Pub Date : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180205
Bianca Tagliaro Beasley, George D. O’Mahony, Sergi Gómez Quintana, A. Temko, E. Popovici
Internet of Things (IoT) security is growing in importance in many applications ranging from biomedical to environmental to industrial applications. Access to data is the primary target for many of these applications. Often IoT devices are an essential part of critical control systems that could affect well-being, safety, or inflict severe financial damage. No current solution addresses all security aspects. This is mainly due to the resource-constrained nature of IoT, cost, and power consumption. In this paper, we propose and analyse a framework for detecting anomalies on a low power IoT platform. By monitoring power consumption and by using machine learning techniques, we show that we can detect a large number and types of anomalies during the execution phase of an application running on the IoT. The proposed methodology is generic in nature, hence allowing for deployment in a myriad of scenarios.
{"title":"Lightweight Anomaly Detection Framework for IoT","authors":"Bianca Tagliaro Beasley, George D. O’Mahony, Sergi Gómez Quintana, A. Temko, E. Popovici","doi":"10.1109/ISSC49989.2020.9180205","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180205","url":null,"abstract":"Internet of Things (IoT) security is growing in importance in many applications ranging from biomedical to environmental to industrial applications. Access to data is the primary target for many of these applications. Often IoT devices are an essential part of critical control systems that could affect well-being, safety, or inflict severe financial damage. No current solution addresses all security aspects. This is mainly due to the resource-constrained nature of IoT, cost, and power consumption. In this paper, we propose and analyse a framework for detecting anomalies on a low power IoT platform. By monitoring power consumption and by using machine learning techniques, we show that we can detect a large number and types of anomalies during the execution phase of an application running on the IoT. The proposed methodology is generic in nature, hence allowing for deployment in a myriad of scenarios.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"8 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132364719","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180179
Yupeng Liu, Liaoyuan Zeng, Shutian Zhou, Lingqing Li, S. McGrath
In this paper, a novel wideband band-pass center frequency tunable filter is proposed based on liquid crystals for applications at millimeter-waves, which uses a micro-strip periodical structure with loading square spiral resonators. The tunable range of the proposed filter is about 1.59 GHz while the external bias voltage of the liquid crystal turns from 0V to 30V. The filter center frequency ranges from 39.08 GHz to 40.67 GHz. And the insertion loss of filter is about 3.9dB. The proposed filter has many advantages, such as continuously electrically tunability, miniaturization, low tuning voltage, etc. Therefore, a great potential of such a filter has been shown for use in the next communication system.
{"title":"A Wideband Millimeter-wave Tunable Filter based on Periodic Square Spiral Structure and Liquid Crystal Material","authors":"Yupeng Liu, Liaoyuan Zeng, Shutian Zhou, Lingqing Li, S. McGrath","doi":"10.1109/ISSC49989.2020.9180179","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180179","url":null,"abstract":"In this paper, a novel wideband band-pass center frequency tunable filter is proposed based on liquid crystals for applications at millimeter-waves, which uses a micro-strip periodical structure with loading square spiral resonators. The tunable range of the proposed filter is about 1.59 GHz while the external bias voltage of the liquid crystal turns from 0V to 30V. The filter center frequency ranges from 39.08 GHz to 40.67 GHz. And the insertion loss of filter is about 3.9dB. The proposed filter has many advantages, such as continuously electrically tunability, miniaturization, low tuning voltage, etc. Therefore, a great potential of such a filter has been shown for use in the next communication system.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"6 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":"133984970","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180211
Nam H. Trinh, Darragh O'Brien
One application of deep learning in medical applications is the use of deep neural networks to classify human speech as healthy or pathological. In such applications, the audio signal is transformed into a spectrogram that captures its time-varying content and the latter “images” are fed into a classifier for classification. A challenge in applying this approach is the shortage of suitable speech data for training purposes. Labelled data acquisition requires significant human effort and/or time-consuming experiments. In this paper, we propose a semi-supervised learning approach that employs a Generative Adversarial Network (GAN) to alleviate the problem of insufficient training data. We compare the classification performance of a traditional classifier and our semi-supervised classifier. We observe that the GAN-based semi-supervised approach demonstrates a significant improvement in terms of accuracy and ROC curve when supplied an equivalent number of training samples.
{"title":"Semi-Supervised Learning with Generative Adversarial Networks for Pathological Speech Classification","authors":"Nam H. Trinh, Darragh O'Brien","doi":"10.1109/ISSC49989.2020.9180211","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180211","url":null,"abstract":"One application of deep learning in medical applications is the use of deep neural networks to classify human speech as healthy or pathological. In such applications, the audio signal is transformed into a spectrogram that captures its time-varying content and the latter “images” are fed into a classifier for classification. A challenge in applying this approach is the shortage of suitable speech data for training purposes. Labelled data acquisition requires significant human effort and/or time-consuming experiments. In this paper, we propose a semi-supervised learning approach that employs a Generative Adversarial Network (GAN) to alleviate the problem of insufficient training data. We compare the classification performance of a traditional classifier and our semi-supervised classifier. We observe that the GAN-based semi-supervised approach demonstrates a significant improvement in terms of accuracy and ROC curve when supplied an equivalent number of training samples.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"27 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":"114925065","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180173
Eoghan Hynes, R. Flynn, Brian A. Lee, Niall Murray
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.
{"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}
Pub Date : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180193
Sowmya Vijayakumar, R. Flynn, Niall Murray
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%.
