the metrics precision, recall, F1-measure, false-positive rate, false-negative rate, specificity, accuracy, and the number of frames per second. All models are tested on the benchmark dataset CDNet, which is a dataset of videos for moving objects under different conditions like low frame rate, shadows, and dynamic background. The results show that using ResNet as a backbone produced promising results compared to other models with respect to most of evaluation metrics.
{"title":"MODSiam: Moving Object Detection using Siamese Networks","authors":"Islam I. Osman, M. Shehata","doi":"10.1109/CCECE47787.2020.9255776","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255776","url":null,"abstract":"Moving object detection is a challenging task in computer vision. A class agnostic model is learned to detect moving objects in a video despite their category. This is done using the proposed MODSiam that takes a single background image of the scene and the current frame as input, then the model extracts features from both inputs and merges then to output the foreground objects. A comparison of using this model with three different backbone convolutional neural networks is presented. The evaluation is done using the metrics precision, recall, F1-measure, false-positive rate, false-negative rate, specificity, accuracy, and the number of frames per second. All models are tested on the benchmark dataset CDNet, which is a dataset of videos for moving objects under different conditions like low frame rate, shadows, and dynamic background. The results show that using ResNet as a backbone produced promising results compared to other models with respect to most of evaluation metrics.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"78 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":"128327739","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255718
Yunfei Bai, A. Rajapakse
To provide more clean energy and satisfy the increasing power demand, microgrids using renewable energy sources (RES) are designed as additional power supplies. Recently, DC microgrids (DCMGs) have gained the attention for their higher power efficiency and simpler configuration compared to AC microgrids (ACMGs). Although DCMGs seem better than ACMGs, lack of protection standard is a critical problem when operating DCMGs. Since DC fault response is completely different as AC fault, AC protection methods cannot be used for DCMGs. In this paper, a ring bus DCMG model is simulated using computer software PSCAD. A combined protection scheme based on cable current derivatives is introduced. This protection scheme not only detects and localizes low resistance DC faults very fast, but also accurately handles high resistance DC faults. The reliability of this scheme is proved by the simulation results.
{"title":"Fault Detection and Localization in a Ring Bus DC Microgrid Using Current Derivatives","authors":"Yunfei Bai, A. Rajapakse","doi":"10.1109/CCECE47787.2020.9255718","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255718","url":null,"abstract":"To provide more clean energy and satisfy the increasing power demand, microgrids using renewable energy sources (RES) are designed as additional power supplies. Recently, DC microgrids (DCMGs) have gained the attention for their higher power efficiency and simpler configuration compared to AC microgrids (ACMGs). Although DCMGs seem better than ACMGs, lack of protection standard is a critical problem when operating DCMGs. Since DC fault response is completely different as AC fault, AC protection methods cannot be used for DCMGs. In this paper, a ring bus DCMG model is simulated using computer software PSCAD. A combined protection scheme based on cable current derivatives is introduced. This protection scheme not only detects and localizes low resistance DC faults very fast, but also accurately handles high resistance DC faults. The reliability of this scheme is proved by the simulation results.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"10 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":"133384988","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255705
J. Makaran
The following paper proposes a method for performing all facets of EMC Testing at temperatures other than ambient. An examination of operational requirements of electronic assemblies is presented through an examination of operating temperature requirements for electronic assemblies from different industrial sectors. This is followed by examination of the requirements of EMC specifications, followed by a proposal of the specifications required for an ideal device to perform EMC testing at temperatures other than ambient.
{"title":"EMC Testing at Temperatures Other than Ambient","authors":"J. Makaran","doi":"10.1109/CCECE47787.2020.9255705","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255705","url":null,"abstract":"The following paper proposes a method for performing all facets of EMC Testing at temperatures other than ambient. An examination of operational requirements of electronic assemblies is presented through an examination of operating temperature requirements for electronic assemblies from different industrial sectors. This is followed by examination of the requirements of EMC specifications, followed by a proposal of the specifications required for an ideal device to perform EMC testing at temperatures other than ambient.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"75 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":"133349445","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255815
E. N. Sadjadi, M. Ebrahimi, Zahra Gachloo
The structure of fuzzy model impacts how well it approximates the nonlinear function, and how many rules are required to gain the desired accuracy. The most of the earlier works rely on diminishing the higher derivation of the fuzzy model in front of the higher derivatives of the real system. However, the smooth compositions are m-time differentiable and will not diminish. This has motivated to derive the relation of required fuzzy rules with the arbitrary accuracy for function approximation through the smooth fuzzy model. The originality of the work is that the approximation error and the number of required fuzzy rules in this paper, rely on the structure of the fuzzy model and the involved s-t compositions, beside the nonlinear properties of the real plant, through a reliable mathematical formulation. Hence, we have presented a prediction-correction algorithm to include all the main factors. It is proved that number of the required rules are lower than those of the earlier works to gain the same level of model accuracy.
