Design and Performance Study of a Dual-Element Multiband Printed Monopole Antenna Array for MIMO Terminals

This letter presents a study on linearly polarized compact multiband multiple-input-multiple-output (MIMO) antenna system for small mobile terminals. The MIMO antenna system consists of two symmetric printed monopole antennas with edge-to-edge separation of 0.097 λ 0 at 900 MHz. Each antenna element has a capacitive feed and is composed of two twisted lines, a parasitic loop, and a shorting trip that generate five resonant modes around 900, 1800, 2100, 3500, and 5400 MHz, covering GSM850/900, DCS, PCS, UMTS, WLAN, and WiMAX frequency bands. Two inverted-L shaped branches and a rectangular slot with one circular end, etched on the ground plane, were introduced to improve the isolation between antenna elements. The isolation achieved is higher than 15 dB in the lower band and 20 dB in the upper bands, leading to an envelope correlation coefficient of less than 0.025. The simulated performance of the designed antenna system has been verified in the experiment

Environment Semantic Aided Communication: A Real World Demonstration for Beam Prediction

Millimeter-wave (mmWave) and terahertz (THz) communication systems adopt large antenna arrays to ensure adequate receive signal power. However, adjusting the narrow beams of these antenna arrays typically incurs high beam training overhead that scales with the number of antennas. Recently proposed vision-aided beam prediction solutions, which utilize \textit{raw RGB images} captured at the basestation to predict the optimal beams, have shown initial promising results. However, they still have a considerable computational complexity, limiting their adoption in the real world. To address these challenges, this paper focuses on developing and comparing various approaches that extract lightweight semantic information from the visual data. The results show that the proposed solutions can significantly decrease the computational requirements while achieving similar beam prediction accuracy compared to the previously proposed vision-aided solutions.Comment: Based on the DeepSense dataset https://deepsense6g.net/. arXiv admin note: text overlap with arXiv:2205.1218

Infectious disease (COVID-19)-related uncertainty and the safe-haven features of bonds markets

Purpose: This study aims to examine the hedge, diversifier and safe-haven properties of bonds against infectious disease-related equity market volatility (IDEMV), like COVID-19. Design/methodology/approach: The authors apply wavelet coherence methodology on the daily data of IDEMV and bond market (US, UK, Japan, Switzerland, Canada, Australia, Sweden, China and Europe) indices from 1 January 2000 to 14 February 2021. Findings: The results show no significant co-movement between these bond indices and IDEMV, thus confirming that they serve as a hedge against IDEMV. However, during the turbulent period like COVID-19, the authors find that the US, UK, Japan, Switzerland, Canada, Australia, Sweden, China and European bond markets act as safe-haven against IDEMV, whereas the UK, US, Japan and Canadian bond markets demonstrate an in-phase and positive co-movement with IDEMV during COVID-19, suggesting their role as a diversifier. Research limitations/implications: The study findings are important for investors and portfolio managers regarding risk management, portfolio diversification and investment strategies. Originality/value: The authors contribute to the fast growing body of work on the financial impacts of COVID-19 as well as to ongoing consideration of whether a bond is a safe-haven investment

Design and Development of MIMO Antennas for WiGig Terminals

This article presents a design for high-gain MIMO antennas with compact geometry. The proposed design is composed of four antennas in MIMO configuration, wherein, each antenna is made up of small units of microstrip patches. The overall geometry is printed on the top layer of the substrate, i.e., Rogers RT-5880 with permittivity of 2.2, permeability of 1.0, dielectric loss of 0.0009, and depth of 0.508 mm. The proposed design covers an area of 29.5 × 61.4 mm2, wherein each antenna covers an area of 11.82 × 25.28 mm2. The dimensions of the microstrip lines in each MIMO element were optimized to achieve a good impedance matching. The design is resonating at 61 GHz, with a wide practical bandwidth of more than 7 GHz, thereby covering IEEE 802.11ad WiGig (58–65 GHz). The average value of gain ranges from 9.45 to 13.6 dBi over the entire frequency bandwidth whereas, the average value of efficiency ranges from 55.5% to 84.3%. The proposed design attains a compact volume, wide bandwidth, and good gain and efficiency performances, which makes it suitable for WiGig terminals

Determination of Yearly Wind Energy Potential and Extraction of Wind Energy Using Wind Turbine for Coastal Cities of Baluchistan, Pakistan

04 March, 2019 Accepted: 24 April, 2019Abstract: Wind energy assessment of Ormara, Gwadar and Lasbela wind sites which are located in provinceBaluchistan is presented. The daily averaged wind speed data for the three sites is recorded for a period of four yearsfrom 2010-2013 at mast heights 7 m, 9.6 m and 23 m. Measured wind data are extrapolated to heights 60 m (Ormara),80 m (Gwadar) and 60 m (Lasbela). Yearly averaged wind speeds are modeled using a two parameters Weibullfunction whose shape (k) and scale (c) parameters are computed using seven well known numerical iterative methods.Reliability of the fitting process is assessed by employing three goodness-of-fit test statistics, namely, RMSE, R2 and χ2tests. Tests indicate that MLE, MLM and EPFM outperformed other Weibull parameter estimation methods for a betterfit behavior. Yearly Weibull pdf and cdf are obtained and Weibull wind characteristics are determined. Wind turbinesEcotecnia 60/1.67 MW and Nordex S77 1500 kW are used to extract wind energy on yearly basis. Estimated yearlyWeibull power densities are in the range 623.00 - 700.13 W/m2, 276.04 – 307.55 W/m2 and 66.85 – 75.93 W/m2 forOrmara, Gwadar and Lasbela respectively. Extracted wind energy values for Ormara and Gwadar using wind turbinesare reported as ca. 8623 kWh and ca. 4622 kWh, respectively

