Search Results (279)

The long-term stability of submerged sandbars and protected shorelines in large alluvial rivers depends on the serviceability of flexible mattresses installed on the riverbed. Distributed fiber optic sensing is one of the few practical methods for monitoring deformation along these underwater systems over engineering-scale distances. Yet BOTDR-derived strain-difference profiles are often heavily contaminated by noise and rarely have reliable clean references. To address this issue, this study develops TrendBlend-BSFormer, a blind self-supervised denoising framework for in situ BOTDR strain data from underwater flexible mattresses. The framework combines four key features: blind-spot masking, a one-dimensional encoder decoder backbone, a Transformer bottleneck for long-range spatial dependence, and a multi-scale trend-detail blending branch with dual signal-noise heads. The framework was validated using annual and daily BOTDR field data from the Yudaizhou shoreline protection project in the Yangtze River, containing 9343 and 9875 valid measurement points, respectively. TrendBlend-BSFormer achieved pseudo-SNR/RMSE/MAE values of 14.22 dB, 15.03 με and 12.05 με for the annual data set and 5.32 dB, 8.02 με and 6.45 με for the daily data set, improving the pseudo-SNR by 1.45 dB and 2.95 dB relative to the published BiLSTM-CNN benchmark. It also reduced the high-frequency energy ratio from 0.172 to 0.011 for the annual data and from 0.424 to 0.112 for the daily data. The denoised profiles suppress isolated spikes while preserving mechanically plausible peaks, valleys, and short-range fluctuations, indicating that blind self-supervised denoising can provide a more physically credible strategy for BOTDR-based monitoring in complex underwater environments. Full article

Machine learning’s computational demands necessitate optimal performance and utilization across diverse hardware architectures. This research compares computing as spiking neural networks (CSNNs, or simulated neuromorphic computing) and regular CNNs on Apple Silicon M3 Pro with Metal Performance Shaders (MPS), and NVIDIA RTX 3070 GPU with CUDA. We run Convolutional Spiking Neural Networks (CSNNs) and traditional CNNs on two datasets (frame-based CIFAR-10; and sequential event-based DVS) to evaluate the suitability of neural net architectures and platforms for different data problems. For both CSNNs and traditional CNNs, Apple Silicon with MPS delivers better energy efficiency but longer processing times for training and inference. NVIDIA with CUDA offers faster computation in training and inference at higher energy costs for CNNs. For CSNNs, frame-based data (CIFAR-10) significantly degraded performance when proper temporal encoding was absent, while event-based data (DVS) proved more naturally suited to the CSNN architecture than frame-based inputs. Though CNNs still achieved higher empirical accuracy in the reported experiments. CSNNs also performed better on Apple Silicon (with MPS) for the sequential event-based data. RAM utilization patterns favored Apple Silicon (with MPS) across both data experiments. The CSNN architecture demanded higher memory resources than CNN, regardless of platform and dataset. NVIDIA (with CUDA) was less energy efficient for spiking neural networks (CSNNs) as compared to Apple Silicon (with MPS). We also compared how the number of time steps affects accuracy and energy consumption across hardware platforms, finding that higher accuracy correlates with energy costs as time steps increase; the accuracy-energy relation seems linear for frame-based data, while for event-based data the energy consumption remains stable increasing at higher time steps. Our cross-platform performance analysis of spiking and regular neural network architectures highlight the importance of matching platform-architecture combinations to a dataset and application requirements. Full article

Safe and intelligent collision avoidance technology is essential for the autonomous underwater vehicle (AUV) to navigate in underwater environments. Most existing spike methods are constrained by a fixed static threshold and are unable to dynamically adjust to threshold changes reasonably, leading to difficulties in robustly adapting to external dynamic interference and thus resulting in insufficient homeostasis and generalization. To address these limitations, inspired by the dynamic threshold changes in biological neural systems, a bioinspired dynamic adaptive threshold (BDAT) is proposed. Combining the spiking neural network with deep reinforcement learning, a novel bioinspired dynamic adaptive threshold planner (BDAT-Planner) framework is constructed for underwater dynamic collision avoidance tasks performed by AUVs in complex, unknown environments. The proposed BDAT-Planner consists of the spiking dynamic adaptive actor network (SDAAN) and the deep critic normal network (DCNN). The BDAT is deployed to each spiking neuron in the SDAAN, dynamically adjusting the spike firing rate through threshold changes and avoiding excessive excitation or inhibition, thus maintaining homeostasis. The spiking encoder and spiking decoder are designed to convert continuous information and spiking sequences. Experimental results from both the training process and evaluation process (ablation studies, comparison experiments, and homeostasis experiments) demonstrate that the proposed BDAT-Planner has achieved superior performance in dynamic collision avoidance and model homeostasis compared to static threshold methods and existing comparison methods. The novel idea of bioinspired dynamic adaptive threshold can maintain model homeostasis and effectively enhance its adaptability to external dynamic interference, which offers significant development potential for promoting the efficient and stable operation of AUVs in marine environments. Full article

