2006 Fourth International Conference on Intelligent Sensing and Information Processing最新文献

{"title":"An Energy-Aware Adaptive Clustering Protocol for Sensor Networks","authors":"A. Garg, M. Hanmandlu","doi":"10.1109/ICISIP.2006.4286054","DOIUrl":"https://doi.org/10.1109/ICISIP.2006.4286054","url":null,"abstract":"This paper presents a new communication protocol for sensor networks which is based on and is an enhancement over the LEACH protocol proposed by Heinzelman et al. The proposed protocol is based on adaptive clustering of the sensor nodes, the nodes with the maximum amount of residual energies being selected as the cluster-heads. This is in contrast with the original LEACH protocol which employs a simple randomized rotation of the cluster-heads. We define a new eligibility criterion for the selection of cluster-heads and introduce data-acknowledgements, advertisement updates and cluster-head reselection to make the protocol robust against node and channel failures. We will show how the utilization of these measures improves the system lifetime and reduces the data losses. With the use of the proposed protocol, the system life is increased by three times as compared to that obtained by using the LEACH protocol. Also, the percentage data loss is reduced by around twenty times.","PeriodicalId":187104,"journal":{"name":"2006 Fourth International Conference on Intelligent Sensing and Information Processing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129119753","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}

{"title":"A Wavelet-Based Approach for Screening Falls Risk in the Elderly using Support Vector Machines","authors":"A. Khandoker, D. Lai, R. Begg, M. Palaniswami","doi":"10.1109/ICISIP.2006.4286092","DOIUrl":"https://doi.org/10.1109/ICISIP.2006.4286092","url":null,"abstract":"Trip related falls are a prevalent and costly threat to the elderly. Early identification of at-risk gait helps prevent falls and injuries. The main aim of this study is to explore the effectiveness of a wavelet based multiscale analysis of a gait variable [minimum foot clearance (MFC)] in extracting features for developing a model using Support Vector Machines (SVM) for automated detection of balance impairment and estimation of the falls risk in the elderly. MFC during continuous walking on a treadmill was recorded on 11 healthy elderly and 10 elderly with balance problems (falls risk) and with a history of tripping falls. The multiscale exponents (beta) between successive wavelet (Wv) coefficient levels after Wnu decomposition of MFC series (512 points) into eight levels from level 2 (Wnu2) to level 256 (Wnu256), were calculated for healthy as well as falls-risk elderly adults. Using receiver operating characteristic (ROC) analysis, the most powerful predictor variable was found to be betaWnu16-Wnu8 (ROCarea = 1.0), followed by betaWnu16-Wnu8 (ROCarea = 0.92). These multiscale exponents were used as inputs to the SVM model to develop relationships between the intrinsic characteristics of gait control and the healthy/falls-risk category. The leave one out technique was utilized for optimal tuning and testing of the SVM model. The maximum accuracy was found to be 100% using a polynomial kernel (d = 4) with C = 10 and the maximum ROC = 1.0, when the SVM model was used to diagnose gait area patterns of healthy and falls risk elderly subjects. For relative risk estimation of all subjects, posterior probabilities of SVM outputs were calculated. In conclusion, these results suggest considerable potential for SVM gait recognition model based on multiscale wavelet features in the detection of gait changes in older adults due to balance impairments and falling behavior. These preliminary results are also encouraging and could be useful not only in the falls risk diagnostic applications but also for evaluating the need for referral for falls prevention intervention (e.g., exercise program to improve balance).","PeriodicalId":187104,"journal":{"name":"2006 Fourth International Conference on Intelligent Sensing and Information Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127359183","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}

{"title":"Mosaic Generation in H.264 Compressed Domain","authors":"Zhi Liu, Zhaoyang Zhang, Liquan Shen","doi":"10.1109/ICISIP.2006.4286052","DOIUrl":"https://doi.org/10.1109/ICISIP.2006.4286052","url":null,"abstract":"This paper presents an efficient approach to mosaic generation in H.264 compressed domain. The proposed mosaic generation approach fully exploits several new coding characteristics of the H.264 standard, i.e., variable block size and the quarter-pel accuracy of motion vector (MV). A uniformly sampled and normalized MV field is first derived from the H.264 compressed video. Then an affine model is used to estimate the global motion from the normalized MV field, and the model parameters are iteratively refined based on outlier rejection scheme. Finally, the most recent and reliable background pixels from multiple frames are warped to the reference frame and then used to integrate the final mosaic. Experimental results demonstrate that the proposed approach is efficient for mosaic generation in H.264 compressed domain.","PeriodicalId":187104,"journal":{"name":"2006 Fourth International Conference on Intelligent Sensing and Information Processing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114133883","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}

