Search Results (311)

This paper presents a novel method for integrating multiple filters into a single board that can be reconfigured through design modifications. The primary objective is to introduce a scalable three-in-one filter, referred to as a triplexer, suitable for diverse applications. The proposed filter is well-suited to applications such as multi-band RF front ends, software-defined radios (SDRs), test instrumentation requiring selectable responses, and compact wireless sensor nodes. The manuscript develops a mathematical model for each filter, enabling adjustment of the cutoff frequency to different values. The model is then expanded to capture the interactions among the three filters and is validated in MATLAB. An experimental four-port filter sample is fabricated to validate the concept. It comprises a $2.85$ GHz low-pass filter (LPF), a $5.10$ GHz band-pass filter (BPF), and a $6.30$ GHz high-pass filter (HPF). The proposed triplexer is designed using step impedance and coupled lines, providing a systematic design approach suitable for various applications due to its adaptability and straightforward structure. The methodology includes calculations in MATLAB, full-wave EM simulation, fabrication on RT/Duroid 5880, and measurements with a four-port network analyzer. The measured results show strong agreement with both calculated and simulated results. Full article

We present a circular complementary split ring resonator (CCSRR) flexible antenna operating in the 1.4 GHz radio-astronomy quiet frequency band. The antenna is designed for microwave non-invasive brain temperature sensing of an infant’s head to aid in the therapeutic hypothermia treatment of hypoxic–ischemic encephalopathy (HIE) and traumatic brain injury (TBI). The proposed metamaterial-inspired antenna is designed on a flexible Kapton substrate with a biocompatible Polydimethylsiloxane (PDMS) protective superstrate layer. For brain temperature measurement, the flexible antenna is placed directly on the scalp to collect thermal noise power from the underlying tissue layers. The received thermal power is to be delivered to a sensitive microwave radiometer. The CCSRR antenna exhibits sharp frequency selectivity at 1.4 GHz with inherent filtering capability, strong field confinement, and excellent suppression of out-of-tissue (external) electromagnetic interference and thermal noise contributions. To closely match the realistic scenario, the CCSRR antenna, initially designed in a planar multi-layer configuration, is investigated in various bending configurations (cylindrical and spherical) with a curvature radius of 55 mm. The results indicate stable performance under bending. Good agreement between simulated and on-body measured results is observed in the desired frequency band. Full article

The increasing risk to Vulnerable Road Users (VRUs) at urban intersections necessitates advanced safety mechanisms capable of operating effectively under diverse conditions, including adverse weather like heavy rain. While optical sensors such as cameras and LiDAR often degrade in poor visibility, Radio Frequency (RF)-based systems offer resilient, all-weather tracking. This paper presents a novel approach to enhancing VRU protection by fusing two RF modalities: radar sensors and Ultra-Wideband (UWB) technology, a strong candidate for Joint Communication and Sensing (JCS). The research, conducted as part of the VIDETEC-2 project, addresses the limitations of existing vehicle-based and infrastructure-based systems, particularly in scenarios involving occlusions and blind spots. By leveraging radar’s environmental robustness alongside UWB’s precise, cost-effective short-range communication and localization, the proposed system delivers the framework for continuous vehicle and VRU tracking. The fusion of these sensor modalities, managed through a hybrid Kalman filter approach integrating an Unscented Kalman Filter (UKF) and an Extended Kalman Filter (EKF), allows reliable VRU tracking even in challenging urban scenarios. The experimental results demonstrate a reduction in tracking uncertainty and highlight the system’s potential to serve as a more accurate and responsive safety mechanism for VRUs at intersections. This work contributes to the development of intelligent road infrastructures, laying the foundation for future advancements in urban traffic safety. Full article

