Millimeter-wave radar has emerged as a preferred modality for privacy-preserving human activity recognition. However, the effectiveness of current deep learning approaches is often severely compromised by data scarcity and incomplete feature representation in complex, real-world environments. To address these challenges, this paper presents a novel meta-learning framework tailored for few-shot radar recognition. Structured time-frequency feature strategy is proposed, representing the systematic extraction of multi-spectral joint temporal-spectral features from raw IQ data. Unlike traditional passive mapping, this strategy utilizes structured temporal alignment to construct distinctive two-dimensional maps that actively compensate for information loss. Synergizing with this, we propose the SAMSCNN architecture, which departs from conventional layer stacking to employ a customized hierarchical multi-scale topology. This architecture innovatively integrates spatially-aware self-attention with meta-learning optimization, enabling the network to rapidly adapt to new tasks while preserving fine-grained signal structures. Experimental results demonstrate state-of-the-art performance, achieving 99.21% accuracy specifically in fall detection. Notably, under severe few-shot conditions with only five training samples, the method sustains an average accuracy of 90.24% across six distinct human activities, significantly outperforming existing methods. Extensive comparative analyses further confirm the framework’s superior robustness across varying scene configurations and environmental interference.

Deep learning has revolutionized medical image segmentation, yet its full potential remains constrained by the paucity of annotated datasets. While diffusion models have emerged as a promising approach for generating synthetic image-mask pairs to augment these datasets, they paradoxically suffer from the same data scarcity challenges they aim to mitigate. Traditional mask-only models frequently yield low-fidelity images due to their inability to adequately capture morphological intricacies, which can critically compromise the robustness and reliability of segmentation models. To alleviate this limitation, we introduce Siamese-Diffusion, a novel dual-component model comprising Mask-Diffusion and Image-Diffusion. During training, a Noise Consistency Loss is introduced between these components to enhance the morphological fidelity of Mask-Diffusion in the parameter space. During sampling, only Mask-Diffusion is used, ensuring diversity and scalability. Comprehensive experiments demonstrate the superiority of our method. Siamese-Diffusion boosts SANet’s mDice and mIoU by 3.6% and 4.4% on the Polyps, while UNet improves by 1.52% and 1.64% on the ISIC2018.

This article presents the design and miniaturization of a wideband monolithic microwave integrated circuit (MMIC) fully distributed efficient power amplifier (FDEPA). Distributed structure arrangement has been applied not only on the auxiliary power amplifier (PA) but also for the main PA. Hence, complicated and bandwidth-limited input matching networks (IMNs) and phase alignment networks are replaced by artificial transmission lines (ATMLs) to simplify the design and save the chip area. At the same time, due to the wide bandwidth characteristic of the distributed main PA, the power back-off (PBO) bandwidth shows good results compared with the conventional single common-source PA arrangement. Besides, a compact wideband on-chip quadrature hybrid power splitter is employed to achieve reasonable power division and phase control. As a proof of concept, an FDEPA prototype has been designed and fabricated in a commercial 0.25-μm GaN-on-SiC process. The chip size is only 3.6 mm×2.55 mm with all the necessary components. From the measurement results, throughout the working band of 3.1-5.5 GHz (56% fractional bandwidth), 40.3-41.6 dBm saturation output power (Psat), namely 1.16-1.58 W/mm2power density are achieved. The associated saturation drain efficiency (DE) is around 46%-55.4% and the 9-dB PBO DE is around 30.3%-47.2%. Under 100-MHz orthogonal frequency division multiplexing (OFDM) signal excitation with 8.5-dB peak-to-average power ratio (PAPR), 29.3%-44.5% average DE has been observed for the whole operating band, while the adjacent channel power ratio (ACPR) is better than -46.2 dBc with the digital pre-distortion (DPD).