{"title":"Discussion on Accuracy of Approximation with Smooth Fuzzy Models","authors":"E. N. Sadjadi, M. Ebrahimi, Zahra Gachloo","doi":"10.1109/CCECE47787.2020.9255815","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255815","url":null,"abstract":"The structure of fuzzy model impacts how well it approximates the nonlinear function, and how many rules are required to gain the desired accuracy. The most of the earlier works rely on diminishing the higher derivation of the fuzzy model in front of the higher derivatives of the real system. However, the smooth compositions are m-time differentiable and will not diminish. This has motivated to derive the relation of required fuzzy rules with the arbitrary accuracy for function approximation through the smooth fuzzy model. The originality of the work is that the approximation error and the number of required fuzzy rules in this paper, rely on the structure of the fuzzy model and the involved s-t compositions, beside the nonlinear properties of the real plant, through a reliable mathematical formulation. Hence, we have presented a prediction-correction algorithm to include all the main factors. It is proved that number of the required rules are lower than those of the earlier works to gain the same level of model accuracy.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"12 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131687992","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255701
B. M. Rocha, G. S. Vieira, Afonso U. Fonseca, H. Pedrini, N. M. Sousa, Fabrízzio Soares
Sugarcane is one of the main crops in the world due to the economic value it promotes by selling its derivatives. A diversity of technologies has been developed to optimize agricultural activities and maximize the productivity of sugarcane crops. In this sense, our primary goal is to contribute to this research area by detecting planting lines and measuring their faults, including the evaluation of curved lines that substantially limit numerous solutions in practical applications. An automatic method that identifies and measures sugarcane planting lines through digital image processing techniques and machine learning algorithms is presented. The proposal is evaluated using a database of real scene images, which were classified by K-Nearest Neighbors (KNN) and prepared with the support of a small unmanned aerial vehicle (UAV). Experimental tests show a low relative error of approximately 1.65% compared to manual mapping in the planting regions. It means that our proposal can identify and measure planting lines accurately, which enables automated inspections with high precision measurements.
{"title":"Evaluation and Detection of Gaps in Curved Sugarcane Planting Lines in Aerial Images","authors":"B. M. Rocha, G. S. Vieira, Afonso U. Fonseca, H. Pedrini, N. M. Sousa, Fabrízzio Soares","doi":"10.1109/CCECE47787.2020.9255701","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255701","url":null,"abstract":"Sugarcane is one of the main crops in the world due to the economic value it promotes by selling its derivatives. A diversity of technologies has been developed to optimize agricultural activities and maximize the productivity of sugarcane crops. In this sense, our primary goal is to contribute to this research area by detecting planting lines and measuring their faults, including the evaluation of curved lines that substantially limit numerous solutions in practical applications. An automatic method that identifies and measures sugarcane planting lines through digital image processing techniques and machine learning algorithms is presented. The proposal is evaluated using a database of real scene images, which were classified by K-Nearest Neighbors (KNN) and prepared with the support of a small unmanned aerial vehicle (UAV). Experimental tests show a low relative error of approximately 1.65% compared to manual mapping in the planting regions. It means that our proposal can identify and measure planting lines accurately, which enables automated inspections with high precision measurements.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"9 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":"127682867","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}
Pub Date : 2020-08-30DOI: 10.1109/ccece47787.2020.9255740
{"title":"Index","authors":"","doi":"10.1109/ccece47787.2020.9255740","DOIUrl":"https://doi.org/10.1109/ccece47787.2020.9255740","url":null,"abstract":"","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"105 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":"117353430","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255714
Thangarajah Akilan, E. Johnson, Gaurav Taluja, Japneet Sandhu, Ritika Chadha
Simultaneous Localization And Mapping (SLAM) is used to predict the trajectory by the Autonomous Navigation Robots (ANR), for instance Self-Driving Cars (SDC). It computes the trajectory through sensing the surroundings, like a visual perception of the environment. This work focuses on the performance improvements of a SLAM model using multimodal learning: (i), early fusion via layer weight enhancement of feature extractors, and (ii), late fusion via score refinement of the trajectory (pose) regressor. The comparative analysis on Apolloscape dataset shows that the proposed fusion strategies improve localization performance significantly. This work also evaluates applicability of various Deep Convolutional Neural Networks (DCNNs) for SLAM.
