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.

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.

Artificial Intelligence (AI) has emerged as a driving force in the fourth industrial revolution, automating labor tasks and transforming various economic sectors. Although it presents significant opportunities to enhance efficiency and accuracy, its implementation requires a solid strategy and the management of ethical challenges, such as algorithmic bias. AI is revolutionizing engineering by optimizing designs, identifying maintenance issues, and improving product quality. Furthermore, it fosters data-driven decision-making and business innovation. However, it is essential to balance its adoption with human skills development and ethical considerations to maintain a competitive edge in a constantly changing world.

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.

The integration of Blockchain technology with Enterprise Resource Planning (ERP) systems presents a novel approach to enhancing organizational data security, transparency, and operational efficiency. This research navigates through the uncharted waters of this integration, exploring its potential, challenges, and implications through a comprehensive investigation and three distinct case studies. The case studies, derived from varied industries, illuminate the practicalities, hurdles, and triumphs experienced by organizations in implementing and operating within a Blockchain-enhanced ERP environment. Through a meticulous comparative analysis, this study aims to unravel the strategic, technical, and organizational layers involved in such integration, providing insights and recommendations for practitioners and researchers embarking on this digital expedition. The findings of this research are anticipated to pave the way for further explorations and developments in the realm of ERP and Blockchain convergence, steering the organizational technological advancements towards a future of secured, transparent, and efficient operational ecosystems.

The impact of AI technologies on different sectors has been profound, and one area where significant changes have occurred is education. In this abstract the integration of artificial intelligence technologies in education is explored, highlighting their potential advantages, challenges and ethical considerations. There are a wide range of tools and techniques in use for the application of AI to education, e.g. Intelligent Coaching Systems, Personalized Learning Platforms, Automated Scoreboards or Virtual Classrooms.

Learning graph topology online with dynamic dependencies is a challenging problem.  Most existing techniques usually assume the generative model to be a diffusion process instigated by a graph shift operator (GSO) and that a first-order method,  such as proximal gradient or least-mean-square (LMS), are used to track the graph topology.  However, they are often susceptible to noisy observations and does not perform well against second-order methods.  This work proposed two forward-backward splitting algorithms called the proximal Newton-iterated extended Kalman filter (PN-IEKF) and PN-IEKF-vector autoregressive (PN-IEKF-VAR) algorithms to track non-causal and causal graph topology with dynamic dependencies, respectively.  The proposed methods directly maximize the posterior probability distribution of the observable graph signal and graph matrix, which make our PN-IEKF framework to be more robust toward additive white Gaussian noise.  The two methods can directly handle streaming data which process them as they become available.  Effectiveness of the proposed methods can be further improved by including a $T$-squared detector in the tracking procedure, which helps to inject proper perturbation to the latent dynamic model such that the time-varying nonstationary graph can be reacquired faster amid abrupt changes in the underlying system. Results on relative error and normalized mean square error using synthetic data on Erd\fH{o}s-R\'enyi graph establish the efficacy of the proposed approach. Simulation results using data from the Dataset for Emotion Analysis Using EEG, Physiological and Video Signals (DEAP) and National Oceanic and Atmospheric Administration (NOAA) are encouraging. Computational and time complexity analysis of the proposed algorithm are given and compared with other algorithms.

Micro-electro mechanical systems (MEMS) accelerometer has been implemented in various applications across industries. To expand its application to textiles, here we propose a facile fabrication and packaging method for fiber-shaped flexible MEMS thermal accelerometer. The sensing structure was fabricated through a standard microfabrication process on a thin polyimide film substrate. The film was then cut into stick shape and packaged inside a flexible resin tube through a facile insertion process. A sensitivity improvement strategy by mitigating the thermal mass around the sensing structure was also introduced. Gravitational acceleration measurement using the proposed sensors was demonstrated using an inclination stage. Finally, measurement of human hand movement using the proposed sensors was demonstrated.

The interference and diffraction are two fundamental phenomena in optics. The patterns evolution phenomena have been shown for the interference and diffraction experiments respectively. In this article, we show for the first time the novel patterns, the Interference + Diffraction Hybrid Patterns of the novel double slit experiment, namely the interference pattern embedded in diffraction patterns. We also show how the patterns evolve, i.e., how the Interference + Diffraction Hybrid Patterns are formed. blob:https://www.techrxiv.org/c7b6a724-f88b-43cd-9693-b4c579429ded It is a challenge to theoretically interpret/describe.

