This letter presents a 76-81-GHz frequency-modulated continuous-wave (FMCW) radar receiver (RX) consisting of a low-noise amplifier (LNA), a zero-intermediate-frequency (IF) mixer with a transimpedance amplifier (TIA), and an analog baseband (BB) in a 65-nm CMOS process. A novel symmetrical balun technique is proposed to generate phase-matched differential signals at the input-with simulated amplitude and phase imbalances below 3 dB and 1 degrees, respectively-optimizing the noise figure (NF) of the LNA. Measurements show that the RX channel achieves an NF of 4.4-6.4 dB with a conversion gain (CG) of up to 70 dB in 76-81 GHz. In summary, this receiver occupies 1.13 mm(2) with an average power consumption of 55.0 mW per channel.

This paper presents a 0.75-1.5 GHz fast locking Fractional-N Phase-Locked Loop (PLL). To meet the requirements of communication systems for the lock time, an open-loop precharge-assisted frequency control scheme is introduced to shorten the lock time without affecting the original loop. The phase noise contribution of Voltage-Controlled Oscillator (VCO) is reduced by minimizing noise current injection and speeding up high-low transition. Implemented in 180 nm CMOS process, the proposed PLL reduces the lock time by 68.9%, achieving a phase noise lower than-92 dBc/Hz at 1 MHz offset and a power consumption of 21 mW.

The multitarget detection is an important scientific proposition for millimeter-wave multiple-input-multiple-output (MIMO) radar systems. Due to the short wavelength at millimeter-wave bands, multichannel MIMO arrays are typically realized with very dense element spacing (often on the order of lambda/2 ), which corresponds to only millimeter-level physical separation. It increases near-field interaction and parasitic coupling through the feed or package, making mutual coupling between adjacent channels severe and deteriorating the sensing performance. However, conventional decoupling methods unavoidably increase system hardware complexity, rendering them incompatible with compact sensing platforms. As a result, sparse MIMO radar is supposed to be an efficient solution. Meanwhile, the nonuniform virtual co-array generated by sparse configurations typically contains missing lags, defined as holes, leading to incomplete virtual-aperture measurements and may degrade direction-of-arrival (DOA) resolution and multitarget separability. Conventional used interpolation-based methods utilize the convex optimization algorithm to fill in the holes, which significantly elevates the computational complexity. In this article, we proposed an enhanced multitarget detection approach using an optimal sparse millimeter-wave radar architecture and polynomial interpolation method to reduce mutual coupling among different sensing channels. In this system, a lightweight sparse MIMO radar array configuration is proposed first. Then, the polynomial interpolation method is introduced to achieve degrees-of-freedom (DOF) enhancement with lower computational and hardware complexity than traditional convex-optimization-based methods. Finally, the proposed approach is experimentally validated through multitarget detection scenarios, successfully identifying 8(6) targets using a 5(4) sensing channels. Simulation and experimental results demonstrate the superiority of the proposed sparse MIMO radar architecture over multitarget detection performance and computational complexity, offering a practical solution for diverse MIMO radar sensing platforms.

This article presents the design and implementation of a 71-76-GHz four-channel phased-array transmitter in 65-nm CMOS. To achieve compact and accurate quadrature signal generation, a nested differential quadrature coupler is proposed and analytically modeled through a lumped-element representation and scattering matrix formulation. The coupler is cascaded with a digitally controlled variable-gain amplifier (VGA) based on a differential cross-coupled Gm-cell (CSCG) array, achieving vector-based phase modulation with stable input impedance during gain transitions. The proposed phase shifter achieves 360 degrees phase coverage with 5.625 degrees resolution and 12-dB attenuation (ATT) range in 1-dB step, with phase and gain errors below 1.17 degrees and 0.21 dB, respectively. The transmitter delivers a saturated output power (P-sat) of 13.82 dBm and an output-referred 1-dB compression point (OP1dB) of 9.7 dBm, consuming 184 mW/channel within a compact area of 1.55 & times; 1.61 mm(2). To validate the design, a transmit phased-array was implemented using a multilayer packaging process, achieving +/- 60 degrees beam scanning without grating lobes. To the best of the authors' knowledge, this work demonstrates the most compact four-channel E-band transmitter to date, making it suitable for substrate-based phased-array implementations and a promising solution for diverse millimeter-wave wireless communication and sensing systems.

