Search Results (1,117)

This paper establishes an integrated framework combining self-induced vibration measurements with deep learning for vibration-based remaining useful life (RUL) prediction of mechanical frame structures in mobile robots. The main innovations comprise (1) a self-induced vibration excitation system that utilizes the robot’s drive wheels to generate controlled mechanical oscillations, using a five-sensor micro-electro-mechanical system (MEMS) accelerometer array to capture non-uniform vibration mode shapes across the robot’s structure, and (2) a processing pipeline for RUL prediction using accelerometer data and early feature fusion in two machine-learning models (long short-term memory (LSTM) and a convolutional neural network (CNN)). Our research methodology includes (i) modal analysis to identify the robot’s natural frequencies, (ii) verification platform evaluation, comparing low-cost MEMS accelerometers against a reference integrated electronic piezoelectric (IEPE) accelerometer, demonstrating industrial-grade measurement quality (coherence > 98%, uncertainty 4.79–7.21%), and (iii) data-driven validation using real data from the mechanical frame, showing that the LSTM model outperforms the CNN with a 2.61× root-mean-square error (RMSE) improvement (R² = 0.99). Our solution demonstrates that early feature fusion provides sufficient information to model degradation and detect faults early at a lower cost, offering a feasible alternative to classical maintenance procedures through combined hardware validation and lightweight software suitable for Industrial Internet-of-Things (IIoT) deployment. Full article

Several commercial seismometers now support CSTP, the real-time communication protocol used in the China Earthquake Early Warning System, but there is still no simple, flexible, and low-cost solution to archive CSTP streams or integrate them into existing data processing systems. In this study, we design and implement SharpCEEWPServer, a lightweight, out-of-the-box graphical server that integrates client management, real-time data reception, visualization, and archiving, and can, via RingServer, convert CSTP real-time streams into widely supported SeedLink streams. Hardware compatibility is evaluated using four commercial CSTP-capable instruments, a forwarding chain is built to assess forwarding functionality and reliability, and concurrency performance is tested using simulated networks with different station counts. The stability under network impairment scenarios and the performance of the forwarding system were also analyzed. The results show that the server can reliably receive and forward real-time data streams, and that laptop-class hardware is sufficient to withstand the load imposed by an M7.0 earthquake scenario when receiving real-time streams from 1000 three-component seismometers. However, the current version’s latency performance can only meet the needs of non-early warning networks. Overall, the proposed server significantly lowers the deployment and usage threshold for new CSTP-capable instruments and provides an efficient, low-cost integration solution for temporary networks in earthquake emergency response and seismic arrays. Full article

Automatic modulation classification (AMC) under low signal-to-noise ratio (SNR) and complex channel conditions remains a significant challenge due to the trade-off between robustness and efficiency. This study proposes a lightweight temporal convolutional network (TCN) and Mamba fusion architecture designed to enhance modulation recognition performance. In the modulation signal denoising stage, a non-local adaptive thresholding denoising module (NATM) is introduced to explicitly improve the effective signal-to-noise ratio. In the parallel feature extraction stage, TCN captures local symbol-level dependencies, while Mamba models long-range temporal relationships. In the output stage, their outputs are integrated through additive layer-wise fusion, which prevents parameter explosion. Experiments were conducted on the RadioML 2016.10A, 2016.10B, and 2018.01A datasets with leakage-controlled partitioning strategies including GroupKFold and Leave-One-SNR-Out cross-validation. The proposed method achieves up to a 3.8 dB gain in the required signal-to-noise ratio at 90 percent accuracy compared with state-of-the-art baselines, while maintaining a substantially lower parameter count and reduced inference latency. The denoising module provides clear robustness improvements under low signal-to-noise ratio conditions, particularly below −8 dB. The results show that the proposed network strikes a balance between accuracy and efficiency, highlighting its application potential in real-time wireless receivers under resource constraints. Full article