In the rapidly evolving landscape of health care, the security and management of patient data emerge as paramount concerns. Traditional centralized systems have shown vulnerabilities, leading to breaches and unauthorized access. This paper delves into the transformative potential of blockchain technology as a solution to these challenges. Blockchain, primarily recognized for its role in cryptocurrency, offers features such as decentralization, immutability, and transparency, making it a promising candidate for secure health care data management. Through an exploration of its fundamental principles and a comparative analysis of two case studies, the practical implementations and outcomes of blockchain in health care settings are highlighted. The findings suggest that while blockchain presents significant advantages, its integration into health care is accompanied by certain limitations and ethical considerations. As the health care industry stands on the cusp of a potential shift in data management, this paper provides insights, recommendations, and a vision for the future, emphasizing the promise and implications of blockchain technology.

The ribbon cutting robot is an approach to inaugurate different events and ceremonies with the help of robot that is interfaced with bluetooth module for communication. The system solely relies on the bluetooth powered control of robot via mobile application and a heating filament is used for cutting of ribbon.

The envisaged fifth-generation (5G) and beyond networks offer unprecedented breakthroughs in media service delivery, stringent requirements, and challenging use cases. In such context, point-to-multipoint (PTM) communications have proved their high efficiency in delivering high-quality multimedia services over wireless networks. For the upcoming 5G system Releases and beyond, the Multicast/Broadcast Services (MBS) capability is an appealing feature to address the ever-growing traffic demands, disruptive multimedia services, massive connectivity, and low-latency applications. This paper addresses the multicast group (MG) channel quality diversity through dynamic single-rate and multi-rate multicasting aided orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) over 5G MBS use cases. We characterize and identify conditions and variables interrelation for an effective coexistence of the benchmark conventional multicast scheme (CMS) and subgrouping based on OMA (S-OMA) and subgrouping based on NOMA (S-NOMA). We propose tailored multicast radio resource management (RRM) strategies aided by K-Means subgrouping and particle swarm optimization (PSO). Moreover, we define the interrelation among the variables that shape the performance of the proposed solution and their dynamic behavior in terms of the users’ reception conditions, multimedia service constraints, and network parameters. The presented results are validated under a wide range of users’ channel quality distributions, and network conditions recreated through link-level simulations of a 5G MBS use case operating in millimeter-wave (mmWave).

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.

Integrated local multi-energy systems are recognized as a promising option to achieve the ambitious energy and climate goals set by the European Commission for 2030. The nature of integrated systems requires a sound combination of interdisciplinary methodologies and complementary tools. Creating an efficient architecture capable of exploiting synergies between tools is therefore crucial for designing, analysing and operating integrated energy systems. The joint application of different tools needed for analysing a local system can be very demanding and time-consuming due to vastly different data structures and functionalities. To address the need for complementary tools, the aim of this paper is to establish and test an integrated modelling architecture allowing the interaction of tools into a modular toolbox for the optimal planning of integrated local multi-energy systems, and also present key preliminary outcomes.

We propose a new decoding algorithm for binary codes called ordered reliability direct error pattern testing (ORDEPT) which is based on completion of partial error patterns arranged in the order of likelihood. Based on ORDEPT we proposed a new adaptive-hybrid algorithm for decoding of product codes. The proposed algorithms outperform state-of-the-art decoders on various metrics for a variety of single and product-like codes.

The series includes two parts, articulating the two novel avenues of research on intelligent fault diagnosis (FD) for nonlinear feedback control systems. In Part I of the series, we design a novel FD paradigm by elaborating an invertible neural network (INN) for feedback control systems.

This paper presents an analytical approach to explore the damping effect of inner loops on grid-forming converters. First, an impedance model is proposed to characterize the behaviors of inner loops, thereby illustrating their influence on output impedance shaping. Then, based on the impedance representation, the complex torque coefficient method is employed to assess the contribution of inner loops to system damping. The interactions among inner loops, outer loops, and the ac grid are analyzed. It reveals that inner loops shape the electrical damping torque coefficient and consequently influence both synchronous and sub-synchronous oscillation modes. The virtual admittance and current control-based inner-loop scheme is employed to illustrate the proposed analytical approach. The case study comprises the analysis of impedance profiles, the analysis of damping torque contributed by inner loops under various grid strengths, and the comparison between dq-frame and αβ-frame realizations of inner loops. Finally, simulation and experimental tests collaborate with theoretical approaches and findings.

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.