Search Results (3,613)

High-performance ethanol sensors with low power consumption show critical applications in environmental monitoring, personal health diagnosis, industry and traffic safety. Herein, MoO₃/Nb₂C MXene heterojunction gas-sensing materials were constructed via a one-step hydrothermal method for MoO₃ nanotube synthesis. The dominant facets of MoO₃ were shifted from the (040) orientation in MoO₃ nanotubes to the (110) and (021) orientations in the MoO₃/Nb₂C MXene composite. Nb₂C nanosheets provide a large number of crystallization sites, preventing the growth of MoO₃ nanotubes during synthesis, inducing a strategic facet release. The sensing performance shows MoO₃/Nb₂C MXene composite reduces the operating temperature down to 120 °C. The 15 wt% Nb₂C MXene-precursor-mixed MoO₃ sensor exhibits an enhanced response of 6.1 toward 100 ppm ethanol, which is higher than that of pristine MoO₃ nanotubes at 120 °C, with response and recovery times of 19 s and 72 s, respectively. The sensors show high selectivity toward ethanol over other VOC gases and good long-term stability over 30 days. This work confirms that crystal engineering is an effective method for reducing operating temperature and enhancing gas-sensing performance, and the sensor shows potential application for ethanol sensing. Full article

The rapidly increasing availability of low-cost commercial UAVs poses significant security challenges for critical infrastructure and law enforcement agencies. This paper presents an integrated LiDAR-based detection and vision-based verification system for an autonomous drone-on-drone aerial interception system. To eliminate the threat of possible dangerous target drones, the interception UAVs presented in this paper use a net to capture them safely in the air. The system addresses the critical limitation of ground-based sensors, which provide insufficient precision for reliable net-based capture operations. Moving beyond simulation-only approaches, the core novelty of this work lies in the successful real-world integration of these sensors on a strictly constrained aerial platform in size, weight and power to achieve sub-meter terminal guidance precision. The developed system uses real-time point cloud processing, DBSCAN clustering, and Moving Horizon Estimation tracking for the detection and tracking of the target. Vision-based verification uses a custom-trained YOLO neural network and achieves over 90% detection rates. The evaluation demonstrates a detection accuracy of less than 0.4 m at ranges exceeding 40 m during dynamic interception scenarios using RTK-GNSS ground truth. The dual-sensor approach successfully completed multiple autonomous interception missions with target detection ranges of up to 60 m, validating the capability of the system for safe, autonomous civilian UAV interception. Full article

The safe and stable operation of air-insulated switchgear (AIS) in high-altitude and low-pressure environments is significantly affected by partial discharge (PD), which accelerates insulation aging and may threaten power system reliability. Therefore, effective online monitoring and fault diagnosis methods are of considerable engineering importance. This paper proposes a PD-type recognition method based on the concentration ratio of two characteristic decomposition gases, CO and NO₂. First, a hybrid numerical model coupling fluid dynamics and plasma chemistry was established to simulate the microscopic decomposition mechanism of air discharge. The simulation results indicate that CO and NO₂ are relatively stable and detectable among the considered air-discharge products and that their generation is promoted by increased average electron energy under low-pressure conditions. Subsequently, an experimental platform was developed to simulate three typical insulation defects, namely point discharge, air-gap discharge, and surface discharge, under different simulated altitudes. Quantitative analysis using Fourier-transform infrared spectroscopy and gas chromatography revealed clear correlations between defect type and gas concentration characteristics. Based on these results, a diagnostic criterion was established under the tested conditions: a CO/NO₂ concentration ratio less than 1 indicates the epoxy-resin-based surface discharge model, whereas a ratio greater than 1 indicates point discharge or air-gap discharge. The latter two types can be further distinguished according to the time-dependent increasing trend of the ratio for air-gap discharge. Finally, based on the observed diffusion characteristics of these gases in the laboratory switchgear model, a low-cost online detection prototype using semiconductor gas sensors was developed. Laboratory validation using three typical single-defect models showed that the proposed method achieved 100% recognition accuracy when sufficient time-series data were available. However, further field validation is required before large-scale industrial application. The proposed CO/NO₂ ratio method provides a potential low-cost auxiliary diagnostic approach for AIS insulation monitoring, particularly under high-altitude and low-pressure conditions. Full article

