Search Results (1,937)

The automation of biodigesters using programmable logic controllers (PLCs) is well established, yet mixing schedules are configured empirically, regardless of substrate characteristics. This study proposes a Substrate-Driven PLC Control (SD-PLC) methodology for fixed-dome biodigesters, in which every timing setpoint is derived from a measured substrate property: the homogenization pulse from the rheological mixing time (Metzner–Otto regime), the maintenance interval from a stratification and a substrate–biomass contact criterion, the duty-cycle ceiling from a net energy balance, and a safety gate from acid–base behavior (pH/EC). The methodology is instantiated on a 13.86 m³ fixed-dome biodigester in the Chillón Valley (Lima, Peru), co-digesting organic waste and guinea pig manure (30:70; theoretical methane potential 371 mL CH₄ g⁻¹ VS; field conversion ≈ 21%). The impeller operates in the transitional regime ($Re\approx 0.9$–$2.6\times {10}^3$), the homogenization pulse is ≈22 min, and the reconciled duty cycle ($\delta \approx 0.13$) remains a factor of four below the energy ceiling (${\delta }^{*}\approx 0.56$). An influence × feasibility matrix identifies a low-cost sensor set (pH, electrical conductivity, temperature, level) that gates mixing. A reduced COD-based dynamic model shows that, under acid shock, continuous mixing suppresses methanogenesis through shear while the absence of mixing fails through contact deficit, so that only the stability-first strategy preserves the process. The principal contribution is the methodology itself—a reproducible mapping from substrate characterization to PLC timing design—rather than the hardware. Full article

This work presents a compact, low-power fully CMOS pulse-frequency-modulation (PFM) temperature sensor fabricated in 180 nm technology, supporting a wide −60 °C to 120 °C sensing range. Subthreshold biasing generates temperature-proportional current, while PFM converts current variations into digital pulse signals to bypass supply voltage swing limitations. The following two core optimizations are proposed: an improved cascode current mirror to suppress channel-length modulation and stabilize thermal current, and a temperature-insensitive monostable circuit eliminating extreme-temperature oscillation failure caused by variable reset pulse width. A four-point off-chip calibration model further compensates static and temperature-dependent loop delay errors. The prototype occupies only 2200 μm² core area and consumes 2.9 μW. With a 5 ms conversion time, it achieves 47 mK RMS temperature resolution, 32 pJ·K² resolution FoM and 0.06 pJ·K⁻²·mm⁻² area FoM, with calibrated measurement inaccuracy limited to +1.8/−1.9 °C (3σ). This RC-free quasi-digital PFM architecture scales well to advanced process nodes and fits aerospace, cryogenic infrared detector and low-power industrial thermal sensing applications. Full article

Hypertension is a major risk factor for cardiovascular disease, yet cuffless blood pressure monitoring remains challenging because most existing methods rely on intermittent cuff-based measurements or multimodal physiological signals. This work proposes PPG-FusionNet, a dual-branch deep learning architecture for simultaneous systolic and diastolic blood pressure estimation using only photoplethysmography (PPG) signals. The model combines a local dilated convolutional encoder and a global AutoCorrelation encoder operating on a shared patch embedding, whose representations are integrated through cross-attention fusion and optimized with a constrained dual-head regression objective. The model was trained and evaluated on the PulseDB benchmark using Bayesian hyperparameter optimization and systematic ablation studies to assess each architectural component. PPG-FusionNet achieved mean absolute errors of 7.46 mmHg for systolic blood pressure and 4.72 mmHg for diastolic blood pressure, with near-zero mean errors and compliance with the ANSI/AAMI standard and BHS Grade B for diastolic estimation. Ablation experiments revealed that trend-seasonal decomposition, despite its success in long-horizon forecasting, degraded performance on short PPG windows, whereas cross-attention fusion and AutoCorrelation improved estimation accuracy. These results demonstrate that heterogeneous dual-branch representation learning provides an effective, scalable framework for cuffless blood pressure estimation from a single PPG sensor. Full article

