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Although smart cards have already provided secure portable storage device, security is still a major concern to electronic data systems. There is a need to improve data security against accidental or unlawful destruction or alteration during transmission or while in storage. The Odd-Even substitution proved to have provided additional confusion technique to DES and was essential in providing adequate security. The limitation of DES to encrypt large data has also been addressed by this research without intensive processing. Unlike the 3DES or AES, the improved DES has lesser computational load.

In this paper, we present a user-adaptive augmented reality (AR) system that augments physical objects with personalized content according to user’s context as well as preferences. Since a user prefers different content according to the context, it reasons the user’s recent content preferences through artificial neural networks trained with the feedback history describing which content the user liked or disliked with respect to his/her context. The system recommends a set of content relevant to the user’s context and preferences. Then, it enables the user to select a preferred content among the recommended set and superimposes the selected content over physical objects.We implemented a prototype illustrating how our system could be used in daily life and evaluate its performance. From experimental results, we could confirm that our system effectively assisted users through personalized content augmentation in mobile computing environment.

Automated Video Surveillance Using Intelligent Middleware presented a Java based system that detects human activities in a security sensitive area and provides alarm for illegal activities identified. The system was developed using Netbeans IDE 6.8 [7] as the working environment, while Java as the programming language [8]. This study enhanced and strengthened existing security, therefore minimizing possibility of missed events which might be a threat to an area. The system composes three major processes: Motion Detection, Subject Identification and Behavior Classification. Motion Detection captures image of any movement detected. Subject Identification screens every captured image by classifying whether the motion is made by human and eliminating those which are caused by wind, animals and other non-human entity. Behavior Classification categorizes the image passed as to what action and outputs alarm if it is considered as illegal. In order to carry out these complex functionalities, a middleware was utilized to maintain continuous data flow from capturing to image processing and to reduce bulk of inputs that are being processed. Neural network [9, 10] was employed as the information processing paradigm for human or non-human and behavior classification. The result shows that the system processed video continuously as it classified behavior automatically.

High range resolution ultra wideband radars attract considerable attention as short range automotive radar for target detection and ranging. Radar signal reflected from a target often contains unwanted echoes called as clutter, so the detection of target is difficult with clutter echoes. Therefore, it is important to investigate the radar detector performance for the better detection of the reflected signals. This paper analyzes the detection performance of non-coherent logarithmic detector in log normal and weibull clutter environment. The detection probability of the detector is obtained with different mean and variance value in log normal clutter environment, and with different shape parameter and scale parameter values in weibull clutter environment at different system bandwidth of 1GHz, 500MHz and 100MHz.

This paper proposed a model based on hierarchical Clustering and Association Rule, which is intended for decision support system. The proposed system is intended to address the shortcomings of other data mining tools on the processing time and efficiency when generating association rules. This study will determine the data structures by implementing the cluster analysis which is integrated in the proposed architecture for data mining process and calculate for associations based on clustered data. The results were obtained using the proposed system as integrated approach and were rendered on the synthetic data. Although, our implementation uses heuristic approach, the experiment shows that the proposed system generated good and understandable association rules, which could be practically explained and use for the decision support purposes.

The study about SMS-based Automatic Billing System of Power Consumption aimed to change the conventional way the power utility provider gathers and handles billing data. The system is composed of two basic parts such as the remote site and the base station. The former calculates and sends power consumption while the latter retrieves meter readings, calculates billing charges and processes payment of the customers. Microsoft Visual Studio 2008 was used to develop the Main Server Software with Visual Basic 2008 as the Integrated Development Environment (IDE) or programming tool and Visual Basic as the programming language. The database was created in Microsoft SQL Server 2005 and ActiveXperts Messaging Server 4.1 was use as an SMS framework that allows the system to send, receive and process SMS. The use of this system will give greater benefit to the electric company and its customers because of the ease and less impediment in gathering meter readings in remote locations and an instant delivery of billing statements to the customer’s cellular phones through SMS technology.

In anomaly intrusion detection, how to model the normal behavior of activities performed by a user is an important issue. To extract the normal behavior as a profile, conventional data mining techniques are widely applied to a finite audit data set. However, these approaches can only model the static behavior of a user in the audit data set. This drawback can be overcome by viewing the continuous activities of a user as an audit data stream. This paper proposes a new clustering algorithm which continuously models a data stream. A set of features is used to represent the characteristics of an activity. For each feature, the clusters of feature values corresponding to activities observed so far in an audit data stream are identified by the proposed clustering algorithm for data streams. As a result, without maintaining any historical activity of a user physically, new activities of the user can be continuously reflected to the on-going result of clustering.

