Search Results (1,037)

The increasing deployment of Internet of Things (IoT) devices introduces significant security challenges, while privacy concerns limit centralized data aggregation for intrusion detection. Federated learning (FL) offers a decentralized alternative, yet the interaction between feature representation, model architecture, and data heterogeneity remains insufficiently understood in IoT malware detection. This study provides a controlled comparative analysis of centralized and federated learning, optionally using amino acid encoding, under IID and Non-IID conditions using a 10,000-sample subset of the CTU–IoT–Malware–Capture dataset. First, we evaluate raw tabular features versus amino acid-based feature encoding, followed by a lightweight multi-layer perceptron (2882 parameters) versus a deeper residual network (70,532 parameters), across binary and multi-class classification tasks. In the binary setting, centralized training achieved up to 98.6% accuracy, while federated IID training reached 98.6%, with differences within statistical variance. Under Non-IID conditions, performance decreased modestly (0.1–0.5 percentage points), and accuracy was consistently lower when using encoded features compared with raw features. The degradation is smaller in deeper architectures and may offer improved stability under highly skewed federated conditions. In the four-class setting, the complex network achieved up to 97.8% accuracy with raw features, while amino acid encoding achieves up to 93.3%. The results show that federated learning can achieve performance comparable to centralized training under moderate heterogeneity, that lightweight architectures are sufficient for low-dimensional IoT traffic features, and that feature compression via amino acid encoding does not inherently mitigate Non-IID effects. These findings clarify the relative impact of representation, heterogeneity, and architectural capacity in practical FL-based IoT intrusion detection systems. Full article

Flexible transparent conductors (FTCs) are key materials that determine the scalability and performance of flexible optoelectronic devices. This study explores the reliability of FTCs with laminated multilayer structures, specifically oxide/metal/oxide (OMO) films, through sequential testing composed of accelerated weathering and cyclic bending. Commercially available ZTO/Ag/ZTO-based FTCs were selected as a model system to study, and Weibull analysis was employed to assess their failure behaviors. Results illustrate that weathered aged samples exhibit significantly impaired bending lifespan compared to unaged samples due to substrate embrittlement. Hence, the surface cracking mechanism alters as the weathering time is prolonged. Not only the weathering time, but also the thickness of the conductive metal layer plays an important role in influencing the bending reliability behaviors of the OMO FTCs. A sequential aging test that combines two-step UV weathering and an interim manual bending demonstrates that surface cracks can induce the degradation of both optical and electrical properties. Intricately complex bending modes would accelerate the deterioration. This study highlights the critical and synergistic roles of weathering aging and cyclic bending on the reliability of OMO FTCs, offering insights for future design and durability assessments of flexible optoelectronic devices. Research results also provide fundamental information for establishing application-specific reliability testing protocols for FTCs. Full article

Mixed-halide perovskite solar cells with the composition Cs_0.1(MA_0.17FA_0.83)_0.9Pb(I_0.83Br_0.17)₃ were fabricated obtaining solar cells as glass/ITO/SnO₂/triple-cation perovskite/HTL/Au, and subsequently used as photoanodes for efficient solar-driven water splitting by attaching commercial catalytic nickel foils to the Au back-contact pads of solar cells. To enable operation in alkaline media, the devices were encapsulated using commercial PET–EVA multilayer films, providing an effective barrier while leaving the Ni foils exposed as the electrochemically active interface. Two operating configurations were investigated and compared: (i) an outside configuration, where the perovskite device powered the external electrochemical cell, and (ii) an immersed configuration, in which the encapsulated perovskite solar cell was directly integrated, together with the Ni catalyst, into the electrolyte. In both configurations, the onset potential for the oxygen evolution reaction shifted from ~1.32 V vs. RHE, when the Ni electrode was not powered by the perovskite solar cell, to ~0.34 V vs. RHE, when the perovskite device powered the Ni foil for both immersed and outside configurations. The immersed configuration delivered the highest performance, achieving a maximum Applied Bias Photon-to-Current Efficiency of ~20% under AM 1.5 G illumination (100 mW cm⁻²), among the highest values reported for perovskite-based photoanodes. Importantly, the enhanced performance does not arise from changes in catalyst composition or direct semiconductor–electrolyte interaction, but from improved photovoltage delivery and reduced resistive losses enabled by the integrated device architecture. These results demonstrate that device architecture is a key factor in controlling photovoltage utilization and charge-transfer kinetics, providing a viable strategy for efficient and scalable perovskite-based photoelectrochemical systems. Full article

