Pub Date : 2020-08-30DOI: 10.1109/CCECE47787.2020.9255732
Elizabeth Hofer, Taufiq Rahman, Ryan Myers, Ismail Hamieh
The retro-reflective characteristics of lane demarcations on roadways can potentially provide robust detection in the infrared spectrum even in poor lighting and weather conditions. This paper explores this idea by training a convolutional neural network using Darknet with YOLO to detect 9 classes of road lines from the Berkeley Deep Drive Dataset (BDD). Although BDD is composed of conventional colour images, they were converted to greyscale prior to training as a solution to the scarcity of datasets in the infrared spectrum. The trained model was evaluated on road scenes acquired by the infrared sensor of an Intel-Realsense camera. From the experimental results, it is concluded that object detection techniques primarily developed for localization and classification of objects in the form of bounding boxes are inherently unsuitable for detecting line shaped objects such roadway lane demarcations. In addition, despite the sub-optimal training and detection approach, the performance showed potential for robust lane detection using infrared images.
即使在恶劣的照明和天气条件下，道路上车道边界的反向反射特性也可能提供强大的红外光谱检测。本文通过使用带有YOLO的Darknet训练卷积神经网络来检测来自Berkeley Deep Drive Dataset (BDD)的9类道路线来探索这一想法。虽然BDD由传统的彩色图像组成，但为了解决红外光谱数据集稀缺的问题，在训练之前将它们转换为灰度图像。用Intel-Realsense摄像机红外传感器采集的道路场景对训练后的模型进行了评估。从实验结果中可以得出结论，主要针对边界框形式的目标定位和分类而开发的目标检测技术本质上不适合检测道路车道划分等线形目标。此外，尽管训练和检测方法不是最优的，但该性能显示了使用红外图像进行鲁棒车道检测的潜力。
{"title":"Training a Neural Network for Lane Demarcation Detection in the Infrared Spectrum","authors":"Elizabeth Hofer, Taufiq Rahman, Ryan Myers, Ismail Hamieh","doi":"10.1109/CCECE47787.2020.9255732","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255732","url":null,"abstract":"The retro-reflective characteristics of lane demarcations on roadways can potentially provide robust detection in the infrared spectrum even in poor lighting and weather conditions. This paper explores this idea by training a convolutional neural network using Darknet with YOLO to detect 9 classes of road lines from the Berkeley Deep Drive Dataset (BDD). Although BDD is composed of conventional colour images, they were converted to greyscale prior to training as a solution to the scarcity of datasets in the infrared spectrum. The trained model was evaluated on road scenes acquired by the infrared sensor of an Intel-Realsense camera. From the experimental results, it is concluded that object detection techniques primarily developed for localization and classification of objects in the form of bounding boxes are inherently unsuitable for detecting line shaped objects such roadway lane demarcations. In addition, despite the sub-optimal training and detection approach, the performance showed potential for robust lane detection using infrared images.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"1048 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":"123145814","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/CCECE47787.2020.9255751
S. Komeylian
Important technological advancement in designing smart array antennas has been encouraged many researchers to concentrate their work on the two main concepts of the direction of arrival (DoA) and beamforming techniques. The preliminary objective of beamforming techniques includes, electronically, the mainbeam in the direction of interest at a certain time and measuring the output power. In this scenario, the main practical challenge resides in achieving maximum output power in which the direction of steered mainbeam coincides with the direction of arrivals. Since the involved problems in most DoA estimation optimizations consist of a lot of unknown parameters including direction of arrivals, SNRs, signal waveforms and samples of noises in the array output, it may become impossible to build a large enough training dataset for covering the distributions for all the aforementioned test data. An alternative way to overcome this constraint which we aim at stressing in this work involves employing support vector machine algorithms for separating unknown components of the actual input in the higher dimensional feature space. In this work, we have implemented the decision directed acyclic graph (DDAG) and Vapnik-Chervonenkis (VC) methods for the least squares support vector machine (LS-SVM) algorithms for estimating DoAs. We have rigorously verified that DoAs are very much affected the antenna array geometries. In addition, we have investigated the quality of the communication channel by the concept of bit error rate (BER).
