Search Results (571)

This paper presents a nanowatt-scale operational transconductance amplifier (OTA) and an electronically tunable third-order low-pass filter (LPF) designed for energy-constrained biomedical signal conditioning. The circuits are implemented in a 65 nm CMOS process and verified through comprehensive schematic-level simulations. Biased in the deep subthreshold region at 1 nA, the OTA achieves a 50 dB low-frequency gain, a 225 Hz unity-gain bandwidth at 10 pF load capacitance and an input-referred noise floor of 1.55 μV/√Hz, with a total power consumption of only 1.75 nW. The integrated third-order LPF provides a wide tuning range (37–668 Hz) via bias current modulation, exhibiting excellent linearity with a THD of 0.059% and a 65.3 dB dynamic range. Monte Carlo and PVT corner analyses demonstrate the design’s theoretical robustness against process variations and environmental fluctuations. ECG signal simulations validate the circuit’s effectiveness in suppressing high-frequency artifacts while preserving morphological integrity, providing a proof-of-concept for ultra-low-power wearable healthcare architectures. Full article

Girth welds are susceptible to defects under high internal pressure and stress. While phased array ultrasonic testing (PAUT) is widely used for non-destructive evaluation, manual inspection remains inefficient and highly dependent on expertise. Furthermore, existing deep learning models often struggle with low accuracy and high complexity. This paper proposes a PAUT defect classification method based on YOLOv8. First, median filtering is employed for denoising, and the results show that noise is effectively reduced while preserving key features, achieving PSNR values of 35.132, 35.938, and 36.138 for slag inclusion, pores, and lack of fusion (LOF), respectively. Subsequently, the spatial enrichment algorithm (SEA) is applied to enhance image details without amplifying noise, yielding a PSNR of 33.71 and an SSIM of 0.96. Finally, the YOLOv8 model is implemented for defect recognition. Experimental results demonstrate that the proposed approach achieves a superior balance between precision and recall with high reliability. This method offers a robust and efficient solution for automated PAUT evaluation in practical engineering applications. Full article

This paper presents a flipped-voltage-follower low-dropout regulator (FVF-LDO) for power supply rejection enhancement and low-power operation in CMOS transimpedance amplifiers for optical receiver applications. The proposed FVF-LDO ensures high stability and reliable regulation over a wide range of load conditions by employing a flipped-voltage follower for fast local feedback and improved power supply rejection, while a super-source follower enhances the transient response through increased current-driving capability. A bandgap reference with a 3-bit trimming DAC is adopted to compensate process variations and support stable LDO operations, achieving a temperature coefficient of 19.6 ppm/°C over a wide range of −25 °C to 125 °C. The FVF-LDO exhibits a 101 mV undershoot under a 100 µA-to-10 mA load step with a 100 ns edge time. When applied to an optoelectronic inverter-based active-feedback transimpedance amplifier (TIA), the regulated supply improves the power supply rejection ratio (PSRR) from −6 dB to −38.3 dB. The proposed optoelectronic TIA realized in a 180 nm CMOS process achieves 67 dBΩ transimpedance gain, 869 MHz bandwidth, 66 dB dynamic range, 6.68 pA/√Hz input-referred noise current spectral density, and 4.68 mW power consumption from a single 1.8 V supply. The proposed TIA chip occupies a core area of 940 × 162 µm². Full article

The use of long-distance transoceanic cables equipped with high-loss loopbacks enables distributed sensing with a resolution determined by amplifier spacing, typically in the order of 50–100 km. Microwave-frequency fiber interferometry is a promising trans-mission technique for investigating long links supported by periodic optical amplification. In this paper, we propose a variant of this technique that ensures compatibility with links containing high-loss loopbacks, thereby transforming the integrated sensing approach into a distributed one. We highlight the critical modifications required to overcome challenges associated with the detection of multiple return signals, and we conduct a proof-of-principle experiment using a two-loop configuration. We demonstrate the concept by detecting and localizing low-frequency (<10 Hz) events—whether human-generated or induced by fiber stretchers—with span-level resolution. This validates the potential of the modified microwave-frequency interferometry approach for transoceanic cable monitoring in scenarios where high-loss loopbacks are present. We also present a theoretical analysis that evaluates the limits of the technique across different frequency ranges, in comparison with optical interferometry methods based on high-spectral-purity fiber lasers. The analysis shows that for long amplifier spacings (~100 km), micro-wave-frequency fiber interferometry exhibits a signal-to-noise ratio advantage at sub-Hz frequencies (<0.1 Hz) compared to state-of-the-art optical interferometers. Full article

