Abstract: The digital preservation of batik, a world intangible cultural heritage, is hindered by the difficulty in performing accurate semantic segmentation on its complex patterns with limited annotated samples. To address this few-shot learning challenge, we constructed a few-shot batik pattern dataset, and proposed a novel network architecture centered on attention weighting and hierarchical decoding. Our method leverages a pre-trained ResNet101 backbone for transfer learning to establish a strong feature foundation. It incorporates a dual-attention module that combines spatial and channel attention to dynamically highlight semantically rich regions and intricate texture boundaries specific to batik. For multi-scale context aggregation, a lightweight module utilizing parallel dilated convolutions is introduced to efficiently capture features from varying receptive fields. Finally, a hierarchical decoder progressively integrates these enhanced, multi-scale features with high-resolution shallow features to reconstruct precise segmentation maps. Comprehensive evaluations on a dedicated batik dataset show that our model achieves state-of-the-art performance, with a mean Intersection over Union (mIoU) of 79.22% and a Pixel Accuracy (PA) of 92.47%. It notably improves over the strong DeepLabV3+ baseline by 3.3% in mIoU and 0.95% in PA, demonstrating its effectiveness for the task of batik pattern segmentation under data-scarce conditions.

Abstract: The mandates of the Circular Economy (CE) and Industry 5.0 necessitate unprecedented life cycle traceability and management for complex products, such as electronics and lithium-ion batteries. The Digital Product Passport (DPP) has emerged as the centralized data framework for this transition. However, effective DPP implementation requires the robust integration of technologies to overcome the technical challenges of scalability, data integrity, and legacy system management. This work shifts from a high-level conceptual overview to an in-depth technical analysis, detailing the integration architecture of the DPP with the Internet of Things (IoT), Digital Twins (DTs), and Artificial Intelligence (AI). Specifically, we focus on how IoT, via embedded sensors and NFC/RFID tags, acts as the dynamic data carrier that feeds DT. For the battery sector, we propose a technical framework that utilizes the DPP to continuously track State of Health (SoH) and State of Charge (SoC), throughout the entire life cycle. AI/Machine Learning is then integrated within the DT to enable accurate predictions of degradation and failure, directly addressing reliability concerns and optimizing the second life and recycling phases. Our focus is on tackling critical bottlenecks, such as interoperability between IT systems and data storage scalability (e.g., via robust Cloud or Blockchain solutions), ensuring the DPP is established not just as a static data repository, but as a dynamic and predictive tool for product reliability management and regulatory compliance.

Abstract: Multimodal Named Entity Recognition (MNER) leverages both textual and visual information to improve entity recognition, particularly in unstructured scenarios such as social media. While existing approaches predominantly rely on raster images (e.g., JPEG, PNG), Scalable Vector Graphics (SVG) offer unique advantages in resolution independence and structured semantic representation—an underexplored potential in multimodal learning. To fill this gap, we propose MNER-SVG, the first framework that incorporates SVG as a visual modality and enhances it with ChatGPT-generated auxiliary knowledge. Specifically, we introduce a Multimodal Similar Instance Perception Module that retrieves semantically relevant examples and prompts ChatGPT to generate contextual explanations. We further construct a Full Text Graph and a Multimodal Interaction Graph, which are processed via Graph Attention Networks (GATs) to achieve fine-grained cross-modal alignment and feature fusion. Finally, a Conditional Random Field (CRF) layer is employed for structured decoding. To support evaluation, we present SvgNER, the first MNER dataset annotated with SVG-specific visual content. Extensive experiments demonstrate that MNER-SVG achieves state-of-the-art performance with an F1 score of 82.23%, significantly outperforming both text-only and existing multimodal baselines. This work validates the feasibility and potential of integrating vector graphics and large language model–generated knowledge into multimodal NER, opening a new research direction for structured visual semantics in fine-grained multimodal understanding.

Abstract: The widescale deployment of radars, distributed across a platform and across multiple platforms for reliable 360° situational awareness (SA), introduces the challenge of radar interference. Interference can broadly be categorised as self-interference (between radars mounted on the same platform) and mutual interference (signals received from radars on other platforms). Both types of interference impede the reliability of SA delivered by such systems, particularly in dense environments where numerous radars operate simultaneously within the same frequency band. This work presents a comprehensive evaluation of a multi-modal beamforming approach that combines unfocused synthetic aperture radar with the traditional Multiple-Input, Multiple-Output beamformer to enhance radar resolution and suppress interference. Additionally, various aspects of sensor configurations defining hardware and software capabilities of state-of-the-art radars are discussed, and a systematic analysis of signal-to-interference-plus-noise ratio at each step of the processing is presented. Extensive simulations and experimental results in both automotive and maritime environments are shown to validate the effectiveness of the proposed approach.

