Real-time Radio Frequency Research Articles

Simulation of a Radio-Frequency Wave Based Bacterial Biofilm Detection Method in Dairy Processing Facilities

This paper describes the principles behind the radio-frequency (RF) sensing of bacterial biofilms in pipes and heat exchangers in a dairy processing plant using an electromagnetic simulation. Biofilm formation in dairy processing plants is a common issue where the absence of timely detection and subsequent cleaning can cause serious illness. Biofilms are known for causing health issues and cleaning requires a large volume of water and harsh chemicals. In this work, milk transportation pipes are considered circular waveguides, and pasteurizers/heat exchangers are considered resonant cavities. Simulations were carried out using the CST studio suite high-frequency solver to determine the effectiveness of the real-time RF sensing. The respective dielectric constants and loss tangents were applied to milk and biofilm. In our simulation, it was observed that a 1 µm thick layer of biofilm in a milk-filled pipe shifted the reflection coefficient of a 10.16 cm diameter stainless steel circular waveguide from 0.229 GHz to 0.19 GHz. Further sensitivity analysis revealed a shift in frequency from 0.8 GHz to 1.2 GHz for a film thickness of 5 µm to 10 µm with the highest wave reflection (S11) peak of ≈−120 dB for a 6 µm thick biofilm. A dielectric patch antenna to launch the waves into the waveguide through a dielectric window was also designed and simulated. Simulation using the antenna demonstrated a similar S11 response, where a shift in reflection coefficient from 0.229 GHz to 0.19 GHz was observed for a 1 µm thick biofilm. For the case of the resonant cavity, the same antenna approach was used to excite the modes in a 0.751 m × 0.321 m × 170 m rectangular cavity with heat exchange fins and filled with milk and biofilm. The simulated resonance frequency shifted from 1.52 GHz to 1.54 GHz, for a film thickness varying from 1 µm to 10 µm. This result demonstrated the sensitivity of the microwave detection method. Overall, these results suggest that microwave sensing has promise in the rapid, non-invasive, and real-time detection of biofilm formation in dairy processing plants.

Radiofrequency ablation-Real-time visualization of lesions and their correlation with underlying parameters.

Lesion durability and transmurality are crucial for successful radiofrequency (RF) ablation. This study provides a model of real-time RF lesion visualization and insights into the role of underlying parameters, as local impedance (LI). A force-sensing, LI-sensing catheter was used for lesion creation in an ex vivo model involving cross-sections of porcine cardiac preparations. During 60 s of RF application, one measurement per second was performed regarding lesion size and available ablation parameters. In total, 1847 measurements from n=36 lesions were performed. Power (20-50W) and contact force (1-5g, 10-15g, 20-25g) were systematically alternated. Lesion formation was most prominent in the first seconds of RF application during which nonlinear lesion growth was observed (max. 1.08mm/s for lesion depth and 2.71mm/s for lesion diameter). Power levels determined the extent of lesion formation in the early phase. After 20 s, lesion size growth velocity approaches 0.1mm/s at all power levels. LI changes were also highest in the first seconds (up to - 12 Ω/s) and decreased to less than - 0.1Ω/s after prolonged application. Lesion formation in irrigated RF ablation is a nonlinear process. Final lesion size resulting from an RF application is mainly influenced by high rates of lesion growth in the first seconds of ablation. LI seems to be a good surrogate for differentiating changes in lesion formation.

