Search Results (5,152)

This paper presents a radiation-hardened 4-bit flash analog-to-digital converter (ADC) implemented in a 22 nm fully depleted silicon-on-insulator (FD-SOI) process for high-reliability applications in radiation environments. To improve single-event upsets (SEU) tolerance, the design introduces a compact fault-tolerant logic scheme based on Dual Modular Redundancy (DMR), offering reliability comparable to Triple Modular Redundancy (TMR) while using two storage nodes instead of three, and a simple XOR-based check in place of a majority voter. A distributed sampling architecture mitigates SEU vulnerabilities in the input path, while thin-oxide devices are used in analog-critical circuits to enhance total ionizing dose (TID) resilience. Post-layout simulations demonstrate SEU detection within 200 ps and correction within ~600 ps. The ADC achieves an active area of 0.089 mm², power consumption below 30 µW, and provides a scalable solution for radiation-tolerant data acquisition in aerospace and other high-reliability systems. Full article

Direct growth of high-quality, Bernal-stacked bilayer graphene (BLG) on dielectric substrates is crucial for electronic and optoelectronic devices, yet it remains hindered by poor film quality, uncontrollable thickness, and high-density grain boundaries. In this work, a facile, catalyst-assisted method to grow high-quality, single-crystalline BLG directly on dielectric substrates (SiO₂/Si, sapphire, and quartz) was demonstrated. A single-crystal monolayer graphene template was first employed as a seed layer to facilitate the homoepitaxial synthesis of single-crystalline BLG directly on insulating substrates. Nanostructure Cu powders were used as the remote catalysis to provide long-lasting catalytic activity during the graphene growth. Transmission electron microscopy confirms the single-crystalline nature of the resulting BLG domains, which validates the superiority of the homoepitaxial growth technique. Raman spectroscopy and electrical measurement results indicate that the quality of the as-grown BLG is comparable to that on metal substrate surfaces. Field-effect transistors fabricated directly on the as-grown BLG/SiO₂/Si showed a room temperature carrier mobility as high as 2297 ± 3 cm² V⁻¹ s⁻¹, which is comparable to BLG grown on Cu and much higher than that reported on in-sulators. Full article

With the large-scale integration of single-phase distributed photovoltaic systems into distribution grids, issues such as mismatched generation and load, overvoltage, and three-phase imbalance may arise in the distribution network. A multi-objective optimization method for flexible distribution networks incorporating a four-leg soft open point (F-SOP) is proposed based on fuzzy chance constraints. First, a mathematical model for the F-SOP’s loss characteristics and power control was established based on the three-phase four-arm topology. Considering the impact of source load uncertainty on voltage regulation, a multi-objective complementary voltage regulation architecture is proposed based on fuzzy chance constraint programming. This architecture integrates F-SOP with conventional reactive power compensation devices. Next, a multi-objective collaborative optimization model for distribution networks is constructed, with network losses, overall voltage deviation, and three-phase imbalance as objective functions. The proposed model is linearized using second-order cone programming. Finally, using an improved IEEE 33-node distribution network as a case study, the effectiveness of the proposed method was analyzed and validated. The results indicate that this method can reduce network losses by 30.17%, decrease voltage deviation by 46.32%, and lower three-phase imbalance by 57.86%. This method holds significant importance for the sustainable development of distribution networks. Full article

Bud break is a critical phenological stage in muscadine grapevines, marking the start of the growing season and the increasing need for irrigation management. Real-time bud detection enables irrigation to match muscadine grape phenology, conserving water and enhancing performance. This study presents BudCAM , a low-cost, solar-powered, edge computing camera system based on Raspberry Pi 5 and integrated with a LoRa radio board , developed for real-time bud detection. Nine BudCAMs were deployed at Florida A&M University Center for Viticulture and Small Fruit Research from mid-February to mid-March, 2024, monitoring three wine cultivars (A27, noble, and Floriana)with three replicates each. Muscadine grape canopy images were captured every 20 min between 7:00 and 19:00, generating 2656 high-resolution (4656 × 3456 pixels) bud break images as a database for bud detection algorithm development. The dataset was divided into 70% training, 15% validation, and 15% test. YOLOv11 models were trained using two primary strategies: a direct single-stage detector on tiled raw images and a refined two-stage pipeline that first identifies the grapevine cordon. Extensive evaluation of multiple model configurations identified the top performers for both the single-stage (mAP@0.5 = 86.0%) and two-stage (mAP@0.5 = 85.0%) approaches. Further analysis revealed that preserving image scale via tiling was superior to alternative inference strategies like resizing or slicing. Field evaluations conducted during the 2025 growing season demonstrated the system’s effectiveness, with the two-stage model exhibiting superior robustness against environmental interference, particularly lens fogging. A time-series filter smooths the raw daily counts to reveal clear phenological trends for visualization. In its final deployment, the autonomous BudCAM system captures an image, performs on-device inference, and transmits the bud count in under three minutes, demonstrating a complete, field-ready solution for precision vineyard management. Full article

