2020 31st Irish Signals and Systems Conference (ISSC)最新文献

{"title":"Affective Computing as a Service (ACaaS)","authors":"W. Murphy, Eoghan Furey, Juanita Blue","doi":"10.1109/ISSC49989.2020.9180158","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180158","url":null,"abstract":"Affective Computing aims to introduce a higher level of computational intelligence to systems, which enables emulation of human affects and emotions. Today those enhanced computing capabilities are seldom found in IT solutions. This paper reviews both Affective Computing and Cloud Computing, presenting the combined outcome in the form of a Software-as-a-Service solution hosted via a Public Cloud Infrastructure. A framework is proposed for the Affective Computing as a Service (ACaaS) solution with the unique consideration that it uses previously created Public Cloud processing services. The framework is then transformed into a working implementation comprising a PHP front-end and a Python back-end. The system is capable of processing text, image, and voice input files and extracting emotional information from them. The results are then presented and evaluated, demonstrating that in most use cases, the multi-modal inputs will facilitate an Affective Computing as a Service solution which will deliver the necessary information for Affective Computing goals. Exploration of the combination of available cloud computing technologies and Affective Computing goals supports research in the area by removing the need for researchers to build their own models. This solution leverages the best available cutting-edge technologies available from large providers. Thereby, the requirement to train new models and the associated overheads are greatly reduced.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"26 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":"121797567","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}

{"title":"Towards a Non-Intrusive Context-Aware Speech Quality Model","authors":"R. Jaiswal, Andrew Hines","doi":"10.1109/ISSC49989.2020.9180171","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180171","url":null,"abstract":"Understanding how humans judge perceived speech quality while interacting through Voice over Internet Protocol (VoIP) applications in real-time is essential to build a robust and accurate speech quality prediction model. Speech quality is degraded in the presence of background noise reducing the Quality of Experience (QoE). Speech Enhancement (SE) algorithms can improve speech quality in noisy environments. The publicly available NOIZEUS speech corpus contains speech in environmental background noise babble, car, street, and train at two Signal-to-noise ratio (SNRs) 5dB and 10dB. Objective Speech Quality Metrics (OSQM) are used to monitor and measure speech quality for VoIP applications. This paper proposes a Context-aware QoE prediction model, CAQoE, which classifies the speech signal context (i.e., noise type and SNR) in order to allow context-specific speech quality prediction. This paper presents experiments conducted to develop the speech context-classification component of the proposed CAQoE model. Speech enhancement algorithms are used in conjunction with an OSQM to estimate Mean Opinion Score (MOS) of noisy and enhanced samples in order to train Machine Learning (ML) classifiers to classify the speech signal context (i.e., noise type and SNR). Results demonstrate that a Decision Tree (DT) classifier has better classification accuracy for the noise classes tested. We present the associated components of the CAQoE model, namely; Voice Activity Detection (VAD) and Speech Quality Model (SQM).","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"19 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":"131696834","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}

{"title":"Path Kinematics for Combined Discrete and Continuous Event Simulation","authors":"John Barry, Joseph Walsh","doi":"10.1109/ISSC49989.2020.9180203","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180203","url":null,"abstract":"Combined discrete and continuous event simulations provide a means of investigating the influence of the many factors affecting the productivity of complex electromechanical systems. This paper describes algorithms and methods for establishing the path kinematics of Cartesian axes of motion pick and place systems which must avoid varying obstacle profiles and which have the potential for path intersections with other pick and place systems within a shared working environment. Where intersections arise, one pick and place device must, in accordance with pre-established prioritization, decelerate and wait for another pick and place device to vacate the zone of conflict. Path kinematics represent a continuous event aspect of the simulation under development while awaiting permission to proceed represents a discrete event aspect of the simulation. A requirement of the research is that the kinematics only include periods of constant acceleration and constant velocity and that any deceleration must continue substantially along the original path. The algorithm and methods presented are concise and may be applicable and convenient to apply in the path control of Cartesian axis of motion devices.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"13 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":"126374372","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}

