2020 31st Irish Signals and Systems Conference (ISSC)最新文献_第6页

Serverless Computing Security: Protecting Application Logic 无服务器计算安全:保护应用逻辑

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180214

Wesley O'Meara, Ruth G. Lennon

引用次数: 6

Multi-Machine Synchronous Islanding Achieved in a Laboratory Test Bed Utilizing PMUs 利用PMUs在实验室试验台实现多机同步孤岛

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180183

Mats-Robin Jacobsen, D. Laverty, R. Best, John Hastings

引用次数: 0

Should WebRTC Prioritise Intelligibility over Speech Quality? webbrtc是否应该优先考虑可理解性而不是语音质量?

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180210

P. Sun, Andrew Hines

{"title":"Should WebRTC Prioritise Intelligibility over Speech Quality?","authors":"P. Sun, Andrew Hines","doi":"10.1109/ISSC49989.2020.9180210","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180210","url":null,"abstract":"Network delay remains a challenge for real-time voice communication on the web. Jitter buffer algorithms have been widely deployed in popular platforms such as webRTC to reduce the impact of delay with playout adjustments. A trade off must be made between speech loss and voice degradations as adjustments can either drop segments resulting in a loss of speech intelligibility or change the rate of playout and impact the pitch or natural sound of the speech. Both options can negatively influence a listener's quality of experience (QoE). Optimising this trade-off requires knowledge of how intelligibility and quality are perceived and priorities when a listener syntheses both factors into a fused QoE judgement. This study conducted two subjective experiments to evaluate intelligibility and quality independently along with a short descriptive analysis to address the interplay between the two factors. The study uses a dataset that simulated listener-end speech under extreme but realistic network delay conditions using webRTC's standard jitter buffer and a variation that prioritised minimisation of packet loss. The results show that intelligibility is a key dimension in quality judgement for the scenarios tested. As a result, this study calls for attention when comparing the quality scores as the overlooked non-traditional quality attributes are proven to be actively contributing to the overall QoE. The descriptive analysis also indicates there is inconsistency in the interpretation of ‘quality’ among the assessors. This finding questions the methodology used in standard QoE subjective experiment designs and proposes adopting a more flexible approach to measure subjective QoE.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"47 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":"129248403","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

Charge Analysis in SAR ADC with Discrete-Time Reference Driver 基于离散时间参考驱动的SAR ADC电荷分析

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-06-01 DOI: 10.1109/ISSC49989.2020.9180184

Fahd A. Shiwani, T. Siriburanon, Jianglin Du, R. Staszewski

引用次数: 0

Neural Ordinary Differential Equation based Recurrent Neural Network Model 基于神经常微分方程的递归神经网络模型

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-05-20 DOI: 10.1109/ISSC49989.2020.9180182

M. Habiba, Barak A. Pearlmutter

{"title":"Neural Ordinary Differential Equation based Recurrent Neural Network Model","authors":"M. Habiba, Barak A. Pearlmutter","doi":"10.1109/ISSC49989.2020.9180182","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180182","url":null,"abstract":"Neural differential equations are a promising new member in the neural network family. They show the potential of differential equations for time-series data analysis. In this paper, the strength of the ordinary differential equation (ODE) is explored with a new extension. The main goal of this work is to answer the following questions: (i) can ODE be used to redefine the existing neural network model? (ii) can Neural ODEs solve the irregular sampling rate challenge of existing neural network models for a continuous time series, i.e., length and dynamic nature, (iii) how to reduce the training and evaluation time of existing Neural ODE systems? This work leverages the mathematical foundation of ODEs to redesign traditional RNNs such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The main contribution of this paper is to illustrate the design of two new ODE-based RNN models (GRU-ODE model and LSTM-ODE) which can compute the hidden state and cell state at any point of time using an ODE solver. These models reduce the computation overhead of hidden state and cell state by a vast amount. The performance evaluation of these two new models for learning continuous time series with irregular sampling rate is then demonstrated. Experiments show that these new ODE based RNN models require less training time than Latent ODEs and conventional Neural ODEs. They can achieve higher accuracy quickly, and the design of the neural network is more straightforward than the previous neural ODE systems.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130965006","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: 12

Neural ODEs for Informative Missingess in Multivariate Time Series 多元时间序列信息缺失的神经ode

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-05-20 DOI: 10.1109/ISSC49989.2020.9180216

