2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)最新文献_第7页

Predicting the Demand in Bitcoin Using Data Charts: A Convolutional Neural Networks Prediction Model 使用数据图预测比特币的需求:卷积神经网络预测模型

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255711

A. Ibrahim, Liam Corrigan, R. Kashef

引用次数: 4

Expanding Cybersecurity Knowledge Through an Indigenous Lens: A First Look 通过本土镜头扩展网络安全知识:第一眼

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255753

Farrah Huntinghawk, C. Richard, S. Plosker, Gautam Srivastava

{"title":"Expanding Cybersecurity Knowledge Through an Indigenous Lens: A First Look","authors":"Farrah Huntinghawk, C. Richard, S. Plosker, Gautam Srivastava","doi":"10.1109/CCECE47787.2020.9255753","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255753","url":null,"abstract":"Decolonization and Indigenous education are at the forefront of Canadian content currently in Academia. Over the last few decades, we have seen some major changes in the way in which we share information. In particular, we have moved into an age of electronically-shared content, and there is an increasing expectation in Canada that this content is both culturally significant and relevant. In this paper, we discuss an ongoing community engagement initiative with First Nations communities in the Western Manitoba region. The initiative involves knowledge-sharing activities that focus on the topic of cybersecurity, and are aimed at a public audience. This initial look into our educational project focuses on the conceptual analysis and planning stage. We are developing a “Cybersecurity 101” mini-curriculum, to be implemented over several one-hour long workshops aimed at diverse groups (these public workshops may include a wide range of participants, from tech-adverse to tech-savvy). Learning assessment tools have been built in to the workshop program. We have created informational and promotional pamphlets, posters, lesson plans, and feedback questionnaires which we believe instill relevance and personal connection to this topic, helping to bridge gaps in accessibility for Indigenous communities while striving to build positive, reciprocal relationships. Our methodology is to approach the subject from a community needs and priorities perspective. Activities are therefore being tailored to fit each community. We hope this will lead to increased awareness and engagement by community members. Two Indigenous student research assistants were hired to assist in this project, which has developed into a blend of community outreach on the topic of security and data protection (most notably with respect to social media and online banking) and a computing education student-led educational research project.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"21 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":"115014976","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

Design and Implementation of an Intelligent System to translate Arabic Text into Arabic Sign Language 阿拉伯语文本智能翻译系统的设计与实现

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255774

T. Jamil

引用次数: 2

ManitobaSat-1: Space Systems Engineering for Student Training ManitobaSat-1:学生训练的空间系统工程

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255722

Jaime Campos, P. Ferguson

引用次数: 1

Application of Saliency Methods for Extracting Tree Features in Outdoor Scenes 显著性方法在室外场景树木特征提取中的应用

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255831

G. S. Vieira, N. M. Sousa, J. P. Félix, J. C. Lima, Fabrízzio Soares

{"title":"Application of Saliency Methods for Extracting Tree Features in Outdoor Scenes","authors":"G. S. Vieira, N. M. Sousa, J. P. Félix, J. C. Lima, Fabrízzio Soares","doi":"10.1109/CCECE47787.2020.9255831","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255831","url":null,"abstract":"The growing demand for accurate results in agricultural environments consolidates the so-called precision agriculture in which saliency analysis has brought possibilities for the effective application of computer vision techniques. The saliency measured by computer algorithms follows a logic of attention similar to the human visual system in which the protuberant regions are identified due to some features that make them more evident and prone to draw more attention. Thus, the salient features are preserved in such a way that the most evocative scene components are highlighted to emphasize the relevant areas. In this paper, we present a saliency map refinement approach, and we use it to compare saliency estimation methods in the detection of trees. Their performance is evaluated by counting the number of areas correctly detected and labeled as a tree, as well as the segments incorrectly categorized as a tree. We present and discuss the results to point out the salience method that best corresponds to the refinement approach we propose. Fourteen saliency methods are compared using an annotated database of manually segmented images that were collected in different scenarios where trees are emphasized in the foreground.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"47 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":"116821233","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