Hardware-accelerated Real-time Drift-awareness for Robust Deep Learning on Wireless RF Data

Proactive and intelligent management of network resource utilization (RU) using deep learning (DL) can significantly improve the efficiency and performance of the next generation of wireless networks. However, variations in wireless RU are often affected by uncertain events and change points due to the deviations of real data distribution from that of the original training data. Such deviations, which are known as dataset drifts, can subsequently lead to a shift in the corresponding decision boundary degrading the DL model prediction performance. To address these challenges, we present hardware-accelerated real-time radio frequency (RF) analytics and drift-awareness modules for robust DL predictions. We have prototyped the proposed design on a Zynq-7000 System-on-Chip that contains an FPGA and an embedded ARM processor. We have used Xilinx Vivado design suite for synthesis and analysis of the HDL design for the proposed solution. To detect dataset drifts, the proposed solution adopts a distance-based technique on FPGA to quantify in real-time the change between the prediction distribution obtained from DL predictions and data distribution of input streaming samples. Using various performance metrics, we have extensively evaluated the performance of the proposed solution and shown that it can significantly improve the DL model robustness in the presence of dataset drifts.

Analog-to-spike encoding and time-efficient RF signal processing with photonic neurons.

The radio-frequency (RF) signal processing in real time is indispensable for advanced information systems, such as radar and communications. However, the latency performance of conventional processing paradigm is worsened by high-speed analog-to-digital conversion (ADC) generating massive data, and computation-intensive digital processing. Here, we propose to encode and process RF signals harnessing photonic spiking response in fully-analog domain. The dependence of photonic analog-to-spike encoding on threshold level and time constant is theoretically and experimentally investigated. For two classes of waveforms from real RF devices, the photonic spiking neuron exhibits distinct distributions of encoded spike numbers. In a waveform classification task, the photonic-spiking-based scheme achieves an accuracy of 92%, comparable to the K-nearest neighbor (KNN) digital algorithm for 94%, and the processing latency is reduced approximately from 0.7 s (code running time on a CPU platform) to 80 ns (light transmission delay) by more than one million times. It is anticipated that the asynchronous-encoding, and binary-output nature of photonic spiking response could pave the way to real-time RF signal processing.

A quantum radio frequency signal analyzer based on nitrogen vacancy centers in diamond

The rapid development of radio-frequency (RF) technologies requires tools which can efficiently monitor the electromagnetic landscape. Broadband real-time RF spectral analyzers need to operate at room temperature, with low power consumption and have a compact design for on-board device integration. Here we describe a Quantum Diamond Signal Analyzer (Q-DiSA) which detects RF signals over a tunable frequency range of 25 GHz with frequency resolution down to 1 MHz, a millisecond temporal resolution and a large dynamic range (40 dB). This approach exploits the room temperature spin properties of an ensemble of nitrogen-vacancy (NV) centers in diamond. Performance is enabled via our analyzer architecture which combines a specific diamond crystallographic cut with a simplified magnetic arrangement. This allows us to maintain the alignment of the magnetic field along the nitrogen-vacancy center axis whilst frequency tuning. These results demonstrate the potential of the Q-DiSA method for real-time broadband spectral analysis.

A Photonics-Inspired Compact Network: Toward Real-Time AI Processing in Communication Systems

Machine learning methods are ubiquitous in communication systems and have proven powerful for applications including radio-frequency (RF) fingerprinting, automatic modulation classification, and signal recovery in communication systems. However, the high throughput requirement of a communication link makes AI models difficult to implement in real-time on edge devices. In this work, we address this issue by improving both the algorithm and hardware to target real-time AI processing in communication systems. For algorithm development, we propose the first compact deep network consisting of a silicon photonic recurrent neural network model in combination with a simplified convolutional neural network classifier to identify RF emitters by their random transmissions. Our model achieves 96.32% classification accuracy over a set of 30 identical ZigBee devices when using 50 times fewer training parameters than an existing state-of-the-art CNN classifier (Merchant et al., 2018). Thanks to the large reduction in network size, we emulate the system using a small-scale FPGA board, the PYNQ-Z1, and demonstrate real-time RF fingerprinting with 0.219 ms latency. In addition, for hardware implementation, we further demonstrate a fully-integrated silicon photonic neural network for fiber nonlinearity compensation (Huang et al., 2021), which improves the received signal by 0.60 dB.