Abstract: The design, fabrication, and characterization of a highly transparent and flexible monopole antenna optimized for the 3–6 GHz frequency band is presented in this paper. In traditional Transparent Conductive Oxide (TCO) designs, there is always a trade-off between the RF efficiency and transparency. Therefore, an Aerosol Jet® 5X system was used to directly print a silver nanoparticle mesh over a 50-µm polyimide (PI) substrate. With this fabrication method, a durable structure was yield which works well both electrically and mechanically with 85% transparency and a gain of −2.5 dBi. In order to demonstrate how the antenna is flexible and compatible with other devices, it was bent over a cylindrical body and was integrated with a commercial solar panel. The results show that impedance matching and radiation characteristics of the antenna remain stable under bending conditions, and no critical decrease was observed in solar energy harvesting. Consequently, this design presents a suitable solution for energy-autonomous IoT systems, smart windows, and CubeSat applications.

Abstract: In this paper, we propose an correntropy weighted extended Kalman filter (CWEKF) method to address the challenges of low estimation accuracy and poor robustness in sensorless rotor speed estimation for doubly-fed induction generators (DFIGs). Firstly, based on Faraday's law of electromagnetic induction and the mechanical motion equation, we derive a DFIG nonlinear state-space model. This model quantifies the sources of nonlinearity arising from cross-coupling terms and product terms, providing a precise model foundation for rotor speed estimation. Secondly, we introduce correntropy theory to design a residual dynamic weighting scheme. By quantifying the local similarity between current and historical residuals, the scheme adaptively adjusts the noise covariance estimation weights, suppressing the interference of outdated data. Combined with the Chi-squared test, we derive an adaptive kernel bandwidth mechanism, balancing the response speed to noise variations and the estimation accuracy in steady-state. Additionally, we further integrate Huber robust weighting and regularization techniques for constructing a hybrid weighting mechanism and optimizing the covariance positive-definiteness correction to address the numerical stability deficiencies of the original algorithm. Using the Lipschitz condition and Lyapunov theory, we prove the mean-square exponential boundedness of the CWEKF estimation error. Finally, we build a DFIG vector control model using MATLAB. Comparative experiments are conducted with EKF, AEKF, and RWEKF under three operating conditions. The results show that the CWEKF has a maximum rotor speed estimation error \( \leq \) 5 r/min, and the response time has been reduced by 65% compared to the traditional EKF, exhibiting significantly improved robustness under parameter variations and strong noise conditions.

Abstract: Thermoelectric generators (TEGs) enable compact waste-heat energy harvesting but require high-gain DC–DC conversion due to their low output voltage for microgrid interfacing. This work proposes a novel TEG-supplied two-stage architecture consisting of a perturb and observe (P&O)-based MPPT boost converter followed by a modified Z-source converter regulated through an advanced control strategy. The modified Z-source topology enables high voltage gain without extreme duty ratios and mitigates switching losses by eliminating diode-related reverse-recovery effects via synchronous operation. To enhance dynamic performance, an advanced model predictive control (MPC) approach is adopted and benchmarked against conventional MPC and sliding mode control (SMC). Simulation results under hot-surface temperature variations demonstrate that the proposed system maintains stable 400-V DC bus regulation at a 100-W output level. In contrast, conventional MPC exhibits switching-frequency deviations that increase switching losses during transients, while conventional SMC suffers from significant voltage deviations. After the temperature variation tests, the proposed control strategy is subjected to a ±20% load test, in which it maintains 400-V regulation with nearly fixed-frequency operation, confirming its superior dynamic suitability for TEG-based systems operating at 50 kHz. The proposed innovative design provides a new perspective for TEG researchers while supporting sustainable waste-heat energy utilization.

