Radio Frequency Sensors Research Articles

Numerical model of N-level cascade systems for atomic Radio Frequency sensing applications

AbstractA ready-to-use numerical model has been developed for the atomic ladder (cascade) systems which are widely exploited in Rydberg Radio Frequency (RF) sensors. The model has been explicitly designed for user convenience and to be extensible to arbitrary N-level non-thermal systems. The versatility and adaptability of the model is validated up to 4-level atomic systems by direct comparison with experimental results from the prior art. The numerical model provides a good approximation to the experimental results and provides experimentalists with a convenient ready-to-use model to optimise the operation of an N-level Rydberg RF sensor. Current sensors exploit the 4-level atomic systems based on alkali metal atoms which require visible frequency lasers and these can be expensive and also suffer from high attenuation within optical fiber. The ability to quickly and simply explore more complex N-level systems offers the potential to use cheaper and lower-loss near-infrared lasers.

Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks.

For any radio frequency (RF) sensor (receiver) to function optimally, the ambient noise field strength, converted to electrical power by the transducer (antenna), must be lower than the in-ternal noise of that sensor. Therefore, knowledge of the expected ambient noise level is essential for the design of sensors for earth observation, atmospheric research, radio astronomy or navigation. The International Telecommunication Union (ITU) provides a model that predicts ambient man-made noise levels, differentiated by frequency, origin and environment. This is entirely empirical model is based on data from the 1960's and 1970's. In recent years, 90,205 noise measurements have been collected to update the model. The analysis of that data set presented here is essential as it shows a pitfall to avoid: despite to size of the data set it is sparce over the parameter space, and unacceptable biases occur when a purely empirical model is based on them. The paper proposes another approach: to create a mathematical model based on physics that can be fine-tuned and validated using these collected measurements, without producing the biases. A revolutionary side effect of such a model would be the linking of two currently isolated domains, that of spectrum management and electromagnetic compatibility.

Design of compact signal source with reduced phase noise for airborne pulse Doppler RF sensor

Abstract In this article, a low phase noise signal source to be used as local oscillator in pulse Doppler radio frequency (PDRF) sensor is proposed. Innovative design techniques for realization of the low phase noise frequency source using phase-locked loop (PLL) and dielectric resonator (DR) are presented. Qualitative investigations have been carried out on the effect of phase noise in PDRF sensor performance. An X-band vibration resistant PLL-based frequency source with phase noise better than −95 dBc at 1 kHz frequency offset has been designed here. It also presents the design of a 7.6 GHz low phase noise, vibration resistant DR oscillator. Systematic analysis of the key design aspects, their thermal-vibrational stability, and ease of integration with hybrid microwave integrated circuits have been disclosed. A prototype board is fabricated, assembled in a compact mechanical enclosure of dimension 55 × 55 × 15 mm3. Finally, developed module is experimentally validated under 7.6 g rms magnitude random vibration test in three axes and compared results with other state of-the-art similar works. The comparison clearly shows the merit of present research work over other similar existing works.

Adaptive Radio Frequency Sensor Enabled by Electromechanically Controlled Stretchable Rectifying Antenna Systems

Adaptive Radio Frequency Sensor Enabled by Electromechanically Controlled Stretchable Rectifying Antenna Systems

Detection of unmanned aerial vehicles using thermal imaging technology

The detection of UAVs can be carried out using their information features in various ranges: acoustic, radio, optical. Acoustic (ultrasonic) sensors, radar, radio frequency sensors, laser locators, electron-optical / IR sensors (direction finders) are used for their registration. This article will consider the possibilities of detecting UAVs using thermal imagers, pay attention to the characteristics of the resolution of thermal imaging systems, the signal-to-noise ratio, the possibilities of polarization processing of recorded thermal radiation, and the results of some foreign studies on the detection (recognition) of UAVs using thermal imaging cameras.