{"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}
Pub Date : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180197
D. Tan, Kevin Meehan
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.
{"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}
Pub Date : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180164
Muhammad Ali Farooq, P. Corcoran
Human thermography is considered to be an integral medical diagnostic tool for detecting heat patterns and measuring quantitative temperature data of the human body. It can be used in conjunction with other medical diagnostic procedures for getting comprehensive medication results. In the proposed study we have highlighted the significance of Infrared Thermography (IRT) and the role of machine learning in thermal medical image analysis for human health monitoring and various disease diagnosis in preliminary stages. The first part of the proposed study provides comprehensive information about the application of IRT in the diagnosis of various diseases such as skin and breast cancer detection in preliminary stages, dry eye syndromes, and ocular issues, liver disease, diabetes diagnosis and last but not least the novel COVID-19 virus. Whereas in the second phase we have proposed an autonomous breast tumor classification system using thermal breast images by employing state of the art Convolution Neural Network (CNN). The system achieves the overall accuracy of 80% and recall rate of 83.33%.
{"title":"Infrared Imaging for Human Thermography and Breast Tumor Classification using Thermal Images","authors":"Muhammad Ali Farooq, P. Corcoran","doi":"10.1109/ISSC49989.2020.9180164","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180164","url":null,"abstract":"Human thermography is considered to be an integral medical diagnostic tool for detecting heat patterns and measuring quantitative temperature data of the human body. It can be used in conjunction with other medical diagnostic procedures for getting comprehensive medication results. In the proposed study we have highlighted the significance of Infrared Thermography (IRT) and the role of machine learning in thermal medical image analysis for human health monitoring and various disease diagnosis in preliminary stages. The first part of the proposed study provides comprehensive information about the application of IRT in the diagnosis of various diseases such as skin and breast cancer detection in preliminary stages, dry eye syndromes, and ocular issues, liver disease, diabetes diagnosis and last but not least the novel COVID-19 virus. Whereas in the second phase we have proposed an autonomous breast tumor classification system using thermal breast images by employing state of the art Convolution Neural Network (CNN). The system achieves the overall accuracy of 80% and recall rate of 83.33%.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"65 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":"132651756","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180218
Hazel Murray, Jerry Horgan, João F. Santos, David Malone, H. Šiljak
Quantum computing has the power to break current cryptographic systems, disrupting online banking, shopping, data storage and communications. Quantum computing also has the power to support stronger more resistant technologies. In this paper, we describe a digital cash scheme created by Dmitry Gavinsky, which utilises the capability of quantum computing. We contribute by setting out the methods for implementing this scheme. For both the creation and verification of quantum coins we convert the algebraic steps into computing steps. As part of this, we describe the methods used to convert information stored on classical bits to information stored on quantum bits.
{"title":"Implementing a Quantum Coin Scheme","authors":"Hazel Murray, Jerry Horgan, João F. Santos, David Malone, H. Šiljak","doi":"10.1109/ISSC49989.2020.9180218","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180218","url":null,"abstract":"Quantum computing has the power to break current cryptographic systems, disrupting online banking, shopping, data storage and communications. Quantum computing also has the power to support stronger more resistant technologies. In this paper, we describe a digital cash scheme created by Dmitry Gavinsky, which utilises the capability of quantum computing. We contribute by setting out the methods for implementing this scheme. For both the creation and verification of quantum coins we convert the algebraic steps into computing steps. As part of this, we describe the methods used to convert information stored on classical bits to information stored on quantum bits.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"11 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":"126711005","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 : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180169
Lisa Moore, Bríd McDevitt, N. Akhtar, Ronan J. Doherty, William N. Scott, J. Connolly
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.
{"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}
Pub Date : 2020-06-01DOI: 10.1109/ISSC49989.2020.9180196
David Watté, Yizhe Hu, T. Siriburanon, R. Staszewski
This paper presents the design of a DPA for 5G telecommunications applications in TSMC (Taiwan Semiconductor Manufacturing Company) 28 nm CMOS. A modified Gilbert cell is proposed to perform the function of amplification and reversing the phase within a compact layout, improving the efficiency. To overcome the linearity issue of DPAs, a non-linear sizing technique of the PA cells is employed. The effective widths of relative bit cells need to be increased to conform to non-linear sizing thus providing linearity with acceptable INL (Integral Non-Linearity)and DNL (Differential Non-Linearity) results. The simulated saturated output power is 11.7 dBm with power added efficiency at 13 percent with the device working off a carrier frequency of 28 GHz.
{"title":"Design of a Non-Linear Sized I/Q Digital PA for 5G mm-Wave Communications in 28 nm CMOS","authors":"David Watté, Yizhe Hu, T. Siriburanon, R. Staszewski","doi":"10.1109/ISSC49989.2020.9180196","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180196","url":null,"abstract":"This paper presents the design of a DPA for 5G telecommunications applications in TSMC (Taiwan Semiconductor Manufacturing Company) 28 nm CMOS. A modified Gilbert cell is proposed to perform the function of amplification and reversing the phase within a compact layout, improving the efficiency. To overcome the linearity issue of DPAs, a non-linear sizing technique of the PA cells is employed. The effective widths of relative bit cells need to be increased to conform to non-linear sizing thus providing linearity with acceptable INL (Integral Non-Linearity)and DNL (Differential Non-Linearity) results. The simulated saturated output power is 11.7 dBm with power added efficiency at 13 percent with the device working off a carrier frequency of 28 GHz.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"10 12 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":"130663119","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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