Electroencephalogram (EEG) signals have a long history of use as a noninvasive approach to measure brain function. An essential component in EEG-based applications is the removal of Ocular Artifacts (OA) from the EEG signals. In this paper we propose a hybrid de-noising method combining Discrete Wavelet Transformation (DWT) and an Adaptive Predictor Filter (APF). A particularly novel feature of the proposed method is the use of the APF based on an adaptive autoregressive model for prediction of the waveform of signals in the ocular artifact zones. In our test, based on simulated data, the accuracy of noise removal in the proposed model was significantly increased when compared to existing methods including: Wavelet Packet Transform (WPT) and Independent Component Analysis (ICA), Discrete Wavelet Transform (DWT) and Adaptive Noise Cancellation (ANC). The results demonstrate that the proposed method achieved a lower mean square error and higher correlation between the original and corrected EEG. The proposed method has also been evaluated using data from calibration trials for the Online Predictive Tools for Intervention in Mental Illness (OPTIMI) project. The results of this evaluation indicate an improvement in performance in terms of the recovery of true EEG signals with EEG tracking and computational speed in the analysis. The proposed method is well suited to applications in portable environments where the constraints with respect to acceptable wearable sensor attachments usually dictate single channel devices.

Automatic Identification and Removal of Ocular Artifacts in EEG—Improved Adaptive Predictor Filtering for Portable Applications

Analysing the face recognition rate of various current face recognition algorithms is absolutely critical in developing new robust algorithms In his paper we report performance analysis of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for face recognition This analysis was carried out on various current PCA and LDA based face recognition algorithms using standard public databases Among various PCA algorithms analyzed, Manual face localization used on ORL and SHEFFIELD database consisting of 100 components gives the best face recognition rate of 100%, the next best was 9970% face recognition rate using PCA based Immune Networks (PCA-IN) on ORL database Among various LDA algorithms analyzed, Illumination Adaptive Linear Discriminant Analysis (IALDA) gives the best face recognition rate of 989% on CMU PIE database, the next best was 98125% using Fuzzy Fisherface through genetic algorithm on ORL database In this paper we report performance analysis of various current PCA and LDA based algorithms for face recognition The evaluation parameter for the study is face recognition rate on various standard public databases The remaining of the paper is organized as follows: Section II provides a brief overview of PCA, Section III presents PCA algorithms analysed, Section IV provides brief overview of LDA, Section V presents LDA algorithms analysed Section VI presents performance analysis of various PCA and LDA based algorithms finally Section VII draws the conclusion II PRINCIPAL COMPONENT ANALYSIS (PCA)

/pdf/performance-analysis-of-pca-based-and-lda-based-algorithms-51zktwin4x.pdf

Performance Analysis of PCA-based and LDA- based Algorithms for Face Recognition

Local SVD based NIR face retrieval

Walsh code based numerical mapping method for the identification of protein coding regions in eukaryotes

A new numerical approach for DNA representation using modified Gabor wavelet transform for the identification of protein coding regions

Uncontrolled lighting Conditions poses obstacle to face recognition. To deal with this problem, this paper proposes a preprocessing scheme using Singular Value Decomposition and Histogram Equalization to enhance and facilitate illumination invariant face recognition. The proposed method first generates synthetic image using Histogram equalization. Original and synthetic images are singular value decomposed; from the estimates of singular values enhanced image is reconstructed. Enhanced image is discrete wavelet decomposed (Haar & Db4) in to different frequency sub bands (LL, LH, HL, HH). The LL sub band is the best approximation of original image with lower-dimensional space and is used as biometric template. Pose Invariant Feature vectors are extracted from this template using Kernel Principal Component Analysis (KPCA). To show the performance, the proposed method is tested on YaleB, ORL benchmarking Databases. The results obtained show the impact of the method and is compared with PCA, KPCA without any preprocessing.

Preprocessing using SVD towards illumination invariant face recognition

Human activities are recognized from the Electrooculogram (EOG) signal generated from the movement of eye. Hence early, accurate preprocessing of EOG signals is important. In recent years, this became an active area of research. The EOG signal captured using acquisition device is corrupted with the noise and device intrinsic, thus pre processing (noise reduction) is first and foremost step in any further analysis & activity recognition. In this paper a novel method of De-noising EOG signals using Dual Tree complex wavelet transform (DT-CWT) is proposed. The Denoising results obtained are compared with conventional wavelet (DWT) de-noising method. To demonstrate the efficacy of the proposed method, SNR calculations and the statistical analysis are evaluated. The proposed method is best suitable for real time EOG based applications like human-machine communication devices for disabled persons, eye movement analysis and gaming applications.

Complex wavelet transform driven de-noising method for an EOG signals

Biometric devices provide secure mechanism towards gaining access. One of the Biometric features is Face and the system implemented is Face Recognition system. The Classical Face Recognition System is implemented with Principal Component Analysis and is successful. PCA is a linear method of extracting the features in a lower dimension space and is severely affected by the Pose and surrounding illumination variation. To implement effective face recognition system, pose variation is to be considered and the problem is well addressed with Kernel PCA (nonlinear PCA). KPCA extracts features in a higher dimension space, there by the system is rugged to pose variation. The illumination variation is accounted for capture range of the front end device and its surrounding and is not dealt in KPCA. In this work Singular Value Decomposition is used to deal with surrounding illumination and wavelets are employed to aid the KPCA in capturing the Multi Scale Features there by making the System robust to pose and illumination variation. To show the performance, the proposed method is tested on YaleB, ORL Databases. The results obtained show the impact of the method and is compared with PCA, KPCA.

Multi scale feature extraction and enhancement using SVD towards secure Face Recognition system

The exceptional speed in increase of genomic data at public databases requires advanced computational tools to perform quick gene analysis. The tools can be devised with the aid of genomic signal processing. The pivotal task in genomic signal processing is numerical mapping. In numerical mapping, the string of nucleotides is transformed into discrete numerical sequence by assigning optimum mathematical descriptor to a nucleotide. The descriptor must be compatible with the further stages of genomic application in order to achieve high efficiency. In this work, a simple numerical mapping method is proposed in which the optimum descriptor value is obtained by applying Gray code concept. The proposed method is evaluated on benchmark databases HRM195 and ASP67 for an identification of protein coding region application. The proposed method exhibits improved exon prediction efficiency in terms of performance accuracy and equal error rate when compared with similar methods.

M. Raman Kumar

Papers

Preprocessing using SVD towards illumination invariant face recognition

Complex wavelet transform driven de-noising method for an EOG signals

Multi scale feature extraction and enhancement using SVD towards secure Face Recognition system

A Numerical Representation Method for a DNA Sequence Using Gray Code Method

Review on DSP based dynamic gene encoding schemes for the detection of protein coding region