The increasing complexity of the Edge–Cloud Continuum (ECC), driven by the rapid expansion of the Internet of Things (IoT) and data-intensive applications, necessitates implementing innovative methods for automated and efficient system management. In this context, recent studies focused on the utilization of self-* capabilities that can be used to enhance system autonomy and increase operational proactiveness. Separately, anomaly detection and adaptive sampling techniques have been explored to optimize data transmission and improve systems’ reliability. The integration of those techniques within a single, lightweight, and extendable self-optimization module is the main subject of this contribution. The module was designed to be well suited for distributed systems, composed of highly resource-constrained operational devices (e.g., wearable health monitors, IoT sensors in vehicles, etc.), where it can be utilized to self-adjust data monitoring and enhance the resilience of critical processes. The focus is put on the implementation of two core mechanisms, derived from the current state-of-the-art: (1) density-based anomaly detection in real-time resource utilization data streams, and (2) a dynamic adaptive sampling technique, which employs Probabilistic Exponential Weighted Moving Average. The performance of the proposed module was validated using both synthetic and real-world datasets, which included a sample collected from the target infrastructure. The main goal of the experiments was to showcase the effectiveness of the implemented techniques in different, close to real-life scenarios. Moreover, the results of the performed experiments were compared with other state-of-the-art algorithms in order to examine their advantages and inherent limitations. With the emphasis put on frugality and real-time operation, this contribution offers a novel perspective on resource-aware autonomic optimization for next-generation ECC. Full article

Background: Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition for which accurate and automated diagnosis is crucial to enable timely intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) serves as one of the key modalities for diagnosing ASD and elucidating its underlying mechanisms. Numerous existing studies using rs-fMRI data have achieved accurate diagnostic performance. However, these methods often rely on a single brain atlas for constructing brain networks and overlook the data heterogeneity caused by variations in imaging devices, acquisition parameters, and processing pipelines across multiple centers. Methods: To address these limitations, this paper proposes a multi-view, low-rank subspace graph structure learning method to integrate multi-atlas and multi-center data for automated ASD diagnosis, termed M³ASD. The proposed framework first constructs functional connectivity matrices from multi-center neuroimaging data using multiple brain atlases. Edge weight filtering is then applied to build multiple brain networks with diverse topological properties, forming several complementary views. Samples from different classes are separately projected into low-rank subspaces within each view to mitigate data heterogeneity. Multi-view consistency regularization is further incorporated to extract more consistent and discriminative features from the low-rank subspaces across views. Results: Experimental results on the ABIDE-I dataset demonstrate that our model achieves an accuracy of 83.21%, outperforming most existing methods and confirming its effectiveness. Conclusions: The proposed method was validated using the publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset. Experimental results demonstrate that the M³ASD method not only improves ASD diagnostic accuracy but also identifies common functional brain connections across atlases, thereby enhancing the interpretability of the method. Full article

