Search Results (1,934)

A low-latency, low-complexity digital demodulator is presented for chirp spread spectrum (CSS)-modulated RF packets targeting low-power IoT wireless systems operating in spectrally congested environments. Conventional CSS receivers rely on fast-fourier transform (FFT)-based synchronization and long preamble sequences, resulting in increased latency and computational complexity. To address these limitations, the proposed receiver employs amplitude-domain synchronization using oversampled sub-chirp windows and maximum likelihood estimation without requiring FFT processing. A digital demodulator co-designed with receiver’s fractional-N phase-locked loop (PLL) architecture enables rapid sub-chirp generation and fast frequency settling, while compensation techniques mitigate symbol boundary offset (SBO) error due to PLL non-idealities during synchronization. The proposed system achieves packet synchronization within 17.5 preamble symbol cycles while maintaining symbol boundary offset estimation error below ±1%. Simulation results demonstrate a syncword misdetection probability below 10⁻³ at SNRs of 9 dB and 1 dB without and with 8× repetition, respectively. In the presence of interferences, the receiver tolerates worst-case in-band signal-to-noise ratio (SIR) levels down to −16.2 dB while consuming 877 µW and 830 µW average power at the digital demodulator, and fractional-N PLL, respectively. Implemented in 65 nm CMOS, the proposed architecture occupies 0.195 mm² active area. Full article

Emerging economies face mounting pressure to adopt sustainable and cost-efficient methods for delivering products and services in urban areas. This study examines the Electric Vehicle Routing Problem with Time Windows (EVRPTW) within a pragmatic urban context. We concentrate on the short-haul delivery network in Marrakesh, Morocco, whose operational viability is influenced by climatic, infrastructural, and regulatory limitations. We present a simulated annealing (SA) metaheuristic, augmented with repair heuristics and a penalty-based cost function, to concurrently reduce routing costs and lateness fines, subject to time-window and battery capacity restrictions. The technique undergoes evaluation through extensive computer tests utilizing realistic instance sets that replicate local demand patterns and charging infrastructure. The penalty-calibrated model demonstrates delivery completion rates of up to 100%, significantly reducing route costs and the number of unserved clients relative to baseline setups. We thoroughly analyze the tuning parameters among several runs. This study intends to provide a useful tool for real-world decision support by fusing extensive literature synthesis with local context validation and by integrating a simulation module that evaluates time-window settings and charging patterns under realistic traffic. Full article

Anomaly detection models trained exclusively on healthy data are widely used in wind turbine condition monitoring because failure data are scarce, heterogeneous, and often unavailable. However, these models produce anomaly indicators that are sensitive not only to fault-related degradation but also to normal operational variability, transient disturbances, and changes in loading conditions. As a result, the practical behavior of an alarm system depends not only on the anomaly detection model but also on the decision rule used to activate and maintain alarm states. This study presents a decision-oriented evaluation of persistence-based alarm confirmation in wind turbine anomaly detection. Four representative techniques are analyzed within a unified framework: Isolation Forest, One-Class Support Vector Machine, Referenced Moving Window Principal Component Analysis using Q-statistic and percentage component weight indicators, and Autoencoder-based reconstruction error. The evaluation combines controlled OpenFAST simulations of rotor unbalance under different severity and noise conditions with an industrial SCADA case study involving a documented main bearing fault. Results show that temporal persistence strongly shapes alarm outcomes across methods and datasets. Low persistence values favor early detection but promote alarms from isolated threshold exceedances, whereas moderate persistence substantially reduces false positives while preserving detection capability in severe and well-observable faults. Excessive persistence increases detection latency and missed detections, particularly for weak, intermittent, or slowly evolving fault signatures. These findings indicate that persistence-based alarm confirmation should be treated as an explicit decision-level configuration variable, rather than as a fixed post-processing or alarm-state heuristic, when designing anomaly detection systems for wind turbine condition monitoring. Full article

