Target tracking using bearing-only measurement with UAV is extensively studied recently years because of its potential value in engineering applications. This brief revisits the problem about slowly moving target tracking using the target circumnavigation scheme. Instead of developing improved algorithm, we mainly present some new results for the typical circumnavigation algorithm in the literature. From a different perspective, the overall convergence analysis is conducted for the typical algorithm under the same conditions assumed in existing work, and it is found that the conclusion made in prior work is not accurate because of the implicity assumption. In this brief, we present a more accurate convergence analysis for the typical target circumnavigation algorithm, simultaneously giving the sufficient conditions for convergence or divergence. Simulation examples validates our results.

A novel metasurface reflector unit cell based on two coupled resonators is proposed and demonstrated for real-time reconfigurable beamforming in the X-band (8-12 GHz). The unit cell is composed of a split-ring resonator (SRR) with tunable capacitance and a dipole-ring resonator (DRR) with tunable resistance, whose collective variations allow for control over the complex reflectance at a desired frequency. To gain physical insight into its working mechanism, the proposed unit cell is modeled as a coupled Lorentz oscillator using the surface susceptibility description of the unit cell. Thereafter, a metasurface reflector based on the proposed unit cell is demonstrated in full-wave simulations to achieve various beamforming examples, such as beam-steering, side-lobe level control, beam-steering with amplitude control, and multi-beam patterns, from a single normally incident plane wave excitation. Three metasurface reflectors are fabricated to experimentally demonstrate the proposed concept; the first is based on a SRR with a varactor diode, the second is based on a DRR with a PIN diode, and the third is based on the proposed coupled SRR-DRR configuration with both varactor and PIN diodes, for simultaneous magnitude and phase control. The metasurface reflector based on the coupled resonator unit cell is experimentally demonstrated to achieve versatile beam transformations including beam-steering with amplitude control and multi-beam patterns.

This paper deals with the implementation of a light fifth generation (5G) base station (gNB) intended  for specific use cases requiring an airborne deployment of the network. We focus on the radio unit (RU) and give details on the components, the implementations, and the technical choices that have been made, driven by the strong constraints inherent to the considered use cases.

Today, the most feasible and easy form of Human-Computer Interaction (HCI) is the use of mobile applications. Mobile applications have become an integral part of our day-to-day lives reaching out to each corner of society and caters to the problems. The applications available are customized, but the incorporation of intelligence especially for the disabled to assist their lives is absent. This motivates the research work specified in this paper is non-commercial application development for society. Designing applications that are highly scalable and intelligent to minimize problems faced by differently-abled , is explicitly covered here. The proposed system makes use of Speech-To-Text and Text-To-Speech conversion techniques for back end and provides enhanced scalability by allowing users to regulate the functionality of the application, customized based on their preferred region, topic and time stamps. Applications and devices meant for the differently-abled do exist, however, most of them have unidirectional and singular functionality with minimum customizability. In most cases, very complicated hardware systems are associated with the proposed work which in turn makes it expensive to maintain. The proposed application aims to offer voice-based functionality automation, remote health monitoring, and pattern recognition for recommendation systems, explicitly for the visually impaired and the solitary elderly The reading of remote health monitoring is close to the actual value with a standard deviation of 1.45% or 0.76 on the average.

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Metaheuristic algorithms for constrained optimiza- tion problems have become popular because of their ease of use and capability to obtain global solutions. However, these population-based algorithms can be computationally expensive and may suffer from low accuracy due to the difficulty in obtaining feasible points. We present a novel algorithm, re- ferred to as SASS-CMODE, by integrating a modified Improved Multi-Operator Differential Evolution (IMODE) algorithm with the Self-Adaptive Spherical Search (SASS) method. IMODE is modified to make it suitable for solving constrained problems, leading to a new algorithm termed Constrained Multi-Operator Differential Evolution (CMODE). SASS-CMODE is capable of achieving solutions with high feasibility rate and high accuracy by utilizing SASS to identify good feasible points and CMODE to achieve accurate solutions with fewer function evaluations. To evaluate its performance, we test SASS-CMODE to 57 engi- neering problems. The results demonstrate its superiority over other state-of-the-art optimization algorithms. SASS-CMODE is also employed to solve a constrained optimization problem on identifying optimal levels of non-pharmaceutical interventions to control an epidemic, showcasing its versatility and applicability in real-world scenarios.