Wearable near-infrared spectroscopy (NIRS) enables non-invasive hemodynamic monitoring, yet conventional CW-NIRS systems suffer from limited temporal resolution, scalp-only measurement, and mandatory manual tuning to compensate for optical heterogeneity across subjects and sites. This work develops NIRSLINK, a modular cascaded wearable NIRS system for high-speed multi-site hemodynamic acquisition. Its flexible probes adopt spring-floating optics with a standardized 30 mm optode separation, integrating dual 735/850 nm LEDs, silicon photodiodes, and a two-stage closed-loop tuning algorithm. A single probe achieves a peak sampling rate of 3 kHz, and up to eight cascaded probes form 52 valid channels, with a signal-to-noise ratio (SNR) of 78.61 ± 7.03 dB and an optical dynamic range (DR) of 101.32 ± 12.41 dB. Phantom experiments verify its millisecond temporal resolution and high sensitivity to blood flow and hemoglobin variations. In vivo trials, including the Valsalva maneuver, forearm occlusion, and two-back cognitive tasks, demonstrate simultaneous recording of hemoglobin concentration shifts, pulse waveforms, and beat-to-beat pulse transit times (PTTs). NIRSLINK supports hemodynamic measurements across multiple anatomical locations, including the forehead, forearm, upper arm, and thigh, covering both cranial and peripheral body regions. The embedded auto-tuning module stabilizes signals from the forehead, forearm, and other regions within the optimal ADC range without manual adjustment, preventing signal saturation and SNR degradation. This scalable adaptive platform overcomes critical drawbacks of traditional wearable NIRS, applicable to cognitive neuroscience, non-invasive cardiovascular assessment, and ambulatory physiological monitoring, and provides design references for multi-site optical sensors. Full article

To investigate the development characteristics of winding interturn insulation partial discharge in a full-scale converter transformer, an interturn insulation defect specimen was installed inside a ±400 kV full-scale converter transformer. A partial-discharge test was conducted under stepwise alternating-current (AC) voltage, while pulse-current, high-frequency, ultra-high-frequency, ultrasonic, sound-pressure, and vibration signals were acquired synchronously. To reduce the subjectivity of discharge-stage classification, a dimensionless discharge-development index was constructed by integrating the 95th-percentile apparent charge, discharge pulse count, and phase occupancy, and the stage-transition points were identified using piecewise-linear change-point analysis. Two change points at approximately 1.9 and 4.3 min divided the discharge process into the inception, development, and severe stages. The apparent charge increased from approximately 1.5 × 10² pC in the inception stage to the order of 10³ pC in the development stage, and then rose sharply to approximately 1.6 × 10⁵ pC in the severe stage. Meanwhile, the discharge activity changed from an intermittent low-intensity state to a continuous high-intensity state. After the test, insulation-paper ablation and carbonization, conductor exposure, and a continuous breakdown channel were observed in the defect region. These post-test observations confirm severe final damage to the interturn insulation, although the time sequence of the individual damage features could not be determined from the final morphology alone. Under the present sensor arrangement, the HF channel provided the earliest identifiable response at approximately 1.9 min, whereas the UHF and internal ultrasonic channels supplied complementary evidence during subsequent discharge development. The external ultrasonic, sound-pressure, and vibration responses were more strongly affected by propagation paths, structural coupling, sensor location, and background disturbance and were therefore treated as supplementary indicators. These findings support a staged multisensor interpretation strategy for interturn partial discharge in a full-scale converter transformer rather than a universal ranking of sensor performance. Full article