For vehicle driving scenarios in complex backgrounds, road sign detection faces challenges such as multi-scale targets, long-distances, and low-resolution. To address these challenges, a detection method based on an improved YOLO11n network is proposed. Firstly, to accommodate the multi-scale characteristics of the targets and improve the network’s ability to detect low-resolution objects and details, a Multi-path Gated Aggregation (MGA) Module is proposed, achieving these objectives via multi-dimensional feature extraction. Secondly, the Neck is improved by designing a network structure that incorporates high-resolution information from the Backbone, thereby enhancing the detection capabilities for small and blurry targets. Finally, an enhanced Spatial Pyramid Pooling—Fast (SPPF) module is proposed, wherein a Group Convolution-Layer Normalization-SiLU structure is integrated across various stages of information passing. By fusing adjacent channel information, it effectively suppresses complex background noise across multiple scales and amplifies road marking features, which consequently boosts the model’s discriminability for distant and obscured targets. Experimental results on a multi-type road sign dataset show that the improved model achieves an mAP@0.5 of 96.96%, which is 1.42% higher than the original model. The mAP@0.5–0.95 and Recall rates are 83.94% and 92.94%, respectively, while the inference speed remains at 134 FPS. Research demonstrates that via targeted modular designs, the proposed approach strikes a superior balance between detection accuracy and real-time efficiency. Consequently, it provides robust technical support for the reliable operation of intelligent vehicle perception systems under complex conditions. Full article

Search coil magnetometers (SCMs) are widely used in space science missions to measure time-varying magnetic fields. However, conventional SCM designs often increase sensor mass and electronic power consumption in order to meet mission-specific sensitivity requirements. This study presents the design and ground-based test results of a space search coil magnetometer (SSCM) concept aimed at reducing sensor mass and electronic power consumption while maintaining practical system operability for platform-constrained missions. Mass reduction was achieved by adopting a rolling-sheet core configuration. In addition, printed circuit board (PCB)-based interconnections between segmented windings were implemented to improve the reproducibility of assembly and mechanical robustness without additional structural complexity. Power reduction was achieved by employing an application-specific integrated circuit (ASIC)-based sensor amplifier and a compact control electronic unit implemented as a modular stack with a 1U CubeSat standard board form factor. Performance tests confirmed the stable operation of the integrated sensor–electronics chain over the target measurement band. The system-level noise-equivalent magnetic induction (NEMI) measured under laboratory conditions was 33 fT/√Hz at 1 kHz. Environmental tests including vibration and thermal cycling were performed to further verify the structural safety and functional stability of the sensor assembly under space-relevant conditions. The proposed SSCM architecture provides a practical approach for implementing low-mass and low-power magnetic field instruments for platform-constrained space missions. Full article

Underwater radiated noise (URN) has attracted increasing attention due to its environmental impact, with cavitation recognized as the dominant source. This study investigates cavitation-generation mechanisms and associated noise radiation for open-type and gap-type wings using high-fidelity numerical simulations. Cavitation noise was predicted using the Ffowcs Williams–Hawkings (FW–H) equation. The Fitzpatrick–Strasberg bubble noise model was independently employed for analysis to relate cavitation dynamics and cavity-volume variation to the resulting acoustic emissions. The results show that the gap-type configuration produces significantly stronger low-frequency noise, with the Tip Leakage Vortex Cavitation (TLVC) contributing up to 15 dB/Hz higher noise levels than the Tip Separation Vortex Cavitation (TSVC). This enhancement is attributed to the strong interaction between TLVC and TSVC, which amplifies cavitation dynamics and acoustic emissions. Analysis of three gap sizes reveals that, for small gaps, this interaction induces periodic cavitation behavior, generating a distinct harmonic component at St ≈ 2. As the gap size increases, the TLVC-TSVC interaction weakens, and the cavitation behavior transitions toward that of the open-type configuration, leading to the disappearance of the tonal component. These findings highlight the critical role of gap-induced vortex interaction in determining URN characteristics. Full article