Ultra-Wideband (UWB) technology enables centimeter-level indoor positioning, but it remains highly sensitive to channel dynamics, multipath and Non-Line-of-Sight (NLoS) propagation. Recent studies increasingly apply Machine Learning (ML) methods to address these issues by modeling nonlinear channel behavior and mitigating ranging bias. This paper presents a comprehensive review and provides a critical synthesis of 169 research works published between 2020 and 2024, offering an integrated overview of how ML techniques are incorporated into UWB-based Indoor Positioning Systems (IPSs). The studies are grouped according to their functional objective, learning algorithm, network architecture, evaluation metrics, dataset, and experimental setting. The results indicate that most approaches apply ML to channel classification and ranging error mitigation, with Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and hybrid CNN–Long Short-Term Memory (LSTM) architectures being among the most common choices due to their ability to capture spatial and temporal patterns in the Channel Impulse Response (CIR). Despite the reported accuracy improvements, scalability and cross-environment generalization remain open challenges, largely due to the scarcity of public datasets and the lack of standardized evaluation protocols. Emerging research trends highlight growing interest in transfer learning, domain adaptation, and federated learning, along with lightweight and explainable models suitable for embedded and multi-sensor systems. Overall, this review summarizes the progress made in ML-driven UWB localization, identifies current gaps, and outlines promising directions toward more robust and generalizable indoor positioning frameworks. Full article

The rapid expansion of Internet of Things (IoT) and mobile devices has intensified the demand for lightweight cryptographic algorithms capable of delivering strong security with minimal computational overhead. This work presents the SMA, a Secure Matrix-Based lightweight block cipher designed to meet these requirements through a 64-bit block and 80-bit key Substitution–Permutation Network (SPN) optimized for constrained environments. The SMA combines a nibble-wise PRESENT S-box with a fully index-based 2D matrix permutation to provide high non-linearity and efficient full-bit diffusion, supported by an enhanced key schedule that increases round-key diversity and mitigates key-dependent weaknesses. The proposed method replaces the complex linear diffusion layers used in existing lightweight ciphers such as GIFT, RECTANGLE, and PRESENT with a low-cost two-dimensional permutation that improves practical performance. Experimental evaluation demonstrates that the SMA achieves 98.5% non-correlated outputs, an average 50% bit error rate under both plaintext and key variations, and a 100% pass rate across fifteen NIST SP 800-22 statistical tests in nine data categories. Software-based implementation further confirms the correctness and applicability of the SMA for IoT-oriented simulation environments. Moreover, no exploitable differential or linear trails were identified across the full 20-round design. These results indicate that the SMA provides strong confusion, diffusion, and statistical randomness while maintaining competitive performance for secure IoT and mobile encryption applications. Full article

Accurate weed detection in crop fields remains a challenging task due to the diversity of weed species and their visual similarity to crops, especially under natural field conditions where lighting and occlusion vary. Traditional methods typically attempt to directly identify various weed species, which demand large-scale, finely annotated datasets and often suffer from low generalization. To address these challenges, this study proposes a novel dual-model framework that simplifies the task by dividing it into two tractable stages. First, a crop segmentation network is used to identify and remove cabbage (Brassica oleracea L. ssp. pekinensis) regions from field images. Since crop categories are visually consistent and singular, this stage achieves high precision with relatively low complexity. The remaining non-crop areas, which contain only weeds and background, are then subdivided into grid cells. Each cell is classified by a second lightweight classification network as either background, broadleaf weeds, or grass weeds. The classification model achieved F₁ scores of 95.1%, 91.1%, and 92.2% for background, broadleaf weeds, and grass weeds, respectively. This two-stage approach transforms a complex multi-class detection task into simpler, more manageable subtasks, improving detection accuracy while reducing annotation burden and enhancing robustness under the tested field conditions. Full article