To meet the requirements of quantitative elemental analysis in the ultraviolet (UV) spectrum, a UV single-photon imaging system was developed, integrating a digital micromirror device (DMD) and a single photon-counting imaging detector, enabling high sensitivity, high resolution, and a wide dynamic range. However, intrinsic geometric distortion poses a significant challenge to accurate spectral calibration. A hybrid correction framework is proposed, cascading polynomial coarse correction with multilayer perceptron (MLP) fine regression, improving calibration accuracy. The method utilizes a full-field dot-array mask projected by the DMD to acquire distortion-reference image pairs. The polynomial model rapidly captures the dominant high-order distortion, while a lightweight MLP performs non-parametric fine regression of residual displacements, achieving a mean error of 0.84 pixels. This approach reduces the root mean square (RMS) error to 1.01 pixels, outperforming traditional direct linear transformation (5.35 pixels) and pure polynomial models (1.33 pixels), while the nonlinearity index decreases from 0.35° to 0.05°. In addition, the method demonstrates stable performance across multi-scale checkerboard patterns ranging from 128 to 280 pixels, with RMS errors remaining around the 1-pixel level. These results validate the high-precision distortion suppression and robust cross-scale performance of the proposed framework. By leveraging DMD-generated patterns for self-calibration, this method eliminates the need for external targets, offering a scalable solution for high-end spectrometer calibration. Full article

Artificial neural network and neuron models have made significant contributions to the area of neurodynamics. Investigating the dynamics of artificial neurons and neural networks is vital in developing brain-like systems and understanding how the brain functions. Neural network models and memristive neurons are currently demonstrating a lot of promise in the study of neurodynamics. In order to model the dynamics of biological synapses, this study explores the complex dynamical behavior of a discrete fractional Hopfield-type neural network using a flux-controlled memristive element with periodic memductance. Hyperbolic tangent and sine are the heterogeneous activation functions that are implemented in the proposed system to improve nonlinearity and replicate various forms of brain activity. Stability and bifurcation analyses are used to illustrate the nonlinear dynamical nature of the constructed network model. We examine how the fractional order $\left(\nu \right)$ and periodical memductance aspects influence the dynamics of the system to emphasize the emerging complex phenomena like multi-layered dynamics and the presence of several distinct dynamical states throughout the system variables. Randomness and complexity of the time series data for the proposed system are illustrated with the help of approximate entropy analysis. These findings could help researchers better understand brain-like memory networks, neuromorphic computers, and the theoretical study of neurological and mental abilities. The study of multi-layer attractors can be useful in advanced sensory devices, neuromorphic devices, and secure communication. Full article

The brain’s primary sensory processing areas often present a topographical organization and are distributed following hierarchical architecture, permitting the integration of the information in higher levels of its hierarchy: a process referred to as multisensory integration. A system with such characteristics naturally computes in a parallel and distributed manner and is based in associations between the different symbols built from our perceptions of the environment. In this work, we take inspiration from the sensory processing areas of the brain and propose proof-of-concept of a multi-layered neuromorphic system with parallel and distributed computing capabilities by means of simulation. The proposed neuromorphic architecture is constituted by identical self-organizing modules which are trained with on-line unsupervised-friendly learning rules, such as the spike-timing-dependent plasticity (STDP). These self-organizing modules are constituted by oxide-based resistive random access memory (OxRAM) devices, which play the analog synaptic role. The different modules display a topographical organization according to the input dataset features they have been trained with and are organized following a hierarchical system. The system exhibits conceptual associative behavior between inputs with clustering capabilities, able to classify inputs which have never been seen before by the system, according to their similarity with the ones it has been trained with. Full article