Turbo Receiver for Polar-Coded OFDM systems with unknown CSI 未知CSI的极化编码OFDM系统Turbo接收机

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255673

Si-yu Zhang, B. Shahrrava

引用次数: 2

Navigation and Obstacle Avoidance System in Unknown Environment 未知环境下的导航与避障系统

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255754

Jaspreet Singh, Marwan Dhuheir, A. Refaey, A. Erbad, Amr M. Mohamed, M. Guizani

引用次数: 4

Unsupervised Manifold Alignment for Wifi RSSI Indoor Localization Wifi RSSI室内定位的无监督流形对准

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255684

Zain Khaliq, Paul Mirdita, A. Refaey, Xianbin Wang

{"title":"Unsupervised Manifold Alignment for Wifi RSSI Indoor Localization","authors":"Zain Khaliq, Paul Mirdita, A. Refaey, Xianbin Wang","doi":"10.1109/CCECE47787.2020.9255684","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255684","url":null,"abstract":"There is a wealth of analysis techniques that researchers across the world are implementing for better indoor localization. The RSSI fingerprinting is one of many techniques used for indoor and outdoor localization. In addition, other fingerprints are used to assist in the localization collected from several sources such as camera, radar, and Lidar. Ideally, a combination of these sources is used to locate the same object. Precisely, these sources are collecting the same data using different dimensions ultimately building upon one big system. Due to different dimensions set by these sources, it often becomes difficult to train the overall system to achieve the task of localization. In this paper, we propose a technique that can be used to incorporate training multiple datasets from different dimensions (e.g. Lidar, camera, and radar) into one global dataset, then train it all at once. This technique is known as the Manifold Alignment. Our proposed manifold alignment algorithm bridges the gap, allowing the inclusion of multiple datasets in our application whilst constraining the computational time and storage that would be required for the system. We assume that our technique is embedded into our system and localization is achieved through either computing our proposed Manifold Alignment algorithm over a local device, edge server, or cloud. Results in this paper show how well the Manifold Alignment Algorithm is beneficial for a localization problem where it is implemented inside a machine learning model that computes the manifold of these datasets.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"27 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":"125439376","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: 4

Towards Smart Trust Management of VANETs 面向VANETs的智能信任管理

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255766

R. Atwah, P. Flocchini, A. Nayak

{"title":"Towards Smart Trust Management of VANETs","authors":"R. Atwah, P. Flocchini, A. Nayak","doi":"10.1109/CCECE47787.2020.9255766","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255766","url":null,"abstract":"In vehicular applications, the cost of additional infrastructure deployments such as roadside units (RSUs), cellular networks, and mobile cloud computing is an obstacle towards the enhancement of the quality of the traffic provided services. The idea of partial reliance on fog computing to support the existing infrastructure is proposed in few research papers. In this paper, we conceive the idea of involving fog nodes in the trust evaluation process. By integrating fog nodes as coordinator resources in Vehicle Ad hoc Networks (VANETs), the quality of services and applications can be significantly enhanced. Possible services fog nodes can perform to reduce the burden on agents include event detection, cluster head selection, and misbehaviour detection. Also, we propose a novel task-based trust experience that evaluates the trustworthiness of a vehicle according to the type of task demanded by examining its historical records. The proposed framework reduces communication demand between the agents by leveraging a vehicle's historical records. Also, the proposed model enables the cluster head to employ not only the most trusted vehicle but also the most competent vehicle for a specific task.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"23 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":"126183709","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 Non-Invasive Wireless Respiratory Monitoring System for Animals' Behavioural Studies 一种用于动物行为研究的无创无线呼吸监测系统

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) Pub Date : 2020-08-30 DOI: 10.1109/CCECE47787.2020.9255713

Kuanghua Qiao, Amanda Nickerson, S. MacDonald, E. Ghafar-Zadeh

引用次数: 0