This study assessed the effect of slicer-made interlocking joints on 3D printed sandwich panels manufactured through fused filament fabrication (FFF) in terms of flexural properties and energy absorption. Composites were prepared with thermoplastic polyurethane (TPU) as the core material and polyamide (PA), polylactic acid (PLA), polyethylene terephthalate (PET) as skin materials for each of the three composites, respectively. In order to assess the implications of internal geometry, 3D printing was done on five infill topologies (Cross-3D, Grid, Gyroid, Line and Honeycomb) at 20% density. All samples had 20% core density and underwent three point bending testing for flexural testing. It was noted that the Grid and Gyroid cores had the best performance in terms of maximum load capacity based on stretch-dominated behavior while Cross-3D and Honeycomb had lower strengths but stable moments during the bending process. Since Cross-3D topology offered the lowest deflection, it was selected for further experiments with slicer added interlocks at the face–core interface. This study revealed the most notable improvements as gains of up to 15% in peak load, 48% in maximum deflection, and 51% in energy absorption compared with the non-interlocked designs. The PET/TPU interlocked demonstrated the best performance in terms of the energy absorption (2.45 J/mm³) and peak load (272.6 N). In contrast, the PA/TPU interlocked exhibited the best flexibility and ductility with a mid-span deformation of 21.34 mm. These results confirm that slicer-generated interlocking interfaces lead to better load capacity and energy dissipation, providing a lightweight, damage-tolerant design approach for additively manufactured sandwich beams. Full article

Fast and accurate indoor occupancy detection is critical for energy efficiency and emergency rescue in the fields of smart building and indoor positioning. However, existing image-based indoor occupancy detection models often neglect small human targets and suffer from large parameters, compromising detection accuracy, real-time performance, and deployment on resource-constrained devices. To address these issues, this study proposes a modified lightweight indoor occupancy detection model based on YOLO v8. Firstly, a patch expanding layer is added to the neck of the YOLO v8 model for reshaping the feature maps of adjacent dimensions into higher-resolution feature maps. Secondly, the standard convolution in the original neck is replaced with the GSConv, boosting the non-linear representation by adding DSC layers and a shuffle operation, efficiently preserving hidden connections between channels. Additionally, the VoV-GSCSP in the neck is designed to adopt one-shot aggregation with GS bottlenecks based on GSConv, followed by a cross-stage partial network module. Experiments on the SCUT-HEAD dataset show the modified lightweight YOLO v8 reduces parameters by 9.3% and computational complexity by 8.6%, while increasing mAP50 by 1.4% compared to the baseline. The proposed model can detect indoor occupancy in a fast and precise manner. Full article

Assurance of the integrity of every weld joint is highly desirable, and defect detection methods that can be applied to welds at high temperatures immediately after welding are required. The laser ultrasonic (LU) method, which generates ultrasonic waves in the target via pulsed laser irradiation, is a well-known technique for non-contact defect detection during welding. Ultrasonic waves excited in ablation mode exhibit large amplitudes and predominantly surface-normal propagation, which has driven extensive research into their application for weld inspection. However, owing to the size and weight of conventional equipment, such systems have largely been limited to bench-top experimental setups. To address this, we developed an LU robotic system incorporating a compact, lightweight laser source and an improved signal-processing system. We conducted experiments to measure signals and to detect backside slits in flat plates and blowholes in lap-fillet welds. Additionally, a method to improve the sensitivity of laser interferometers was investigated and demonstrated on smut-covered areas near weld beads. Full article

Background/Objectives: Cardiovascular diseases are the leading cause of mortality worldwide according to the World Health Organization (WHO), highlighting the need for accessible tools for early detection. Automated classification systems based on signal processing and machine learning offer a non-invasive alternative to support clinical diagnosis. Methods: This study implements and evaluates machine learning models for distinguishing normal and abnormal heart sounds using a hybrid feature extraction approach. Recordings labeled as normal, murmur, and extrasystolic were obtained from the PASCAL dataset and subsequently binarized into two classes. Multiple numerical datasets were generated through statistical features derived from Mel-Frequency Cepstral Coefficients (MFCCs) and Daubechies wavelet analysis. Each dataset was standardized and used to train four classifiers: support vector machines, logistic regression, random forests, and decision trees. Results: Model performance was assessed using accuracy, precision, recall, specificity, F1-score, and area under curve. All classifiers achieved notable results; however, the support vector machine model trained with 26 MFCCs and Daubechies-4 wavelet coefficients obtained the best performance. Conclusions: These findings demonstrate that the proposed hybrid MFCC–Wavelet framework provides competitive diagnostic accuracy and represents a lightweight, interpretable, and computationally efficient solution for computer-aided auscultation and early cardiovascular screening. Full article

