2020 31st Irish Signals and Systems Conference (ISSC)最新文献_第2页

Low Cost Embedded Multimodal Opto-Inertial Human Motion Tracking System 低成本嵌入式多模态光惯性人体运动跟踪系统

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180190

Mariusz P. Wilk, M. Walsh, B. O’flynn

{"title":"Low Cost Embedded Multimodal Opto-Inertial Human Motion Tracking System","authors":"Mariusz P. Wilk, M. Walsh, B. O’flynn","doi":"10.1109/ISSC49989.2020.9180190","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180190","url":null,"abstract":"Human motion tracking systems are widely used in various application spaces, such as motion capture, rehabilitation, or sports. There exists a number of such systems in the State-Of-The-Art (SOA) that vary in price, complexity, accuracy and the target applications. With the continued advances in system integration and miniaturization, wearable motion trackers gain in popularity in the research community. The opto-inertial trackers with multimodal sensor fusion algorithms are some of the common approaches found in SOA. However, these trackers tend to be expensive and have high computational requirements. In this work, we present a prototype version of our opto-inertial, motion tracking system that offers a low-cost alternative. The 3D position and orientation are determined by fusing optical and inertial sensor data together with knowledge about two external reference points using a purpose-designed data fusion algorithm. An experimental validation was carried out on one of the use cases that this system is intended for, i.e. barbell squat in strength training. The results showed that the total RMSE in position and orientation was 32.8 mm and 0.89 degree, respectively. It operated in real-time at 20 frames per second.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"1 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":"129559912","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

Digital Image Exchange using a No-key(s) Protocol with Phase-only Encryption 数字图像交换使用无密钥(s)协议与阶段加密

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180215

J. Blackledge, N. Mosola

引用次数: 0

Time-Domain Multiply-Accumulator using Digital-to-Time Multiplier for CNN Processors in 28-nm CMOS 使用数字时间乘法器的时域乘加器用于CNN处理器在28纳米CMOS

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180202

Xutong Wu, T. Siriburanon, R. Staszewski

引用次数: 0

Detection of ringforts from aerial photography using machine learning 利用机器学习从航空摄影中检测环形堡垒

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180159

Keith Phelan, D. Riordan

引用次数: 4

Open Source Power Quality Meter with cloud monitoring 开源电能质量计与云监控

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180176

Cathal Ferry, J. Connolly

{"title":"Open Source Power Quality Meter with cloud monitoring","authors":"Cathal Ferry, J. Connolly","doi":"10.1109/ISSC49989.2020.9180176","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180176","url":null,"abstract":"Energy saving and energy conservation are fast becoming key ideologies in the construction and creation of modern data centres and IT infrastructure. This applies to large scale deployments and on smaller to more intermediate scale sites. Data centres consume large quantities of energy and contribute to carbon dioxide (CO2) emissions. Reducing CO2output using methods such as sustainable power generation and better energy efficiency can help mitigate against the effects of global warming. This paper proposes methods of saving energy in IT equipment by monitoring key power statistics such as power factor to determine the efficiency of the power being used by network equipment. This is achieved using an open-source power factor meter which is not only low cost but also accurate. The meter measures power factor as well as true power, apparent power, reactive power, mains voltage, current, and mains frequency to determine the energy efficiency of the installation or equipment. Readings are measured using three primary sensors; a current transformer, voltage transformer, and a mains frequency sensor. The system is designed for use with single-phase systems and incorporates a local HMI and a cloud-based CMS system. All of the software and hardware elements used are open source and therefore low cost.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"295 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":"132800909","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

On-demand updates after a node failure in a wireless network 无线网络中节点故障后的按需更新

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180207

Davide Villa, Chih-kuang Lin, Adam Kuenzi, Michael Lang

引用次数: 0

Flexible Manufacturing using Automated Material Handling and Autonomous Intelligent Vehicles 使用自动化物料搬运和自动智能车辆的柔性制造

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180172

Con Cronin, A. Conway, Anshul Awasthi, Joseph Walsh

引用次数: 0

Implementing wearable sensor technology for the determination of a biomarker profile for cancer-related fatigue 实施可穿戴传感器技术，用于确定癌症相关疲劳的生物标志物概况

