Fast Thermodynamic Study on a Silicon Nanotransistor at Cryogenic Temperatures.

Cryogenic temperatures are crucial for the operation of semiconductor quantum electronic devices, yet the heating effects induced by microwave or laser signals used for quantum state manipulation can lead to significant temperature variations at the nanoscale. Therefore, probing the temperature of individual devices in working conditions and understanding the thermodynamics are paramount for designing and operating large-scale quantum computing systems. In this study, we demonstrate high-sensitivity fast thermometry in a silicon nanotransistor at cryogenic temperatures using RF reflectometry. Through this method, we explore the thermodynamic processes of the nanotransistor during and after a laser pulse and determine the dominant heat dissipation channels in the few-kelvin temperature range. These insights are important to understand thermal budgets in quantum circuits, with our techniques being compatible with microwave and laser radiation, offering a versatile approach for studying other quantum electronic devices in working conditions.

RF shimming in the cervical spinal cord at 7 T.

PURPOSE: Advancing the development of 7 T MRI for spinal cord imaging is crucial for the enhanced diagnosis and monitoring of various neurodegenerative diseases and traumas. However, a significant challenge at this field strength is the transmit field inhomogeneity. Such inhomogeneity is particularly problematic for imaging the small, deep anatomical structures of the cervical spinal cord, as it can cause uneven signal intensity and elevate the local specific absorption ratio, compromising image quality. This multisite study explores several RF shimming techniques in the cervical spinal cord. METHODS: Data were collected from 5 participants between two 7 T sites with a custom 8Tx/20Rx parallel transmission coil. We explored two radiofrequency (RF) shimming approaches from an MRI vendor and four from an open-source toolbox, showcasing their ability to enhance transmit field and signal homogeneity along the cervical spinal cord. RESULTS: The circularly polarized (CP), coefficient of variation (CoV), and specific absorption rate (SAR) efficiency shim modes showed the highest B1 + efficiency, and the vendor-based "patient" and "volume" modes showed the lowest B1 + efficiency. The coefficient of variation method produced the highest CSF/spinal cord contrast on T2*-weighted scans (ratio of 1.27 ± 0.03), and the lowest variation of that contrast along the superior-inferior axis. CONCLUSION: The study's findings highlight the potential of RF shimming to advance 7 T MRI's clinical utility for central nervous system imaging by enabling more homogenous and efficient spinal cord imaging. Additionally, the research incorporates a reproducible Jupyter Notebook, enhancing the study's transparency and facilitating peer verification.

Bilateral orthogonality generative acquisitions method for homogeneous T 2 * images using parallel transmission at 7 T.

PURPOSE: The novel bilateral orthogonality generative acquisitions method has been developed for homogeneous T 2 * $$ {\mathrm{T}}_2^{\ast } $$ images without the effects of transmit field inhomogeneity using a parallel-transmission (pTx) system at 7 T. THEORY AND METHODS: A new method has been introduced using four low-angle gradient-echo (GRE) acquisitions to obtain homogeneous T 2 * $$ {\mathrm{T}}_2^{\ast } $$ contrast by removing the effects of transmit field inhomogeneity in the pTx system. First, two input images are obtained in circularly polarized mode and another mode in which the first transmit channel or channel group have an additional transmit phase of π. The last two acquisitions are single-channel acquisitions for a dual-channel system or single-channel group acquisitions for more than two channels. The introduced method is demonstrated in dual-channel and eight-channel pTx systems using phantom and whole-brain in vivo experiments. Noise performance of the proposed method is also tested against the ratio of two GRE acquisitions and the TIAMO (time-interleaved acquisitions of modes) method. RESULTS: Th new method results in more homogeneous T 2 * $$ {\mathrm{T}}_2^{\ast } $$ contrast in the final images than the compared methods, particularly in the low-intensity regions of circularly polarized-mode images for the images obtained via ratio of the two GRE acquisitions. CONCLUSION: The introduced method is easy to implement, robust, and provides homogeneous T 2 * $$ {\mathrm{T}}_2^{\ast } $$ images of the whole brain using pTx systems with any number of channels, compared with the ratio of the two GRE images and the TIAMO method.

Combined Therapy of Chemotherapy and Radiofrequency Ablation for Pancreatic Cancer Patients With Metachronous Hepatic Metastatic Lesions After Radical Pancreatic Resection.