{"title":"Data Sharing Strategy for Guaranteeing Quality-of-Service in VoD Application","authors":"D. Sujatha, K. Girish, K. Venugopal, L. Patnaik","doi":"10.1109/ICISIP.2006.4286062","DOIUrl":"https://doi.org/10.1109/ICISIP.2006.4286062","url":null,"abstract":"The phenomenal growth in the distributed multimedia applications has accelerated the popularity of Video-on-Demand (VoD) system. The vital task of multimedia applications is to satisfy diverse client's request for distinct video with confined resources by using assorted Quality-of-Service (QoS) procedures. In this paper a fusion of data sharing techniques like batching and recursive patching is applied in the local server for ensuring Quality-of-Service to the clients and enabling higher throughput. The network resources are apportioned appropriately using batching and the time difference between the requests is minified by recursive patching. The suggested algorithm renders the entire video to the clients using true VoD, near VoD using multicast or broadcast scheme depending on popularity of the video. The experimental results indicate that our approach accomplishes 2% reduction in blocking ratio and throughput is 10% -15% greater than the Poon 's strategy [15], which depicts that not only the resources are efficiently utilized but also a suitable Quality-of-Service is provided to each client.","PeriodicalId":187104,"journal":{"name":"2006 Fourth International Conference on Intelligent Sensing and Information Processing","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134164597","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}

{"title":"Routing Protocols for Landslide Prediction using Wireless Sensor Networks","authors":"K. Tejaswi, P. Mehta, R. Bansal, C. Parekh, S. Merchant, U. Desai","doi":"10.1109/ICISIP.2006.4286057","DOIUrl":"https://doi.org/10.1109/ICISIP.2006.4286057","url":null,"abstract":"Landslide prediction and early warning system is an important application where sensor networks can be deployed to minimize loss of life and property. Due to the dense deployment of sensors in landslide prone areas, clustering is an efficient approach to reduce redundant communication from co-located sensors. In this paper we propose two distributed clustering and multi-hop routing protocols, CAMP and HBVR, for this problem. While CAMP is a new clustering and routing protocol, HBVR is an enhancement of BVR with HEED. We further enhance CAMP and HBVR with TEEN, a threshold based event driven protocol. TEEN is most suitable protocol for this application since different rock types can have different thresholds for stress values. Simulation results show that CAMP-TEEN gives the best performance with respect to network life time and energy consumption.","PeriodicalId":187104,"journal":{"name":"2006 Fourth International Conference on Intelligent Sensing and Information Processing","volume":"20 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132025653","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}

{"title":"Language Independent Automated Segmentation of Speech using Bach scale filter-banks","authors":"G. Ananthakrishnan, H. G. Ranjani, A. Ramakrishnan","doi":"10.1109/ICISIP.2006.4286074","DOIUrl":"https://doi.org/10.1109/ICISIP.2006.4286074","url":null,"abstract":"This correspondence describes a method for automated segmentation of speech. The method proposed in this paper uses a specially designed filter-bank called Bach filter-bank which makes use of 'music' related perception criteria. The speech signal is treated as continuously time varying signal as against a short time stationary model. A comparative study has been made of the performances using Mel, Bark and Bach scale filter banks. The preliminary results show up to 80 % matches within 20 ms of the manually segmented data, without any information of the content of the text and without any language dependence. The Bach filters are seen to marginally outperform the other filters.","PeriodicalId":187104,"journal":{"name":"2006 Fourth International Conference on Intelligent Sensing and Information Processing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131359632","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}

{"title":"Gesture Based Robot Control","authors":"V. S. Rao, C. Mahanta","doi":"10.1109/ICISIP.2006.4286082","DOIUrl":"https://doi.org/10.1109/ICISIP.2006.4286082","url":null,"abstract":"Vision based techniques provide a natural way for controlling robots. In this paper, we present a visual gesture recognition system for controlling robots by using fuzzy-C - means clustering algorithm. The proposed method is applied for recognizing both static and dynamic hand gestures. In dynamic hand gesture recognition, instead of processing all video frames, key frames are extracted by using Hausdorff' distance method. After key frame extraction, a sequence of static gesture recognition operations is done for recognizing these key frames. The proposed technique requires training prior to its operation. Once trained, the system is ready for recognizing new gestures. A gesture database, consisting of 10 static gesture classes and 500 gesture samples per class and 3 different dynamic gestures, is created. The proposed method is successfully tested for recognizing 5000 new static gestures and 9 dynamic gestures.","PeriodicalId":187104,"journal":{"name":"2006 Fourth International Conference on Intelligent Sensing and Information Processing","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132260708","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}