Accuracy and clinical feasibility of 3D-myocardial thickness map measured by cardiac computed tomogram

BackgroundAlthough myocardial thickness is an important variable for therapeutic catheter ablation of cardiac arrhythmias, quantification of wall thickness has been overlooked. We developed a software (AMBER) that measures 3D-myocardial thickness using a cardiac computed tomogram (CT) image, verified its accuracy, and tested its clinical feasibility.MethodsWe generated 3D-thickness maps by calculating wall thickness (WT) from the CT images of 120 patients’ hearts and a 3D-phantom model (PhM). The initial vector field of the Laplace equation was oriented to calculate WT with the field lines derived from the 3D mesh. We demonstrate the robustness of the Laplace WT algorithm by comparing with the real thickness of 3D-PhM, echocardiographically measured left ventricular (LV) WT, and regional left atrial (LA) WT reported from previous studies. We conducted a pilot case of catheter ablation for atrial fibrillation (AF) utilizing real-time LAWT map-guided radiofrequency (RF) energy titration.ResultsAMBER 3D-WT had excellent correlations with the real thickness of the PhM (R = 0.968, p < 0.001) and echocardiographically measured LVWT in 10 patients (R = 0.656, p = 0.007). AMBER 3D-LAWT (n = 120) showed a relatively good match with 12 previously reported regional LAWT. We successfully conducted pilot AF ablation utilizing AMBER 3D-LAWT map-guided real-time RF energy titration.ConclusionWe developed and verified an AMBER 3D-cardiac thickness map measured by cardiac CT images for LAWT and LVWT, and tested its feasibility for RF energy titration during clinical catheter ablation.

FPGA-Assisted Real-Time RF Wireless Data Analytics System: Design, Implementation, and Statistical Analyses

A wide range of new ultra reliable low latency communication (URLLC) applications in next generation (NG) wireless systems demand real-time radio frequency (RF) data analytics of channel utilization (CU) that can help in making proactive resource allocation decisions. However, such real-time RF data analytics require processing of tens of millions of in-phase and quadrature (IQ) samples per second and sending huge quantities of samples to a resource allocating entity is not practical. We present design and implementation of an RF data analytics system which utilizes field-programmable gate arrays (FPGAs) at the network edge to process real-time streaming IQ samples from RF transceiver. FPGAs process millions of samples per second and output low-overhead descriptive statistics of wireless CU, such as mean CU values, maximum CU values, and entire histograms to obtain probability distribution of CU values, to a resource controller server where a quantile estimation based technique is used to detect congestion in CU in real-time. The FPGA-based modules are implemented on Xilinx's Zynq-7000 devices mounted with RF transceivers. We evaluate the performance of the implemented analytics system using extensive measurements, testing, and statistical analyses that are performed in both laboratory and over-the-air environments.

A New Indoor Location Algorithm Based on Radio Frequency Fingerprint Matching

Indoor positioning technology plays an important role in the production and life of today's society. Modern people's clothing, food, housing, and transportation are increasingly closely related to Location Based Service. In this paper, an indoor location algorithm based on the radio frequency fingerprint matching is offered. In the offline phase, by deploying a small number of radio signal intensity location regions, the location selection mechanism is used to predict the location of mobile terminals, which can avoid the instability of radio frequency signals caused by time-lapse and the impact of changes in Access Point (AP) facilities. In the online stage, the location engine receives real-time Radio Frequency fingerprints and updates the fingerprint location by mapping time and equipment. Finally, several main localization algorithms are simulated and compared to the aspect of location accuracy. The proposed new method reduces the location error, improves the location accuracy by 1 to 2 meters, and achieves 100% within 2.5 meters.

Filter Optimization for Real-Time Digital Processing of Radio Frequency Signals: Application to Oscillator Metrology.