PURPOSE: Hepatic metastasis frequently occurs in patients who have undergone radical pancreatic resection for pancreatic cancer. Besides chemotherapy, various local treatment approaches targeting hepatic lesions have been explored. However, research on radiofrequency ablation (RFA) as a localized therapy for hepatic metastasis is limited. Therefore, we conducted this retrospective study to provide clinical evidence. METHODS: This is a single-center, retrospective, cohort study. After radical pancreaticoduodenectomy, 32 patients developed metachronous hepatic metastasis with fewer than 3 lesions, the largest of which was less than 3 cm in diameter. These patients underwent combined treatment with chemotherapy and RFA. After 8 weeks of chemotherapy, patients received RFA for hepatic lesions. Additional chemotherapy was administered, and the patients' tumor status and survival were monitored. The primary endpoint of this study was overall survival (OS). Factors affecting OS were analyzed using the Cox risk model. RESULTS: Among the 32 patients, the mean OS was 28.4 months. Univariate and multivariate Cox regression analysis revealed that the time (in months) of liver metastasis (HR = 0.04, 95% CI: 0.01 to 0.19; P < 0.001), the number of liver metastases (HR = 7.08, 95% CI: 1.85 to 27.08, P = 0.004), and PD (progressive disease) response to the second round of chemotherapy (HR = 29.50, 95% CI: 1.46 to 597.27; P = 0.027) were independent predictors of poorer survival. CONCLUSION: Combined therapy with RFA and chemotherapy is safe in patients with hepatic metastasis after radical pancreaticoduodenectomy. Early recurrence (≤12 months), three liver metastatic lesions, and a poor response to the second round of chemotherapy were associated with poor survival.

Enhancing waste sludge solubilization through radio frequency treatment perforating bacterial cells.

Sludge solubilization is known as a rate-limiting step of anaerobic digestion. Although radio frequency (RF) has been applied for sludge pretreatment due to its similar thermal effect as microwave, the potential non-thermal effects of RF treatment remain controversial. In this study, we demonstrate that RF pretreatment enhances the solubilization and lysis of sludge by 8.02%-19.69% through both thermal and non-thermal mechanisms with less energy input. Scanning electron microscope images provide direct evidence that RF-induced microcurrents penetrated bacterial cells, leading to the release of intracellular substances through formed pores. Additionally, the non-thermal effect of RF treatment which could weaken the cell protection and accelerate the lysis rate involves the disruption of binding forces between extracellular polymeric substances and microbial cells. On average, the utilization of RF at a frequency of 27.12 MHz demonstrates its efficacy as a sludge pretreatment technique, as evidenced by a 13.39% reduction in energy consumption and a 16.9% improvement in treatment performance compared to conductive heating (CH). The findings of this study elucidate the possible mechanism of RF treatment of sludge and could establish a theoretical basis for the practical application of RF treatment in sludge management.

Micro-environmental personal radio-frequency electromagnetic field exposures in Melbourne: A longitudinal trend analysis.

BACKGROUND: A knowledge gap exists regarding longitudinal assessment of personal radio-frequency electromagnetic field (RF-EMF) exposures globally. It is unclear how the change in telecommunication technology over the years translates to change in RF-EMF exposure. This study aims to evaluate longitudinal trends of micro-environmental personal RF-EMF exposures in Australia. METHODS: The study utilised baseline (2015-16) and follow-up (2022) data on personal RF-EMF exposure (88 MHz-6 GHz) measured across 18 micro-environments in Melbourne. Simultaneous quantile regression analysis was conducted to compare exposure data distribution percentiles, particularly median (P50), upper extreme value (P99) and overall exposure trends. RF-EMF exposures were compared across six exposure source types: mobile downlink, mobile uplink, broadcast, 5G-New Radio, Others and Total (of the aforementioned sources). Frequency-specific exposures measured at baseline and follow-up were compared. Total exposure across different groups of micro-environment types were also compared. RESULTS: For all micro-environmental data, total (median and P99) exposure levels did not significantly change at follow-up. Overall exposure trend of total exposure increased at follow-up. Mobile downlink contributed the highest exposure among all sources showing an increase in median exposure and overall exposure trend. Of seven micro-environment types, five of them showed total exposure levels (median and P99) and overall exposure trend increased at follow-up.

