Search Results (513)

Reconfigurable intelligent surface (RIS)-aided index modulation (IM) shows great potential for next-generation wireless communications. Nevertheless, obtaining channel state information (CSI) for RIS-based IM incurs high pilot overhead, particularly for multi-domain IM. In this paper, we integrate orthogonal frequency division multiplexing into RIS-aided differential reflecting modulation (DRM) communications, introducing the differential reflecting frequency modulation (DRFM) system. In DRFM, information bits are jointly conveyed through the activation permutations of reflecting patterns, grouped carriers, and constellation symbols. The transmitter combines the differentially coded reflecting-time block and the time–frequency block using the Kronecker product. This allows DRFM to operate without relying on CSI at the transmitter, RIS, or receiver. Moreover, we design a novel high-rate quadrature amplitude modulation (QAM) scheme for DRFM. Compared to PSK-based DRFM, this QAM scheme can boost either the throughput or the performance of DRFM. Simulation results illustrate the superiority of the DRFM system, along with an acceptable SNR penalty, compared to non-differential modulation with coherent detection. At the same spectral efficiency, the proposed QAM-aided DRFM outperforms schemes using traditional PSK, amplitude phase shift keying (APSK), and star-QAM constellation modulations. Full article

The growing incorporation of distributed energy resources (DER) in power distribution grids, although pivotal to the energy transition, increases operational variability and amplifies the exposure to disturbances that can compromise resilience and the continuity of service during contingencies. Addressing these challenges requires both a shift toward flexible distribution architectures and the adoption of advanced power electronics interfacing systems. In this setting, this paper proposes a resilience-oriented strategy for medium-voltage (MV) distribution systems and clustered hybrid AC/DC microgrids interfaced through solid-state transformers (SSTs). When a fault occurs along an MV feeder segment, the affected microgrids naturally transition to islanded operation. However, once their local generation and storage become insufficient to sustain autonomous operation, the proposed framework reconfigures the power routing within the cluster by activating an emergency low-voltage DC (LVDC) power path that bypasses the faulted MV section. This mechanism enables controlled power sharing between microgrids during prolonged MV outages, ensuring the supply of priority loads without oversizing SSTs or reinforcing existing infrastructure. Experimental validation on a reduced-scale SST prototype demonstrates stable grid-forming and grid-following operation. The reliability of the proposed scheme is supported by both steady-state and transient experimental results, confirming accurate voltage regulation, balanced sinusoidal waveforms, and low current tracking errors. All tests were conducted at a switching frequency of 50 kHz, highlighting the robustness of the proposed architecture under dynamic operation. Full article

In the context of fifth-generation (5G) communications and the dawn of sixth-generation (6G) networks, a surged societal demand on bandwidth and data rate and more stringent commercial requirements on transmission efficiency, cost, and reliability are increasingly evident and, hence, driving the maturity of reconfigurable millimeter-wave (mmW) and terahertz (THz) devices and systems, in particular, liquid crystal (LC)-based tunable solutions for delay line phase shifters (DLPSs). However, the field of LC-combined electronics has witnessed only incremental developments in the past decade. First, the tuning principle has largely been unchanged (leveraging the shape anisotropy of LC molecules in microscale and continuum mechanics in macroscale for variable polarizability). Second, LC-enabled devices’ performance has yet to be standardized (suboptimal case by case at different frequency domains). In this context, this work points out three underestimated knowledge gaps as drawn from our theoretical designs, computational simulations, and experimental prototypes, respectively. The first gap reports previously overlooked physical constraints from the analytical model of an LC-embedded coaxial DLPS. A new geometry-dielectric bound is identified. The second gap deals with the lack of consideration in the suboptimal dispersion behavior in differential delay time (DDT) and differential delay length (DDL) for LC phase-shifting devices. A new figure of merit (FoM) is proposed and defined at the V-band (60 GHz) to comprehensively evaluate the ratios of the DDT and DDL over their standard deviations across the 54 to 66 GHz spectrum. The third identified gap deals with the in-depth explanation of our recent experimental results and outlook for partial leakage attack analysis of LC phase shifters in modern eavesdropping. Full article

