{"title":"3-D Object Recognition System using Ultrasound","authors":"C. Koley, B.L. Midya","doi":"10.1109/ICISIP.2005.1619419","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619419","url":null,"abstract":"The patterns of ultrasonic reflected echoes from objects contain information about the geometric shape, size, orientation and the surface material properties of the reflector. Accurate estimation of the ultrasonic echo signal pattern is essential for recognition of the target object. We propose a method to classify different objects having specific geometric shape such as cylindrical, rectangular, sphere and conical of different size and material. Here continuous wavelet transform (CWT) has been used for feature extraction. In the present work an attempt has been made to classify the pattern inherent in the features extracted through CWT of different echo signals with the help of two different machine learning algorithms like self organizing feature map (SOFM) and support vector machine (SVM). CWT allows a time domain signal to be transformed into time frequency domain such that frequency characteristics and the location of particular features in a time series may be highlighted simultaneously. Thus it allows accurate extraction of features from the non-stationary signals like ultrasonic echo envelop. SOFM transforms the input of arbitrary dimension into a one or two dimensional discrete map subject to a topological (neighbourhood preserving) constraint. In the present work the SOFM algorithm with Kohonen's learning and SVM in regression mode has been used to classify the patterns inherent in the features extracted through CWT of different echo envelop","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"86 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":"123122485","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":"Improved PCA-Based Personal Identification Method Using Invariance Moment","authors":"C. Nadee, P. Kumhom, K. Chamnongthai","doi":"10.1109/ICISIP.2005.1619443","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619443","url":null,"abstract":"Since PCA-based teeth-image personal identification method (K. Prajuabklang, et al., 2004) is not robust against reflection and orientation, registered persons in database are rejected around 7%. This paper proposes a method to improve the PCA-based teeth-image personal identification method. In this method, the teeth image failed from the matching in the PCA-based system is reconsidered by feeding back the image to eliminate the reflection and the rotation problems. The enhanced teeth image is fed back to PCA process in order to rescue misclassified teeth-image. In the experiments, 25 teeth images are tested with 20-teeth database. The results revealed that of the 7% errors caused by the two problems, 5% are correctly identified because of the proposed method","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":"114679743","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":"Basins of Attraction of Cellular Automata Based Associative Memory and Its Rule Space","authors":"P. Maji, P. Pal Chaudhuri","doi":"10.1109/ICISIP.2005.1619422","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619422","url":null,"abstract":"In this paper, we analytically establish two important observations reported in (P. Maji et al., 2003) and (N. Ganguly et al., 2002) - the nature of the basins of attraction of a special class of non-linear cellular automata (CA), referred to as generalized multiple attractor CA (GMACA) (P. Maji et al., 2003); and the characteristics of the evolved GMACA rule space (N. Ganguly et al., 2002). Characterization of the basins of attraction of the GMACA ensures the sparse network of CA as a powerful pattern recognizer for memorizing unbiased patterns. An in-depth analysis of GMACA rule space has established that more heterogeneous CA rules are capable of executing complex computation like pattern recognition. That is, the rule space of the pattern recognizing CA lies at the edge of chaos","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"13 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":"116777684","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":"Spectral Estimation Using Constrained Autoregressive (CAR) Model","authors":"N. Jain, S. Dandapat","doi":"10.1109/ICISIP.2005.1619421","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619421","url":null,"abstract":"In this work, a spectral estimation technique using a novel autoregressive model, constrained autoregressive (CAR) model, is proposed. CAR model is based on constraining one of the model parameters of an autoregressive model. This helps obtain a modified or desired AR spectrum for the signal. Constraining different AR parameters or changing the values of a particular parameter results in dissimilar AR spectrum for the signal. The value of this constrained parameter can be used for externally controlling the gain or improving the spectral resolution between two peaks in the spectrum. By constraining the Mth parameter, aM, in a M-order model the resolution between two closely spaced peaks present in the signal spectrum can be improved. Similarly, by constraining the a0 parameter and assigning it different values the spectral gain can be controlled","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"55 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":"123749471","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}
B. Sundaram, M. Palaniswami, S. Reddy, M. Sinickas
{"title":"Radar Localization with multiple Unmanned Aerial Vehicles using Support Vector Regression","authors":"B. Sundaram, M. Palaniswami, S. Reddy, M. Sinickas","doi":"10.1109/ICISIP.2005.1619441","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619441","url":null,"abstract":"This paper presents a first attempt to solve the geolocation problem using support vector regression (SVR). This paper proposes a method to pinpoint the location of stationary, hostile radar using the time difference of arrival (TDoA) of the same characteristic pulse emitted by the radar at 3 different unmanned aerial vehicles (UAVs) flying in a fixed triangular formation. The performance of the proposed SVR method is compared with a variation of the Taylor series method (TSM) used for solving the same problem and currently deployed by the DSTO, Australia on the Aerosonde Mark III UAVs. The robustness to error of the SVR method is explored and compared with the TSM. Extended applications of the SVR approach to more general localization scenarios in wireless sensor networks are proposed for further work","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"19 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":"122178971","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":"Naive-Bayes Classification using Fuzzy Approach","authors":"P. Radha