Electromagnetic field exposure monitoring of commercial 28-GHz band 5G base stations in Tokyo, Japan.

Fifth generation (5G) wireless communication is being rolled out around the world. In this work, the latest radio frequency electromagnetic field (EMF) exposure measurement results on commercial 28-GHz band 5G base stations (BSs) deployed in the urban area of Tokyo, Japan, are presented. The measurements were conducted under realistic traffic conditions with a 5G smartphone and using both omnidirectional and horn antennas. First and foremost, in all cases, the electric-field (E-field) intensity is much lower (<-38 dB) than the exposure limits. The E-field intensities for traffic-off cases do not show any significant difference between the two antennas with the maximum being 3.6 dB. For traffic-on cases, the omnidirectional antenna can undesirably capture the radio wave from the smartphone in some cases, resulting in a 7-13 dB higher E-field intensity than that using the horn antenna. We also present comparative results between 4G long term evolution BSs and sub-6-GHz band and 28-GHz band 5G BSs and provide recommendations on acquiring meaningful EMF exposure data. This work is a further step toward the standardization of the measurement method regarding quasi-millimeter/millimeter wave 5G BSs.

Ultrasound Spectral Combined With Clinical Pathological Parameters in Prediction of Axillary Lymph Node Metastatic in Breast Cancer.

OBJECTIVES: To explore the clinical value of the nomogram based on ultrasound spectral combined with clinical pathological parameter in predicting axillary lymph node metastasis in breast cancer. METHODS: We prospectively gathered clinicopathologic and ultrasonic data from 240 patients confirmed breast cancer. The risk factors of axillary lymph node metastasis were analyzed by univariate and multivariate logistic regression, and the prediction model was established. The model calibration, predictive ability, and diagnostic efficiency in the training set and the testing set were analyzed by receiver operating characteristic curve and calibration curve analysis, respectively. RESULTS: Univariate analysis showed that lymph node metastasis was related with tumor size, Ki-67, axillary ultrasound, ultrasound spectral quantitative parameter, internal echo, and calcification (P < .05). Multivariate logistic regression analysis showed that the Ki-67, axillary ultrasound, quantitative parameter (the mean of the mid-band fit in tumor and posterior tumor) were independent risk factors of axillary lymph node metastasis (P < .05). The models developed using Ki-67, axillary ultrasound, and quantitative parameters for predicting axillary lymph node metastasis demonstrated an area under the receiver operating characteristic curve of 0.83. Additionally, the prediction model exhibited outstanding predictability for axillary lymph node metastasis, as evidenced by a Harrell C-index of 0.83 (95% confidence interval 0.73-0.93). CONCLUSION: Axillary ultrasound combined with Ki-67 and ultrasound spectral parameters has the potential to predict axillary lymph node metastasis in breast cancer, which is superior to axillary ultrasound alone.

Passive UHF RFID-based real-time intravenous fluid level sensor.

Ultra high frequency (UHF) passive radio frequency identification (RFID) tag-based sensors are proposed for intravenous (IV) fluid level monitoring in medical Internet of Things (IoT) applications. Two versions of the sensor are proposed: a binary sensor (i.e., full vs. empty state sensing) and a real-time (i.e., continuous level) sensor. The operating principle is demonstrated using full-wave electromagnetic simulation at 910 MHz and validated with experimental results. Generalized Additive Model (GAM) and random forest algorithms are employed for each interrogation dataset. Real-time sensing is accomplished with small deviations across the models. A minimum of 72% and a maximum of 97% of cases are within a 20% error for the GAM model and 62% to 98% for the random forest model. The proposed sensor is battery-free, lightweight, low-cost, and highly reliable. The read range of the proposed sensor is 4.6 m.

Design and research of multi-information fusion RFID sensor for fruit and vegetable quality detection.

