To mitigate the persistent risk of thermal runaway during automated battery disassembly, safety systems require transition from offline assessment to real-time intervention. This paper introduces a novel T-shaped RFID tag that enables full lifecycle data traceability and enhances fire safety during battery recycling without additional hardware costs. The tag design ensures reliable RF signal transmission in fully metallic environments through spatial decoupling of sensing and communication functions. By employing a persistence-time-based algorithm, the system acquires inter-cell temperature data from battery modules. A continuous risk assessment framework leverages spatiotemporal correlation analysis of this temperature data, enabling robust regional fault prediction and early anomaly detection to prevent thermal runaway propagation. Experimental validation demonstrates that the proposed approach facilitates accurate identification of internal thermal runaway onset. The solution can be integrated with robotic systems to inform disassembly trajectory planning and enhance operational safety throughout the recycling process.
Long Range (LoRa) backscattering has gained increasing popularity, as it enables wireless ultra-low-power long-range communication by reflecting and modulating ambient signals without active Radio Frequency (RF) components. Although operation in the microwatt range is possible, the design of backscatter tags requires balancing synchronization sensitivity, spectral behavior, implementation complexity, and power consumption. This contribution derives design guidelines for LoRa backscatter tags by analyzing two key subsystems: the envelope-detector-based synchronization path and impedance-modulated backscatter transmission. In ambient systems synchronization sensitivity limits the achievable operating range, which is often related to the envelope detector. Therefore, we evaluate low-power amplification strategies, showing that amplifying the demodulated Low-Frequency (LF) signal improves sensitivity by up to 8 dB with only 7μW additional power consumption. Furthermore, amplifier performance is shown to depend on closed-loop bandwidth and LF small-signal behavior rather than gain-bandwidth product alone. In addition, we analyze different impedance-state configurations for backscattering with respect to harmonic suppression, signal strength, implementation complexity, and power consumption. The results reveal that increasing the number of impedance states reduces harmonics but introduces trade-offs between switching complexity and energy consumption. Configurations with up to eight states provide a good balance between spectral performance and implementation effort, while two-state modulation remains preferable for maximizing data rates. Overall, the results presented provide design guidelines for optimizing LoRa backscatter tags in terms of spectral performance, energy efficiency, and communication range, and are also applicable to non-LoRa backscatter communication systems.
To address the challenges of limited coverage range, severe self-interference (SI), and spectrum contraction trends faced by traditional ultra-high frequency radio frequency identification (UHF RFID) technology, a passive channelized cellular IoT system is proposed. A distributed cellular architecture consisting of base stations, helper stations, and tags is established, leveraging in-band frequency division multiplexing (FDM) technology to design a channelized filtering structure for decoupling bidirectional link interference. A temporally-coupled automatic gain control-based self-interference cancellation scheme with a power-compensated joint cancellation structure is developed to counteract fluctuating interference, enhancing adaptability in high-dynamic reflection environments. An in-band segmented linkage frequency hopping (IBSLFH) mechanism based on ping-pong operation is proposed, enabling low-latency dynamic channel switching via channelized frequency hopping (FH) network strategy and extending the system FH range across the entire 5 MHz band. Experimental results demonstrate that the system achieves an effective operating range of 105 m, with a tag recognition rate (TRR) exceeding 90%, at an uplink rate of 160 kbps. Moreover, it attains a TRR of up to 70% in indoor cellular deployments. This provides a practical engineering pathway for long-distance and high-density IoT application.
Space debris continues to emerge as a critical threat to the safety of human-made space technologies. We address this issue by developing a low-complexity integrated sensing and communication (ISAC) design that integrates linear frequency modulated (LFM) and orthogonal frequency division multiplexing (OFDM) waveforms for joint debris tracking and inter-satellite communication. In the proposed method, the two waveforms are non-orthogonally combined and share the available power resource, allowing each waveform to perform its designated task, namely, debris tracking via the LFM signal and inter-satellite communication via the OFDM waveform. To alleviate the signal-to-interference-plus-noise ratio (SINR) degradation at the radar receiver, a Sage-Husa adaptive Kalman filter (SHAKF) is incorporated to enhance the sensing measurements’ quality under low SINR conditions. Unlike the conventional Kalman filter (KF), the SHAKF method recursively estimates the state noise covariance matrix (SNCM) and observation noise covariance matrix (ONCM), reducing its reliance on accurately known noise statistics. In our considered ISAC application, however, occasional disturbances may cause the covariance estimates to become non-positive-definite, resulting in unstable state estimation and potential filter failure. To improve the robustness of the SHAKF implementation, modified SNCM and ONCM estimation techniques are incorporated and adapted to the considered ISAC-based debris-tracking framework. The resulting integrated approach is evaluated through Monte Carlo simulations, which demonstrates improved space-debris sensing performance compared with the conventional KF, while simultaneously enabling reliable inter-satellite communication.
