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In this paper, battery supported transportation systems called electrical vehicles (EV) and the wireless charging topologies are presented. The operation and state of the art techniques for wireless inductive power transfer (WIPT), compensation of IPT techniques, parallel–series (P–S) compensation topology studies are presented. The techniques are majorly based on current fed dc-dc converter topologies. This paper presents the detailed evaluation of electrical parameters, comparison of various qualitative features of power transfer techniques, IPT for EV application, the control strategies and challenges for EV battery charging. The parameters are evaluated and the results are presented in MATLAB/SIMULINK.

At present various watermarking techniques are in use in the field of digital images. The medical image watermarking creates a greater degree of the distortion as a result of the embedded image, which is a big challenge in the medical applications as it affects the decision making process. Certain watermarking technique results vary with different images and also the performance seems to be inconsistent when various images are considered for watermarking. The key point of this work is to enhance the quality and security in order to accomplish distortion free watermarking by utilizing a watermarking technique which subject to pixel weight. For this 2 level DWT is used to obtain the best region for embedding for which the dragonfly optimization algorithm is applied.

© 2015 IEEE. This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program. The task is to develop Swahili ASR and KWS systems within two weeks using as little as 3 hours of transcribed data. Multilingual acoustic representations proved to be crucial for building these systems under strict time constraints. The paper discusses several key insights on how these representations are derived and used. First, we present a data sampling strategy that can speed up the training of multilingual representations without appreciable loss in ASR performance. Second, we show that fusion of diverse multilingual representations developed at different LORELEI sites yields substantial ASR and KWS gains. Speaker adaptation and data augmentation of these representations improves both ASR and KWS performance (up to 8.7% relative). Third, incorporating un-transcribed data through semi-supervised learning, improves WER and KWS performance. Finally, we show that these multilingual representations significantly improve ASR and KWS performance (relative 9% for WER and 5% for MTWV) even when forty hours of transcribed audio in the target language is available. Multilingual representations significantly contributed to the LORELEI KWS systems winning the OpenKWS15 evaluation.

We fabricated and characterized disorder-free GaSb photovoltaic devices on Si. The fabrication process relies on epitaxial lift-off of pixelated GaSb membranes from GaSb substrates and their transfer onto Si. We demonstrated that our approach maintains the structural integrity and optoelectronic functionality of the GaSb absorbers after the epitaxial transfer. The performance parameters of the pixelated solar cells are V oc ~ 0.1V, J sc ~ 17 mA/cm2, FF ~ 29% and h~ 1.2%, which compare favorably to the characteristics of a 5x5 mm2, GaSb solar cells on a GaSb substrate.

Access to lattice matched narrow bandgap semiconductors is a challenge for improved efficiency of GaAs based multi-junction solar cells (MJSCs). GaSb has a bandgap of 0.72 eV and is ideally suited to be used as a Near-Infrared subcell for MJSCs. However, when used as a replacement for Germanium in GaAs based MJSCs, the large lattice mismatch between GaSb and GaAs results in significant threading dislocation density in the GaSb subcell. The threading dislocation density in the GaSb epilayer can be reduced to a certain extent by the realization of 90° interfacial misfit dislocation arrays (IMF) between the GaSb and GaAs layers. The substrate temperature during the growth of the GaSb epi-layer on GaAs has a strong impact on the threading dislocation density while making use of the IMF technique. In this study, several substrate temperatures (ranging from 350°C to 540°C) are explored to achieve reduced threading dislocation density which is measured using both plan-view Transmission Electron Microscopy (TEM) and X-Ray Diffraction (XRD) rocking curve analysis. A low growth temperature (of 420°C) shows the reduction of threading dislocation to the level of ~1.3×108 cm–2.

Precision timing properties of high energy physics detectors in high luminosity colliders has become an important requirement for the success of particle detection, of which new calorimeter with timing precision of few tens of ps is the main interest. In this paper, we presented the first calorimetry and timing performance measurement of Cadmium-Telluride (CdTe) as the active material of sampling calorimeter, using 2 GeV to 200 GeV electron beam from CERN beamlines. A good energy performance is achieved due to the better sensitivity of CdTe to X ray photons in the secondary shower than other semi-conducting sensors such as silicon. The timing resolution of the tested CdTe sensor is measured to be down to 21 ps under the best conditions.

The objective of this research was to determine various motivational factors that influence high school students towards intended careers in engineering. This study utilized data from the High School Longitudinal Study of 2009, which surveyed over 24,000 students in 9th grade and again in 11th grade. We classified students into four categories of intentions: 1) Not STEM Intending, those who did not select a STEM occupation in either the 2009 or 2011 administrations; 2) Leavers, students who selected a STEM occupation in 2009 but not in 2011, 3) Newcomers, those who did not originally specify a STEM career in 2009, but who did in 2011; and 4) Stayers, those who chose STEM careers in both 2009 and 2011. Repeated measures analysis of variance modeled the extent to which the motivational variables, measured for both mathematics and science, were related to students' career intentions. Results show that occupational intentions change dramatically between 9th and 11th grades, and that the relationship between STEM intention and motivation is highly time-sensitive: Of the 6,788 STEM intending students in 2009 (29% of the total sample), only 3,560 remained STEM intending in 2011 (48% attrition).

