Radio Frequency Sensors Research Articles

Fusion-Vital: Video-RF Fusion Transformer for Advanced Remote Physiological Measurement

Remote physiology, which involves monitoring vital signs without the need for physical contact, has great potential for various applications. Current remote physiology methods rely only on a single camera or radio frequency (RF) sensor to capture the microscopic signatures from vital movements. However, our study shows that fusing deep RGB and RF features from both sensor streams can further improve performance. Because these multimodal features are defined in distinct dimensions and have varying contextual importance, the main challenge in the fusion process lies in the effective alignment of them and adaptive integration of features under dynamic scenarios. To address this challenge, we propose a novel vital sensing model, named Fusion-Vital, that combines the RGB and RF modalities through the new introduction of pairwise input formats and transformer-based fusion strategies. We also perform comprehensive experiments based on a newly collected and released remote vital dataset comprising synchronized video-RF sensors, showing the superiority of the fusion approach over the previous single-sensor baselines in various aspects.

Tracking of Bacteriophage Predation on Pseudomonas aeruginosa Using a New Radiofrequency Biofilm Sensor.

Confronting the challenge of biofilm resistance and widespread antimicrobial resistance (AMR), this study emphasizes the need for innovative monitoring methods and explores the potential of bacteriophages against bacterial biofilms. Traditional methods, like optical density (OD) measurements and confocal microscopy, crucial in studying biofilm-virus interactions, often lack real-time monitoring and early detection capabilities, especially for biofilm formation and low bacterial concentrations. Addressing these gaps, we developed a new real-time, label-free radiofrequency sensor for monitoring bacteria and biofilm growth. The sensor, an open-ended coaxial probe, offers enhanced monitoring of bacterial development stages. Tested on a biological model of bacteria and bacteriophages, our results indicate the limitations of traditional OD measurements, influenced by factors like sedimented cell fragments and biofilm formation on well walls. While confocal microscopy provides detailed 3D biofilm architecture, its real-time monitoring application is limited. Our novel approach using radio frequency measurements (300 MHz) overcomes these shortcomings. It facilitates a finer analysis of the dynamic interaction between bacterial populations and phages, detecting real-time subtle changes. This method reveals distinct phases and breakpoints in biofilm formation and virion interaction not captured by conventional techniques. This study underscores the sensor's potential in detecting irregular viral activity and assessing the efficacy of anti-biofilm treatments, contributing significantly to the understanding of biofilm dynamics. This research is vital in developing effective monitoring tools, guiding therapeutic strategies, and combating AMR.

Next-Generation Security: Detecting Suspicious Liquids Through Software Defined Radio Frequency Sensing and Machine Learning

Next-Generation Security: Detecting Suspicious Liquids Through Software Defined Radio Frequency Sensing and Machine Learning

Digitally-defined ultrathin transparent wireless sensor network for room-scale imperceptible ambient intelligence

Wireless and battery-free radio-frequency (RF) sensors can be used to create physical spaces that ambiently sense and respond to human activities. Making such sensors ultra-flexible and transparent is important to preserve the aesthetics of living environments, accommodate daily activities, and functionally integrate with objects. However, existing RF sensors are unable to simultaneously achieve high transparency, flexibility, and the electrical conductivity required for remote room-scale operation. Here, we report 4.5 µm RF tag sensors achieving transparency exceeding 90% that provide capabilities in room-scale ambient wireless sensing. We develop a laser-assisted water-based adhesion-reversion process to digitally realize computer-aided RF design at scale. By individually tagging multiple objects and regions of the human body, we demonstrate multiplexed wireless tracking of human-environment interactions and physiological signals at a range of up to 8 m. These radio-frequency identification sensors open opportunities for non-intrusive wireless sensing of daily living spaces for applications in health monitoring and elderly care.

A CFO-Assisted Algorithm for Wireless Time-Difference-of-Arrival Localization Networks: Analytical Study and Experimental Results

Localization of wireless transmitters is traditionally done using Radio Frequency (RF) sensors that measure the propagation delays between the transmitter and a set of anchor receivers. One of the major challenges of wireless localization systems is the need for anchor nodes to be time-synchronized to achieve accurate localization of a target node. Using a reference transmitter is an efficient way to synchronize the anchor nodes Over-The-Air (OTA), but such algorithms require multiple periodic messages to achieve tight synchronization. In this paper, we propose a new synchronization method that only requires a single message from a reference transmitter. The main idea is to use the Carrier Frequency Offset (CFO) from the reference node, alongside the Time of Arrival (ToA) of the reference node messages, to achieve tight synchronization. The ToA allows the anchor nodes to compensate for their absolute time offset, and the CFO allows the anchor nodes to compensate for their local oscillator drift. Additionally, using the CFO of the messages sent by the reference nodes and the target nodes also allow us to estimate the speed of the targets. The error of the proposed algorithm is derived analytically and is validated through controlled laboratory experiments. Finally, the algorithm is validated by realistic outdoor vehicular measurements with a software-defined radio testbed.