The energy performance of a building reflects its typical energy use and is influenced by factors such as the building envelope (insulation and windows), system efficiency (particularly for heating, cooling, and domestic hot water), and the integration of renewable energy sources. Improving energy performance helps save energy, boost energy independence and security, lower energy costs, and reduce the need for grid investments. Standardizing energy performance assessments enables benchmarking and comparison of building efficiency, encouraging informed decision-making. In this context, the paper presents a knowledge discovery-driven intelligent decision-making system, designed, developed, and tested to identify the best strategies for prioritizing buildings in the envelope process. The system combines data mining techniques with statistical analysis to precisely rank and thoroughly evaluate low-energy-performance buildings and to develop scenario-based strategies for enveloping the buildings to achieve high energy efficiency (associated with nearly zero-energy buildings) under real-world conditions. Testing of the proposed intelligent decision-making system was conducted using a real building database of approximately 3900 records, uploaded from the Romanian central administration website. Under the highest-performance scenario of the envelope-priority strategy, which includes nearly zero-energy building standards, energy savings exceeded 50% across all categories: 51.70% for healthcare, 53.40% for residential, 60.11% for administrative and office buildings, and 69.92% for educational institutions. Overall, the average savings across all building types were 59.81% (644.86 GWh/year). Full article

The motion of AC electromagnetic actuators exhibits complex electrical–magneto–mechanical coupling characteristics. A three-phase AC contactor is taken as the typical research object in this paper. Using the finite-element method (FEM) and mesh deformation technique, the commercial software COMSOL Multiphysics is adopted to analyze its static electromagnetic characteristics, together with the operational coil current response and movable core displacement. In addition, the static correlation between the magnetic force, air gap, and time-varying magnetic force curves in the movement process are obtained. An experimental platform is established to measure the magnetic force of electromagnetic actuators. The experiment results demonstrate the feasibility of the proposed simulation method. The normalized root mean square errors between simulated and measured static magnetic forces are below 8% under all tested coil voltages. Furthermore, the effect of coil voltage phase angle on dynamic operational characteristics is thoroughly investigated. Combined with the closing time and final velocity of the movable core, the recommended operating window and its corresponding phase angle are determined. Full article

Walnut multiplication technology for obtaining high-quality planting material consists of grafting, followed by forcing bionts in protected spaces under controlled microclimatic conditions, and completed by acclimatization under field conditions. The present research substantiates the hypothesis that the use of protected spaces (polyethylene tunnels) enables rigorous control of limiting factors. The main objectives of the paper are the comparative evaluation of two grafting methods (chip and patch budding) on the grafting success of eight native genotypes (‘Anica’, ‘Grădinar’, ‘Miroslava’, and ‘Velnița’, ‘Bălțăți’, ‘Belcești’, ‘Săbăuani’, and ‘Șorogari’) grafted on ‘Bălțați’ local biotype, determining the optimal moment of grafting by identifying the time window (April vs. August) that maximizes the success rate of the grafting association. The study, carried out from 2022 to 2024, evaluated the performance of chip and patch budding executed under high-tunnel conditions, quantifying the scion/rootstock growth, callus formation, and anatomical symbiont similarity through cross-sectional microscopy and image analysis software to measure vessel number, density, and diameter; the results are presented as the mean values of three annual repetitions across the experimental period. Preliminary results indicate a superior efficiency of the chip budding method, with a 51.3% success rate compared to 32.9% for the patch budding method. Another objective of the study was the ranking of the experimental variants. Thus, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision analysis method, ranked the experimental variants and identified the chip budding performed in April (variant a1/b2) as the optimal solution across all analyzed physiological and morphological parameters. These findings are highly significant for the nursery sector, as they demonstrate that transitioning from unpredictable field conditions to controlled high-tunnel conditions stabilizes production outcomes. By establishing a clear methodological hierarchy and a precise chronological window, this study provides actionable guidelines to standardize walnut multiplication, mitigate seasonal climate risks, and substantially increase the output of high-quality certified planting material. Full article

This study presents experimental results from high-speed interferometric measurements on a transonic compressor blade cascade, where three of the five blades were torsionally oscillated at various frequencies up to $150\mathrm{Hz}$ and different inter-blade phase angles. The primary research objective is to develop and validate a non-intrusive methodology capable of quantifying unsteady flow fields surrounding aeroelastically unstable components. The resulting flow field images demonstrate the potential of the method. Unlike classical interferometric methods, the proposed approach has less stringent requirements for the optical quality of the test section windows. This advantage allows for the use of organic-glass windows, which are necessary for investigating highly loaded compressor blade cascades. Such windows are required to accommodate the suction slots used to maintain a representative Axial Velocity Density Ratio (AVDR). Unlike the classical schlieren technique, the method provides quantitative results with high spatial and temporal resolution, while the synthetic schlieren images can also be produced. The method proved suitable for measurements in the harsh environment of transonic flow through oscillating blades and is capable of capturing important unsteady flow phenomena. Full article