Unmanned vehicles in health monitoring and medicine delivery with swarm algorithm innovations

The article delves into modern approaches and innovative technologies designed to monitor the health of military personnel using unmanned vehicles, while also assessing their potential for delivering medicines in combat environments. It thoroughly examines the wide range of sensors and technologies that enable remote measurement of vital signs, including but not limited to thermal imaging cameras, photoplethysmography sensors, and radio frequency sensors, highlighting their effectiveness in various operational contexts. The article also reviews numerous real-world applications of drones in both medical and humanitarian projects, showcasing the transformative impact these technologies can have in providing timely healthcare and support in areas where traditional medical infrastructure may be lacking. In addition to discussing the practical benefits of using unmanned vehicles for healthcare delivery, the article also addresses the challenges posed by their integration into military operations, such as logistical, technical, and regulatory hurdles. Furthermore, it presents a detailed example of how these innovative systems can be effectively combined to enhance the medical care provided to military personnel, especially during combat, where traditional access to medical services is often limited or compromised. The article proposes several key areas for future research and development, emphasizing the importance of continued technological advancements to fully harness the potential of unmanned vehicles for medical applications in the military sector. These areas include improving sensor accuracy, developing better communication systems, and creating more autonomous and efficient delivery mechanisms. Here are examples that prove, the article suggests that unmanned vehicles could play a critical role in revolutionizing both battlefield medicine and military logistics, offering new solutions for healthcare delivery in challenging environments.

A Vision and Proof of Concept for New Approach to Monitoring for Safer Future Smart Transportation Systems.

Transportation infrastructure experiences distress due to aging, overuse, and climate changes. To reduce maintenance costs and labor, researchers have developed various structural health monitoring systems. However, the existing systems are designed for short-term monitoring and do not quantify structural parameters. A long-term monitoring system that quantifies structural parameters is needed to improve the quality of monitoring. In this work, a novel Transportation Rf-bAsed Monitoring (TRAM) system is proposed. TRAM is a multi-parameter monitoring system that relies on embeddable backscatter-based, batteryless, and radio-frequency sensors. The system can monitor structural parameters with 3D spatial and temporal information. Laboratory experiments were conducted on a 1D scale to evaluate and examine the sensitivity and reliability of the monitored structural parameters, which are displacement and water content. In contrast to other existing methods, TRAM correlates phase change to the change in concerned parameters, enabling long-term monitoring.

RF-GymCare: Introducing Respiratory Prior for RF Sensing in Gym Environments

Exercise-induced sudden death has become an increasingly serious health issue in recent years. Fortunately, respiratory monitoring can serve as an effective early prevention solution. In particular, radio frequency (RF) sensing has emerged as a promising solution due to its contactless nature and privacy preservation. Existing RF-based respiratory monitoring solutions for homes, offices, or vehicles would suffer from significant performance degradation when deployed in typical gym workouts, where respiratory and physical movements are mixed in the RF signals in an unpredictable manner, leading to a blind source separation (BSS) problem. What is worse, the prior assumptions of source independence in existing BSS algorithms no longer hold, leaving respiratory monitoring in gym scenarios as an open issue. In this paper, we propose RF-GymCare, which introduces respiratory priors into the BSS problem and utilizes knowledge distillation to transfer these priors, aiming to learn the demixing weight from mixed signals. With the demixing weight, the respiratory signal can be obtained through a linear combination of the mixed signals. Our experiments with 13 hours of data across nine types of exercises demonstrate its superiority over existing methods, achieving a median similarity of 0.75 and a median rate error of 0.5 respiration per minute (RPM), respectively.

Validation of a micro-doppler radar for measuring gait modifications during multidirectional visual perturbations

