Energy storage behaviour of some binary alkali borate glasses by thermoluminescence studies

Thermoluminescence behaviour of a series of binary alkali borate glasses has been investigated to study their energy storage mechanism. Sodium borate glasses of varying composition have been prepared and their glow curves recorded after exposing them to X-rays (CuK radiation, 30 kV, 10 mA) of different dosages at room temperature. The effect of the nature and concentration of alkali oxide and the dose of irradiation on the nature of thermoluminescent glow curves were also studied. Borate glasses containing different concentrations of Na₂O exhibit significantly different glow curves. These glow curves have been analysed and the nature of traps responsible for TL emission are tentatively identified. The broad and complex nature of the glow pattern is attributed to distribution of trap depths in these materials. The viability of borate glasses in the construction of TL dosimeters are discussed.     

Text and non-text separation in offline document images: a survey

International Journal on Document Analysis and Recognition (IJDAR) - Separation of text and non-text is an essential processing step for any document analysis system. Therefore, it is important to...     

Multi-scale RoIs selection for classifying multi-spectral images

Multidimensional Systems and Signal Processing - The applications of object-based image analysis (OBIA) in remote sensing studies have received a considerable amount of attention over the recent...     

3D Face Recognition Using a Fusion of PCA and ICA Convolution Descriptors

Neural Processing Letters - This paper introduces a hybrid filter bank-based convolutional network to develop a 3D face recognition system in different orientations. The filter banks approach has...     

Understanding NFC-Net: a deep learning approach to word-level handwritten Indic script recognition

Neural Computing and Applications - This paper presents a deep learning architecture modified for resource-constrained environments, called Non-Fully-Connected Network or NFC-Net, based on...     

Expressions Recognition of North-East Indian (NEI) Faces

Facial expression is one of the major distracting factors for face recognition performance. Pose and illumination variations on face images also influence the performance of face recognition systems. The combination of three variations (facial expression, pose and illumination) seriously degrades the recognition accuracy. In this paper, three experimental protocols are designed in such a way that the successive performance degradation due to the increasing variations (expressions, expressions with illumination effect and expressions with illumination and pose effect) on face images can be examined. The whole experiment is carried out using North-East Indian (NEI) face images with the help of four well-known classification algorithms namely Linear Discriminant Analysis (LDA), K-Nearest Neighbor algorithm (KNN), combination of Principal Component Analysis and Linear Discriminant Analysis (PCA + LDA), combination of Principal Component Analysis and K-Nearest Neighbor algorithm (PCA + KNN). The experimental observations are analyzed through confusion matrices and graphs. This paper also describes the creation of NEI facial expression database, which contains visual static face images of different ethnic groups of the North-East states. The database is useful for future researchers in the area of forensic science, medical applications, affective computing, intelligent environments, lie detection, psychiatry, anthropology, etc.     

High-speed face recognition using self-adaptive radial basis function neural networks

In this work, we have proposed a self-adaptive radial basis function neural network (RBFNN)-based method for high-speed recognition of human faces. It has been seen that the variations between the images of a person, under varying pose, facial expressions, illumination, etc., are quite high. Therefore, in face recognition problem to achieve high recognition rate, it is necessary to consider the structural information lying within these images in the classification process. In the present study, it has been realized by modeling each of the training images as a hidden layer neuron in the proposed RBFNN. Now, to classify a facial image, a confidence measure has been imposed on the outputs of the hidden layer neurons to reduce the influences of the images belonging to other classes. This process makes the RBFNN as self-adaptive for choosing a subset of the hidden layer neurons, which are in close neighborhood of the input image, to be considered for classifying the input image. The process reduces the computation time at the output layer of the RBFNN by neglecting the ineffective radial basis functions and makes the proposed method to recognize face images in high speed and also in interframe period of video. The performance of the proposed method has been evaluated on the basis of sensitivity and specificity on two popular face recognition databases, the ORL and the UMIST face databases. On the ORL database, the best average sensitivity (recognition) and specificity rates are found to be 97.30 and 99.94%, respectively using five samples per person in the training set. Whereas, on the UMIST database, the above quantities are found to be 96.36 and 99.81%, respectively using eight samples per person in the training set. The experimental results indicate that the proposed method outperforms some of the face recognition approaches.     

A quality based routing protocol for wireless mesh networks

Wireless mesh networks can provide low-cost solutions for extending the reach of wireless access points by using multi-hop routing over a set of stationary wireless routers. The routing protocol for these networks may need to address quality considerations to meet the requirements of the user. In this paper, we present a quality based routing protocol for wireless mesh networks that tries to maximize the probability of successful transmissions while minimizing the end-to-end delay. The proposed routing protocol uses reactive route discoveries to collect key parameters from candidate routes to estimate the probability of success and delay of data packets transmitted over them. To achieve accurate route quality assessments, a new route quality metric is proposed that uses performance models of data packet transmissions as opposed to estimating route quality from the transmission of control packets, which have different transmission characteristics. These models are developed after careful evaluations of multi-hop wireless transmissions and validated by computer simulations. Relevant parameters that can be used to assess the route quality metric using these models are explained. Extensive performance evaluations of the proposed quality based routing protocol are presented and its benefits in comparison to some other known routing protocols are discussed.