We propose a Dual-Layer Meta-Learning Network for Few-Shot Named Entity Recognition, where the network can selectively retain positive training signals from the memory chain to enhance the meta-model's learning capability and filter out interference from non-positive signals. Additionally, to mitigate the parameter explosion caused by the dual-layer network, we further use Chebyshev polynomials to fit the token classification function for entity span detection and employ the Kolmogorov-Arnold Network to fit the prototype-oriented classification function for entity span classification. This effectively reduces the runtime and GPU usage of the dual-layer structure.

The growing demand for high-performance components in wireless communication and automotive systems, especially for radar applications, has driven the need for dual-band power amplifiers (PAs) operating at 60GHz and 77GHz. These frequency bands are particularly beneficial for automotive radar systems, integral to Advanced Driver Assistance Systems (ADAS) and autonomous driving technologies, as they offer enhanced resolution, reduced interference, and faster data transmission rates. This paper presents the design and development of a dual-band PA based on a novel coupled-line dual-frequency matching structure. The PA’s innovative input and output matching networks utilize a unique coupler design to achieve simultaneous impedance matching at both 60GHz and 77GHz. Through comprehensive simulation, optimal matching impedances for both frequencies were identified, enabling the PA to achieve an output power of 12 dBm at 60GHz and 10 dBm at 77GHz, with power-added efficiencies of 24.4% and 13.85%, respectively. The design also incorporates a two-stage power amplifier configuration that ensures high efficiency and gain across the dual bands. Experimental validation was performed using a small-signal test system, demonstrating excellent performance, with a peak power gain of 12.4 dB at 60GHz and 9.8 dB at 77GHz. This dual-band PA design is particularly well-suited for integration into automotive radar systems, thanks to its compact size, high power efficiency, and ability to support wideband matching. Furthermore, this work presents a highly efficient, wideband solution for next-generation automotive radar and communication systems operating in the millimeter-wave frequency range.

Recent advancements have marked significant progress in photoacoustic imaging as an effective method for acquiring deep bio-tissue visuals in modern medical clinical therapy and the efficacy of U-Net and its variants has been established for imaging quality enhancement in this field. Unlike common computer vision datasets such as ImageNet [1] and PASCAL VOC [2], biomedical images exhibit highly structured patterns, low spatial resolution, and single-channel modality, as shown in Fig. 1. Additionally, the U-Net parameters trained for medical super-resolution tasks demonstrate a high sparsity ratio, making them suitable for implementation on edge-computing platforms. Therefore, developing an energy-efficient photoacoustic imaging setup in this area is a natural progression. However, this development is constrained by the current neural network architectures, which are built around a U-Net backbone. The multi-stage feature extractor, skip connection integration across different blocks, and the encoder-decoder backbone design pose significant challenges to cutting-edge computational hardware platforms. In this study, a scalable, sparsity-supported neural network accelerator architecture for bio-tissue imaging quality enhancement is proposed to meet the stringent requirements of latency and energy efficiency, as depicted in Fig. 2. This architecture achieves desired performance improvements by exploring the sparsity possibilities in neural network during the training process and implementing an end-to-end pixel-first hardware design to minimize data movement and support sparsity computation. Compared with the state-of-the-art related works, this optimized architecture has achieved minimum on-chip storage overhead and the fastest frame for the application of photoacoustic imaging quality enhancement. The scalable architecture has also been implemented on a Xilinx XCZU9EG FPGA and attains a performance of PSNR@ 24 dB and a frame rate of 164 fps at a working frequency of 250 MHz.

This paper proposes a compact sub-THz frequency conversion module for D-band transceivers, fabricated using a 28-nm CMOS process. The module integrates an injection-locked frequency multiplier (ILFM) for Tx signal frequency up-conversion and an active Gilbert double-balanced mixer for Rx signal frequency down-conversion. The system was tested on a probe station. Utilizing a tunable coupling-coil technique and optimized inductance, the ILFM achieves a locking range of 105.2-125.5 GHz with an output power of -4.5 dBm. The Gilbert mixer demonstrates a conversion loss of -6.5 dB across the same range with an LO power of -4.2 dBm. The active region of ILFM and mixer chips occupy areas of 0.31 mm2and 0.51 mm2, respectively.