Automating radio frequency (RF) amplifier design remains challenging because existing methods suffer from the curse of dimensionality, weak use of domain knowledge, and poor transferability, leading to low data efficiency. Meanwhile, although large language models (LLMs) have shown promise in many scientific domains, applying them directly to RF sizing is nontrivial due to the numerical nature of circuit optimization and the reliance on domain-specific design flows. To address this, this paper proposes RFAmpDesigner, a multi-agent framework that automates RF amplifier sizing. It introduces a resource-allocation middleware that reframes high-dimensional parameter tuning as a low-dimensional resource distribution problem, making it easier to inject sizing knowledge into general-purpose LLMs. The framework also follows standard design practice, enabling LLMs to distinguish between high- and low-cost actions and search in parallel. To realize a self-evolving optimization process, the framework employs retrieval-augmented generation (RAG) to reuse past knowledge and experience from memory base. As a proof of concept, we apply RFAmpDesigner to low noise amplifiers of varying complexity. The experimental results show that it can automatically synthesize designs with fractional bandwidths ranging from 10% to 80% and center frequencies from 10 GHz to 50 GHz. To the best of our knowledge, this work develops the first LLM-driven approach for RF amplifier sizing that operates on design concepts instead of treating netlists as text, offering a novel solution to mitigate data scarcity in RF design.

4D millimeter-wave radar plays a critical role in object detection for autonomous driving and robotics under all-weather and all-lighting conditions. Recently, the virtual-point-based approaches have attracted widespread attention due to their ability to address radar data sparsity by complementing the depth of image instance points with the nearest 3D points. However, existing radar-camera fusion methods based on virtual points simply incorporate virtual points into the raw radar points as a form of data augmentation, overlooking the potential of virtual points that have an inherent association with both radar and images. To address these issues, we present a novel radar-camera fusion network, RCVAFusion, for 3D object detection. Specifically, we first design an association branch that employs Object Area Sampling (OAS) and Virtual-Raw Points Depth Lifting (VRPDL). This branch facilitates a deep interaction between radar geometric features and image semantic features through the medium of virtual points to generate an association feature. Then, we introduce the Dual-step Feature Aggregation (DFA) to promote feature fusion from radar, image, and association branches by establishing aggregation priorities based on feature similarity in two steps. Experimental results on the TJ4DRadSet and View-of-Delft (VoD) datasets demonstrate that our method efficiently fuses radar and camera through virtual points and achieves state-of-the-art performance.

This article presents a K/Ka-band transmit/receive (T/R) front-end for jointed sensing and communication (JSAC) applications. A reconfigurable matching network for both signal reception and transmission is realized using the proposed triple-coupled transformer (TCT) technique, achieving low power loss and a compact footprint. The T/R switch at the center tap of the low-noise amplifier (LNA) coil enhances the T/R isolation by cutting off the coupled leakage power from the power amplifier (PA) coil. The chip was fabricated in 65-nm bulk CMOS and occupies a 1.18-mm(2) core area. In the RX mode, it achieves a power gain of 24.7-27.6 dB and an NF less than 5 dB at 17.7-29.5 GHz. In the TX mode, it achieves a maximum saturated output power (P-sat) of 20.5 dBm with a power added efficiency (PAE) of 21.8% and an output-referred 1-dB gain compression point (OP1dB) of 20.2 dBm with a PAE of 20.6% at 24 GHz. The measured OP1dB is larger than 16.7 dBm over the 20-31-GHz frequency range. The chip demonstrates low noise figure (NF) and high output power among K/Ka-band silicon-based T/R front-ends.

