2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA)最新文献_第5页

Electronic nose purging technique for confined space application 密闭空间电子鼻净化技术的应用

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) Pub Date : 2017-03-01 DOI: 10.1109/CSPA.2017.8064948

M. Bakar, A. Abdullah, F. S. A. Sa'ad, S. Shukor, M. S. Kamis, A. A. Razak, M. H. Mustafa

{"title":"Electronic nose purging technique for confined space application","authors":"M. Bakar, A. Abdullah, F. S. A. Sa'ad, S. Shukor, M. S. Kamis, A. A. Razak, M. H. Mustafa","doi":"10.1109/CSPA.2017.8064948","DOIUrl":"https://doi.org/10.1109/CSPA.2017.8064948","url":null,"abstract":"Confined space is an enclosed area with limited space to perform work activity which could contribute towards atmospheric hazards accidents. To avoid this, the environment needs to be observed. The air sample can be monitored using the integration of Electronic Nose (e-nose) and mobile robot. This work proposed the development of e-nose purging technique for confined space application. An active carbon filter was installed at the e-nose body to perform purging. The e-nose system comprises of sensors chamber, electric air pumps, controller unit, wireless communication and a computer for data analysis. The Analog to Digital Converter (ADC) readings from gas sensors response is measured and defined into parts per million (ppm) and percentage level. To verify e-nose discriminative ability, two parameters are used which include normal laboratory air sample and hand sanitizer air sample. Principal Component Analysis (PCA) developed the discriminant analysis models from data collection. Results obtained proved the developed e-nose is successful in purging and it is reliable to produce correct measurement with high discriminative ability.","PeriodicalId":445522,"journal":{"name":"2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA)","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116054802","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

Appraising effect of environmental parameter toward mangrove area: A review 环境参数对红树林的影响评价综述

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) Pub Date : 2017-03-01 DOI: 10.1109/CSPA.2017.8064967

Noorita Sahriman, A. Samad, M. Z. Zainal, N. Ghazali, M. A. Abbas

引用次数: 1

Design and development of flexible end-effector based on Bernoulli principle 基于伯努利原理的柔性末端执行器的设计与开发

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) Pub Date : 2017-03-01 DOI: 10.1109/CSPA.2017.8064973

Muhammad Faiz Mansor, R. Sam, M. Masrie, Z. Janin

{"title":"Design and development of flexible end-effector based on Bernoulli principle","authors":"Muhammad Faiz Mansor, R. Sam, M. Masrie, Z. Janin","doi":"10.1109/CSPA.2017.8064973","DOIUrl":"https://doi.org/10.1109/CSPA.2017.8064973","url":null,"abstract":"In this study, a flexible end-effector based on Bernoulli principle showing in good handling type of flat workpiece without any damage. End-effector is operating on the principle of generating a high-speed stream of air between end-effector and the surface of the product, thereby creating a vacuum that levitates product. By this creating end-effector by Bernoulli principle, an innovative approach lift up workpiece in a variable of size and weight. End-effector from this paper is about the lifting of workpiece object and understanding theory with different diameter of end-effector to be compare which is more efficient. It will design by SolidWorks software for 3D drawing and fabricate end effector by 3D-printer. A flexible end-effector has been developed in this project based on Bernoulli principle. The lifting force generated and the force exerted on the workpiece. The application of automation will include for handling the workpiece. Development of the end-effector will help industry for handling workpiece such as in food industry without getting damage which increases year by year to become the principal industry.","PeriodicalId":445522,"journal":{"name":"2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128759073","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

Design and development of an enhanced computer based notification system 设计和开发一个增强的基于计算机的通知系统

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) Pub Date : 2017-03-01 DOI: 10.1109/CSPA.2017.8064971

K. D. Angay, W. Buclatin, J. Lontoc, S. Mendoza, J. Poniente

引用次数: 1

Parallel processing cell score design of linear gap penalty smith-waterman algorithm 线性间隙罚smith-waterman算法并行处理单元分数设计

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) Pub Date : 2017-03-01 DOI: 10.1109/CSPA.2017.8064969

