2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE)最新文献

Different neural networks approaches for identification of obstructive sleep apnea 识别阻塞性睡眠呼吸暂停的不同神经网络方法

2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) Pub Date : 2018-07-05 DOI: 10.1109/ISCAIE.2018.8405485

Sarah Qasim Ali, A. Hossen

{"title":"Different neural networks approaches for identification of obstructive sleep apnea","authors":"Sarah Qasim Ali, A. Hossen","doi":"10.1109/ISCAIE.2018.8405485","DOIUrl":"https://doi.org/10.1109/ISCAIE.2018.8405485","url":null,"abstract":"Obstructive sleep apnea (OSA) is one of the most common breathing-related sleep disorders affecting individuals of different age groups, genders and origins. It is characterized by short-duration of cessations in breathing during sleep due to the collapse of the upper airway. The golden standard and reliable test for the detection of OSA is conducted by specialized physicians performing a polysomnographic sleep study. However, this test is time/labor consuming, expensive and cumbersome. In this paper, a non-invasive technique employing three different artificial neural networks to analyze spectral and statistical features of the Heart Rate Variability (HRV) signal to identify OSA subjects from normal control is investigated. The artificial networks include the single perceptron network, the feedforward network with back-propagation and the probabilistic neural network. The highest performance on MIT standard data is achieved by the feedforward network with back propagation using wavelet-based frequency domain features with specificity, sensitivity, and accuracy of 90%, 100% and 96.67%, respectively.","PeriodicalId":333327,"journal":{"name":"2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE)","volume":"8 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130370196","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: 8

The development of sports science knowledge management systems through CommonKADS and digital Kanban board 通过CommonKADS和数字看板开发体育科学知识管理系统

2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) Pub Date : 2018-07-05 DOI: 10.1109/ISCAIE.2018.8405455

Sirikorn Santirojanakul

{"title":"The development of sports science knowledge management systems through CommonKADS and digital Kanban board","authors":"Sirikorn Santirojanakul","doi":"10.1109/ISCAIE.2018.8405455","DOIUrl":"https://doi.org/10.1109/ISCAIE.2018.8405455","url":null,"abstract":"The role of the sports scientist is becoming more complex and deals with high risk regarding the future of athletes. The challenge of this study is to address the lack of collaboration among stakeholders. The traditional reporting system between experts and others stakeholders creates more waiting time. This research develops the Sports Science Knowledge Management system (SSKM) based on the Common KADS and Kanban board. CommonKADS is one of the effective modeling frameworks used for investigating both the organization model and task model. The Sport Authority of Thailand (SAT), was selected as a case study to propose this framework. The results have shown that SSKM can be utilized to improve the performance of the sports scientist's reporting system. Furthermore, the digital Kanban board can support collaboration and communication challenges that occur within the sports scientists, executive, staff, and sport association. This digital Kanban board can create a helpful method for managing workflow and measuring the outcomes of multifaceted task. The digital Kanban board display types of sport competitions, sports associations, sports scientists, and athletic evaluations.","PeriodicalId":333327,"journal":{"name":"2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE)","volume":"72 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123526138","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: 4

Timing analysis for Diffie Hellman Key Exchange In U-BOOT using Raspberry pi U-BOOT中Diffie Hellman密钥交换的时序分析

2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) Pub Date : 2018-07-05 DOI: 10.1109/ISCAIE.2018.8405472