In this paper, we propose Time-Marked Word (TMW) lists as a replacement for the lattices and Confusion Networks (CNs) widely used as indexing vehicles for Spoken Term Detection (STD). In a TMWlist, candidates are simply tagged with posterior probabilities and time information and stored as a large list of words: the additional ordering present in a lattice or CN is discarded. TMW lists compactly summarize a large ASR search space. Representing a large search space is critical for STD metrics such as ATWV that heavily penalize misses of rare keywords. Comparisons on the OpenKWS 2014 Tamil limited language pack task [1] show that the new TMW-based indexing results in better performance while being faster and having a smaller footprint.

In this paper we describe progress we have made in detecting out-of-vocabulary words (OOVs) for a speech-to-speech translation system for the purpose of playing back audio to the user for clarification and correction. Our OOV detector follows a strategy of first identifying a rough location of the OOV and then merging adjacent decoded words to cover the true OOV word. We show the advantage of our OOV detection strategy and report on improvements using a real-time implementation of a new Convolutional Neural Network acoustic model. We discuss why commonly used metrics for OOV detection do not meet our needs and explore an overlap metric as well as a Jaccard metric for evaluating our ability to detect the OOVs and localize them accurately in time. We have found different metrics to be useful at different stages of development.

In this paper, we present a fast, vocabulary independent algorithm for spoken term detection (STD) that demonstrates a word-based index is sufficient to achieve good performance for both in-vocabulary (IV) and out-of-vocabulary (OOV) terms. Previous approaches have required that a separate index be built at the sub-word level and then expanded to allow for matching OOV terms. Such a process, while accurate, is expensive in both time and memory. In the proposed architecture, a word-level confusion network (CN) based index is used for both IV and OOV search. This is implemented using a flexible WFST framework. Comparisons on 3 Babel languages (Tagalog, Pashto and Turkish) show that CN-based indexing results in better performance compared with the lattice approach while being orders of magnitude faster and having a much smaller footprint.

The paper describes a state-of-the-art keyword search (KWS) system in which significant improvements are obtained by using Convolutional Neural Network acoustic models, a two-step speech segmentation approach and a simplified ASR architecture optimized for KWS. The system described in this paper had the best performance in the 2013 DARPA RATS evaluation for both Levantine and Farsi.

Keyword search, in the context of low resource languages, has emerged as a key area of research. The dominant approach in keyword search is to use Automatic Speech Recognition (ASR) as a front end to produce a representation of audio that can be indexed. The biggest drawback of this approach lies in its the inability to deal with out-of-vocabulary words and query terms that are not in the ASR system output. In this paper we present an empirical study evaluating various approaches based on using confusion models as query expansion techniques to address this problem. We present results across four languages using a range of confusion models which lead to significant improvements in keyword search performance as measured by the Maximum Term Weighted Value (MTWV) metric.

Automatic speech recognition is a core component of many applications, including keyword search. In this paper we describe experiments on acoustic modeling, language modeling, and decoding for keyword search on a Cantonese conversational telephony corpus collected as part of the IARPA Babel program. We show that acoustic modeling techniques such as the bootstrapped-and-restructured model and deep neural network acoustic model significantly outperform a state-of-the-art baseline GMM/HMM model, in terms of both recognition performance and keyword search performance, with improvements of up to 11% relative character error rate reduction and 31% relative maximum term weighted value improvement. We show that while an interpolated Model M and neural network LM improve recognition performance, they do not improve keyword search results; however, the advanced LM does reduce the size of the keyword search index. Finally, we show that a simple form of automatically adapted keyword search performs 16% better than a preindexed search system, indicating that out-of-vocabulary search is still a challenge.

The paper describes a state-of-the-art spoken term detection system in which significant improvements are obtained by diversifying the ASR engines used for indexing and combining the search results. First, we describe the design factors that, when varied, produce complementary STD systems and show that the performance of the combined system is 3 times better than the best individual component. Next, we describe different strategies for system combination and show that significant improvements can be achieved by normalizing the combined scores. We propose a classifier-based system combination strategy which outperforms a highly optimized baseline. The system described in this paper had the highest accuracy in the 2012 DARPA RATS evaluation.

Spoken content in languages of emerging importance needs to be searchable to provide access to the underlying information. In this paper, we investigate the problem of extending data fusion methodologies from Information Retrieval for Spoken Term Detection on low-resource languages in the framework of the IARPA Babel program. We describe a number of alternative methods improving keyword search performance. We apply these methods to Cantonese, a language that presents some new issues in terms of reduced resources and shorter query lengths. First, we show score normalization methodology that improves in average by 20% keyword search performance. Second, we show that properly combining the outputs of diverse ASR systems performs 14% better than the best normalized ASR system.