S. Junid, M. Idros, A. Razak, F. N. Osman, N. Tahir

引用次数: 6

Deep neural network approach to frog species recognition 基于深度神经网络的蛙类识别

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) Pub Date : 2017-03-01 DOI: 10.1109/CSPA.2017.8064946

Norsalina Hassan, D. A. Ramli, H. Jaafar

{"title":"Deep neural network approach to frog species recognition","authors":"Norsalina Hassan, D. A. Ramli, H. Jaafar","doi":"10.1109/CSPA.2017.8064946","DOIUrl":"https://doi.org/10.1109/CSPA.2017.8064946","url":null,"abstract":"Automatic frog species recognition based on acoustic signal has received attention among biologists for environmental studies as it can detect, localize and document the declining population of frog species efficiently compared to the manual survey. In this study, we investigate the possibility of the use of Deep Neural Network (DNN) as a classifier for a frog species recognition system. The Mel-Frequency Cepstral Coefficients (MFCCs) is utilized as features and prior to the feature extraction, we also investigate the capability of automatic segmentation of syllables based on the Sinusoidal Modulation (SM), Energy with Zero Crossing Rate (E+ZCR) and Short-Time Energy with Time Average Zero Crossing Rate (STE+STAZCR). We also evaluate several DNN parameter's setting so as to discover the optimum parameter values for our developed system. 55 different species of frog with 2674 syllables from our in-house database have been tested. Experimental results based on DNN classifier showed that the STE+STAZCR method gives the accuracy of 99.03%, which reveals the viability of DNN as a classifier. In future, further research on DNN parameter optimization will be conducted for system improvement.","PeriodicalId":445522,"journal":{"name":"2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115402705","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: 7

Holy Qur'an speech recognition system Imaalah checking rule for warsh recitation 神圣古兰经语音识别系统Imaalah检查规则洗涤背诵

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) Pub Date : 2017-03-01 DOI: 10.1109/CSPA.2017.8064962

B. Yousfi, A. Zeki

引用次数: 7

Classification of the syllables sound using wavelet, Renyi entropy and AR-PSD features 基于小波、人义熵和AR-PSD特征的音节语音分类

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) Pub Date : 2017-03-01 DOI: 10.1109/CSPA.2017.8064931

Domy Kristomo, Risanuri Hidayat, I. Soesanti

引用次数: 6

Robust arrhythmia classifier using wavelet transform and support vector machine classification 基于小波变换和支持向量机分类的鲁棒心律失常分类器

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) Pub Date : 2017-03-01 DOI: 10.1109/CSPA.2017.8064959

Nyoke Goon Chia, Y. Hau, M. N. Jamaludin

引用次数: 1

Neonatal sleep state identification using deep learning autoencoders 使用深度学习自动编码器识别新生儿睡眠状态

2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA) Pub Date : 2017-03-01 DOI: 10.1109/CSPA.2017.8064956

L. Fraiwan, K. Lweesy

{"title":"Neonatal sleep state identification using deep learning autoencoders","authors":"L. Fraiwan, K. Lweesy","doi":"10.1109/CSPA.2017.8064956","DOIUrl":"https://doi.org/10.1109/CSPA.2017.8064956","url":null,"abstract":"Neonatal sleep state analysis provides a tool for diagnosis of several possible physiological disorders in newborns. The sleep state identification is a time consuming procedure where the sleep state is determined in epochs of 60 second for an entire sleep recording. A new technique for automated sleep state identification in neonates is proposed. The proposed system comprises two major step; feature extraction and classification. Twelve features were extracted from a single EEG recording. The features extracted were based on statistical parameters extracted from both temporal and spectral domains of the EEG signal. The total number of recordings used was 29 EEG recordings acquired from newborns (14 preterm infants and 15 fullterm). The classification was done based on deep autoencoder neural networks. The structure of the network used was two autoencoder layers and one softnet output layer. The performance of the proposed system was evaluated for both preterm and fullterm records assembled in one group of data using 10 fold cross validation. Also, the performance was tested for the two groups separately. The reported accuracy was 0.804 for the entire data sets.","PeriodicalId":445522,"journal":{"name":"2017 IEEE 13th International Colloquium on Signal Processing & its Applications (CSPA)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122815727","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: 22