A New Evolutionary Bayesian Approach Incorporating Additive Path Correction for Nonlinear Inverse Problems

An evolutionary form of a generalized Bayesian update method, which is strictly derivative- free yet directed through an additive update term based purely on the statistical moments of the design variables, is proposed for nonlinear inverse problems in general and applied in particular to an optical imaging problem, the ultrasound modulated optical tomography (UMOT). The additive update term, which bypasses most pitfalls of a conventional weight- based Bayesian update, results from a change of measures aimed at driving appropriately derived observation-prediction error terms or increments of cost functionals to zero-mean Brownian martingales. This constitutes a novel characterization corresponding to the extremization of the cost functional(s), where the design unknowns are represented as diffusion processes evolving with respect to a continuously parameterized iteration variable. This leads to a recursive prediction-update algorithm to implement the search. The scheme offers freedom from sample degeneracy and the accompanying divergence of the conventional weight-based Bayesian update schemes. We obtain the order of convergence of the conditioned process and also establish that the solutions are stable against tolerable variations in the regularizing noise terms, even as the original inverse problem remains severely ill-posed. Numerical evidence on solutions to the UMOT problem also confirms substantive improvements in the reconstruction efficacy through the proposed method vis-\`a- vis a Gauss-Newton approach, especially where the regularized quasi-Newton direction has low sensitivity to variations in the design unknowns.Comment: 42 pages, 3 figures (not yet published in a refereed journal or any conference proceedings

Fingerprint Recognition Using Minutia Score Matching

The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a person's life. A minutia matching is widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this paper we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves the quality of the image and extract the minutiae from the thinned image. The false matching ratio is better compared to the existing algorithm.Comment: 8 Page

A Kushner-Stratonovich Monte Carlo Filter Applied to Nonlinear Dynamical System Identification

A Monte Carlo filter, based on the idea of averaging over characteristics and fashioned after a particle-based time-discretized approximation to the Kushner-Stratonovich (KS) nonlinear filtering equation, is proposed. A key aspect of the new filter is the gain-like additive update, designed to approximate the innovation integral in the KS equation and implemented through an annealing-type iterative procedure, which is aimed at rendering the innovation (observation-prediction mismatch) for a given time-step to a zero-mean Brownian increment corresponding to the measurement noise. This may be contrasted with the weight- based multiplicative updates in most particle filters that are known to precipitate the numerical problem of weight collapse within a finite-ensemble setting. A study to estimate the a-priori error bounds in the proposed scheme is undertaken. The numerical evidence, presently gathered from the assessed performance of the proposed and a few other competing filters on a class of nonlinear dynamic system identification and target tracking problems, is suggestive of the remarkably improved convergence and accuracy of the new filter.Comment: 51 pages, 6 figure

FPGA Based Efficient Multiplier for Image Processing Applications Using Recursive Error Free Mitchell Log Multiplier and KOM Architecture

The Digital Image processing applications like medical imaging, satellite imaging, Biometric trait images etc., rely on multipliers to improve the quality of image. However, existing multiplication techniques introduce errors in the output with consumption of more time, hence error free high speed multipliers has to be designed. In this paper we propose FPGA based Recursive Error Free Mitchell Log Multiplier (REFMLM) for image Filters. The 2x2 error free Mitchell log multiplier is designed with zero error by introducing error correction term is used in higher order Karastuba-Ofman Multiplier (KOM) Architectures. The higher order KOM multipliers is decomposed into number of lower order multipliers using radix 2 till basic multiplier block of order 2x2 which is designed by error free Mitchell log multiplier. The 8x8 REFMLM is tested for Gaussian filter to remove noise in fingerprint image. The Multiplier is synthesized using Spartan 3 FPGA family device XC3S1500-5fg320. It is observed that the performance parameters such as area utilization, speed, error and PSNR are better in the case of proposed architecture compared to existing architecture

Similarity based Dynamic Web Data Extraction and Integration System from Search Engine Result Pages for Web Content Mining

There is an explosive growth of information in the World Wide Web thus posing a challenge to Web users to extract essential knowledge from the Web. Search engines help us to narrow down the search in the form of Search Engine Result Pages (SERP). Web Content Mining is one of the techniques that help users to extract useful information from these SERPs. In this paper, we propose two similarity based mechanisms; WDES, to extract desired SERPs and store them in the local depository for offline browsing and WDICS, to integrate the requested contents and enable the user to perform the intended analysis and extract the desired information. Our experimental results show that WDES and WDICS outperform DEPTA [1] in terms of Precision and Recall.Comment: 8 page