{"title":"Implementation and Evaluation of LS-SVM Optimization Methods for Estimating DoAs","authors":"S. Komeylian","doi":"10.1109/CCECE47787.2020.9255751","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255751","url":null,"abstract":"Important technological advancement in designing smart array antennas has been encouraged many researchers to concentrate their work on the two main concepts of the direction of arrival (DoA) and beamforming techniques. The preliminary objective of beamforming techniques includes, electronically, the mainbeam in the direction of interest at a certain time and measuring the output power. In this scenario, the main practical challenge resides in achieving maximum output power in which the direction of steered mainbeam coincides with the direction of arrivals. Since the involved problems in most DoA estimation optimizations consist of a lot of unknown parameters including direction of arrivals, SNRs, signal waveforms and samples of noises in the array output, it may become impossible to build a large enough training dataset for covering the distributions for all the aforementioned test data. An alternative way to overcome this constraint which we aim at stressing in this work involves employing support vector machine algorithms for separating unknown components of the actual input in the higher dimensional feature space. In this work, we have implemented the decision directed acyclic graph (DDAG) and Vapnik-Chervonenkis (VC) methods for the least squares support vector machine (LS-SVM) algorithms for estimating DoAs. We have rigorously verified that DoAs are very much affected the antenna array geometries. In addition, we have investigated the quality of the communication channel by the concept of bit error rate (BER).","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"12 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":"121268608","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/CCECE47787.2020.9255731
Maryam Butt, G. Naghdy, F. Naghdy, Geoffrey Murray, H. Du
Robot-assisted motor training provides an efficient alternative to conventional rehabilitation methods used for poststroke patients. The re-learning of lost motor functions happens through neuroplasticity in the brain. Electroencephalogram (EEG) provides an effective method for assessing neuroplasticity. Movement-related cortical potential (MRCP), an EEG-derived time-domain pattern, indicates changes due to motor skills gained as a result of the training. This study aims to perform a two-stage robot-assisted rehabilitation program on brain stem stroke patients consisting of a total of 24 training sessions and to assess whether significant motor recovery and neuroplasticity induction are achieved after the first stage or after completing both stages of the designed rehabilitation program. Three brain stem stroke patients were recruited for hand motor training on AMADEO rehabilitation robot for 8 weeks consisting of two stages of 4 weeks each. Three assessments methods which include standard clinical tests, hand strength and range of movement measurements using AMADEO assessment tool, as well as EEG signal acquisition, were performed at the beginning of all the training sessions (week 0), after completion of the first stage of rehabilitation (week 4) and after completion of both stages of the training sessions (week 8). The experimental results demonstrate that all brain stem stroke patients show significant functional hand motor recovery, as indicated by clinical tests, hand strength, and range of movement measurements, after completing 8 weeks of the training. Moreover, MRCP signal negative peak showed a significant decrease in its amplitude when the patients completed two phases of rehabilitation training, indicating neuroplasticity induction.
{"title":"Assessment of Neuroplasticity Using EEG Signal in Rehabilitation of Brain Stem Stroke Patients","authors":"Maryam Butt, G. Naghdy, F. Naghdy, Geoffrey Murray, H. Du","doi":"10.1109/CCECE47787.2020.9255731","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255731","url":null,"abstract":"Robot-assisted motor training provides an efficient alternative to conventional rehabilitation methods used for poststroke patients. The re-learning of lost motor functions happens through neuroplasticity in the brain. Electroencephalogram (EEG) provides an effective method for assessing neuroplasticity. Movement-related cortical potential (MRCP), an EEG-derived time-domain pattern, indicates changes due to motor skills gained as a result of the training. This study aims to perform a two-stage robot-assisted rehabilitation program on brain stem stroke patients consisting of a total of 24 training sessions and to assess whether significant motor recovery and neuroplasticity induction are achieved after the first stage or after completing both stages of the designed rehabilitation program. Three brain stem stroke patients were recruited for hand motor training on AMADEO rehabilitation robot for 8 weeks consisting of two stages of 4 weeks each. Three assessments methods which include standard clinical tests, hand strength and range of movement measurements using AMADEO assessment tool, as well as EEG signal acquisition, were performed at the beginning of all the training sessions (week 0), after completion of the first stage of rehabilitation (week 4) and after completion of both stages of the training sessions (week 8). The experimental results demonstrate that all brain stem stroke patients show significant functional hand motor recovery, as indicated by clinical tests, hand strength, and range of movement measurements, after completing 8 weeks of the training. Moreover, MRCP signal negative peak showed a significant decrease in its amplitude when the patients completed two phases of rehabilitation training, indicating neuroplasticity induction.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"51 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":"123418255","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/CCECE47787.2020.9255698
Arjama Halder, A. Attaran, W. Handler, B. Chronik
As a part of this work a small patch antenna probe was developed to measure the variation in the electric field produced by gradient coils within an MRI in the presence of any active implantable medical devices (AIMDs). This probe was designed, fabricated, and tested within a gradient coil mimicking dB/dt exposure platform. A 2×1 cm small patch antenna followed by an instrumentational amplifier was chosen to measure the electric fields. Probe was fabricated using a 4-layer PCB. The fabricated probe was used to monitor the electric fields within the phantom in the gradient coil environment. To verify the observed behavior of the probe a simulation study was performed using Sim4Life. This study aims to assess the performance of this probe in a tissue mimicking environment within the coil.