Acoustic fingerprints can be used for device-to-device authentication due to manufacturing-induced variations in microphones and speakers. However, previous works have focused mostly on recognizing single devices from a set of multiple devices, which may not be sufficiently realistic since in practice, a single device has to be recognized from a very large pool of devices that are not available for training machine learning classifiers. Therefore, in this work, we focus on one-class classification algorithms, namely one-class Support Vector Machine and the local outlier factor. As such, learning the fingerprint of a single device is sufficient to recognize the legitimate device and reject all other attempts to impersonate it. The proposed application can also rely on cloud-based deployment to free the smartphone from intensive computational tasks or data storage. For the experimental part, we rely both on smartphones and an automotive-grade Android headunit, exploring in-vehicle environments as the main area of application. We create a dataset consisting of more than 5000 measurements and achieve a recognition rate ranging from 50% to 100% for different devices under various environmental conditions such as distance, altitude, and component aging. These conditions also serve as our limitations, however, we propose different solutions for overcoming them, which are part of our threat model. Full article

This work presents a comprehensive study of GaN-on-Si pseudo-vertical MOSFETs focusing on single-trench and multi-trench designs. Thanks to a gate-first process flow based on an Al₂O₃/TiN MOS stack, both fabricated devices exhibit promising transistor behavior, with steady normally OFF operation, very low gate leakage current, and good switching performance. Following the extraction of a low effective channel mobility, the frequency dependence of gate-to-source C–V characteristics is studied. In addition, using TCAD Sentaurus Synopsys simulations, the impact of positive fixed charge and donor-type defects at the p-GaN/dielectric interface is investigated, together with the frequency dependency. Finally, by comparing experimental and simulated results, a mechanism is proposed linking the observed threshold voltage shift to the presence of sharp trench-bottom micro-trenching. This mechanism may further explain the origin of the additional C–V hump observed at high frequencies, which could arise from charge trapping at the p-GaN/dielectric interface or from charge inversion in the p-GaN region. Full article

Due to the outstanding thermal stability of diamond film, diamond films have extensive application prospects in fields such as electronics, optics, biomedicine, and aerospace, and are one of the important materials driving the development of modern science and technology. Moreover, the cost of single-crystal diamond substrates is high, and it is difficult to achieve large-scale batch production. A direct current arc plasma jet chemical vapor deposition method, combined with post-treatment steps such as nano-diamond seed crystal implantation, surface modification, and high-temperature annealing, is used to prepare high-quality diamond films. The relationship between the thermal conductivity and optical properties of diamond films is analyzed in detail. The experimental results showed that diamond film has a relatively smooth surface, with a surface roughness that can reach 3 nm. As the temperature rises, diamond films exhibit good crystal orientation and thermal stability, the FWHM of reflection peaks become smaller, and thermal conductivity can reach 1734 W/(m·K). The infrared testing analysis also confirmed that the diamond film has excellent thermal diffusion properties. When the diamond film is applied to power device chips, it can effectively reduce the junction temperature of 30 °C. The preparation method proposed in this paper is expected to break through the cost and scale limitations of high-performance diamond films, thereby promoting the wide application of diamond films in industries. Full article

Background/Objectives: Although significant advances in duodenoscope reprocessing have been introduced since mid-2010s—including enhanced cleaning protocols, disposable distal endcaps, and the introduction of fully single-use duodenoscopes—residual contamination and infection risks remain unresolved. Moreover, repeated reprocessing may cause cumulative damage to the polymer surfaces, elevator mechanisms, and internal channels of the duodenoscopes, making them more susceptible to residual contamination. To minimize the duodenoscope polymer degradation caused by intensive use and reprocessing, new alternatives are urgently needed. In this context, calcium peroxide nanoparticles coated with sodium alginate (CaO₂–Alg NPs), synthesized by our group, were tested for the first time as a disinfectant capable of combating nosocomial pathogens while reducing device deterioration associated with repeated investigations and reprocessing. Methods: The disinfectant properties of the CaO₂–Alg NPs were evaluated under biomimetic conditions using reference bacterial strains commonly associated with nosocomial infections. In addition, the compatibility of the nanoparticles with the polymeric duodenoscope coatings was assessed after simulated intensive use. The external polymer coating was structurally and morphologically characterized by Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), Atomic Force Microscopy (AFM), and Scanning Electron Microscopy (SEM). Results: The nanoparticles exhibited important antimicrobial activity against the reference bacterial strains Staphylococcus aureus, Escherichia coli, Enterococcus faecalis, and Klebsiella pneumoniae after only 20 min of incubation. Intensive exposure to the CaO₂–Alg NPs did not cause additional structural or morphological damage to the duodenoscope’s external polymers and did not alter their anti-adhesive properties. Conclusions: The CaO₂–Alg NPs appear to be a safe and effective disinfectant for the duodenoscope reprocessing, offering both antimicrobial efficacy and material compatibility. Full article

