Hong Kong Polytechnic University

About: Hong Kong Polytechnic University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Tourism & Population. The organization has 29633 authors who have published 72136 publications receiving 1956312 citations. The organization is also known as: HKPU & PolyU.

Fiber‐Based Wearable Electronics: A Review of Materials, Fabrication, Devices, and Applications

Abstract: Fiber-based structures are highly desirable for wearable electronics that are expected to be light-weight, long-lasting, flexible, and conformable Many fibrous structures have been manufactured by well-established lost-effective textile processing technologies, normally at ambient conditions The advancement of nanotechnology has made it feasible to build electronic devices directly on the surface or inside of single fibers, which have typical thickness of several to tens microns However, imparting electronic functions to porous, highly deformable and three-dimensional fiber assemblies and maintaining them during wear represent great challenges from both views of fundamental understanding and practical implementation This article attempts to critically review the current state-of-arts with respect to materials, fabrication techniques, and structural design of devices as well as applications of the fiber-based wearable electronic products In addition, this review elaborates the performance requirements of the fiber-based wearable electronic products, especially regarding the correlation among materials, fiber/textile structures and electronic as well as mechanical functionalities of fiber-based electronic devices Finally, discussions will be presented regarding to limitations of current materials, fabrication techniques, devices concerning manufacturability and performance as well as scientific understanding that must be improved prior to their wide adoption

Some basic cryptographic requirements for chaos-based cryptosystems

Abstract: In recent years, a large amount of work on chaos-based cryptosystems have been published. However, many of the proposed schemes fail to explain or do not possess a number of features that are fundamentally important to all kind of cryptosystems. As a result, many proposed systems are difficult to implement in practice with a reasonable degree of security. Likewise, they are seldom accompanied by a thorough security analysis. Consequently, it is difficult for other researchers and end users to evaluate their security and performance. This work is intended to provide a common framework of basic guidelines that, if followed, could benefit every new cryptosystem. The suggested guidelines address three main issues: implementation, key management and security analysis, aiming at assisting designers of new cryptosystems to present their work in a more systematic and rigorous way to fulfill some basic cryptographic requirements. Meanwhile, several recommendations are made regarding some practical aspects of analog chaos-based secure communications, such as channel noise, limited bandwith and attenuation.

Effect of valence electron concentration on stability of fcc or bcc phase in high entropy alloys

Abstract: Phase stability is an important topic for high entropy alloys (HEAs), but the understanding to it is very limited. The capability to predict phase stability from fundamental properties of constituent elements would benefit the alloy design greatly. The relationship between phase stability and physicochemical/thermodynamic properties of alloying components in HEAs was studied systematically. The mixing enthalpy is found to be the key factor controlling the formation of solid solutions or compounds. The stability of fcc and bcc solid solutions is well delineated by the valance electron concentration (VEC). The revealing of the effect of the VEC on the phase stability is vitally important for alloy design and for controlling the mechanical behavior of HEAs.

Real-time compressive tracking

Abstract: It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. While much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, these mis-aligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from the multi-scale image feature space with data-independent basis. Our appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is adopted to efficiently extract the features for the appearance model. We compress samples of foreground targets and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.