2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)最新文献_第6页

Evaluation of muscle fatigue degree using surface electromyography and accelerometer signals in fall detection systems 使用表面肌电图和加速度计信号在跌倒检测系统中评估肌肉疲劳程度

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120573

J. Ocampo, Jonathan A. Dizon, Clarence Vinzcent I. Reyes, John Joseph C. Capitulo, Juncarl Kevin G. Tapang, Seigfred V. Prado

{"title":"Evaluation of muscle fatigue degree using surface electromyography and accelerometer signals in fall detection systems","authors":"J. Ocampo, Jonathan A. Dizon, Clarence Vinzcent I. Reyes, John Joseph C. Capitulo, Juncarl Kevin G. Tapang, Seigfred V. Prado","doi":"10.1109/ICSIPA.2017.8120573","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120573","url":null,"abstract":"Fall events are common to elderly people due to their deteriorating muscle structures caused by old age. Their relatively weaker bodies make them prone to accidents such as falls even when performing daily tasks. These fall events may leave physical or psychological consequences among them. Commonly, these events are associated with one or more identifiable risk factors such as weakness, unsteady gait, confusion, environment, and certain medications. Previous researches have shown that these events can be prevented using fall detection mechanisms. In this study, we investigate whether the analysis of muscle fatigue degree may enhance the performance of existing fall detection systems that utilize both surface electromyography (SEMG) and accelerometer (ACC) sensors. SEMG and ACC signals were measured and recorded from 20 healthy study volunteers. A series of pre-defined activities that mimic fall events were performed by the study volunteers. These activities were conducted in a controlled environment. Acquired SEMG signals were pre-processed to eliminate unwanted signals and distortion. Discriminative features were then extracted from the clean signals, and these extracted features were combined with the accelerometer data for classification using an Artificial Neural Network (ANN) classifier. Results showed that the combination of SEMG and ACC data have relatively increased the accuracy of fall detection systems.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126776868","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

Development of WirelessHART adapter with industrial transmitter for process monitoring 用于过程监控的带有工业变送器的WirelessHART适配器的研制

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120624

ThasarathaRao Supramaniam, R. Ibrahim, S. Hassan, Kishore Bingi

引用次数: 5

A self-learning approach for pan-sharpening of multispectral images 多光谱图像泛锐化的自学习方法

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120606

Mohammad Khateri, H. Ghassemian

引用次数: 3

A novel superpixel approach utilizing depth and temporal cues 一种利用深度和时间线索的新型超像素方法

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120667

Shengda Luo, A. Leung, Yong Liang

引用次数: 0

A robust image hashing based on discrete wavelet transform 基于离散小波变换的鲁棒图像哈希算法

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120651

S. Singh, G. Bhatnagar

引用次数: 20

Stochastic diagonal approximate greatest descent in convolutional neural networks 卷积神经网络的随机对角近似最大下降

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120653

H. Tan, K. Lim, H. Harno

{"title":"Stochastic diagonal approximate greatest descent in convolutional neural networks","authors":"H. Tan, K. Lim, H. Harno","doi":"10.1109/ICSIPA.2017.8120653","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120653","url":null,"abstract":"Deep structured of Convolutional Neural Networks (CNN) has recently gained intense attention in development due to its good performance in object recognition. One of the crucial components in CNN is the learning mechanism of weight parameters through backpropagation. In this paper, stochastic diagonal Approximate Greatest Descent (SDAGD) is proposed to train weight parameters in CNN. SDAGD adopts the concept of multistage control system and diagonal Hessian approximation for weight optimization. It can be defined into two-phase optimization. In phase 1, when an initial guessing point is far from the solution, SDAGD constructs local search regions to determine the step length of next iteration at the boundary of search region. Subsequently, when the solution is at the final search region, SDAGD will shift to phase 2 by approximating Newton method to obtain a fast weight convergence. The calculation of Hessian in diagonal approximation results in less computational cost as compared to full Hessian calculation. The experiment showed that SDAGD learning algorithm could achieve misclassification rate of 8.85% on MNIST dataset.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133951177","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

Unsupervised classification of Intrusive igneous rock thin section images using edge detection and colour analysis 基于边缘检测和颜色分析的侵入火成岩薄片图像无监督分类

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120669

Silvia Joseph, Hamimah Ujir, I. Hipiny

引用次数: 10

A low-cost vibration analyser for analogue electromagnetic shaker 模拟电磁激振器的低成本振动分析仪

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120619

Faruq Muhammad Foong, C. Thein, B. L. Ooi, A. Aziz

{"title":"A low-cost vibration analyser for analogue electromagnetic shaker","authors":"Faruq Muhammad Foong, C. Thein, B. L. Ooi, A. Aziz","doi":"10.1109/ICSIPA.2017.8120619","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120619","url":null,"abstract":"This paper proposes a low-cost vibration analyser for a vibration-based shaker. The system comprises a refurbished analogue shaker, laser displacement sensors and data acquisition (DAQ) device. The laser displacement sensor captures the vibrational motion generated by the shaker. The data obtained from the laser displacement sensor is then amplified and transferred to a computer using a DAQ device. The system demonstrated close proximity to the analytical and simulation results in terms of determining the natural frequency of a cantilever beam, hence verifying the capability of the system for vibration analysis. The limitations of the system come from the analogue shaker and the sensitivity of the laser displacement sensors. Due to the attribute of an analogue machine, the shaker can only be operated manually. A sweep run is preferred to accommodate the low precision of the shaker. A frequency sweep speed between the range of 0.03 to 0.07 Hz per second should be used to ensure that the true amplitude of the test specimen can be obtained. A sampling rate of 20 kHz for the DAQ device is sufficient for the proposed system, while maintaining a proportional data size.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121296323","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: 2

Improved color scene classification system using deep belief networks and support vector machines 改进了基于深度信念网络和支持向量机的彩色场景分类系统

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120575

V. Sowmya, A. Ajay, D. Govind, K. Soman

{"title":"Improved color scene classification system using deep belief networks and support vector machines","authors":"V. Sowmya, A. Ajay, D. Govind, K. Soman","doi":"10.1109/ICSIPA.2017.8120575","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120575","url":null,"abstract":"In general, the three main modules of the color scene classification systems are image decolorization, feature extraction and classification. The work presented in this paper focuses on image decolorization and classification as two stages. The first stage or objective of this paper is to improve the performance of the color scene classification system using deep belief networks (DBN) and support vector machines (SVM). Therefore, color scene classification system termed as AGMM-DBN-SVM is proposed using the existing feature extraction technique called bags of visual words (BoW) derived from the dense scale-invariant feature transform (SIFT) and adapted gaussian mixture models (AGMM). The second stage of the presented work is to combine the proposed AGMM-DBN-SVM classification models obtained for the two different image decolorization methods called rgb2gray and singular value decomposition (SVD) based color-to-grayscale image mapping techniques to significantly increase the performance of the proposed color scene classification system. The effectiveness of the proposed framework is experimented on Oliva Torralba (OT) scene dataset containing 8 different classes. The classification rate of the proposed color scene classification system applied on OT 8 scene dataset is significantly greater than the one of the existing benchmarks color scene classification system developed using AGMM and SVM.","PeriodicalId":268112,"journal":{"name":"2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121067985","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

PAPR and BER reduction in MU-MIMO-OFDM systems via a set of waveforms 通过一组波形降低MU-MIMO-OFDM系统的PAPR和BER

2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120579

Zee Ang Sim, R. Reine, Z. Zang, Lenin Gopal

引用次数: 5