Abstract: The development of cutting-edge vehicle communication technology targeted at enhancing road safety, traffic efficiency, and autonomous mobility has been expedited by the emergence of intelligent transportation systems. Direct wireless connection between vehicles is made possible via vehicle-to-vehicle (V2V) communication, which speeds up the exchange of vital safety data like speed, trajectory, braking status, and road conditions. By incorporating Artificial Intelligence (AI) into V2V networks, predictive skills are improved, enabling cars to foresee possible risks and react proactively as opposed to reactively.With an emphasis on its operational architecture, supporting technologies, recent research advancements, and future network paradigms, this study provides a thorough scientific overview of V2V communication. The report demonstrates how next-generation wireless technologies, edge computing, and AI-driven analytics are converting vehicle networks into intelligent safety ecosystems. Important issues are also looked at, such as interoperability, scalability, cyber security hazards, and latency limitations. The study comes to the conclusion that AI-enabled V2V communication will be a key component of completely autonomous and accident-free transportation systems.

Abstract: Trajectory planning algorithms are essential in human-robot collaboration (HRC), as they must generate efficient trajectories for seamless interaction. Given the risks and complexity of testing in real-world scenarios, a virtual environment was developed in Unity 3D, integrating a digital twin of the UR3 robot that delivers workpieces to a user equipped with a Meta Quest device. The RRT, RRT-Star (RRTS), and RRT-Connect (RRTC) algorithms were evaluated using ANOVA and Tukey post-hoc tests, considering the following response variables: safety, feasibility, smoothness, and computation time across three experimental scenarios characterized by (i) low, (ii) medium, and (iii) high levels of movement of the participant’s left hand. The statistical results indicate that RRTC exhibited the best performance in terms of smoothness and computation time. Based on these findings, a multicriteria decision-making analysis was conducted using the Analytic Hierarchy Process (AHP), combining quantitative evidence derived from the statistical analysis with expert judgments supported by bibliographic references. This multicriteria analysis enabled the coherent integration of the different evaluation criteria and concluded that RRTC is the most suitable alternative for collaborative assembly tasks in CHR environments.

Abstract: Years of empirical research and technical effort have brought the world into an era defined by modern engineering amenities. The Brushless Direct Current or BLDC motor for electric propulsion systems stands as a significant innovation within this modern period. These motor drives are currently utilized across various sectors including automation systems and electric vehicles along with robotics and industrial applications. While popular control methods like fuzzy logic or PWM offer distinctive functionalities they often struggle with the nonlinear behavior and load variations inherent in BLDC motors. High speed configurations and parametric varieties further complicate stability. To enhance performance a rugged and quickly adaptable controller is required to minimize ripples and improve response times. This study proposes an adaptive PID controller that combines the strengths of a PID autotuner with a standard PID framework. The autotuner provides self adjusting parameters to handle nonlinearities and speed variations through a frequency response estimation process while the fast responsive PID controller compensates for the slow performance of the autotuning phase. These elements work in tandem to automatically readjust parameters for superior accuracy. Using the MATLAB simulation platform a benchmark motor system was developed to verify these results. The proposed design was compared against traditional PID and FPA speed controllers. Results demonstrate that the adaptive PID controller achieves less ripple and an overshoot of less than one percent while maintaining excellent load performance. This research contributes a reliable and highly adaptable control solution for modern BLDC motor systems. Benchmark results in MATLAB Simulink confirm that the proposed controller maintains an overshoot threshold of less than 1% and consistently yields lower torque ripple than existing PID and FPA models.

Abstract: This paper presents the design, modeling, and numerical validation of a compact artificial magnetic conductor (AMC)–backed flexible UHF RFID tag antenna intended for on-body biomedical and wearable sensing applications. Human tissue proximity typically causes severe detuning, radiation efficiency degradation, and increased specific absorption rate (SAR) for conventional RFID tag antennas. To address these limitations, a miniaturized AMC metasurface based on a modified Jerusalem-cross geometry with meandered and interdigitated features is developed on a high-permittivity biocompatible substrate using CST Studio Software. Full-wave simulations demonstrate that the proposed design, with an ultra-compact footprint of 0.0246 λ2 (32.12 mm × 64.24 mm), functions as an effective shielding element, significantly enhancing the tag antenna gain and reading range by an order of magnitude compared to conventional on-body tags, while simultaneously reducing backward radiation and SAR. The antenna demonstrates robust platform tolerance and excellent isolation from the human body, ensuring high reliability. Fabricated on a thin, flexible, biocompatible, silicon-doped dielectric substrate, this device also functions as an epidermal antenna for on-skin health parameter sampling. This research paves the way for advanced, non-invasive wearable medical devices with superior performance.