While digital payments have transformed e-commerce, they are not without drawbacks. The most noticeable issues arise from extended payment settlement times and the costs incurred from card fees, largely due to reliance on credit card payment networks. To address these shortfalls, real-time payments have been introduced by various central banks. Although real-time payment systems have been available for some time and present numerous advantages, their adoption in Australia is still limited. This paper provides an in-depth overview of Australia’s real-time payment network architecture, the New Payment Platform (NPP), including details of its service products and presents several use cases. While the aim is to inspire further innovations in the payment system utilising the NPP platform, the paper also examines the reasons for the limited adoption of real-time payments despite their clear advantages over traditional digital payment methods.

Recent quadruped robots achieve high performance motion control relying on optimization and reinforcement learning. However, there's ongoing research to achieve high performance motion control through dynamic motion implementation based on simple and dominant principles. In this paper, a control approach is proposed that employs admittance control in the rotating workspace to project the SLIP dynamics onto articulated legs, which describes the dynamic behavior of the compliant leg. Furthermore, a comprehensive control framework is presented that allows the motion control of the quadruped robot exploiting the SLIP dynamics by establishing a Jacobian that relates the motions of trunk and SLIP-realized legs. Notably, the robotic leg used in this work features high reduction ratio gears, typically unsuitable for the legged robots due to low backdrivability and significant joint nonlinearity. Through the vertical jumping motion, the effectiveness of the proposed method is validated, which enables the quadruped robot motion control with SLIP-realized legs, even with high gear ratio actuators.

This article describes the design science research process involved in developing a new tool for teaching K-12 children about the complete machine learning workflow, specifically with image classifiers. The tool was designed alongside 36 6-hour classroom workshops in 12 schools with 213 4th and 7th grade children, for which research permission was obtained from guardians and the municipality. Additionally, a usability study provides further design insights along with a co-design process involving teachers. The goal is to address the limitations with existing tools that have been critiqued in literature by first detailing the design principles that must guide such tool development.

Innovations in optical networks created new technological challenges as routing and spectrum allocation (RSA) problem, fragmented spectrum, the need for rapid and efficient channel restoration, and put pressure on the operation and maintenance. With a lot of lots of variables or knobs to adjust, it is crucial to improve the automation as traditional algorithms are not able to handle the network efficiently. So, this requires sensors, network abstraction, actuators and SDN (Software Defined Networking) in order to run algorithms on top, control, manage the network and make decisions. In addition to this, there are the requirements for low-margin systems and probabilistic shaping. So, machine learning (ML) provides a collection of techniques to adapt to this dynamic and flexible environment and provide a network that learn from experience, optimize and make the networks agile, robust, dynamic, and smarter. At the same time, network automation together with machine learning may cause an explosion in power consumption, making this solution costly, inefficient and not sustainable.

In the last years, location-aware services and network management have driven the demand for user location estimation in mobile networks. Nevertheless, the location obtained from user terminals is not usually accessible to mobile operators. In addition, available cell Key Performance Indicators (KPI) vary highly from network to network, and only a few of them are always enabled widely. Currently prevalent Machine Learning (ML) based solutions have achieved high precisions, but they are bounded to a trained scenario, restricting their application to new areas. We propose a method for creating scenario-agnostic prediction models which solves the above problems by applying feature engineering, over a small set of easily obtainable KPIs, applicable for any ML method. Finally, the performance of the proposed method is demonstrated using a real network dataset.

Functional and sensory augmentation of living structures, such as the human skin and plant epidermis, offers vast opportunities for biology-machine interface, wearable health, and environmental monitoring. However, current sensor and electronic formats could be obstructive to their hosts’ inherent sensations or physiological changes. Challenges are also faced in widening the augmentation of living structures without drastically increasing the global environmental and ecological burdens. Here, we demonstrate imperceptibly augmented living systems, through in situ tethering of poly (3,4-ethylenedioxythiophene) : polystyrene sulfonate (PEDOT:PSS)-based organic bioelectronic fibres. Customising fibre tether patterns and modalities enable applications from biopotential acquisition and skin-gated organic electrochemical transistors, to augmented touch and plant interfaces. The open networks formed by the intrinsically substrate-free fibres provide a biomorphic interface, while supporting direct coupling with microelectronics and e-textiles. We further demonstrate conceptual fibre formats for on-demand device repair, upgrade, and recycle, or for enhancing electromechanical stability against touch. Our work may unfold wearable technologies that are simultaneously biologically-adaptable and sustainable.

