This study presents a novel biometric approach to identify operators, given only streams of their control movements within a manual control task setting. In the present task subjects control a simulated, remotely operated robotic arm, attempting to dock onto a satellite in orbit. The proposed methodology utilizes the Hierarchical Temporal Memory (HTM) algorithm to distinguish operators by their unique control behaviors. Results presented compare the identification performance of HTM with Dynamic Time Warping (DTW) and Edit Distance on Real Sequences (EDR), in both static and real-time data settings. The HTM method outperformed both DTW and EDR in the real- time setting, and matched DTW in the static setting. Observed superior performance of the HTM algorithm lays the foundation for the extension of the proposed methodology to other motion- monitoring applications, such as real-time workload assessment, motion/simulator sickness onset or distraction detection. The data gathered in the study was posted to IEEE-dataport, DOI: 10.21227/wpyf-r927

In this paper, we propose a novel cooperative multi-relay transmission scheme for mobile terminals to exploit spatial diversity. By improving the timeliness of measured channel state information (CSI) through deep learning (DL)-based channel prediction, the proposed scheme remarkably lowers the probability of wrong relay selection arising from outdated CSI in fast time-varying channels. It inherits the simplicity of opportunistic relaying by selecting a single relay, avoiding the complexity of multi-relay coordination and synchronization. Numerical results reveal that it can achieve full diversity gain in slow-fading channels and substantially outperforms the existing schemes in fast-fading wireless environments. Moreover, the computational complexity brought by the DL predictor is negligible compared to off-the-shelf computing hardware.

Terahertz (THz)-band communications are a key enabler for future-generation wireless communication systems that promise to integrate a wide range of data-demanding applications. Recent advancements in photonic, electronic, and plasmonic technologies are closing the gap in THz transceiver design. Consequently, prospect THz signal generation, modulation, and radiation methods are converging, and the corresponding channel model, noise, and hardware-impairment notions are emerging. Such progress paves the way for well-grounded research into THz-specific signal processing techniques for wireless communications. This tutorial overviews these techniques with an emphasis on ultra-massive multiple-input multiple-output (UM-MIMO) systems and reconfigurable intelligent surfaces, which are vital to overcoming the distance problem at very high frequencies. We focus on the classical problems of waveform design and modulation, beamforming and precoding, index modulation, channel estimation, channel coding, and data detection. We also motivate signal processing techniques for THz sensing and localization.

Dear Editor: This manuscript is already online in techrxiv (https://doi.org/10.36227/techrxiv.12376427.v1), and the co-authors are not correctly associated with the published preprint so we are submitting this again by associating the co-authors. We have also improved the similarity report, the similarity index is 11% of this submission. Thank you. Abstract: Air pollution is one of the significant causes of mortality and morbidity every year. In recent years, many researchers have focused their attention on the associations of air pollution and health. These studies used two types of data in their studies, i.e., air pollution data and health data. Feature engineering is used to create and optimize air quality and health features. In order to merge these datasets residential address, community/county/block/city and hospital/school address are used. Using residence address or any location becomes a spatial problem when the Air Quality Monitoring (AQM) stations are concentrated in urban areas within the regions and an overlap in the AQM stations in urban areas coverage area, which raises the question that how to associate the patients with the relevant AQM station. Also, in most of the studies the distance of patients to the AQM stations is also not taken into account. In this study, we propose a four-part spatial feature engineering algorithm to find the coordinates for health data, calculate distances with AQM stations and associate health records to the nearest AQM station. Hence, removing the limitations of current air pollution health datasets. The proposed algorithm is applied as a case study in Klang Valley, Malaysia. The results show that the proposed algorithm can generate air pollution health dataset efficiently and the algorithm also provides the radius facility to exclude the patients who are situated far away from the stations.

Previously, a conductivity invariance phenomena (CIP) has been discovered – at a certain lift-off, the inductance change of the sensor due to a test sample is immune to conductivity variations, i.e. the inductance – lift-off curve passes through a common point at a certain lift-off, termed as conductivity invariance lift-off. However, this conductivity invariance lift-off is fixed for a particular sensor setup, which is not convenient for various sample conditions. In this paper, we propose using two parameters in the coil design – the horizontal and vertical distances between the transmitter and the receiver to control the conductivity invariance lift-off. The relationship between these two parameters and the conductivity invariance lift-off is investigated by simulation and experiments and it has been found that there is an approximate linear relationship between these two parameters and the conductivity invariance lift-off. This is useful for applications where the measurements have restrictions on lift-off, e.g. uneven coating thickness which limits the range of the lift-off of probe during the measurements. Therefore, based on this relationship, it can be easier to adjust the configuration of the probe for a better inspection of the test samples.