In high-resolution maritime radar working in scanning mode, the classification and identification of ships require the recovery of the ship's high-resolution range profiles (HRRPs) from radar returns. The return signal from the ship is a complex sparse signal interfered by non-Gaussian sea clutter. In this article, three sparse optimization methods matching the non-Gaussian characteristics of sea clutter, i.e., the sparse optimization matching K-distribution method, the sparse optimization matching generalized Pareto distribution method, the sparse optimization matching CGIG distribution method, are proposed to estimate complex HRRPs of ships. The compound Gaussian model is used to describe the non-Gaussianity of sea clutter, and the sparsity of ships' complex HRRPs is constrained by the random distribution with one parameter. In the three methods, the Anderson-Darling test is used to search the parameters of the sparse constraint model. Besides, the non-Gaussian characteristics of sea clutter depend on the marine environment parameters and radar operating parameters. For different scenarios, the minimal criterion of the Kolmogorov-Smirnov distance is used to select the best model from the three compound Gaussian models, and then select the corresponding proposed methods. Simulated and measured radar data are used to evaluate the performance of the proposed methods and the results show that the proposed methods obtain better estimates of ship HRRPs compared to the recent SRIM method and the classical SLIM method.

Phased arrays are an important subsystem for satellite communication with flexible beam steering capability. The multibeam technique demonstrates superior performance with multiple independent beams sharing the same array simultaneously. This letter proposed a 256-element K-band phased array with two concurrent beams and reconfigurable polarization capability, along with active axial ratio optimization method for large scanning angle. Experimental results indicate that the array has +/- 55 degrees beam steering in both azimuth and elevation planes with 3.8 dB gain loss without grating lobes. The 3 dB beamwidth is about 6 degrees with sidelobes below -13.5 dB and G/T value of -0.86 dB/K per beam at normal direction. The phased array features wideband (17.7 GHz to 21.2 GHz), reconfigurable polarizations and axial ratio optimization capability, offering a reliable solution for diverse multibeam communication systems.

Recent advances in resolution have positioned 4D radar as a promising sensor for autonomous driving, leading to growing research interest in radar-based perception. Many organizations have released related datasets to explore the potential of radar sensing; however, radar information quality-an important factor affecting perception performance-is often overlooked. This neglect is partly due to a limited understanding of the radar pipeline, particularly among people without a radar signal processing background. Moreover, even when this issue is recognized, closed radar pipelines in existing datasets hinder in-depth investigation of radar information quality. To fill this gap, we propose FlexRadar, a dataset with a fully open radar pipeline for systematic study of radar information quality, along with diverse sensor data to support fusion perception research. In this paper, we provide a comprehensive description and analysis of the dataset. We establish two benchmarks and conduct extensive experiments to demonstrate the effectiveness of the data and the benefits of the open radar pipeline. This work is expected to broaden researchers' perspectives on radar-based perception.

This letter proposes an AI-driven synergistic scheme for wideband low-noise amplifier (LNA) synthesis. The scheme consists of a high-fidelity electromagnetic (EM) surrogate model and a hybrid optimization algorithm to manage automatic physical parameter sizing. The EM surrogate model employs a residual squeeze-and-excitation (ResSE) network to capture complex mapping from the physical parameters and frequency to the scattering parameters. Subsequently, the hybrid optimization algorithm utilizes a nonlinear self-adaptive hyperparameter (NSH)-particle swarm optimization (PSO) algorithm to balance design space coverage and convergence, and employs a scout mechanism to minimize the number of time-consuming Spectre simulations. To validate the proposed scheme, a 7.5–18.0-GHz LNA is synthesized and fabricated in a 65-nm CMOS, achieving a peak $S_{21}$ of 21.6dB, a $\leqslant -8.6$ dB $S_{11}$ , a noise figure (NF) of 2.7–4.3dB, and 36.1-mW power consumption. The entire automated synthesis consumes 23.4 h, significantly reducing design time compared to manual approaches that require more than one week. Measured results exhibit high fidelity with simulations and a competitive figure of merit (FoM), which demonstrates a scalable and efficient design paradigm for autonomous RFIC synthesis.