Ambient health sensing on passive surfaces using metamaterials.

Ambient sensors can continuously and unobtrusively monitor a person's health and well-being in everyday settings. Among various sensing modalities, wireless radio-frequency sensors offer exceptional sensitivity, immunity to lighting conditions, and privacy advantages. However, existing wireless sensors are susceptible to environmental interference and unable to capture detailed information from multiple body sites. Here, we present a technique to transform passive surfaces in the environment into highly sensitive and localized health sensors using metamaterials. Leveraging textiles' ubiquity, we engineer metamaterial textiles that mediate near-field interactions between wireless signals and the body for contactless and interference-free sensing. We demonstrate that passive surfaces functionalized by these metamaterials can provide hours-long cardiopulmonary monitoring with accuracy comparable to gold standards. We also show the potential of distributed sensors and machine learning for continuous blood pressure monitoring. Our approach enables passive environmental surfaces to be harnessed for ambient sensing and digital health applications.

From Field to Fork: The Role of AI and IoT in Agriculture

In "From Field to Fork: The Role of AI and IoT in Agriculture," we explore the transformative impact of cutting-edge technology in the farming industry and its journey through the food supply chain. This article delves into how the Internet of Things (IoT) and Artificial Intelligence (AI) are revolutionizing the way food is grown, processed, and brought to market. It looks at key technologies such as Radio Frequency Identification (RFID), sensor networks, and Cloud Computing, highlighting their role in enhancing food production, streamlining transportation, and improving distribution methods. The review sheds light on the growing importance of big data, gathered from sensors in the fields and online social media platforms, in refining agricultural methods, from monitoring crops using drones to bringing supply chains into the modern age. The paper also examines the vital function of AI in fine-tuning farming practices, safeguarding food quality, and ensuring food safety. Additionally, it addresses the need for secure data management in agricultural production and discusses how blockchain technology can provide reliable traceability from the field to consumers' tables. Wrapping up with an overview of current trends and future prospects, the article emphasizes the crucial role of AI and IoT not just in enhancing agricultural efficiency and minimizing waste, but also in their potential to meet Keywords: Fork, AI and IoT, Agriculture, food.

Unilateral RF sensors based on parallel-plate architecture for improved surface-scan MRI analysis of commercial pouch cells

Rapid expansion of the Li-ion pouch cell market is driven by the looming problem of permanent depletion of natural gas reservoirs and by the growing demand for high-performance portable devices and electric vehicles. Safety and performance of Li-ion cells have been two main focal points of the extensive battery research. Surface-scan Magnetic Resonance Imaging (MRI) is an operando method designed for the accurate detection of substandard battery cells and for monitoring electrochemical processes with high spatial and temporal resolutions. Intercalation-dependent magnetism and charge transfer processes in the cell's electrodes give rise to characteristic magnetic field patterns outside the cell. For accurate mapping of such patterns, we proposed the concept of a unilateral radio-frequency (RF) sensor, a flat thin resonator encapsulating a proton-rich solid-state detection medium. When the pouch cell is placed in direct contact with the sensor, the magnetic field patterns propagate inside the detection medium, and the corresponding spatial distribution of Larmor precession frequencies can be detected with MRI. In this work, we developed and evaluated a series of RF sensor configurations based on parallel-plate architecture enhanced by arrays of distributed capacitors. The parallel-plate approach does not suffer from RF interference with pouch cells and provides excellent sensitivity and B1-field homogeneity. The optimal configuration of the parallel-plate sensor depends on the dimensions of the pouch cell and the distribution of parallel capacitors. This article includes the results of experimental tests, RF-field simulations, and strategies to further improve the surface-scan MRI method.

