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2005 3rd International Conference on Intelligent Sensing and Information Processing最新文献

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Modified Spectral Subtraction Method for Enhancement of Noisy Speech 改进的频谱减法增强带噪语音
2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619427
P. Krishnamoorthy, S. Prasanna
{"title":"Modified Spectral Subtraction Method for Enhancement of Noisy Speech","authors":"P. Krishnamoorthy, S. Prasanna","doi":"10.1109/ICISIP.2005.1619427","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619427","url":null,"abstract":"This paper proposes a modified spectral subtraction method for enhancing speech corrupted by additive background noise. The method is based on identifying and enhancing speech regions in the noisy speech signal. The speech regions are identified by computing the kurtosis and energy values from the noisy speech signals. For speech corrupted by additive background noise, kurtosis and energy values will be relatively high in speech regions compared to nonspeech regions. This property is exploited in deriving a weight function. The noisy signal is multiplied by the weight function to attenuate the noise in the nonspeech regions. The weighted speech signal is processed only in the speech regions by the spectral subtraction method to enhance speech components in the detected speech regions. The proposed method is effective in attenuating the noise components compared to the spectral subtraction method especially in nonspeech regions.","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128877809","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: 11
Distributed Estimation from Relative Measurements in Sensor Networks 传感器网络中相对测量的分布式估计
2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619440
P. Barooah, J. Hespanha
{"title":"Distributed Estimation from Relative Measurements in Sensor Networks","authors":"P. Barooah, J. Hespanha","doi":"10.1109/ICISIP.2005.1619440","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619440","url":null,"abstract":"We consider the problem of estimating vector-valued variables from noisy \"relative\" measurements. The measurement model can be expressed in terms of a graph, whose nodes correspond to the variables being estimated and the edges to noisy measurements of the difference between the two variables. We take the value of one particular variable as a reference and consider the optimal estimator for the differences between the remaining variables and the reference. This type of measurement model appears in several sensor network problems, such as sensor localization and time synchronization. Two algorithms are proposed to compute the optimal estimate in a distributed, iterative manner. The first algorithm implements the Jacobi method to iteratively compute the optimal estimate, assuming all communication is perfect. The second algorithm is robust to temporary communication failures, and converges to the optimal estimate when certain mild conditions on the failure rate are satisfied. It also employs an initialization scheme to improve accuracy in spite of the slow convergence of the Jacobi method","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133946909","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: 45
A Retinomorphic Sensor Network for Image Preprocessing 一种用于图像预处理的视胚传感器网络
2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619425
S. Sarkar, Kuntal Ghosh, K. Bhaumik
{"title":"A Retinomorphic Sensor Network for Image Preprocessing","authors":"S. Sarkar, Kuntal Ghosh, K. Bhaumik","doi":"10.1109/ICISIP.2005.1619425","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619425","url":null,"abstract":"The mammalian retina is actually an intelligent sensor network. It is capable of extracting very rich edge information in the cortex, from the image falling on it, before transferring the computed values to the brain node, located further interior, for carrying out more sophisticated information processing tasks, and all in real time. In this paper a simple two dimensional sensor network for implementing the mammalian retina on a three-dimensional vision chip has been suggested. Although the inner layers of retina are known to elicit non-linear responses, we have shown here that even such a simple linear analogue can yield some very effective tools for the purpose of retina-like image preprocessing","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124508399","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
MANET Internet Access using Reactive Mobile Agent Protocol 使用响应式移动代理协议的MANET互联网接入
2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619414
A. Velmurugan, R. Rajaram
{"title":"MANET Internet Access using Reactive Mobile Agent Protocol","authors":"A. Velmurugan, R. Rajaram","doi":"10.1109/ICISIP.2005.1619414","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619414","url":null,"abstract":"Internet-based mobile ad hoc networking (MANET) is an emerging technology that supports self-organizing mobile networking infrastructures, and is one which is expected to be of great use in commercial and military applications. A new routing scheme for mobile ad hoc network is proposed in this paper. In the proposed mechanism, ad hoc on-demand distance vector (AODV) is modified to achieve a link between a MANET and the Internet with the aid of ant-like mobile agents (ALMA). Through extensive simulations it is proven in this paper the proposed AODV-ALMA hybrid routing technique reduces end-to-end delay to make a quicker route discovery and provides high connectivity as compared to AODV","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124554567","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
A Multimodal Audio Visible and Infrared Surveillance System (MAVISS) 一种多模态音频、可见和红外监视系统(MAVISS)
2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619428
A. Mittal, P. Kumar
{"title":"A Multimodal Audio Visible and Infrared Surveillance System (MAVISS)","authors":"A. Mittal, P. Kumar","doi":"10.1109/ICISIP.2005.1619428","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619428","url":null,"abstract":"This paper presents a low cost surveillance system employing multimodal information (visible, infrared and audio signals) for monitoring small area and detecting alarming events. To ensure efficient and robust operation, the system captures different aspects of the environment using audio and video information. Infrared imagery is usedfor night and other low level lighting situations. The visual processing module of the system uses a motion based approach for detecting objects, and employs Kalman filter model for tracking its motion. Environmental sound is recognized by processing audio signals to extract features in the form of Mel-Frequency Cepstral coefficients (MFCC), which are then used for classification by Dynamic Time Warping (DTW) technique. Semantic rules are proposed to identify alarming events by using information from audio and video module. Experimental results are shown on some typical sequences and publicly available dataset.","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128047593","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: 9
