Advanced Materials Research Vols. 403-408

Abstract: This paper proposes a novel approach of content based image retrieval based on Log Gabor Wavelet Transform (LGWT). It is observed that LGWT better represents an image compared to Gabor Wavelet Transform (GWT). Experimental results illustrate the comparative analysis of proposed retrieval system and the retrieval system based on GWT feature descriptor. It is verified that LGWT based retrieval system improves the average precision and average recall (55.46% and 32.03% respectively) from GWT based retrieval system (50.61% and 31.63% respectively). All the experiments are performed on Corel 1000 natural image database.

Abstract: In this paper, we obtain the general solution and investigate the Hyers-Ulam-Rassias stability of the Generalized Quadratic functional equation in non-Archimedean fuzzy normed spaces.

Abstract: This paper presents a fast and reliable algorithm for fingerprint verification. Our proposed fingerprint verification algorithm is based on image-based fingerprint matching. The improved orientation feature vector of two fingerprints has been compared to compute the similarities at a given threshold. Fingerprint image has been aligned by rotating through an angle before feature vector is computed and matched. Row and Column variance feature vector of orientation image will be employed. The algorithm has been tested on the FVC2002 Databases. The performance of algorithm is measured in terms of GAR and FAR. At a threshold level of 1.1 % and at 5.7% FAR the GAR observed is 97.83%. The improved Feature vector will lower imposter acceptance rate at reasonable GAR and hence yields better GAR at lower FAR. The proposed algorithm is computationally very efficient and can be implemented on Real-Time Systems.

Abstract: A frequency dependent approach to defining a dynamic relative gain array (DRGA) is discussed. The approach assumes the availability of a dynamic transfer function based process model for control loop pairing analysis. Two examples are considered: one in which the traditional RGA (based on steady-state gain matrix) gives the correct pairing recommendation and the other in which the traditional RGA suggests wrong pairings particularly in the frequency range of interest. The calculations pertaining to analysis of control loop pairing is performed using MATLAB (version 7.0.1). An inaccurate indication of the amount of interaction present is discussed. The first example uses 2x2 transfer function model [1] and the second one uses 3x3 transfer function model [2].

Abstract: In this paper, a proposal of a new and unusual framework to detect and extract the text from the images and video frames have been presented. In the past various methods have been presented for detection and localization of text in images and video frames. In this paper, a comparison has been made between several text detection methods and proposed method for text detection in images and video frames. The proposed method is carried out by edge detection, and the projection profile method is used to localize the text region better. Various experiments have been carried out to evaluate and compare the performance of the proposed algorithm. Experimental results tested from a large dataset have demonstrated that the proposed method is effective and practical. Various parameters like average time, precision and recall rates and analyzed for both existing and proposed method to determine the success and limitation of our method.

Abstract: A new image indexing and retrieval algorithm known as local binary pattern (LBP) correlogram is presented in this paper. LBP histogram captures only the patterns distribution in a texture while the spatial correlation between the pair of patterns is gathered by LBP correlogram. Multi-resolution texture decomposition and color correlation has been efficiently used in the proposed method where multi-resolution texture images are computed using Gaussian filter for collection of LBPs from these particular textures. Eventually, feature vectors are constructed by making into play the auto-correlation that exists between binary patterns. The retrieval results of the proposed method are examined on different texture image databases viz Brodatz database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and small set of rotated Brodatz database (DB4), and shows a major improvement in terms of average retrieval rate as when weighed against with LBP histogram and some existing transform domain technique.

Abstract: An efficient currency recognition system is vital for the automation in many sectors such as vending machine, rail way ticket counter, banking system, shopping mall, currency exchange service etc. The paper currency recognition is significant for a number of reasons. a) They become old early than coins; b) The possibility of joining broken currency is greater than that of coin currency; c) Coin currency is restricted to smaller range. This paper discusses a technique for paper currency recognition. Three characteristics of paper currencies are considered here including size, color and texture. By using image histogram, plenitude of different colors in a paper currency is calculated and compared with the one in the reference paper currency. The Markov chain concept has been considered to model texture of the paper currencies as a random process. The method discussed in this paper can be used for recognizing paper currencies from different countries. This paper also represents a currency recognition system using ensemble neural network (ENN). The individual neural networks in an ENN are skilled via negative correlation learning. The purpose of using negative correlation learning is to skill the individuals in an ensemble on different parts or portion of input patterns. The obtainable currencies in the market consist of new, old and noisy ones. It is sometime difficult for a system to identify these currencies; therefore a system that uses ENN to identify them is discussed. Ensemble network is much helpful for the categorization of different types of currency. It minimizes the chances of misclassification than a single network and ensemble network with independent training.

Abstract: In the last two decades in areas like banking, finance and medical research privacy policies restrict the data owners to share the data for data mining purpose. This issue throws up a new area of research namely privacy preserving data mining. In this paper, we proposed a privacy preservation method by employing Particle Swarm Optimization (PSO) trained Auto Associative Neural Network (PSOAANN). The modified (privacy preserved) input values are fed to a decision tree (DT) and a rule induction algorithm viz., Ripper for rule extraction purpose. The performance of the hybrid is tested on four benchmark and bankruptcy datasets using 10-fold cross validation. The results are compared with those obtained using the original datasets where privacy is not preserved. The proposed hybrid approach achieved good results in all datasets.

Abstract: In an ID-Based cryptosystem, identity of users are used to generate their public and private keys. In this system private key is generated by trusted private key generator (PKG). Unlike traditional PKI, this system enables the user to use public keys without exchanging public key certificates. With the exploitation of bilinear pairing, several secure and efficient ID-Based signature schemes have been proposed till now. In this paper, we have proposed an ID-Based signature scheme from bilinear pairing based on Ex-K-Plus problem. The proposed scheme is existentially unforgeable in the random oracle model under the hardness of K-CAA problem. Our scheme is also unforgeable due to hardness of ex-k-plus problem and computationally more efficient than other existing schemes.

Abstract: It is important to detect multi simultaneous events in Wireless Sensor Networks (WSNs). WSNs are usually consist of tiny sensor nodes which are often deployed in harsh environment and facing the fault conditions. In this paper, a fault tolerant distributed two-event detection method based on the Bayesian approach is proposed for WSNs. In addition, a data fusion algorithm is employed to involve the statuses of the neighborhoods in the decision of each sensing point. The proposed distributed method is used to detect two events simultaneously in a WSN with 200 sensors that distributed randomly. Also some random faults occur in all sensing nodes. Results show the accuracy and significant performance of the proposed two events detection approach.