One of the common issues in the R&D of new drugs is the failure of clinical trials caused by the species-specific inadequacy of animal models to assess drugs’ efficiency and safety. Therefore, systems like organ-on-a-chip and, particularly, liver-on-a-chip (LOC) can be an efficient tool for recapitulating in vivo-like human physiology at the microscale. This review focuses on discussing LOC design, emphasizing its architecture and validation to reveal the trends in searching for a balance between biomimetics and functionality. We found that the huge variety of already published models can be divided into five groups based on their configuration complexity: flat one-channel, flat two-channel, vertically stacked multilayered, hexagonal-patterned, and multi-well chips. While researchers attempt to recapitulate the liver’s histology and its functions in detail by increasing the complexity of devices’ architectonics, industrial companies prefer to promote more simple and flexible solutions. Thus, the LOC designs of the future require neglecting some liver characteristics to make them standardizable and sustainable, which could facilitate their introduction into the market and clinics. Full article

The on-chip detection of circularly polarized light is pivotal for advancing applications in quantum optics, information processing, and spectroscopic sensing. However, conventional chiral metasurfaces often suffer from complex multilayer fabrication, material incompatibility, or modest performance, hindering their integration with photonic circuits. Here, we introduce a monolithic all-silicon metasurface that overcomes these limitations through a singular structural innovation. By strategically truncating four corners of a conventional Z-shaped meta-atom, we induce a hybridization of optical modes that profoundly enhances chiral light–matter interaction. This deliberately engineered perturbation yields a colossal circular dichroism with an extinction ratio exceeding 66 dB, a performance that surpasses existing state-of-the-art designs by approximately three orders of magnitude. Furthermore, the proposed metasurface exhibits remarkable fabrication robustness, owing to its single-layer architecture and CMOS-compatible material. We demonstrate that this exceptional metasurface can be directly integrated with a Mercury Cadmium Telluride (MCT) photodetector to form a highly efficient, compact circular polarization detector. Our work provides a simple yet powerful paradigm for creating high-performance chiral photonic devices, paving the way for their widespread adoption in integrated optoelectronics. Full article

The self-directed channel (SDC) class of memristors employs a multilayer architecture that is designed to enable robust Ag ion conduction, long cycling lifetime, and thermal stability. While several layers contribute to mechanical and chemical reliability, two layers primarily govern the electrical behavior: the amorphous Ge–chalcogenide active layer that is adjacent to the bottom electrode and the overlying metal–chalcogenide source layer. In this work, we investigate how the variation in the chalcogen species in these two layers influences switching characteristics in the pre-write regime, both in the pristine state and after a write/erase cycle, as well as the conduction behavior at room temperature. The devices were fabricated using Ge-rich chalcogenides containing O, S, Se, or Te, combined with SnS, SnSe, or Ag₂Se metal–chalcogenide layers. The DC current-voltage measurements were analyzed using the standard linearization approaches to examine whether the transport behavior in the pre-write regime exhibits characteristics that are associated with Ohmic, Schottky, Poole–Frenkel, or space charge limited conduction. These measurements specifically probe the pre-write region of the I-V curve, where early ionic redistribution and structural rearrangement precede the abrupt formation of the conductive channels responsible for the resistive switching. The results show that the chalcogen composition strongly affects the threshold voltage, the resistance window, and the onset of field-enhanced transport, reflecting the differences in ionic distribution and channel formation dynamics. The results indicate that transport evolves with a bias and a compliance current, transitioning between regimes that are influenced by the interface injection and bulk-limited conduction, depending on the material stack. These findings clarify the role of chalcogen chemistry in governing the SDC switching behavior and provide guidance for the material selection in application-specific device design. Full article