Software-defined radio (SDR) provides stability, flexibility, and reconfigurability to radio frequency signal processing. Applied to oscillator characterization in the context of ultrastable clocks, stringent filtering requirements are defined by spurious signal or noise rejection needs. Since real-time radio frequency processing must be performed in a field-programmable array to meet timing constraints, we investigate optimization strategies to design filters meeting rejection characteristics while limiting the hardware resources required and keeping timing constraints within the targeted measurement bandwidths. The presented technique is applicable to scheduling any sequence of processing blocks characterized by a throughput, resource occupation and performance tabulated as a function of configuration characteristics, as is the case for filters with their coefficients and resolution yielding rejection and the number of multipliers.

Design and development of FPGA-based real-time RF cavity simulator using LabVIEW

ABSTRACT To understand the behaviour of RF (Radio Frequency) cavity in different machine operating conditions and to optimise the low-level radio frequency systems, FPGA (Field Programmable Gate Array)-based real-time RF cavity simulator is presented in this paper. LabVIEW is used for programming of FPGA to make this simulator user-friendly. For the implementation of the RF cavity, a suitable transfer function is derived in a continuous domain. By using bilinear transformation technique, this transfer function is discretised for implementation on FPGA. A Simulink model for this RF cavity is also implemented for verification of results. Here, Indus-2 RF cavity parameters are considered and fill time of cavity took a comparison factor.

ICE-MoCha: Intelligent Crowd Engineering using Mobility Characterization and Analytics.

Human injuries and casualties at entertaining, religious, or political crowd events often occur due to the lack of proper crowd safety management. For instance, for a large scale moving crowd, a minor accident can create a panic for the people to start stampede. Although many smart video surveillance tools, inspired by the recent advanced artificial intelligence (AI) technology and machine learning (ML) algorithms, enable object detection and identification, it is still challenging to predict the crowd mobility in real-time for preventing potential disasters. In this paper, we propose an intelligent crowd engineering platform using mobility characterization and analytics named ICE-MoCha. ICE-MoCha is to assist safety management for mobile crowd events by predicting and thus helping to prevent potential disasters through real-time radio frequency (RF) data characterization and analysis. The existing video surveillance based approaches lack scalability thus have limitations in its capability for wide open areas of crowd events. Via effectively integrating RF signal analysis, our approach can enhance safety management for mobile crowd. We particularly tackle the problems of identification, speed, and direction detection for the mobile group, among various crowd mobility characteristics. We then apply those group semantics to track the crowd status and predict any potential accidents and disasters. Taking the advantages of power-efficiency, cost-effectiveness, and ubiquitous availability, we specifically use and analyze a Bluetooth low energy (BLE) signal. We have conducted experiments of ICE-MoCha in a real crowd event as well as controlled indoor and outdoor lab environments. The results show the feasibility of ICE-MoCha detecting the mobile crowd characteristics in real-time, indicating it can effectively help the crowd management tasks to avoid potential crowd movement related incidents.

Design and Implementation of an RFID-GSM-Based Vehicle Identification System on Highways

In this paper, a real-time radio frequency identification (RFID)-GSM-based vehicle identification system compatible with IEEE802.15.4 is designed and implemented at 2.4 GHz, which provides a full automation of highway scanning far away from the monitoring station. Once the RFID reader broadcasts “auto-highway-scanning” RF signal, each tagged vehicle within the RF field of 80 m from the reader runs a collision-avoidance scheme involving two strategies. First, the unique tag’s CC2530F256 SoC MAC address is used to generate a fixed waiting time. The second strategy utilizes tag’s CC2530F256 pseudorandom number generator to add a random value to the first strategy output. This strategy makes the proposed scheme dynamic and secure, because it prevents the attackers from accessing to the tag ID by estimating fixed waiting time of the first strategy. Such a collision-avoidance scheme overcomes the conventional methods constraints, such as tag population estimation latency in Aloha-based methods and time-consuming lengthy queries in Tree-based protocols. Simulation results show that the collision is avoided by using the carrier sensing capability and the identification efficiency of 63% is achieved by the proposed scheme, which is more efficient than conventional tag anti-collision schemes, such as ISO/IEC 18000-7, CSMA non persistent, CSMA p-persistent, QT, CT, and QWT. Besides, experimental results prove that the above-mentioned scheme works properly.

