Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255711
A. Ibrahim, Liam Corrigan, R. Kashef
Traditional time series modeling techniques emphasize on predicting cryptocurrencies using classically structured data representation as numerical features to present the time-series datasets. In this paper, a novel approach to analyze time-series data charts using a modified Convolutional Neural Networks (CNNs) is proposed. The CNNs have been adopted to recognize subtle and undetectable patterns within images of time-series data charts. Our approach has been proven to achieve significant results, suggesting a need for further research into this new method for time series modeling, especially for Bitcoin.
{"title":"Predicting the Demand in Bitcoin Using Data Charts: A Convolutional Neural Networks Prediction Model","authors":"A. Ibrahim, Liam Corrigan, R. Kashef","doi":"10.1109/CCECE47787.2020.9255711","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255711","url":null,"abstract":"Traditional time series modeling techniques emphasize on predicting cryptocurrencies using classically structured data representation as numerical features to present the time-series datasets. In this paper, a novel approach to analyze time-series data charts using a modified Convolutional Neural Networks (CNNs) is proposed. The CNNs have been adopted to recognize subtle and undetectable patterns within images of time-series data charts. Our approach has been proven to achieve significant results, suggesting a need for further research into this new method for time series modeling, especially for Bitcoin.","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":"130398395","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/CCECE47787.2020.9255753
Farrah Huntinghawk, C. Richard, S. Plosker, Gautam Srivastava
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.
{"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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255774
T. Jamil
Sign Language is a widely known medium of communication used by the deaf, hard of hearing individuals, and those who are unable to speak physically because of any reason. There is no common or universal version of sign language. This is especially true for Arabic Sign Language (ArSL) which has various versions depending on the Arab country where it is being used. In this paper, we have designed an intelligent system which can parse Arabic text, keeping in mind the context of the text, and then translate the text into Arabic Sign Language. Preliminary testing of the system has shown an overall accuracy of 85% in correctly translating Arabic words into the Arabic Sign Language.
{"title":"Design and Implementation of an Intelligent System to translate Arabic Text into Arabic Sign Language","authors":"T. Jamil","doi":"10.1109/CCECE47787.2020.9255774","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255774","url":null,"abstract":"Sign Language is a widely known medium of communication used by the deaf, hard of hearing individuals, and those who are unable to speak physically because of any reason. There is no common or universal version of sign language. This is especially true for Arabic Sign Language (ArSL) which has various versions depending on the Arab country where it is being used. In this paper, we have designed an intelligent system which can parse Arabic text, keeping in mind the context of the text, and then translate the text into Arabic Sign Language. Preliminary testing of the system has shown an overall accuracy of 85% in correctly translating Arabic words into the Arabic Sign Language.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"62 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":"132287713","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/CCECE47787.2020.9255722
Jaime Campos, P. Ferguson
ManitobaSat-1 is a CubeSat project implementing project management approaches found in other industries that enable efficient system engineering practices. One of the major roles of a systems engineer is to control requirements that dictate product specifications, functions, and related tasks. These functions provide engineers a metric that better reflects the technical progress of a project compared to Earned Value management. This project management approach combines the roles of systems engineer with project manager by using requirements verification activities to capture the technical progress of a project, providing managers with a meaningful metric to monitor the health of the project.
{"title":"ManitobaSat-1: Space Systems Engineering for Student Training","authors":"Jaime Campos, P. Ferguson","doi":"10.1109/CCECE47787.2020.9255722","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255722","url":null,"abstract":"ManitobaSat-1 is a CubeSat project implementing project management approaches found in other industries that enable efficient system engineering practices. One of the major roles of a systems engineer is to control requirements that dictate product specifications, functions, and related tasks. These functions provide engineers a metric that better reflects the technical progress of a project compared to Earned Value management. This project management approach combines the roles of systems engineer with project manager by using requirements verification activities to capture the technical progress of a project, providing managers with a meaningful metric to monitor the health of the project.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"6 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":"114949665","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/CCECE47787.2020.9255831
G. S. Vieira, N. M. Sousa, J. P. Félix, J. C. Lima, Fabrízzio Soares
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.