Contactless fall detection with radio frequency sensors for the aging population

AbstractBackgroundFalls remain a primary concern for People with Dementia (PwD) living in long‐term care facilities (LTCFs) despite many prevention and intervention methods that have tried. Current fall detection monitoring systems use wearable or vision‐based sensors. The problem is research has shown that many seniors will not consistently wear a device on their body or allow cameras in their living space because of privacy concerns that severely limit the technology user acceptance. The current standard of care is self‐report or eyewitness and therefore fall events are often undetected and underreported due to human error. Hence, we are proposing a solution to address this unmet need with a novel fall management system engineered with radio frequency (RF) sensors that do not require an individual to wear or interact with a device.MethodSix healthy participants simulated three types of falls, medial and lateral, anterior and posterior, and collapse in the Dementia and Elderly Care Robotics and Sensing (DECRS) Lab, University of Minnesota Duluth. The sensor was installed in the lab as it would be in the resident room of an LCTF. The simulated room included a bed, chair, and mattress on the floor to protect the participants.ResultOur study found that the contactless fall detection and activity system identified falls at overall sensitivity rate of 89% with a specificity of 100% in 748 tests and activity was identified at 90%. Our system identified “medial and lateral” falls with a 92% sensitivity, “anterior and posterior” falls with a 91% sensitivity, and “collapse” falls with a 75% sensitivity.ConclusionContactless sensing holds tremendous potential for monitoring individuals living with AD/ADRD and the elderly population for aging in place. LTCFs that are short staffed could benefit by utilizing tools that augment the capabilities of nurses with a continuous collection of patient‐generated health data (PGHD) and a notification system that is integrated with existing workflows and only send alerts for potential events that would require immediate attention.

Contactless fall detection with radio frequency sensors for the aging population

AbstractBackgroundFalls remain a primary concern for People with Dementia (PwD) living in long‐term care facilities (LTCFs) despite many prevention and intervention methods that have tried. Current fall detection monitoring systems use wearable or vision‐based sensors. The problem is research has shown that many seniors will not consistently wear a device on their body or allow cameras in their living space because of privacy concerns that severely limit the technology user acceptance. The current standard of care is self‐report or eyewitness and therefore fall events are often undetected and underreported due to human error. Hence, we are proposing a solution to address this unmet need with a novel fall management system engineered with radio frequency (RF) sensors that do not require an individual to wear or interact with a device.MethodSix healthy participants simulated three types of falls, medial and lateral, anterior and posterior, and collapse in the Dementia and Elderly Care Robotics and Sensing (DECRS) Lab, University of Minnesota Duluth. The sensor was installed in the lab as it would be in the resident room of an LCTF. The simulated room included a bed, chair, and mattress on the floor to protect the participants.ResultOur study found that the contactless fall detection and activity system identified falls at overall sensitivity rate of 89% with a specificity of 100% in 748 tests and activity was identified at 90%. Our system identified “medial and lateral” falls with a 92% sensitivity, “anterior and posterior” falls with a 91% sensitivity, and “collapse” falls with a 75% sensitivity.ConclusionContactless sensing holds tremendous potential for monitoring individuals living with AD/ADRD and the elderly population for aging in place. LTCFs that are short staffed could benefit by utilizing tools that augment the capabilities of nurses with a continuous collection of patient‐generated health data (PGHD) and a notification system that is integrated with existing workflows and only send alerts for potential events that would require immediate attention.

Exploiting Wireless Communication Using Software-defined Radio Frequency Sensing For E-health APPLICATIONS

Exploiting Wireless Communication Using Software-defined Radio Frequency Sensing For E-health APPLICATIONS

Highly sensitive RF sensor based on microstrip meander line for measuring microfluidics dielectric properties of alcohol–alcohol mixtures

Abstract In this paper, a radio frequency sensor for measuring microfluidics dielectric properties is designed based on microstrip meander line. The meander sensor replaces the straight transmission lines with meander transmission lines in the part of the half-wavelength path difference to improve the sensitivity of the sensor and reduce its size. According to the experimental results, the meander sensor based on the meander line has higher accuracy and a lower relative error than the straight sensor in measuring methanol–ethanol mixtures with different molar fractions. The relative error measured by the meander sensor after calibration with an adjustable cavity is less than 1%. It is easier to detect the very slight variation in dielectric properties brought about by microfluidics. The detection technique can be further applied for the accurate detection of dielectric properties of valuable biological samples, providing a more concise and convenient way.