{"title":"Measuring Electric Fields Produced by MRI Gradient Coils Using a Patch Antenna Probe","authors":"Arjama Halder, A. Attaran, W. Handler, B. Chronik","doi":"10.1109/CCECE47787.2020.9255698","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255698","url":null,"abstract":"As a part of this work a small patch antenna probe was developed to measure the variation in the electric field produced by gradient coils within an MRI in the presence of any active implantable medical devices (AIMDs). This probe was designed, fabricated, and tested within a gradient coil mimicking dB/dt exposure platform. A 2×1 cm small patch antenna followed by an instrumentational amplifier was chosen to measure the electric fields. Probe was fabricated using a 4-layer PCB. The fabricated probe was used to monitor the electric fields within the phantom in the gradient coil environment. To verify the observed behavior of the probe a simulation study was performed using Sim4Life. This study aims to assess the performance of this probe in a tissue mimicking environment within the coil.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"38 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":"126655099","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/CCECE47787.2020.9255785
Matheus Y. Ataka, Lucas L. Bacci, Thiago M. Lima, R. F. R. Pereira, E. Costa, L. Liboni
This paper proposes an analysis of the lightning performance of High Voltage Direct Current – HVDC transmission systems with Voltage Source Converters – VSCs. The lightning protection is composed of AC metal-oxide surge arresters at the sending and receiving ends of a HVDC transmission line. These lightning protection devices are designed to operate under AC power signal; nevertheless, there are several applications in which conventional AC surge arresters have been used in HVDC transmission systems with Line Commuted Converters – LCCs. In this context, this paper investigates the performance of this same lightning protection apparatus when applied to a VSC-HVDC transmission systems.
{"title":"Lighting Protection of VSC-HVDC Transmission Systems using ZnO Surge Arresters","authors":"Matheus Y. Ataka, Lucas L. Bacci, Thiago M. Lima, R. F. R. Pereira, E. Costa, L. Liboni","doi":"10.1109/CCECE47787.2020.9255785","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255785","url":null,"abstract":"This paper proposes an analysis of the lightning performance of High Voltage Direct Current – HVDC transmission systems with Voltage Source Converters – VSCs. The lightning protection is composed of AC metal-oxide surge arresters at the sending and receiving ends of a HVDC transmission line. These lightning protection devices are designed to operate under AC power signal; nevertheless, there are several applications in which conventional AC surge arresters have been used in HVDC transmission systems with Line Commuted Converters – LCCs. In this context, this paper investigates the performance of this same lightning protection apparatus when applied to a VSC-HVDC transmission systems.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"18 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":"126960339","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/CCECE47787.2020.9255692
Bashir Ghariba, M. Shehata, Peter F. McGuire
Human Visual System (HVS) has the ability to focus on specific parts of the scene, rather than the whole scene. This phenomenon is one of the most active research topics in the computer vision and neuroscience fields. Recently, deep learning models have been used for visual saliency prediction. In this paper, we investigate the performance of five state-of-the-art deep neural networks (VGG-16, ResNet-50, Xception, InceptionResNet-v2, and MobileNet-v2) for the task of visual saliency prediction. In this paper, we train five deep learning models over the SALICON dataset and then use the trained models to predict visual saliency maps using four standard datasets, namely: TORONTO, MIT300, MIT1003, and DUT-OMRON. The results indicate that the ResNet-50 model outperforms the other four and provides a visual saliency map that is very close to human performance.