Grain number is a primary agronomic trait for targeted yield improvement, with the prospect of enhanced grain production leading to greater food security. Given the complex polygenic nature of the grain number trait, large sample sizes are essential for effective QTL identification. The implementation of trained computer vision models for grain detection offers a timely and cost-effective solution for rapid QTL isolation. In this study, we trained a grain detection model using Ultralytics’ You Only Look Once (YOLOv11) framework. Training was completed on 1000 images of barley spikes, derived from a doubled haploid (DH) population descended from Hindmarsh and RGT Planet. The trained model, termed BarleyGC, achieved satisfactory accuracy metrics (mAP50–95 = 71.9%, recall = 96.7%, precision = 97.1%). Phenotypic characterisation of the DH population was completed with BarleyGC on a distinct collection of 973 images. The Pearson correlation coefficient (r) between model and manual-derived counts for the trait of grain number per spike was 0.895 (p < 0.0001), and 92.4% of all measurements fell within three grains of the manual measurement. Downstream QTL analysis on the phenotype data (n = 153 DH lines), revealed a QTL peak at position 224.959 cM on the genetic map (LOD = 3.14), named qGN-2H. The QTL region contained 21 candidate genes—including HORVU2Hr1G092290 (HORVU.MOREX.r3.2HG0184740), encoding the six-rowed spike 1 (Vrs1) gene—a well-characterised major regulator of row-type divergence and lateral spikelet development. Our study demonstrates the power of the YOLOv11 framework for grain quantification, with BarleyGC capable of grain detection directly from images of intact spikes in two-rowed barley varieties—thus achieving accelerated sample processing for the grain number trait. Full article

Integrating on-device learning into autonomous systems requires neural network frameworks that achieve both high energy efficiency and low latency. While spiking neural networks (SNNs) provide a promising event-driven paradigm, implementing hardware-efficient learning remains a challenge due to the computational overhead of error signaling and global gradients. This paper introduces a hardware-oriented hierarchical spiking predictive coding (SPC) framework designed for end-to-end event-driven systems. The proposed architecture implements an implicit prediction error encoding mechanism through local lateral and supervisory feedback connections, eliminating the need for dedicated error-storage memory or complex inter-layer error communication. The entire framework is structured and parameterized for physical implementation, utilizing digital-aligned simulations and arithmetic operations. We evaluate the system on neuromorphic datasets using a fixed 1 ms temporal resolution to mirror real-time hardware constraints. Experimental results demonstrate that the SPC framework can effectively identify stimuli from transient event streams, achieving stable on-device learning. Our work provides a practical path toward deploying low-power, scalable hierarchical spiking networks in resource-constrained environments. Full article

Neuromorphic technologies are attracting increasing interest in neuroengineering, as they provide an event-driven, spike-based computational framework that is well suited to temporally structured, sparse, and resource-constrained biological systems. Compared with conventional computing pipelines, neuromorphic approaches enable tighter integration of sensing, encoding, inference, feedback, and actuation under low-power and low-latency conditions. These features make them particularly relevant for wearable, implantable, and other edge-native neuroengineering applications. This review examines neuromorphic neuroengineering from four closely related perspectives: neuromorphic neurostimulation and adaptive actuation; tactile and sensory biointerfaces; spiking neural network (SNN)-based biosignal processing and state decoding; and wearable or implantable neuromorphic platforms. Across these domains, we highlight how neuromorphic systems may facilitate edge-native, closed-loop architectures that operate closer to the body and respond selectively to meaningful state changes. Neurorehabilitation is further discussed as an important translational context, as it involves long-term use, multimodal sensing, adaptive intervention, and substantial real-world deployment constraints. At present, however, the evidence base remains fragmented and is still largely dominated by device demonstrations and proof-of-concept studies rather than robust translational validation. Overall, neuromorphic approaches offer a promising systems-level pathway toward neuroengineering platforms that are not only computationally efficient but also adaptive, deployable, and responsive in real-world settings. Full article

