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.

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.

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.

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.

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.

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.

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.

Transferring Learned Behaviors between Similar and Different Radios.

Transfer learning (TL) techniques have proven useful in a wide variety of applications traditionally dominated by machine learning (ML), such as natural language processing, computer vision, and computer-aided design. Recent extrapolations of TL to the radio frequency (RF) domain are being used to increase the potential applicability of RFML algorithms, seeking to improve the portability of models for spectrum situational awareness and transmission source identification. Unlike most of the computer vision and natural language processing applications of TL, applications within the RF modality must contend with inherent hardware distortions and channel condition variations. This paper seeks to evaluate the feasibility and performance trade-offs when transferring learned behaviors from functional RFML classification algorithms, specifically those designed for automatic modulation classification (AMC) and specific emitter identification (SEI), between homogeneous radios of similar construction and quality and heterogeneous radios of different construction and quality. Results derived from both synthetic data and over-the-air experimental collection show promising performance benefits from the application of TL to the RFML algorithms of SEI and AMC.

Compact Wideband Tapered Slot Antenna Using Fan-Shaped and Stepped Structures for Chipless Radio-Frequency-Identification Sensor Tag Applications.

In this paper, two kinds of miniaturization methods for designing a compact wideband tapered slot antenna (TSA) using either fan-shaped structures only or fan-shaped and stepped structures were proposed. First, a miniaturization method appending the fan-shaped structures, such as quarter circular slots (QCSs), half circular slots (HCSs), and half circular patches (HCPs), to the sides of the ground conductor for the TSA was investigated. The effects of appending the QCSs, HCSs, and HCPs sequentially on the input reflection coefficient and gain characteristics of the TSA were compared. The compact wideband TSA using the first miniaturization method showed the simulated frequency band for a voltage standing wave ratio (VSWR) less than 2 of 2.530-13.379 GHz (136.4%) with gain in the band ranging 3.1-6.9 dBi. Impedance bandwidth was increased by 29.7% and antenna size was reduced by 39.1%, compared to the conventional TSA. Second, the fan-shaped structures combined with the stepped structures (SSs) were added to the sides of the ground conductor to further miniaturize the TSA. The fan-shaped structures based on the HCSs and HCPs were appended to the ground conductor with the QCSs and SSs. The compact wideband TSA using the second miniaturization method had the simulated frequency band for a VSWR less than 2 of 2.313-13.805 GHz (142.6%) with gain in the band ranging 3.0-8.1 dBi. Impedance bandwidth was increased by 37.8% and antenna size was reduced by 45.9%, compared to the conventional TSA. Therefore, the increase in impedance bandwidth and the size reduction effect of the compact wideband TSA using the second miniaturization method were better compared to those using the first method. In addition, sidelobe levels at high frequencies decreased while gain at high frequencies increased. A prototype of the compact wideband TSA using the second miniaturization method was fabricated on an RF-35 substrate to validate the simulation results. The measured frequency band for a VSWR less than 2 was 2.320-13.745 GHz (142.2%) with measured gain ranging 3.1-7.9 dBi.

Design of a Cavity for the High-Power Radio-Frequency Quadrupole Coupler Test for the ANTHEM Project.

The ANTHEM (Advanced Technologies for Human-centered Medicine) Radio-Frequency Quadrupole (RFQ) will employ eight coaxial power couplers, which will be magnetically coupled to the device through a loop antenna. The coupler design can support up to 140 kW in continuous wave operation. This paper presents the design of the cavity used for high-power testing, with the primary objectives of both optimizing the coupling between the couplers and ensuring operations at the designated operating frequency. Furthermore, the paper encompasses thermal and structural assessments conducted through numerical simulations.

A Sparse Recovery Algorithm for Suppressing Multiple Linear Frequency Modulation Interference in the Synthetic Aperture Radar Image Domain.

In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method.

GNSS Radio Frequency Interference Monitoring from LEO Satellites: An In-Laboratory Prototype.

The disruptive effect of radio frequency interference (RFI) on global navigation satellite system (GNSS) signals is well known, and in the last four decades, many have been investigated as countermeasures. Recently, low-Earth orbit (LEO) satellites have been looked at as a good opportunity for GNSS RFI monitoring, and the last five years have seen the proliferation of many commercial and academic initiatives. In this context, this paper proposes a new spaceborne system to detect, classify, and localize terrestrial GNSS RFI signals, particularly jamming and spoofing, for civil use. This paper presents the implementation of the RFI detection software module to be hosted on a nanosatellite. The whole development work is described, including the selection of both the target platform and the algorithms, the implementation, the detection performance evaluation, and the computational load analysis. Two are the implemented RFI detectors: the chi-square goodness-of-fit (GoF) algorithm for non-GNSS-like interference, e.g., chirp jamming, and the snapshot acquisition for GNSS-like interference, e.g., spoofing. Preliminary testing results in the presence of jamming and spoofing signals reveal promising detection capability in terms of sensitivity and highlight room to optimize the computational load, particularly for the snapshot-acquisition-based RFI detector.

Efficacy of Fractional Micro-plasma Radio Frequency Technology in Treating Hypertrophic Burn Scars in Asian Patients Under General Anesthesia: A Retrospective Study of 104 Cases.

