2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)最新文献_第7页

Graph theory for the discovery of non-parametric audio objects 图论用于发现非参数音频对象

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) Pub Date : 2012-07-02 DOI: 10.1109/ISSPA.2012.6310498

C. Srinivasa, M. Bouchard, R. Pichevar, Hossein Najaf-Zadeh

引用次数: 0

Design of a neuromuscular disorders diagnostic system using human movement analysis 基于人体运动分析的神经肌肉疾病诊断系统的设计

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) Pub Date : 2012-07-02 DOI: 10.1109/ISSPA.2012.6310660

C. O’Reilly, R. Plamondon

引用次数: 41

Blind source separation towards decentralized modal identification using compressive sampling 基于压缩采样的分散模态识别盲源分离

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) Pub Date : 2012-07-02 DOI: 10.1109/ISSPA.2012.6310463

A. Sadhu, Bo Hu, S. Narasimhan

引用次数: 16

Learning optimal warping window size of DTW for time series classification 学习时间序列分类DTW的最优翘曲窗大小

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) Pub Date : 2012-07-02 DOI: 10.1109/ISSPA.2012.6310488

Qian Chen, Guyu Hu, Fang-lin Gu, Peng Xiang

引用次数: 21

System-level noise of an ultra-wideband tracking system 超宽带跟踪系统的系统级噪声

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) Pub Date : 2012-07-02 DOI: 10.1109/ISSPA.2012.6310630

William C. Suski, Salil Banerjee, A. Hoover

引用次数: 10

The SIMCA algorithm for processing ground penetrating radar data and its use in landmine detection 探地雷达数据处理的SIMCA算法及其在地雷探测中的应用

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) Pub Date : 2012-07-02 DOI: 10.1109/ISSPA.2012.6310699

A. Sengodan, W. Cockshott

{"title":"The SIMCA algorithm for processing ground penetrating radar data and its use in landmine detection","authors":"A. Sengodan, W. Cockshott","doi":"10.1109/ISSPA.2012.6310699","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310699","url":null,"abstract":"The main challenge of ground penetrating radar (GPR) based land mine detection is to have an accurate image analysis method that is capable of reducing false alarms. However an accurate image relies on having sufficient spatial resolution in the received signal. But because the diameter of an AP mine can be as low as 2cm and many soils have very high attenuations at frequencies above 3GHz, the accurate detection of landmines is accomplished using advanced algorithms. Using image reconstruction and by carrying out the system level analysis of the issues involved with recognition of landmines allows the landmine detection problem to be solved. The SIMCA ('SIMulated Correlation Algorithm') is a novel and accurate landmine detection tool that carries out correlation between a simulated GPR trace and a clutter1 removed original GPR trace. This correlation is performed using the MATLAB® processing environment. The authors tried using convolution and correlation. But in this paper the correlated results are presented because they produced better results. Validation of the results from the algorithm was done by an expert GPR user and 4 other general users who predict the location of landmines. These predicted results are compared with the ground truth data.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116512175","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

Incorporating user specific normalization in multimodal biometric fusion system 在多模态生物特征融合系统中纳入用户特定归一化

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) Pub Date : 2012-07-02 DOI: 10.1109/ISSPA.2012.6310596

Messaoud Bengherabi, F. Harizi, A. Guessoum, M. Cheriet

{"title":"Incorporating user specific normalization in multimodal biometric fusion system","authors":"Messaoud Bengherabi, F. Harizi, A. Guessoum, M. Cheriet","doi":"10.1109/ISSPA.2012.6310596","DOIUrl":"https://doi.org/10.1109/ISSPA.2012.6310596","url":null,"abstract":"The aim of this paper is to investigate the user-specific two-level fusion strategy in the context of multimodal biometrics. In this strategy, a client-specific score normalization procedure is applied firstly to each of the system outputs to be fused. Then, the resulting normalized outputs are fed into a common classifier. The logistic regression, non-confidence weighted sum and the likelihood ratio based on Gaussian mixture model are used as back-end classifiers. Three client-specific score normalization procedures are considered in this paper, i.e. Z-norm, F-norm and the Model-Specific Log-Likelihood Ratio MSLLR-norm. Our first findings based on 15 fusion experiments on the XM2VTS score database show that when the previous two-level fusion strategy is applied, the resulting fusion classifier outperforms the baseline classifiers significantly and a relative reduction of more than 50% in the equal error rate can be achieved. The second finding is that when using this two-level user-specific fusion strategy, the design of the final classifier is simplified and performance generalization of baseline classifiers is not straightforward. A great attention must be given to the choice of the combination normalization-back-end classifier.","PeriodicalId":248763,"journal":{"name":"2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126497544","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

Feedback-free and hybrid distributed video coding using neural networks 基于神经网络的无反馈混合分布式视频编码

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) Pub Date : 2012-07-02 DOI: 10.1109/ISSPA.2012.6310608

Isaac Nickaein, M. Rahmati, S. S. Ghidary, A. Zohrabi

引用次数: 4

A genetic algorithm based clustering approach for improving off-line handwritten digit classification 基于遗传算法的聚类方法改进离线手写体数字分类

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) Pub Date : 2012-07-02 DOI: 10.1109/ISSPA.2012.6310471

S. Impedovo, Francesco Maurizio Mangini, G. Pirlo

引用次数: 4

Hyperspectral band selection based on graph clustering 基于图聚类的高光谱波段选择

2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) Pub Date : 2012-07-02 DOI: 10.1109/ISSPA.2012.6310665

R. Hedjam, M. Cheriet

引用次数: 22