Search Results (231)

This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of $-55\mathrm{dBm}$ and a conversion gain varying from $-2.5\mathrm{dB}$ to $-9\mathrm{dB}$ depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications. Full article

XSPICE models for a generic transmission line, a microstrip line, and coupled microstrips are presented. The developed models extend general-purpose circuit simulation tools using RF circuits design features. The models could be used for circuit simulation in frequency, DC, and time domains for any active or passive RF or microwave schematic (including microwave monolithic integrated circuits—MMICs) involving transmission lines. The presented models could be used with any circuit simulation backend supporting XSPICE extensions and could be integrated without patching the core simulator code. The presented XSPICE models for microstrip lines take into account the frequency dependency of characteristic impedance and dispersion. The models were designed using open-source circuit simulation software. This study provides a practical example of the low-noise RF amplifier (LNA) design with Ngspice simulation backend using the proposed models. Full article

Two-dimensional (2D) materials have been proposed for use in a multitude of applications, with graphene being one of the most well-known 2D materials. Despite their potential to contribute to innovative solutions, the fabrication of such devices still faces significant challenges. One of the key challenges is the fabrication at a wafer-level scale, a fundamental step for allowing reliable and reproducible fabrication of a large volume of devices with predictable properties. Overcoming this barrier will allow further integration with sensors and actuators, as well as enabling the fabrication of complex circuits based on 2D materials. This work presents the fabrication steps for a process that allows the on-wafer fabrication of active and passive radiofrequency (RF) devices enabled by graphene. Two fabrication processes are presented. In the first one, graphene is transferred to a back gate surface using critical point drying to prevent cracks in the graphene. In the second process, graphene is transferred to a flat surface planarized by ion milling, with the gate being buried beneath the graphene. The fabrication employs a damascene-like process, ensuring a flat surface that preserves the graphene lattice. RF transistors, passive RF components, and antennas designed for backscatter applications are fabricated and measured, illustrating the versatility and potential of the proposed method for 2D material-based RF devices. The integration of graphene on devices is also demonstrated in an antenna. This aimed to demonstrate that graphene can also be used as a passive device. Through this device, it is possible to measure different backscatter responses according to the applied graphene gating voltage, demonstrating the possibility of wireless sensor development. With the proposed fabrication processes, a flat graphene with good quality is achieved, leading to the fabrication of RF active devices (graphene transistors) with intrinsic f_T and f_max of 14 GHz and 80 GHz, respectively. Excellent yield and reproducibility are achieved through these methods. Furthermore, since the graphene membranes are grown by Chemical Vapor Deposition (CVD), it is expected that this process can also be applied to other 2D materials. Full article

Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R² increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R² = 0.86, RMSE = 2.55%). Full article

This paper presents a Virtualized Computational Radio Frequency Identification (VCRFID) solution that utilizes far-field UHF RF for sensing, computing, and self-powering at the edge. A standard UHF RFID system is asymmetric as it consists of a relatively large, complex “reader”, which acts as an RF transmitter and controller for a number of small simple battery-less “tags”, which work in passive mode as they communicate and harvest RF energy from the reader. Previously proposed Computational RFID (CRFID) solutions enhance the standard RFID tags with microcontrollers and sensors in order to gain enhanced functionality, but they end up requiring a relatively high level of power, and thus ultimately reduced range, which limits their use for many Internet-of-Things (IoT) application scenarios. Our VCRFID solution instead keeps the functionality of the tags minimalistic by only providing a sensor interface to be able to capture desired environmental data (temperature, humidity, vibration, etc.), and then transmit it to the RFID reader, which then performs all the computational load usually carried out by a microcontroller on the tag in prior work. This virtualization of functions enables the design of a circuit without a microcontroller, providing greater flexibility and allowing for wireless reconfiguration of tag functions over RF for a 97% reduction in energy consumption compared to prior energy-harvesting RFID tags with microcontrollers. The target application is Industry 4.0 where our VCRFID solution enables battery-less fine-grain monitoring of vibration and temperature data for pumps and motors for predictive maintenance scenarios. Full article