Ultra-high-pressure and extreme-pressure metrology plays a critical role in advanced manufacturing, geoscience, high-pressure physics, and frontier materials research. As pressure ranges extend from hundreds of MPa to several GPa and beyond, pressure generation and measurement are increasingly constrained by material strength, structural deformation, the state of pressure-transmitting media, sealing reliability, sensor drift, and incomplete traceability chains, imposing higher requirements on pressure metrology. This review systematically examines measurement technologies and traceability routes for ultra-high-pressure and extreme-pressure ranges. The development of controlled-clearance piston gauges is summarized, with emphasis on uncertainty reduction, range extension, and calibration automation. Drop-weight-based primary standards for dynamic pressure are also reviewed as an established route for the traceable calibration of high-amplitude, millisecond-scale hydraulic pressure pulses. Progress in secondary ultra-high-pressure standards is reviewed, including ultra-high-pressure gauges and piezoresistive, resonant, fiber-optic, and triboelectric sensors, with focus on range extension, high-accuracy measurement, environmental adaptability, and emerging pressure-sensitive mechanisms. Measurement methods for extreme pressure, including ruby fluorescence, Raman spectroscopy, X-ray diffraction, phase-transition points, and equations of state, are compared in terms of applicability and limitations. Current challenges in ultra-high-pressure and extreme-pressure metrology are further discussed, together with the prospects of quantum pressure sensing based on nitrogen-vacancy centers in nanodiamonds, providing a reference for future research and metrological infrastructure development. Full article

Flexible pressure sensors serve as core sensing components for wearable health monitoring systems, electronic skins for soft robots, and flexible human–machine interaction devices. Benefiting from the interfacial electric double-layer polarization effect, iontronic sensing delivers far higher pressure response sensitivity than conventional parallel-plate capacitive sensors, endowing it with distinctive advantages in the detection of weak physiological signals. Nevertheless, current dense ionic thin-film dielectric layers suffer from limited deformation space under compression and poor low-pressure sensing capability. Mainstream high-precision micropillar arrays are fabricated via photolithography, 3D printing, and metal etching molds, which require costly equipment and complicated fabrication procedures, making large-area mass production unfeasible. Random frosted concave-convex microstructures feature disordered dimensions, leading to severe device hysteresis and narrow linear ranges, which fail to achieve ultrahigh sensitivity alongside a wide pressure detection range simultaneously. To address the aforementioned multiple bottlenecks, this paper proposes a low-cost resin template replication process to fabricate TPU-based ionic thin-film dielectric layers with ordered micropillar array microstructures. Combined with inkjet-printed silver conductive PI flexible electrodes, an iontronic flexible pressure sensor with a sandwich layered structure is constructed. Multi-dimensional investigations including microscopic morphology characterization, electromechanical sensing performance calibration, and human wearable application tests are systematically implemented to thoroughly elucidate the synergistic enhancement mechanism of the arrayed micropillars. Test results demonstrate that the effective pressure detection range of the sensor spans 0–1038 kPa, accommodating ultra-low pressures such as pulse signals as well as medium-to-high-pressure loads including joint bending. The sensitivity reaches 23.27 kPa⁻¹ within the low-pressure range of 0–200 kPa and remains stable at 3.52 kPa⁻¹ in the high-pressure range of 200–1038 kPa, with piecewise linear fitting correlation coefficients of 0.93 and 0.96 respectively. Both the response time and recovery time of the device are 40 ms, and the hysteresis error throughout the loading-unloading cycle is merely 2.62%. After 20,000 consecutive cyclic loading-unloading tests, the peak capacitance output only decays by 5.1%, verifying outstanding mechanical fatigue resistance and electrical stability. Validations in multi-scenario applications prove that the sensor can accurately capture human physiological and motion signals including radial artery pulses, laryngeal deformation induced by multi-syllable vocalization, and multi-angle bending of fingers and elbow joints, suitable for home-based health monitoring, quantitative rehabilitation training, flexible tactile interaction and other scenarios. The entire fabrication process eliminates high-precision micro-nano processing equipment such as photolithography systems, plasma etchers and 3D printers; only general chemical raw materials and conventional laboratory instruments are adopted. The reusable templates enable low manufacturing costs and large-area coating forming, offering a novel low-cost technical solution for the engineering implementation and industrialization of high-performance iontronic flexible pressure sensors. Full article