BackgroundChanges in spatio-temporal gait parameters and their variability during balance-challenging tasks are markers of motor performance linked to fall risk. Radio frequency (RF) sensors hold great promise towards achieving continuous remote monitoring of these parameters. Research questionsTo establish the concurrent validity of RF-based gait metrics extracted using micro-Doppler (µD) signatures and to determine whether these metrics are sensitive to gait modifications created by multidirectional visual perturbations. MethodsFifteen participants walked overground in a virtual environment (VE) and VE with medio-lateral (ML) and antero-posterior (AP) perturbations. An optoelectronic motion capture system and one RF sensor were used to extract the linear velocity of the trunk and estimate step time (ST), step velocity (SV), step length (SL), and their variability (STV, SVV, and SLV). Intra-class coefficient for consistency (ICC), mean and standard deviation of the differences (MD), 95 % limits of agreement, and Pearson correlation coefficients (r) were used to determine concurrent validity. One-way repeated-measures analysis of variance was used to analyze the main and interaction effects of visual conditions. ResultsAll outcomes showed good to excellent reliability (r>0.795, ICC>0.886). Average gait parameters showed good to excellent agreement, with values obtained with the RF sensor systematically smaller than the values obtained with the markers (MD of 0.001 s, 0.09 m/s, and 0.06 m). Gait variability parameters showed poor to moderate agreement, with values obtained with the RF sensor systematically larger than those obtained with the markers (MD of 1.9 %-3.9 %). Both measurement systems reported decreased SL and SV during ML perturbations, but the gait variability parameters extracted with the radar were not able to detect the higher STV and SLV during this condition. SignificanceThe radar µD signature is a valid and reliable method for the assessment of average spatio-temporal gait parameters but gait variability measures need to be viewed with caution because of the lower levels of agreement and sensitivity to ML visual perturbations. This work represents an initial investigation for the development of a low-cost system that will facilitate aging-in-place by providing remote monitoring of gait in natural settings.

U-TAG: Electromagnetic Wireless Sensing System for Robotic Hand Pre-Grasping.

In order to perform complex manipulation and grasp tasks, robotic hands require sensors that can handle increasingly demanding functionality and degrees of freedom. This research paper proposes a radiofrequency sensor that uses a wireless connection between a probe and a tag. A compact and low-profile antenna is mounted on the hand and functions as a probe to read a printed passive resonator on the plastic object being targeted, operating within a pre-touch sensing range. The grasping strategy consists of four stages that involve planar alignment in up-to-down and left-to-right directions between the probe and tag, the search for an appropriate distance from the object, and rotational (angular) alignment. The real and imaginary components of the probe-input impedance are analyzed for different orientation strategies and positioning between the resonator on the object and the probe. These data are used to deduce the orientation of the hand relative to the target object and to determine the optimal position for grasping.

Rydberg-atom-based radio-frequency sensors: amplitude-regime sensing.

Rydberg atom-based radio frequency electromagnetic field sensors are drawing wide-spread interest because of their unique properties, such as small size, dielectric construction, and self-calibration. These photonic sensors use lasers to prepare atoms and read out the atomic response to a radio frequency electromagnetic field based on electromagnetically induced transparency, or related phenomena. Much of the theoretical work has focused on the Autler-Townes splitting induced by the radio frequency wave. The amplitude regime, where the change in transmission observed on resonance is measured to determine electric field strength, has received less attention. In this paper, we deliver analytic expressions that are useful for calculating the absorption coefficient in the amplitude regime. Our main goal is to describe the analytic expressions for the absorption coefficient and demonstrate their validity over a large range of the interesting parameter space. The effect of the thermal motion of the atoms is explicitly addressed. The analytic formulas for the absorption coefficient for different types of Doppler broadening are compared to estimate the sensitivity under conditions where it is limited by the laser shot noise. Residual Doppler shifts are shown to limit sensitivity. The expressions, approximations and descriptions presented in the paper are important for understanding the absorption of Rydberg atom-based sensors in the amplitude regime. This provides insight into the physics of multi-level interference phenomena.

Image-based intrusion detection system for GPS spoofing cyberattacks in unmanned aerial vehicles