While blind beamforming is a promising approach for improving the interrogation range and throughput of multi-antenna radio frequency identification (RFID) systems, conventional algorithms often exhibit slow convergence or even fail to converge in large-scale antenna-array settings. To address this issue, we propose a novel blind adaptive beamforming (BABF) method, termed approximate gradient perturbation BABF (AGP-BABF), which innovatively solves the tag-backscattering power maximization problem by applying both positive and negative perturbations to the individual reader antenna weights. The introduced perturbations enable more flexible beam steering without a priori channel knowledge, thereby accelerating convergence to near-optimal weights and improving convergence reliability in both monostatic and bistatic configurations. Comprehensive numerical simulations demonstrate that the proposed AGP-BABF significantly outperforms conventional BABF methods in terms of interrogation range and reliably converges to the near-optimal beamforming weights. Finally, the improvements in convergence speed and interrogation range are further validated through experiments on a software-defined radio testbed built with a USRP X310 and GNU Radio.
Light Fidelity (LiFi) for intra-satellite communication offers significant advantages, including flexible Assembly, Integration and Testing Phase (AIT) and reduced harness complexity. This paper presents a simulation framework for light propagation, primarily aimed at optimizing positioning, spatial structures, and system parameters for intra-satellite LiFi architectures. Initial simulations included generic scenarios such as obstacle simulation and transmitter–receiver alignment, followed by integration of a complete 3D CubeSat model. Results demonstrate the framework’s capability to support the optimization and visualization of optical communication structures, enabling virtual analysis that complements physical test setups in early development stages. The framework has been extended to incorporate diffuse reflection and simple wavelength-independent refraction, while supporting iterative reuse of simulation results and channel characteristics enabling iterative transmitter and receiver placement optimization.
This paper presents a battery-less NFC sensor tag designed for contactless physiological monitoring, where extended read range and robust energy harvesting are critical to ensure reliable sensing. The sensor tag integrates three functional blocks: an electrochemical enzymatic sensor for analyte detection, the SIC4341 interface chip for data acquisition and wireless communication, and a dual-coil NFC antenna for efficient power transfer and signal exchange. The antenna is implemented on a thin, flexible polyimide substrate, with two in-phase coils printed on opposite sides and connected in series through a via hole. This configuration increases the overall inductance and magnetic flux, enhancing the tag communication range while maintaining a compact footprint. A full RLC circuit model is analyzed and tuned to operate at the standard NFC frequency of 13.56 MHz, with calibration to maintain resonance. The proposed device successfully demonstrates battery-less sensing of glucose levels under real operating conditions. Additionally, electromagnetic simulations and experimental validation confirm that the dual-coil design enables an almost 50% improvement in communication distance over a conventional single-coil layout, and is capable of extracting enough energy through inductive coupling over the large distance to drive the IC and sensing operation.