This paper proposes Non-Uniform Adaptive Acquisition (NUAA), a signal-feature-matched non-uniform adaptive photonic sampling framework that recovers broadband radio-frequency (RF) waveforms from highly sparse programmable non-uniform photonic sampling points. A 200 MHz mode-locked laser together with five electrical optical delay lines (EDLs; motor-actuated optical delay units) arranges the non-uniform sampling instants. Benefiting from the joint design of the photodetector/track-and-hold amplifier (PD/THA) response model and programmable non-uniform optical pulse spacing, a low-bandwidth PD infers neighboring pulse amplitudes from their deterministic superposition at the readout. In numerical simulations of this physical forward operator, that construction corresponds to a 1 THz equivalent sampling rate on the 1 ps EDL grid, while the electrical front end operates at a 1 GHz average sampling rate (cascaded PD–THA analog 3 dB bandwidth $\approx 0.676$ GHz). Under severe blocking interference and low signal-to-noise ratio (SNR), the numerical simulations show that the strongest broadband chirplet result uses a scene prior with support locking: with the NUAA–MU (Mamba–Unfolding) reconstructor at 0.1% multi-coset sparsity, all $N_{\mathrm{trial}}=50$ Monte Carlo trials succeed within 200 ms (Wilson 95% CI $[93, 100]\%$; cumulative-best NMSE $-28.0$ dB), whereas the configuration without a scene prior is substantially weaker in the same window. A scene prior may come from known radar or communication waveform families, coarse occupancy reported by a companion sensor, or accumulation across related tasks. Full article

Continuous-wave mud pulse telemetry offers high spectral efficiency and transmission rates, making it an important technology for high-speed information transmission under complex well conditions. However, surface-received signals are highly susceptible to periodic pressure pulsations generated by mud pumps, which degrade phase extraction and symbol decision performance. Dual-pressure-sensor delayed differential processing can exploit the correlated propagation characteristics of pump noise between two measurement locations to suppress its correlated components; however, its performance depends on accurately matching the propagation delay and amplitude compensation coefficient. To specifically address the dynamic variation in the pump noise propagation relationship between two measurement locations under actual operating conditions, a joint delay–amplitude compensation method is developed, in which pump noise suppression is formulated as the joint estimation of the signal propagation delay and amplitude compensation coefficient. Built upon LMS-based time delay estimation, the proposed method employs an enhanced time-varying step-size LMS time delay estimation algorithm (HTVSS-LMSTDE) to improve dynamic retracking capability following changes in propagation delay. A sliding-window weighted least-squares method (SWLS) is further introduced to estimate the amplitude compensation coefficient and correct differential mismatch caused by variations in the amplitude transfer ratio. With non-pump interference modeled as additive white Gaussian noise independent of the telemetry signal and pump noise, simulation results demonstrate that, when the propagation delay and amplitude transfer ratio vary simultaneously, the proposed method yields delay estimates and amplitude compensation coefficients close to their theoretically optimal values. Field wellbore tests further verify that the proposed method effectively attenuates low-frequency pump noise interference in continuous-wave mud pulse telemetry signals while preserving the BPSK-modulated information. Full article

Flexible pressure sensors featuring high sensitivity, a broad detection range, and excellent stability are pivotal components for high-precision electronic skins and human health monitoring. To circumvent the limitations of existing sensors in maintaining high responsiveness across extensive pressure ranges, herein, a novel flexible capacitive pressure sensor is developed based on a 3D porous ionogel foam composite (IL/EG/PVA@MF) coupled with a planar electrode array. This device leverages the synergistic structural engineering of the 3D hyperelastic melamine foam (MF) skeleton and the pressure-regulated fringe-field distribution and iontronic interfacial polarization of the porous ionogel. Experimental evaluations demonstrate that the sensor achieves a high normalized sensitivity of 62.45 kPa⁻¹ (2–10 kPa) and maintains reliable piecewise linear sensing performance across a broad working range of 0–50 kPa, accompanied by a rapid response time of within 8 ms. Furthermore, the sensor exhibits outstanding performance consistency after 6000 compression-release cycles at 50 kPa, verifying its good mechanical durability. In practical applications, the device can monitor diverse physiological signals with high fidelity, ranging from subtle radial artery pulses to large-scale joint movements and specific coughing patterns, underscoring its broad potential for integrated wearable systems and intelligent healthcare. Full article