The increasing demand for high-capacity, energy-efficient wireless networks poses significant challenges in maintaining spectral efficiency, minimizing interference, and ensuring sustainability. Traditional direct-link communication suffers from signal degradation due to path loss, multipath fading, and interference, limiting overall performance. To mitigate these challenges, this paper proposes a unified RIS framework that integrates passive and active Reconfigurable Intelligent Surfaces (RISs) for enhanced communication and ambient RF energy harvesting. Our methodology optimizes RIS-assisted beamforming using successive convex approximation (SCA) and adaptive phase shift tuning, maximizing desired signal reception while reducing interference. Passive RIS efficiently reflects signals without external power, whereas active RIS employs amplification-assisted reflection for superior performance. Evaluations using realistic urban macrocell and mmWave channel models reveal that, compared to direct links, passive RIS boosts SNR from 3.0 dB to 7.1 dB, and throughput from 2.6 Gbps to 4.6 Gbps, while active RIS further enhances the SNR to 10.0 dB and throughput to 6.8 Gbps. Energy efficiency increases from 0.44 to 0.67 (passive) and 0.82 (active), with latency reduced from 80 ms to 35 ms. These performance metrics validate the proposed approach and highlight its potential applications in urban 5G networks, IoT systems, high-mobility scenarios, and other next-generation wireless environments. Full article

This paper presents a highly linear two-stage InGaP/GaAs power amplifier integrated circuit (PAIC) using a dynamic predistorter for 5G small-cell applications. The proposed predistorter, based on a diode-connected transistor, utilizes a supply voltage to accurately control the linearization characteristics by adjusting its dc current. It is connected in parallel with an inter-stage of the two-stage PAIC through a series configuration of a resistor and an inductor, and features a shunt capacitor at the base of the transistor. These passive components have been optimized to enhance the linearization performance by managing the RF signal’s coupling to the diode. Using these optimized components, the AM−AM and AM−PM nonlinearities arising from the nonlinear resistance and capacitance in the diode can be effectively used to significantly flatten the AM−AM and AM−PM characteristics of the PAIC. The proposed predistorter was applied to the 2.6 GHz two-stage InGaP/GaAs HBT PAIC. The IC was tested using a 5 × 5 mm² module package based on a four-layer laminate. The load network was implemented off-chip on the laminate. By employing a continuous-wave (CW) signal, the AM−AM and AM−PM characteristics at 2.55–2.65 GHz were improved by approximately 0.05 dB and 3°, respectively. When utilizing the new radio (NR) signal, based on OFDM cyclic prefix (CP) with a signal bandwidth of 100 MHz and a peak-to-average power ratio (PAPR) of 9.7 dB, the power-added efficiency (PAE) reached at least 11.8%, and the average output power was no less than 24 dBm, achieving an adjacent channel leakage power ratio (ACLR) of −40.0 dBc. Full article

In the context of the booming development of precision agriculture and plant phenotyping, plant 3D reconstruction technology has become a research hotspot, with widespread applications in plant growth monitoring, pest and disease detection, and smart agricultural equipment. Given the complex geometric and textural characteristics of plants, traditional 2D image analysis methods are difficult to meet the modeling requirements, highlighting the growing importance of 3D reconstruction technology. This paper reviews active vision techniques (such as structured light, time-of-flight, and laser scanning methods), passive vision techniques (such as stereo vision and structure from motion), and deep learning-based 3D reconstruction methods (such as NeRF, CNN, and 3DGS). These technologies enhance crop analysis accuracy from multiple perspectives, provide strong support for agricultural production, and significantly promote the development of the field of plant research. Full article

BAW filters have been widely used in RF circuits, and their combination with integrated passive inductors is one of the most common forms of BAW filters. However, the large size of passive inductors increases the area of the filter, making it unable to meet packaging requirements. At the same time, their low quality factor (Q) severely degrades the performance of the BAW filter. This paper presents a miniaturized wide band BAW filter with small-size high-Q active inductor. The active inductor is implemented by a circuit topology with three common-source amplifiers constructed with N-type transistors. The three-stage topology uses a small-size transistor in the middle stage to reduce the parasitic capacitance at the input node, achieving a large inductive bandwidth. The simulation results show that the active inductor has variable inductance from 1 nH to 10 nH, and a quality factor of up to 4 K from 2 to 7 GHz. The 30 × 30 μm² active inductor is embedded in a 4.55–5.05 GHz BAW filter ladder so as to substantially decrease filter size. Simulation results indicate that the BAW filter based on the active inductor achieves a low insertion loss of −1.1 dB, out-of-band rejection of −35 dB on the left side, and out-of-band rejection of −53 dB on the right side. Compared to the traditional passive inductor, this active inductor significantly improves the performance of the BAW filter while occupying a much smaller chip size of 0.83 × 0.75 mm². Full article