{"title":"Statistical Classification of Mammograms Using Random Forest Classifier","authors":"L. Vibha, G. Harshavardhan, K. Pranaw, P. D. Shenoy, K. Venugopal, L. Patnaik","doi":"10.1109/ICISIP.2006.4286091","DOIUrl":"https://doi.org/10.1109/ICISIP.2006.4286091","url":null,"abstract":"A woman in general has 12% chance of developing breast cancer and a 3.5% chance of dying from this disease, hence detection of cancer has received considerable attention in the recent years. Mammogram is an X-ray of the breast used to detect and diagnose breast cancer and other abnormalities. The aim of a screening mammogram is to detect a tumor that cannot be physically detected. This paper proposes a Decision Forest Classifier (DFC) for classifying mammograms. Results of screening the mammograms are organised by classification and finally grouped into three categories i.e., Normal, Benign and malign. Experimental results obtained indicate that the proposed method performs relatively well with the classification accuracy reaching nearly 90.45% in comparison with the already existing algorithms.","PeriodicalId":187104,"journal":{"name":"2006 Fourth International Conference on Intelligent Sensing and Information Processing","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134351579","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}

{"title":"Open Sensor Web Architecture: Core Services","authors":"Xingchen Chu, T. Kobialka, B. Durnota, R. Buyya","doi":"10.1109/ICISIP.2006.4286069","DOIUrl":"https://doi.org/10.1109/ICISIP.2006.4286069","url":null,"abstract":"As sensor network deployments begin to grow there emerges an increasing need to overcome the obstacles of connecting and sharing heterogeneous sensor resources. Common data operations and transformations exist in deployment scenarios and can be encapsulated into a layer of software services that hide the complexity of the underlying infrastructure from the application developer. NICTA Open Sensor Web Architecture (NOSA) is built upon the Sensor Web Enablement (SWE) standard defined by the Open Geospatial Consortium (OGC), which is composed of a set of specifications, including SensorML, Observation & Measurement, Sensor Collection Service, Sensor Planning Service and Web Notification Service. NOSA presents a reusable, scalable, extensible, and interoperable service oriented Sensor Web architecture that (i) conforms to the SWE standard; (ii) integrates Sensor Web with Grid Computing and (in) provides middleware support for Sensor Webs.","PeriodicalId":187104,"journal":{"name":"2006 Fourth International Conference on Intelligent Sensing and Information Processing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134042522","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}

{"title":"A Rough Set Classifilcation Based Approach to Detect Hotspots in NOAA/AVHRR Images","authors":"R. S. Gautam, D. Singh, A. Mittal","doi":"10.1109/ICISIP.2006.4286076","DOIUrl":"https://doi.org/10.1109/ICISIP.2006.4286076","url":null,"abstract":"India accounts for the greatest concentration of coal fires in world. Nearly half of the subsurface mine fires (hotspots) in Indian coalfields exist in Jharia (Jharkhand) region. Careful attention is required in this direction for mapping, monitoring and detecting these hotspots. Present paper utilizes the potential of operational satellite images to detect hotspots in Jharia region. Proposed algorithm consists of two steps: (1) marking potential hotspot pixels in NOAA/AVHRR image using different AVHRR channel statistics (i.e. average & variance), and (2) generating rules using the potential hotspots pixel information obtained in first step, in order to classify seen or unseen AVHRR images in hotspots and non-hotspots classes. Rough set theory is emerging as a new powerful tool for learning classification rules. In this paper, we propose a rough set based method to classify NOAA/A VHRR images of Jharia region in order to determine the spatial allocation of hotspots. Instead of applying all induced rules for classifying AVHRR images, only those generated rules take part in the classification process which meet the user specified criteria, thus simplifying the whole classification procedure. Proposed algorithm appears to detect hotspots successfully with throughout greater than 90% classification accuracy.","PeriodicalId":187104,"journal":{"name":"2006 Fourth International Conference on Intelligent Sensing and Information Processing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132098428","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}