2020 31st Irish Signals and Systems Conference (ISSC)最新文献_第5页

Lightweight Anomaly Detection Framework for IoT 物联网轻量级异常检测框架

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180205

Bianca Tagliaro Beasley, George D. O’Mahony, Sergi Gómez Quintana, A. Temko, E. Popovici

引用次数: 1

A Wideband Millimeter-wave Tunable Filter based on Periodic Square Spiral Structure and Liquid Crystal Material 基于周期性方形螺旋结构和液晶材料的宽带毫米波可调谐滤波器

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180179

Yupeng Liu, Liaoyuan Zeng, Shutian Zhou, Lingqing Li, S. McGrath

引用次数: 1

Semi-Supervised Learning with Generative Adversarial Networks for Pathological Speech Classification 基于生成对抗网络的半监督学习病理语音分类

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180211

Nam H. Trinh, Darragh O'Brien

引用次数: 3

An Evaluation of Lower Facial Micro Expressions as an Implicit QoE Metric for an Augmented Reality Procedure Assistance Application 下面部微表情作为增强现实程序辅助应用的隐式QoE度量的评价

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180173

Eoghan Hynes, R. Flynn, Brian A. Lee, Niall Murray

{"title":"An Evaluation of Lower Facial Micro Expressions as an Implicit QoE Metric for an Augmented Reality Procedure Assistance Application","authors":"Eoghan Hynes, R. Flynn, Brian A. Lee, Niall Murray","doi":"10.1109/ISSC49989.2020.9180173","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180173","url":null,"abstract":"Augmented reality (AR) has been identified as a key technology to enhance worker utility in the context of increasing automation of repeatable procedures. AR can achieve this by assisting the user in performing complex and frequently changing procedures. Crucial to the success of procedure assistance AR applications is user acceptability, which can be measured by user quality of experience (QoE). An active research topic in QoE is the identification of implicit metrics that can be used to continuously infer user QoE during a multimedia experience. A user's QoE is linked to their affective state. Affective state is reflected in facial expressions. Emotions shown in micro facial expressions resemble those expressed in normal expressions but are distinguished from them by their brief duration. The novelty of this work lies in the evaluation of micro facial expressions as a continuous QoE metric by means of correlation analysis to the more traditional and accepted post-experience self-reporting. In this work, an optimal Rubik's Cube solver AR application was used as a proof of concept for complex procedure assistance. This was compared with a paper-based procedure assistance control. QoE expressed by affect in normal and micro facial expressions was evaluated through correlation analysis with post-experience reports. The results show that the AR application yielded higher task success rates and shorter task durations. Micro facial expressions reflecting disgust correlated moderately to the questionnaire responses for instruction disinterest in the AR application.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126413606","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: 5

A Comparative Study of Machine Learning Techniques for Emotion Recognition from Peripheral Physiological Signals 基于外围生理信号的情绪识别的机器学习技术比较研究

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180193

Sowmya Vijayakumar, R. Flynn, Niall Murray

{"title":"A Comparative Study of Machine Learning Techniques for Emotion Recognition from Peripheral Physiological Signals","authors":"Sowmya Vijayakumar, R. Flynn, Niall Murray","doi":"10.1109/ISSC49989.2020.9180193","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180193","url":null,"abstract":"Recent developments in wearable technology have led to increased research interest in using peripheral physiological signals for emotion recognition. The non-invasive nature of peripheral physiological signal measurement via wearables enables ecologically valid long-term monitoring. These peripheral signal measurements can be used in real-time in many ways including health and emotion classification. This paper investigates the utility of peripheral physiological signals for emotion recognition using the publicly available DEAP database. Using this database (which contains electroencephalogram (EEG) signals and peripheral signals), this paper compares eight machine learning models in the classification of valence and arousal emotion dimensions. These were applied to the peripheral physiological signals only. These models operate on three groupings of the peripheral data: (i) the raw peripheral physiological signals; (ii) individual feature sets extracted from each peripheral signal; and (iii) a fusion data set made of the combined features from the individual peripheral signals. The results indicate that support vector machine, linear discriminant analysis and logistic regression give the best recognition results on all three data groups considered. The feature fusion data set, which is made up by fusing all the features from the peripheral signals, gives the best recognition accuracy on both valence and arousal dimensions. In addition, subject dependency for emotion classification from peripheral signals is examined and significant individual variability is observed. The recognition rate varies between each participant from 10% to 87.5%.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133700099","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: 14

