Search Results (1,687)

Mixers are integral components in RF circuits for frequency conversion and are present in almost all RF front-ends. The relentless advancement of mobile communication standards, particularly towards 5G-Advanced and 6G, imposes ever more stringent and multi-dimensional performance requirements on mixer design. While previous surveys have capably summarized mixer technologies, this review distinguishes itself by providing a comprehensive and critical examination of millimeter-wave and sub-THz silicon-based integrated mixers, with explicit coverage extended from core RF bands to beyond 170 GHz. We place particular emphasis on the unique challenges and trade-offs inherent to silicon (CMOS and SiGe BiCMOS) platforms at these high frequencies. This work first summarizes the structural frameworks and underlying principles of mixers, examines multiple mixer variants, and performs an in-depth analysis of their key performance characteristics, encompassing conversion gain, noise figure (with distinctions between single-sideband (SSB) and double-sideband (DSB) definitions), isolation, and related metrics. Then, it compares and discusses the design of several mixers, especially analyzing their innovative points and key technologies, while critically evaluating their inherent limitations and trade-offs. Furthermore, a dedicated section synthesizes the most recent research trends, including heterogeneous integration, AI/ML-assisted design, and mixer architectures for integrated sensing and communication (ISAC), thereby addressing a notable gap in the current literature. Finally, it concludes with an outlook on future challenges and opportunities for mixers in next-generation communication systems. Full article

Multiple-input multiple-output (MIMO) radar is widely adopted in the fields of forward-looking imaging and target recognition, but its azimuth imaging resolution is fundamentally limited by the size of the physical aperture. Aiming to achieve higher imaging resolution than the theoretical value, an image super-resolution reconstruction method based on the horizontal attention generative adversarial network (HA-GAN) is proposed in this paper. In detail, the horizontal attention mechanism is introduced into the generator to enhance the azimuth resolution, and then the high-resolution (HR) images can be obtained through the adversarial learning between the generator network and the discriminator network. The numerical results demonstrate that the proposed method can break through the theoretical limitation of MIMO azimuth imaging. Moreover, compared to some state-of-the-art methods, the proposed method demonstrates superior performance on sidelobe suppression and super-resolution reconstruction at a low signal-to-noise ratio (SNR). Furthermore, the method’s effectiveness and generalization capability are extensively validated using simulation data, real-world experiments on a millimeter-wave MIMO system, and the public CRUW and RADAL datasets. Overall, the experimental results demonstrate that HA-GAN significantly enhances angular resolution and target recoverability, establishing it as a robust solution for high-precision forward-looking radar imaging. Full article

This paper presents two capacitors fabricated using the metallic nanowire membrane (MnM) interposer technology operating at mmWaves. Standard 2D interdigital capacitors (IDCs) are designed to operate up to 70 GHz, which presents a straightforward and non-complex fabrication. In comparison, this work also proposes an improved device that is more compact and exhibits large capacitance density, as high-performance vias enable the realization of high-depth capacitors. The fabrication process of 3D devices presents advanced maturity and innovation as it takes advantage of the porous nature of the interposer material to overcome the device complexity, and is also described in detail. Both capacitor types are modeled by a numerical lumped-element model that also considers parasitics. The 3D capacitors were successfully fabricated and characterized up to 70 GHz, displaying capacitance values between 30 fF and 160 fF and self-resonant frequencies in good agreement with mmWave applications. The quality factor of these devices, measured at 40 GHz, lies between 16 and 4, and the superficial capacitance density is between 4 pF/mm² and 8 pF/mm², showing that these devices are indeed promising for mmWave applications. These devices present considerably larger capacitance density compared to 2D traditional capacitors fabricated on the high-performance substrate, highlighting the advantage of 3D fabrication using nanowire growth. In addition, thin-film resistances are simulated and fabricated, projecting their functions as an RF-choke in a bias tee configuration using Ti thin film sputtering deposition step that is also part of the capacitors fabrication. Full article

