Search Results (2,273)

For high-voltage signal detection applications, an auto-zero and chopper operational amplifier (OPA) is proposed in this paper. With the auto-zero and chopper technique, the OPA adopts an eight-channel Ping-Pong mechanism to reduce the high-frequency ripple and glitch generated by chopper modulation. The main transconductor effectively suppresses low-frequency noise and offset by combining input coarse and output fine auto-zero. A common-mode voltage tracking circuit is presented to ensure constant gate-source and gate-substrate voltages of the chopper, which reduces the charge injection caused by threshold voltage drift of their transistors and improves output signal resolution. The OPA is implemented using CMOS 180 nm BCD process. The post-simulation results show that the unit gain bandwidth (UGB) is 2.5 MHz and common-mode rejection ratio (CMRR) is 137 dB when the power supply voltage is 5–55 V. The noise power spectral density (PSD) is 6.6 nV/√Hz, and the offset is about 47 µV. The overall circuit consumes current of 960 µA. Full article

Voltage-gated Ca²⁺ channels (VGCCs) are regulated by four CaVβ subunits (CaVβ1–CaVβ4), each showing specific expression patterns in excitable cells. While primarily known for regulating VGCC function, CaVβ proteins also have channel-independent roles, including gene expression modulation. Among these, CaVβ1 is expressed in skeletal muscle as multiple isoforms. The adult isoform, CaVβ1D, localizes at the triad and modulates CaV1 activity during Excitation–Contraction Coupling (ECC). In this study, we investigated the lesser-known embryonic/perinatal CaVβ1 isoforms and their roles in neuromuscular junction (NMJ) formation, maturation, and maintenance. We found that CaVβ1 isoform expression is developmentally regulated through differential promoter activation. Specifically, CaVβ1A is expressed in embryonic muscle and reactivated in denervated adult muscle, alongside the known CaVβ1E isoform. Nerve injury in adult muscle triggers a shift in promoter usage, resulting in re-expression of embryonic/perinatal Cacnb1A and Cacnb1E transcripts. Functional analyses using aneural agrin-induced AChR clustering on primary myotubes demonstrated that these isoforms contribute to NMJ formation. Additionally, their expression during early post-natal development is essential for NMJ maturation and long-term maintenance. These findings reveal previously unrecognized roles of CaVβ1 isoforms beyond VGCC regulation, highlighting their significance in neuromuscular system development and homeostasis. Full article

Existing nighttime visibility enhancement methods primarily focus on improving overall brightness under low-light conditions. However, nighttime road images are also affected by glare, glow, and flare from complex light sources such as streetlights and headlights, making it challenging to suppress locally overexposed regions and recover fine details. To address these challenges, we propose a Nighttime Road Glare Suppression Network (NRGS-Net) for glare removal and detail restoration. Specifically, to handle diverse glare disturbances caused by the uncertainty in light source positions and shapes, we designed a gated positional attention (GPA) module that integrates positional encoding with local contextual information to guide the network in accurately locating and suppressing glare regions, thereby enhancing the visibility of affected areas. Furthermore, we introduced an improved Uformer backbone named LCAtransformer, in which the downsampling layers adopt efficient depthwise separable convolutions to reduce computational cost while preserving critical spatial information. The upsampling layers incorporate a residual PixelShuffle module to achieve effective restoration in glare-affected regions. Additionally, channel attention is introduced within the Local Context-Aware Feed-Forward Network (LCA-FFN) to enable adaptive adjustment of feature weights, effectively suppressing irrelevant and interfering features. To advance the research in nighttime glare suppression, we constructed and publicly released the Night Road Glare Dataset (NRGD) captured in real nighttime road scenarios, enriching the evaluation system for this task. Experiments conducted on the Flare7K++ and NRGD, using five evaluation metrics and comparing six state-of-the-art methods, demonstrate that our method achieves superior performance in both subjective and objective metrics compared to existing advanced methods. Full article