{"title":"Multimodality Weight and Score Fusion for SLAM","authors":"Thangarajah Akilan, E. Johnson, Gaurav Taluja, Japneet Sandhu, Ritika Chadha","doi":"10.1109/CCECE47787.2020.9255714","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255714","url":null,"abstract":"Simultaneous Localization And Mapping (SLAM) is used to predict the trajectory by the Autonomous Navigation Robots (ANR), for instance Self-Driving Cars (SDC). It computes the trajectory through sensing the surroundings, like a visual perception of the environment. This work focuses on the performance improvements of a SLAM model using multimodal learning: (i), early fusion via layer weight enhancement of feature extractors, and (ii), late fusion via score refinement of the trajectory (pose) regressor. The comparative analysis on Apolloscape dataset shows that the proposed fusion strategies improve localization performance significantly. This work also evaluates applicability of various Deep Convolutional Neural Networks (DCNNs) for SLAM.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"8 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":"126965568","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255809
Osama Bondoq, K. Abugharbieh, Abdullah Hasan
This work proposes a novel master-slave latch D-type Flip-Flop. It consists of a reset-set slave latch and an asymmetrical single data input master latch. By reducing the number of stages and removing signal conditioning circuitry in the master latch, setup time has been significantly reduced and power consumption has improved. The proposed flip-flop is competitive to other state of the art low power flip-flops in addition to the conventional Transmission Gate Flip-flop (TGFF) in terms of performance, power consumption and area. In simulations, the proposed flip-flop has achieved up to 71.5% improvement in setup time, 36.5% improvement in D-Q delay time and up to 56.5% less power delay product (PDP) with 10% data activity compared to Topologically Compressed Flip-Flop (TCFF), which is a low power flip-flop. Further, it has achieved 11% smaller circuit area compared with TGFF. This work includes the proposed flip-flop's circuit schematic, layout design and simulations using Hspice tool with 28nm CMOS technology and a 1V supply voltage at 1 GHz clock (CLK).
{"title":"A D-Type Flip-Flop with Enhanced Timing Using Low Supply Voltage","authors":"Osama Bondoq, K. Abugharbieh, Abdullah Hasan","doi":"10.1109/CCECE47787.2020.9255809","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255809","url":null,"abstract":"This work proposes a novel master-slave latch D-type Flip-Flop. It consists of a reset-set slave latch and an asymmetrical single data input master latch. By reducing the number of stages and removing signal conditioning circuitry in the master latch, setup time has been significantly reduced and power consumption has improved. The proposed flip-flop is competitive to other state of the art low power flip-flops in addition to the conventional Transmission Gate Flip-flop (TGFF) in terms of performance, power consumption and area. In simulations, the proposed flip-flop has achieved up to 71.5% improvement in setup time, 36.5% improvement in D-Q delay time and up to 56.5% less power delay product (PDP) with 10% data activity compared to Topologically Compressed Flip-Flop (TCFF), which is a low power flip-flop. Further, it has achieved 11% smaller circuit area compared with TGFF. This work includes the proposed flip-flop's circuit schematic, layout design and simulations using Hspice tool with 28nm CMOS technology and a 1V supply voltage at 1 GHz clock (CLK).","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"34 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":"127931545","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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255672
Anas Ibrahim, Yue Zhou, M. Jenkins, M. Naish, A. L. Trejos
The study of the characteristics and behaviour of tremor for people suffering from Parkinson's disease (PD) is an important first step in developing a new method to predict future tremor signals, their onset and the active tremor instances. The current approaches to detect tremor are limited to tremor estimators that rely on simple tremor models, or on deep brain probing that is invasive in nature. Thus, a new method that is noninvasive and that can capture tremor complexity to predict when tremor is active is needed. In this work, a new approach is presented using neural networks (NNs) and data from inertial measurement units (IMUs) to predict tremor onset and classify the active tremor instances in the wrist and metacarpophalangeal (MCP) joints of the index finger and thumb. The developed model showed an accuracy of 92.9% in predicting and detecting tremor onset, and therefore can be considered a reliable tool that has the potential to be integrated with wearable assistive devices for suppressing tremor.
{"title":"Parkinson's Tremor Onset Detection and Active Tremor Classification Using a Multilayer Perceptron","authors":"Anas Ibrahim, Yue Zhou, M. Jenkins, M. Naish, A. L. Trejos","doi":"10.1109/CCECE47787.2020.9255672","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255672","url":null,"abstract":"The study of the characteristics and behaviour of tremor for people suffering from Parkinson's disease (PD) is an important first step in developing a new method to predict future tremor signals, their onset and the active tremor instances. The current approaches to detect tremor are limited to tremor estimators that rely on simple tremor models, or on deep brain probing that is invasive in nature. Thus, a new method that is noninvasive and that can capture tremor complexity to predict when tremor is active is needed. In this work, a new approach is presented using neural networks (NNs) and data from inertial measurement units (IMUs) to predict tremor onset and classify the active tremor instances in the wrist and metacarpophalangeal (MCP) joints of the index finger and thumb. The developed model showed an accuracy of 92.9% in predicting and detecting tremor onset, and therefore can be considered a reliable tool that has the potential to be integrated with wearable assistive devices for suppressing tremor.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"20 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":"131522172","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}
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