2015 International Conference on Systems, Signals and Image Processing (IWSSIP)最新文献

Ground penetrating radar survey inside a historical monument 探地雷达探测历史遗迹

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2015-11-02 DOI: 10.1109/IWSSIP.2015.7314240

A. Chelmuș

引用次数: 1

Visually lossless image compression extension for JPEG based on just-noticeable distortion evaluation 视觉无损图像压缩扩展的JPEG基于刚刚明显的失真评估

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2015-11-02 DOI: 10.1109/IWSSIP.2015.7314220

Zhe Wang, S. Simon, Y. Baroud, Seyyed Mahdi Najmabadi

{"title":"Visually lossless image compression extension for JPEG based on just-noticeable distortion evaluation","authors":"Zhe Wang, S. Simon, Y. Baroud, Seyyed Mahdi Najmabadi","doi":"10.1109/IWSSIP.2015.7314220","DOIUrl":"https://doi.org/10.1109/IWSSIP.2015.7314220","url":null,"abstract":"A visually lossless image encoding extension for JPEG is presented. Such extension enables an efficient implementation of perceptual coding by reusing existing widespread software libraries and hardware IP cores for JPEG. For any pixel in a decoded image, the proposed algorithm guarantees a maximum distortion bounded by the just-noticeable distortion (JND) threshold measured based on the input image. Perceptual coding is performed in three steps: (1) standard transform domain coding, (2) spatial domain distortion visibility analysis by JND model and (3) spatial domain residual coding. Such scheme has been implemented in this work as an extension for JPEG based on a low complexity JND model. The encoder determines if a pixel block in a standard JPEG output image contains distortions beyond the visibility threshold given by the JND model. If it is true then the locations and the values of such distortions are encoded as side information. Quantization step size for the distortion values, i.e. perceptual residuals, are chosen based on the visibility threshold. Experimental results show that in terms of compression efficiency, the proposed perceptual encoding extension outperforms the standard JPEG encoder by 50% for a visually lossless compression of images.","PeriodicalId":249021,"journal":{"name":"2015 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116066757","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

Test of six image quality assessment methods 六种图像质量评价方法的试验

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2015-11-02 DOI: 10.1109/IWSSIP.2015.7313930

Tomasz Grzywalski, R. Stasinski

引用次数: 0

An efficient troubleshooting testing scenario for IPTV over DSL lines: Practical examples 通过DSL线路进行IPTV的有效故障排除测试场景:实际示例

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2015-11-02 DOI: 10.1109/IWSSIP.2015.7314219

N. Skaljo, A. Begovic, N. Behlilovic

引用次数: 1

Crowd density estimation based on voxel model in multi-view surveillance systems 基于体素模型的多视点监控系统人群密度估计

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2015-11-02 DOI: 10.1109/IWSSIP.2015.7314215

Pawel Gardzinski, Krzysztof Kowalak, Lukasz Kaminski, S. Mackowiak

引用次数: 7

Multi-channel sub-Nyquist cross-spectral estimation for modal analysis of vibrating structures 振动结构模态分析的多通道亚nyquist交叉谱估计

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2015-11-02 DOI: 10.1109/IWSSIP.2015.7314232

K. Gkoktsi, B. Tausiesakul, A. Giaralis

{"title":"Multi-channel sub-Nyquist cross-spectral estimation for modal analysis of vibrating structures","authors":"K. Gkoktsi, B. Tausiesakul, A. Giaralis","doi":"10.1109/IWSSIP.2015.7314232","DOIUrl":"https://doi.org/10.1109/IWSSIP.2015.7314232","url":null,"abstract":"Operational modal analysis (OMA) is a widely used construction verification and structural health monitoring technique aiming to obtain the modal properties of vibrating civil engineering structures subject to ambient dynamic loads by collecting and processing structural response acceleration signals. Motivated by the need for cost-efficient OMA using wireless sensor networks which acquire and transmit measurements at a lower than the Nyquist rate, a novel OMA approach is put forth to derive modal properties directly from sub-Nyquist sampled (compressed) acceleration measurements from arrays of sensors. This is achieved by adopting sub-Nyquist deterministic non-uniform multi-coset sampling devices and by extending a previously proposed in the literature power spectrum blind sampling method for single-channel spectral estimation of stochastic processes to treat the case of multiple channel cross-spectral estimation. The standard frequency domain decomposition is used to obtain the modal properties from the cross-spectral matrix derived directly from the sub-Nyquist measurements. The applicability and efficiency of the proposed approach is exemplified by retrieving mode shapes of a white-noise excited simply supported steel beam with good accuracy according to the widely used modal assurance criterion using 70% less than the Nyquist rate measurements.","PeriodicalId":249021,"journal":{"name":"2015 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132721516","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