Electromechanical systems operating under viscoelastic loads require precise modeling due to the highly nonlinear behavior of the load. An automatic bread machine is a practical example where dough represents a dynamic viscoelastic load sensitive to hydration. As found in this paper, increasing the water content leads to a decrease in the torque and the required mechanical power. An integrated approach combining MATLAB/Simulink and Simscape modeling, experimental measurements, and a PCA-based regression model is presented. The tests were conducted with three types of flour (type 500, type 1850, and rye–wheat) at hydrations of 52%, 58%, and 63% with over 6000 measurements recorded for each combination. The regression models achieve moderate predictability (R² = 0.64–0.96) model performance that varies across flour types. Increasing the dough hydration from 52% to 63% reduces the torque by approximately 22–46% across the tested flour types, while the angular velocity rises slightly (from about 147.9 to 151.9 rad/s). A descriptive decrease in energy consumption of up to around 6% was observed within the sampled batches with the system efficiency remaining within a narrow range around η ≈ 0.67. Within the studied levels (52–63%), the minimum load was observed at 58%. The proposed integrated model reliably describes the interaction between the electric motor, the mechanical gear, and the viscoelastic load, and it offers a basis for energy optimization and the implementation of low-cost sensor systems for intelligent control in the bread-making process. Full article

In remote areas where sugar beets are grown on a large scale, inadequate ground-based communication networks can easily lead to information silos in farmland, as well as technical challenges such as uneven node power consumption and short lifespans during the long-term operation of wireless sensor networks. To address these challenges, a real-time field environment monitoring system for sugar beet fields based on the Beidou satellite system and ZigBee wireless sensor networks has been developed, employing a three-tier architecture comprising a perception layer, a network layer, and an application layer. The system uses ARM as the core of the data acquisition nodes and integrates sensors for temperature, humidity, light intensity, atmospheric pressure, and dissolved oxygen with a Beidou positioning module. Field data are aggregated via a ZigBee mesh network and transmitted remotely using a dual-link Beidou short message protocol. To prevent uneven energy consumption in ZigBee networks, an improved energy-balanced routing algorithm, Energy-Balanced Low-Energy Adaptive Clustering Hierarchy (EB-LEACH), is proposed. By optimizing cluster head election, adaptive competition radius mechanisms, and inter-cluster multi-hop routing strategies through multi-factor weighting, the algorithm achieves a globally balanced distribution of network energy consumption. Our experimental tests demonstrate that, compared to the traditional LEACH protocol, this algorithm increases the number of rounds until the first node fails by 87.3%, extends the network half-life by 110.48%, and improves total packet delivery by 118.3%. Our test results indicate that the improved routing algorithm performs better, and the accuracy of the sensor measurements meets the practical requirements for environmental monitoring in sugar beet fields. Full article

Comprehensive monitoring of cerebral hemodynamics has led to significant advances in Functional Near-Infrared Systems (fNIRS), particularly in terms of hardware design and development of wearable platforms. These advancements have established fNIRS devices as valuable tools in research and clinical practices; however, most existing literature focuses predominantly on clinical applications or high-level system performance. This review provides a rigorous, bottom-up analysis of bio-instrumentation architectures, evaluating the low-level trade-offs in component selection and circuit design that define modern wearable fNIRS performance. In this paper, we have identified and compared key hardware components of modern fNIRS technologies, including optical sensors, signal conditioning elements, control units, power systems, and communication modules. Significant progress has been made in terms of optical tomography, head coverage and conformity, multimodal integration, hyperscanning, motion tolerance, user comfort, and miniaturization. The paper underscores how systems may have unique architectures although they follow the same foundational principle. It also aims to identify the trade-offs existing in current fNIRS devices. Overall, this paper presents an overview of where we stand in terms of fNIRS development and attempts to trace an outline of the next generation of devices. Full article