Maritime transportation accounts for approximately 80 percent of global trade volume, with modern vessels increasingly reliant on Software-Defined Radio (SDR) technologies for communication and navigation. However, the very flexibility and reconfigurability that make SDRs advantageous also introduce complex radio frequency vulnerabilities exposing ships to threats that jeopardize vessel security, and this disrupts global supply chains. This survey paper systematically examines the security landscape of maritime SDR systems through a threat modeling lens. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we analyzed 84 peer-reviewed publications (from 2002 to 2025) and applied the STRIDE framework to identify and categorize maritime SDR threats. We identified 44 distinct threat types, with tampering attacks being most prevalent (36 instances), followed by Denial of Service (33 instances), Repudiation (30 instances), Spoofing (23 instances), Information Disclosure (24 instances), and Elevation of Privilege (28 instances). These threats exploit vulnerabilities across device, software, network, message, and user layers, targeting critical systems including Global Navigation Satellite Systems, Automatic Identification Systems, Very High Frequency or Digital Selective Calling systems, Electronic Chart Display and Information Systems, and National Marine Electronics Association 2000 networks. Our analysis reveals that maritime SDR threats are multidimensional and interdependent, with compromises at any layer potentially cascading through entire maritime operations. Significant gaps remain in authentication mechanisms for core protocols, supply chain assurance, regulatory frameworks, multi-layer security implementations, awareness training, and standardized forensic procedures. Further analysis highlights that securing maritime SDRs requires a proactive security engineering that integrates secured hardware architectural designs, cryptographic authentications, adaptive spectrum management, strengthened international regulations, awareness education, and standardized forensic procedures to ensure resilience and trustworthiness. Full article

A mechanically reconfigurable phased array antenna (MRPA) with switchable radiation and scattering characteristics is presented. By adjusting the height of each array element, a continuous aperture phase response is achieved, enabling mechanical beam steering without electronic phase shifters. In the radiation mode, a height-induced phase gradient is used to steer the beam, while in the scattering mode, the same height–phase mapping mechanism produces multi-element phase cancellation for radar cross-section (RCS) reduction. An 8 × 8 prototype operating at 7.9 GHz is designed and validated. The array achieves beam steering up to ±45° with a peak realized gain of 21.5 dBi and an aperture efficiency of 87.6%. Moreover, more than 10 dB monostatic RCS reduction is obtained over a wide frequency range from 3 to 38 GHz. The proposed design provides a unified mechanical approach for radiation enhancement and scattering suppression in multifunctional phased arrays. Full article

Recent advances in deep learning have had a significant impact on biomedical applications, driving precise actions in automated diagnostic processes. However, integrating neural networks into medical devices requires meeting strict requirements regarding computing power, energy efficiency, reconfigurability, and latency, essential conditions for real-time inference. Field-Programmable Gate Array (FPGA) architectures provide a high level of flexibility, performance, and parallel execution, thus making them a suitable option for the real-world implementation of machine learning (ML) and deep learning (DL) models in systems dedicated to the analysis of physiological signals. This paper presents a review of intelligent algorithms for electrocardiogram (ECG) signal classification, including Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Convolutional Neural Networks (CNNs), which have been implemented on FPGA platforms. A comparative evaluation of the performances of these hardware-accelerated solutions is provided, focusing on their classification accuracy. At the same time, the FPGA families used are analyzed, along with the reported performances in terms of operating frequency, power consumption, and latency, as well as the optimization strategies applied in the design of deep learning hardware accelerators. The conclusions emphasize the popularity and efficiency of CNN architectures in the context of ECG signal classification. The study aims to offer a current overview and to support specialists in the field of FPGA design and biomedical engineering in the development of accelerators dedicated to physiological signals analysis. Full article