BACKGROUND: Laser and other energy devices have been widely used in the minimally invasive treatment of scars. Among various technologies, Fractional Micro-Plasma Radio Frequency Technology (FMRT) has gained extensive consensus in the treatment of various types of scars and skin disorders, such as wrinkles, skin laxity, and pigmentation. OBJECTIVE: This study is a retrospective clinical trial aimed at assessing the effectiveness and safety of FMRT for hypertrophic burn scars treatment in the Asian population under different anesthesia methods. METHODS: A total of 104 patients with hypertrophic burn scars treated in our department from May 2018 to May 2022 were selected. Scar assessment scales were applied to observe changes in scars before and after FMRT treatment. RESULTS: A prospective study of 104 patients found that female patients were more likely to undergo laser treatment under general anesthesia (P < 0.05). Postoperative VSS total score, VSS total score difference, and immediate postoperative pain score were all better with general anesthesia compared to local anesthesia (P < 0.05). There were more significant improvements in scar color, vascular distribution, and flexibility (P < 0.05). When comparing the treatment outcomes between females and males, it was found that general anesthesia patients were superior to local anesthesia patients in terms of color score, vascular distribution score, flexibility score, and postoperative VSS total score 6 months after the final treatment. General anesthesia patients had a shorter hospital stay. Overall treatment evaluation was better for female general anesthesia patients than male patients. CONCLUSION: General anesthesia combined with FMRT is an effective, safe, and more acceptable treatment method for hypertrophic burn scars in the Asian population. BULLET POINTS: In the Asian population, the combined use of general anesthesia and Fractional Micro-Plasma Radio Frequency Technology (FMRT) is an effective, safe, and accepted method for treating skin scars. LEVEL OF EVIDENCE II: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Acousto-optic-based time domain electric field sensor for magnetic resonance imaging applications.

An acousto-optic (AO)-based electric field sensor is presented for time domain measurement under magnetic resonance imaging (MRI). A fully MR-compatible sensor is designed and fabricated using a phase-shifted fiber Bragg grating mechanically coupled to a piezoelectric transducer. Mechanical resonance of the piezoelectric transducer is matched to the operating frequencies of commonly used MRI systems to increase the sensitivity of the sensor. Sensitivity of the sensor is measured as 1.27 mV/V/m, with a minimum detectable electric field of 4.4 mV/m/√/Hz. Directivity of the sensor is measured with a 18 dB orthogonal component rejection. The dynamic range of the sensor is calculated as 117 dB/Hz, which allows the measurement of electric fields up to 3.2 kV/m. In MRI studies, the AO sensor was able detect local hot spots around a reference implant accurately with high signal-to-noise ratio. AO sensor exhibited similar or better performance when compared with commercially available MRI compatible electric field sensors. Furthermore, the small size of the sensor with the flexible fiber optic link could allow in situ measurements of electric fields during critical interventional procedures such as pacemaker lead or deep brain stimulator placement as an MRI dosimeter during diagnostic scans.

Evaluating the effects of graphene nanoparticles combined radio-frequency thawing on the physicochemical quality and protein conformation in hairtail (Trichiurus lepturus) dorsal muscle.

BACKGROUND: The thawing process is an essential step for a frozen marine fish. The present study aimed to investigate the effects of graphene magnetic nanoparticles combined radio-frequency thawing methods on frozen hairtail (Trichiurus lepturus) dorsal muscle. Seven thawing methods were used: air thawing, 4 °C cold storage thawing, water thawing, radio-frequency thawing (RT), radio frequency thawing combined with graphene nanoparticles (G-RT), radio frequency thawing combined with graphene oxide nanoparticles (GO-RT) and radio-frequency thawing combined with graphene magnetic nanoparticles (GM-RT). The thawing loss and centrifugal loss, electric conductivity, total volatile basic nitrogen, thiobarbituric acid reactive substances and color of thawed hairtail dorsal muscle were determined. The carbonyl content, total sulfhydryl groups, Ca2+ -ATPase activity, raman spectroscopy measurements and Fourier-transform infrared spectrometry measurements were determined using myofibrillar extracted from the dorsal muscle of hairtail. The water distribution was determined using low-field NMR techniques. RESULTS: The results demonstrated that the RT, G-RT, GO-RT and GM-RT could significantly shorten the thawing time. Moreover, GO-RT and GM-RT efficiently preserved the color of fish dorsal muscle and reduced the impact of thawing on fish quality by reducing lipid and protein oxidation. Meanwhile, the myofibrillar protein structure thawed by GO-RT and GM-RT were more stable and had a more stable secondary structure, which maintained strong systemic stability at the same time as slowing down protein oxidation. CONCLUSION: The results showed that GO-RT and GM-RT can significantly improve the thawing efficiency at the same time as effectively maintaining and improving the color and texture of thawed fish, slowing down the oxidation of proteins and lipids, and maintaining a good quality of thawed fish meat. © 2023 Society of Chemical Industry.

Design of a volumetric cylindrical coil-tuned at 298 MHz for 7 T imaging.

The concept of a 2D cylindrical High Pass Ladder (2D c-HPL) is used in the development of this ultra high radio frequency (UHRF) volumetric head coil for 7T tuned at the Larmor frequency of 298 MHz. The architecture of the 2D c-HPL helps to overcome the challenges associated with non-uniform magnetic field distribution. The prototype consists of an individual resonating array of inductance-capacitance (LC) elements and each component is tuned to the precise f o frequency. The tuning of the (i) inductance, (ii) capacitance, (iii) mesh size, and (iv) coupling coefficient play critical roles to attain the desired Larmor frequency. For this proof-of-concept, the prototype of a volumetric head coil consists of a cylindrical array size of 4 ×6, with individual LC components of inductance magnitude, 98 nH and four fixed value capacitors and one tunable capacitor that allowed to achieve the desired precession frequency, f r = 298 M H z . The model was tested for three different f o values of 269 MHz, 275 MHz and 286 MHz. The mutual coupling and the eigenfrequencies were compared through bench testing and dispersion equation. The experimental data were in good agreement (< 5%) with the theoretical eigenfrequencies from the dispersion relation. The theoretical eigenfrequencies and the experimental eigenfrequencies are in good agreement for eigenmodes (1,2), (1,3), (2,2), (2,3) and (4,3).