This paper presents the design and analysis of dual-band monolithic microwave integrated circuit (MMIC) switches, including a single pole double throw (SPDT) and a single pole four throw (SP4T). With a novel on-chip coupled-line (OCL) topology, the input signal can be switched into low- or high-band paths to create dual-band characteristics. By carefully selecting the electrical lengths of OCLs and device size for corresponding shunt-FETs, the operating frequencies for low- and high-bands can be determined. This brings about improved insertion loss (IL) and isolation (ISO) in a compact structure. With the proposed techniques, two switch prototypes have been designed and fabricated in a 0.25-$\mu $m GaN-on-SiC process for high-power capability. The SPDT consists of a low-band path and a high-band path. It achieves an average IL/ISO of 1.0/32 dB with the best input 1-dB compression points (IP1dB) of 37.2 dBm at a low-band of DC-15 GHz; and an average IL/ISO of 2.0/28.5 dB with the best IP1dB of 32.8 dBm at a high-band of 20-40 GHz, respectively. The return loss is better than 11 dB for each port. The SP4T achieves a fully integrated dual-band transmit/receive (T/R) switch with doubled low-/high-band paths. It shows an average IL/ISO of 2.26/27.5 dB with the best IP1dB of 31.2 dBm at a low-band of 5-15 GHz; and an average IL/ISO of 2.7/26.5 dB with the best IP1dB of 30 dBm at a high-band of 20-30 GHz have been achieved, respectively. Better than 11.3 dB return loss is obtained for each port. The chip sizes are$1.8\times 0.9$mm2 for the SPDT and$2.2\times 1.7$mm2 for the SP4T.

This article presents the design theory and implementation of a fully integrated coupled-line-based load-modulated balanced amplifier (CLLMBA) monolithic microwave integrated circuit (MMIC). To facilitate the design, a novel design method is proposed for the CLLMBA to precisely control the load modulation and output power back-off (OBO) level by arranging the current ratio among the control amplifier (CA) and balanced amplifiers (BAs). Moreover, to further expand the working bandwidth, the coupled-line couplers are adopted in the CLLMBA. Subsequently, the physical dimensions and operating conditions of the three sub-amplifiers are selected accurately based on load modulation analysis at the fundamental frequency. It leads to properly modulated impedances and cancels the output matching networks for sub-amplifiers. Besides, meandering lange couplers are adopted by double metal layers and air-bridges for a compact layout. To validate the proposed techniques, a CLLMBA prototype is implemented and fabricated in a commercial 0.25-$\mu $m GaN HEMT process with the die size of$3.1\times 2.3$mm2. The measurement result exhibits a 38.1-39.3 dBm saturated output power with a 45.8%-57.6% saturated drain efficiency (DE), and a 31.7%-42.3% DE at 10-dB OBO from 4 to 6 GHz. Furthermore, under a 100 MHz orthogonal frequency division multiplexing (OFDM) signal with 8.5 dB peak-to-average power ratio (PAPR), the average DE is 32.8%-40.6% and the adjacent channel leakage ratio (ACLR) after digital predistortion is better than −47.5 dBc.

This brief presents a 360° tunable phase shifter (PS) with low phase error in a 0.25-$\mu $m GaN-on-SiC HEMT process. To achieve these features, the design incorporates two key innovations: a novel switched-bandpass phase-shifting cell (PSC) topology and a Q-learning-based optimization algorithm, both applied for the first time in monolithic microwave integrated circuit (MMIC) PS designs. The adverse effects of the charge trapping effect in GaN HEMT switches are mitigated by using a nonlinear equivalent circuit model. A PS prototype consisting of a fifth-order bandpass PSC and two third-order bandpass PSCs with a core area of$1.25\times 2.5$mm2 is designed, fabricated, and measured. Experimental results demonstrate a low rms phase error of less than 7.0°, along with high power linearity characterized by an IP$_{\mathrm {1\,dB}}$of 37 dBm and an IIP3 of 48 dBm, over a frequency range from 4.1 to 5.3 GHz.

Facial-expression-based pain assessment represents a promising computer vision application in medical diagnostics. However, improving the generalization capability of pain recognition models remains challenging due to the susceptibility of pain-related features to individual differences. The lack of large-scale pain-related datasets hinders the conventional approach relying on data diversity to enhance model generalization. Recent methods to solve this problem include feature descriptors for video classification and frame-level similarity calculation. The former captures better emotion-related features effectively, while the latter shows better generalization. We propose a two-stage framework combining the two strategies to address reference data limitations and ensure robust frame-level recognition. The initial stage introduces a simple but high-efficiency feature descriptor for emotion detail capture, which outputs a reference frame and an initial pain level score. In the second stage, we add a hash layer into the transformer and embed the transformer into the Siamese network to get the final pain scores by comparing the test frames with reference images obtained in the first stage. Experimental evaluations on UNBC McMaster and BioVid Heat Pain databases showcase our approach’s state-of-the-art accuracy, robust generalization, and faster processing speed.