With the increasing demand for fruits and vegetables in the market, the development of cold chain logistics has put forward higher requirements for the quality of fruits and vegetables in storage. To ensure the freshness of fruits and vegetables during storage and transportation and avoid unnecessary loss, it is necessary to conduct real-time detection of their odor to ensure their quality. Therefore, based on nano-composite materials combined with Radio Frequency Identification (RFID) technology, this paper designs an integrated RFID sensor that can simultaneously detect temperature, carbon dioxide, and ethanol concentrations. The test results show that the sensor has a high sensitivity of 0.25 dB/°C, 0.011 dB/ppm, and 0.65 MHz/ppm for detecting temperature, carbon dioxide, and ethanol concentration, respectively. The sensor also uses Printed Circuit Board (PCB) technology to make the sensor base, which has the advantages of low cost, easy portability, and mass production capability. The results obtained evidence that the system meets the requirements of environmental monitoring for fruit and vegetable storage, runs stably, and has a high use value.

Dielectric Properties of Materials Used for Microwave-Based NOx Gas Dosimeters.

Nitrogen oxides (NOx), primarily generated from combustion processes, pose significant health and environmental risks. To improve the coordination of measures against excessive NOx emissions, it is necessary to effectively monitor ambient NOx concentrations, which requires the development of precise and cost-efficient detection methods. This study focuses on developing a microwave- or radio frequency (RF)-based gas dosimeter for NOx detection and addresses the optimization of the dosimeter design by examining the dielectric properties of LTCC-based (Low-Temperature Co-fired Ceramics) sensor substrates and barium-based NOx storage materials. The measurements taken utilizing the Microwave Cavity Perturbation (MCP) method revealed that these materials exhibit more pronounced changes in dielectric losses when storing NOx at elevated temperatures. Consequently, operating such a dosimeter at high temperatures (above 300 °C) is recommended to maximize the sensor signal. To evaluate their high-temperature applicability, LTCC substrates were analyzed by measuring their dielectric losses at temperatures up to 600 °C. In terms of NOx storage materials, coating barium on high-surface-area alumina resolved issues related to limited NOx adsorption in pure barium carbonate powders. Additionally, the adsorption of both NO and NO2 was enabled by the application of a platinum catalyst. The change in dielectric losses, which provides the main signal for an RF-based gas dosimeter, only depends on the stored amount of NOx and not on the specific type of nitrogen oxide. Although the change in dielectric losses increases with the temperature, the maximum storage capacity of the material decreases significantly. In addition, at temperatures above 350 °C, NOx is mostly weakly bound, so it will desorb in the absence of NOx. Therefore, in the future development of a reliable RF-based NOx dosimeter, the trade-off between the sensor signal strength and adsorption behavior must be addressed.

Galileo and BeiDou AltBOC Signals and Their Perspectives for Ionospheric TEC Studies.

For decades, GNSS code measurements were much noisier than phase ones, limiting their applicability to ionospheric total electron content (TEC) studies. Ultra-wideband AltBOC signals changed the situation. This study revisits the Galileo E5 and BeiDou B2 AltBOC signals and their potential applications in TEC estimation. We found that TEC noises are comparable for the single-frequency AltBOC phase-code combination and those of the dual-frequency legacy BPSK/QPSK phase combination, while single-frequency BPSK/QPSK TEC noises are much higher. A two-week high-rate measurement campaign at the ACRG receiver revealed a mean 100 sec TEC RMS (used as the noise proxy) of 0.26 TECU, 0.15 TECU, and 0.09 TECU for the BeiDou B2(a+b) AltBOC signal and satellite elevations 0-30°, 30-60°, and 60-90°, correspondingly, and 0.22 TECU, 0.14 TECU, and 0.09 TECU for the legacy B1/B3 dual-frequency phase combination. The Galileo E5(a+b) AltBOC signal corresponding values were 0.25 TECU, 0.14 TECU, and 0.09 TECU; for the legacy signals' phase combination, the values were 0.19 TECU, 0.13 TECU, and 0.08 TECU. The AltBOC (for both BeiDou and Galileo) SNR exceeds those of BPSK/QPSK by 7.5 dB-Hz in undisturbed conditions. Radio frequency interference (the 28 August 2022 and 9 May 2024 Solar Radio Burst events in our study) decreased the AltBOC SNR 5 dB-Hz more against QPSK SNR, but, due to the higher initial SNR, the threshold for the loss of the lock was never broken. Today, we have enough BeiDou and Galileo satellites that transmit AltBOC signals for a reliable single-frequency vTEC estimation. This study provides new insights and evidence for using Galileo and BeiDou AltBOC signals in high-precision ionospheric monitoring.