Objective: This study aimed to evaluate the diagnostic accuracy of two cone beam computed tomography (CBCT) devices using 18 imaging modalities in detecting root fractures—vertical, horizontal, and oblique—in teeth with intracanal post systems. Materials and methods: Ninety-six were extracted; single-rooted mandibular premolars were endodontically treated and restored with Bundle, Reforpost, or Splendor Single Adjustable posts. Controlled fractures of different types were induced using a universal testing machine. Each tooth was scanned with NewTom 7G and NewTom Go (Quantitative Radiology, Verona, Italy) under nine imaging protocols per device; varying in dose and voxel size, yielding 1728 CBCT images. Three observers (a professor of endodontics; a specialist; and a postgraduate student in endodontics) independently evaluated the images. Results: Observers demonstrated almost perfect agreement (κ ≥ 0.81) with the gold standard in fracture detection using NewTom 7G. No significant differences were found in sensitivity, specificity, or accuracy across voxel size and dose parameters for both devices in detecting fracture presence (p > 0.05). Similarly, both devices displayed comparable performance in identifying horizontal and oblique fractures (p > 0.05). However, in NewTom Go, regular and low doses with different voxel sizes showed reduced sensitivity and accuracy in detecting vertical fractures across all post systems (p ≤ 0.05). Conclusions: NewTom 7G, with its advanced detector system and smaller voxel sizes, provides superior diagnostic accuracy for root fractures. In contrast, NewTom Go displays reduced sensitivity for vertical fractures at lower settings. Clinical relevance: CBCT device selection and imaging protocols significantly affect the diagnosis of vertical root fractures. Full article

Substantial improvements in the performance of optical interconnects based on multi-mode fibers are required to support emerging single-channel data transmission rates of 200 Gb/s and 400 Gb/s. Future optical components must combine very high modulation bandwidths—supporting signaling at 100 Gbaud and 200 Gbaud—with reduced spectral width to mitigate chromatic-dispersion-induced pulse broadening and increased brightness to further restrict flux-confining area in multi-mode fibers and thereby increase the effective modal bandwidth (EMB). A particularly promising route to improved performance within standard oxide-confined VCSEL technology is the introduction of multiple isolated or optically coupled oxide-confined apertures, which we refer to collectively as multi-aperture (MA) VCSEL arrays. We show that properly designed MA VCSELs exhibit narrow emission spectra, narrow far-field profiles and extended intrinsic modulation bandwidths, enabling longer-reach data transmission over both multi-mode (MMF) and single-mode fibers (SMF). One approach uses optically isolated apertures with lateral dimensions of approximately 2–3 µm arranged with a pitch of 10–12 µm or less. Such devices demonstrate relaxation oscillation frequencies of around 30 GHz in continuous-wave operation and intrinsic modulation bandwidths approaching 50 GHz. Compared with a conventional single-aperture VCSELs of equivalent oxide-confined area, MA designs can reduce the spectral width (root mean square values < 0.15 nm), lower series resistance (≈50 Ω) and limit junction overheating through more efficient multi-spot heat dissipation at the same total current. As each aperture lases in a single transverse mode, these devices exhibit narrow far-field patterns. In combination with well-defined spacing between emitting spots, they permit tailored restricted launch conditions in MMFs, enhancing effective modal bandwidth. In another MA approach, the apertures are optically coupled such that self-injection locking (SIL) leads to lasing in a single supermode. One may regard one of the supermodes as acting as a master mode controlling the other one. Streak-camera studies reveal post-pulse oscillations in the SIL regime at frequencies up to 100 GHz. MA VCSELs enable a favorable combination of wavelength chirp and chromatic dispersion, extending transmission distances over MMFs beyond those expected for zero-chirp sources and supporting transfer bandwidths up to 60 GHz over kilometer-length SMF links. Full article