Accurate lesion classification in Wireless Capsule Endoscopy (WCE) images is vital for early diagnosis and treatment of gastrointestinal (GI) cancers. However, this task is confronted with challenges like tiny lesions and background interference. Additionally, WCE images exhibit higher intra-class variance and inter-class similarities, adding complexity. To tackle these challenges, we propose Decoupled Supervised Contrastive Learning for WCE image classification, learning robust representations from zoomed-in WCE images generated by Saliency Augmentor. Specifically, We use uniformly down-sampled WCE images as anchors and WCE images from the same class, especially their zoomed-in images, as positives. This approach empowers the Feature Extractor to capture rich representations from various views of the same image, facilitated by Decoupled Supervised Contrastive Learning. Training a linear Classifier on these representations within 10 epochs yields an impressive 92.01% overall accuracy, surpassing the prior state-of-the-art (SOTA) by 0.72% on a blend of two publicly accessible WCE datasets. Code is available at: https://github.com/Qiukunpeng/DSCL.

This paper presents a new design method for phase shifters that, for the first time, manipulates out-of-band transmission zeros (TZs) generated by shunt resonators to achieve the phase reconfiguration. Compared with conventional designs, the proposed out-of-band reconfiguration approach minimizes the impact on in-band performance during the reconfiguration, thereby substantially reducing in-band phase errors and magnitude ripples. With a derived closed-form phase slope formula, theoretical analysis is given to guide the unit design. Meanwhile, also based on this formula, the phase slope alignment is achieved for the entire phase shifter by utilizing the Particle Swarm Optimization (PSO) algorithm to optimize each individual resonator. To validate the proposed design method, two$C$-band prototypes with different circuit topologies are designed, fabricated and measured. Both prototypes demonstrate a low phase error ($\pm$15.6$^{\circ}$and$\pm$5.0$^{\circ})$and magnitude ripple (0.7 dB and 0.6 dB), across a 360$^{\circ}$phase tuning range, surpassing most existing phase shifters. This work is expected to be a good candidate for modern industrial applications including phased array radars, high precision phase modulators and other intelligent communication or sensing systems.

regularization model is hindered by their hypothesis of inherent sparsity, causing unreal estimations of surface-like targets. Inspired by the edge-preserving property of total variation (TV), we propose a new complex-valued TV (CTV)-driven interpretable neural network with nested topology, i.e., CTV-Net, for 3-D SAR imaging. In our scheme, based on the 2-D holography imaging operator, the CTV-driven optimization model is constructed to pursue precise estimations in weakly sparse scenarios. Subsequently, a nested algorithmic framework, i.e., complex-valued TV-driven fast iterative shrinkage thresholding (CTV-FIST), is derived from the theory of proximal gradient descent (PGD) and FIST algorithm, theoretically supporting the design of CTV-Net. In CTV-Net, the trainable weights are layer-varied and functionally relevant to the hyperparameters of CTV-FIST, which aims to constrain the algorithmic parameters to update in a well-conditioned tendency. All weights are learned by end-to-end training based on a two-term cost function, which bounds the measurement fidelity and TV norm simultaneously. Under the guidance of the SAR signal model, a reasonably sized training set is generated, by randomly selecting reference images from the MNIST set and consequently synthesizing complex-valued label signals. Finally, the methodology is validated, numerically and visually, by extensive SAR simulations and real-measured experiments, and the results demonstrate the viability and efficiency of the proposed CTV-Net in the cases of recovering 3-D SAR images from incomplete echoes.

In this article, a fully integrated 1.0–11.0-GHz wideband distributed power amplifier (DPA) monolithic microwave integrated circuit (MMIC) design is presented. Particularly, a coupled technique with bandpass (CTB) characteristic between the kth output node and the ($k+1$)th input node of amplification units (AUs) is adopted in the DPA design. It generates an additional signal reuse path (SRP) to reuse part of the output signal to superimpose the input signal, and then they will be reamplified to the output artificial transmission line (O-ATML). Moreover, due to the bandpass characteristic, the signal reuse can be manipulated to target the upper cutting edges of the working band to alleviate sharp gain and power roll-off. By carefully controlling the SRP, the overall gain, output power, and bandwidth are enhanced and extended. The systematic design approach for the DPA is detailed with circuit implementations and optimizations. To validate the proposed concept, a DPA MMIC prototype is implemented and fabricated in a commercial 0.25-$\mu $m gallium nitride (GaN)-on-silicon carbide (SiC) high-electron-mobility transistor (HEMT) process. It shows the compact layout within a die size of 3.36 mm2. Under 28-V VDD power supply, the measured results show a flat$14.8\pm 1.0$-dB small-signal gain with 10.0-GHz wide operating bandwidth and good impedance matching conditions. A saturated output power (${P} _{\text {sat}}$) of 7.25 W with peak power-added efficiency (PAE) exceeding 38.7% is achieved. The proposed DPA obtains around 1.54–2.16-W/mm2 power density associated with an average PAE of 34.5% over the entire frequency range.