Traffic accidents involving pedestrians are a serious problem in Japan, particularly for vulnerable road users (VRUs), including pedestrians and cyclists. Furthermore, road conditions, particularly at intersections without traffic lights and minor intersections in residential areas, contribute to accidents. The development of advanced driver assistance systems (ADASs) for drivers continues to advance in addressing traffic accidents. Various studies have been conducted to develop ADAS using multiple devices, including cameras, LiDAR, radars, and other detection devices. Since radar cannot detect small objects such as VRUs, this study proposes VRU radars as active tags that transmit an extraordinarily fast chirp modulation-based frequency-modulated continuous wave (FCM-FMCW) radar signal with long chirp periods, which generates an impulse signal for the car radar. It can detect the presence of VRUs and determine the number of VRUs by counting the impulses on the car radar. The experimental results confirmed that in realistic environments, the radars rarely identify VRUs because the Radar Cross Sections (RCS) of VRUs are significantly smaller than the vehicle targets and surrounding objects on a road. These facts validate the necessity of the proposed concept. According to the experiments using radar evaluation boards, the proposed system does not cause adverse effects on car radar. In this paper, the range and spatial spectra with and without interference mitigation are compared. A radar evaluation board can generate impulses at a maximum distance of approximately 22 meters away, which is obtained by varying the distances between the car radar and VRUs radars. The violin plots over a certain number of frames can be used as a metric to identify the presence of VRUs and the number of VRUs.
As the rapid advent of quantum computing and artificial intelligence (AI) has threatened the heuristic hardness of conventional cryptographic standards, hardware-rooted security primitives have become essential for post-quantum resilience. This work proposes a new memory-free physical unclonable function (PUF) architecture tailored for secure wireless identification, authentication, and anti-counterfeiting applications. The core mechanism exploits a non-Foster parity-time (PT)-symmetric analog circuit comprising an active −RLC oscillator coupled with a passive RLC resonator via a negative mutual capacitance realized with negative capacitance converter (NCC). The active oscillator serves as a gain stage via a negative resistance converter (NRC). The non-Foster PT-symmetric circuit exhibits a dramatic eigenfrequency bifurcation effect at a divergent exceptional point (DEP), for which hypersensitivity can amplify entropy and stochastic electronic property variations of analog circuits, thereby enabling on-demand generation of unique electromagnetic fingerprints. Furthermore, such randomized oscillatory signals can be digitized into a binary bit-sequence to modulate the backscattered RF signal. Our simulation results demonstrate that the proposed PUF instance based on the non-Foster PT-symmetric circuit can display excellent cryptographic performance in terms of randomness and uniqueness. The architecture further supports reconfigurable PUF encryption key generation, ensuring an expansive and secure key space for seamless integration into existing IoT platform and RFID ecosystems. This work establishes a promising framework for robust physical-layer security and hardware-level protection against emerging cryptanalytic threats in the AI and quantum era.
Channel State Information (CSI) based RF sensing enables device free human activity recognition (HAR) and indoor location recognition, making it attractive for smart RFID-enabled and RF-assisted sensing environments. However, CSI-based sensing relies on fine-grained amplitude and phase variations that are sensitive to numerical precision, quantization noise, and edge-device constraints. Existing deep learning models for CSI processing are computationally intensive and do not explicitly account for CSI-specific signal characteristics, limiting their deployment on resource-constrained platforms. This paper presents an RF-aware and deployment-oriented deep learning framework for CSI based joint HAR and location recognition. We propose DB-XNet, a lightweight dual-branch convolutional architecture that separately processes CSI amplitude and phase, and adaptively fuses them through cross-channel attention, thereby preserving complementary RF information under resource constraints. On the ARIL benchmark, DB-XNet achieves state-of-the-art performance with 95.40% HAR accuracy and 99.30% location accuracy, while maintaining a compact footprint of only 0.54M parameters and a model size of 2.08 MB. We further perform a systematic compression aware study using multiple structured pruning methods and custom bit width quantization (2-8 bits), and validate the findings on the JUST RF CSI dataset. The results show that HAR is more sensitive to compression than location recognition, highlighting the stronger dependence of HAR on fine-grained CSI dynamics. After applying combined pruning and quantization, DB-XNet achieves up to a $5 imes $ inference speedup on a Raspberry Pi 4, demonstrating its feasibility for real-time low-power RF sensing applications.