In recent years, 4D imaging radar has gained wide attention in autonomous driving for its robustness against harsh weather and ability to output target velocity. Nevertheless, mainstream 4D radar-camera fusion methods only support front-view perception, lacking mature solutions for surround-view sensing. Directly expanding these pipelines to full 360° coverage introduces excessive computation cost and limits real-world deployment. To tackle these limitations, this work proposes Sparse4D-Radar, an efficient robust surround-view multi-modal fusion framework. We first design a Deformable Fusion module to embed radar-camera features into sparse queries, constructing the lightweight base version Sparse4D-Radar-Base. Two dedicated modules are further introduced to boost localization accuracy and modality stability: Velocity-Consistency Sampling (VCS) refines features via radar velocity cues for motion awareness, and Adaptive Modality Gating (AMG) dynamically adjusts cross-modal fusion weights according to feature confidence. Combining all components, we build Sparse4D-Radar-Acc for high-precision detection demands. Comprehensive experiments on OmniHD-Scenes verify that our approach achieves state-of-the-art surround-view 3D detection performance. Compared with prior arts, our method obtains over 7

—Artificial intelligence (AI) enabled biomedical robots rely on tactile perception, but tactile sensing is limited by force induced variability that mixes material signals with contact pressure. Here, we introduce a pressure-labelled tactile intelligence framework built on a bioinspired tactile sensor (BT sensor) with a self-powered triboelectric element and fingertip like architecture, delivering decoupled dynamic tactile and static pressure information. The sensor integrates fast-adapting (FA) and slow-adapting (SA) elements based on contact electrification and electrostatic induction, respectively, for synchronous acquisition of transient responses and sustained pressure signals. A cross-talk-suppressed decoupling circuit separates triboelectric signals into dynamic and static components, enabling the SA output to serve as a reliable pressure reference for pressure labelled learning. Within a feature augmented framework combining physics informed descriptors with a one dimensional convolutional neural network (1D CNN), the pressure labelled strategy achieves 99.2% recognition accuracy across ten materials and elevates aggregate cross pressure recognition from 50.0% for dynamic-only sensing to near-perfect robustness, demonstrating suppression of force induced variability. Integrated with a robotic manipulator, it identifies three dimensional (3D) printed organ model materials with 100% accuracy. These results establish a controlled proof-of-concept for pressure-labelled tactile material recognition in anatomically relevant biomedical model systems.

This letter presents a 76–81-GHz frequency-modulated continuous-wave (FMCW) radar receiver (RX) consisting of a low-noise amplifier (LNA), a zero-intermediate-frequency (IF) mixer with a transimpedance amplifier (TIA), and an analog baseband (BB) in a 65-nm CMOS process. A novel symmetrical balun technique is proposed to generate phase-matched differential signals at the input—with simulated amplitude and phase imbalances below 3 dB and 1°, respectively—optimizing the noise figure (NF) of the LNA. Measurements show that the RX channel achieves an NF of 4.4–6.4 dB with a conversion gain (CG) of up to 70 dB in 76–81 GHz. In summary, this receiver occupies 1.13 mm2 with an average power consumption of 55.0 mW per channel.