A reconfigurable analog beamformer for the use case of multiband Global Navigation Satellite System (GNSS) multiantenna receiver systems is designed and tested. The beamformer board operates in all existing GNSS frequency bands. In this paper, the two commonly used GNSS bands, the E1/L1 and E5a/L5 GNSS bands at 1.575 GHz and 1.176 GHz, respectively, are studied. An analog weighting of the complex excitation of up to 14 individual channels is realized using attenuators and phase shifters, digitally controlled by proprietary PC software. We present an analysis of the relative errors between the channels and a simple calibration of constant errors which is applied and validated. The beamformer is then demonstrated in an exemplary test case, to generate an ad hoc pattern from an array of antennas. Full article

The widespread adoption of low-power devices and microelectronic systems has intensified the need for efficient energy harvesting solutions. While cantilever-beam piezoelectric energy harvesters (PEHs) are popular for their simplicity, their performance is often limited by conventional mass block designs. This study addresses this by proposing a comprehensive structural optimization framework for a triangular cantilever PEH to significantly enhance its electromechanical conversion efficiency. The methodology involved a multi-stage approach: first, an embedded coupling design was introduced to connect the mass block and cantilever beam, improving space utilization and strain distribution. Subsequently, the mass block’s shape was optimized. Furthermore, a negative Poisson’s ratio (NPR) honeycomb structure was integrated into the cantilever beam substrate to induce biaxial strain in the piezoelectric layer. Finally, a variable-density mass block was implemented. The synergistic combination of all optimizations—embedded coupling, NPR substrate, and variable-density mass block—culminated in a total performance enhancement of 69.07% (17.76 V) in voltage output and a 44.34% (28.01 Hz) reduction in resonant frequency. Through experimental testing, the output performance of the prototype machine showed good consistency with the simulation results, successfully verifying the effectiveness of the structural optimization method proposed in this study. These findings conclusively show that strategic morphological reconfiguration of key components is highly effective in developing high-performance, low-frequency adaptive piezoelectric energy harvesting systems. Full article

This paper proposes an analog retuning strategy that strengthens the functional longevity of photovoltaic (PV) systems operating within circular-economy environments. Although PV modules can be relocated from large generation sites to low-demand rural or remote settings, their electrical behavior offers no adjustable quantities capable of extending service duration. In many cases, even after formal disposal or decommissioning, these solar panels still retain a considerable portion of their energy-generation capability and can operate for many additional years before their output becomes negligible, making second-life deployment both technically viable and economically attractive. In contrast, the associated power-electronic converters contain modifiable gate-driver parameters that can be reconfigured to moderate transient phenomena and lessen device stress. The method introduced here adjusts the external gate resistance in conjunction with coordinated switching-frequency adaptation, reducing overshoot, ringing, and steep $dv/dt$ slopes while preserving the original switching-loss budget. A unified analytical framework connects stress mitigation, ripple evolution, and projected lifetime enhancement, demonstrating that deliberate analog tuning can substantially increase the endurance of aged semiconductor hardware without compromising suitability for second-life PV applications. Analytical results are supported by experimental validation, including hardware measurements of switching waveforms and energy dissipation under multiple gate-resistance configurations. Full article

We propose a novel scheme for generating high-frequency millimeter-wave signals by exploiting period-one (P1) dynamics in a mutual injection configuration of two spin-polarized vertical-cavity surface-emitting lasers (spin-VCSELs). The frequency of the generated millimeter-wave signal is jointly determined by the birefringence rate of the spin-VCSEL and the frequency detuning between the two lasers. By leveraging the complex dynamics of free-running spin-VCSELs, we explore the coupling of three distinct dynamic states: continuous-wave (CW) injected into CW, CW injected into P1 oscillation, and P1 oscillation injected into P1 oscillation. Our results reveal that these interactions not only enhance the tunability and frequency of the millimeter-wave output but also significantly reduce the linewidth, offering substantial advantages for reconfigurable photonic systems. This study demonstrates the remarkable potential of mutually injected spin-VCSELs for generating high-performance, tunable photonic millimeter waves and highlights their promising applications in advanced communication and radar systems. Full article