Frequency tunable coherent perfect absorption and lasing in radio-frequency system for ultrahigh-sensitive sensing

A coherent perfect absorption-lasing (CPAL) point in non-Hermitian physics systems has infinite output amplitudes (or quality factor) theoretically and has been utilized for applications. However, practical implementation of CPAL systems faces extreme challenges because of the deviations of electronic components from ideal values, which makes it difficult to attain the CPAL state. Moreover, the amplitude-based output is easily interfered by noises or environments and is inferior as compared to frequency shift-based sensing. Through the investigation of the effects of component deviations on general CPAL systems, we propose an eigenstate tuning method specifically tailored for radio-frequency (RF) CPAL systems. The method enables the realization of CPAL states at any desired frequency with dynamic ranges of signal amplitudes exceeding 60 dB experimentally or to work at a frequency shift-based sensing. Then, a CPAL-based RF sensor system is developed for verification, showing an ultrahigh sensitivity of 1.9 dB/10 fF by measuring output coefficient variation or 9.4 MHz/pF by measuring frequency shift, significantly broadening the application scope of CPAL systems.

Preliminary feasibility of a wrist-worn receiver to measure medication adherence via an ingestible radiofrequency sensor.

Adherence to medications is a complex task that requires complex biobehavioral support. To better provide tools to assist with medication adherence, digital pills provide an option to directly measure medication taking behaviors. These systems comprise a gelatin capsule with radiofrequency emitter, a wearable Reader that collects the radio signal and a smartphone app that collects ingestion data displays it for patients and clinicians. These systems are feasible in measuring adherence in the real-world, even in stigmatized diseases like HIV treatment adherence. While the current iteration of the digital pill system utilizes a wearable Reader worn like a necklace, preliminary feedback demonstrated that a miniaturized system that was worn on the wrist could be more functional in the real-world. This paper therefore describes the development and preliminary field testing of a wrist-borne wearable Reader to facilitate acquisition of oral HIV pre-exposure prophylaxis (PrEP) adherence data among individual prescribed PrEP.

Detection of Jamming Attacks via Source Separation and Causal Inference

Jamming attacks to hinder communication capabilities are becoming a critical aspect of wireless networks. A challenging issue is the detection of reactive jammers that perform spectrum sensing and attack the network only when legitimate communication is in progress. In this scenario, we introduce a novel framework for reactive jamming detection using a patrol of radio-frequency (RF) sensors external to the network to be protected. The solution relies on two key components: i) a novel underdetermined blind source separation (UBSS) method that, starting from the signal mixtures observed by the RF patrollers, is capable of separating the jamming temporal profile from the network nodes’ transmission profiles; ii) a new jamming detection based on causal inference called all-versus-one transfer entropy (AvOTE). The framework is then applied to a case study where the victim network is a Long Range (LoRa)-based internet of things (IoT) system with star topology. The solution outperforms a state-of-the-art method and an approach that attempts to find the causal relationship via time series correlation, exhibiting very good performance in the presence of shadowing. Indeed, a detection probability of 90% is achieved with a false alarm probability of 6% in the presence of nuisances such as collisions and severe shadowing.

First-Order Individual Gas Sensors as Next Generation Reliable Analytical Instruments.

It is conventionally expected that the performance of existing gas sensors may degrade in the field compared to laboratory conditions because (i) a sensor may lose its accuracy in the presence of chemical interferences and (ii) variations of ambient conditions over time may induce sensor-response fluctuations (i.e., drift). Breaking this status quo in poor sensor performance requires understanding the origins of design principles of existing sensors and bringing new principles to sensor designs. Existing gas sensors are single-output (e.g., resistance, electrical current, light intensity, etc.) sensors, also known as zero-order sensors (Karl Booksh and Bruce R. Kowalski, Analytical Chemistry, DOI: 10.1021/ac00087a718). Any zero-order sensor is undesirably affected by variable chemical background and sensor drift that cannot be distinguished from the response to an analyte. To address these limitations, we are developing multivariable gas sensors with independent responses, which are first-order analytical instruments. Here, we demonstrate self-correction against drift in two types of first-order gas sensors that operate in different portions of the electromagnetic spectrum. Our radiofrequency sensors utilize dielectric excitation of semiconducting metal oxide materials on the shoulder of their dielectric relaxation peak and achieve self-correction of the baseline drift by operation at several frequencies. Our photonic sensors utilize nanostructured sensing materials inspired by Morpho butterflies and achieve self-correction of the baseline drift by operation at several wavelengths. These principles of self-correction for drift effects in first-order sensors open opportunities for diverse emerging monitoring applications that cannot afford frequent periodic maintenance that is typical of traditional analytical instruments.

A Voxel Vascular Structure-based Mannequin-like Arm Electromagnetic Model for Radio Frequency Biomedical Sensors.