Implementing Gesture Recognition in a Sign Language Learning Application 在手语学习应用中实现手势识别

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180197

D. Tan, Kevin Meehan

{"title":"Implementing Gesture Recognition in a Sign Language Learning Application","authors":"D. Tan, Kevin Meehan","doi":"10.1109/ISSC49989.2020.9180197","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180197","url":null,"abstract":"Artificial Intelligence (AI) has become increasingly prevalent in contemporary times. It has a wide variety of application areas which can almost replicate tasks that humans would normally perform. Many companies that are using this form of technology are making efficiencies by replacing humans with AI agents. However, researchers are still making efforts to find ways to enhance artificial intelligence to be more ‘human-like’. Gesture recognition is a form of human computer interaction in which AI has the potential to improve. Similar to humans, AI has the ability to ‘see’ and recognise gestures. Sign language is a language that a small proportion of the human population know and use. However, it is slowly gaining popularity and more resources are being provided in order to learn the language. Some people tend to go for in-person classes whereas others tend to go online or use applications to self-learn. This research discovers the success of technology such as gesture recognition to assist in learning sign language. The main research aim is to determine whether gesture recognition can assist self-learners in learning the language. The research has explored the use of Convolutional Neural Networks (CNN) to detect shapes that represent sign language form. The research demonstrated different accuracies based on a small sample size of 10 participants using three different types of datasets: non pre-processed, skin mask, and Sobel filtered images. The CNN model trained with the skin mask dataset was overall the most suitable model in identifying gestures from images; however, the CNN model trained with the non pre-processed dataset was slightly more accurate in recognising the American Sign Language (ASL) gestures in realtime. All CNN models demonstrated accuracy levels above 70% proving that the CNN has the ability to recognise sign language gestures.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"144 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132470068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Infrared Imaging for Human Thermography and Breast Tumor Classification using Thermal Images 红外成像用于人体热成像和乳腺肿瘤热图像分类

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180164

Muhammad Ali Farooq, P. Corcoran

引用次数: 15

Implementing a Quantum Coin Scheme 实现量子币方案

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180218

Hazel Murray, Jerry Horgan, João F. Santos, David Malone, H. Šiljak

引用次数: 3

Preliminary investigations of the agreement between two wearable accelerometers for use in clinical studies 两个可穿戴加速度计在临床研究中使用的协议的初步调查

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180169

Lisa Moore, Bríd McDevitt, N. Akhtar, Ronan J. Doherty, William N. Scott, J. Connolly

{"title":"Preliminary investigations of the agreement between two wearable accelerometers for use in clinical studies","authors":"Lisa Moore, Bríd McDevitt, N. Akhtar, Ronan J. Doherty, William N. Scott, J. Connolly","doi":"10.1109/ISSC49989.2020.9180169","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180169","url":null,"abstract":"Healthcare providers continue to address the challenges caused by aging populations, chronic disease management, hospitalization costs, as well as significant legal risks. Characterisation of physical activity and sedentary behaviour within ambulatory conditions is becoming increasingly popular in view of growing evidence for the health implications of these tendencies. Wearable technology can potentially offer a solution to these problems by using state-of-the-art sensors and wearable systems as well as secure and effective networks of communication between patients and clinicians. The primary aim of this study is to investigate levels of agreement between two wearable sensors for use in clinical studies. Establishing validity of these wearable devices is of particular interest as they may be utilised in future clinical studies to monitor sleep and activity patterns over prolonged time periods. Initial visualization of data from physical activity and periods of inactivity show high similarity between devices for ambulatory conditions and standardized activities. However, future steps concerning alignment of timestamps needs to be utilized in order to coordinate the devices' outputs.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127436090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Design of a Non-Linear Sized I/Q Digital PA for 5G mm-Wave Communications in 28 nm CMOS 基于28nm CMOS的5G毫米波通信非线性I/Q数字PA设计

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180196

David Watté, Yizhe Hu, T. Siriburanon, R. Staszewski

引用次数: 0