This paper proposes integrated guidance and control design to intercept a non-maneuvering target at a pre-specified time of interception. The problem is addressed considering nonlinear engagement kinematics and the interceptor is steered using the combined effects of canard as well as tail configurations (dual control interceptors). Different formulations of time-to-go, without the restrictive assumption of interceptor’s small heading angle, have been used in deriving the guidance commands, allowing the proposed strategies to remain effective over a wide range of impact time values. A weighted effort allocation scheme, in canard and tail deflections, has been proposed to generate the required lateral acceleration. The overall design uses sliding mode control owing to its simplicity of design. Finally, simulations are presented for various scenarios, including impaired actuator, vindicating the efficacy of the proposed technique.

In this paper, we revisit the asymptotic reverse-waterfilling characterization of the nonanticipative rate distortion function (NRDF) derived for a time-invariant multidimensional Gauss-Markov processes with mean-squared error (MSE) distortion in [1]. We show that for certain classes of time-invariant multidimensional Gauss-Markov processes, the specific characterization behaves as a reverse-waterfilling algorithm obtained in matrix form ensuring that the numerical approach of [1, Algorithm 1] is optimal. In addition, we give an equivalent characterization that utilizes the eigenvalues of the involved matrices reminiscent of the well-known reverse-waterfilling algorithm in information theory. For the latter, we also propose a novel numerical approach to solve the algorithm optimally. The efficacy of our proposed iterative scheme compared to similar existing schemes is demonstrated via experiments. Finally, we use our new results to derive an analytical solution of the asymptotic NRDF for a correlated time-invariant two-dimensional Gauss-Markov process.

While self-mixing interferometry(SMI) has proven to be suitable for displacement measurement and other sensing applications, its characteristic self-mixing signal shape is strongly governed by the non-linear phase equation which forms relation between perturbed and unperturbed phase of self-mixing laser. Therefore, while it is desirable for robust estimation of displacement of moving target, the algorithms to achieve this must have an objective strategy that can be achieved by understanding the characteristic of extracting knowledge of the perturbed phase from the unperturbed phase. Therefore, it has been proved and shown that such a strategy must not involve sole methods where the perturbed phase is a continuous function of the unperturbed phase (e.g: Taylor series or fixed-point methods) or through successive displacements (e.g: variations of Gauss-Seidel method). The subset of this strategy is to perform spectral filtering of the perturbed phase followed by perturbative or homotopic deformation. A less computationally expensive approach of this strategy is adopted to achieve displacement with a mean error of 62.2nm covering all feedback regimes, when the coupling factor ‘C’ is unknown.

The existing image retrieval approaches focus on the behavior of a single user only in each query without considering the correlation of the behaviors of multiple users in performing similar queries. In fact, users would have similar behaviors while they have similar expectations during queries. Accordingly, this paper therefore proposes the interactive image retrieval framework with the Similar Behavior Learning model. The framework consists of two stages. In the first stage, the framework retrieves images with the content-based feature vector as preliminary query result for user selection. In the second stage, the SBL model determines the similarity of the user behavior and annotates label code to the selected images instantly. The images are indexed by label code can be retrieved more efficiently. Meanwhile, the selected images in preliminary result are used as additional information for retrieving better results at the end of the current query. Experiments show the promising results.

In three-phase three-wire (3P3W) voltage-source converter (VSC) systems, utilization of filter inductors with deep saturation characteristics is often advantageous due to the improved size, cost, and efficiency. However, with the use of conventional synchronous frame current control (CSCC) methods, the inductor saturation results in significant dynamic performance loss and poor steady-state current waveform quality. This paper proposes an inverse dynamic model based compensation (IDMBC) method to overcome these performance issues. Accordingly, a review of inductor saturation and core materials is performed, and the motivation on the use of saturable inductors is clarified. Then, two-phase exact modelling of the 3P3W VSC control system is obtained and the drawbacks of CSCC have been demonstrated analytically. Based on the exact modelling, the inverse system dynamic model of the nonlinear system is obtained and employed such that the nonlinear plant is converted to a fictitious linear inductor system for linear current regulators to perform satisfactorily.

Current waveform quality and current control bandwidth are the figures of merit for current controllers in three-phase three-wire (3P3W) voltage-source converter (VSC) systems. When saturable inductors are employed as converter side inductors due to cost, size, and energy conversion efficiency benefits in 3P3W VSCs with conventional synchronous frame current control (CSCC); substantial drawbacks arise by means of these figures of merit. In the preceding part of this study, an inverse dynamic model based compensation (IDMBC) method has been proposed to deal with these drawbacks. Complementarily, this paper presents a thorough analytical investigation of the control system characteristics of CSCC and IDMBC methods that affect the current waveform quality and current control bandwidth. The analyses are verified via dynamic response and waveform quality simulations and experiments that employ saturable inductors reaching $1/9^{th}$ of the zero-current inductance at full current. The results obtained demonstrate the suitability of the IDMBC method for 3P3W VSCs employing saturable inductors.