Radio Frequency (RF) sensor is widely used to monitor physiological signals. Generally, RF sensor simulation is mostly done using a layered model, which sometimes cannot model the accurate properties in the real world. A voxel vascular structure-based mannequin-like arm electromagnetic model (VVS-MaM) is proposed to evaluate the RF sensor, which mainly gathers the real physiological signal. This model is built with high-precision Magnetic Resonance Imaging (MRI), and it can finish fast simulation while there is also a voxel-like part in it which means it has the advantages of both the layered model and the real human model. After modelling, both simulation and in-vivo experiments are designed to test this sensor. In the simulation, the simulated standard resonant frequency of the equivalent model is 1.8137 GHz, and the relative error of the VVS-MaM is 0.012 GHz, which is closer to the standard value than the layer model result of 0.049 GHz. Meanwhile, in the in-vivo experiments, an RF sensor based on a composite right/left-handed transmission line (CRLH-TL) and complementary split resonator rings (CSRRs) are fabricated, and the measurements from the real experiments are gathered and stored to compare with that of the simulation. The comparison shows that the relative error of the VVS-MaM (0.08804 GHz)is closer to the in-vivo measurements than that of the layer model (0.09891 GHz), which validates the performance of VVS-MaM.Clinical Relevance-Radio Frequency, magnetic resonance imaging, scattering parameter, composite right/left-handed, complementary split resonator ring.

Systolic Time Interval Extraction in Hypertensive and Hypotensive Pig Models Using Wearable Near-Field Radio-Frequency Sensors.

Screening and monitoring for cardiovascular diseases (CVDs) can be enabled by analyzing systolic time intervals (STIs). As CVDs have a strong causal correlation with hypertension, it is important to validate STI sensor accuracy in hypertensive hearts to ensure consistent performance in this prevalent cardiac disease state. This work presents STI extraction using a non-invasive near-field radio-frequency (RF) sensor during normotension, hypertension, and hypotension in a pig model. Waveform features of semilunar and atrioventricular valve dynamics during systole were extracted to derive isovolumic contraction time (ICT) and left ventricular ejection time (LVET), benchmarked by a phonocardiogram and aortic catheterization. Study-wide mean relative ICT and LVET errors were -4.4ms and -3.6ms, respectively, demonstrating high accuracy during both normal and abnormal systemic pressures.Clinical relevance- This work demonstrates accurate STI extraction with relative error less than 5 ms from a non-invasive near-field RF sensor during normotensive, hypotensive, and hypertensive systemic pressures, validating the sensor's accuracy as a screening tool during this disease state.

Modeling and Dynamic Radar Cross-Section Estimation of Chaff Clouds for Real-Time Simulation

Chaff is a passive jammer widely used to disrupt radar or radio-frequency sensors. A mass of chaff fibers dispersed in the air is commonly referred to as a chaff cloud. It is nearly impossible to numerically simulate in real-time the enormous amount of chaff fibers composing the chaff cloud. In this paper, we model the behavior of numerically estimated chaff clouds as probability density functions (PDFs) and apply approximation techniques to estimate the radar cross-section (RCS) of the chaff cloud in real time. To model the aerodynamics of the chaff cloud, we represented the combination of PDFs as functions of time and wind speed. The applied approximation techniques—vector radiative transfer and generalized equivalent conductor method—showed a computation time that cannot be achieved by low-frequency methods such as the method of moments or finite-difference time-domain. Moreover, the dynamic RCS results of the approximation techniques showed a similar trend to those of other studies simulating similar situations. The proposed scheme is effective for real-time chaff cloud simulation, and the modeled dynamics and estimated dynamic RCSs can be a standard baseline for developing new analysis methods for chaff clouds. In the future, the proposed scheme will extend to more chaff fibers and more diverse environmental parameters.

Role of presence of gold nanoparticles on enhancement of RF biosensing sensitivity

In this work, we explore the enhancement of sensitivity of sensing of biomolecules using Radio Frequency (RF) based sensing by utilizing gold particles. In particular, we investigate the impact of various parameters of gold particles such as shape, size and arrangements on enhancing sensitivity. The work uses a Microstrip Patch Antenna designed to resonate at 96.84 GHz. The sensor is facile, rapid and efficient and uses a sample volume of 17.5 nL. The results indicate that the presence of gold particles helps in improving the performance of RF sensors in detecting biomolecules. However, the extent of improvement depends on the properties of gold particles and is in the range of 30%–80% for the different cases studied in this work.