Digital beamforming is a crucial technology that enables fifth-generation (5G) devices to operate on millimeter wave (mmW) radio frequencies. However, linearizing massive arrays at the transmitter (TX) side remains a significant challenge as the complexity scales with the number of antennas. This work focuses on the orthogonal frequency division multiplexing (OFDM) modulation in 5G and beyond networks and presents low-complexity linearization approaches that can be used before the precoder. These approaches do not scale with the number of antennas and provide flexible control of the linearization performance across different parts of the spectrum. Simulation results for a 64-element uniform linear array (ULA) demonstrate that the proposed techniques can achieve performance comparable to conventional multi-digital pre-distortion (DPD) with only 8.91% and 3.52% of the complexity.

The Internet of Things (IoT) technologies reserves a large latent capacity in dealing with the emerging fall detection problem of elder people. The radar-based IoT methods are considered one of the optimum solutions to indoor fall detection problems. In this article, a millimeter-wave frequency modulated continuous wave (FMCW) radar-based fall detection method using the pattern contour vector (PCV) is proposed. The soft fall motions, which were not considered in most previous literature, are studied and analyzed. The motion attributes of velocity, intensity, and trajectory can distinguish sudden and soft fall motions from nonfall ones. PCVs of Doppler time (DT) map (DT-PCV), regional Power Burst Curve (rPBC), and PCVs of range time (RT) map (RT-PCV), interpreting the aforementioned attributes, respectively, are used as the inputs of the two convolutional neural networks (CNNs). The experimental results show that the proposed method can detect sudden and soft fall motions with high accuracy, sensitivity, and specificity.

This article presents a 3-stage millimeter-wave low-noise amplifier (LNA) monolithic microwave integrated circuit (MMIC) design for broadband applications. The proposed structure consists of cascading common-source (CS) LNA with coupled interstage feedback (CIF) paths at the second and third stages. By tuning the coupling factor and the inductance of the CIF, enhanced overall gain and extended working bandwidth can be achieved simultaneously. The proposed CIF formed by a simple on-chip edge-coupled parallel metal-lines features small size and high flexibility. To facilitate circuit design and optimization, an equivalent circuit for the proposed CIF structure is developed. Besides, to address the trade-off between impedance matching and noise performance, a noise matching strategy focusing more on higher frequencies is presented to achieve good noise performance. With the proposed techniques, an LNA prototype is demonstrated in a commercial 0.15-$\mu \text{m}$GaAs E-mode pHEMT process. The fabricated LNA has a die size of 1.15 mm2 and DC power consumption of 109 mW. The measurement results show a peak gain of 25.6 dB with a 3-dB bandwidth of 9~42 GHz, the minimal noise figure (NF) of 1.91 dB, a group delay of 68.7± 20.5 ps, and 20.8 dBm best OIP3 under 2.0-V VDD.

In this paper, a 2-stage compact wideband low-noise amplifier (LNA) monolithic microwave integrated circuit (MMIC) with multiple resistive feedback (MRFB) is presented. From the DC point of view, the proposed MRFB functions as a self-biasing structure to bias transistors in the optimal condition, improving the noise figure (NF) and linearity. Meanwhile, by employing MRFB with source degeneration and input inductor, the proposed LNA achieves wideband flat gain at the AC side. In comparison with traditional topologies, a wide bandwidth of more than 11.5 GHz with low noise figure of less than 2.5 dB can be achieved. To verify the proposed LNA structure, a chip prototype is fabricated in a 0.15-$\mu \text{m}$GaAs E-mode pHEMT process with a compact die size of only 0.75 mm2 including all the testing pads. From the measurement results, the proposed LNA circuit features a 1.0 to 12.5 GHz 3-dB working bandwidth (172% fractional bandwidth), 23.6 peak gain, 1.51 dB minimum NF, 66.7± 15 ps group delay, and 24.3/12.6 dBm best OIP3/OP1dB, respectively. The total DC power is around 87.5 mW from a single 2.5-V power supply.

