Optical and electrical characteristics of (LiCl)x(P2O5)1-x glass.

Homogeneous (LiCl) x (P2O5)1 − x glasses were synthesised using a melt-quenching method for x = 0.1–0.6 in the interval of 0.05. The amorphous structure of the samples was evident by the X-ray diffraction spectrum. The short range structures of the binary phosphate samples were examined by Fourier transform infrared spectroscopy, whilst the density of the samples was measured as supportive data for the investigations. The results of refractive indices as measured using an ellipsometer reveal the homogeneity of samples and was found to depend on the glass composition. The electrical properties of the glasses were investigated by ac impedance spectroscopy from 10 mHz to 1 MHz for temperatures ranging from room temperature to 573 K. An estimation of the bulk resistivity was obtained by taking the intercepts on the real axis at low frequencies of the complex impedance plot. The dc conductivities derived from the reciprocal of resistivity values were found to obey the Arrhenius relationship, and its activation energy shows a decreasing trend with the increase in LiCl content in the glass. Lastly, an equivalent circuits consisting of real and complex capacitors is proposed to describe the dielectric response of the glass

Sign Language Recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set

3D Hand Pose Reconstruction With ISOSOM

We present an appearance-based 3D hand posture estimation method that deter-mines a ranked set of possible hand posture candidates from an unmarked hand image, based on an analysis by synthesis method and an image retrieval algorithm. We formulate the posture estimation problem as a nonlinear, many-to-many map-ping problem in a high dimension space. A general algorithm called ISOSOM is proposed for nonlinear dimension reduction, applied to 3D hand pose reconstruc-tion to establish the mapping relationships between the hand poses and the image features. In order to interpolate the intermediate posture values given the sparse sampling of ground-truth training data, the geometric map structure of the samples’ manifold is generated. The experimental results show that the ISOSOM algorithm performs better than traditional image retrieval algorithms for hand pose estima-tion

Towards faster activity search using embedding-based subsequence matching

Event search is the problem of identifying events or activity of interest in a large database storing long sequences of activity. In this paper, our topic is the problem of identifying activities of interest in databases where such activities are represented as time series. In the typical setup, the user presents a query that represents an activity of interest, and the system needs to retrieve the most similar activities stored in the database. We focus on the case where the best database matches are not segmented a priori: the database contains representations of long, continuous activity, that occurs throughout relatively extensive periods of time, and, given a query, there are no constraints as to when exactly a database match starts and ends within the longer activity pattern where it is contained. Using the popular DTW measure, the best database matches can be found using dynamic programming. However, retrieval time is linear to the size of the database and can become too long as the database size becomes larger. To achieve more efficient retrieval time, we apply to this problem a recently proposed technique called Embedding-based Subsequence Matching (EBSM), and we demonstrate that using EBSM we can obtain significant speedups in retrieval time

BoostMap: A Method for Efficient Approximate Similarity Rankings

This paper introduces BoostMap, a method that can significantly reduce retrieval time in image and video database systems that employ computationally expensive distance measures, metric or non-metric. Database and query objects are embedded into a Euclidean space, in which similarities can be rapidly measured using a weighted Manhattan distance. Embedding construction is formulated as a machine learning task, where AdaBoost is used to combine many simple, 1D embeddings into a multidimensional embedding that preserves a significant amount of the proximity structure in the original space. Performance is evaluated in a hand pose estimation system, and a dynamic gesture recognition system, where the proposed method is used to retrieve approximate nearest neighbors under expensive image and video similarity measures. In both systems, BoostMap significantly increases efficiency, with minimal losses in accuracy. Moreover, the experiments indicate that BoostMap compares favorably with existing embedding methods that have been employed in computer vision and database applications, i.e., FastMap and Bourgain embeddings