With the increasing frequency of global climate change and natural disasters, the resilience and stability of port power grids have become crucial for ensuring continuous port operations. This study proposes a three-stage resilience optimization method for port power grids under disaster scenarios, aiming to enhance their supply capacity and operational flexibility across the pre-disaster, during-disaster, and post-disaster phases. In the pre-disaster stage, the model considers the uncertainty of photovoltaic (PV) generation and the reconfigurability of the grid, optimizing the quantity and spatial layout of mobile energy storage systems with the objective of minimizing configuration and load-shedding risk costs, thereby improving system disturbance resistance. During the disaster, the model integrates the dynamic coordination of distributed generators, PV units, and storage systems to minimize load-shedding costs and achieve staged restoration and multi-source energy coordination. In the post-disaster stage, considering the failure of lines and nodes caused by disasters, a topology reconstruction and source-load coordination optimization strategy is developed to ensure rapid power restoration and critical load supply. Simulation studies based on an improved IEEE 33-bus system demonstrate that the proposed robust optimization model in the pre-disaster phase significantly enhances risk resistance and system resilience, while the incorporation of mobile energy storage further improves the system’s flexibility and black-start capability. This research provides an effective theoretical foundation and practical framework for post-disaster recovery and resilience enhancement of port power grids. Full article

A scheme of a photonic convolution processor based on a tunable electro-optic frequency comb is proposed. The optical frequency comb (OFC) is generated using a dual-parallel Mach–Zehnder modulator (DPMZM) driven by an RF signal. By adjusting the working parameters of the DPMZM, OFCs with different line number and frequency spacing can be produced to reconfigure the convolution kernel dimensions. A linearly chirped fiber Bragg grating (LCFBG) is employed to implement interleaving of temporal and spectral dimensions. The interleaved signals are sampled at specific time and summed after optoelectronic conversion, and the convolution operation is completed. In this work, using a 10 GHz RF signal, a 4-line OFC with a frequency spacing of 20 GHz and a 2-line OFC with a spacing of 40 GHz are generated to obtain a 2 × 2 and 1 × 2 convolution kernels, respectively. The convolution results are fed into an electronic pooling layer and a fully connected layer for classifying the images of the MNIST handwritten digit dataset. The results demonstrate that a classification accuracy of 95.7% is achieved using the 2 × 2 convolution kernel, and a higher classification accuracy of 96.5% is obtained with the 1 × 2 convolution kernel. Full article

With the rapid advancement of detection technologies, traditional static electromagnetic absorbers increasingly struggle to meet controllable stealth requirements across diverse dynamic environments. To achieve active and controllable modulation of electromagnetic reflection characteristics, this paper proposes a transparent reconfigurable metamaterial absorber based on an origami structure. By adjusting the folding angles of the indium tin oxide (ITO)-polyethylene terephthalate (PET) film, the structure achieves reversible deformation from the vertical state to the horizontal state. This enables continuous modulation of the reflectance from below −10 dB (absorbing state) to nearly 0 dB (reflecting state) within the 4–18.9 GHz frequency range, with a relative bandwidth exceeding 130% and excellent angular stability. The energy loss and current distribution under different states are analyzed, revealing the mechanisms behind broadband absorption and deep modulation. Experimental measurements of the fabricated metamaterial align well with simulation results. Leveraging its flexible structure, reversible modulation capability, and angular stability, this origami-inspired reconfigurable metamaterial demonstrates promising application potential in the fields of adaptive electromagnetic camouflage and stealth protection. Full article