Motivated by the necessity of acquiring wall parameters for through-the-wall radar, a novel and general time domain model is proposed to measure the thickness and permittivity of single-layered slab-shaped materials, by exploiting the delays of the two surface reflections in the bistatic radar scheme. First, the two surface delays are formulated as functions of the unknown permittivity and thickness, as well as the accessible incident angle, and a nonlinear equation set is formed. Then based on a geometric analysis, in two separate bistatic delay tests with different antenna separations and standoff distances, the condition of identical incident angle is established. As such, the intra-wall delay is the same for the two delay tests, leading to a significant simplification and a closed-form solution to the equation set. Finally, a three-antenna test setup is constructed, with which the desired parameters can be acquired conveniently and accurately by performing bistatic tests at a set of standoff distances. Simulation and experiment show that our method can achieve high accuracy and strong robustness against noise.

Mathematically, 3-D synthetic aperture radar (SAR) imaging is a typical inverse problem, which, by nature, can be solved by applying the theory of sparse signal recovery. However, many reconstruction algorithms are constructed by exploring the inherent sparsity of imaging space, which may cause unsatisfactory estimations in weakly sparse cases. To address this issue, we propose a new perceptual learning framework, dubbed as PeFIST-Net, for 3-D SAR imaging, by unfolding the fast iterative shrinkage-thresholding algorithm (FISTA) and exploring the sparse prior offered by the convolutional neural network (CNN). We first introduce a pair of approximated sensing operators in lieu of the conventional sensing matrices, by which the computational efficiency is highly improved. Then, to improve the reconstruction accuracy in inherently nonsparse cases, a mirror-symmetric CNN structure is designed to explore an optimal sparse representation of roughly estimated SAR images. The network weights control the hyperparameters of FISTA by elaborated regularization functions, ensuring a well-behaved updating tendency. Unlike directly using pixelwise loss function in existing unfolded networks, we introduce the perceptual loss by defining loss term based on high-level features extracted from the pretrained VGG-16 model, which brings higher reconstruction quality in terms of visual perception. Finally, the methodology is validated on simulations and measured SAR experiments. The experimental results indicate that the proposed method can obtain well-focused SAR images from highly incomplete echoes while maintaining fast computational speed.

In the research topic of three-dimensional (3D) SAR imaging, the sparsity-enforcing techniques offer promise in shortening sensing time and improving reconstruction accuracy. However, many of them only explore the sparse prior of 3D SAR images, which leads to biased estimations in cases of non-sparse scenarios. To remedy this problem, we propose a new network with learned low-rank and sparse priors, i.e., LLRS-Net, to obtain improved reconstructions from sparsely sampled 3D SAR echoes. In our scheme, a two-stage reconstruction algorithmic framework (LSRA) is derived based on sparse and low-rank priors. Wherein, the first stage recovers the measurements from their limited observations by exploring the low-rank prior, while the second estimates the final 3D SAR images with a fast-iterative optimization. Theoretically inspired by LRSA, the LLRS-Net is designed into a cascaded network structure. In LLRS-Net, the trainable weights serve as independent variables and control the algorithmic hyper-parameters via regularizing functions, ensuring a well-conditioned updating tendency. By end-to-end training, the network weights are updated automatically under the guidance of a compound loss function constraining both the outputs of two stages. Finally, the methodology is validated on simulations and measured experiments. These results show that the proposed framework outperforms many state-of-the-art imaging algorithms in recovering 3D SAR images from incomplete echo data.

Inevitable inaccuracies of 3-D synthetic aperture radar (3-D SAR) imaging geometry may cause undesired blurs in reconstructed images. Recent advances show impressive results in integrating error estimation into sparse imaging. However, the concept is still challenging in 3-D SAR due to the cumbersome high-dimensional processing. To address this problem, we propose a model-driven 3-D SAR autofocusing network with learned sparsity (AFLS-Net) by applying the recent emerging deep unfolding technique. In our scheme, we first construct a kernel-based observation model with consideration of motion-induced phase errors, which avoids the memory-consuming matrix calculations in the conventional matrix–vector form. Then, a joint sparse imaging and autofocusing algorithm is derived based on the framework of block coordinate descent. In addition, by mapping the computational steps, the AFLS-Net is designed to further improve the autofocusing accuracy and efficiency in which a shallow two-path convolutional neural network (CNN) is embedded to explore the implicit sparse prior, by which the reconstruction accuracy can be improved. Meanwhile, the batchwise autofocusing module is designed to obtain a robust estimation by jointly optimizing subcost functions associated with a batch of independent measurements. Finally, the methodology is validated in both simulations and laboratory 3-D SAR experiments. The experimental results suggest that the proposed method obtains better autofocusing quality compared to other comparison baselines in reconstructing 3-D SAR images from incomplete and error-polluted echoes.