Visible Light Positioning (VLP) is widely regarded as a promising technology for high-precision indoor localization due to its immunity to radio-frequency interference and compatibility with existing Light-Emitting Diode (LED) lighting infrastructure. Despite recent progress, current VLP systems remain fundamentally limited by nonlinear received signal strength (RSS) characteristics, unknown transmitter orientations, and dynamic indoor disturbances. Existing solutions typically address these challenges in isolation, resulting in limited robustness and scalability. This paper proposes SCENE-VLP (Sequential Deep Learning with Feature Compression and Optimal State Estimation), a structured positioning framework that integrates feature compression, temporal sequence modeling, and probabilistic state refinement within a unified estimation pipeline. Specifically, SCENE-VLP combines Principal Component Analysis (PCA) and Denoising Autoencoders (DAE) for linear and nonlinear observation conditioning, Gated Recurrent Units (GRU) for modeling temporal dependencies in RSS sequences, and Kalman-based filtering (KF/EKF) for recursive state-space refinement. The framework is formulated as a hierarchical approximation of the nonlinear observation model, linking data-driven measurement learning with Bayesian state estimation. A systematic ablation study across multiple scenarios, including same-dataset evaluation and cross-dataset generalization, demonstrates that each component provides complementary benefits. Feature compression reduces redundancy while preserving dominant signal structure; GRU significantly improves robustness over static regression; and recursive filtering consistently reduces positioning error compared to unfiltered predictions. While both KF and EKF improve performance, EKF provides incremental refinement under mild nonlinearities. Extensive simulations conducted on an indoor dataset collected from a realistic deployment with eight ceiling-mounted LEDs and a single photodetector (PD) show that SCENE-VLP achieves sub-decimeter localization accuracy, with P50 and P95 errors of 1.84 cm and 6.52 cm, respectively. Cross-scenario evaluation further confirms stable generalization and statistically consistent improvements. These results demonstrate that the structured integration of observation conditioning, temporal modeling, and Bayesian refinement yields measurable gains beyond partial pipeline configurations, establishing SCENE-VLP as a robust and scalable solution for next-generation indoor visible light positioning systems. Full article

This paper presents the design and optimization of highly efficient radio frequency power amplifiers (RFPAs) for driving acousto-optic tunable filters (AOTFs) in spaceborne applications. High efficiency is critical in such applications to minimize power consumption, heat dissipation, and enhance system reliability. However, RFPAs typically generate significant harmonic content and heat, which can induce thermal effects and compromise the optical measurement accuracy of AOTFs. This work investigates the trade-offs among efficiency, bandwidth, harmonic suppression, and tunable output power. Analytical modeling and parametric optimization are employed to derive practical design strategies. The results offer valuable insights for the development of efficient RF driving systems for AOTFs. Full article

This paper presents the design of a highly reconfigurable interdigital bandpass filter (BPF) developed through a three-stage design approach. In the first stage, the influence of four low-loss dielectric substrates on the filter response is systematically analyzed to identify the optimal configuration. The selected substrate demonstrates excellent performance, achieving an input return loss of −38 dB, an insertion loss of −0.9 dB at 4.30 GHz, and a wide passband corresponding to a bandwidth (BW) of 2.20 GHz. In the second stage, two variable capacitors were incorporated into the baseline geometry, enabling manual tuning of the center frequency ($f_0$) from 5.10 to 6.34 GHz, with $\left(S_{11}\right)$ better than −25 dB and ${(S}_{12})$ close to −0.60 dB. In the final stage, the capacitors were replaced by SMV1413 varactor diodes, transforming the design into a fully voltage-controlled tunable filter. This configuration provides continuous frequency agility from 4.70 to 5 GHz without modifying the physical structure, while achieving $\left(S_{11}\right)$ levels down to −40 dB and insertion loss as low as −0.7 dB. The proposed architecture offers a compact, low-loss, and electrically reconfigurable solution, making it a promising solution for next-generation RF front-ends, adaptive wireless systems, and cognitive radio applications. Two independent Electromagnetic solvers (EM) were employed to validate the filter’s performance: an EM based on the Finite Integration Technique and the Advanced Design System 2026 (ADS) solver using the Method of Moments (MoM). The close agreement between the results produced by both platforms confirms the accuracy and robustness of the proposed reconfigurable bandpass filter structure. Full article