Temperature has a significant impact on the operation and performance of electronic systems. Conventional approaches focus on stabilizing electronic systems to maintain functionality under unfavorable thermal conditions, typically at the expense of increased consumption. This paper adopts a multi-objective approach to identify the Pareto-optimal (PO) trade-off across varying temperatures between functionality and consumption of low-power radio transceivers used in the Internet of Things (IoT) and wireless sensor networks. Building upon the established two-segment PO trade-off controlled by supply voltage and output power settings, between engaged and achieved transmission power, parameters directly associated with energy consumption and transmission quality, we analyze the influence of temperature on the Pareto front. We find that decreasing the temperature improves both engaged power and achieved transmission power simultaneously. Therefore, we propose a novel Pareto-optimal temperature-opportunistic wireless communication approach that exploits temperature variability by selecting favorable temperature conditions for transmission. We also identify the spatio-temporal potential of temperature variations across a four-dimensional network deployment space, particularly in temperature-dynamic urban environments of smart city infrastructure supporting massive IoT. Experiments on a modern Texas Instruments CC1200 transceiver confirm that the power savings of approx 30% and nearly 450 times increase in achieved transmission power are attainable for a temperature difference of 60 °C, corresponding to realistic conditions between the ambient air and a black-painted surface. Full article

Fiber Bragg Grating (FBG) sensors are widely used for strain and temperature monitoring due to their high sensitivity, compact size, electromagnetic immunity, and multiplexing capability. While conventional FBG interrogators remain bulky and costly, Photonic Integrated Circuit (PIC) platforms provide a promising route toward compact, scalable, and low-power FBG interrogation. However, the choice of architecture strongly determines the achievable resolution, bandwidth, multiplexing capacity, and robustness. This review compares on-chip demodulation architectures, evaluating their performance in resolution, bandwidth, and interrogation speed. We show that the optimal architecture depends strongly on the application: AWG-based schemes excel in compact, multi-FBG readout; ring-resonator systems are highly effective for tunable filtering; and interferometric phase-domain schemes offer the highest sensitivity for dynamic strain sensing. Despite these architectural advances, practical deployment remains constrained by system-level bottlenecks. These challenges primarily include source/detector integration, fiber–chip coupling, packaging robustness, and thermal drift. Overcoming these barriers requires a shift in future development from isolated photonic-device optimization toward comprehensive, system-level co-design. Full article

This paper presents the design of a batteryless near-field communication (NFC) multi-sensor node with an integrated adaptive power-management system for sensing applications. The work focuses on harvesting energy from a 13.56 MHz NFC field to power an ultra-low power sensing platform. The design consists of the TI RF430FRL152H, an integrated NFC transponder with an embedded MSP430 microcontroller core and ferroelectric random-access memory (FRAM) non-volatile memory. The system combines an ISO/IEC 15693 NFC front end, a tuned loop antenna for optimized power harvesting, and multiple analog and digital sensor interfaces, and a firmware architecture for intermittent harvested energy operation. The aforementioned design performs on-demand data acquisition, logs measurements in the FRAM, and communicates the measured results through an ISO15693 compliant NFC link while powered entirely by the reader’s radio-frequency (RF) field. Since NFC provides only limited harvested power, efficient energy management is critical. The proposed scheme continuously monitors the storage capacitor voltage and activates each sensor only when sufficient energy is available. After every measurement, the system reassesses the stored charge before triggering the next acquisition, ensuring stable multi-sensor operation. A BMP390 temperature and pressure sensor and the on-chip temperature sensor demonstrate the platform’s capability. Experimental results show that the system harvests 1.064 mW (1.85 V, 560 µA), achieves a wireless operating range of up to 40 mm, and delivers a response time of 800 ms, demonstrating its suitability for low-power temperature and pressure sensing applications. Full article