Biodiesel is a promising, renewable, and environmentally friendly alternative fuel. Numerous studies have focused on improving the biodiesel production process from various feedstocks using different activation methods and catalysts. However, the reaction times typically range from tens of minutes to hours. This study presents, for one of the first systematic studies exploring time, the potential of using millimeter-wave electromagnetic radiation generated by a gyrotron as an activation method for biodiesel production reactions. Esterification was carried out using free fatty acids and fatty waste, specifically brown grease (BG), in the presence of the Lewis acid catalyst AlCl₃. Complete conversion of oleic acid was achieved after only 0.4 s of exposure to millimeter waves. When BG was used as the feedstock, a biodiesel yield of 73–76% was obtained within only 3.0 s. Gyrotron-based electromagnetic activation was benchmarked against conventional thermal and sonication-assisted methods, demonstrating high effectiveness. This study presents an efficient and novel process that reduces reaction times while utilizing fatty waste as a feedstock, aligning with the principles of green chemistry, the circular economy, and sustainable development. Full article

Terahertz (THz) sub-millimeter wave imaging offers unique capabilities for stand-off biometrics through concealment, yet it suffers from severe sparsity, low resolution, and high noise. To address these limitations, we introduce a novel unified Multi-Task Learning (MTL) network centered on a custom shared U-Net-like THz data encoder. This network is designed to simultaneously solve three distinct critical tasks on concealed THz facial data, given a limited dataset of approximately 1400 THz facial images of 20 different identities. The tasks include concealed face verification, facial posture classification, and a generative reconstruction of unconcealed faces from concealed ones. While providing highly successful MTL results as a standalone solution on the very challenging dataset, we further studied the expansion of this architecture via a cross-modal teacher-student approach. During training, a privileged visible-spectrum teacher fuses limited visible features with THz data to guide the THz-only student. This distillation process yields a student network that relies solely on THz inputs at inference. The cross-modal trained student achieves better latent space in terms of inter-class separability compared to the single-modality baseline, but with reduced intra-class compactness, while maintaining a similar success in the task performances. Both THz-only and distilled models preserve high unconcealed face generative fidelity. Full article

Road target detection is a crucial aspect of current research in automotive advanced driver assistance systems and intelligent transportation systems, where accuracy, speed, and lightweight design are key considerations. Compared to various sensors employed in driving assistance systems, millimeter-wave radar offers advantages such as all-weather operation, low hardware cost, strong penetration capability, and the ability to extract rich spatial information about targets. This paper tackles the challenges posed by the characteristics of Range-Angle map data from 77 GHz Frequency-Modulated Continuous Wave radar—namely, non-visible light imagery, abstract representation, rich fine details, and overlapping features. To this end, this paper proposes MMPFNet, a lightweight model based on the hypergraph mechanism with attention enhancement, as an extension of YOLOv13. First, an M-DSC3k2 module is proposed based on the hypergraph mechanism to enhance attention toward small targets. Second, a detection head with a double-bottleneck inverted MBConv-block structure is designed to improve the model’s accuracy and generalization capability. Third, a lightweight PPLConv module is customized to transform the backbone network, enhancing the model’s lightweight design while slightly reducing its accuracy. Considering the differences from traditional visible light datasets, the Focus Expansion-IoU loss function is introduced into the model to focus attention on different regression samples. The MMPFNet model achieves significant improvements in detecting common road targets such as pedestrians, bicycles, cars, and trucks on the Frequency-Modulated Continuous Wave radar Range-Angle dataset compared to the baseline YOLOv13n model: mAP50-95 increases by 16%, precision improves by 6%, and recall rises by 8.7%. MMPFNet is also evaluated on other non-visible light datasets such as CRUW-ONRD and soundprint datasets. Compared to commonly used detection models like FCOS and RetinaNet, MMPFNet achieves significant performance gains, attaining state-of-the-art results. Full article

People counting and occupancy monitoring play a vital role in applications such as intelligent building management, safety control, and resource optimization in future smart cities. Conventional camera and infrared-based methods often suffer from privacy risks, lighting dependency, and limited robustness in complex indoor environments. In this paper, we present a 60 GHz millimeter-wave (mmWave) radar-based occupancy monitoring system that enables accurate and privacy-preserving people counting. The proposed system leverages echo signals processed through Doppler and range spectrogram and analyzed by an enhanced ResNet-50 deep learning model to classify motion states and count individuals. Experimental results collected in a typical indoor environment demonstrate that the system achieves 95.45% accuracy across 6 classes of movements and 98.86% accuracy for people counting (0–3 persons). The method also shows strong adaptability under limited data and robustness to Gaussian blur interference, providing an efficient and reliable solution for intelligent indoor occupancy monitoring. Full article