{"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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255673
Si-yu Zhang, B. Shahrrava
In this paper, a turbo receiver for polar-coded orthogonal frequency division multiplexing(OFDM) systems for frequency-selective fading channels with unknown channel state information(CSI) is proposed. The receiver iteratively exchanges soft information between an expectation-maximization(EM) symbol detector and a soft polar decoder that is based on the belief propagation(BP) algorithm. By utilizing such receiver, the error-correcting performance of the system can be significantly improved even with unknown CSI. Simulation results show that by using the proposed turbo receiver, around 5dB coding gain at a bit-error rate of 5 × 10 −2 can be obtained compared to the receiver that detects symbols and implements decoding separately with unknown CSI.
{"title":"Turbo Receiver for Polar-Coded OFDM systems with unknown CSI","authors":"Si-yu Zhang, B. Shahrrava","doi":"10.1109/CCECE47787.2020.9255673","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255673","url":null,"abstract":"In this paper, a turbo receiver for polar-coded orthogonal frequency division multiplexing(OFDM) systems for frequency-selective fading channels with unknown channel state information(CSI) is proposed. The receiver iteratively exchanges soft information between an expectation-maximization(EM) symbol detector and a soft polar decoder that is based on the belief propagation(BP) algorithm. By utilizing such receiver, the error-correcting performance of the system can be significantly improved even with unknown CSI. Simulation results show that by using the proposed turbo receiver, around 5dB coding gain at a bit-error rate of 5 × 10 −2 can be obtained compared to the receiver that detects symbols and implements decoding separately with unknown CSI.","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":"134190843","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/CCECE47787.2020.9255754
Jaspreet Singh, Marwan Dhuheir, A. Refaey, A. Erbad, Amr M. Mohamed, M. Guizani
Recently, drones have been used in many different applications such as search and rescue operations, extinguishing fires, and environment mapping. As the number of moving drones increases in the sky, the collisions risk increases. In this paper, we present a system model, prototype, and preliminary evaluation for UAV obstacle avoidance. The obstacle avoidance system prototype uses ultrasonic sensors for obstacle detection, S-BUS communication protocol for drone control, and Savitzky-Golay filter for data smoothing.
{"title":"Navigation and Obstacle Avoidance System in Unknown Environment","authors":"Jaspreet Singh, Marwan Dhuheir, A. Refaey, A. Erbad, Amr M. Mohamed, M. Guizani","doi":"10.1109/CCECE47787.2020.9255754","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255754","url":null,"abstract":"Recently, drones have been used in many different applications such as search and rescue operations, extinguishing fires, and environment mapping. As the number of moving drones increases in the sky, the collisions risk increases. In this paper, we present a system model, prototype, and preliminary evaluation for UAV obstacle avoidance. The obstacle avoidance system prototype uses ultrasonic sensors for obstacle detection, S-BUS communication protocol for drone control, and Savitzky-Golay filter for data smoothing.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"32 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":"122339420","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/CCECE47787.2020.9255684
Zain Khaliq, Paul Mirdita, A. Refaey, Xianbin Wang
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.
{"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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255766
R. Atwah, P. Flocchini, A. Nayak
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.
{"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}
Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255713
Kuanghua Qiao, Amanda Nickerson, S. MacDonald, E. Ghafar-Zadeh
This paper presents a novel non-invasive wireless device for animal breathing measurement. A flexible sensor is used to convert the berthing rate into a parodic resistive change. An interface circuit is designed to accurately measure the resistive signal, detect the breathing rate and transfer the recorded data to the computer wirelessly. Herein we demonstrate and discuss the functionality of the proposed system on a dog. Based on this result, the proposed system can reliably be used for animal behavioural studies.
{"title":"A Non-Invasive Wireless Respiratory Monitoring System for Animals' Behavioural Studies","authors":"Kuanghua Qiao, Amanda Nickerson, S. MacDonald, E. Ghafar-Zadeh","doi":"10.1109/CCECE47787.2020.9255713","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255713","url":null,"abstract":"This paper presents a novel non-invasive wireless device for animal breathing measurement. A flexible sensor is used to convert the berthing rate into a parodic resistive change. An interface circuit is designed to accurately measure the resistive signal, detect the breathing rate and transfer the recorded data to the computer wirelessly. Herein we demonstrate and discuss the functionality of the proposed system on a dog. Based on this result, the proposed system can reliably be used for animal behavioural studies.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"48 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":"124087575","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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