M. Habiba, Barak A. Pearlmutter

{"title":"Neural ODEs for Informative Missingess in Multivariate Time Series","authors":"M. Habiba, Barak A. Pearlmutter","doi":"10.1109/ISSC49989.2020.9180216","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180216","url":null,"abstract":"Informative missingness is unavoidable in the digital processing of continuous time series, where the value for one or more observations at different time points are missing. Such missing observations are one of the major limitations of time series processing using deep learning. Practical applications, e.g., sensor data, healthcare, weather, generates data that is in truth continuous in time, and informative missingness is a common phenomenon in these datasets. These datasets often consist of multiple variables, and often there are missing values for one or many of these variables. This characteristic makes time series prediction more challenging, and the impact of missing input observations on the accuracy of the final output can be significant. A recent novel deep learning model called GRU-D is one early attempt to address informative missingness in time series data. On the other hand, a new family of neural networks called Neural ODEs (Ordinary Differential Equations) are natural and efficient for processing time series data which is continuous in time. In this paper, a deep learning model is proposed that leverages the effective imputation of GRU-D, and the temporal continuity of Neural ODEs. A time series classification task performed on the PhysioNet dataset demonstrates the performance of this architecture.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"361 9","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120893928","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: 6

Revealing the Dynamic Relationship Between Neural Population Activities in Corticoraphe System 揭示皮质皮质系统中神经种群活动的动态关系

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-05-06 DOI: 10.1109/ISSC49989.2020.9180170

C. Behera, Ruairi O’Sullivan, J. Sanchez-Bornot, Alok Joshi, G. Prasad, T. Sharp, KongFatt Wong-Lin

{"title":"Revealing the Dynamic Relationship Between Neural Population Activities in Corticoraphe System","authors":"C. Behera, Ruairi O’Sullivan, J. Sanchez-Bornot, Alok Joshi, G. Prasad, T. Sharp, KongFatt Wong-Lin","doi":"10.1109/ISSC49989.2020.9180170","DOIUrl":"https://doi.org/10.1109/ISSC49989.2020.9180170","url":null,"abstract":"Studies have shown that the firing activity of single neurons in brainstem dorsal raphe nucleus (DRN) is linked to slow-wave oscillations in the cortex, especially the frontal cortex. However, most studies consist of either single DRN neuronal or single-channel electrocorticogram (ECoG) recording. Hence, it is unclear how a population of DRN neurons with electrophysiologically diverse characteristics can coordinate and relate to the oscillatory rhythms in different cortical regions. In this work, we explored the technical feasibility of such an investigation. We simultaneously recorded extracellularly a group of DRN neurons and three cortical regions using electrocorticogram (ECoG) in two anaesthetized SERT-Cre mice. The cortical regions were the two bi-hemispheric frontal and one (right) occipital regions. We then used coherence analysis to quantify the relationship between DRN neurons and cortical activity rhythms. We also computed the coherence between firing activities of DRN neurons to quantify their relationship. We found slow-firing DRN neurons with regular and irregular spiking characteristics, potentially serotonergic neurons, were more likely to have stronger relationships with cortical ECoG signals, especially the frontal cortex. Moreover, the DRN neurons were generally found to be weakly correlated with each other. Future investigation with more samples and analytical methods will be conducted to validate our results.","PeriodicalId":351013,"journal":{"name":"2020 31st Irish Signals and Systems Conference (ISSC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127990138","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

Non-Intrusive Load Monitoring Algorithm for PV Identification in the Residential Sector 住宅小区光伏识别的非侵入式负荷监测算法

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-05-06 DOI: 10.1109/ISSC49989.2020.9180192

Moreno Jaramillo, A. M. Jaramillo, D. Laverty, Jesús Martínez, del Rincón, P. Brogan, D. Morrow

引用次数: 10

Context-aware robotic arm using fast embedded model predictive control 基于快速嵌入式模型预测控制的情境感知机械臂

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-05-06 DOI: 10.1109/ISSC49989.2020.9180217

Shane Trimble, W. Naeem, S. McLoone, Pantelis Sopasakis

引用次数: 2

Minimising Impact of Local Congestion in Networks-on-Chip Performance by Predicting Buffer Utilisation 通过预测缓冲区利用率最小化局部拥塞对片上网络性能的影响

2020 31st Irish Signals and Systems Conference (ISSC) Pub Date : 2020-05-06 DOI: 10.1109/ISSC49989.2020.9180165

Aqib Javed, J. Harkin, L. McDaid, Junxiu Liu

引用次数: 2