Wireless Sensor Networks (WSNs) play a key role in bridging the physical and digital worlds, enabling applications ranging from environmental monitoring to health-related sensing. However, the limited energy resources available to sensor nodes remain a major challenge for long-term deployments. This paper proposes an ML-enhanced adaptive sensing framework built on the e-LiteSense architecture to improve energy efficiency in WSNs. The proposed approach dynamically adjusts the sensing interval by leveraging predictive models that anticipate the evolution of monitored physical parameters. By incorporating prediction into the sensing control loop, the system proactively adapts sampling frequency according to environmental dynamics and node energy conditions. Three forecasting models were initially considered: ARIMA, Random Forest (RF), and Long Short-Term Memory (LSTM). Based on preliminary evaluation, RF and LSTM were selected for the adaptive sensing scheme due to their superior predictive performance. Experimental results across multiple sensing scenarios show that the proposed approach accurately tracks the evolution of monitored parameters while significantly reducing the number of sensing operations. The reduction in sampling events reaches up to approximately 60%, leading to substantial energy savings and extended sensor lifetime. These results demonstrate that integrating predictive ML techniques into adaptive sensing schemes represents an effective strategy for improving the sustainability and operational efficiency of WSN deployments.
It is well established that replacing the traditional continuous-wave signals with pulsed-wave signals improves the performance of passive UHF (Ultra High Frequency) RFID (Radio Frequency Identification) systems in terms of wireless power transfer. However, the impact of this change on the performance of these systems in terms of information transmission has been little analyzed. Based on the maximum ratio combining principle, this paper describes the optimal multi-carrier receiver processing that maximizes the information performance of passive UHF RFID systems in pulsed-wave mode. In addition to the theoretical description of the processing, numerical simulations are performed to test the estimators required for the proposed combination process. Finally, experimental measurements are performed to confirm the feasibility of the proposed processing with real data and verify the performance stated in the theory.
This paper presents a comprehensive investigation of a scalable manufacturing framework for advanced flexible radio-frequency (RF) devices based on screen-printing technology. The study systematically evaluates the electrical and electromagnetic performance of multiple conformal components spanning microwave to sub-terahertz frequency ranges. First, a low-loss flexible microstrip transmission line (FML) is designed and experimentally characterized from 1 GHz to 20 GHz under various bending conditions. The results demonstrate minimal degradation in S-parameters, with phase variation remaining below 10 degrees at a 45 degrees bend, confirming its suitability for high-speed digital and analog interconnects. Building upon this platform, a whispering gallery mode (WGM) resonator-based sensor is developed for liquid characterization, specifically for monitoring alcohol concentration in water. The sensor maintains stable operation under mechanical deformation, highlighting its applicability in confined and dynamically varying environments. Notably, this work demonstrates that phase variation provides a highly sensitive and robust sensing metric, offering improved immunity to environmental noise compared to conventional amplitude- or frequency-based approaches. Furthermore, the proposed fabrication process is extended to millimeter-wave applications through the realization of an ultrathin dual-polarized conformal transmissive band-stop frequency selective surface (FSS) operating at 79.25 GHz and 84 GHz, as well as reflective intelligent surfaces (RIS) at 30 GHz and 60 GHz enabling high-gain beam and beam redirection for enhancing signal-to-noise-ratio and field of view (FoV) in wireless communications. Furthermore, the FSS maintains functionality under extreme bending conditions up to 80 degrees. Ultimately, to assess scalability toward higher frequencies, dielectric properties of candidate substrates are characterized using vector network analyzer (VNA)-based quasi-optical and terahertz time-domain spectroscopy (THz-TDS) methods. The simplicity, cost-effectiveness, and versatility of the proposed screen-printing process make it a promising candidate for next-generation flexible communication, sensing, and intelligent surface applications.
This paper presents, for the first time, a low-cost, flexible Substrate-Integrated Nonradiative Dielectric (SINRD) waveguide. The proposed Flex SINRD operates in the 22-26 GHz frequency band, exhibiting an average insertion loss of approximately -4.78 dB and a return loss better than -20 dB. The impact of key design parameters on the operational bandwidth and transmission performance is analyzed. The Flex SINRD is fabricated using screen-printing technology on a polyetherimide substrate (ε = 3.08, tan δ = 0.00276 at 10 GHz) with a thickness of 0.125 mm. Measurement results from the printed prototypes demonstrate excellent agreement with commercial EM simulations, underscoring the potential of this technology for flexible antenna feeds, conformal arrays, wearable wireless communication systems, or integrated flexible RF front-end applications.