Abstract: This paper investigates the feasibility of interior permanent magnet (IPM) rotor for a 1MW-class high-speed permanent magnet synchronous machines (PMSMs) in hybrid propulsion system of electrified aviation. A double-layer IPM machine and a surface-mounted PM (SPM) benchmark machine with Halbach-array PMs, which are typical employed in the aviation applications, are designed using the same design specifications, the same stator, double-three-phase winding layout, physical air-gap length, outer and inner diameters of rotor, and the same materials. The rotor robustness of IPM machine using high-strength iron material has been verified through mechanical strength analysis with outstanding safety factor margin. The electromagnetic performances of IPM and SPM benchmark machines are compared. It is found that the IPM design can achieve similar high torque/power density and high efficiency to the SPM benchmark machine, using 48% less rare-earth PM materials and simpler rotor structure without carbon-fiber sleeve for easy manufacturing. The investigation confirms the feasibility of IPM topology for MW-class high speed aviation propulsion machines for lower cost and more sustainable purposes.

Abstract: High-performance difference patterns (DPs) are critical for compact and inte-grated microwave array systems, particularly in monopulse tracking and beam-scanning applications. However, the design of monopulse phased arrays with steep slopes, high directivity, low sidelobes, and symmetric main lobes remains challenging due to con-straints imposed by the array aperture and radome structure. In this paper, a novel design method is proposed to maximize the DP directivities for monopulse linear and planar phased arrays composed of microstrip patch antennas. The DP synthesis problem is first formulated as a nonconvex optimization model for directivity maximization. By fixing the reference phase of the DP slope and applying a first-order Taylor expansion of the quad-ratic function, the original problem is decomposed into a sequence of convex subproblems that can be solved efficiently. The proposed method fully exploits the flexibility of the phased array feed network, enabling directivity enhancement without altering the geo-metric configuration of the monopulse array. Finally, two numerical examples employing a radome-enclosed linear phased array and a uniform planar phased array are presented to demonstrate effectiveness of the proposed method in achieving the monopluse array DP synthesis with high directivity and symmetric main-lobes.

Abstract: In the era of renewable dominated grids and integration of dynamic load such as EV charging stations has increased the operational challenges in multifolds particularly in DC microgrids (DC MG). Traditional battery dominated grid’s energy management strategies (EMS) are often not capable of handling fast transients due to limitations of battery electrochemistry. To overcome this limitation, an hierarchical hybrid energy management strategy is proposed that uses the combination of data driven and metaheuristic algorithms. The designed optimization framework consists of particle swarm optimization (PSO) and neural network (NN) implemented in central controller of 4 bus ringmain DC MG. A efficient decoupling of fast and slow storage dynamics is performed, where supercapacitor (SC) is optimized using NN and battery is optimized using PSO. This selective optimization reduces the computational overhead on the PSO making it more feasible for realtime implementation. The designed hybrid PSO-Neural EMS framework is initially designed on MATLAB and further validated on realtime hardware setup. Robustness of the control scheme is verified with various case studies such as, renewable intermittency, dynamic loading and partial shading scenarios. An effective optimization of SC in both transient and heavy load scenarios is observed. LabVIEW interfacing is used for MODBUS based interaction with PV emulators and DC-DC converters.

Abstract: The growing importance of integrating renewable energy sources (RES) into mainline railway traction networks stems from the sector's substantial electricity demands, traditionally met by carbon-intensive thermal generation. This paper addresses the potential of wind and solar power to enhance energy efficiency and reduce emissions in rail transport. It details the devel-opment of digital models for simulating DC traction power systems (TPS) coupled with RES, specifically wind turbines. Given the complexity of TPS, effective integration requires digital modeling that accounts for their unique properties. The proposed methodology, based on phase coordinates algorithms, offers a universal and comprehensive framework. It enables the identi-fication of various operational modes (normal, emergency, special) for diverse network com-ponents, including traction networks, transmission lines, and transformers. These models were used to simulate real-world train operations, generating data on electrical parameter dynamics and transformer thermal conditions. The results confirm that wind integration can improve energy efficiency, validating the methodology's practical applicability for RES projects in DC traction networks, including advanced high-voltage systems.