Wearable device-based Human Activity Recognition (HAR) is widely used in health management, rehabilitation, and personal safety. While contemporary HAR research effectively classifies a wide range of discrete activities, there remains a significant gap in organizing these heterogeneous motions into a structured intensity framework suitable for continuous risk assessment. Furthermore, many high-performing models rely on computationally intensive architectures that hinder real-time deployment on resource-constrained wearables. We propose an on-device method for estimating five-level activity intensity in real time using only accelerometer signals from a commercial smartwatch. To bridge the gap between simple identification and intensity modeling, 13 dynamic and emergency-like wrist motions were integrated with 11 daily activities from the PAMAP2 dataset, yielding 21 activities mapped onto an ordinal five-level intensity scale. A finetuned Multi-Layer Perceptron (MLP) classifier trained on this integrated dataset achieved 0.939 accuracy and a quadratic weighted kappa (QWK) of 0.971. The model was deployed on a Galaxy Watch 7, achieving $<1$ ms inference latency and a size $<0.1$ MB, confirming real-time feasibility. This approach demonstrates that organizing diverse activities into a lightweight, intensity-aware framework provides a robust foundation for safety-aware monitoring systems under real-world, on-device constraints. Full article

Dye-sensitized solar cells (DSCs) have garnered significant attention due to their high power conversion efficiency and low production cost-effectiveness. In this study, we developed a hierarchically structured three-layer TiO₂ photoanode via hydrothermal synthesis to significantly enhance DSC performance. The optimized device achieved a short-circuit current density of 16.92 mA/cm² and a photoelectric conversion efficiency of 8.34%, representing improvements of 15.67% and 20.5%, respectively, compared to traditional DSCs with a single-layer TiO₂ photoanode in our study. The significance lies in the rational design principle rather than absolute efficiency. This performance enhancement stems from the complementary functions of each architectural layer: (1) a bottom layer of TiO₂ nanocrystals providing high surface area for dye adsorption, (2) an intermediate layer of vertically aligned TiO₂ nanorods enabling efficient electron transport, and (3) a top layer of TiO₂ microspheres simultaneously boosting dye loading and light harvesting through enhanced light scattering. Our findings demonstrate that rational design of multi-layered photoanode architectures can effectively address the competing demands of surface area, charge transport, and light management in high-performance DSCs. Full article

Visual localization plays a critical role for mobile robots to estimate their position and orientation in GPS-denied environments. However, its efficiency, robustness, and generalization are fundamentally undermined by severe viewpoint changes and dramatic appearance variations, which present persistent challenges for image-based feature representation and pose estimation under real-world conditions. Recently, map-free visual relocalization (MFVR) has emerged as a promising paradigm for lightweight deployment and privacy isolation on edge devices, while how to learn compact and invariant image tokens without relying on structural 3D maps still remains a core problem, particularly in highly dynamic or long-term scenarios. In this paper, we propose the Debiased Multiplex Tokenizer as a novel method (termed as DMT-Loc) for efficient and versatile MFVR to address these issues. Specifically, DMT-Loc is built upon a pretrained vision Mamba encoder and integrates three key modules for relative pose regression: First, Multiplex Interactive Tokenization yields robust image tokens with non-local affinities and cross-domain descriptions. Second, Debiased Anchor Registration facilitates anchor token matching through proximity graph retrieval and autoregressive pointer attribution. Third, Geometry-Informed Pose Regression empowers multi-layer perceptrons with a symmetric swap gating mechanism operating inside each decoupled regression head to support accurate and flexible pose prediction in both pair-wise and multi-view modes. Extensive evaluations across seven public datasets demonstrate that DMT-Loc substantially outperforms existing baselines and ablation variants in diverse indoor and outdoor environments. Full article