High-accuracy angular speed measurement using incremental rotary encoders is essential for the optimized operation of electric drives and the improvement of energy efficiency in industrial applications. Conventional encoder-based speed estimation relies on frequency- or period-based methods, whose accuracy is limited to complementary operating regions. Hybrid schemes based on stationary Kalman filters have been proposed to fuse both methods; however, since their noise covariances remain fixed, they achieve near-optimal performance only around a single rotational speed, and their accuracy deteriorates over the rest of the speed range. To overcome this limitation, this work proposes two adaptive Kalman filter variants that fuse frequency- and period-based measurements with online covariance adaptation: the measurement-adaptive Kalman filter (MA-KF) and the dual-adaptive Kalman filter (DA-KF). Both are evaluated in simulation and benchmarked against a stationary Kalman filter (S-KF) and the conventional frequency- and period-based estimators. The results demonstrate that Kalman-based estimators achieve significantly lower relative errors than classical methods across the entire speed range, with the maximum relative error of the DA-KF variant remaining below 0.27% while providing a dynamic response comparable to or faster than period-based techniques. Since the analysis is based on an ideal encoder model, these figures should be interpreted as an upper bound on the achievable performance, which experimental validation will further refine. Full article

Optical Camera Communication (OCC) enables data reception using common CMOS cameras and commercial webcams. However, applying multi-level modulation with rolling-shutter sensors is constrained by temporal acquisition parameters that may be undocumented or not directly accessible, making it challenging since many existing solutions rely on specialized hardware or require high processing complexity. This paper demonstrates that reliable multi-level OCC can be achieved using only unmodified commercial hardware and straightforward signal processing by experimentally characterizing and validating a 4-level pulse width modulation (4-PWM) link. Data are encoded in the duty cycle of the transmitted signal and recoveblack from the width of the captublack rolling-shutter stripes. Two internal timing parameters are estimated directly from the captublack images without access to the internal camera timing: the row readout period (34.38 $\mathsf{\mu }$s), obtained from the spatial periodicity of the stripes, and the effective integration time (490 $\mathsf{\mu }$s), inferblack from the deformation of the received constellation with carrier frequency. A single-parameter model is derived to describe this deformation and is validated at two carrier frequencies differing by a factor of four, pblackicting constellation compression, a fixed point at a duty cycle of 0.5, and constellation collapse (followed by inversion) when the exposure-to-carrier-period ratio reaches 0.5. We evaluate system performance under different exposure settings, showing that automatic camera control strongly degrades multi-level detection (BER of 0.290, with mean image level variation constrained to $0.14\%$ compablack to $61\%$ under fixed exposure). Under optimal fixed-exposure operating conditions, a prospective 15 min transmission achieved zero bit errors over 35,878 bits at 40 bps, corresponding to a $95\%$ upper confidence bound on the BER of $8.4\times {10}^{-5}$. These results reveal a practical balance between cost, complexity, and performance, demonstrating that 4-PWM rolling-shutter OCC is a viable solution for Internet of Things (IoT) signaling and low-rate data transmission using commercially available devices. Full article