One major challenge in cellular neuroscience is to elucidate how the accurate alignment of presynaptic release sites with postsynaptic densely clustered ligand-gated ion channels at chemical synapses is achieved upon synapse assembly. The clustering of neurotransmitter receptors at postsynaptic sites is a key moment of synaptogenesis and determinant for effective synaptic transmission. The number of the ionotropic neurotransmitter receptors at these postsynaptic sites of both excitatory and inhibitory synapses is variable and is regulated by different mechanisms, thus allowing the modulation of synaptic strength, which is essential to tune neuronal network activity. Several well-regulated processes seem to be involved, including lateral diffusion within the plasma membrane and local anchoring as well as receptor endocytosis and recycling. The molecular mechanisms implicated are numerous and were reviewed recently in great detail. The role of pre-synaptically released neurotransmitters within the complex regulatory apparatus organizing the postsynaptic site underneath presynaptic terminals is not completely understood, even less for inhibitory synapses. In this mini review article, we focus on this aspect of synapse formation, summarizing and contrasting findings on the functional role of the neurotransmitters glycine and γ-aminobutyric acid (GABA) for initiation of postsynaptic receptor clustering and regulation of Cl⁻ channel receptor numbers at inhibitory synapses gathered over the last two decades. Full article

Hyperspectral image (HSI) classification is an important task in the field of remote sensing, with far-reaching practical significance. Most Convolutional Neural Networks (CNNs) only focus on local spatial features and ignore global spectral dependencies, making it difficult to completely extract spectral information in HSI. In contrast, Vision Transformers (ViTs) are widely used in HSI due to their superior feature extraction capabilities. However, existing Transformer models have challenges in achieving spectral–spatial feature fusion and maintaining local structural consistency, making it difficult to strike a balance between global modeling capabilities and local representation. To this end, we propose a Prompt-Gated Transformer with a Spatial–Spectral Enhancement (PGTSEFormer) network, which includes a Channel Hybrid Positional Attention Module (CHPA) and Prompt Cross-Former (PCFormer). The CHPA module adopts a dual-branch architecture to concurrently capture spectral and spatial positional attention, thereby enhancing the model’s discriminative capacity for complex feature categories through adaptive weight fusion. PCFormer introduces a Prompt-Gated mechanism and grouping strategy to effectively model cross-regional contextual information, while maintaining local consistency, which significantly enhances the ability for long-distance dependent modeling. Experiments were conducted on five HSI datasets and the results showed that overall accuracies of 97.91%, 98.74%, 99.48%, 99.18%, and 92.57% were obtained on the Indian pines, Salians, Botswana, WHU-Hi-LongKou, and WHU-Hi-HongHu datasets. The experimental results show the effectiveness of our proposed approach. Full article

The exponential growth of autonomous systems demands robust security mechanisms that can operate within the extreme constraints of real-time embedded environments. This paper introduces SMART DShot, a groundbreaking machine learning-enhanced framework that transforms the security landscape of unmanned aerial vehicle motor control systems through seamless integration of adaptive timing correction and real-time anomaly detection within Digital Shot (DShot) communication protocols. Our approach addresses critical vulnerabilities in Electronic Speed Controller (ESC) interfaces by deploying four synergistic algorithms—Kalman Filter Timing Correction (KFTC), Recursive Least Squares Timing Correction (RLSTC), Fuzzy Logic Timing Correction (FLTC), and Hybrid Adaptive Timing Correction (HATC)—each optimized for specific error characteristics and attack scenarios. Through comprehensive evaluation encompassing 32,000 Monte Carlo test iterations (500 per scenario × 16 scenarios × 4 algorithms) across 16 distinct operational scenarios and PolarFire SoC Field-Programmable Gate Array (FPGA) implementation, we demonstrate exceptional performance with 88.3% attack detection rate, only 2.3% false positive incidence, and substantial vulnerability mitigation reducing Common Vulnerability Scoring System (CVSS) severity from High (7.3) to Low (3.1). Hardware validation on PolarFire SoC confirms practical viability with minimal resource overhead (2.16% Look-Up Table utilization, 16.57 mW per channel) and deterministic sub-10 microsecond execution latency. The Hybrid Adaptive Timing Correction algorithm achieves 31.01% success rate (95% CI: [30.2%, 31.8%]), representing a 26.5% improvement over baseline approaches through intelligent meta-learning-based algorithm selection. Statistical validation using Analysis of Variance confirms significant performance differences (F(3,1996) = 30.30, p < 0.001) with large effect sizes (Cohen’s d up to 4.57), where 64.6% of algorithm comparisons showed large practical significance. SMART DShot establishes a paradigmatic shift from reactive to proactive embedded security, demonstrating that sophisticated artificial intelligence can operate effectively within microsecond-scale real-time constraints while providing comprehensive protection against timing manipulation, de-synchronization, burst interference, replay attacks, coordinated multi-channel attacks, and firmware-level compromises. This work provides essential foundations for trustworthy autonomous systems across critical domains including aerospace, automotive, industrial automation, and cyber–physical infrastructure. These results conclusively demonstrate that ML-enhanced motor control systems can achieve both superior security (88.3% attack detection rate with 2.3% false positives) and operational performance (31.01% timing correction success rate, 26.5% improvement over baseline) simultaneously, establishing SMART DShot as a practical, deployable solution for next-generation autonomous systems. Full article