{"title":"IoT Personal Air Quality Monitor","authors":"S. M. Grath, C. Flanagan, L. Zeng, Conor O'Leary","doi":"10.1109/ISSC49989.2020.9180199","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180199","url":null,"abstract":"With more attention being paid to environmental issues in recent years, air quality monitoring is becoming more important. It has been possible to monitor air quality successfully for many years, but monitoring has traditionally been both expensive and immobile, thus restricted in application. To improve urban environments, air quality monitoring has to be widespread, ubiquitous, cheap, and rapidly responsive. Good, timely data is the key to first identifying, then tackling air pollution issues. This paper develops an alternative, cheap, IoT-based air quality monitor, which can track air pollution in real time, and transmit the relevant data rapidly through a low power wide area network. A large network of such monitors can generate a vast amount of data, which may then be processed and analyzed in the cloud in real time, and correlated with time of day, month, year or weather and other factors.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"5 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":"116668750","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}

{"title":"Audio Pre-Processing and Neural Network Models for Identification of Orthopedic Reamers in Use","authors":"M. Hanlon, B. Jackson, J. Rice, Joseph Walsh, D. Riordan","doi":"10.1109/ISSC49989.2020.9180175","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180175","url":null,"abstract":"In order for a successful outcome of Total Hip Arthroplasty (THA) to occur, implant stability is a key concern. A means to achieve this is ensuring that the implant has an optimum seating within the femur cavity. This is achieved during surgery by progressive reaming of the cavity interior. Both under and over reaming have undesirable effects towards implant longevity and post-operative prognosis, and so through education/experience, orthopedic surgeons have learned to anticipate when optimal reaming occurs. The work presented here is part of a larger research project which seeks to use bone resonance as an indicator of good implant seating. Here we present results of initial work using several neural network models on classification of orthopedic reamer type on the basis of sound signature. These results are discussed in the context of an interesting feature found when comparing differing audio- preprocessing methods. Despite identical audio raw data being used for both representations, the models that used the Mel Spectrograms categorically outperformed those which used the STFT Spectrogram.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"73 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":"129588004","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}

{"title":"A Statistically Significant Test to Evaluate the Order or Disorder of a Binary String","authors":"J. Blackledge, N. Mosola","doi":"10.1109/ISSC49989.2020.9180178","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180178","url":null,"abstract":"This paper addresses a basic problem in regard to the analysis of a finite binary string or bit stream (of compact support), namely, how to tell whether the string is representative of non-random or intelligible information (involving some form of periodicity, for example), whether it is the product of an entirely random process or whether it is something in between the two. This problem has applications that include cryptanalysis, quantitative finance, machine learning, artificial intelligence and other forms of signal and image processing involving the general problem of how to distinguishing real noise from information embedded in noise, for example. After providing a short introduction to the problem, we focus on the application of information entropy for solving the problem given that this fundamental metric is an intrinsic measure on information in regard to some measurable system. A brief overview on the concept of entropy is given followed by examples of how algorithms can be design to compute the binary entropy of a finite binary string including important variations on a theme such as the BiEntropy. The problem with computing a single metric of this type is that it can be representative of similar binary strings and lacks robustness in terms of its statistically significance. For this reasons, the paper presents a solution to the problem that is based on the Kullback-Leibler Divergence (or Relative Entropy) which yields a measure of how one probability distribution is different from another reference probability distribution. By repeatedly computing this metric for different reference (simulated or otherwise) random finite binary strings, it is shown how the distribution of the resulting signal changes for intelligible and random binary strings of a finite extent. This allows a number of standard statistical metrics to be computed from which the foundations for a machine learning system can be developed. A limited number of results are present for different natural languages to illustrate the approach, a prototype MATLAB function being provide for interested readers to reproduce the results given as required, investigate different data sets and further develop the method considered.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"55 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120857567","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}