This work investigates performance enhancement in phased antenna arrays arranged using a quasi-periodic concentric hexagonal ring array structure. The proposed PAA employs an unconventional geometric layout in which antenna elements are placed on concentric hexagonal rings, with inter-ring spacing determined by the Thue-Morse sequence. The impact of this quasi-periodic variation on array performance is systematically analyzed in terms of half-power beamwidth, sidelobe level, and scan capability, focused on the grating lobes behavior. Simulation results demonstrate that the proposed concentric hexagonal rings array configuration yields measurable improvements if compared with standard ones, especially in terms of grating lobes mitigation, highlighting its potential for advanced phased array applications.
Radar-based contactless monitoring of respiratory rate (RR) plays an important role in a person's life as it provides a new way of patient observation that does not require physical contact, which medical professionals need for monitoring patients in neonatal intensive care units (NICU) because it safeguards their well-being while maintaining their comfort and stopping the spread of diseases. In this paper, we conduct a comprehensive evaluation of various machine learning (ML) models that use continuous wave (CW) radar data to determine noncontact RR measurements. In a previous paper, we showed that the random forest (RF) algorithm can accurately predict RR but is not up to mark for medical purposes. This study systematically investigates different ML algorithms, which include linear regression (LR), support vector regression (SVR), gradient boosting regression (GBR), XGBoost regression (XGBR), and light gradient boosting machine regression (LGBMR) to evaluate their ability to make predictions and their system performance. These algorithms alone do not provide an accurate prediction of RR monitoring, so we introduce a new hybrid algorithm called stacked ensemble regression (SER), which utilizes an SER framework that integrates various base regressors through a meta-learning method to achieve shared benefits, which allow it to capture both vast linear patterns and small nonlinear changes within respiration data. The proposed SER method achieves an accuracy of 92.24%, which surpasses the performance of XGBR, which achieves an accuracy of 89.65%. The proposed framework creates a strong and expandable system that enables monitoring respiratory patterns through both physical and remote methods while developing radar-based respiratory assessment technology for medical use in hospitals and home care environments.
In nuclear facilities, modern inventory systems which enable RFID technology have been proposed to accelerate tracking of assets. Prior to regular inventory rounds, assets must be “commissioned” with passive RFID tags, which requires establishing a one-to-one correspondence between asset and tag. We outline and formally evaluate a proposed hybrid barcode-RFID commissioning process for nuclear material containers. Using principles from statistical design and analysis of experiments, we explore commissioning error rates and process step times as a function of controllable settings, taking into account relevant uncontrollable environmental factors. Broadly, this work presents a formal experimental design and analysis framework for preliminary studies of RFID-enabled processes.
Passive RFID tags offer a cost-effective and scalable solution for tracking numerous deployed assets. However, in forested environments, signal attenuation and multipath effects generally limit RFID spatial accuracy to the meter level. Conversely, while cameras employing stereo vision can achieve centimeter-level precision, relying solely on computer vision fails to resolve issues arising from spatial association ambiguity and partial occlusions in dense settings. Fusing these modalities allows systems to harness the high-accuracy benefits of vision while retaining the robust, non-line-of-sight identification advantages of RFID. Yet, a primary challenge in achieving this, which is the central focus of this paper, lies in accurately associating the disparate trajectories generated by these two sensors. To overcome this limitation, we introduce a novel camera–RFID fusion framework that integrates depth and object information with advanced trajectory-matching algorithms. By successfully bridging the meter-to-centimeter accuracy gap, the proposed approach helps achieve reliable tag localization even when assets temporarily leave the camera's field of view. To the best of our knowledge, this represents the first application of camera–RFID fusion for asset tracking in natural forested environments.
This article shows that it is possible to estimate the impedance of a remote antenna connected to a UHF RFID chip. The proposed method relies on the measurement of four backscattered field values and the knowledge of only two load impedance values. The field values can be obtained using the autotuning functionality available in modern UHF RFID chips. The method does not make any assumption about the antenna geometry and can be performed multiple times to obtain the impedance of the antenna over a given bandwidth (or any other parameter). More importantly, the method is not limited by the number of autotune capacitors in the chip. The method allows one to directly transform a UHF RFID tag into a sensor if the relation between the antenna impedance and a physical quantity is known. Thanks to the proposed method, a significant part of the UHF RFID tags already deployed in the field can be transformed into sensors.