Aquaponics is a water-reusing, circular form of controlled-environment agriculture, but its sustainability benefits depend on reliable, constraint-aware nutrient dosing under delayed inflow effects. Aquaponics involves coupling hydroponics with aquaculture but is difficult to control because the greenhouse/crop state at the current time step ($t$) must anticipate water-quality changes that arrive at the next time step ($t+1$), under hard EC–pH and dose constraints. We propose the Analysis System for Nutrient Requirements in Hydroponics (ASNRH), a two-module, constraint-aware framework that directly regresses next-step elemental supplementation (N, P, K; mg·L⁻¹). First, the Fish-farm By-product Prediction Module (FBPM) uses a lightweight GRU forecaster to predict inflow chemistry at $t+1$ (e.g., NH₄⁺/NO₂⁻/NO₃⁻, alkalinity) from standard aquaculture sensors. Second, the Nutrient Requirement Prediction Module (NRPM) encodes the current hydroponic and crop state at t in parallel with the FBPM inflow at $t+1$ via a dual-branch architecture and fuses both representations to produce non-negative dose recommendations while penalizing forecasted EC/pH violations and excessive actuation volatility. The data pipeline assumes low-cost greenhouse and aquaculture sensors with chronological, leakage-free splits. A protocol-first simulation evaluates ASNRH against time-series and rule-based baselines using accuracy metrics (MAE/RMSE/R²), EC/pH violation rates, and robustness under missingness/noise; ablations isolate the contributions of the inflow branch, constraint-aware losses, and lightweight physics priors. The framework targets deployability in decoupled or coupled aquaponics by structurally resolving $t$ vs. $t+1$ asynchrony and internalizing domain constraints during learning; procedures are specified to support reproducibility and subsequent field trials. By operationalizing anticipatory dosing from reused aquaculture byproducts under EC/pH feasibility constraints, ASNRH is designed to support sustainability goals such as reduced nutrient wastage and fewer corrective water exchanges in coupled or decoupled aquaponics. Full article

We propose a distribution-aware framework for unsupervised outlier detection that transforms multivariate data into one-dimensional neighborhood statistics and identifies anomalies through fitted parametric distributions. This directly addresses central difficulties of high-dimensional data—including sparsity of observations, the concentration of pairwise distances, hubness phenomena in nearest-neighbor graphs, and general effects of the curse of dimensionality that degrade classical distance-based scoring. Supported by the Cumulative Distribution Function (CDF) Superiority Theorem and validated through Monte Carlo simulations, the method connects distributional modeling with Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) consistency and produces interpretable, probabilistically calibrated scores. Across 23 real-world datasets, the proposed parametric models demonstrate competitive or superior detection accuracy with strong stability and minimal tuning compared with baseline non-parametric approaches. The framework is computationally lightweight and robust across diverse domains, offering clear probabilistic interpretability and substantially lower computational cost than conventional non-parametric detectors. These findings establish a principled and scalable approach to outlier detection, showing that statistical modeling of neighborhood distances can achieve high accuracy, transparency, and efficiency within a unified parametric framework. Full article

Natural fibers are increasingly used as sustainable, lightweight, and low-cost alternatives to glass fibers in polymer composites. However, their inherent hydrophilicity and surface polarity limit compatibility with non-polar polymer matrices. Both gas/solid and plasma fluorination modify only the surface of lignocellulosic materials. Mild conditions are mild, with reactivity governed by fluorine concentration, temperature, and material composition. Surface energy is typically assessed through contact-angle measurements and surface analytical techniques that quantify changes in hydrophobicity and chemical functionalities. In wood, fluorination proceeds preferentially in lignin-rich regions, making lignin a key component controlling reactivity and the spatial distribution of fluorinated groups. Natural fibers follow the same logic as for flax, which is a representative example of lignin content. Applications of fluorinated bio-based materials include improved moisture resistance, enhanced compatibility in composites, and functional surfaces with tailored wetting properties. Scalability depends on safety, cost, and process control, especially for direct fluorination. Durability of the treatment varies with depth of modification, and environmental considerations include the potential release of fluorinated species during use or disposal. Full article