Radio Frequency Fingerprinting (RFF) enables passive physical-layer device authentication by exploiting unintentional hardware variations in wireless transmitters. Neuromorphic implementations are attractive, given their potential for low-latency, energy-efficient inference capability under Size, Weight, and Power (SWaP) constraints at the edge. A new RFF capability is demonstrated here using recently introduced Radio Frequency Resonate-and-Fire (RF-RAF) neurons and eight WirelessHART devices. Performance is evaluated for RF-RAF-generated fingerprints against the established Gabor Transform (GTX) baseline using three classifier architectures: Random Forest (RndF), Convolutional Neural Network (CNN), and a Time-Incremented Spiking Neural Network (TI-SNN). The results show that RF-RAF fingerprints achieve an average classification accuracy of 96.7% across all three classifier types and consistently outperform GTX fingerprints at all evaluated fingerprint sizes. This performance persists under time-span-matched conditions, and the RF-RAF versus GTX benefit is not solely attributable to input data utilization. The TI-SNN surpasses 94% classification accuracy using M = 4 time step RF-RAF fingerprints with approximately 100 spikes per inference—a 4× larger GTX fingerprint requires approximately 1000 spikes to achieve the same classification accuracy. RF-RAF fingerprints offer two additional benefits: they are natively non-negative, which supports efficient neuromorphic hardware implementation, and they provide greater flexibility in fingerprint size selection. It is concluded that RF-RAF neurons provide an efficient neuromorphic-native encoding pathway for device RFF discrimination and offer improved accuracy–efficiency tradeoffs in training and inference for various classifier architectures. Full article

The surface spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key target for the development of Coronavirus Disease 2019 (COVID-19) vaccines. Nevertheless, the mutations in the S protein, particularly in its receptor-binding domain region, have resulted in a reduced or complete loss of immunogenicity and/or protective efficacy in early vaccines against the Omicron variant and subvariants. Accordingly, continuous efforts are required to develop effective vaccines against multiple Omicron subvariants to reduce current and future threats. In this study, we designed an mRNA vaccine targeting the S protein of a recent Omicron-XEC subvariant (XEC-S-mRNA) and assessed its immunogenicity, including its broad neutralizing activity, and its protective efficacy against multiple Omicron subvariants. Our results demonstrated that the lipid nanoparticle-formulated mRNA vaccine formed an appropriate particle size with strong stability and successful antigen expression. It elicited durable cellular immune responses and broad neutralizing antibodies against multiple early and recent Omicron subvariants, thereby cross-protecting transgenic mice from challenge with a heterologous Omicron strain (KP.3). Moreover, the vaccine-induced neutralizing antibodies alone were sufficient to prevent Omicron-KP.3 infection. Overall, this study shows promise for further development of the candidate vaccine against current and future Omicron infections. Full article

Background: Porcine epidemic diarrhea virus (PEDV) remains a major threat to the global swine industry, highlighting the urgent need for safe and effective next-generation vaccines. mRNA vaccines have emerged as a promising platform due to their rapid development and favorable safety profile. Objectives: This study aimed to design and perform the preliminary evaluation of a PEDV multi-epitope mRNA vaccine using an immunoinformatics-guided strategy combined with experimental validation. Methods: Immunoinformatics tools were used to identify B-cell and cytotoxic T lymphocyte (CTL) epitopes from the PEDV spike (S), membrane (M), and nucleocapsid (N) proteins. Selected epitopes were assembled into a multi-epitope antigen (E). mRNA constructs encoding S1, S2, and antigen E were synthesized via in vitro transcription and encapsulated into lipid nanoparticles (LNPs). Expression was evaluated in HEK293T cells, and immunogenicity was assessed in mice measuring antigen-specific antibody responses and cytokine levels following immunization. Results: The mRNA constructs exhibited high structural integrity and efficient intracellular translation. The LNP formulations showed good physicochemical stability and delivery efficiency. Immunization with the antigen E mRNA-LNP formulation induced significantly higher PEDV-specific IgG levels compared with control groups. Elevated cytokine levels further indicated activation of both humoral and cellular immune responses. Conclusions: This study presents a feasible workflow for the development of a PEDV multi-epitope mRNA vaccine. The antigen E construct demonstrated favorable immunogenicity in a mouse model, supporting its potential as a promising construct for further investigation and optimization. Although further studies are required to validate antigen expression at the protein level and to further characterize immune mechanisms, these findings provide preliminary evidence supporting the feasibility of multi-epitope mRNA vaccines for PEDV prevention. Full article