Future 6G-enabled smart hospital infrastructures will support latency-critical medical operations such as robotic surgery, autonomous monitoring, and real-time clinical decision systems, which require communication mechanisms that ensure both ultra-low latency and long-term cryptographic security. Existing security solutions either rely on classical cryptographic protocols that are vulnerable to quantum attacks or deploy isolated post-quantum primitives without providing a unified framework for secure real-time medical command transmission. This research presents a latency-aware, multi-layered post-quantum security architecture for 6G-enabled smart hospital environments. The proposed framework establishes an end-to-end secure command transmission pipeline that integrates hardware-rooted device authentication, post-quantum key establishment, hybrid payload protection, dynamic access enforcement, and tamper-evident auditing within a coherent system design. In contrast to existing approaches that focus on individual security mechanisms, the architecture introduces a structured integration of Kyber-based key encapsulation and Dilithium digital signatures with hybrid AES-based encryption and legacy-compatible key transport, while Physical Unclonable Function authentication provides hardware-bound device identity verification. Zero Trust access control, metadata-driven anomaly detection, and blockchain-style audit logging provide continuous verification and traceability, while threshold cryptography distributes cryptographic authority to eliminate single points of compromise. The proposed architecture is evaluated using a discrete-event simulation framework representing adversarial conditions in realistic 6G medical communication scenarios, including replay attacks, payload manipulation, and key corruption attempts. Experimental results demonstrate improved security and operational efficiency, achieving a 48% reduction in detection latency, a 68% reduction in false-positive anomaly detection rate, and a 39% improvement in end-to-end round-trip latency compared to conventional RSA-AES-based architectures. These results demonstrate that the proposed framework provides a practical and scalable approach for achieving post-quantum secure and low-latency command transmission in next-generation 6G smart hospital systems. Full article

Zinc titanate (ZnTiO₃) is a chemically stable and non-toxic oxide perovskite whose photovoltaic potential remains largely unexplored due to its wide indirect bandgap. This study evaluates whether oxygen-vacancy (F-center) engineering can tailor its electronic structure and improve its suitability as a photovoltaic absorber. Density Functional Theory (DFT) calculations using VASP (PAW − GGA/PBE + U) were performed to evaluate structural stability, electronic properties, and electron affinity, while optical absorption was modeled through a combined Tauc–Gaussian approach. Device performance was assessed via SCAPS-1D simulations in an FTO/ZnO/ZnTiO₃/Spiro-OMeTAD architecture. Oxygen vacancies induce bandgap narrowing from ~2.96 eV to ~1.47 eV and generate Ti-3d-dominated donor-like and deep intragap states. The calculated electron affinity is ~3.77 eV. Simulated single-layer devices reach Voc ≈ 1.11 V, Jsc ≈ 8.27 mA·cm⁻², FF ≈ 83%, and a maximum efficiency of ~7.65%, primarily limited by moderate absorption strength and defect-assisted recombination. Multilayer configurations indicate that geometric optimization can significantly enhance projected efficiency, approaching 19.25% under idealized conditions. Although vacancy engineering extends visible-light absorption, the intrinsic indirect band-gap character constrains the ultimate photovoltaic performance of ZnTiO₃. Full article

This study presents a computational, simulation-based investigation of the dielectric response of human hand tissues, skin, fat, muscle, and bone to radiofrequency (RF) electromagnetic fields emitted by mobile devices. The widespread adoption of handheld devices and the deployment of fifth-generation (5G) networks, including millimetre-wave (mmWave) bands, have intensified concerns regarding localized human exposure to RF radiation, particularly in the hand, which serves as the primary interface during device operation. Using validated dielectric property datasets, numerical simulations were performed across the frequency range of 0.5–40 GHz, employing the Finite-Difference Time-Domain (FDTD) method to solve Maxwell’s equations, with analytical evaluations conducted in Maple-18. A heterogeneous multilayer hand phantom was developed, and simulations were conducted under controlled exposure conditions, including a transmitted power of 1 W, antenna gain of 2 dBi, and incident power density of 5 W/m², consistent with ICNIRP and NCC safety guidelines. Tissue responses were assessed over a temperature range of 10–40 °C to account for thermal variability. The results demonstrate strong frequency- and temperature-dependent behaviour of dielectric properties, intrinsic impedance, reflection coefficient, attenuation, and specific absorption rate (SAR). At lower frequencies (<1 GHz), RF energy penetrated more deeply with distributed absorption and relatively low SAR values, whereas higher frequencies (3–40 GHz) produced highly localized absorption in superficial tissues, particularly skin and muscle. Increasing temperature led to significant increases in permittivity, conductivity, and SAR, with up to a twofold enhancement observed between 10 °C and 40 °C. These findings confirm that 5G and mmWave exposures result in predominantly surface-confined energy deposition in hand tissues. The study provides a robust computational framework for evaluating hand device electromagnetic interactions and offers quantitative insights relevant to antenna design, exposure compliance assessment, and the development of evidence-based safety guidelines. Full article