This paper examines precise timestamping of LoRaWAN messages (particularly beacons) to enable wide-area synchronization for end devices without GNSS. The need for accuracy demands hardware-level timestamping architectures, possibly using time-domain cross-correlation (matched filtering) against internally generated chirp references. Focusing on Time-of-Arrival (TOA) estimation from raw IQ samples, the authors analyze effects of non-idealities—additive white Gaussian noise (AWGN), Carrier Frequency Offset (CFO), Sampling Phase and Frequency Offset (SPO and SFO, respectively), and radio parameters such as spreading factor (SF) and sampling rate of the baseband signals. A MATLAB (R2020) simulation mimics preamble detection and Start-of-Frame Delimiter (SFD) timestamping while sweeping SF (7, 9, 12), sampling rates (0.25–10 MSa/s), SNR (−20 to +20 dB), and CFO/SFO offsets (−10–10 ppm frequency deviation). Errors are evaluated in terms of mean and dispersion, the latter represented by the P95–P5 range metric. Results show that oversampling not only improves temporal resolution, but sub-microsecond error dispersion can be achieved with high sampling rates in favorable SNR and SF cases. Indeed, SPO and SNR greatly contribute to error dispersion. On the other hand, higher SF values increase correlation robustness at the cost of longer chirps, making SFO a dominant error source; ±10 ppm SFO can induce roughly ±3 μs SFD bias for SF12. CFO largely cancels after up-/down-chirp averaging. As a concluding remark, matched-filter hardware timestamping can ensure sub-μs errors thanks to oversampling but requires SFO compensation for accurate real-world synchronization in practice. Full article

Electromagnetic interference (EMI) shielding materials are essential for ensuring the reliable operation of electronic devices and safeguarding human health, yet conventional metal-polymer materials are non-biodegradable, energy-intensive, and difficult to recycle. This study prepared a biodegradable paper-based shielding material; renewable cellulose filter paper was employed as the sole substrate, and graphene was integrated to construct an electromagnetic shielding network. A low-cost paper-based electromagnetic shielding preparation method was developed, and the performance of the material was analyzed in electromagnetic shielding applications. Samples were fabricated through a simple impregnation-evaporation-lamination process. It has a thickness of 1 mm for single layers and a maximum conductivity of 21.3 S/m. The influence of sample thickness on electromagnetic shielding in the X-band (8.2–12.4 GHz) was investigated, when the graphene filter cake loading reached 20 wt%, the SE_T values for triple-layer electromagnetic shielding papers reach 36 dB at 8.2 GHz and 33 dB at 12.4 GHz. A phone box for indoor environments and a card holder with anti-radio-frequency identification (RFID) functionality were designed. Furthermore, achievable design solutions for an EMI shielding wallpaper in medical and artistic installations were proposed. Full article

While circuit parameter optimization has matured significantly, the systematic discovery of novel circuit topologies remains a bottleneck in analog design automation. This work presents SymXplorer, an open-source Python framework designed for automated topology exploration through symbolic modeling of analog components. The framework enables a component-agnostic approach to architecture-level synthesis, integrating stability analysis and higher-order filter exploration within a streamlined API. By modeling non-idealities as lumped parameters, the framework accounts for physical constraints directly within the symbolic analysis. To facilitate circuit sizing, SymXplorer incorporates a multi-objective optimization toolbox featuring Bayesian optimization and evolutionary algorithms for simulation-in-the-loop evaluation. Using this framework, we conduct a systematic search for differential Common-Gate (CG) Bandpass Transimpedance Amplifier (TIA) topologies tailored for 5G New Radio (NR) Radio-over-Fiber applications. We propose a novel, orthogonally tunable Bandpass TIA architecture identified by the tool. Implementation in 65 nm CMOS technology demonstrates the efficacy of the framework. Post-layout results exhibit a tunable gain of 30–50 dBΩ, a center frequency of 3.5 GHz, and a tuning range of 500 MHz. The design maintains a power consumption of less than 400 μW and an input-referred noise density of less than 50 pA/$\sqrt{\mathrm{Hz}}$ across the passband. Finally, we discuss how this symbolic framework can be integrated into future agentic EDA workflows to further automate the analog design cycle. SymXplorer is open-sourced to encourage innovation in symbolic-driven analog design automation. Full article