{"title":"Performance Evaluation of Pre-Trained CNN Models for Visual Saliency Prediction","authors":"Bashir Ghariba, M. Shehata, Peter F. McGuire","doi":"10.1109/CCECE47787.2020.9255692","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255692","url":null,"abstract":"Human Visual System (HVS) has the ability to focus on specific parts of the scene, rather than the whole scene. This phenomenon is one of the most active research topics in the computer vision and neuroscience fields. Recently, deep learning models have been used for visual saliency prediction. In this paper, we investigate the performance of five state-of-the-art deep neural networks (VGG-16, ResNet-50, Xception, InceptionResNet-v2, and MobileNet-v2) for the task of visual saliency prediction. In this paper, we train five deep learning models over the SALICON dataset and then use the trained models to predict visual saliency maps using four standard datasets, namely: TORONTO, MIT300, MIT1003, and DUT-OMRON. The results indicate that the ResNet-50 model outperforms the other four and provides a visual saliency map that is very close to human performance.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"76 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":"122754431","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/CCECE47787.2020.9255812
M. Thiam, Sengthavy Phommixay, Moustapha Diop, M. Doumbia, M. Wade
This paper investigates the impact of the grid connection on the DC and AC bus voltages of a microgrid (MG), which is composed of a photovoltaic (PV), a battery energy storage system (BESS), and a diesel generator (DG). The considered MG is able to connect to the local distribution grid. An incremental conductance algorithm is implemented for the maximum power point tracking (MPPT) of the PV modules. Proportional-integral control is used for the MPPT and DC bus voltage regulation through the bidirectional converter connected with a BESS. A three-level inverter controlled by the space vector pulse width modulation is proposed in this study and is connected through the LC filter to interface with the AC bus, DG, and main grid. Two scenarios are investigated for MG operation: isolated and grid-connected modes.
{"title":"DC and AC Voltage Investigation in Isolated and Grid-Connected Hybrid Microgrid","authors":"M. Thiam, Sengthavy Phommixay, Moustapha Diop, M. Doumbia, M. Wade","doi":"10.1109/CCECE47787.2020.9255812","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255812","url":null,"abstract":"This paper investigates the impact of the grid connection on the DC and AC bus voltages of a microgrid (MG), which is composed of a photovoltaic (PV), a battery energy storage system (BESS), and a diesel generator (DG). The considered MG is able to connect to the local distribution grid. An incremental conductance algorithm is implemented for the maximum power point tracking (MPPT) of the PV modules. Proportional-integral control is used for the MPPT and DC bus voltage regulation through the bidirectional converter connected with a BESS. A three-level inverter controlled by the space vector pulse width modulation is proposed in this study and is connected through the LC filter to interface with the AC bus, DG, and main grid. Two scenarios are investigated for MG operation: isolated and grid-connected modes.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"316 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":"114466906","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/CCECE47787.2020.9255688
Md. Nasmus Sakib Khan Shabbir, Xiaodong Liang
For faults diagnosis in a Variable Frequency Drive (VFD)-fed induction motor, a Discrete Fast Fourier Transform (DFFT) and coherence analysis-based approach is proposed in this paper. To identify signature harmonics that maintain a strong correlation between a healthy and a faulty cases and are present under various conditions, a coherence analysis is conducted. After signature harmonics are identified, fault diagnosis can be carried out by comparing magnitudes of the fundamental and signature harmonics under various healthy and faulty conditions. Magnitudes of the fundamental voltage and the third harmonic voltage can serve as parameters to detect the five types of faults. The fifth harmonic current can effectively detect the occurrence of a fault although it cannot distinguish the fault types. The combination of the fundamental voltage and the third harmonic voltage from the stator voltage and the fifth harmonic current from the stator current can lead to effective fault diagnosis. The proposed approach is verified using two motor loading conditions.