High performance and reliability are the main goals of parallel and distributed computing systems. To increase the performance and reliability of the systems, various checkpoint schemes have been proposed in the literature for decades. However, the lack of general analytical models has been an obstacle to compare the performance of systems employing different checkpoint schemes. This paper develops an analytical model to evaluate the relative response time of systems employing checkpoint schemes. The model has been applied to evaluate the relative response time of systems employing RFC (Roll-Forward Checkpoint), DMR-F (Double Modular Redundancy for Forward recovery), and DST (Duplex with Self-Test) schemes. The result shows the feasibility of the model developed in the paper.

A framework for semantic annotation of human motion sequences is proposed in this paper. Motion capture technology is widely used for manufacturing animation but it has a significant weakness due to the lack of an industry wide standard for archiving and retrieving motion capture data. It is difficult for animators to retrieve the desired motion sequences from motion capture files as there is no semantic annotation on already captured motion data. Our goal is to improve the reusability of motion capture data. To archive our goal first, we propose a standard format for integrating different motion capture file formats. Second, we define motion ontologies that are used to annotate and semantically organize human motion sequences. This ontology-based approach provides the means for discovering and exploiting the information and knowledge surrounding motion capture data.

In this paper, iris recognition system using wavelet packet and support vector machines is presented. It specifically uses the multiresolution decomposition of 2-D discrete wavelet packet transform for extracting the unique features from the acquired iris image. This method of feature extraction is well suited to describe the shape of the iris while allowing the algorithm to be translation and rotation invariant. The SVM approach for comparing the similarity between the similar and different irises can be assessed to have the feature’s discriminative power. We have showed that the proposed method for human iris recognition gave a way of representing iris patterns in an efficient manner and thus had advantages of saving both time and space. Thanks to the efficiency of the proposed method, it can be easily applied to the real problems.

An efficient method of classifying form is proposed in this paper. Our method identifies a small number of matching areas by their distinctive images with respect to their layout structure and then form classification is performed by matching only these local regions. The process is summarized as follows. First, the form is partitioned into rectangular regions along the locations of lines of the forms. The disparity in each partitioned region of the comparing form images is measured. The penalty for each partitioned area is computed by using the preprinted text, filled-in data, and the size of a partitioned area. The disparity and penalty are considered to compute the score to select final matching areas. By using our approach, the redundant matching areas are not processed and a feature vector of good quality can be extracted.

We propose a model for the recognition of unconstrained digits that may be touched with neighbor ones or damaged by noises such as lines. The recognition of such digits seems to be rather paradoxical because it requires the segmentation of them into understandable units, but proper segmentation needs a priori knowledge of the units and this implies recognition capability. To break up the loop of their interdependencies, we combine two schemes, hypothesis testing and data reconstruction, motivated by the human information system. Hypothesis is set up on the basis of the information obtained from the results of the basic segmentation, and reconstruction of the information is carried out with the knowledge of a guessed digit and then testing for its validity is performed. Since our model tries to construct a guessed digit from input image it can be successful in a variety of situations such as that a digit contains strokes that do not belong to to it, that neighbor digits are touched with each other, and that there are some occluding things like lines. The recognition results of this model for 100 handwritten numeral strings belonging to NIST database and for some artificial digits damaged by line demonstrate the potential its capacity.

We describe in this paper efficient techniques for iris recognition system with high performance from the practical point of view. These techniques range every step for an iris recognition system from the image acquisition step to the final step, the pattern matching, and contain as follows: a method of evaluating the quality of an image in the image acquisition step and excluding it from the subsequent processing if it is not appropriate, a bisection-based Hough transform method on the edge components for detecting the center of the pupil and localizing the iris area from an eye image, an elastic body model for transforming the localized iris area into a simple coordination system, and a compact and efficient feature extraction method which is based on 2D multiresolution wavelet transform. By exploiting these techniques, we can improve the system performance in terms of computationally efficient, and more accurate and robust against noises.

In this paper, we are proposing an efficient method of classifying form that is applicable in real life. Our method will identify a small number of local regions by their distinctive images with respect to their layout structure and then by using the DP (Dynamic Programming) matching to match only these local regions. The disparity score in each local region is defined and measured to select the matching regions. Genetic Algorithm will also be applied to select the best regions of matching from the viewpoint of a performance. Our approach of searching and matching only a small number of structurally distinctive local regions would reduce the processing time and yield a high rate of classification.