Accurate partial discharge (PD) detection and insulation diagnosis are essential for ensuring the operational safety of high-voltage (HV) power equipment, while wideband high sensitivity PD detection is extremely imperative since most of the PD events reflecting potential insulation defects have low strength (<1 pC) with wideband features. However, conventional wideband detection methods such as high-frequency (HF) antennas often suffer from the trade-off between low sensitivity, increased noise and the bandwidth requirements due to their low-Q features in the wideband sensing, and the vulnerability to in-band narrowband interference, especially in complex industrial environments. To address these challenges, this paper proposes a novel high sensitivity super-wideband PD detection system for Industrial Internet of Things (IIoT) applications integrating a reconfigurable high-frequency antenna sensor (RHAS), a bandwidth-reconfigurable low-noise amplifier (BRLNA), and a sub-band synthesis algorithm with built-in narrowband interference rejection (NIR) function. The RHAS captures weak PD signals across 48 high-Q sub-bands, which are individually amplified and digitally synthesized to reconstruct the wideband signal with enhanced sensitivity. The proposed system enables remote, clamp-free PD detection with strong NIR capability near grounded conductors, significantly outperforming the traditional sensors such as High-Frequency Current Transformers (HFCTs). Experimental results demonstrate a detection bandwidth from 1.4 MHz to 98.2 MHz (194.38% relative bandwidth) and a minimum detectable PD level of 0.3 pC. Compared with HFCT based systems, the proposed method achieves a 0.4–4.2 dB improvement in PD signal-to-noise ratio (SNR) and a 6.1–8.4 dB increase in total system gain. These results validate the effectiveness and robustness of the proposed approach for high-sensitivity wideband PD monitoring.

This paper presents a cryogenic qubit controller for scalable superconducting quantum computing. A phase-selection digital power amplifier (DPA) topology is utilized for the XY-driving pulses generation. With a compact digital extensive architecture, the amplitude modulation and qubit phase rotation can be directly implemented at the phase-selection DPA stage for power reduction. A multi-phase envelope-tracking supply unit is also employed to enhance the power efficiency. The controller was designed and simulated in 28nm bulk CMOS technology. The controller can cover a band of 4-6GHz with an output power of at least -8dBm. The simulated SNR/SFDR is better than 55dB/46dB with a phase rotation error of less than 0.7°. The envelope-tracking supply unit can reduce the DPA power consumption by at least 24% compared to a constant power supply. The total power consumption of the controller is 3.64mW.

This paper presents a MEMS-based wake-up receiver (WuRX) operating at 825 MHz, with low-power and high-sensitivity. To enhance selectivity and interference rejection, a high-Q MEMS resonator is incorporated, co-designed with the low noise amplifier (LNA) to implement resonant noise matching (RNM), providing passive gain to mitigate the input-referred noise. The receiver is implemented in TSMC 65nm CMOS technology, achieving a sensitivity of −70 dBm at 10-3BER while consuming a mere 2.83 µW of power at a 1 V power supply. Additionally, it attains a SIR of 20 dB at a 2 MHz offset from the center frequency.

The timely detection of partial discharge (PD) of high-voltage (HV) power equipment is crucial to mitigate serious consequences such as the degradation of insulation and equipment failure. The ultra-high frequency (UHF) detection method stands out for its efficacy in this regard. In this study, a novel UHF antenna sensor is proposed for PD detection. The antenna's offset structure enables it to detect PD events near a conducting ground wire without being clamped to the wire like the typical high-frequency current transformer (HFCT). Meanwhile, its equivalent circuit is modeled as a ladder-structure band-pass filter (within the consideration of mutual inductances) to realize its wideband properties. Fabricated on a substrate and integrated with a low-noise amplifier, the antenna sensor exhibits a broad impedance bandwidth between 1 MHz and 108 MHz, as demonstrated through measurements in an anechoic chamber. In-lab and on-site systematic experiments affirm the efficiency of the proposed antenna sensor in PD detection. Notably, the Phase-Resolved Partial Discharge (PRPD) pattern is distinctly observable at the backend through Internet connectivity, further confirming the overall monitoring capabilities.

Partial discharge (PD) is an essential indication of insulation degradation in high-voltage power equipment like gas-insulated switchgears (GIS). However, in certain applications that are exposed to intense external noise, traditional PD detection methods often encounter numerous challenges due to their vulnerability to noise and interference. This paper proposes a low-noise ultra-high frequency (UHF) sensing system for PD detection and classification. In analog front end, the noise performance is optimized using a broadband noise-shaping network (BNSN) integrated with a wideband printed monopole antenna (PMA). In digital back end, the combination of noise cancellation, wavelet time scattering (WTS) based features extraction and a support vector machine (SVM), yields 95% correct classification. Simulation and Comparative experimental results validate superior noise performance, effectiveness and accuracy of this UHF sensing system.