The operations of unmanned aerial vehicles (UAVs) are susceptible to cybersecurity risks, mainly because of their firm reliance on the Global Positioning System (GPS) and radio frequency (RF) sensors. GPS and RF sensors are vulnerable to potential threats, such as spoofing attacks that can cause the UAVs to behave erratically. Since these threats are widespread and potent, it is imperative to develop effective intrusion detection systems. In this paper, we propose an image-based intrusion detection system for detecting GPS spoofing cyberattacks based on a deep learning methodology. We combine convolutional neural networks with Principal Component Analysis (PCA) to reduce the dimensionality of the dataset features, data augmentation to increase the size and diversity of the training dataset, and transfer learning to improve the proposed model’s performance with limited data to design a fast, accurate, and general method. Extensive numerical experiments demonstrate the effectiveness of the proposed solution carried out using benchmark datasets. We achieved an accuracy of 100% within a running time of 120.64 s at 0.3529 ms latency and a detection time of 2.035 s in the case of the training dataset. Further, using this trained model, we achieved an accuracy of 99.25% within a detection time of 2.721 s on an unseen dataset that was unrelated to the one used for training the model. In contrast, other models, such as Inception-v3, showed lower accuracy on unseen datasets. However, Inception-v3 performance improved significantly after Bayesian optimization, with the Tree-structured Parzen Estimator reaching 99.06% accuracy. Our results demonstrate that the proposed image-based intrusion detection method outperforms the existing solutions while providing a general model for detecting cyberattacks included in unseen datasets.

A Glycemic Status Classification Model Using a Radiofrequency Noninvasive Blood Glucose Monitor.

Despite significant efforts in the development of noninvasive blood glucose (BG) monitoring solutions, delivering an accurate, real-time BG measurement remains challenging. We sought to address this by using a novel radiofrequency (RF) glucose sensor to noninvasively classify glycemic status. The study included 31 participants aged 18-65 with prediabetes or type 2 diabetes and no other significant medical history. During control sessions and oral glucose tolerance test sessions, data were collected from both a RF sensor that rapidly scans thousands of frequencies and concurrently from a venous blood draw measured with an US Food and Drug Administration (FDA)-cleared glucose hospital meter system to create paired observations. We trained a time series forest machine learning model on 80% of the paired observations and reported results from applying the model to the remaining 20%. Our findings show that the model correctly classified glycemic status 93.37% of the time as high, normal, or low.

Proprioceptive swarms for celestial body exploration

The exploration of small celestial bodies has been a very active field ever since the dawn of space exploration. However, traditional approaches based on monolithic landers allow only the area in the immediate vicinity to the landing spot to be examined. Using mobile systems like rovers allow for better coverage, but at considerable cost and complexity. In this work, we propose an approach based on swarms of small nonmaneuverable agents, equipped with a sensor package, that are launched from a base station and made to land across the entire surface of the asteroid or comet. The methodology is based on multilateration using small range-finding radio frequency sensors aboard the agents; this enables position determination relative to the base station. Through dynamics simulation, we show that this approach is feasible even in highly irregular gravity fields where closed-form solutions for orbital mechanics are not available, such as the case of comet 67P/Churyumov–Gerasimenko. We show a sensitivity analysis of the absolute position error of the agents which originated from range-measurement error. The surface coverage is evaluated against the numerosity of the swarm. Finally, we show that allowing for a subset of agents to follow a long-period orbit around the object enables better localization of the landed agents, thus increasing the overall performance.

Advancing Wireless Strain Sensing with Low-cost Printable Supersensitive Radiofrequency Patches

Engineering structures experience changes in material and functional properties due to structural damage, induced by sudden events or gradual degradation over time. Structural Health Monitoring (SHM) is crucial, utilizing real-time monitoring systems with various sensors. Suitable sensors for monitoring structures are vital for future economies. They should feature sensitivity, cost-effectiveness, easy integration, and reduced complexity. Current strain sensors, like strain gauges and Fiber-optic Brag Gratings (FBG), are sensitive but have drawbacks, including wiring complexities, installation time, and limited lifetimes. Introducing wireless sensors as a solution, enables autonomous strain monitoring, overcoming the drawbacks of traditional wired sensors. Chipless Radio Frequency (RF) technology, specifically passive RF sensors, stands out for smart structure development. These sensors, relying on RF readers for power, offer compactness, non-intrusiveness, longer lifetimes, and cost-effectiveness, making them promising for monitoring large structures. This research aims to develop an innovative chipless RF strain sensor for wireless communication, emphasizing enhanced sensitivity and seamless integration into real structures for integrity monitoring. First, the structure and processing technique of such sensors will be presented. Second, an external readout coil connected to a vector network analyzer was used to make electromagnetic coupling with the receiver coil in the sensor and resonate the sensor on its resonant frequency. When experiencing strain, the signal activates a transmission line mechanism, increasing the attenuation along the capacitive element. The consequence is a strong variation in the effective sensor length making a remarkable variation in the resonant frequency. We demonstrate the exceptional gauge factor in the resonant frequency at different strain levels, which gives our sensor a big advantage, compared to pure resistive sensors and geometrically related capacitive sensors. We also demonstrate that such sensors can be fabricated in large quantities at very low cost as they are by design compatible with classical printing technologies.