In this study, we propose a bidirectional chemical sensor platform based on a reconfigurable ion-sensitive field-effect transistor (R-ISFET) architecture. The device incorporates Ni-silicide Schottky barrier source/drain (S/D) contacts, enabling ambipolar conduction and bidirectional turn-on behavior for both p-type and n-type configurations. Channel polarity is dynamically controlled via the program gate (PG), while the control gate (CG) suppresses leakage current, enhancing operational stability and energy efficiency. A dual-control-gate (DCG) structure enhances capacitive coupling, enabling sensitivity beyond the Nernst limit without external amplification. The extended-gate (EG) architecture physically separates the transistor and sensing regions, improving durability and long-term reliability. Electrical characteristics were evaluated through transfer and output curves, and carrier transport mechanisms were analyzed using band diagrams. Sensor performance—including sensitivity, hysteresis, and drift—was assessed under various pH conditions and external noise up to 5 V_pp (i.e., peak-to-peak voltage). The n-type configuration exhibited high mobility and fast response, while the p-type configuration demonstrated excellent noise immunity and low drift. Both modes showed consistent sensitivity trends, confirming the feasibility of complementary sensing. These results indicate that the proposed R-ISFET sensor enables selective mode switching for high sensitivity and robust operation, offering strong potential for next-generation biosensing and chemical detection. Full article

This article presents VRF-DETR, a lightweight real-time object detection framework for aerial remote sensing images, aimed at addressing the challenge of insufficient receptive fields for easily confused categories due to differences in height and perspective. Based on the RT-DETR architecture, our approach introduces three key innovations: the multi-scale receptive field adaptive fusion (MSRF²) module replaces the Transformer encoder with parallel dilated convolutions and spatial-channel attention to adjust receptive fields for confusing objects dynamically; the gated multi-scale context (GMSC) block reconstructs the backbone using Gated Multi-Scale Context units with attention-gated convolution (AGConv), reducing parameters while enhancing multi-scale feature extraction; and the context-guided fusion (CGF) module optimizes feature fusion via context-guided weighting to resolve multi-scale semantic conflicts. Evaluations were conducted on both the VisDrone2019 and UAVDT datasets, where VRF-DETR achieved the mAP₅₀ of 52.1% and the mAP_50-95 of 32.2% on the VisDrone2019 validation set, surpassing RT-DETR by 4.9% and 3.5%, respectively, while reducing parameters by 32% and FLOPs by 22%. It maintains real-time performance (62.1 FPS) and generalizes effectively, outperforming state-of-the-art methods in accuracy-efficiency trade-offs for aerial object detection. Full article