Multiplication is a fundamental operation in digital signal processing, embedded computing, and nanoscale arithmetic data paths, where area, delay, and energy efficiency are critical design constraints. However, nanoscale multiplier design is challenged by high interconnect complexity, frequent wire crossings, clock-zone synchronization issues, and the rapid growth of area and latency with operand size. Quantum-dot cellular automata (QCA) technology offers a promising post-CMOS platform for compact arithmetic circuit realization through field-coupled computation and transistor-free switching. This paper presents a single-layer QCA-based Dadda Tree Multiplier (DTM) using layout-aware integration of compact half-adder, full adder, XOR, and carry-skip adder modules. The proposed design emphasizes partial-product compression, routing compactness, clock-aware organization, and area-efficient final accumulation. Functional verification is performed using QCADesigner 2.0.3, while energy-related behavior is evaluated using QCADesigner-E under the conventional QCA simulation framework. The proposed DTM consists of 4282 cells and occupies 6.14 μm². Compared with a recent compact QCA multiplier baseline, the proposed architecture reduces cell count by 59.12% and occupies area by 39.80%, while maintaining competitive clocking latency. These results indicate that layout-aware integration of arithmetic modules can substantially improve the area efficiency of QCA-based multipliers, making the proposed design a compact arithmetic core for future nanoscale embedded and signal-processing systems. Full article

This article presents a comprehensive analytical, numerical, and experimental study of the amplification and detection of weak signals in magnetically coupled electromagnetic systems, using an architecture consisting of three magnetically coupled coils. A rigorous mathematical model of the system is developed, which includes the formulation of the mutual inductance matrix and a state-space representation that captures the dynamic interaction between the coils. It is important to note that the electromagnetic subsystem is linear and that the stochastic resonance effect is achieved by incorporating an external nonlinear bistable element. In this configuration, a weak periodic signal below a threshold is applied to the primary coil, while a controlled source of Gaussian white noise is injected into a secondary coil. A third coil functions as a sensing element, capturing the superimposed magnetic response resulting from coupling effects. The voltage induced in the sensor coil is subsequently processed by a bistable nonlinear element implemented via a Schmitt trigger, which provides the nonlinearity and bistability necessary to enable stochastic resonance and the detection of the weak periodic signal. The conditions of the SR are analyzed in terms of noise intensity, coupling coefficients, and system parameters, highlighting the existence of an optimal noise level that maximizes the signal-to-noise ratio (SNR) at the output. A detailed simulation framework has been developed in MATLAB/Simulink, enabling a systematic exploration of the parameter space and the validation of theoretical predictions. The simulation results are further supported by experimental measurements obtained from a physical prototype, which show agreement with the proposed model. The main contribution of this work lies in demonstrating that magnetically coupled electromagnetic structures can effectively interact with nonlinear bistable elements to exploit stochastic resonance in the detection of weak signals, even when the electromagnetic domain itself remains linear. The results demonstrate that magnetic coupling is an effective mechanism for mediating constructive interactions between noise and weak signals, thereby improving the detection of the latter. These results extend the applicability of stochastic resonance to hybrid electromagnetic systems and demonstrate its relevance in practical applications. Potential applications include ultra-sensitive magnetic detection, low-power signal detection, magnetic transducers, and robust signal recovery in noisy electromagnetic environments, particularly in contexts where conventional linear amplification fails. Full article

