The objective of this article was to study the effects of Chi meditation on heart rate variability (HRV). For this purpose, the statistical and spectral measures of HRV from the RR intervals were analyzed. In addition, it is concerned with finding adequate Auto-Regressive Moving Average (ARMA) model orders for spectral analysis of the time series formed from RR intervals. Therefore, Akaike's Final Prediction Error (FPE) was taken as the base for choosing the model order. The results showed that overall the model order chosen most frequently for FPE was p = 8 for before meditation and p = 5 for during meditation. The results suggested that variety of orders in HRV models upon different psychological states could be due to some differences in intrinsic properties of the system.

Speech quality of VoIP system is degraded due different network layer problems such as packet loss delay and jitter and external noise. This paper compares the quality of speech signal that is implemented on digital signal processor using G.729 audio data compression algorithm with the ITU-T G.711 PCM coder implemented using modified digital filtering algorithm. PESQ (ITU-T P.862, Perceptual Evaluation of Speech Quality) is used to evaluate the performance. The results indicate that our proposed architecture has better performance.

In the context of Acoustic Echo Cancellation (AEC), sparseness level of acoustic impulse response (AIR) varies greatly in mobile environments. The modified sparseness-controlled Improved PNLMS (MSC-IPNLMS) algorithm proposed in this paper, exploits the sparseness measure of AIR using l1, l2 and l∞ norms. The MSC-IPNLMS is the modified version of SC-IPNLMS which uses sparseness measure based on l1 and l2 norms. Sparseness measure using l1, l2 and l∞ norms is the good representation of both sparse and dense impulse response, where as the measure which uses l1 and l2 norms is the good representation of sparse impulse response only. The MSC-IPNLMS is based on IPNLMS which allocates adaptation step size gain in proportion to the magnitude of estimated filter weights. By estimating the sparseness of the AIR, the proposed MSC-IPNLMS algorithm assigns the gains for each step size such that the proportionate term of the IPNLMS will be given higher weighting for sparse impulse responses. For a less sparse impulse response, a higher weighting will be given to the NLMS term. Simulation results, with input as white Gaussian noise (WGN), show the improved performance over the SC-IPNLMS algorithm in both sparse and dense AIR.

Handwritten signatures are the most commonly used method for authentication of a person as compared to other biometric authentication methods. For this purpose Neural Networks (NN) can be applied in the process of verification of handwritten signatures that are electronically captured. This paper presents a real time or online method for recognition and verification handwritten signatures by using NN architecture. Various features of signature such as height, length, slant, Hu's moments etc are extracted and used for training of the NN. The objective of online signature verification is to decide, whether a signature originates from a given signer. This recognition and verification process is based on the instant signature image obtained from the genuine signer and a few images of the original signatures which are already part reference database. The process of Devnagari signature verification can be divided it into sub-processes as pre-processing, feature extraction, feature matching, feature comparison and classification. This stepwise analysis allows us to gain a better control over the precision of different components

Automated Vehicle detection and classification is an important component of intelligent transport system. Due to significant importance in various fields such as traffic accidents avoidance, toll collection, congestion avoidance, terrorist activities monitoring, security and surveillance systems, intelligent transport system has become important field of study. Various technologies have been used for detecting and classifying vehicles automatically. Automated vehicle detection is broadly divided into two types- Hardware based and software based detection. Various algorithms have been implemented to classify different vehicles from videos. In this paper an efficient and economical solution for automatic vehicle detection and classification is proposed. The proposed system first isolates the object through background subtraction followed by vehicle detection using ontology. Vehicle detection is based on low level features such as shape, size, and spatial location. Finally system classifies vehicles into one of the known classes of vehicle based on size.

Irish recognition has always been an attractive goal for researchers. The identification of the person based on iris recognition is very popular due to the uniqueness of the pattern of iris. Although a number of methods for iris recognition have been proposed by many researchers in the last few years. This paper proposes statistical texture feature based iris matching method for recognition using K-NN classifier. Statistical texture measures such as mean, standard deviation, entropy, skewness etc., and six features are computed of normalized iris image. K-NN classifier matches the input iris with the trained iris images by calculating the Euclidean distance between two irises. The performance of the system is evaluated on 500 iris images, which gives good classification accuracy with reduced FAR/FRR.

In this paper, we propose a handwritten character recognition system for Malayalam language. The feature extraction phase consists of gradient and curvature calculation and dimensionality reduction using Principal Component Analysis. Directional information from the arc tangent of gradient is used as gradient feature. Strength of gradient in curvature direction is used as the curvature feature. The proposed system uses a combination of gradient and curvature feature in reduced dimension as the feature vector. For classification, discriminative power of Support Vector Machine (SVM) is evaluated. The results reveal that SVM with Radial Basis Function (RBF) kernel yield the best performance with 96.28% and 97.96% of accuracy in two different datasets. This is the highest accuracy ever reported on these datasets.

Steganography is an art that involves communication of secret data in an appropriate carrier, eg. images, audio, video, etc. with a goal to hide the very existence of embedded data so as not to arouse an eavesdropper's suspicion. In this paper, a steganographic technique with high level of security and having a data hiding capacity close to 20% of cover image data has been developed. An adaptive and matched bit replacement method is used based on the sensitivity of Human Visual System (HVS) at different intensities. The proposed algorithm ensures that the generated stego image has a PSNR greater than 38.5 and is also resistant to visual attack. A three level security is infused into the algorithm which makes data retrieval from the stego image possible only in case of having all the right keys.