Spiking neural networks (SNNs) offer inherent advantages in processing temporal information. However, their network topologies are predominantly algorithm-generated, lacking constraints from biological brain connectivity, which limits their bio-plausibility. In our previous work, we constructed a spiking neural network (SNN) by incorporating the topological structure of functional brain networks derived from fMRI data of healthy subjects and proposed an fMRISNN model. This model was further employed as the reservoir layer of a liquid state machine (LSM) to build a speech recognition framework. In this framework, the Lyon ear model and the BSA were used to encode speech signals into spike sequences; however, this approach suffers from high computational cost and limited adaptability to temporal variations. To address these limitations, we propose an enhanced Mel-frequency cepstral coefficient (MFCC)-driven sparse spike encoding method. For the speech recognition task, we systematically compare the two preprocessing pipelines in terms of spike number, spike sparsity, encoding time, and downstream speech recognition performance. Experimental results show that the proposed method generates substantially fewer spikes, achieves markedly higher sparsity, and requires significantly less encoding time, while maintaining nearly the same recognition accuracy under the same LSM-based framework. These findings indicate that improved speech input representation can enhance the computational efficiency of SNN-based speech recognition without compromising recognition capability. In addition, the fMRISNN model significantly outperforms several baseline models with algorithmically generated topologies. Compared with mainstream models reported in the literature, although the deep convolutional neural network (CNN) still achieves higher absolute recognition accuracy, the fMRISNN exhibits clear advantages in terms of model parameter size and theoretical energy efficiency. Full article

Pre-harvest sprouting (PHS) in wheat is a significant global challenge influenced by climate. This study aimed to decipher the genetic underpinnings of PHS and identify resistance genes using 309 recombinant inbred lines (RILs) derived from the “Berkut” × “Worrakatta” cross. Methods: Phenotypic assessment of PHS traits was performed using the whole-spike sprouting method across various environments, complemented by quantitative trait loci (QTL) analysis employing a wheat 50 K SNP chip. Results showed high PHS rates in both parental lines across multiple environments. Progeny exhibited substantial variation in PHS rates, with coefficients of variation ranging from 0.16 to 0.19 and phenotypic variation ranging from 23.92% to 100%, suggesting pronounced transgressive segregation. Nine QTLs associated with PHS were identified on chromosomes 1AL, 1DL, 2AL, 2AS, 2BS, 3DS, 4BL, and 7BL. These loci accounted for 2.67% to 6.39% of the phenotypic variation. Notably, the enhancer alleles at four loci—1DL, 2BS, 4BL, and 7BL—originated from “Worrakatta”, and “Berkut” contributed the enhancer alleles at the remaining five loci. Two QTLs, QPHS.xjau-1AL.1 and QPHS.xjau-1AL.2, were stable across multiple environments. Specifically, QPHS.xjau-1AL.1 was present in three environments and explained 3.86% to 6.39% of the phenotypic variation, while QPHS.xjau-1AL.2 appeared in one environment under average conditions, explaining 2.67% to 4.87% of the variation. Our study also identified eight candidate genes associated with wheat PHS, including those encoding Myb transcription factors that influence flavonoid biosynthesis and grain color, as well as genes involved in stress response and gibberellin biosynthesis, which are crucial for plant growth and development. These genes represent vital targets for enhancing wheat PHS resistance. Full article