Real-time broadband radio frequency spectrum analyzer based on parametric spectro-temporal analyzer (PASTA).

A real-time broadband radio frequency (RF) spectrum analyzer is proposed and experimentally demonstrated to rapidly measure the RF spectrum of broadband optical signal. Cross phase modulation in the highly-nonlinear fiber is used to convert the RF spectrum carried by the pump to the optical spectrum of the probe signal, then the optical spectrum is real-time analyzed with the parametric spectro-temporal analyzer (PASTA) technology. The system performances are investigated in detail, including bandwidth, resolution, frame rate, and dynamic range. It achieves large RF bandwidth of over 800 GHz, as well as 91-MHz frame rate without sacrificing the resolution. It is noted that 91-MHz frame rate is several orders of magnitude improvement over those previous reported all-optical RF spectrum analyzers. As a proof-of-concept demonstration, this real-time broadband RF spectrum analyzer successfully characterizes the ultra-short pulse trains with repetition rate of 160GHz, which is far beyond capability of the conventional electrical spectrum analyzer. It presents a new way to implement rapid and broadband RF spectrum measurement, and would be of great interests for some ultrafast scenarios, where the real-time RF spectrum analysis can be applied.

Prediction of radiofrequency ablation lesion formation using a novel temperature sensing technology incorporated in a force sensing catheter

Real-time radiofrequency (RF) ablation lesion assessment is a major unmet need in cardiac electrophysiology. The purpose of this study was to assess whether improved temperature measurement using a novel thermocoupling (TC) technology combined with information derived from impedance change, contact force (CF) sensing, and catheter orientation allows accurate real-time prediction of ablation lesion formation. RF ablation lesions were delivered in the ventricles of 15 swine using a novel externally irrigated-tip catheter containing 6 miniature TC sensors in addition to force sensing technology. Ablation duration, power, irrigation rate, impedance drop, CF, and temperature from each sensor were recorded. The catheter "orientation factor" was calculated using measurements from the different TC sensors. Information derived from all the sources was included in a mathematical model developed to predict lesion depth and validated against histologic measurements. A total of 143 ablation lesions were delivered to the left ventricle (n = 74) and right ventricle (n = 69). Mean CF applied during the ablations was 14.34 ± 3.55g, and mean impedance drop achieved during the ablations was 17.5 ± 6.41 Ω. Mean difference between predicted and measured ablation lesion depth was 0.72 ± 0.56 mm. In the majority of lesions (91.6%), the difference between estimated and measured depth was ≤1.5 mm. Accurate real-time prediction of RF lesion depth is feasible using a novel ablation catheter-based system in conjunction with a mathematical prediction model, combining elaborate temperature measurements with information derived from catheter orientation, CF sensing, impedance change, and additional ablation parameters.

A Compact, High Power Capable, and Tunable High Directivity Microstrip Coupler

A coupler is an indispensable component to sample the forward and reflected power for the real-time radio frequency (RF) power monitoring system. The directivity of a coupler is a critical factor to achieve accurate RF power measurements. This paper proposes a microstrip coupler with a tunable high directivity circuit to accurately measure the reflected RF power. The directivity tuner composed of passive components adjusts phase and amplitude of the coupled RF signal, and cancel out the leakage signal from the RF input port at the coupled reflection port. The experimental results, which agree with simulation results, show that the microstrip coupler with the directivity tuner circuit has a compact size (~ 0.07 λg x 0.05 λg), high power capability (up to 1 kW), and high directivities (more than 40 dB) at operating frequency bands (f = 297.3 MHz, 400 MHz, and 447 MHz, respectively) for magnetic resonance imaging (MRI) applications.