SQL Injection is a well-known vulnerability that has persisted in web applications for decades. A widely held assumption among developers is that even when SQL Injection is present, hashing or encrypting sensitive data using SQL-provided cryptographic functions, such as sha256() or md5(), renders stolen data unrecoverable. This paper challenges that assumption directly. We demonstrate that invoking cryptographic functions within SQL statements does not protect plaintext credentials against an attacker who already has SQL Injection access, not because the hash functions are weak but because their plaintext arguments are transiently exposed in DBMS in-memory monitoring views before the hash function executes. We exploit this window using a technique that we call SQLSnoop, which repurposes built-in SQL looping constructs to poll the monitoring view at high frequency within a single injected statement. We demonstrate SQLSnoop against four major RDBMS platforms: MySQL, MSSQL, Oracle, and PostgreSQL. Systematic quantitative evaluation is conducted on MySQL, while feasibility on MSSQL, Oracle, and PostgreSQL is confirmed through working Proof-of-Concept implementations against each platform’s respective in-memory monitoring view. Our evaluation on MySQL shows attack success rates consistently above 90%, reaching 100% at 1.2 or more virtual CPU cores, and holding across all four Data Manipulation Language operations. The key practical implication is that SQL-layer hashing is fundamentally insufficient as a defense against SQL Injection, and sensitive data must be hashed at the application layer before the SQL statement is constructed. Full article

Emotion recognition plays a crucial role in human–computer interaction, health monitoring, and affective computing by analysing physiological signals. Despite recent advancements, current research still faces challenges, including the lack of effective fusion strategies for diverse physiological modalities, difficulties in handling high-dimensional feature representations, and limited use of efficient temporal modelling techniques to capture complex emotional patterns. This study proposes a deep learning-based approach that fuses multiple physiological modalities, including Electroencephalography (EEG), Electrooculography (EOG), Electromyography (EMG), Galvanic Skin Response (GSR), Respiratory Rate (RR), Skin Temperature (SKT), and Photoplethysmography (PPG), to improve emotion recognition. Arousal and valence ratings were binarized into two classes (low/high) using a threshold of 4.5, formulating a binary classification problem. In addition to utilising Bidirectional Long Short-Term Memory (Bi-LSTM), the study employs Temporal Convolutional Networks (TCN), a widely used approach for time-series analysis, to efficiently capture temporal dependencies. The proposed model optimises feature selection through channel-wise strategies, incorporates advanced learning rate scheduling, and reduces computational overhead. Furthermore, window-wise, block-wise, and trial-wise evaluation protocols were investigated to assess the impact of temporal information leakage on emotion recognition performance. Using the DEAP dataset for validation, the proposed TCN-based approach achieved classification accuracies of 88.42% for valence and 86.35% for arousal under an overlapping block-wise evaluation protocol, demonstrating improved performance in binary emotion recognition and highlighting the importance of leakage-aware model assessment. Full article

The microstructure of metal matrix composites is inherently governed by fabrication routes and processing parameters, yet technological and physical constraints often prevent the realization of intended structural designs. In particle-reinforced composites produced via casting, interactions between the solidification front and inclusions frequently lead to agglomeration, segregation, and hence, a non-uniform distribution of the inclusions concentration. To mitigate these effects, post-processing techniques such as Friction Stir Processing offering particular promise for cast materials by refining microstructures and enhancing phase homogeneity. This study addresses these challenges by application of Fourier transform analysis to characterize stochastic inclusion distributions. Building on the Windows Washing method, we extend its application to heterogeneous media with varying inclusion concentrations. Through computer simulations and experimental analysis of real composites, we demonstrate that discrete Fourier transform can reveal hidden stochastic periodicity. The proposed framework provides a pathway toward improved predictive models and optimization strategies for metal matrix composites processing and performance. Full article