With the widespread deployment of automotive millimeter-wave radars, mutual interference and broadband noise severely degrade the signal-to-noise ratio (SNR) of range–Doppler (RD) maps, leading to the loss of weak targets. Existing deep learning methods rely on difficult-to-obtain paired training samples and often cause excessive target smoothing due to a lack of physical constraints. To address these challenges, this paper proposes RIME-Net, a physics-guided unpaired learning framework designed to jointly achieve radar interference mitigation and weak target enhancement. First, based on a cycle-consistent adversarial architecture, we designed the Interference Mitigation Network (IM-Net). IM-Net integrates spectral consistency loss and identity mapping constraints, learning a robust mapping from the interference domain to the clean domain without paired supervision, effectively suppressing low-rank interference and preserving signal integrity. Second, to recover target details attenuated during denoising, we propose the saliency-aware Target Enhancement Network (TE-Net). TE-Net combines multi-scale residual blocks and channel-spatial attention mechanisms, selectively enhancing weak target features based on saliency priors. Extensive experiments on diverse datasets show that RIME-Net significantly outperforms existing supervised and model-driven methods in terms of SINR, recall, and structural similarity, providing a robust solution for reliable radar perception in complex electromagnetic environments. Full article

Terahertz waves are located in the “transition zone” between millimeter waves and infrared light. Terahertz video synthetic aperture radar (THz-ViSAR) utilizes the high operating frequency, strong radar cross-section intensity, and high azimuth repetition frequency of terahertz waves to detect and track ground moving targets. The conventional methods for detecting moving targets do not take into account the imaging characteristics of moving targets in THz-ViSAR. The constant false alarm rate (CFAR) detection method is used together with other methods to detect moving targets, resulting in unsatisfactory detection performance. This article proposes a new detection method for single channel slow-moving targets in THz-ViSAR based on shadows and light spots, which extracts the features of the shadow and spot areas of the moving target, and determines the position and direction of the moving target through the identification of the shadow and spot areas. The progressiveness of this method is verified by simulation and experimental tests. Full article

This paper presents a water-based reflect-transmit antenna (WBRTA) array for millimeter-wave (mm-wave) applications. The WBRTA array incorporates the low-permittivity polylactic acid (PLA)- and high-permittivity water-based unit cells. The low permittivity PLA unit cells provide better transmission, whereas the water-based unit cell offers good reflections due to a very high permittivity. Therefore, the WBRTA enables simultaneous beam splitting in reflection and transmission modes across a wider bandwidth. In addition, depending on the distribution and configuration of the water- and PLA-based unit cells, the WBRTA enables beam tilting of up to 45° in the reflection and transmission modes simultaneously. The proposed WBRTA offers peak gains of 25.2 dBi in transmission and 24 dBi in reflection at the central frequency. The corresponding sidelobe levels (SLLs) are −22 dB for transmission and −17 dB for reflection, while cross-polarization (x-pol) levels remain below −81 dB. In addition, the wide operational bandwidth, low sidelobe levels, and high polarization purity make the proposed WBRTA relevant as an enabling antenna structure for positioning-oriented sensing functions in future mmWave wireless systems. Full article

This work presents a comprehensive quantum transport modeling and simulation framework to evaluate parasitic effects and radio frequency (RF) performance in stacked silicon (Si) nanosheet (NS) lateral gate-all-around (LGAA) nFETs targeting the sub-2 nm technology node. Leveraging the non-equilibrium Green’s function (NEGF) method, the proposed framework integrates detailed modeling of parasitic resistances (R_para) and capacitances (C_para) to enable a holistic analysis of both intrinsic and extrinsic figures-of-merit, including transconductance (g_m), output conductance (g_d), cutoff frequency (f_T), and maximum oscillation frequency (f_max). The effects of nanosheet geometry, crystal orientations, and dual-k spacers on high-frequency performance are systematically investigated. The analysis reveals key design trade-offs, with optimized device configurations yielding f_T exceeding 400 GHz and f_max approaching 1.2 THz. These findings highlight the potential of stacked NS LGAA-nFETs for future millimeter-wave and terahertz applications, providing critical insights into parasitics management and quantum-transport-aware design strategies at advanced CMOS nodes. Full article