This paper describes recent engineering designs that allow full-duplex SerDes connectivity between a number of cascaded Xilinx radio frequency system-on-chip (RF-SoC) and VCU FPGA systems. The design allows for unlimited scalability with all-to-all connectivity across FPGA systems and RF-SoCs that allow for bidirectional data transport in streaming mode at a capacity of 50 Gbps per ADC-DAC channel. A custom massively parallel systolic-array architecture supporting 8 parallel data streams from time-interleaved ADC/DACs allow real-time matrix–vector-multiplication (MVM). The MVM can be 8 × 8, 8 × 16, …, 8 × 1024 in supported matrix size, and is demonstrated in real time sustained throughput of 1 TeraMAC/second, for matrix size 8 × 512. The MVM is the building block supporting machine learning and filtering, with the computational graph split across FPGA systems using the SerDes connections. The RF data processed by the FPGA chain can be further utilized for higher-level AI workloads on an NVIDIA DGX Spark platform connected to the system. We demonstrate two platforms in which ZCU111 and ZCU1285 RF-SoC boards perform direct-RF data acquisition, while compute engines operating in real time on VCU128 and VCU129 FPGA boards showcase both digital beamforming and polyphase FIR filterbanking in a real-time bandwidth of 1.0 GHz. Full article

Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article

High-performance low dropout regulator (LDO) chips are core components that provide clean power for high-precision sensors, radio frequency (RF) circuits, low noise amplifiers and other noise-sensitive circuits. In the reported literature, the designed LDO chip has advantages in certain parameters, but it cannot meet all the requirements of a high power supply rejection ratio (PSRR), low output noise and low standby current at the same time, which makes the high-end applications of LDOs greatly limited. In this paper, an LDO chip with high PSRR, low output noise and low standby current has been designed and fabricated. By increasing the loop gain, introducing an improved feedforward path, and adopting isolated power supply, the PSRR of the LDO at different frequency bands is greatly improved. By optimizing the design of the error amplifier (EA) and adding a low-pass filter to filter out the reference noise, the output voltage noise of the LDO is reduced. Within the depletion process and an optimized reference structure, the standby power consumption of the LDO is reduced without damaging the output voltage accuracy. The chip is taped out with SMIC’s 0.18 μm/5 V/BCD process. The measured PSRR of the chip is as high as 95dB at a frequency of 1 kHz, and the high-frequency (1 MHz) PSRR is above 45 dB. The amplitude of integrated output noise is below 5.4 μVrms within the frequency range of 10 Hz to 100 KHz. When the load current is zero, the measured standby current is less than 400 nA. The test results indicate that the chip has excellent performance in terms of PSRR, output noise and standby power consumption. Full article

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

Global Navigation Satellite Systems (GNSSs) play a critical role in ensuring the safety of modern transportation across all domains, including aviation, road, rail, and maritime navigation. However, recent years have seen a significant increase in radio frequency interference, including signal masking (jamming) and data deception (spoofing) attacks against GNSSs. These threats can severely compromise human safety, disrupt logistics chains, and undermine essential public services. This study offers a structured holistic overview of the most common forms and impacts of GNSS interference. It also presents practical, resilient solutions to reduce vulnerabilities. Both technological (e.g., redundancy, filtering, alternative navigation) and organizational (e.g., regulation, training, risk assessment) strategies are discussed. The findings highlight that building GNSS resilience is not optional—it is necessary to protect transportation systems that rely on satellite navigation. The summary may be of particular interest to legislators, transport authorities, logistics operators, and policymakers. Full article

This paper provides a comprehensive review of recent advancements in radio-frequency (RF) multifunctional components with integrated filtering characteristics, including tunable filtering attenuators, filtering power dividers, filtering couplers, and filtering Butler matrices, all of which play critical roles in wireless communication systems. With the increasing demand for miniaturization, integration, and low-loss performance in RF front-ends, multifunctional components with filtering characteristics have become essential. This review first introduces tunable attenuators and filtering attenuators based on various technologies such as PIN diodes, graphene-based structures, and RF-MEMS switches, and also analyzes their advantages, limitations, and performance. Then, we discuss filtering power dividers developed from Wilkinson structures, three-line coupled structures, resonator-based coupling matrix methods, and SSPP-waveguide hybrids. Furthermore, filtering couplers and filtering Butler matrices are reviewed, highlighting their capability to simultaneously achieve amplitude and phase control, making them suitable for multi-beam antenna feeding networks. Finally, a brief conclusion is summarized. Future research directions, such as hybrid technologies, novel materials, broadband and multi-band designs, and antenna-matrix co-design, are suggested to further enhance the performance and practicality of multifunctional RF components for next-generation wireless communication systems. Full article