Krishna, S. K. De","doi":"10.1109/ICISIP.2005.1619413","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619413","url":null,"abstract":"Data mining is the quest for knowledge in databases to uncover previously unimagined relationships in the data. This paper generalizes Naive-Bayes classification technique using fuzzy set theory, when the available numerical probabilistic information is incomplete or partially correct. We consider a training dataset, where attribute values have certain similarities in nature. Though nothing can replace precise and complete probabilistic information, a useful classification system for data mining can be built even with imperfect data by introducing domain-dependent constraints. This observation is analyzed here based on fuzzy proximity relations for the domain of each attribute. The study shows that this approach is highly suitable for real-world applications, especially when databases contain uncertain information","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"24 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":"123661415","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":"ECG Signal Compression using Discrete Sinc Interpolation","authors":"M. Sabarimalai Manikandan, S. Dandapat","doi":"10.1109/ICISIP.2005.1619406","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619406","url":null,"abstract":"This paper presents a novel ECG data compression algorithm based on discrete sinc interpolation (DSI) technique. The compression and decompression of ECG data is achieved using discrete sinc interpolation (DSI), which is realized by an efficient discrete Fourier transform (DFT). The proposed algorithm is evaluated using MIT-BIH arrhythmia database (sampled at 360 Hz with 11 bits resolution). The performance of the proposed DSI based algorithm is compared with the performance of the widely used ECG data compression algorithms such as AZTEC, FAN, Hilton and Djohan algorithms. It is observed that higher compression ratio (CR) is achieved with a relatively lower percentage RMS difference (PRD) by DSI algorithm. The diagnostic distortion is measured in terms of average absolute error (AAE), which is lower in case of the DSI algorithm compared to the AZTEC and FAN algorithm","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"125 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":"115871686","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":"Knowledge Discovery in Distributed Biological Datasets Using Fuzzy Cellular Automata","authors":"P. Maji, Chandra Das","doi":"10.1109/ICISIP.2005.1619430","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619430","url":null,"abstract":"Recent advancement and wide use of highthroughput technologies for biological research are producing enormous size of biological datasets distributed worldwide. Data mining techniques and machine learning methods provide useful tools for knowledge discovery in this field. The goal of this paper is to present the design of a pattern classifier to mine distributed biological dataset. The proposed classifier is built around a special class of computing model termed as Fuzzy Cellular Automata (FCA). A concrete example of the effectiveness of this approach is provided by demonstrating its success in gene identification problem. Extensive experimental results confirm the scalability of the FCA to handle distributed biological datasets. Application of the proposed model to solve gene identification problem establishes the FCA as the classifier ideally suited for biological data mining in a distributed environment.","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"25 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132782785","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":"Identifying Individuality Using Mental Task Based Brain Computer Interface","authors":"Ramaswamy Palaniappan","doi":"10.1109/ICISIP.2005.1619442","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619442","url":null,"abstract":"In recent years, numerous Brain Computer Interface (BCI) technologies have been developed to assist the disabled. In this paper, mental task based BCI is proposed for a different purpose: to identify the individuality of a person. The idea is based on the classification of electroencephalogram (EEG) signals recorded when a user thinks of either one or two mental tasks. As different individuals have different thought processes, this idea would be appropriate for individual identification. To increase the inter-subject differences, EEG data from six electrodes are used instead of one. Sixth order autoregressive features are computed from EEG signals and classified by Linear Discriminant classifier using a modified 10 fold cross validation procedure, which gave an average error of 0.95% when tested on 400 EEG patterns from four subjects. Though the method would have to undergo further development to obtain repeatable good accuracy; this initial study has shown the huge potential of the method over existing biometric identification systems as it is impossible to be faked.","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":"122680208","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":"Analysis, Simulation and Testing of a Micromirror with Rotational Serpentine Springs","authors":"Jianliang You, M. Packirisamy, I. Stiharu","doi":"10.1109/ICISIP.2005.1619439","DOIUrl":"https://doi.org/10.1109/ICISIP.2005.1619439","url":null,"abstract":"In this paper, a 2-DOF model for electrostatically actuated torsional micromirrors with relatively soft stiff rotational serpentine springs is presented. The analytical stiffness formulae for this rotational serpentine spring are also presented. FEA simulations for static performance have been verified by the experimental values. Such validation was implemented through fabrication of the micromirror on a SOI wafer by MicraGEM micromachining process, PSD sensor based test set-up for static properties, and the corresponding tests. Due to the soft stiffness of rotational serpentine springs designed, the fabricated torsional micromirror could be rotated to some angle under low applied bias. The simulated pull-in voltage 17.2 V is close to the actual value but much smaller than those of previously reported large size torsional micromirrors. The deviation of the simulated static displacements from experimental results could be mainly due to the tolerance of fabrication, the slender beam and linear structural assumptions. However, with relatively lower applied voltages of actuation, these torsional micromirrors that use the rotational serpentine springs can be integrated on the same microchip with CMOS circuits, showing their promising potential for industrial applications","PeriodicalId":261916,"journal":{"name":"2005 3rd International Conference on Intelligent Sensing and Information Processing","volume":"137 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":"122770356","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}