Liquid State Machines (LSMs), a reservoir computing model based on recurrent spiking neural networks, provide a powerful framework for solving spatiotemporal classification tasks by leveraging rich temporal dynamics and event-driven processing. Although the traditional LSM formulation assumes a fixed, randomly generated reservoir, recent research has explored optimization strategies to improve liquid dynamics. However, most existing approaches focus primarily on optimizing synaptic connectivity or reservoir structure, while the role of neuron-level parameters remains largely underexplored. This work proposes a neuroevolutionary strategy based on a Genetic Algorithm (GA) that encodes both neuron configurations and their spatial positions, explicitly treating neuron-level parameters as optimization targets. By evolving neuron-specific parameters and spatial positions, the method induces diverse reservoir dynamics. Unlike approaches that directly optimize synaptic weights, the proposed representation maintains an encoding whose dimensionality scales linearly with the number of neurons. The approach was evaluated on four synthetic benchmark tasks, including one Frequency Recognition task and three Pattern Recognition tasks, using compact reservoirs composed of only 20 Leaky Integrate-and-Fire neurons. Despite the small reservoir size, the method achieved state-of-the-art or highly competitive performance, reaching mean accuracies of up to 99.71%. In the most challenging case (PR12), performance improved when the reservoir size was increased to 64 neurons. The method was further evaluated on two real-world datasets, N-MNIST and the Free Spoken Digit Dataset (FSDD), using reservoirs of 300 neurons, achieving 90.65% and 81.47% accuracy, respectively, while using substantially fewer neurons than many existing LSM-based approaches. These results highlight the potential of evolving neuron configurations and spatial organization to produce compact and effective liquid reservoirs. Full article

Deep learning models have achieved high accuracy in forecasting problems, but at the cost of large computational energy demand. Brain-inspired spiking neural networks (SNNs) offer a promising, low-power alternative, yet their adoption for time-series forecasting has been limited by information loss from binary spikes and degraded performance in deeper networks. This paper proposes a fully spiking framework that bridges this gap by improving both the encoding and propagation of information in SNNs. The framework introduces a hybrid Delta-Rate encoding mechanism that captures both abrupt changes and gradual trends in time-series data, and a Mem-Spike mechanism that transmits analog membrane potential values to preserve fine-grained information between spiking layers. We further employ residual membrane connections to maintain signal flow in deep spiking networks. Using two public energy load datasets, our enhanced SNNs consistently outperform conventional spiking models, improving prediction accuracy by up to 61.6% and mitigating degradation in multi-layer networks. Notably, it narrows the gap to the selected deep learning baseline (LSTM), achieving comparable accuracy in some settings while requiring only about 10% of the estimated inference energy of that baseline under a common operation-level model. These results show that, within the empirical scope considered here, enhanced conventional SNNs can improve time-series forecasting accuracy while retaining favorable estimated efficiency. Full article

Brain–machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their generalizability. In this work, we propose a Single-Direction CNN-LSTM decoder inspired by motor cortex encoding mechanisms, which separately models extension and flexion dynamics through parallel CNN-LSTM branches. Each branch extracts spatial–temporal features from neural spike data and predicts directional joint variables, which are then combined by subtraction to yield the net angular velocity and torque of upper-limb joints. Using invasive recordings from a macaque during a 2D center-out reaching task, we demonstrate that our decoder achieves comparable performance to a conventional CNN-LSTM when trained on all tasks, while significantly outperforming both CNN-LSTM and linear regression baselines in cross-target generalization scenarios. Moreover, the model can capture physiologically meaningful co-contraction patterns, providing richer insights into motor control. These results suggest that incorporating neuroscience-inspired modular decoding into deep neural architectures enhances robustness and adaptability across tasks, offering a promising pathway for BMI applications in prosthetics and rehabilitation. Full article

The convergence of sensing and processing is a critical frontier in the development of energy-efficient spiking edge intelligence. This paper presents a novel hardware implementation of a sensory neuron evolving from the leaky integrate-and-fire (LIF) model by coupling a volatile memristor with an LC tank circuit. The proposed memristor–resistor–inductor–capacitor (MRLC) neuron embeds electromagnetic sensing directly into neuronal dynamics, enabling direct transduction of proximity information into spike trains. We demonstrate that the circuit functions as a metal-sensitive proximity sensor with spiking output in both simulation and physical experiments. Moreover, the MRLC neuron exhibits rich dynamical regimes, including regular spiking, bursting with 2–5 spikes per burst, and quasi-chaotic behavior, as well as sensing memory provided by hysteresis-like multistability, which is a notable advancement over simple rate-encoding LIF neurons. Full article