Herding on Fundamental/Nonfundamental Information During the COVID-19 Outbreak and Cyber-Attacks : Evidence From the Cryptocurrency Market

We provide an empirical analysis of herding behavior in cryptocurrency markets during COVID-19 and periods of cyber-attacks, differentiating between fundamental and nonfundamental herding. The results show that herding behavior is driven by fundamental information during the full sample period and the cyber-attack days. However, herding is not prevalent during the COVID-19 outbreak, either when reacting to fundamental or nonfundamental information. This finding suggests heterogeneity in the behaviors of participants in the cryptocurrency markets during the COVID-19 period.© The Author(s) 2021. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).fi=vertaisarvioitu|en=peerReviewed

Novel one time signatures (NOTS) : a compact post-quantum digital signature scheme

The future of the hash based digital signature schemes appears to be very bright in the upcoming quantum era because of the quantum threats to the number theory based digital signature schemes. The Shor's algorithm is available to allow a sufficiently powerful quantum computer to break the building blocks of the number theory based signature schemes in a polynomial time. The hash based signature schemes being quite efficient and provably secure can fill in the gap effectively. However, a draw back of the hash based signature schemes is the larger key and signature sizes which can prove a barrier in their adoption by the space critical applications, like the blockchain. A hash based signature scheme is constructed using a one time signature (OTS) scheme. The underlying OTS scheme plays an important role in determining key and signature sizes of a hash based signature scheme. In this article, we have proposed a novel OTS scheme with minimized key and signature sizes as compared to all of the existing OTS schemes. Our proposed OTS scheme offers an 88% reduction in both key and signature sizes as compared to the popular Winternitz OTS scheme. Furthermore, our proposed OTS scheme offers an 84% and an 86% reductions in the signature and the key sizes respectively as compared to an existing compact variant of the WOTS scheme, i.e. WOTS +

A Deep-Unfolded Spatiotemporal RPCA Network For L+S Decomposition

Low-rank and sparse decomposition based methods find their use in many applications involving background modeling such as clutter suppression and object tracking. While Robust Principal Component Analysis (RPCA) has achieved great success in performing this task, it can take hundreds of iterations to converge and its performance decreases in the presence of different phenomena such as occlusion, jitter and fast motion. The recently proposed deep unfolded networks, on the other hand, have demonstrated better accuracy and improved convergence over both their iterative equivalents as well as over other neural network architectures. In this work, we propose a novel deep unfolded spatiotemporal RPCA (DUST-RPCA) network, which explicitly takes advantage of the spatial and temporal continuity in the low-rank component. Our experimental results on the moving MNIST dataset indicate that DUST-RPCA gives better accuracy when compared with the existing state of the art deep unfolded RPCA networks

Data-driven prognosis method using hybrid deep recurrent neural network

Prognostics and health management (PHM) has attracted increasing attention in modern manufacturing systems to achieve accurate predictive maintenance that reduces production downtime and enhances system safety. Remaining useful life (RUL) prediction plays a crucial role in PHM by providing direct evidence for a cost-effective maintenance decision. With the advances in sensing and communication technologies, data-driven approaches have achieved remarkable progress in machine prognostics. This paper develops a novel data-driven approach to precisely estimate the remaining useful life of machines using a hybrid deep recurrent neural network (RNN). The long short-term memory (LSTM) layers and classical neural networks are combined in the deep structure to capture the temporal information from the sequential data. The sequential sensory data from multiple sensors data can be fused and directly used as input of the model. The extraction of handcrafted features that relies heavily on prior knowledge and domain expertise as required by traditional approaches is avoided. The dropout technique and decaying learning rate are adopted in the training process of the hybrid deep RNN structure to increase the learning efficiency. A comprehensive experimental study on a widely used prognosis dataset is carried out to show the outstanding effectiveness and superior performance of the proposed approach in RUL prediction. © 2020 Elsevier B.V

Towards a low complexity scheme for medical images in scalable video coding

Medical imaging has become of vital importance for diagnosing diseases and conducting noninvasive procedures. Advances in eHealth applications are challenged by the fact that Digital Imaging and Communications in Medicine (DICOM) requires high-resolution images, thereby increasing their size and the associated computational complexity, particularly when these images are communicated over IP and wireless networks. Therefore, medical research requires an efficient coding technique to achieve high-quality and low-complexity images with error-resilient features. In this study, we propose an improved coding scheme that exploits the content features of encoded videos with low complexity combined with flexible macroblock ordering for error resilience. We identify the homogeneous region in which the search for optimal macroblock modes is early terminated. For non-homogeneous regions, the integration of smaller blocks is employed only if the vector difference is less than the threshold. Results confirm that the proposed technique achieves a considerable performance improvement compared with existing schemes in terms of reducing the computational complexity without compromising the bit-rate and peak signal-to-noise ratio. © 2013 IEEE