In this article, a low-power low-intermediate-frequency (Low-IF) receiver front-end is presented for Bluetooth Low Energy (BLE) body area network (BAN) applications. The receiver employs an input matching network, an inductorless self-biased inverter-based low-noise transconductance amplifier (LNTA), a single-balanced passive mixer, a common-gate transimpedance amplifier (TIA), and a Gm-C complex filter for image suppression. Native MOS devices are adopted to support low-voltage operation and reduce static power consumption. The interstage on-chip coupling capacitor between the RF front-end and the TIA is removed by aligning the DC operating points of the two stages. The receiver front-end is implemented in a 55 nm standard CMOS process and occupies an active area of 0.081 ${\mathrm{mm}}^2$, excluding bonding pads. Post-layout simulations show that the proposed design achieves 45.2 dB gain, 7.2 dB noise figure, and 28.1 dB image rejection ratio over the 2.4–2.48 GHz band, while consuming 537 $\mathsf{\mu }$W. The proposed front-end is suitable for low-power BLE BAN sensor nodes. Full article

This paper presents a K-band four-channel phased array beamformer with temperature compensation in 65 nm CMOS for 5G and satellite communications. The beamformer includes a four-way power divider/combiner, four RF channels, and digital control circuits. Each RF channel comprises a receive chain, a transmit chain, and a pair of receive/transmit (TX/RX) single-pole double-throw (SPDT) switches. The receive chain consists of a low-noise amplifier (LNA), a six-bit reflective-type phase shifter (RTPS), a drive amplifier (DA), two temperature-compensation attenuators (TCAs), and a six-bit attenuator (ATT); the transmit chain integrates a power amplifier (PA), two TCAs, a six-bit RTPS, a DA, and a six-bit ATT. Measurements show the chip exhibits 0–4.5 dB gain, noise figure (NF) < 7.8 dB, root mean square (RMS) phase error < 3.5°, and RMS gain error < 0.4 dB in receive mode operating in 19–23 GHz. In transmit mode operating in 21–23 GHz, it provides 6–10 dB gain range, RMS phase error < 3.4°, RMS gain error < 0.25 dB, and output power at 1 dB compression point (OP1dB) > 6.5 dBm. In addition, the receive and transmit gain variations are within 0.8 dB and 0.4 dB, respectively, when temperature ranges from −55 °C to 85 °C. With a compact footprint of 3.5 × 4.8 mm², the beamformer consumes 110 mW (receive) and 190 mW (transmit) DC power per channel. Full article

This work introduces a trimming technique based on eTrim technology to minimize both the input-referred offset voltage and its temperature drift in the operational amplifiers. The proposed low-voltage op-amp utilizes the body effect to maintain a constant bandwidth across the rail-to-rail input common-mode range under low supply voltages. During input common-mode transitions, the current in the folded cascode stage remains stable, ensuring a robust output stage. Furthermore, a specialized gain-boosting structure enhances the low-frequency gain while preventing occasional latch-up during low-voltage power-up. A pin-multiplexing scheme is employed for trimming data input, thereby eliminating the need for dedicated trimming pins and mitigating post-package parameter variations. At room temperature, a constant-current injection mechanism reduces the DC offset to microvolt levels. At high temperature, temperature-compensated current injection cancels the first-order drift component. Implemented in a low-voltage operational amplifier, post-layout simulation results demonstrate that with a 100-pF capacitive load, the amplifier achieves a gain–bandwidth product exceeding 10 MHz, a low-frequency gain greater than 140 dB, and an input-referred noise of 2.54 µVp-p for the P-channel input and 3.95 µVp-p for the N-channel input. The trimming process reduces the residual offset to the microvolt range and effectively suppresses offset drift, ensuring accurate offset compensation across the specified temperature range. Full article