Intelligent predictions for flow pattern and phase fraction of a horizontal gas-liquid flow

In-situ measurement of phase fraction of a gas-liquid flow is closely related to the production efficiency in natural gas extraction. However, the measurement accuracy can be affected by the co-existed multiple flow patterns. This study proposes an intelligent strategy that identifies the flow pattern followed by a phase fraction prediction. For flow pattern recognition, we establish a bidirectional long short-term memory (BI-LSTM) network whose inputs are time-series phases of a Radio Frequency Sensor (RFS). The accuracy is 92.4 % over four classical flow patterns. The time-series phases of RFS are agreed well with the axial imaging from a Wire-Mesh Sensor (WMS). Two predictive models are developed for gas fraction: dimensionless analysis model (DAM) based on RFS and gas Froude number, and neural network model (NNM) with the phases of RFS and the recognized flow pattern. The mean absolute errors (MAE) are 3.2 % and 1.5 % for DAM and NNM, respectively. It is concluded that a NNM, incorporated with RFS and flow pattern by BI-LSTM, can intelligently predict gas fraction with high-accuracy. As the present strategy decouples the pattern recognition and gas fraction prediction into two networks, the complexity of a NNM is reduced which benefits the in-situ measurement practice.

Hydrogel-based radio frequency H2S sensor for in situ periodontitis monitoring and antibacterial treatment

Periodontitis, a chronic disease, can result in irreversible tooth loss and diminished quality of life, highlighting the significance of timely periodontitis monitoring and treatment. Meanwhile, hydrogen sulfide (H2S) in saliva, produced by pathogenic bacteria of periodontitis, is an important marker for periodontitis monitoring. However, the easy volatility and chemical instability of the molecule pose challenges to oral H2S sensing. Here, we report a wearable hydrogel-based radio frequency (RF) sensor capable of in situ H2S detection and antibacterial treatment. The RF sensor comprises an agarose hydrogel containing conjugated silver nanoparticles-chlorhexidine (AG-AgNPs-CHL hydrogel) integrated with split-ring resonators. Adhered to a tooth, the hydrogel-based RF sensor enables wireless transmission of sensing signals to a mobile terminal and a concurrent release of the broad-spectrum antibacterial agent chlorhexidine without complex circuits. With the selective binding of the AgNPs to the sulfidion, the RF sensor demonstrates good sensitivity, a wide detection range (2–30 μM), and a low limit of detection (1.2 μM). Compared with standard H2S measurement, the wireless H2S sensor can distinguish periodontitis patients from healthy individuals in saliva sample tests. The hydrogel-based wearable sensor will benefit patients with periodontitis by detecting disease-related biomarkers for practical oral health management.

Direction-finding for unmanned aerial vehicles using radio frequency methods

Nowadays, drones, also known as unmanned aerial vehicles (UAVs), are used for a wide range of purposes in the civil, industrial, and military domains. The greater the number of UAVs, the more serious the potential security risks correlated to them. To address these risks, it became necessary to develop systems that can detect, locate, and possibly neutralize such an object. The detection can be performed using different sensors such as imaging, radar, acoustic, and radio frequency (RF) sensors. The localization of the target drone, which can be done using direction finding (DF) methods, is an essential part of such a system. Thus, an RF-based DF testbed is designed and experimentally assessed in this paper. The testbed is implemented using software defined radio (SDR) platforms from the Universal Software Radio Peripheral (USRP) family as hardware equipment, and the MUSIC algorithm for processing the signals received on four different phase-coherent RF channels. A DJI Mavic Air UAV is used as target. For assessing the DF estimation performance, the direction obtained from the drone flight record is compared with the one estimated using the proposed DF testbed. Two different scenarios, a static one and a dynamic one, are considered to highlight the effectiveness of the proposed DF testbed across various conditions. The obtained results indicate an average estimation error of 1.15 degrees from the drone's actual position, in the case of the static scenario, and of 1.86 degrees in the case of the dynamic scenario, results which are on parity with or surpassed those of other drone DF systems described in the literature. The paper also outlines the benefits and challenges of the current approach.