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180194

N. Akhtar, M. Kelly, William N. Scott, J. Connolly

{"title":"Implementing wearable sensor technology for the determination of a biomarker profile for cancer-related fatigue","authors":"N. Akhtar, M. Kelly, William N. Scott, J. Connolly","doi":"10.1109/ISSC49989.2020.9180194","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180194","url":null,"abstract":"Cancer Related Fatigue (CRF) is a well-recognised symptom of malignant breast disease and may affect up to 70% of those undergoing therapy or deemed to be in remission. The condition is frequently subject to unpredictable recurrence that can result in unavoidable and unforeseen detriment to quality of life. Moreover, management of the condition can place significant financial burden on health and social care facilities. CRF is distinct from normal tiredness which may be resolved by periods of sleep or rest. Customers' extensive use of wearable technologies has contributed to the evolution of clinical trial procedures and, as a result, health data can also be obtained using wearables [1]. New technologies have the potential to improve data accuracy and timeliness, improve efficiency and increasing patient engagement in the clinical trial process Medical quality tracking devices are already supporting patient care in several clinical areas [1]. The main aim of this study is to define an accurate fatigue baseline for individuals diagnosed with breast cancer to determine potential relationships between possible fatigue markers, measurable daily activity and individual perceptions of fatigue.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"384 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":"123956475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Derivation of E-model Equipment Impairment Factors for Narrowband and Wideband Opus Codec Using the Instrumental Method 用仪器法推导窄带和宽带工作码编解码器的e型设备损伤因子

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180160

Mohannad Al-Ahmadi, P. Počta, H. Melvin

{"title":"Derivation of E-model Equipment Impairment Factors for Narrowband and Wideband Opus Codec Using the Instrumental Method","authors":"Mohannad Al-Ahmadi, P. Počta, H. Melvin","doi":"10.1109/ISSC49989.2020.9180160","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180160","url":null,"abstract":"Real-time multimedia applications like Web realtime communication WebRTC support a wide range of codecs, from the standard narrowband up to fullband codecs. The IETF standardized Opus codec is the default codec utilized by WebRTC speech and audio applications, by supporting a wide range of bitrates. In current best effort networks, network impairments such as packet loss, delay and jitter affect the quality of VoIP. To assess the impact of such impairments in order to estimate the quality experienced by the end users of speech applications, the E-model standardized in ITU-T Rec. G.107 can be used. In this paper we derive codec-specific parameters required by the E-model to estimate the quality degradation in speech applications deploying narrowband and wideband Opus codec, namely the equipment impairment factor Ie and packet loss robustness factor Bpl. We followed the ITU-T methods designed for this purpose and share the results arising from all the experiments covering all the narrowband and wideband Opus codec conditions. The derived values make it possible to integrate the E-model in realtime communication applications including WebRTC to assess the quality experienced by the end user.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"23 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":"131353980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gender Classification using Twitter Text Data 使用Twitter文本数据进行性别分类

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180161

Pradeep Vashisth, Kevin Meehan

{"title":"Gender Classification using Twitter Text Data","authors":"Pradeep Vashisth, Kevin Meehan","doi":"10.1109/ISSC49989.2020.9180161","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180161","url":null,"abstract":"Increasingly content sharing websites such as social media have become very popular in many countries across the world. Classifying the gender of a person based on these short messages is an interesting research area that could benefit legal investigation, forensics, marketing analysis, advertising and recommendation. This research will explore the use of Natural Language Processing (NLP) techniques and tweets in a gender classification system. This investigation will compare multiple techniques such as Bag of Words (Term Frequency - Inverse Document Frequency), Word Embedding (W2Vec, GloVe) and traditional Machine Learning techniques (Logistic Regression, Support Vector Machine and Naïve Bayes) in this context. A new dataset has been generated to be used as part of this study comprising of the user gender and associated tweets. This dataset was developed due to the unavailability of any public standard dataset with the volume required to perform this investigation. The results have determined that the traditional Bag of Words model did not provide any significant results in classification. However, word embedding models have significantly performed better using multiple machine learning techniques. Therefore, the word embedding models have been proven to be the most effective technique in classifying gender based on twitter text data.","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":"127212712","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: 15