{"title":"A DFFT and Coherence Analysis-Based Fault Diagnosis Approach for Induction Motors Fed by Variable Frequency Drives","authors":"Md. Nasmus Sakib Khan Shabbir, Xiaodong Liang","doi":"10.1109/CCECE47787.2020.9255688","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255688","url":null,"abstract":"For faults diagnosis in a Variable Frequency Drive (VFD)-fed induction motor, a Discrete Fast Fourier Transform (DFFT) and coherence analysis-based approach is proposed in this paper. To identify signature harmonics that maintain a strong correlation between a healthy and a faulty cases and are present under various conditions, a coherence analysis is conducted. After signature harmonics are identified, fault diagnosis can be carried out by comparing magnitudes of the fundamental and signature harmonics under various healthy and faulty conditions. Magnitudes of the fundamental voltage and the third harmonic voltage can serve as parameters to detect the five types of faults. The fifth harmonic current can effectively detect the occurrence of a fault although it cannot distinguish the fault types. The combination of the fundamental voltage and the third harmonic voltage from the stator voltage and the fifth harmonic current from the stator current can lead to effective fault diagnosis. The proposed approach is verified using two motor loading conditions.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"58 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":"128209158","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/CCECE47787.2020.9255671
Samal Munidasa, Parastoo Baghaei Ravari, Edward Shim, Olivia Lin, E. Ghafar-Zadeh
This paper presents the characterization of a smart bedsheet developed by Studio 1 Labs, which could be used to monitor the movement of an infant at night in order to detect and prevent sleep-related disorders. This smart bedsheet consists of an array of conductive fabrics to be used as pressure sensors to track the baby's movement. Electrical impedance spectroscopy (EIS) has been performed using the Metrohm Autolab potentiostat on a single and two-fabric interface. The results of this study will provide the information required to develop a sensitive and reliable smart bedsheet.
本文介绍了 Studio 1 实验室开发的智能床单的特性，该床单可用于监测婴儿夜间的活动，以检测和预防与睡眠有关的疾病。这种智能床单由导电织物阵列组成，可用作压力传感器来跟踪婴儿的运动。使用 Metrohm Autolab 恒电位仪对单层和双层织物界面进行了电阻抗光谱分析（EIS）。这项研究的结果将为开发灵敏可靠的智能床单提供所需的信息。
{"title":"A Bedsheet for Baby Monitoring at Night: Measurement and Characterization Results","authors":"Samal Munidasa, Parastoo Baghaei Ravari, Edward Shim, Olivia Lin, E. Ghafar-Zadeh","doi":"10.1109/CCECE47787.2020.9255671","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255671","url":null,"abstract":"This paper presents the characterization of a smart bedsheet developed by Studio 1 Labs, which could be used to monitor the movement of an infant at night in order to detect and prevent sleep-related disorders. This smart bedsheet consists of an array of conductive fabrics to be used as pressure sensors to track the baby's movement. Electrical impedance spectroscopy (EIS) has been performed using the Metrohm Autolab potentiostat on a single and two-fabric interface. The results of this study will provide the information required to develop a sensitive and reliable smart bedsheet.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"31 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":"128288284","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/CCECE47787.2020.9255738
E. Mohammadi, Javad Khodabakhsh, G. Moschopoulos, R. Fadaeinedjad
The performance of a photovoltaic (PV) system is significantly affected in snowy conditions. Snow accumulation on a PV module causes the shadow on the module and as a result the irradiance level received by the module and its generated power are reduced. During the snow removal and snow sliding on a PV module, its performance can be different considering the module landscape or portrait orientations, the number and location of bypass diodes, used in the module. In the present study, a snow-covered PV module is modeled using MATLAB/Simulink software considering the snow sliding as the snow removal process. A commercial PV module with three bypass diodes is modeled and its performance is investigated in snowy conditions considering portrait and landscape orientations. The result of the study shows how the module orientation and bypass diodes affect the performance of the PV system in snowy conditions. The results of the study can be considered as practical guidelines for installing different PV modules in snowy conditions.
{"title":"A Study on the Performance of PV Modules in Snowy Conditions Considering Orientation of Modules","authors":"E. Mohammadi, Javad Khodabakhsh, G. Moschopoulos, R. Fadaeinedjad","doi":"10.1109/CCECE47787.2020.9255738","DOIUrl":"https://doi.org/10.1109/CCECE47787.2020.9255738","url":null,"abstract":"The performance of a photovoltaic (PV) system is significantly affected in snowy conditions. Snow accumulation on a PV module causes the shadow on the module and as a result the irradiance level received by the module and its generated power are reduced. During the snow removal and snow sliding on a PV module, its performance can be different considering the module landscape or portrait orientations, the number and location of bypass diodes, used in the module. In the present study, a snow-covered PV module is modeled using MATLAB/Simulink software considering the snow sliding as the snow removal process. A commercial PV module with three bypass diodes is modeled and its performance is investigated in snowy conditions considering portrait and landscape orientations. The result of the study shows how the module orientation and bypass diodes affect the performance of the PV system in snowy conditions. The results of the study can be considered as practical guidelines for installing different PV modules in snowy conditions.","PeriodicalId":296506,"journal":{"name":"2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)","volume":"163 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":"128546226","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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