Synthetic aperture radar (SAR), as an active microwave imaging system, has the capability of all-weather and all-time observation. In response to the challenges of aircraft detection in SAR images due to the complex background interference caused by the continuous scattering of airport buildings and the demand for real-time processing, this paper proposes a YOLOv7-MTI recognition model that combines the attention mechanism and involution. By integrating the MTCN module and involution, performance is enhanced. The Multi-TASP-Conv network (MTCN) module aims to effectively extract low-level semantic and spatial information using a shared lightweight attention gate structure to achieve cross-dimensional interaction between “channels and space” with very few parameters, capturing the dependencies among multiple dimensions and improving feature representation ability. Involution helps the model adaptively adjust the weights of spatial positions through dynamic parameterized convolution kernels, strengthening the discrete strong scattering points specific to aircraft and suppressing the continuous scattering of the background, thereby alleviating the interference of complex backgrounds. Experiments on the SAR-AIRcraft-1.0 dataset, which includes seven categories such as A220, A320/321, A330, ARJ21, Boeing737, Boeing787, and others, show that the mAP and mRecall of YOLOv7-MTI reach 93.51% and 96.45%, respectively, outperforming Faster R-CNN, SSD, YOLOv5, YOLOv7, and YOLOv8. Compared with the basic YOLOv7, mAP is improved by 1.47%, mRecall by 1.64%, and FPS by 8.27%, achieving an effective balance between accuracy and speed, providing research ideas for SAR aircraft recognition. Full article

Sonar is the primary tool used for detecting small targets at long distances underwater. Due to the influence of the underwater environment and imaging mechanisms, sonar images face challenges such as a small number of target pixels, insufficient data samples, and uneven category distribution. Existing target detection methods are unable to effectively extract features from sonar images, leading to high false positive rates and affecting the accuracy of target detection models. To counter these challenges, this paper presents a novel sonar small-target detection framework named DCE-Net that refines the YOLOv8 architecture. The Detail Enhancement Attention Block (DEAB) utilizes multi-scale residual structures and channel attention mechanism (AM) to achieve image defogging and small-target structure completion. The lightweight spatial variation convolution module (CoordGate) reduces false detections in complex backgrounds through dynamic position-aware convolution kernels. The improved efficient multi-scale AM (MH-EMA) performs scale-adaptive feature reweighting and combines cross-dimensional interaction strategies to enhance pixel-level feature representation. Experiments on a self-built sonar small-target detection dataset show that DCE-Net achieves an mAP@0.5 of 87.3% and an mAP@0.5:0.95 of 41.6%, representing improvements of 5.5% and 7.7%, respectively, over the baseline YOLOv8. This demonstrates that DCE-Net provides an efficient solution for underwater detection tasks. Full article

This review explores the advantages of ivabradine in the management of cardiac surgery patients, particularly highlighting its heart rate (HR)-reducing properties, its role in minimizing the impact of atrial fibrillation, and its contributions to improving left ventricular diastolic function, as well as reducing pain, stress, and anxiety. In parallel, studies provide evidence that ivabradine influences endothelial inflammatory responses through mechanisms such as biomechanical modulation. Unlike traditional beta-blockers that may induce hypotension, ivabradine selectively inhibits hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, allowing for effective HR reduction without compromising blood pressure stability. This characteristic is particularly beneficial for patients at risk of atrial fibrillation post-surgery, where HR control is crucial for cardiovascular stability. This is an area in which ivabradine appears to play a role prophylactically, possibly in combination with beta-blockers. Furthermore, ivabradine has been associated with enhanced diastolic parameters in left ventricular function, reflecting its potential to improve surgical outcomes in patients with compromised heart function. In addition to its cardiovascular benefits, it appears to alleviate psychological stress and anxiety, common in postoperative settings, by moderating the neuroendocrine response to stress, thereby reducing stress-induced hormone levels. Furthermore, it has notable analgesic properties, contributing to pain management through its action on HCN channels in both the peripheral and central nervous systems. Collectively, these findings indicate that ivabradine may serve as a valuable therapeutic agent in the perioperative care of cardiac surgery patients, addressing both physiological and psychological challenges during recovery. Full article