High-voltage direct-current (HVdc) transmission lines are gaining more attention as an integral part of modern power system networks. Monitoring the dc current is important for metering and the development of dynamic line rating control schemes. However, this has been a challenging task, and there is a need for wireless sensing methods with high accuracy and a dynamic range. Conventional methods require direct contact with the high-voltage conductors and utilize bulky and complex equipment. In this paper, an ultra-high-frequency (UHF) radio frequency identification (RFID)-based sensor is introduced for the monitoring of the dc current of an HVdc transmission line. The sensor is composed of a passive RFID tag with a custom-designed antenna, integrated with a Hall effect magnetic field device and an RF power harvesting unit. The dc current is measured by monitoring the dc magnetic field around the conductor using the Hall effect device. The internal memory of the RFID tag is encoded with the magnetic field data. The entire RFID sensor can be wirelessly powered and interrogated using a conventional RFID reader. The advantage of this approach is that the sensor does not require batteries and does not need additional maintenance during its lifetime. This is an important feature in a high-voltage environment where any maintenance requires either an outage or special equipment. In this paper, the detailed design of the RFID sensor is presented, including the antenna design and measurements for both the RFID tag and the RF harvesting section, the microcontroller interfacing design and testing, the magnetic field sensor calibration, and the RF power harvesting section. The UHF RFID-based magnetic field sensor was fabricated and tested using a laboratory experimental setup. In the experiment, a 40 mm-diameter-aluminum conductor, typically used in 500 kV HVdc transmission lines carrying a dc current of up to 1200 A, was used to conduct dc current tests for the fabricated sensor. The sensor was placed near the conductor such that the Hall effect device was close to the surface of the conductor, and readings were acquired by the RFID reader. The sensitivity of the entire RFID sensor was 30 mV/mT, with linear behavior over a magnetic flux density range from 0 mT to $4.5$ mT. Full article

This article presents a comprehensive analysis of the technical characteristics of advanced versatile materials used in chipless radio-frequency identification (RFID) tags and antennas. The focus is on materials that are used as radiators and substrates. Crucial aspects include flexibility, weight, size, gain, environmental sustainability, efficiency, fabrication time and type, and cost. A comprehensive set of tables are presented that summarize and compare material properties. The materials include flexible high-tech ink substances, graphene, and liquid crystals, as well as metamaterials which possess properties that allow for an increased bandwidth. Printing techniques are discussed for high-performance high-resolution fabricated tags. This paper contributes by systematically comparing emerging materials for chipless RFID tags, highlighting their impact on performance and sustainability. It also provides practical guidance for material selection and fabrication techniques to enable next-generation wireless applications. It presents a broad understanding of various materials and their use. The paper provides direction for the deployment and utilization of inexpensive passive chipless RFID tags in future intelligent wireless networks. The advancement of chipless RFID is largely driven by the development of innovative materials, especially in the realm of advanced materials and smart materials, which enable the creation of more cost-effective, flexible, and scalable RFID systems. Full article

Amplitude equalizers play an important role in microwave transmission systems, and their performance can directly improve the signal transmission quality. Nowadays, the miniaturized equalizer is in good need of a highly integrated radio frequency (RF) front end. In this paper, a wafer-level amplitude equalizer based on an integrated passive device (IPD) process is proposed. Moreover, the equalizer circuit containing two resonance points within the transmission frequency band is also proposed. The amplitude equalizer operates at the center frequency of 3.88 GHz, with two resonance points of 2.9 GHz and 5.2 GHz; the minimum insertion loss is 1.17 dB, the maximum attenuation is 4 dB, and the in-band voltage standing wave ratio (VSWR) is less than 1.6:1. All the measured results are in good agreement with the designed results. The size of the proposed equalizer is 0.8 mm × 0.65 mm × 0.1 mm (0.011λ × 0.008λ × 0.001λ), which shows great potential in the miniaturization application of the RF microsystem. Furthermore, the new equalizer circuit with two resonance points is especially suitable for wavy in-band transmission. Full article