Target localization using dual tone frequency radio 使用双音无线电定位目标

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2015-11-02 DOI: 10.1109/IWSSIP.2015.7314199

D. Gleich, M. Malajner, P. Planinsic

引用次数: 1

Applied machine learning classifiers for medical applications: Clarifying the behavioural patterns using a variety of datasets 医疗应用的应用机器学习分类器:使用各种数据集澄清行为模式

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2015-11-02 DOI: 10.1109/IWSSIP.2015.7314218

A. Aljaaf, D. Al-Jumeily, A. Hussain, P. Fergus, M. Al-Jumaily, Naeem Radi

{"title":"Applied machine learning classifiers for medical applications: Clarifying the behavioural patterns using a variety of datasets","authors":"A. Aljaaf, D. Al-Jumeily, A. Hussain, P. Fergus, M. Al-Jumaily, Naeem Radi","doi":"10.1109/IWSSIP.2015.7314218","DOIUrl":"https://doi.org/10.1109/IWSSIP.2015.7314218","url":null,"abstract":"Machine-learning (ML) techniques have grown to be among the leading research topics within the health care systems and particularly for clinical decision support systems (CDSS), which are commonly used in helping physicians to make more accurate diagnosis. However, applying these techniques for CDSS is most likely would face a lack of criteria for adequate use. Therefore, a range of recent studies have focused on evaluating different machine learning classifiers with the aim of identifying the most appropriate classifier to be used for particular decision making problem-domain. The majority of these studies have used a single dataset within a certain medical-related classification domain. Nevertheless, evaluating machine-learning classifiers with one sample of data appears to be unsatisfying, perhaps it is not reflecting the classifiers capabilities or their behavioral patterns under different circumstances. In this study, five well-known supervised machine-learning classifiers were examined using five different real-world datasets with a range of attributes. The main aim was to illustrate not only the impact of the datasets volume and attributes on the evaluation, but also and more importantly, present the classifiers capabilities and shortcomings under certain conditions, which potentially provide a guidance or instructions to help health analysts and researchers to determine the most suitable classifier to address a particular medical-related decision making problem.","PeriodicalId":249021,"journal":{"name":"2015 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115677497","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

Advance flood detection and notification system based on sensor technology and machine learning algorithm 基于传感器技术和机器学习算法的先进洪水检测和通知系统

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2015-11-02 DOI: 10.1109/IWSSIP.2015.7314188

Mohammed Khalaf, A. Hussain, D. Al-Jumeily, P. Fergus, I. Idowu

{"title":"Advance flood detection and notification system based on sensor technology and machine learning algorithm","authors":"Mohammed Khalaf, A. Hussain, D. Al-Jumeily, P. Fergus, I. Idowu","doi":"10.1109/IWSSIP.2015.7314188","DOIUrl":"https://doi.org/10.1109/IWSSIP.2015.7314188","url":null,"abstract":"Floods are common natural disasters that cause severe devastation of any country. They are commonly caused by precipitation and runoff of rivers, particularly during periods of excessively high rainy season. Due to global warming issues and extreme environmental effects, flood has become a serious problem to the extent of bringing about negative impact to the mankind and infrastructure. To date, sensor network technology has been used in many areas including water level fluctuation. However, efficient flood monitoring and real time notification system still a crucial part because Information Technology enabled applications have not been employed in this sector in a broad way. This research presents a description of an alert generating system for flood detection with a focus on determining the current water level using sensors technology. The system then provides notification message about water level sensitivity via Global Communication and Mobile System modem to particular authorise person. Besides the Short Message Service, the system instantaneously uploads and broadcast information through web base public network. Machine-learning algorithms were conducted to perform the classification process. Four experiments were carried out to classify flood data from normal and at risk condition in which 99.5% classification accuracy was achieved using Random Forest algorithm. Classification using Bagging, Decision Tree and HyperPipes algorithms achieved accuracy of 97.7 %, 94.6% and 89.8 %, respectively.","PeriodicalId":249021,"journal":{"name":"2015 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128198001","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: 16

System level simulation of LTE MBSFN networks with high mobility users 具有高移动性用户的LTE MBSFN网络系统级仿真

2015 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2015-11-02 DOI: 10.1109/IWSSIP.2015.7313927

Illia Safiulin, Stefan Schwarz, M. Rupp

引用次数: 8