Efficient Mining of Contrast Patterns and Their Applications to Classification 对比模式高效挖掘及其在分类中的应用
2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619410
K. Ramamohanarao, J. Bailey, H. Fan
{"title":"Efficient Mining of Contrast Patterns and Their Applications to Classification","authors":"K. Ramamohanarao, J. Bailey, H. Fan","doi":"10.1109/ICISIP.2005.1619410","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619410","url":null,"abstract":"Data mining is one of the most important areas in the 21st century with many wide ranging applications. These include medicine, finance, commerce and engineering. Pattern mining is amongst the most important and challenging techniques employed in data mining. Patterns are collections of items which satisfy certain properties. Emerging patterns are those whose frequencies change significantly from one dataset to another. They represent strong contrast knowledge and have been shown to be very successful for constructing accurate and robust classifiers. In this paper, we examine various kinds of contrast patterns. We also investigate efficient pattern mining techniques and discuss how to exploit patterns to construct effective classifiers","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128187509","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: 47
A new design of low pass filter by Gaussian derivative family 基于高斯导数族的低通滤波器的新设计
2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619432
S. Karmakar, Kuntal Ghosh, R. Saha, S. Sarkar, S. Sen
{"title":"A new design of low pass filter by Gaussian derivative family","authors":"S. Karmakar, Kuntal Ghosh, R. Saha, S. Sarkar, S. Sen","doi":"10.1109/ICISIP.2005.1619432","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619432","url":null,"abstract":"Exploiting the models of human visual system based on Gaussian derivatives and their non-localization property in spectral domain, a new design of low pass filter is proposed in this work. The filter is designed by a weighted combination of the Gaussian derivative family which makes the passband of this filter almost equiripple. This design principle can be extended for the bandpass filter also because of the bandpass nature of the Gaussian derivatives in spectral domain. This work is applied in the design of adaptive digital filters for sigma-delta based ultrasound beamformer","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"82 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128156438","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
Generic Feature Extraction for Classification using Fuzzy C - Means Clustering 基于模糊C均值聚类的分类通用特征提取
2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619409
K. Srinivasa, A. Singh, A. Thomas, K. Venugopal, L. Patnaik
{"title":"Generic Feature Extraction for Classification using Fuzzy C - Means Clustering","authors":"K. Srinivasa, A. Singh, A. Thomas, K. Venugopal, L. Patnaik","doi":"10.1109/ICISIP.2005.1619409","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619409","url":null,"abstract":"Knowledge discovery and data mining (KDD) process includes preprocessing, transformation, data mining and knowledge extraction. The two important tasks of data mining are clustering and classification. In this paper, we propose a generic feature extraction for classification using fuzzy C-means (FCM) clustering. The raw data is preprocessed, normalized and then data points are clustered using the fuzzy C-means technique. Feature vectors for all the classes are generated by extracting the most relevant features from the corresponding clusters and used for further classification. Artificial neural network and support vector machines are used to perform the classification task. Experiments are conducted on four datasets and the accuracy obtained by performing specific feature extraction for a particular data set is compared with the generic feature extraction scheme. The algorithm performs relatively well with respect to classification results when compared with the specific feature extraction technique","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129084157","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: 21
A Low Complexity Technique for Data Fusion in Digital Images and Lossless Retrieval 一种低复杂度的数字图像数据融合与无损检索技术
2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619420
P. Meher, M. R. Meher
{"title":"A Low Complexity Technique for Data Fusion in Digital Images and Lossless Retrieval","authors":"P. Meher, M. R. Meher","doi":"10.1109/ICISIP.2005.1619420","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619420","url":null,"abstract":"A simple technique based on integer discrete Hartley transform (DHT) is presented for embedding data of various different forms in digital images. Not only it involves significantly less computational complexity, but also accommodates considerably more payload and offers better visual quality over the existing schemes. Due to the lossless reversal behaviour of the integer DHT, the proposed technique can also be used for lossless retrieval of the embedded data as well as the host image from the embedded image","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133773971","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
A Novel Approach for Electrical Load Forecasting Using Distributed Sensor Networks 基于分布式传感器网络的电力负荷预测新方法
2005 3rd International Conference on Intelligent Sensing and Information Processing Pub Date : 2005-12-14 DOI: 10.1109/ICISIP.2005.1619434
B.P. Challa, S. Challa, R. Chakravorty, S. Deshpande, D. Sharma
{"title":"A Novel Approach for Electrical Load Forecasting Using Distributed Sensor Networks","authors":"B.P. Challa, S. Challa, R. Chakravorty, S. Deshpande, D. Sharma","doi":"10.1109/ICISIP.2005.1619434","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619434","url":null,"abstract":"Electrical market often demands accurate forecasting of electrical load for planning and operation of the power infrastructure. Current models can forecast load from half hour up to 24 hours and are based on aggregate temperature for the entire day. Although these models work very well, they do not consider the intermediate real time information between time intervals to forecast load which introduce many uncertainties pertaining to factors such as climatic conditions, geographic locations etc. Furthermore, such intermediate real time information is costly and difficult to obtain. With the aid of distributed sensor networks, real time information can easily be obtained which can lead to precise planning and operation of power systems. Such information can easily improve electrical load forecasting and reduce uncertainty which can have a direct impact on the customer. We propose new and improved models for electricity load forecasting by incorporating real-time weather (temperature) information arising from the low-cost distributed sensor networks","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124842123","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
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