In this paper, package integration of glass–based passive components for 5G new radio (NR) millimeter–wave (mm wave) bands and an analysis of their system performance are presented. Passive components such as diplexers and couplers covering 5G NR mm wave bands n257, n258 and n260 are modeled, designed, fabricated and characterized individually along with their integrated versions. Non–contiguous diplexers are designed using three different types of filters, hairpin, interdigital and edge–coupled, and combined with a broadband coupler to emulate a power detection and control circuitry block in an RF transmitter chain. A panel–compatible semi–additive patterning (SAP) process is utilized to form high–precision redistribution layers (RDLs) on laminated glass substrate, onto which fine features with tight tolerance are added to fabricate these structures. The diplexers exhibit low insertion loss, low VSWR and high isolation, and have a small footprint. A system performance analysis using a co–simulation technique is presented for the first time to quantify the distortion in amplitude and phase produced by the fabricated passive component block in terms of error vector magnitude (EVM). Moreover, the scalability of this approach to compare similar passive components based on their specifications and signatures using a system–level performance metric such as EVM is discussed. Full article

In this work, AlGaN/GaN high-electron-mobility transistors (HEMTs) with a nanocrystalline diamond (NCD)/SiN_x trench dual-passivated (TDP) structure were promoted, which demonstrated superior performance with a higher saturation output current (I_dss) of 1.266 A/mm, a higher maximum transconductance (G_mmax) of 0.329 S/mm, and a lower resistance (R_on) of 2.64 Ω·mm. Thermal simulations revealed a peak junction temperature of 386.36 K for TDP devices under V_ds/V_gs = 30 V/0 V, representing 13.7% and 4.5% reductions versus SiN_x single-passivated (SP, 447.59 K) and dual-passivated (DP, 404.58 K) devices, respectively. The results suggested that compared to conventional SP and DP devices, TDP devices can effectively suppress the self-heating effect, thereby improving output characteristics while maintaining superior RF small-signal characteristics. Moreover, the results of numerical simulations indicated that the enhanced electrothermal performance of TDP devices was predominantly attributed to the mitigation of temperature-induced degradation in electron mobility and drift velocity, thereby preserving their high power and high frequency capabilities. These results highlighted the significant potential of TDP devices to improve the performance of GaN HEMTs in high-power and high-frequency applications. Full article

The accurate estimation of forest aboveground biomass (AGB) is essential for effective forest resource management and carbon stock assessment. However, the estimation accuracy of forest AGB is often constrained by scarce in situ measurements and the limitations of using a single data source or retrieval model. This study proposes a multi-source data integration framework using Sentinel-1 (S-1) and Sentinel-2 (S-2) data along with eight predictive models (i.e., multiple linear regression—MLR; Elastic-Net; support vector regression (with a linear kernel and polynomial kernel); k-nearest neighbor; back-propagation neural network—BPNN; random forest—RF; and gradient-boosting tree—GBT). With airborne light detection and ranging (LiDAR)-derived AGB as a reference, a three-stage optimization strategy was developed, including stepwise feature selection (SFS), hyperparameter optimization, and multi-decision vector fusion (MDVF) model construction. Initially, the optimal feature subsets for each model were identified using SFS, followed by hyperparameter optimization through a grid search strategy. Finally, eight models were evaluated, and MDVF was implemented to integrate outputs from the top-performing models. The results revealed that LiDAR-derived AGB demonstrated a strong performance (R² = 0.89, RMSE = 20.27 Mg/ha, RMSE_r = 15.90%), validating its effectiveness as a supplement to field measurements, particularly in subtropical forests where traditional inventories are challenging. SFS could adaptively select optimal variable subsets for different models, effectively alleviating multicollinearity. Satellite-based AGB estimation using the MDVF model yielded robust results (R² = 0.652, RMSE = 31.063 Mg/ha, RMSE_r = 20.4%) through the synergy of S-1 and S-2, with R² increasing by 4.18–7.41% and the RMSE decreasing by 3.55–5.89% compared to the four top-performing models (BPNN, GBT, RF, MLR) in the second optimization stage. This study aims to provide a cost-effective and precise strategy for large-scale and spatially continuous forest AGB mapping, demonstrating the potential of integrating active and passive satellite imagery with airborne LiDAR to enhance AGB mapping accuracy and support further ecological monitoring and forest carbon accounting. Full article