2009 IEEE International Conference on Signal and Image Processing Applications最新文献_第5页

Feature analysis of acoustic emission signals in Time-Frequency representation from partial discharge sources using Self-Organizing Map 基于自组织映射的局部放电源声发射信号时频特征分析

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478716

Z. Zakaria, Y. Thayoob, M. R. Samsudin, P. Ghosh, M. Chai

引用次数: 1

Texture segmentation via statistical wavelet transform modeling 基于统计小波变换建模的纹理分割

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478622

N. Nikooienejad, H. Amindavar, K. Faez

引用次数: 0

Task-based visual saliency for intelligent compression 智能压缩的基于任务的视觉显著性

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478703

Patrick Harding, Neil M Roberston

引用次数: 4

Automatic removal of extracardiac hotspots in technetium-99m myocardial perfusion SPECT 99m心肌灌注SPECT自动去除心外热点

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478667

W. Tan, G. Coatrieux, R. Besar, B. Solaiman

引用次数: 0

Generalized Ridgelet-Fourier for M×N images: Determining the normalization criteria M×N图像的广义ridge - fourier:确定归一化准则

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478681

M. Mustaffa, F. Ahmad, R. Mahmod, S. Doraisamy

引用次数: 2

Bayesian decision for the fusion center of a distributed network in Cognitive Radio 认知无线电分布式网络融合中心的贝叶斯决策

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478709

S. Mosleh, A. Tadaion, M. Derakhtian

{"title":"Bayesian decision for the fusion center of a distributed network in Cognitive Radio","authors":"S. Mosleh, A. Tadaion, M. Derakhtian","doi":"10.1109/ICSIPA.2009.5478709","DOIUrl":"https://doi.org/10.1109/ICSIPA.2009.5478709","url":null,"abstract":"Cognitive Radio (CR) allows for usage of licensed frequency band by unlicensed Secondary Users (SU)s. However, these SUs need to monitor the spectrum continuously to avoid possible interference with the licensed Primary users (PU)s. In order to avoid causing harmful interference to a PU that is in operation, spectrum sensing is needed for the CRs. One of the great challenges of spectrum sensing is to detect the presence of the PU with little information about the channel and the signal transmitted from the PU. Cooperative spectrum sensing has been shown to greatly increase the probability of detecting the PU. Cooperative spectrum sensing refers to the spectrum sensing methods where local spectrum sensing information from multiple SUs are combined for PU detection. Distributed spectrum sensing methods have the potential to increase the spectral estimation reliability and decrease the probability of interference of CRs to primary communications. This paper considers the performance of a distributed Bayesian detection system consisting of N sensors and a fusion center, in which the decision rules of the sensors have been given and the decisions of different sensors are mutually independent conditioned on both hypotheses. Theoretical analysis on the performance of this fusion center is carried out. We obtain the conditions for the fusion center such that achieves the minimum average cost of making a decision for the overall system is smaller than the local risk of each sensor. The performance of our detector is compared to the traditional AND, OR and Majority decision fusion rules and numerical results show that AND, OR and Majority decision fusion rules are the special cases of the Bayesian fusion rule. In addition, we derive a lower bound for the risk of the fusion center for any given priori probability of hypothesis.","PeriodicalId":400165,"journal":{"name":"2009 IEEE International Conference on Signal and Image Processing Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123594610","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

Codebook constrained iterative and Parametric Wiener filter speech enhancement 码本约束迭代和参数维纳滤波语音增强

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478717

S. Chehresa, M. Savoji

引用次数: 7

Multi-pose head tracking using colour and edge features fuzzy aggregation for driver assistant system 基于颜色和边缘特征模糊聚合的驾驶员辅助多姿态头部跟踪

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478682

Hadi Seyedarabi, S. M. Bakhshmand, S. Khanmohammadi

引用次数: 4

Autonomously normalized horizontal differentials as features for HMM-based Omni font-written OCR systems for cursively scripted languages 自主规范化水平差分作为草书脚本语言的基于hmm的Omni字体OCR系统的特性

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478619

M. Attia, M. Rashwan, M. El-Mahallawy

{"title":"Autonomously normalized horizontal differentials as features for HMM-based Omni font-written OCR systems for cursively scripted languages","authors":"M. Attia, M. Rashwan, M. El-Mahallawy","doi":"10.1109/ICSIPA.2009.5478619","DOIUrl":"https://doi.org/10.1109/ICSIPA.2009.5478619","url":null,"abstract":"Automatic font-written Optical Character Recognition (OCR) is highly desirable for numerous modern information technology (IT) applications. Reliable font-written OCR's for Latin scripts are readily in use since long. For cursively scripted languages, that are the mother tongues of over one fourth of the world population, such OCR's are however not available at a robust and reliable performance. In this regard, the main challenge is the mandatory connectivity of characters/ligatures (i.e. graphemes) that has to be resolved simultaneously upon the recognition of these graphemes. Among the various approaches tried over decades, Hidden Markov Models (HMM)-based OCR's seem to be the most promising as they capitalize on the ability of HMM decoders to achieve segmentation and recognition simultaneously similar to the widely used HMM-based automatic speech recognition (ASR). Unlike ASR's, what is missing in HMM-based OCR's is the definition of a rigorously founded features vector capable to robustly achieving minimal “font type/size-independent” (omnifont) word error rates comparable to those realized with Latin scripts. Here comes the contribution of this paper that introduces such a sound features vector design, and experimentally shows its superiority in this regard.","PeriodicalId":400165,"journal":{"name":"2009 IEEE International Conference on Signal and Image Processing Applications","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121192992","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

Threshold-free detection of maternal heart rate from abdominal electrocardiogram 从腹部心电图无阈值检测母体心率

2009 IEEE International Conference on Signal and Image Processing Applications Pub Date : 2009-11-01 DOI: 10.1109/ICSIPA.2009.5478697

M. Algunaidi, M. A. Mohd. Ali

{"title":"Threshold-free detection of maternal heart rate from abdominal electrocardiogram","authors":"M. Algunaidi, M. A. Mohd. Ali","doi":"10.1109/ICSIPA.2009.5478697","DOIUrl":"https://doi.org/10.1109/ICSIPA.2009.5478697","url":null,"abstract":"This work is involved the threshold-free detection of QRS complexes in an abdominal electrocardiogram (AECG) waveform. The precision in the identification of QRS complexes is of great importance for on-line maternal heart rate calculation and, will lead to on line fetal heart detection,. During the last century much work has been carried out in this field, using various methods ranging from filtering and threshold methods, through wavelet methods, to neural networks, and others, each of which has different effectiveness and weaknesses. Although their performance is generally good, but, the main weakness is that, they are threshold dependent. In the proposed algorithm a RR moving interval is calculated, based on normal maximum and minimum heart rate (HR). This has the advantage of ensuring that every R-peak is contained between the edges of the moving interval. Thus the effectiveness of this algorithm is that, it is threshold independent, and after every peak detection the RR moving interval is updated to calculate the next peak contained between its edges. The complete algorithm is implemented using MATLAB 7.4. The method is validated using 20 recorded data. The average sensitivity and average positive predictivity of the detection method are 99.05% and 99.8% respectively.","PeriodicalId":400165,"journal":{"name":"2009 IEEE International Conference on Signal and Image Processing Applications","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126581924","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