Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

Broadband Tissue Mimicking Phantoms and a Patch Resonator for Evaluating Noninvasive Monitoring of Blood Glucose Levels

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This post-acceptance version of the paper is essentially complete, but may differ from the official copy of record, which can be found at the following web location (subscription required to access full paper): http://dx.doi.org/10/1109/TAP.2014.2313139

Towards Accurate Dielectric Property Retrieval of Biological Tissues for Blood Glucose Monitoring

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This post-acceptance version of the paper is essentially complete, but may differ from the official copy of record, which can be found at the following web location (subscription required to access full paper): http://dx.doi.org/10/1109/TMTT.2014.2365019

On-Body Channel Measurement Using Wireless Sensors

© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This post-acceptance version of the paper is essentially complete, but may differ from the official copy of record, which can be found at the following web location (subscription required to access full paper): http://dx.doi.org/10.1109/TAP.2012.219693

Antennas and Propagation of Implanted RFIDs for Pervasive Healthcare Applications

© 2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.This post-acceptance version of the paper is essentially complete, but may differ from the official copy of record, which can be found at the following web location (subscription required to access full paper): http://dx.doi.org/10.1109/JPROC.2010.205101

Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding

Entity alignment is the task of finding entities in two knowledge bases (KBs) that represent the same real-world object. When facing KBs in different natural languages, conventional cross-lingual entity alignment methods rely on machine translation to eliminate the language barriers. These approaches often suffer from the uneven quality of translations between languages. While recent embedding-based techniques encode entities and relationships in KBs and do not need machine translation for cross-lingual entity alignment, a significant number of attributes remain largely unexplored. In this paper, we propose a joint attribute-preserving embedding model for cross-lingual entity alignment. It jointly embeds the structures of two KBs into a unified vector space and further refines it by leveraging attribute correlations in the KBs. Our experimental results on real-world datasets show that this approach significantly outperforms the state-of-the-art embedding approaches for cross-lingual entity alignment and could be complemented with methods based on machine translation

Mechanical properties and behaviour of concrete reinforced with spiral-shaped steel fibres under dynamic splitting tension

Concrete exhibits excellent resistance to compressive forces, but is brittle and weak in tension. Various types of fibres have been investigated by many researchers to improve the ductility and energy absorption capability of concrete materials and structures under static and blast and impact loadings. Spiral-shaped steel fibres were recently proposed as reinforcement in a concrete matrix and it was found that spiral fibre reinforcement can significantly improve the ductility, crack control ability and energy absorption capacity of concrete material under static and impact compressive loads. This paper presents an experimental study of the static and dynamic properties of steel fibre reinforced concrete (SFRC) materials under splitting tension. SFRC materials mixed with spiral-shaped steel fibres of different volume fractions were prepared and tested. A high-speed camera was used to capture the deformation, failure and crack opening process of the tested specimens. The contribution of spiral fibres to the mechanical properties and behaviour of concrete at high strain rate under splitting tension was investigated. Analyses of the test results revealed the effectiveness of spiral fibres in improving the performance of SFRC (e.g. crack control, energy absorption capability and more pronounced rate sensitivity under dynamic splitting loading). Moreover, crack opening and closing (pull-back by spiral fibres) processes were observed, demonstrating the excellent bonding and outstanding performance of spiral steel fibres in maintaining the integrity of the concrete material, thus resulting in significant improvements in impact resistance and energy dissipation

Language Transfer of Audio Word2Vec: Learning Audio Segment Representations without Target Language Data

Audio Word2Vec offers vector representations of fixed dimensionality for variable-length audio segments using Sequence-to-sequence Autoencoder (SA). These vector representations are shown to describe the sequential phonetic structures of the audio segments to a good degree, with real world applications such as query-by-example Spoken Term Detection (STD). This paper examines the capability of language transfer of Audio Word2Vec. We train SA from one language (source language) and use it to extract the vector representation of the audio segments of another language (target language). We found that SA can still catch phonetic structure from the audio segments of the target language if the source and target languages are similar. In query-by-example STD, we obtain the vector representations from the SA learned from a large amount of source language data, and found them surpass the representations from naive encoder and SA directly learned from a small amount of target language data. The result shows that it is possible to learn Audio Word2Vec model from high-resource languages and use it on low-resource languages. This further expands the usability of Audio Word2Vec.Comment: arXiv admin note: text overlap with arXiv:1603.0098