A model-independent technique to determine one-dimensional radio source structure from interplanetary scintillation (IPS) observations

We outline a method of deriving one-dimensional phaseless visibility along solar wind direction from interplanetary scintillation power spectrum, together with the known visibility of a calibration source. The method is illustrated briefly. Details may be found in Edwin Jayaraj (1990).Comment: 3 pages, 1 figure, 1 table, method used by S Edwin Jayaraj for MPhil project of Madurai Kamaraj Universit

A Dataset and Preliminary Results for Umpire Pose Detection Using SVM Classification of Deep Features

In recent years, there has been increased interest in video summarization and automatic sports highlights generation. In this work, we introduce a new dataset, called SNOW, for umpire pose detection in the game of cricket. The proposed dataset is evaluated as a preliminary aid for developing systems to automatically generate cricket highlights. In cricket, the umpire has the authority to make important decisions about events on the field. The umpire signals important events using unique hand signals and gestures. We identify four such events for classification namely SIX, NO BALL, OUT and WIDE based on detecting the pose of the umpire from the frames of a cricket video. Pre-trained convolutional neural networks such as Inception V3 and VGG19 net-works are selected as primary candidates for feature extraction. The results are obtained using a linear SVM classifier. The highest classification performance was achieved for the SVM trained on features extracted from the VGG19 network. The preliminary results suggest that the proposed system is an effective solution for the application of cricket highlights generation.Comment: To be published at the 2018 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2018), 18-21 NOV, 2018, BENGALURU, INDI

Two Stage Prediction Process with Gradient Descent Methods Aligning with the Data Privacy Preservation

Privacy preservation emphasize on authorization of data, which signifies that data should be accessed only by authorized users. Ensuring the privacy of data is considered as one of the challenging task in data management. The generalization of data with varying concept hierarchies seems to be interesting solution. This paper proposes two stage prediction processes on privacy preserved data. The privacy is preserved using generalization and betraying other communicating parties by disguising generalized data which adds another level of privacy. The generalization with betraying is performed in first stage to define the knowledge or hypothesis and which is further optimized using gradient descent method in second stage prediction for accurate prediction of data. The experiment carried with both batch and stochastic gradient methods and it is shown that bulk operation performed by batch takes long time and more iterations than stochastic to give more accurate solution.Comment: 14 page

Static Analysis, Design Modification and Modal Analysis of Structural Chassis Frame

The chassis frame is an important part in a truck and it carries the whole load acting on the truck as well as \ud different parts of the automobile. So it must be strong enough to resist the shock, twist, vibration and other \ud stresses. Maximum stress and maximum deflection are important criteria for design of the chassis. The objective\ud of present is to determine the maximum stress, maximum deflection and to recognize critical regions under \ud static loading condition. Static structural analysis of the chassis frame is carried out by FEA Method. The \ud structural chassis frame is modeled using PRO-E wildfire 4.0 software. The Pre-processing has done with \ud HYPERMESH software; then the problem has been solved through RADIOSS and the post processing was done\ud by HYPERVIEW. The results obtained like maximum shear stress, Von-mises stress and maximum deflections \ud are used for improving design modification. Modal analysis of the chassis frame done using ANSYS \ud WORKBENCH. Through modal analysis, natural frequencies and corresponding vibration mode shapes of the \ud structure are obtained

QoS group based optimal retransmission medium access protocol for wireless sensor networks

This paper presents, a Group Based Optimal Retransmission Medium Access (GORMA) Protocol is designed that combines protocol of Collision Avoidance (CA) and energy management for low-cost, short-range, low-data rate and low-energy sensor nodes applications in environment monitoring, agriculture, industrial plants etc. In this paper, the GORMA protocol focuses on efficient MAC protocol to provide autonomous Quality of Service (QoS) to the sensor nodes in one-hop QoS retransmission group and two QoS groups in WSNs where the source nodes do not have receiver circuits. Hence, they can only transmit data to a sink node, but cannot receive acknowledgement control signals from the sink node. The proposed protocol GORMA provides QoS to the nodes which work independently on predefined time by allowing them to transmit each packet an optimal number of times within a given period. Our simulation results shows that the performance of GORMA protocol, which maximize the delivery probability of one-hop QoS group and two QoS groups and minimize the energy consumption.Comment: 9 pages in IEEE format and 6 figure