Yasin Fitri Alias, H. Hashim

{"title":"Timing analysis for Diffie Hellman Key Exchange In U-BOOT using Raspberry pi","authors":"Yasin Fitri Alias, H. Hashim","doi":"10.1109/ISCAIE.2018.8405472","DOIUrl":"https://doi.org/10.1109/ISCAIE.2018.8405472","url":null,"abstract":"In Diffie-Hellman Key Exchange (DHKE), two parties need to communicate to each other by sharing their secret key (cipher text) over an unsecure communication channel. An adversary or cryptanalyst can easily get their secret keys but cannot get the information (plaintext). Brute force is one the common tools used to obtain the secret key, but when the key is too large (etc. 1024 bits and 2048 bits) this tool is no longer suitable. Thus timing attacks have become more attractive in the new cryptographic era where networked embedded systems security present several vulnerabilities such as lower processing power and high deployment scale. Experiments on timing attacks are useful in helping cryptographers make security schemes more resistant. In this work, we timed the computations of the Discrete Log Hard Problem of the Diffie Hellman Key Exchange (DHKE) protocol implemented on an embedded system network and analyzed the timing patterns of 1024-bit and 2048-bit keys that was obtained during the attacks. We have chosen to implement the protocol on the Raspberry-pi board over U-BOOT Bare Metal and we used the GMP bignum library to compute numbers greater than 64 bits on the embedded system.","PeriodicalId":333327,"journal":{"name":"2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128358651","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

Exploring antecedent factors toward knowledge sharing intention in E-learning 网络学习中知识共享意愿的前因因素探讨

2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) Pub Date : 2018-07-05 DOI: 10.1109/ISCAIE.2018.8405453

Raisiffah Kunthi, R. Wahyuni, Mochammad Umar Al-Hafidz, D. I. Sensuse

引用次数: 5

Effects of permanent and recoverable component of NBTI mechanisms on flip flop circuits designed using planar MOSFET and FinFET NBTI机制的永久和可恢复分量对采用平面MOSFET和FinFET设计的触发器电路的影响

2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) Pub Date : 2018-07-05 DOI: 10.1109/ISCAIE.2018.8405460

M. F. Zainudin, H. Hussin, A. Halim, J. Karim

引用次数: 2

Classification of twilight zone proteins using a structure-based phylogenetic approach 利用基于结构的系统发育方法对模糊带蛋白进行分类

2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) Pub Date : 2018-07-05 DOI: 10.1109/ISCAIE.2018.8405437

Siti Aisyah Mohd Taha, Y. Zakaria

{"title":"Classification of twilight zone proteins using a structure-based phylogenetic approach","authors":"Siti Aisyah Mohd Taha, Y. Zakaria","doi":"10.1109/ISCAIE.2018.8405437","DOIUrl":"https://doi.org/10.1109/ISCAIE.2018.8405437","url":null,"abstract":"The emerging knowledge in drug discovery has heightened the need to study the classification of proteins in order to understand their structure, functions and evolutionary relationship. Due to high vulnerability of protein sequence to change throughout evolution, it is difficult to identify protein homology of distant evolutionarily-related proteins. These proteins are also known to be structurally homologous, thus, the structural approach was a more suitable method. This study focused on the methods for classifying twilight zone proteins using structure-based phylogenetic approach. However, since protein homology plays a major role in protein classification, finding the best alignment tool is the most crucial step. The classification of proteins was constructed by clustering 15 folds at their superfamily level. These proteins belonged to four main SCOPe classes which are the all alpha proteins (Class A), all beta proteins (Class B), wound alpha beta proteins (Class C) and mixed alpha beta proteins (Class D). Protein homology was identified using structural alignment tools which are FATCAT-F and FATCAT-R, while the sequence alignment was conducted using T-COFFEE. Classification tree was constructed using the Unweighted Pair Group Method of Arithmetic Mean (UPGMA) and the clusters were validated using Adjusted Rand Index (ARi), pseudo-jackknife confidence interval and manual observation of clusters. Results show that the structural approach produced better classification than the sequence-based method by producing clusters with higher resemblance to SCOPe for three main SCOPe classes (Class A, Class C and Class D). Moreover, FATCAT-R was able to cluster proteins more accurately than FATCAT-F with higher ARi results for a majority of protein folds. On the other hand, T-COFFEE was able to cluster Class B proteins more accurately than FATCAT-F and FATCAT-R.","PeriodicalId":333327,"journal":{"name":"2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE)","volume":"95-98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131059653","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

Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm 基于粒子群算法的锂离子电池充电状态估计改进NARX神经网络模型

2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) Pub Date : 2018-07-05 DOI: 10.1109/ISCAIE.2018.8405498