Measurement Campaign of Radio Frequency Interference in a Portion of the C-Band (4-5.8 GHz) for the Sardinia Radio Telescope.

Radio frequency interference (RFI) analysis is crucial for ensuring the proper functioning of a radio telescope and the quality of astronomical observations, as human-generated interference can compromise scientific data collection. The aim of this study is to present the results of an RFI measurement campaign in the frequency range of 4-5.8 GHz, a portion of the well-known C-band, for the Sardinia Radio Telescope (SRT), conducted in October-November 2023. In fact, this Italian telescope, managed by the Astronomical Observatory of Cagliari (OAC), a branch of the Italian National Institute for Astrophysics (INAF), was recently equipped with a new C-band receiver that operates from 4.2 GHz to 5.6 GHz. The measurements were carried out at three strategically chosen locations around the telescope using the INAF mobile laboratory, providing comprehensive coverage of all possible antenna pointing directions. The results revealed several sources of RFI, including emissions from radar, terrestrial and satellite communications, and wireless transmissions. Characterizing these sources and assessing their frequency band occupation are essential for understanding the impact of RFI on scientific observations. This work provides a significant contribution to astronomers who will use the SRT for scientific observations, offering a suggestion for the development of mitigation strategies and safeguarding the radio astronomical environment for future observational campaigns.

Crescent Antennas as Sensors: Case of Sensing Brain Pathology.

Microstrip crescent antennas offer compactness, conformability, low profile, high sensitivity, multi-band operability, cost-effectiveness and ease of fabrication in contrast to bulky, rigid horn, helical and Vivaldi antennas. This work presents crescent sensors for monitoring brain pathology associated with stroke and atrophy. Single- and multi-element crescent sensors are designed and validated by software simulations. The fabricated sensors are integrated with glasses and experimentally evaluated using a realistic brain phantom. The performance of the sensors is compared in terms of peak gain, directivity, radiation performance, flexibility and detection capability. The crescent sensors can detect the pathologies through the monitoring of backscattered electromagnetic signals that are triggered by dielectric variations in the affected tissues. The proposed sensors can effectively detect stroke and brain atrophy targets with a volume of 25 mm3 and 56 mm3, respectively. The safety of the sensors is examined through the evaluation of Specific Absorption Rate (peak SAR < 1.25 W/Kg, 100 mW), temperature increase within brain tissues (max: 0.155 °C, min: 0.115 °C) and electric field analysis. The results suggest that the crescent sensors can provide a flexible, portable and non-invasive solution to monitor degenerative brain pathology.

UAV Detection Using Reinforcement Learning.

Unmanned Aerial Vehicles (UAVs) have gained significant popularity in both military and civilian applications due to their cost-effectiveness and flexibility. However, the increased utilization of UAVs raises concerns about the risk of illegal data gathering and potential criminal use. As a result, the accurate detection and identification of intruding UAVs has emerged as a critical research concern. Many algorithms have shown their effectiveness in detecting different objects through different approaches, including radio frequency (RF), computer vision (visual), and sound-based detection. This article proposes a novel approach for detecting and identifying intruding UAVs based on their RF signals by using a hierarchical reinforcement learning technique. We train a UAV agent hierarchically with multiple policies using the REINFORCE algorithm with entropy regularization term to improve the overall accuracy. The research focuses on utilizing extracted features from RF signals to detect intruding UAVs, which contributes to the field of reinforcement learning by investigating a less-explored UAV detection approach. Through extensive evaluation, our findings show the remarkable results of the proposed approach in achieving accurate RF-based detection and identification, with an outstanding detection accuracy of 99.7%. Additionally, our approach demonstrates improved cumulative return performance and reduced loss. The obtained results highlight the effectiveness of the proposed solution in enhancing UAV security and surveillance while advancing the field of UAV detection.

Development of Wearable Devices for Collecting Digital Rehabilitation/Fitness Data from Lower Limbs.