Reflector antenna systems used in applications such as radio astronomy and radiometry are required to have a stable and predictable receiving sensitivity response as a function of frequency, among other characteristics. It is well known that a chromatic aberration, or ripple, occurs in the frequency response of the these systems due to the multiple reflections between the feed antenna and reflector surface. In clear aperture optics, such as an offset Gregorian system, this effect is drastically reduced by removing the feed out of the optical path. However, this paper shows how diffraction mechanisms may cause further ripples in the response, whose structure is a strong function of reflector pointing angle. For the analysis herein the reflector/feed interaction is completely removed from the simulation to highlight the diffraction interference effects, which are illuminated by a graphical illustration of the mechanisms at play.

The effect of Radiation on the semiconductor-oxide interface, inducing interface trap states, has generally only been experimentally measured, which makes it difficult to quantify the impact of this radiation on device electrostatics. For an Ultra-Thin-Body (UTB) MOS device, the 1-D Band structure along the direction of confinement, if solved selfconsistently with the 1-D Poisson’s equation, while varying the band edge energy (∆Eedge) at the Si−SiO2 interface, can enable the quantification of the effect of interface trap states on channel electrostatics. In this work, we present an approach to correlate the radiation dose to the band edge energy (∆Eedge), thus enabling the band structure-based approach to be used to quantify the effect of these radiation-induced traps on the device electrostatics. We show a methodology that co-relates the interface charge induced by ∆Eedge and the charge yield, due to different radiating particles, on the Si − SiO2 interface. After identifying appropriate values of ∆Eedge for different particles and doses, the degradation due to radiation on the channel electrostatics can be accurately simulated with the atomistic band structure-based methodology. We also show an approach to extend this methodology to lower device temperatures, thus effectively quantifying the effect of radiation dose on UTB device electrostatics for a wide range of device temperatures.

Machine learning-enabled medical imaging analysis has become a vital part of the automatic diagnosis system. However, machine learning, especially deep learning models have been shown to demonstrate a systematic bias towards certain subgroups of people. For instance, they yield a preferential predictive performance to males over females, which is unfair and potentially harmful especially in healthcare scenarios. In this literature survey, we give a comprehensive review of the current progress of fairness studies in medical image analysis (MedIA) and healthcare. Specifically, we first discuss the definitions of fairness, the source of unfairness and potential solutions. Then, we discuss current research on fairness for MedIA categorized by fairness evaluation and unfairness mitigation. Furthermore, we conduct extensive experiments to evaluate the fairness of different medical imaging tasks. Finally, we discuss the challenges and future directions in developing fair MedIA and healthcare applications.

This manuscript presents a novel cloud detection algorithm utilizing a neural network technique, developed for identifying cloudy and clear pixels over sea ice in MISR images. Our methodology is based on an extensive multi-angular dataset covering various Arctic regions in different seasons, demonstrating strong performance metrics, including F score and Accuracy. We believe that this research contributes significantly to the remote sensing domain and offers a fresh approach to enhancing cloud detection accuracy over sea ice.

In this paper, we study the problem of extremely large (XL) multiple-input multiple-output (MIMO) channel estimation in the Terahertz (THz) frequency band, considering the presence of propagation delays across the entire array apertures, which leads to frequency selectivity, a problem known as beam squint. Multi-carrier transmission schemes which are usually deployed to address this problem, suffer from high peak-to-average power ratio, which is specifically dominant in THz communications where low transmit power is realized. Diverging from the usual approach, we devise a novel channel estimation problem formulation in the time domain for single-carrier (SC) modulation, which favors transmissions in THz, and incorporate the beam-squint effect in a sparse vector recovery problem that is solved via sparse optimization tools. In particular, the beam squint and the sparse MIMO channel are jointly tracked by using an alternating minimization approach that decomposes the two estimation problems. The presented performance evaluation results validate that the proposed SC technique exhibits superior performance than the conventional one as well as than state-of-the-art multi-carrier approaches.

In this article, we have assessed the transition from the conventional monolithic approach to integrating enterprise applications in Digital Experience Platforms (DXPs) to the modern paradigm of Microservices-based integration across a unified platform. Implementing Microservices based DXPs presents its own set of challenges, particularly in the realm of distributed systems, encompassing the intricacies of distributed computing and distributed databases. This article delves into the pertinent requirements and hurdles associated with the adoption of Microservices in DXPs. Digital Experience Platforms (DXPs) represent a unified strategy for integrating diverse technology stacks available in the market, catering to a wide array of touchpoints, including wearable devices, web interfaces, interactive voice response (IVR), and mobile applications.

This article studies the security gains and challenges in IBN from the aspect of enabling concepts and technologies. Furthermore, the article highlights potential solutions to existing challenges, outlines the standardization efforts, and summarizes the most important research gaps to advance future research in this direction.