The mechanism by which voltage-gated ion channels open and close has been the subject of intensive investigation for decades. For a large class of potassium channels and related sodium channels, the consensus has been that the gating current preceding the main ionic current is a large movement of positively charged segments of protein from voltage-sensing domains that are mechanically connected to the gate through linker sections of the protein, thus opening and closing the gate. We have pointed out that this mechanism is based on evidence that has alternate interpretations in which protons move. Very little literature considers the role of water and protons in gating, although water must be present, and there is evidence that protons can move in related channels. It is known that water has properties in confined spaces and at the surface of proteins different from those in bulk water. In addition, there is the possibility of quantum properties that are associated with mobile protons and the hydrogen bonds that must be present in the pore; these are likely to be of major importance in gating. In this review, we consider the evidence that indicates a central role for water and the mobility of protons, as well as alternate ways to interpret the evidence of the standard model in which a segment of protein moves. We discuss evidence that includes the importance of quantum effects and hydrogen bonding in confined spaces. K⁺ must be partially dehydrated as it passes the gate, and a possible mechanism for this is considered; added protons could prevent this mechanism from operating, thus closing the channel. The implications of certain mutations have been unclear, and we offer consistent interpretations for some that are of particular interest. Evidence for proton transport in response to voltage change includes a similarity in sequence to the H_v1 channel; this appears to be conserved in a number of K⁺ channels. We also consider evidence for a switch in -OH side chain orientation in certain key serines and threonines. Full article

Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m⁻¹ spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article

AlGaN-based high-electron-mobility transistors are critical for next-generation power electronics and radio-frequency applications, yet achieving stable enhancement-mode operation with a high threshold voltage remains a key challenge. In this work, we designed p-AlGaN superlattices with different structures and performed energy band structure simulations using the device simulation software Silvaco. The results demonstrate that thin barrier structures lead to reduced acceptor incorporation, thereby decreasing the number of ionized acceptors, while facilitating vertical hole transport. Superlattice samples with varying periodic thicknesses were grown via metal-organic chemical vapor deposition, and their crystalline quality and electrical properties were characterized. The findings reveal that although gradient-thickness barriers contribute to enhancing hole concentration, the presence of thick barrier layers restricts hole tunneling and induces stronger scattering, ultimately increasing resistivity. In addition, we simulated the structure of the enhancement-mode HEMT with p-AlGaN as the under-gate material. Analysis of its energy band structure and channel carrier concentration indicates that adopting p-AlGaN superlattices as the under-gate material facilitates achieving a higher threshold voltage in enhancement-mode HEMT devices, which is crucial for improving device reliability and reducing power loss in practical applications such as electric vehicles. Full article

Carbon nanotube field-effect transistors (CNTFETs) are becoming a strong competitor for the next generation of high-performance, energy-efficient integrated circuits due to their near-ballistic carrier transport characteristics and excellent suppression of short-channel effects. However, CNT FETs with large diameters and small band gaps exhibit obvious bipolarity, and gate-induced drain leakage (GIDL) contributes significantly to the off-state leakage current. Although the asymmetric gate strategy and feedback gate (FBG) structures proposed so far have shown the potential to suppress CNT FET leakage currents, the devices still lack scalability. Based on the analysis of the conduction mechanism of existing self-aligned gate structures, this study innovatively proposed a design strategy to extend the length of the source–drain epitaxial region (L_ext) under a vertically stacked architecture. While maintaining a high drive current, this structure effectively suppresses the quantum tunneling effect on the drain side, thereby reducing the off-state leakage current (I_off = 10⁻¹⁰ A), and has good scaling characteristics and leakage current suppression characteristics between gate lengths of 200 nm and 25 nm. For the sidewall gate architecture, this work also uses single-walled carbon nanotubes (SWCNTs) as the channel material and uses metal source and drain electrodes with good work function matching to achieve low-resistance ohmic contact. This solution has significant advantages in structural adjustability and contact quality and can significantly reduce the off-state current (I_off = 10⁻¹⁴ A). At the same time, it can solve the problem of off-state current suppression failure when the gate length of the vertical stacking structure is 10 nm (the total channel length is 30 nm) and has good scalability. Full article