In side-channel analysis, simple power analysis (SPA) is a widely used technique for recovering secret information by exploiting differences between operations in traces. However, in realistic measurement environments, SPA is often hindered by noise, temporal misalignment, and weak or transient leakage, which obscure secret-dependent features in single or very few power traces. In this paper, we provide a systematic analysis of moving-skewness-based trace preprocessing for enhancing asymmetric leakage characteristics relevant to SPA. The method computes local skewness within a moving window along the trace, transforming the original signal into a skewness trace that emphasizes distributional asymmetry while suppressing noise. Unlike conventional smoothing-based preprocessing techniques, the proposed approach preserves and can even amplify subtle leakage patterns and spike-like transient events that are often attenuated by low-pass filtering or moving-average methods. To further improve applicability under different leakage conditions, we introduce feature-driven window-selection strategies that align preprocessing parameters with various leakage characteristics. Both simulated datasets and real measurement traces collected from multiple cryptographic platforms are used to evaluate the effectiveness of the approach. The experimental results indicate that moving-skewness preprocessing improves leakage visibility and achieves higher SPA success rates compared to commonly used preprocessing methods. Full article

This study addresses the inefficiencies, subjectivity, and poor adaptability to lighting variations inherent in traditional candling methods used in large-scale egg incubation. We developed a high-throughput transmissive imaging system capable of capturing 30 eggs simultaneously. Based on this system, we propose PDA-YOLO, an enhanced YOLOv8-based object detection model featuring a position-decoupled attention strategy. Specifically, a lightweight C2f-SE module is integrated into the backbone to amplify subtle feature responses in low-contrast regions, while a CBAM is deployed prior to the detection head to mitigate background clutter through precise spatial attention. Experimental results on a self-constructed Hailan White egg dataset show that at the critical 60 h incubation stage, PDA-YOLO achieves a Recall of 91.5% and an mAP@0.5 of 97.4%, outperforming the YOLOv8 baseline while maintaining a real-time inference speed of 62.1 FPS. Grad-CAM visualizations confirm the model’s ability to focus on vascular textures and suppress noise. Furthermore, the model demonstrates robust performance under varying illumination (180–540 lumens), effectively mitigating missed detections in low light and recognition degradation from overexposure. This work provides a scalable, real-time solution for non-destructive, early-stage detection of poultry health and fertilization status in commercial hatcheries. Full article

The automatic reading recognition of digital instruments is crucial for achieving metering automation and intelligent inspection. However, in non-standardized industrial environments, the masking effect caused by the coupling of non-uniform low-light conditions and the reflective surfaces of instrument panels severely degrades the displayed information, significantly limiting the recognition performance. Conventional image processing methods, while aiming to restore the imaging quality of instrument panels through low-light enhancement, inevitably introduce overexposure and indiscriminately amplify background noise during this process. To address the two key challenges of illumination recovery and noise suppression in the process of restoring panel image quality under non-uniform low-light conditions, this paper proposes a coarse-to-fine cascaded perception framework (CFCP). First, a lightweight YOLOv10 detector is employed to coarsely localize the meter reading region under non-uniform illumination conditions. Second, an Adaptive Illumination Correction Module (AICM) is designed to decouple and correct the illumination component at the pixel level, effectively restoring details in dark areas. Then, an Illumination-invariant Feature Perception Module (IFPM) is embedded at the feature level to dynamically perceive illumination-invariant features and filter out noise interference. Finally, the refined detection results are fed into a lightweight sequence recognition network to obtain the final meter readings. Experiments on a self-built industrial digital instrument dataset show that the proposed method achieves 93.2% recognition accuracy, with 17.1 ms latency and only 7.9 M parameters. Full article

Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands. Full article

In the present study, the limiter component with excellent low insertion loss and leakage power characteristics is used at the beginning of the receiving path, mounted at the rear end of the antenna of the AESA radar system, to protect the low noise amplifier (LNA) from excessive input power. The main components required for the X-band transmit/receive module are designed and manufactured mainly using bare-type components to reduce the module size. In this paper, we develop the limiter component, which is a key component, and verify whether it can secure performance that can be operated from the system perspective by mounting it on the receiving path of the transmit/receive module. The performance results of the limiter component unit obtained insertion loss of less than 0.615 dB at 10 GHz and leakage power of less than +16.8 dBm in the X-band. The main performance of the receiving path in the transmit/receive module unit obtained results of a noise figure of less than 3.2 dB and a gain of more than 37 dB (including two stages of LNA). Full article