Interactive learning of natural sign language with radar

AbstractOver the past decade, there have been great advancements in radio frequency sensor technology for human–computer interaction applications, such as gesture recognition, and human activity recognition more broadly. While there is a significant amount of study on these topics, in most cases, experimental data are acquired in controlled settings by directing participants what motion to articulate. However, especially for communicative motions, such as sign language, such directed data sets do not accurately capture natural, in situ articulations. This results in a difference in the distribution of directed American Sign Language (ASL) versus natural ASL, which severely degrades natural sign language recognition in real‐world scenarios. To overcome these challenges and acquire more representative data for training deep models, the authors develop an interactive gaming environment, ChessSIGN, which records video and radar data of participants as they play the game without any external direction. The authors investigate various ways of generating synthetic samples from directed ASL data, but show that ultimately such data does not offer much improvement over just initialising using imagery from ImageNet. In contrast, an interactive learning paradigm is proposed by the authors in which model training is shown to improve as more and more natural ASL samples are acquired and augmented via synthetic samples generated from a physics‐aware generative adversarial network. The authors show that the proposed approach enables the recognition of natural ASL in a real‐world setting, achieving an accuracy of 69% for 29 ASL signs—a 60% improvement over conventional training with directed ASL data.

Non-invasive Blood Glucose Monitoring Using a Radiofrequency Sensor and a Machine Learning Model

Diabetes Mellitus (DM) affects over 530 million people globally and is a condition characterized by high blood glucose (BG) that can result in severe long-term health consequences if poorly managed. To mitigate the risks associated with DM, it is crucial to regulate BG levels through regular monitoring. Current methods of monitoring BG come with drawbacks in the form of pain and expense. With the rapid increase in prevalence of DM, there is an increasing need for economical, accurate, and non-invasive continuous measures of BG. Here we present a validation for a novel sensor that rapidly scans through a wide range of radio frequencies (RF) and uses machine learning (ML) techniques for the purpose of non-invasive BG measurement. After model training, the RF sensor and ML techniques employed in this study can predict BG at a level significantly better than chance, as shown below. Data were collected from 13 healthy participants over a series of tests lasting 2 – 3 hours. During each test, participants placed their forearm on an RF sensor that measured their BG levels using sweeps across the 500 MHz – 1500 MHz range at 0.1 MHz intervals. Participants ingested 37.5 grams of glucose solution to generate BG readings across normoglycemic and hyperglycemic ranges. Each sweep took approximately 22 seconds, including a one second pause between sweeps. Across 110 tests, we collected 3,311 BG observations. Concurrent measurements from a Continuous Glucose Monitor (CGM) were taken as reference. Data were divided using a 60-20-20 (training-validation-test) split and trained on a Light Gradient-Boosting Machine (lightGBM) model to predict BG values. A ‘blind’ evaluation of model performance was determined using the held-out test dataset. We predicted BG values in the held-out test dataset with a Mean Absolute Relative Difference (MARD) of 10.8% in the normoglycemic range and 15.9% in the hyperglycemic range (11.27% overall MARD). These results were significantly better than a chance model (two-sample t-test, p <0.01). In a Surveillance Error Grid analysis of model accuracy, 89.0% of predictions fell in Risk Level 0, 10.1% in Risk Level 1, and 0.9% in Risk Level 2. These results demonstrate that the novel RF sensor and ML techniques described can predict a reference BG value in the population under study. More research is underway to refine and expand these methods using a gold-standard blood glucose reference device and among a broader participant population with an expanded glucose range. DK and VS are consultants for and own stock in Know Labs. JA and KC are employed by and have stock options in Know Labs. CW and KP are consultants for Know Labs. NA. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.