Abstract: Buildings located in highly urbanized areas have not been considered for photovoltaic (PV) deployment on building walls due to limitation of ground and rooftops space. As the need for increasing energy demand due to population growth in cities, and the advancements in the efficiency of semi-transparent (ST-PV) solar cell technology, the integration of ST-PV modules into building windows, become feasible. The present article proposes a novel methodology for calculating the incident solar energy on PV vertical modules deployed on building walls and windows facing the southern direction and obscured by a nearby building in front. The present work analyses analytically, for the first time, the incident energy and its distribution on PV vertical modules along a wall height. Monthly and annually direct beam, diffuse and global energies are calculated for different wall height, building separation and orientation. The results shows, for example, that both the front and rear building walls receive the same amount of annual direct beam energy 913 kWh/m² for a distance 25 m between the buildings. Decreasing the distance from 25 m to 10 m, decreases the annual incident global energy on the rear-building wall by 15 %.

Abstract: A digital pre-distortion (DPD) scheme for concurrent multi-beam transmission in fully digital multiple-input, multiple-output (MIMO) systems, using fast Fourier transform (FFT) beamforming and so-called virtual array processing, is proposed. In a MIMO array with nonlinear power amplifiers (PAs), transmitting multiple beams concurrently yields intermodulation products that end up in both user and non-user directions. In the setting with few users in a large array, the array dimension will typically be much larger than the number of generated intermodulation products. At the same time, linearization per-PA is excessively costly for large arrays. This work shows that is instead possible to linearize the system by producing predistorted user beams, and non-user intermodulation products, through DPD processing in a virtual array, of a much smaller dimension than the physical array. Theoretical derivations and simulation examples show how this approach can lead to manyfold reductions in DPD complexity.

Abstract: To solve the problem of constructing the frequency responses (FR) of filters on switched capacitors, which belong to the class of electronic circuits with a periodically changing structure, a method for modeling them in Micro-Cap and Delta Design environments is proposed. It allows you to evaluate the nature of changes in the FR of such filters in the time domain. As an example, a comparative analysis of the frequency response of a second-order analog bandpass filter, as well as two bandpass filter circuits with switching resistors and capacitors, is given. An assessment of the current state of EDA and trends in their development is given.

Abstract: This work investigated the structural, morphological, electrical and photovoltaic properties of n-ZnNiO/p-Si heterostructures. ZnNiO nanocomposite thin films were fabricated on p-Si (100) substrates using pulsed laser deposition, enabling the formation of n-type oxide/p-type silicon heterojunctions. The crystalline structure and surface morphology of the deposited thin films were examined using X-ray diffraction and scanning electron microscopy, revealing well-defined crystalline features and uniform surface morphology. The electrical characteristics were analyzed through current–voltage measurements, allowing the extraction of key diode parameters. In addition, the optoelectronic response under ultraviolet illumination was investigated, demonstrating pronounced photosensitivity in the UV spectral range. Several important electrical and optoelectronic parameters relevant to ultraviolet photodetection were determined and discussed. The obtained results indicate that ZnNiO-based heterostructures combined with silicon substrates constitute a promising material platform for advanced optoelectronic and ultraviolet applications.

Abstract: On-chip integration of two-dimensional (2D) materials provides a promising route for implementing nonlinear integrated photonic devices that break existing barriers and unlock new capabilities. Although 2D materials with ultrahigh optical nonlinearity have driven this technological progress, their high optical absorption also constitutes an Achilles’ heel. Whether 2D materials can overcome their intrinsic absorption and generate net gain (NG) via optical parametric amplification (OPA) processes is a critical and intriguing question, which is central to many nonlinear optical applications. Recently [1], we experimentally demonstrated enhanced OPA and achieved NG in silicon nitride waveguides integrated with 2D graphene oxide (GO) under pulsed pumping. Based on material parameters from this work, this perspective systematically analyzes the feasibility of achieving NG in more widely used, yet more challenging, scenarios involving silicon waveguides incorporating GO and continuous-wave pumping. The results show that a gap still exists toward achieving this goal, but it can be bridged through combined efforts in optimizing waveguide structure, reducing loss of GO, and improving GO’s thermal stability. We also investigate different waveguide structures as well as other 2D materials, and analyze the gap in each case. This work provides a critical roadmap and useful guidance for future developments towards achieving NG via OPA in integrated photonic devices incorporating 2D materials.