Geometric information of scenes available with four dimensional (4-D) light fields (LFs) pave the way for post-capture refocusing. Light filed refocusing methods proposed so far have been limited to a single planar or a volumetric region of a scene. In this paper, we demonstrate simultaneous refocusing of multiple volumetric regions in LFs. To this end, we employ a 4-D sparse finite-extent impulse response (FIR) filter consisting of multiple hyperfan-shaped passbands. We design the 4-D sparse FIR filter as an optimal filter in the least-squares sense. Experimental results confirm that the proposed filter provides 64% average reduction in computational complexity with negligible degradation in the fidelity of multi-volumetric refocused LFs compared to a 4-D nonsparse FIR filter.

The paper aims to raise awareness in the domain of security and privacy concerns in network communication that takes place among machines. Knowing that there are lots of possibilities that an attacker or hacker may get a slight chance of causing exploits from network vulnerabilities leads to threats at personal and organizational level. In this paper, research has been carried out using survey methodology to gather user viewpoints on general awareness and importance of network security and privacy. The results will be used to support the overall significance of how a network should behave and work on behalf of the users. The main goal as developers and engineers is to prioritize and improve user satisfaction and data protection standard. Therefore, this paper will also discuss the methodologies and possible ways to offer the best strategies for protection against security and privacy attacks.

Multiunit activity (MUA) has been proposed to mitigate the robustness issue faced by single-unit activity (SUA)-based brain-machine interfaces (BMIs). Most MUA-based BMIs still employ a binning method for estimating firing rates and linear decoder for decoding behavioural parameters. The limitations of binning and linear decoder lead to suboptimal performance of MUA-based BMIs. To address this issue, we propose a method which consists of Bayesian adaptive kernel smoother (BAKS) as the firing rate estimation algorithm and deep learning, particularly quasi-recurrent neural network (QRNN), as the decoding algorithm. We evaluated the proposed method for reconstructing (offline) hand kinematics from intracortical neural data chronically recorded from the primary motor cortex of two non-human primates. Extensive empirical results across recording sessions and subjects showed that the proposed method consistently outperforms other combinations of firing rate estimation algorithm and decoding algorithm. Overall results suggest the effectiveness of the proposed method for improving the decoding performance of MUA-based BMIs.

In this paper, we address the problem of energy efficient resource optimization for downlink transmission in user-centric ultra-dense networks enabled by wireless access via nonorthogonal multiple access and wireless backhaul via beamforming. Our objective is to maximize the system energy efficiency by optimizing user/access point scheduling, subchannel assignment, and power allocation jointly. The problem is formulated as a nonconvex mixed-integer nonlinear programming problem which is NP-hard. We then transform it into a convex subproblem using the sum-of-ratios decoupling and the iterative successive convex approximation method. An overall algorithm is further developed to solve the subproblem iteratively. Simulation results show that the proposed algorithm has improved the system-wide energy efficiency significantly when compared to the benchmark scheme.

Full-duplex (FD) transmission has already been regarded and developed as a promising method to improve the utilization efficiency of the limited spectrum resource, as transmitting and receiving are allowed to simultaneously occur on the same frequency band. Nowadays, benefiting from the recent development of intelligent reflecting surface (IRS), some unique electromagnetic (EM) functionalities, like wavefront shaping, focusing, anomalous reflection, absorption, frequency shifting, and nonreciprocity can be realized by soft-controlled elements at the IRS, showing the capability of reconfiguring the wireless propagation environment with no hardware cost and nearly zero energy consumption. To jointly exploit the virtues of both FD transmission and IRS, in this article we first introduce several EM functionalities of IRS that are profitable for FD transmission; then, some designs of FD-enabled IRS systems are proposed and discussed, followed by numerical results to demonstrate the obtained benefits. Finally, the challenges and open problems of realizing FD-enabled IRS systems are outlined and elaborated upon.

This paper comments on our recently published conference paper entitled “An Initial Study on the Relationship Between Meta Features of Dataset and the Initialization of NNRW”. We point out that the above-mentioned article has a typographical error in proving that using Gamma distribution to initialize NNRW is not a good choice, and give the corresponding correct proof.

In a context of a virus that is transmissive by sputtering, wearing masks appear necessary to protect the wearer and to limit the propagation of the disease. Currently, we are facing the 2019-20 coronavirus pandemic. Coronavirus disease 2019 (COVID-19) is an infectious disease with first symptoms similar to the flu. COVID-19 appeared first in China and very quickly spreads to the rest of the world. The COVID-19 contagiousness is known to be high by comparison with the flu. In this paper, we propose a design of a mobile application for permitting to everyone having a smartphone and being able to take a picture to verify that his/her protection mask is correctly positioned on his/her face. Such application can be particularly useful for people using face protection mask for the first time and notably for children and old people. The designed method exploits Haar-like feature descriptors to detect key features of the face and a decision-making algorithm is applied. Experimental results show the potential of this method in the validation of the correct mask wearing. To the best of our knowledge, our work is the only one that currently proposes a mobile application design “CheckYourMask” for validating the correct wearing of protection mask.