The automation of quality control in agricultural food processing, particularly the detection of incomplete peeling in taro, constitutes a critical frontier for ensuring food safety and optimizing production efficiency in the Industry 4.0 era. However, this domain is fraught with significant technical challenges, primarily stemming from the inherent visual characteristics of residual peel: extremely minute scales relative to the vegetable body, highly irregular morphological variations, and the dense occlusion of objects on industrial conveyor belts. To address these persistent impediments, this study introduces a comprehensive solution comprising a specialized dataset and a novel detection architecture. We established the Taro Peel Industrial Dataset (TPID), a rigorously annotated collection of 18,341 high-density instances reflecting real-world production conditions. Building upon this foundation, we propose MDB-YOLO, a lightweight, multi-dimensional bionic detection model evolved from the YOLOv8s architecture. The MDB-YOLO framework integrates a synergistic set of innovations designed to resolve specific detection bottlenecks. To mitigate the conflict between background texture interference and tiny target detection, we integrated the C2f_EMA module with a Wise-IoU (WIoU) loss function, a combination that significantly enhances feature response to low-contrast residues while reducing the penalty on low-quality anchor boxes through a dynamic non-monotonic focusing mechanism. To effectively manage irregular peel shapes, a dynamic feature processing chain was constructed utilizing DySample for morphology-aware upsampling, BiFPN_Concat2 for weighted multi-scale fusion, and ODConv2d for geometric preservation. Furthermore, to address the issue of missed detections caused by dense occlusion in industrial stacking scenarios, Soft-NMS was implemented to replace traditional greedy suppression mechanisms. Experimental validation demonstrates the superiority of the proposed framework. MDB-YOLO achieves a mean Average Precision (mAP50-95) of 69.7% and a Recall of 88.0%, significantly outperforming the baseline YOLOv8s and advanced transformer-based models like RT-DETR-L. Crucially, the model maintains high operational efficiency, achieving an inference speed of 1.1 ms on an NVIDIA A100 and reaching 27 FPS on an NVIDIA Jetson Xavier NX using INT8 quantization. These findings confirm that MDB-YOLO provides a robust, high-precision, and cost-effective solution for real-time quality control in agricultural food processing, marking a significant advancement in the application of computer vision to complex biological targets. Full article

The miniaturisation of electronic devices has intensified the demand for compact, high-performance lithium-ion batteries. This review synthesises recent progress in microscale battery development, focusing on microfabrication techniques, nanostructured materials, porosity-engineered architectures, and strategies for reducing non-active components. It explores both top–down and bottom–up fabrication methods, the integration of nanomaterials, the role of gradient electrode architectures in enhancing ion transport and energy density, along with strategies to reduce non-active components, such as separators and current collectors, to maximise volumetric efficiency. Advances in top–down and bottom–up fabrication methods, including photolithography, laser structuring, screen printing, spray coating, mechanical structuring, and 3D printing, enable precise control over electrode geometry and enhance ion transport and material utilisation. Nanostructured anodes, cathodes, electrolytes, and separators further improve conductivity, mechanical stability, and cycling performance. Gradient porosity designs optimise ion distribution in thick electrodes, while innovations in ultra-thin separators and lightweight current collectors support higher energy density. Remaining challenges relate to scalability, mechanical robustness, and long-term stability, especially in fully integrated micro-battery architectures. Future development will rely on hybrid fabrication methods, advanced material compatibility, and data-driven optimisation to bridge laboratory innovations with practical applications. By integrating microfabrication and nanoscale engineering, next-generation LIBs can deliver high energy density and long operational lifetimes for miniaturised and flexible electronic systems. Full article