M. Lipu, A. Hussain, M. Saad, A. Ayob, M. A. Hannan

{"title":"Improved recurrent NARX neural network model for state of charge estimation of lithium-ion battery using pso algorithm","authors":"M. Lipu, A. Hussain, M. Saad, A. Ayob, M. A. Hannan","doi":"10.1109/ISCAIE.2018.8405498","DOIUrl":"https://doi.org/10.1109/ISCAIE.2018.8405498","url":null,"abstract":"This paper aims to develop an accurate estimation technique for computing state of charge (SOC) of a lithium-ion battery using recurrent neural network algorithm. Nonlinear autoregressive with exogenous input (NARX) model is a well-known subclass of the recurrent neural network which has proven to be very effective and computationally rich for controlling dynamic system and predicting time series. However, the accuracy of recurrent NARX neural network depends on the amount of input and output order as well as a number of neurons in a hidden layer. Therefore, this study presents an improved recurrent NARX neural network based SOC estimation with particle swarm optimization (PSO) algorithm for finding the best value of input delays, feedback delays and a number of neurons in a hidden layer. The proposed model uses three most significant factor such as current, voltage and temperature without considering battery model. The model robustness is checked at low temperature (0°C), medium temperature (25°C), and high temperature (45°C). The US06 drive cycle is selected for model training and testing. The effectiveness of the proposed approach is compared with the back-propagation neural network (BPNN) optimized by PSO based on the SOC error, root mean square error (RMSE) and mean absolute error (MAE) and average execution time (AET). The results prove that the proposed model has higher estimation speed and achieves higher accuracy in reducing RMSE and MAE by 53% and 50% than BPNN based PSO model at 25°C.","PeriodicalId":333327,"journal":{"name":"2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117084245","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: 19

Cancelable biometrics technique for iris recognition 虹膜识别的可取消生物特征技术

2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) Pub Date : 2018-07-05 DOI: 10.1109/ISCAIE.2018.8405512

Musab A. M. Ali, N. Tahir

引用次数: 24

Concurrent MAC unit design using VHDL for deep learning networks on FPGA 基于FPGA的深度学习网络并行MAC单元设计

2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) Pub Date : 2018-04-28 DOI: 10.1109/ISCAIE.2018.8405440

Hossam O. Ahmed, M. Ghoneima, M. Dessouky

{"title":"Concurrent MAC unit design using VHDL for deep learning networks on FPGA","authors":"Hossam O. Ahmed, M. Ghoneima, M. Dessouky","doi":"10.1109/ISCAIE.2018.8405440","DOIUrl":"https://doi.org/10.1109/ISCAIE.2018.8405440","url":null,"abstract":"Deep neural network algorithms have proven their enormous capabilities in wide range of artificial intelligence applications, specially in Printed/Handwritten text recognition, Multimedia processing, Robotics and many other high end technological trends. The most challenging aspect nowadays is to overcome the extremely computational processing demands in applying such algorithms, especially in real-time systems. Recently, the Field Programmable Gate Array (FPGA) has been considered as one of the optimum hardware accelerator platform for accelerating the deep neural network architectures due to its large adaptability and the high degree of parallelism it offers. In this paper, the proposed 8-bits fixed-point parallel multiply-accumulate (MAC) unit architecture aimed to create a fully-customize MAC unit for the Convolutional Neural Networks (CNN) instead of depending on the conventional DSP blocks and embedded memories units on the FPGAs architecture silicon fabrics. The proposed 8-bits fixed-point parallel multiply-accumulate (MAC) unit architecture is designed using VHDL language and can performs a computational speed up to 4.17 Giga Operation per Second (GOPS) using high-density FPGAs.","PeriodicalId":333327,"journal":{"name":"2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127706390","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: 13

A promising power-saving technique: Approximate computing 一种很有前途的节能技术:近似计算

2018 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE) Pub Date : 2018-04-28 DOI: 10.1109/ISCAIE.2018.8405486

Junqi Huang, T. Kumar, Haider Abbas

引用次数: 5