This article presents a V-band highly integrated frequency-modulated continuous-wave (FMCW) radar transceiver fabricated in 65-nm CMOS for indoor sensing applications. The chip features a four-channel receiver (RX) and a three-channel transmitter (TX), enabling multi-input multi-output (MIMO) capabilities. A 15-GHz frequency synthesizer with injection-locking frequency multipliers is employed to generate sawtooth FMCW signals. To achieve a wide chirp bandwidth (BW) with high phase linearity, reconfigurable capacitor banks are employed in the frequency multipliers and drivers to over-come the limitation of the locking range and nonlinear phase response. Furthermore, a fast-settling circuit is designed to reduce the settling time at the end of a sawtooth sweep. The TX delivers a maximum output power of 14.3 dBm, and the RX achieves a minimum noise figure (NF) of 7.8 dB at 5-MHz intermediate frequency (IF) and an adjustable gain of 18-82 dB including 8/56-dB RF/IF gain range. The FMCW signal generator achieves an 8-GHz chirp BW with 80-MHz/us chirp rate, with the measured phase noise -95.3 dBc/Hz at 1-MHz offset from a 60-GHz carrier. The radar transceiver occupies 4.8 x 2.8 mm(2) are area and consumes 674 mW. Using a substrate-integrated waveguide (SIW) slot antenna array with a 14-dBi gain, the radar system achieves a measured range resolution of 3.5 cm and an angular resolution of 17 degrees, and demonstrates indoor sensing capabilities along with a digital signal processing (DSP) platform.

This article presents a compact 64–98GHz power-combining frequency doubler with magnetic enhanced harmonic reflectors. The 2nd harmonic outputs of two identical push-push frequency doublers are combined to improve the maximum output power. Two ways share an L-C harmonic reflector, and each way employs a C-L harmonic reflector. Two C-L harmonic reflectors are magnetically coupled to enhance each other and save area. Fabricated in a 65-nm bulk CMOS technology, the doubler achieves a 3dB bandwidth of 64–98GHz, a peak saturation output power of 11.4dBm, a maximum drain efficiency of 21.1%, a maximum conversion gain of 3.3dB, with a core area of only 0.053mm2.

Background Depression is a common mental disorder characterized by prolonged loss of interest and low mood, accompanied by symptoms such as sleep disturbances and cognitive impairments. In severe cases, there may be a tendency toward suicide. Depression can be caused by a series of highly complex pathological mechanisms; However, its key pathogenic mechanism remains unclear. As a novel programmed cell death (PCD) pathway and inflammatory cell death mode, pyroptosis involves a series of tightly regulated gene expression events. It may play a significant role in the pathogenesis and management of depression by modulating neuroinflammatory processes. In addition, a large number of studies have shown that various pharmacologically active natural products can regulate pyroptosis through multiple targets and pathways, demonstrating significant potential in the treatment of depression. These natural products offer advantages such as low costs and minimal side effects, making them a viable supplement or alternative to traditional antidepressants. In this review, we summarized recent research on natural products that regulate pyroptosis and neuroinflammation to improve depression. The aim of this review was to contribute to a scientific basis for the discovery and development of more natural antidepressants in the future. Methods To review the antidepressant effects of natural products targeting pyroptosis-mediated neuroinflammation, data were collected from the Web of Science, ScienceDirect databases, and PubMed to classify and summarize the relationship between pyroptosis and neuroinflammation in depression, as well as the pharmacological mechanisms of natural products. Results Multiple researches have revealed that pyroptosis-mediated neuroinflammation serves as a pivotal contributory factor in the pathological process of depression. Natural products, such as terpenoids, terpenes, phenylethanol glycosides, and alkaloids, have antidepressant effects by regulating pyroptosis to alleviate neuroinflammation. Conclusion We comprehensively reviewed the regulatory effects of natural products in depression-related pyroptosis pathways, providing a uniquely insightful perspective for the research, development, and application of natural antidepressants. However, future research should further explore the modulatory mechanisms of natural products in regulating pyroptosis, which is of great importance for the genration of effective antidepressants.