Heart failure (HF) remains a leading cause of recurrent hospitalisations worldwide, largely driven by acute episodes of decompensation. Early identification of impending decompensation could enable timely intervention and potentially prevent costly admissions. Non-invasive wearable devices have emerged as promising tools for continuously monitoring physiological parameters and detecting early signs of deterioration. This review summarises recent advances in wearable technologies designed to predict HF decompensation and appraises their ability to generate clinically useful alerts. It will examine various modalities designed to monitor different aspects of cardiorespiratory physiology that have the potential to detect abnormalities preceding heart failure decompensation. Broadly, these devices either monitor physical activity capacity and cardiac function or monitor changes in pulmonary fluid congestion. We will also cover evidence exploring whether these devices can generate timely alerts for interventions to improve patient outcomes and reduce hospitalisations. However, despite advances in these technologies, challenges remain regarding their accuracy and usability for remote monitoring, as well as concerns with data storage, processing, patient adherence, and integration into existing healthcare workflows. While current limitations exist, previous results warrant further research into this area, with a focus on larger randomised trials, exploring both single- and multi-sensor systems, using artificial intelligence and cost-effectiveness analysis. Overall, non-invasive wearables represent an opportunity to create a more proactive approach to HF management, with the potential to shift the paradigm from reactive treatment to anticipatory care. Full article

This study aims to compare different classifiers in the context of distinguishing two classes of signals: nonlinear electrocardiography (ECG) signals and stochastic artifacts occurring in ECG signals. The ECG signals from a single-lead wearable Movesense device were analyzed with a set of eight features: variance (VAR), three fractal dimension measures (Higuchi fractal dimension (HFD), Katz fractal dimension (KFD), and Detrended Fluctuation Analysis (DFA)), and four entropy measures (approximate entropy (ApEn), sample entropy (SampEn), and multiscale entropy (MSE) for scales 1 and 2). The minimum-redundancy maximum-relevance algorithm was applied for evaluation of feature importance. A broad spectrum of machine learning models was considered for classification. The proposed approach allowed for comparison of classifier features, as well as providing a broader insight into the characteristics of the signals themselves. The most important features for classification were VAR, DFA, ApEn, and HFD. The best performance among 34 classifiers was obtained using an optimized RUSBoosted Trees ensemble classifier (sensitivity, specificity, and positive and negative predictive values were 99.8, 73.7%, 99.8, and 74.3, respectively). The accuracy of the Movesense device was very high (99.6%). Moreover, the multifractality of ECG during sleep was observed in the relationship between SampEn (or ApEn) and MSE. Full article

Background/Objectives: Dysregulated gamma oscillations are associated with cognitive dysfunction. Auditory stimulation at 40 Hz enhances neural activity in brain regions associated with learning, attention, and memory. This study assessed the safety and acceptability of 40 Hz amplitude-modulated auditory stimulation in healthy older people. Auditory stimuli were created using popular songs, where vocals and background music were separated and independently amplitude-modulated at 40 Hz with different modulation depths to generate periodic 40 Hz gamma waveforms. Methods: In this open-label, single-arm study, healthy participants aged ≥65 years received 40 Hz amplitude-modulated auditory stimulation daily via a smartphone for 28 days through earphones/headphones. Safety was assessed through adverse event (AE) monitoring and changes in clinical scores for depression, cognitive function, and hearing thresholds. Acceptability was evaluated by adherence rates, listening time, dropout reasons, volume levels, intent for future use, and subjective impressions of the sound source on a 7-point Likert scale. Results: Among 28 participants (mean age 69.1 years, 53.6% female), six reported 12 AEs, with six considered device-related (e.g., ear discomfort, jaw pain, musculoskeletal stiffness). The AEs observed were mild or moderate. Scores for cognitive function, depression, and hearing thresholds did not worsen during the study period. Adherence was observed in 96.4%, with 85.7% expressing interest in continuing. Most participants rated the sounds’ unnaturalness between 2 and 3 and discomfort between 1 and 3 on the 7-point Likert scale. Conclusions: The intervention was well tolerated and acceptable in study participants, with no major safety concerns identified. Auditory stimulation did not cause severe discomfort or reduce acceptability. Further studies should explore the long-term effects and broader clinical applications. Full article