Non-contact measurement plays a crucial role in monitoring the heart rate of preterm and low birth weight infants in the neonatal intensive care unit (NICU). Addressing the challenges of weak heartbeat signals easily overwhelmed by noise in non-contact heart rate detection for these neonates, this paper proposes a millimeter-wave radar-based heart rate detection method using adaptive subband variable step-size least mean square (LMS) filtering. The innovative approach divides the chest echo signal into multiple subbands, employing an error-based variable step-size update strategy in each subband. By utilizing the abdominal signal as a reference, the heartbeat information is enhanced through adaptive filtering, and the results from various subbands are fused. The heart rate estimation is achieved by combining the fused results with time-frequency analysis using wavelet transform. Experimental results on data collected from multiple preterm infants in the NICU demonstrate that the proposed algorithm can reduce the root mean square error (RMSE) of preterm infant heart rate estimation to below 5 Beats Per Minute (BPM), providing a novel solution for the application of millimeter-wave radar in NICU heart rate monitoring. Full article

This paper presents a robust approach for achieving accurate indoor positioning and autonomous navigation of quadcopters through the fusion of multiple radars and an inertial measurement unit (IMU) named hybrid-filtered Radar–Inertial Odometry (Hybrid-RIO). The Hybrid-RIO system integrates four-directional Frequency-Modulated Continuous-Wave (FMCW) millimeter-wave (mmWave) radars simultaneously with a high-performance IMU to continuously estimate the quadcopters’ position, velocity, and orientation even in low-light and indoor environments. The autonomous flight commands from the system further enable indoor navigation without requiring human intervention. Experimental results reveal notable advancements in both the accuracy and consistency of positioning. The integration of the proposed Hybrid-RIO approach holds promise in a wide spectrum of domains, including cave exploration, tunnel rescue operations, and indoor navigation solutions. Full article

This work presents a millimeter-wave half-mode substrate integrated waveguide filter with high selectivity, using through glass via technology. Compared to a traditional printed circuit board, the benefits of high precision and integration afforded by the glass-based process enable the substrate-integrated waveguide to be employed at a higher operating frequency. A novel negative coupling structure is proposed for achieving a quasi-elliptic function response, and its coupling mechanism is investigated to explore the properties of the finite transmission zeros. The proposed coupling slots allow for flexible adjustment of the coupling between the half-mode substrate integrated waveguide cavities from positive to negative by modulating the corresponding geometrical parameters. As a prototype, a glass-based fourth-order bandpass filter is synthesized, simulated, fabricated and measured. Subsequently, good matching is captured, confirming the validity of the topology. The proposed glass-based negative coupling structure is promising for realizing substrate integrated waveguide filters with a quasi-elliptic function response, especially operating at millimeter-wave band. Full article

To address the tradeoff between environmental robustness and fine-grained accuracy in single-sensor human behavior recognition, this paper proposes a non-contact system fusing 77 GHz SIFT (mmWave) radar and a 40 kHz ultrasonic array. The system leverages radar’s long-range penetration and low-visibility adaptability, paired with ultrasound’s centimeter-level short-range precision and electromagnetic clutter immunity. A synchronized data acquisition platform ensures multi-modal signal consistency, while wavelet transform (for radar) and STFT (for ultrasound) extract complementary time–frequency features. The proposed Attention-CNN-BiLSTM architecture integrates local spatial feature extraction, bidirectional temporal dependency modeling, and salient cue enhancement. Experimental results on 1600 synchronized sequences (four behaviors: standing, sitting, walking, falling) show a 98.6% mean class accuracy with subject-wise generalization, outperforming single-sensor baselines and traditional deep learning models. As a privacy-preserving, lighting-agnostic solution, it offers promising applications in smart homes, healthcare monitoring, and intelligent surveillance, providing a robust technical foundation for contactless behavior recognition. Full article