This study presents an AI-assisted long-distance fiber Bragg grating (FBG)-based sensing approach for simultaneous temperature and vibration measurement using a single bare FBG sensor. To address the strong coupling between temperature- and vibration-induced effects in the wavelength time series, a signal processing framework based on adaptive variational mode decomposition (AVMD) is developed. With power-spectral-density-guided parameter selection, the mixed wavelength signal is separated into a low-frequency temperature-related component and a high-frequency vibration-related component, enabling stable temperature–vibration decoupling within a single-sensor architecture. Experiments conducted with a 10 km fiber link between the sensor and the interrogator demonstrate that the proposed method can stably track the dominant vibration frequency under various temperature and vibration conditions, while the reconstructed low-frequency component remains consistent with the thermal evolution trend even in the presence of vibration. Random vibration tests and low-frequency vibration resolution analysis further confirm the stability and practicality of the proposed approach under long-distance fiber transmission conditions. In addition, an AI-assisted condition-monitoring scheme is demonstrated using a one-dimensional convolutional autoencoder trained solely with normal wavelength time-series data. Rather than relying on raw reconstruction error alone, the diagnostic layer derives a latent transition score from encoder bottleneck features through temporal pooling, L2 normalization, cosine-distance evaluation, smoothing, and baseline removal. Deviations from steady operating conditions can thereby be preliminarily indicated, highlighting the potential for integrating physics-driven signal processing with data-driven artificial intelligence in long-distance fiber sensing systems. Full article

This paper presents a column-parallel adaptive-gain single-slope (SS) analog-to-digital converter (ADC) for high-speed high-dynamic-range (HDR) CMOS image sensors. Conventional adaptive-gain approaches often rely on dual-ramp generation or duplicated column circuits, which increase area and power overhead. In contrast, the proposed architecture achieves adaptive-gain operation using a single global ramp shared across all columns. A reconfigurable capacitive attenuation network embedded inside each column comparator locally scales the ramp at the comparator input, enabling seamless transition between high-gain operation for low-level signals and unity-gain operation for large signals within a single exposure and readout cycle. To suppress mode-dependent offsets while maintaining low noise, a configurable dual-source-follower ramp buffer symmetrically buffers the ramp and reference voltages during auto-zeroing and is reconfigured as a full-sized buffer during unity-gain conversion. Switching-induced column offsets are compensated using optical black pixels and lightweight digital processing. The ADC is implemented in a 110 nm CMOS image sensor process and validated through post-layout simulations including extracted parasitics and Monte Carlo mismatch analysis. The core ADC consumes 36.8 µW per column. Simulation results demonstrate linearity error below 1% without missing codes and show that the proposed AG_x8-to-AG_x1 configuration extends the effective dynamic range up to 78.3 dB. Full article

Partial discharge (PD) detection effectively identifies insulation defects in power equipment. Radio frequency (RF) methods for PD detection offer promising advantages due to their non-invasive measurement capability and ability to locate discharge sources. However, microstrip antennas used as RF sensors for PD detection suffer from narrow bandwidth and limited installation flexibility. To address these limitations, this paper presents a novel flexible microstrip antenna design. By incorporating a partial ground plane and oblique-cut meandering techniques and optimizing the structural parameters using an improved whale optimization algorithm (I-WOA), the operating bandwidth is expanded from 0.612–0.625 GHz to 0.346–2.0 GHz, while the overall size is reduced to 75.3% of its original dimensions. The antenna’s performance was validated through GTEM cell measurements and PD calibration pulse tests, confirming its suitability for RF detection of PD in power equipment such as transformers and cable joints. Notably, when the antenna was conformally wrapped around a cable joint, the response amplitude increased by 14%. This study contributes to the development of a low-cost, broadband, and flexibly installable RF sensor for partial discharge detection. Full article

Cosmic-Ray Neutron Sensing (CRNS) has become a standard method for non-invasive soil moisture monitoring at the field scale. With most CRNS sensors being derivatives from scientific nuclear equipment, the development of instruments based on alternative neutron detection technologies is a major development goal for CRNS. We present a modular instrument family based on boron-10-lined proportional counters, specifically designed for long-term autonomous field operation. The system is controlled by a data logger supporting various telemetry options and external SDI-12 environmental sensors, while the frontend electronics use pulse-height and pulse-length information to suppress non-neutron background and electronic noise. Our results show high energy efficiency, with the latest generation close to 50 mW, allowing solar-powered operation even in challenging environments. The performance of the instruments is validated within long-term field deployments in different settings, showing that boron-10-based systems provide a scalable, low-power and cost-efficient alternative for the next generation of CRNS monitoring networks. Full article