Fault location in multi-modular redundant systems

This paper describes a systematic design of a general m-order fault location network to detect and locate all possible subcritical faults in a multi-modular redundant system using decision elements. The fault distribution pattern propagating through various levels of the fault location network of a multi-modular redundant system is presented. The conditions for fault detection and location are also developed.     

Textural feature based face recognition for single training images

A novel face recognition algorithm using single training face image is proposed. The algorithm is based on textural features extracted using the 2D log Gabor wavelet. These features are encoded into a binary pattern to form a face template which is used for matching. Experimental results show that on the colour FERET database the accuracy of the proposed algorithm is higher than the local feature analysis (LFA) and correlation filter (CF) based face recognition algorithms even when the number of training images is reduced to one. In comparison with recent single training image based face recognition algorithms, the proposed 2D log Gabor wavelet based algorithm shows an improvement of more than 3% in accuracy.     

Biometric classifier update using online learning: A case study in near infrared face verification

The performance of a large scale biometric system may deteriorate over time as new individuals are continually enrolled. To maintain an acceptable level of performance, the classifier has to be re-trained offline in batch mode using both existing and new data. The process of re-training can be computationally expensive and time consuming. This paper presents a new biometric classifier update algorithm that incrementally re-trains the classifier using online learning and progressively establishes a decision hyperplane for improved classification. The proposed algorithm incorporates soft labels and granular computing in the formulation of a 2ν$\nu$-Online Granular Soft Support Vector Machine (SVM) to re-train the classifier using only the new data. Granular computing makes it adaptive to local and global variations in data distribution, while soft labels provide resilience to noise. Each time data is acquired, new support vectors that are linearly independent are added and existing support vectors that do not improve the classifier performance are removed. This constrains the size of the support vectors and significantly reduces the training time without compromising the classification accuracy. The efficacy of the proposed online learning strategy is validated in a near infrared face verification application involving different covariates. The results obtained on a heterogeneous near infrared face database of 328 subjects show that in all experiments using different feature extraction and classification algorithms the proposed online 2ν$\nu$-Granular Soft Support Vector Machine learning approach is 2–3 times faster while achieving a high level of accuracy similar to offline training using all data.     

A mosaicing scheme for pose-invariant face recognition.

Mosaicing entails the consolidation of information represented by multiple images through the application of a registration and blending procedure. We describe a face mosaicing scheme that generates a composite face image during enrollment based on the evidence provided by frontal and semiprofile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a user's face image. In the proposed scheme, the side profile images are aligned with the frontal image using a hierarchical registration algorithm that exploits neighborhood properties to determine the transformation relating the two images. Multiresolution splining is then used to blend the side profiles with the frontal image, thereby generating a composite face image of the user. A texture-based face recognition technique that is a slightly modified version of the C2 algorithm proposed by Serre et al. is used to compare a probe face image with the gallery face mosaic. Experiments conducted on three different databases indicate that face mosaicing, as described in this paper, offers significant benefits by accounting for the pose variations that are commonly observed in face images.     

Design of reconfigurable fault-tolerant VLSI/WSI processor array structures

This paper presents a new reconfiguration technique for VLSI/WSI processor arrays. The fault-tolerant capabilities of both interstitial redundancy and time redundancy are combined to provide optimal reconfiguration. Results obtained through Monte Carlo simulations show that with the proposed reconfiguration technique, a very high yield and chip area utilization is achieved. It is also shown that in the presence of harsh environments, where a high rate of transient faults occur, the proposed algorithm is more robust compared to the existing approaches.     

Evolutionary neural network modeling for software cumulative failure time prediction

An evolutionary neural network modeling approach for software cumulative failure time prediction based on multiple-delayed-input single-output architecture is proposed. Genetic algorithm is used to globally optimize the number of the delayed input neurons and the number of neurons in the hidden layer of the neural network architecture. Modification of Levenberg–Marquardt algorithm with Bayesian regularization is used to improve the ability to predict software cumulative failure time. The performance of our proposed approach has been compared using real-time control and flight dynamic application data sets. Numerical results show that both the goodness-of-fit and the next-step-predictability of our proposed approach have greater accuracy in predicting software cumulative failure time compared to existing approaches.