Accurate actuator modeling is critical for robust design validation and sim-to-real control transfer in humanoid robotics. Yet, in practice, developers rely on simplified actuator models built from sparse datasheets or offline system identification, which often omit internal control logic, saturation, sensor dynamics, and electromechanical actuator dynamics. This limits model fidelity under changing conditions and contributes to sim-to-real failures. We propose actuator Digital Twins (DTs) as a scalable solution for predictive simulation. In this work, predictive simulation is defined as the forward computation of joint position and actuator torque from prescribed reference trajectories, controller parameters, mechanical configuration, and initial conditions, with prediction accuracy evaluated against measurements from the physical actuator. We validate a DT of the Pulsar PULSE115 quasi-direct-drive actuator that reproduces the actuator electromechanical dynamics, physical operating limits, sensing characteristics, and embedded cascaded controller executed at 10 kHz on a 1-DOF pendulum testbed, comparing real-world experiments with simulations using both the DT and a simplified model. Across varying trajectories and configurations, the DT maintains low error-from-real, while the simplified model degrades outside its tuned regime, particularly under changes in trajectory dynamics, mechanical load, and controller gains. We further embed the DT in a 4-DOF humanoid arm simulation and show that it runs significantly faster than the real-time version, achieving a simulation speedup factor of approximately 6.3× on a standard laptop. These results demonstrate that actuator-specific electromechanical and embedded control modeling improves the forward prediction of physical actuator behavior while remaining computationally practical for multi-joint robotic simulation. Full article

Photoplethysmography-derived pulse rate variability (PPG-derived PRV) is increasingly used in wearable and camera-based biomedical sensors as a low-burden alternative to electrocardiography-derived heart rate variability (ECG-derived HRV), but its agreement with ECG-derived measurements remains uncertain. This systematic review and meta-analysis evaluated the accuracy of PPG-derived PRV compared with ECG-derived HRV in healthy or apparently healthy non-clinical populations, focusing on the root mean square of successive differences (RMSSD) and the standard deviation of normal-to-normal intervals (SDNN). Forty-three studies were included in the qualitative synthesis; 33 were summarized narratively, and 10 unique studies provided sufficient data for quantitative synthesis. Eight studies contributed to each RMSSD and SDNN meta-analysis. The pooled absolute standardized error was 0.188 (95% CI: 0.066 to 0.309; I² = 11.69%) for RMSSD and 0.134 (95% CI: 0.014 to 0.255; I² = 0%) for SDNN. Sensitivity analyses supported the robustness of RMSSD findings, whereas SDNN estimates were directionally stable but less robust in statistical significance. Because the quantitative synthesis included only 10 studies and was based predominantly on selected resting or controlled conditions, the pooled estimates should not be generalized to sleep, exercise, stress, or free-living settings or interpreted as evidence of interchangeability. Full article

Point-of-care (POC) technologies are promising tools to decentralize and accelerate the diagnosis of rare diseases. Among them, electrochemical immunosensors offer advantages such as high sensitivity, low cost, portability, low sample consumption, and suitability for use in resource-limited settings. However, the performance of these devices is dependent on electrode surface properties, which influence electron transfer, biomolecule immobilization, and analytical sensitivity. In this work, screen-printed carbon electrodes (SPCEs) were modified through two strategies: (i) gold electrodeposition and (ii) cold plasma treatment. The modified electrodes were functionalized with EDC/NHS, followed by the immobilization of anti-SMN antibodies and electrochemical characterization using cyclic voltammetry and differential pulse voltammetry. The impact of each modification approach on the electrochemical response and reproducibility of the sensor was evaluated. Gold electrodeposition resulted in higher and more reproducible electrochemical responses, demonstrating improved electron transfer properties and surface homogeneity. The primary objective of this study was to investigate how different surface modification strategies affect the electrochemical performance of SPCE-based immunosensors, employing the detection of Survival Motor Neuron (SMN) protein, a biomarker associated with Spinal Muscular Atrophy (SMA), as a proof-of-concept application. The resulting platform successfully differentiated specific and non-specific protein recognition events through distinct electrochemical patterns, demonstrating the suitability of gold-modified SPCEs for immunosensing applications. These findings provide insights into the influence of surface engineering strategies on sensor performance and support the future development of optimized electrochemical platforms for biomarker detection. Full article