Unmanned Aerial Vehicles (UAVs) are integral components of future 6G networks, offering rapid deployment, enhanced line-of-sight communication, and flexible coverage extension. However, UAV communications in low-altitude environments face significant challenges, including rapid link variations due to attitude instability, severe signal blockage by urban obstacles, and critical sensitivity to transmitter–receiver alignment. While traditional planar reconfigurable intelligent surfaces (RIS) show promise for mitigating these issues, they exhibit inherent limitations such as angular sensitivity and beam squint in wideband scenarios, compromising reliability in dynamic UAV scenarios. To address these shortcomings, this paper proposes and evaluates a spherical-cap reflective intelligent surface (ScRIS) specifically designed for dynamic low-altitude communications. The intrinsic curvature of the ScRIS enables omnidirectional reflection capabilities, significantly reducing sensitivity to UAV attitude variations. A rigorous analytical model founded on Generalized Sheet Transition Conditions (GSTCs) is developed to characterize the electromagnetic scattering of the curved metasurface. Three distinct 1-bit RIS unit cell coding arrangements, namely alternate, chessboard, and random, are investigated via numerical simulations utilizing CST Microwave Studio and experimental validation within a mechanically stirred reverberation chamber. Our results demonstrate that all tested ScRIS coding patterns markedly enhance electromagnetic field uniformity within the chamber and reduce the lowest usable frequency (LUF) by approximately 20% compared to a conventional metallic spherical reflector. Notably, the random coding pattern maximizes phase entropy, achieves the most uniform scattering characteristics and substantially reduces spatial field autocorrelation. Furthermore, the combined curvature and coding functionality of the ScRIS facilitates simultaneous directional focusing and diffuse scattering, thereby improving multipath diversity and spatial coverage uniformity. This effectively mitigates communication blind spots commonly encountered in UAV applications, providing a resilient link environment despite UAV orientation changes. To validate these findings in a practical context, we conduct link-level simulations based on a reproducible system model at 3.5 GHz, utilizing electromagnetic scale invariance to bridge the fundamental scattering properties observed in the RC to the application band. The results confirm that the ScRIS architecture can enhance link throughput by nearly five-fold at a 10 km range compared to a baseline scenario without RIS. We also propose a practical deployment strategy for urban blind-spot compensation, discuss hybrid planar-curved architectures, and conduct an in-depth analysis of a DRL-based adaptive control framework with explicit convergence and complexity analysis. Our findings validate the significant potential of ScRIS as a passive, energy-efficient solution for enhancing communication stability and coverage in multi-band 6G networks. Full article

The safe and stable operation of distribution networks relies on the real-time monitoring, analysis, and feedback of power quality data. However, with the continuous advancement of distribution network construction, the number of distributed power electronic devices has increased significantly, leading to frequent power quality issues such as voltage fluctuations, harmonic pollution, and three-phase unbalance in distribution terminals. Therefore, the augmentation and processing of power quality data have become crucial for ensuring the stable operation of distribution networks. Traditional methods for augmenting and processing power quality data fail to consider the differentiated characteristics of burrs in signal sequences and neglect the comprehensive consideration of both time-domain and frequency-domain features in disturbance identification. This results in the distortion of high-frequency fault information, and insufficient robustness and accuracy in identifying Power Quality Disturbance (PQD) against the complex noise background of distribution networks. In response to these issues, we propose a power quality data augmentation and processing method for distribution terminals considering high-frequency sampling. Firstly, a burr removal method of the sampling waveform based on a high-frequency filter operator is proposed. By comprehensively considering the characteristics of concavity and convexity in both burr and normal waveforms, a high-frequency filtering operator is introduced. Additional constraints and parameters are applied to suppress sequences with burr characteristics, thereby accurately eliminating burrs while preserving the key features of valid information. This approach avoids distortion of high-frequency fault information after filtering, which supports subsequent PQD identification. Secondly, a PQD identification method based on a dual-channel time–frequency feature fusion network is proposed. The PQD signals undergo an S-transform and period reconfiguration to construct matrix image features in the time–frequency domain. Finally, these features are input into a Convolutional Neural Network (CNN) and a Transformer encoder to extract highly coupled global features, which are then fused through a cross-attention mechanism. The identification results of PQD are output through a classification layer, thereby enhancing the robustness and accuracy of disturbance identification against the complex noise background of distribution networks. Simulation results demonstrate that the proposed algorithm achieves optimal burr removal and disturbance identification accuracy. Full article