Lower extremity exercises are considered a standard and necessary treatment for rehabilitation and a well-rounded fitness routine, which builds strength, flexibility, and balance. The efficacy of rehabilitation programs hinges on meticulous monitoring of both adherence to home exercise routines and the quality of performance. However, in a home environment, patients often tend to inaccurately report the number of exercises performed and overlook the correctness of their rehabilitation motions, lacking quantifiable and systematic standards, thus impeding the recovery process. To address these challenges, there is a crucial need for a lightweight, unbiased, cost-effective, and objective wearable motion capture (Mocap) system designed for monitoring and evaluating home-based rehabilitation/fitness programs. This paper focuses on the development of such a system to gather exercise data into usable metrics. Five radio frequency (RF) inertial measurement unit (IMU) devices (RF-IMUs) were developed and strategically placed on calves, thighs, and abdomens. A two-layer long short-term memory (LSTM) model was used for fitness activity recognition (FAR) with an average accuracy of 97.4%. An intelligent smartphone algorithm was developed to track motion, recognize activity, and calculate key exercise variables in real time for squat, high knees, and lunge exercises. Additionally, a 3D avatar on the smartphone App allows users to observe and track their progress in real time or by replaying their exercise motions. A dynamic time warping (DTW) algorithm was also integrated into the system for scoring the similarity in two motions. The system's adaptability shows promise for applications in medical rehabilitation and sports.

Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence.

The proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the realms of RF fingerprint (RFF) based on applying the Jensen-Shannon divergence (JSD) to the statistical distribution of noise in RF signals to identify Bluetooth devices. Thus, through a detailed case study, Bluetooth RF noise taken at 5 Gsps from different devices is explored. A noise model is considered to extract a unique, universal, permanent, permanent, collectable, and robust statistical RFF that identifies each Bluetooth device. Then, the different JSD noise signals provided by Bluetooth devices are contrasted with the statistical RFF of all devices and a membership resolution is declared. The study shows that this way of identifying Bluetooth devices based on RFF allows one to discern between devices of the same make and model, achieving 99.5% identification effectiveness. By leveraging statistical RFFs extracted from noise in RF signals emitted by devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of RF identification techniques, which could be useful in forensic processes.

RFID Adaptive Parallel Response Collision Tree Algorithm Based on Lock-Bit.

This paper proposes the Lock-Position-Based RFID Adaptive Parallel Collision Tree (LAPCT) algorithm to address the issues of excessive time slots required in the identification process of collision tree algorithms for multiple tags and the high communication complexity between the reader and multiple tags. The LAPCT algorithm adopts a single-query multiple-response mechanism and dynamically divides the response sub-cycle numbers in the identification cycle based on an adaptive strategy. It uses Manchester encoding to lock collision positions and generate a common query prefix, effectively reducing the number of reader queries. This reduction in queries decreases the total number of required time slots and transmitted bits during the reader-tag communication process, thereby improving the efficiency of multiple tag recognition. Theoretical and simulation experiments demonstrate that compared to similar algorithms, the LAPCT algorithm achieves a maximum reduction of 37% in total time slots required, a maximum improvement of 30% in recognition efficiency, and a maximum reduction of 90% in communication complexity. Furthermore, with an increase in the number of tags, the performance advantages of the LAPCT algorithm become more pronounced, making it suitable for large-scale tag scenarios.

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks.

The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of -10 dB using the LSTM network and fused features.

Development of 2400-2450 MHz Frequency Band RF Energy Harvesting System for Low-Power Device Operation.

Recently, there has been an increasing fascination for employing radio frequency (RF) energy harvesting techniques to energize various low-power devices by harnessing the ambient RF energy in the surroundings. This work outlines a novel advancement in RF energy harvesting (RFEH) technology, intending to power portable gadgets with minimal operating power demands. A high-gain receiver microstrip patch antenna was designed and tested to capture ambient RF residue, operating at 2450 MHz. Similarly, a two-stage Dickson voltage booster was developed and employed with the RFEH to transform the received RF signals into useful DC voltage signals. Additionally, an LC series circuit was utilized to ensure impedance matching between the antenna and rectifier, facilitating the extraction of maximum power from the developed prototype. The findings indicate that the developed rectifier attained a peak power conversion efficiency (PCE) of 64% when operating at an input power level of 0 dBm. During experimentation, the voltage booster demonstrated its capability to rectify a minimum input AC signal of only 50 mV, yielding a corresponding 180 mV output DC signal. Moreover, the maximum power of 4.60 µW was achieved when subjected to an input AC signal of 1500 mV with a load resistance of 470 kΩ. Finally, the devised RFEH was also tested in an open environment, receiving signals from Wi-Fi modems positioned at varying distances for evaluation.