There has been a strong interest in using deep neural networks (DNNs) for modeling the power amplifier (PA) non-linearity and designing the digital pre-distortion (DPD) circuit. Most DNNs only accept real-valued inputs since the baseband signal has in-phase and quadrature (I/Q) components. As a result, their entire structures can be highly complex. In this paper, we are interested in reducing the complexity of such structures by exploiting both the envelope-dependent terms and residual learning. To acquire such an efficient structure, we propose a novel methodology executed over two consecutive steps; at first, we estimate the best input combinations to a shallow NN that allows it to achieve a threshold value of NMSE. Then, we exploit these combinations as inputs to our proposed structure and increase the network depth until we obtain our system's actual requirements. Finally, our optimized structure (ODNN) performance has been evaluated using MATLAB simulation and real measurements. For a 15 MHz test signal, ODNN achieves lower NMSE than conventional DNN by 2.13 dB and 3.08 dB for Doherty PA behavioral modeling and its DPD design, respectively. For a broader 40 MHz test signal, ODNN achieves lower NMSE by 0.94 dB and 1.94 dB. Moreover, in all previous scenarios, ODNN reduces the complexity of DNN by 26.40%.

A practical power amplifier (PA) has nonlinear characteristics that distort the output signal and hence increase the transmission error. Digital pre-distortion (DPD) has been widely accepted to compensate for the PA nonlinearity. However, in direct-conversion transmitters (DCTs), DPD performance is affected by in-phase and quadrature (IQ) imbalance. In this paper, we utilize the Iterative Learning Control (ILC) algorithm to design a DPD scheme to compensate for PA nonlinearity under IQ imbalance. We first prove that ILC is applicable in such a scenario. This proof is validated using simulations which show that ILC is able to estimate the PA ideal input. The estimated ideal input is then exploited in training a neural network (NN)-based DPD model. We provide the complexity estimation of our proposed scheme using the number of real multiplications. Finally, we demonstrate the performance advantage of our proposed scheme in comparison with other existing polynomial based approaches through simulations and measurements.

Three-dimensional-printing technologies have been receiving great attention for a wide variety of applications in recent years for cost effectiveness, eco-friendliness, and process simplicity in complicated structures. This paper describes the challenges and solutions of applying 3-D-printing technologies in fabricating passive millimeter-wave (mmWave) and terahertz (THz) devices. First, we review the state-of-the-art dielectric 3-D-printed passive mmWave and THz devices. Then, we focus on our novel 3-D-printed metallic passive mmWave and THz devices such as horn antennas and waveguides. Next, we analyze the dimensional tolerance and surface roughness of various 3-D-printing technologies in order to provide a guide for choosing an appropriate technology for specific applications. Finally, we summarize the current work and identify the future studies in material powder refinement, surface treatment, optimization of the print process, development of hybrid dielectric and metallic 3-D-printing technology for realizing not only simple mmWave and THz devices but also sophisticated mmWave and THz systems.

Wireless power delivery and telemetry have enabled completely implantable neural devices. Current day implants are controlled, monitored, and powered wirelessly, eliminating the need for batteries and prolonging the lifetime. A brief overview of wireless platforms for such implantable devices is presented in this paper alongside an in-depth discussion of wireless platform for peripheral nerve implants covering design requirements, link design, and safety. Initial acute studies on the performance of the wireless power and data links in rodents are also presented.

In this design contest, the design methodology leading to a high performance Millimeter-wave amplifier in 0.13 μm SiGe BiCMOS is elaborated. Equivalent circuit models of the utilized cascode shielding structure are developed to assist the amplifier design. Meanwhile, final layouts of the passive connections are verified by 3D electromagnetic simulation in ANSYS HFSS. The implemented amplifier obtained a gain more than 45 dB in band, which is the gain record of silicon-based amplifiers in W-band.

In this paper, a novel wideband circularly polarized slot antenna is presented. The slot antenna with four microstrip line feeds orientated to have phases of 0deg, 90deg, 180deg and 270deg, using a feed network comprising a pair of broadband 900 hybrid was found to deliver a measured impedance bandwidth of 77.8% from 1.02 to 2.32 GHz for SWR3dBi. The simulated impedance bandwidth is 89.1% from 1.02 to 2.66 GHz for SWR3dBi. Good agreement is observed between simulation and measurement.