Continuous Calibration of Microring Weights for Analog Optical Networks

Continuously configurable weighted addition is a key function for multivariate analog signal processing. The wavelength-division multiplexed version of weighted addition based on microrings can apply the strengths of photonic integration, wideband photonic information processing, and multiwavelength networks toward new approaches to ultrafast analog distributed processing. In this letter, we experimentally demonstrate single-channel functionality using a 5-GHz signal, showing a continuous range of complementary (+/−) weighting in a bank of silicon microrings using a feedforward controller and balanced detection technique. Weight tuning accuracy with a dynamic range of 9.2 dB (i.e., 3.1 bits) is shown, and steps to improve performance are discussed. Systems of more microring weights and weight banks could address hitherto unsolvable problems in real-time radio frequency processing and high-performance control.

Control of intravascular catheters using an array of active steering coils.

To extend the concept of deflecting the tip of a catheter with the magnetic force created in an MRI system through the use of an array of independently controllable steering coils located in the catheter tip, and to present methods for visualization of the catheter and/or surrounding areas while the catheter is deflected. An array of steering coils made of 42-gauge wire was built over a 2.5 Fr (0.83 mm) fiber braided microcatheter. Two of the coils were 70 turn axial coils separated by 1 cm, and the third was a 15-turn square side coil that was 2 x 4 mm2. Each coil was driven independently by a pulse width modulation (PWM) current source controlled by a microprocessor that received commands from a MATLAB routine that dynamically set current amplitude and direction for each coil. The catheter was immersed in a water phantom containing 1% Gd-DTPA that was placed at the isocenter of a 1.5 T MRI scanner. Deflections of the catheter tip were measured from image-based data obtained with a real-time radio frequency (RF) spoiled gradient echo sequence (GRE). The small local magnetic fields generated by the steering coils were exploited to generate a hyperintense signal at the catheter tip by using a modified GRE sequence that did not include slice-select rewinding gradients. Imaging and excitation modes were implemented by synchronizing the excitation of the steering coil array with the scanner by ensuring that no current was driven through the coils during the data acquisition window; this allowed visualization of the surrounding tissue while not affecting the desired catheter position. Deflections as large as 2.5 cm were measured when exciting the steering coils sequentially with a 100 mA maximum current per coil. When exciting a single axial coil, the deflection was half this value with 30% higher current. A hyperintense catheter tip useful for catheter tracking was obtained by imaging with the modified GRE sequence. Clear visualization of the areas surrounding the catheter was obtained by using the excitation and imaging mode even with a repetition time (TR) as small as 10 ms. A new system for catheter steering is presented that allows large deflections through the use of an integrated array of steering coils. Additionally, two imaging techniques for tracking the catheter tip and visualization of surrounding areas, without interference from the active catheter, were shown. Together the demonstrated steerable catheter, control system and the imaging techniques will ultimately contribute to the development of a steerable system for interventional MRI procedures.

A framework for statistical wireless spectrum occupancy modeling

In this paper, we propose a novel spectrum occupancy model designed to generate accurate temporal and frequency behavior of various wireless transmissions. Our proposed work builds upon existing concepts in open literature in order to develop a more accurate time-varying spectrum occupancy model. This model can be employed by wireless researchers for evaluating new wireless communication and networking algorithms and techniques designed to perform dynamic spectrum access (DSA). Using statistical characteristics extracted from actual radio frequency measurements, first- and second-order parameters are employed in a statistical spectrum occupancy model based on a combination of several different probability density functions (PDFs) defining various features of a specific spectrum band with several concurrent transmissions. To assess the accuracy of the model, the output characteristics of the proposed spectrum occupancy model are compared with realtime radio frequency measurements in the television and paging bands.

1281: Sonazoid-Enhanced Sonographically Guided Real-time Radiofrequency Ablation for Liver Tumors

1281: Sonazoid-Enhanced Sonographically Guided Real-time Radiofrequency Ablation for Liver Tumors