{"title":"Comparison of Mathematical and Physical Phase Noise Performance in Fractional-N Synthesizers","authors":"Kyle Jansen, Michael Peter Kennedy","doi":"10.1109/ISSC49989.2020.9180168","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180168","url":null,"abstract":"Fractional-N frequency synthesizers are widely employed in wireless communications to produce sinusoidal carrier signals. Traditionally, analog synthesizers have offered the best phase noise performance whilst digital synthesizers are more flexible and have also demonstrated excellent phase noise performance. In addition, hybrid architectures have looked to combine the benefits of both analog and digital. This paper provides a qualitative analysis of the performances for each architecture with an eye towards identifying their respective performance limits.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"38 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":"126065481","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}

{"title":"Improving Academic Performance Amongst First Years Computer Science Students Through Goal-Setting","authors":"R. Donovan, Jamie Cotter, Ruairi O'Reilly","doi":"10.1109/ISSC49989.2020.9180154","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180154","url":null,"abstract":"Academic performance across Computer Science (CS) courses in the Republic of Ireland is underwhelming. CS undergraduates are statistically the most likely cohort in the country not to progress past year one of their studies. Insufficient motivation to pursue CS studies has been demonstrated to be a significant cause of poor CS academic performance. Goal-setting programs are an efficient, cost-effective, and student empowering way to boost motivation. Goal-setting is the formulation of a set of activities intended to motivate an individual to the desired goal state. This paper provides an experimental design for assessing the effectiveness of a written goal-setting program on academic performance concerning individual differences. Participants are randomly assigned either to the written goal-setting program or an active control task via an online platform. The goal-setting program requires participants to articulate both a desired future life and a feared future life. The program also requires participants to: identify goals and sub-goals across several domains (e.g. family, health, study); the benefits that achieving their goals would have for themselves for their connected group; the daily habits they could develop to make their ideal future more likely to occur. This study also investigates how personality (via a high-resolution personality model) and cognitive differences influence goal-setting effectiveness. Differences in both Semester 1 performance and the number of students who progress to Semester 2 are assessed between experimental groups. An ANCOVA analysis will assess whether the effectiveness of an experimental task varied based on individual differences in personality and/or cognitive ability.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"143 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":"128397940","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}

{"title":"Copyright","authors":"","doi":"10.1109/issc49989.2020.9180177","DOIUrl":"https://doi.org/10.1109/issc49989.2020.9180177","url":null,"abstract":"","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"18 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":"117176461","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}

{"title":"Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models","authors":"Benn Henderson, P. Yogarajah, B. Gardiner, M. McGinnity, Kitty Forster, B. Nicholas, D. Wimpory, J. Wanigasinghe","doi":"10.1109/ISSC49989.2020.9180201","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180201","url":null,"abstract":"Autism Spectrum Disorder (ASD) is a developmental disorder that is prevalent globally. Research into detecting autism traditionally focused on behavioural aspects of the condition, however, more recently, focus has shifted to more objective alternatives using techniques such as machine learning and gait analysis. Gait measurements, having been used for person identification, varies from person to person, introducing a lot of intra-subject variance. This applies to the 8 spatial-temporal features used in this study, representing the time that an individual spends in each phase of a gait cycle, collected using a Vicon motion tracking system. The features were averaged across each gait trial that the subjects performed, producing a second set of features with reduced intra-subject variance. Four common classifiers, a Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forests (RF) and a Decision Tree (DT) classifier, were all trained using the two feature sets and their classification rates were compared. The results show that for the RF classifier, reducing the intra-subject variance, was able to successfully increase the classification power. The KNN and DT classifiers experienced a minimal decrease in accuracy, where the SVM suffered the greatest loss when intra-subject variance was reduced. Results overall show that the effect intra-subject variance has on classification power depends heavily on the suitability of the classifier to the initial problem as well as size and class balance of the data.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"29 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":"114440623","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}