Optical nonreciprocity (ONR) plays an important role in laser technique, optical communications, quantum information, etc. Realizing ONR at the quantum level of few photons or single photons is also essential for quantum communications or quantum networks. In this work, we propose a hybrid configuration composed of a quantum dot–metallic nanoparticle (QD-MNP) composite in a ring cavity to achieve ONR at the few-photon level via optical bistability. With the surface plasmon effect of the MNP, the bistable property and regime of photons producing ONR in an asymmetric ring cavity including a QD and an MNP inside can be significantly improved in comparison with the case of a single QD. By using the bistability effect, giant ONR can be achieved in an optimal window of numbers of input photons. The detuning and the coupling strength coefficients of the hybrid system can be adjusted and utilized to optimize the performance of the quantum nonreciprocity. This work may find promising applications in quantum nonreciprocal devices and photonic quantum circuits. Full article

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

Pipelines remain the primary mode of oil and gas transportation but are vulnerable to leaks that pose environmental and safety risks, particularly in two-phase flow systems. Conventional detection methods often struggle under transient multiphase conditions, while many data-driven studies rely on static evaluation metrics that do not reflect continuous monitoring requirements. This study develops a machine learning framework for leak detection using OLGA-simulated datasets from a previously published study, comprising approximately 180,000 labelled samples across nine leak scenarios and one no-leak case. Pressure, temperature, and mass-flow variables were enhanced through feature engineering to capture nonlinear leak behaviour. Random forest and extreme gradient boosting (XGBoost) classifiers were trained using an 80/20 stratified split with synthetic minority oversampling technique (SMOTE)-based balancing applied only to training data. XGBoost achieved 99.2% accuracy and reduced false positives by 53% relative to random forest while maintaining near-zero false negatives. A sliding-window suspicion framework extended static classification into time-dependent detection, producing delays of between 9.81 s and 82.04 s with zero false alarms in the no-leak scenario. Physical validation using pressure, flow, and fast Fourier transform (FFT) analysis confirmed that detections correspond to genuine hydraulic disturbances, demonstrating the reliability and physical credibility of the proposed framework. Full article

Double-skin façade (DSF) systems are important for energy efficiency because they effectively utilize natural ventilation and daylight. However, the uninterrupted vertical gaps in these systems may pose safety risks in the event of a fire by causing the rapid spread of smoke and hot gases. This study presents a hybrid approach that combines computational fluid dynamics (CFD)-based simulations and machine learning (ML) techniques to predict heat flow and fire-room control-volume heat release rate (FR-HRR). Within the scope of the study, 400 different scenarios were modeled with different combinations of basic natural ventilation design parameters consisting of gap width, gap height, window opening area, and air inlet and outlet area. The data obtained were evaluated with different ML models, including Fine Tree, Bagged Tree, Support Vector Machine, and Artificial Neural Network models; in particular, the Fine Tree model gave the most successful results with high accuracy rates (R² = 0.99 for FR-HRR; R² = 0.91 for heat flow). The analysis showed that DSF gap width provided a dominant model-based contribution within the investigated CFD-generated dataset. This approach provides a preliminary CFD-informed ML framework for the rapid comparative assessment of fire-related responses in open-boundary naturally ventilated DSF configurations during the early design stage. Full article

Maize hybrid seed production is a core factor in increasing maize yield. It is the key to ensure seed purity to remove tassels from female plants. This paper analyzes the inherent connections between seed production agronomy, biomechanics, computer vision, and intelligent devices at the system engineering level. The paper first elaborates on the role of crop growth models and genetic male sterility techniques in expanding the time window of mechanical operations. Secondly, based on the perception decision execution framework, this paper discusses how the biomechanical characteristics of male spikes directly determine the dynamic parameter design of the male removal actuator; in-depth analysis was conducted on the performance and limitations of deep learning algorithms in handling lighting changes, leaf occlusion, and high-throughput recognition in unstructured field environments. In addition, this paper compares the technical game between the detasseling success rate and leaf damage rate of two mainstream execution paths, cutting and extraction. This review highlights that future research should focus on the development of lightweight intelligent operation platforms and full-life-cycle digital decision-making systems, to realize high-efficiency and low-damage precision detasseling of seed maize. Full article