A W-band frequency-modulated continuous-wave (FMCW) radar, implemented in 65-nm CMOS, is proposed for intelligent transportation system (ITS) applications in this article. The system integrates four transmitters (TXs) and four receivers (RXs), along with a frequency synthesizer and a local oscillator (LO) distribution network. Both the low-noise amplifier (LNA) and the power amplifier (PA) adopt multistage cascaded topologies with magnetically coupled resonators (MCRs) to enable broadband operation. An ultrawideband class-B mixer, implemented with only an active switching core, supports continuous operation from 20 to 110 GHz. Furthermore, an LO distribution network featuring three cascaded frequency doublers achieves frequency octupling from an 11-13-GHz synthesizer, enabling wide-bandwidth (BW) modulation. Under the default configuration, the four TX and RX channels achieve a maximum TX output power of 13.4 dBm with 12.8% drain efficiency, an RX conversion gain (CG) of 64.4 dB, a minimum RX NFssb of 8.4 dB, an RX in-band (IB) IP1dB from -49.4 to -43.6 dBm @3MHz offset, and an RX out-of-band (OOB) IP1dB from -17 to -11.1 dBm at 10-kHz offset across 90-98 GHz. The measured phase noise is -94.06 dBc/Hz at 1-MHz offset with a 90.4-GHz carrier. The root-mean-square (rms) error is 3.52 MHz (0.044%) for a sawtooth chirp with an 8-GHz range and a 20-MHz/ mu s chirp rate. Each TX/RX element consumes 208.5/76.5 mW, respectively, and the entire chip occupies a 4.5x 3.8 mm(2) area. To validate the radar operation, a slot substrate-integrated waveguide (SIW) antenna array, with a flip-chip chip-scale package (FCCSP) transceiver, is designed and fabricated on a Rogers 3003G2 PCB. The multiple-input-multiple-output (MIMO) radar prototype achieves a distance resolution of 2.85 cm and an angular resolution of 13 degrees with a field of view (FOV) of 144 degrees.

brief presents a Ka-band frequency-modulation continuous wave (FMCW) transceiver (TRX) in 65-nm CMOS for low-power, high-resolution radar detection. The low-noise amplifier (LNA) employs current multiplexing and Gm-boosting techniques to enhance energy efficiency. The proposed primary-coil current splitter improves the gain and minimizes crosstalk between the I/Q mixers. The transceiver achieves a 15.6-dBm maximum transmitter (TX) output power with a 35.3% drain efficiency (DE) at 34.6 GHz, a 49-dB receiver (RX) conversion gain (CG), and a 4.9-dB minimum double-sideband noise figure (NFdsb). When operating at low-voltage mode, it achieves a 7.68-dBm TX output power with a 23.8% DE, a 41-dB RX CG, and a 9.17-dB RX NFdsb. The measured phase noise is-100.6 dBc/Hz at 1-MHz offset with a 32.4-GHz carrier. The rms frequency error is 2.94 MHz (0.073%) for a sawtooth chirp with a 4-GHz chirp bandwidth and a 200-mu s chirp-up time. The proposed transceiver achieves wide bandwidth and low power consumption among Ka-band FMCW radar TRXs. An inverted shunt-fed patch antennas are also fabricated on an evaluation board using Rogers 4550F to demonstrate high gain and broadband operation over the air (OTA).

This paper presents a 71-86-GHz receiver (RX) with 5-GHz IF bandwidth (BW) in 65-nm CMOS, which can be applied to high-speed and large-capacity wireless communications. The proposed receiver integrates a low-noise amplifier (LNA), a down-conversion mixer, and a baseband (BB) circuit that utilizes a mutually-coupled inductive load for wideband IF. Measurements show the receiver achieves a maximum conversion gain (CG) of 28 dB and a minimum single-sideband noise figure (NFssb) of 6.5 dB at 77-GHz. The measured error vector magnitude (EVM) is less than -34 dB for 0.6 Gb/s 64-QAM signals. The receiver occupies a chip area of 1.45 x 1.09 mm(2) with power consumption of 123.4 mW.