Robust Hybrid Algorithm of PSO and SOCP for Grating Lobe Suppression and against Array Manifold Mismatch

Based on Particle Swarm Optimization (PSO) and Second-Order Cone Programming (SOCP) algorithm, this paper proposes a hybrid optimization method to suppress the grating lobes of sparse arrays and improve the robustness of array layout. With the peak side-lobe level (PSLL) as the objective function, the paper adopts the particle swarm optimization as a global optimization algorithm to optimize the elements’ positions, the convex optimization as a local optimization algorithm to optimize the elements’ weights. The effectiveness of the grating lobes suppression (as low as -32.13 dB) by this method is illustrated through its application to the sparse linear array when the actual steering vector is known. To enhance the robustness of the optimized array, a rebuilt robust convex optimization model is adopted in the optimization of both array excitations and layout. When the array manifold mismatch error is 1cm, the PSLL by the robust algorithm can be compressed to -27dB, compared to that of -24dB by the ordinary optimization. Results of a set of representative numerical experiments show that the algorithm proposed in this paper can obtain a more robust array layout and matched elements’ weight coefficients to avoid the huge degradation of the array pattern performance in the presence of array manifold mismatch errors. The good performance of pattern synthesis demonstrates the effectiveness of the proposed robust algorithm

Text localisation for roman words from shop signage / Nurbaity Sabri ... [et al.]

Text localisation determinesthe location of the text in an image. This process is performed prior to text recognition. Localising text on shop signage is a challenging task since the images of the shop signage consist of complex background, and the text occurs in various font types, sizes, and colours. Two popular texture features that have been applied to localise text in scene images are a histogram of oriented gradient (HOG) and speeded up robust features (SURF). A comparative study is conducted in this paper to determine which is better with support vector machine (SVM) classifier. The performance of SVM is influenced by its kernel function and another comparative study is conducted to identify the best kernel function. The experiments have been conducted using primary data collected by the authors. Resultsindicate that HOG with quadratic kernel function localises text for shop signage better than SURF

The survey on Near Field Communication

PubMed ID: 26057043Near Field Communication (NFC) is an emerging short-range wireless communication technology that offers great and varied promise in services such as payment, ticketing, gaming, crowd sourcing, voting, navigation, and many others. NFC technology enables the integration of services from a wide range of applications into one single smartphone. NFC technology has emerged recently, and consequently not much academic data are available yet, although the number of academic research studies carried out in the past two years has already surpassed the total number of the prior works combined. This paper presents the concept of NFC technology in a holistic approach from different perspectives, including hardware improvement and optimization, communication essentials and standards, applications, secure elements, privacy and security, usability analysis, and ecosystem and business issues. Further research opportunities in terms of the academic and business points of view are also explored and discussed at the end of each section. This comprehensive survey will be a valuable guide for researchers and academicians, as well as for business in the NFC technology and ecosystem.Publisher's Versio

Using BDH for the Message Authentication in VANET

The transport message security provided by vehicles in VANETs is quite important; vehicle message should be real-time and it will be not complicated to validate message calculation. The method proposed in the essay is mainly to validate the identity by means of Bilinear Diffie-Hellman method, and make vehicles validate the authenticity of RSU and TA’s identity and the effectiveness of key. RSU and TA only need to validate vehicle identity, without helping vehicles produce any key. When vehicle identity validation is completed, vehicles will produce public value and transmit it to other RSU and vehicles, while other vehicles could validate the identity through the message from the sender and public value from RSU. The advantages of the method proposed in this essay are listed as follows. (1) Vehicles, RSU, and TA can validate mutual identities and the effectiveness of keys. (2) Vehicles can produce public value functions automatically, thus reducing key control risks. (3) Vehicles do not need to show certificates to validate their identities, preventing the certificates from attacking because of long-term exposure. (4) Vehicles adopt a pseudonym ID challenge to validate their own identities during the process of handoff. (5) Vehicle messages can be validated using the Bilinear Diffie-Hellman (BDH) method without waiting for the RSU to validate messages, thus improving the instantaneity of messaging. The method proposed in the essay can satisfy source authentication, message integrity, nonrepudiation, privacy, and conditional untraceability requirements

Past, Present, and Future of EEG-Based BCI Applications

An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed

Ubiquitous Technologies for Emotion Recognition

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions