Radio Frequency Sensors Research Articles

A Dual Band Radio Frequency Sensor Antenna for the Measurement of Ambient Radiations at the GSM 800/900/1800/1900 MHz Bands

A Dual Band Radio Frequency Sensor Antenna for the Measurement of Ambient Radiations at the GSM 800/900/1800/1900 MHz Bands

Jugular Venous Pulse Waveform Extraction From a Wearable Radio Frequency Sensor.

Many prevalent heart diseases can be indicated by the features of the jugular venous pulse (JVP), an efficacious indicator of right heart health. However, JVP dynamics are not widely utilized in clinical settings as its observation and sensing remain cumbersome. Non-invasive measures of cardiac behavior, including the JVP, are of growing interest to enable continuous and at-home monitoring of cardiac disorders. In this work, we propose a wearable near-field radio-frequency (RF) sensor affixed with a neck collar on the clavicle over the internal jugular vein to enable non-invasive JVP sensing. We employed a complex vector injection signal processing method to extract repeatable JVP waveform features in multiple postures. With a 21-subject human study, we demonstrated morphologically consistent JVP sensing with consistent a-, c-, and v-wave feature timings, benchmarked by synchronous electrocardiogram and phonocardiogram. Further, inter-postural experiments demonstrated the capability of the proposed system to quantify morphological changes to the JVP which are present in many cardiac disorders. The results of this work suggest the proposed near-field RF sensor is capable of non-invasive JVP monitoring, potentially enabling improved sensing in both clinical and ambulatory environments.

ISACoT: Integrating Sensing with Data Traffic for Ubiquitous IoT Devices

The intention of leveraging Radio-Frequency (RF) resources for diverse sensing purposes has grown increasingly keen, thanks to the ever-expanding deployment of IoT devices using RF communications to maintain connectivity. Whereas the original idea was to integrate sensing and communications (a.k.a. ISAC) for individual IoT devices, most proposals in the past decade simply converted such devices (e.g., Wi-Fi or LoRa) into RF sensors without attending to their communication nature. Therefore, we argue that the device architecture has to be overhauled so as to achieve the ISAC ambition. To this end, we propose ISACoT as the framework for enabling ISAC over IoT devices. We categorize ISACoT's extensions over existing devices into four aspects, namely time, frequency, space, and protocol. We argue that, as the multistatic communication infrastructure of IoT is adverse to device-free sensing (e.g., lack of precise time synchronization), the keystone of ISACoT should be operating sensing in a monostatic mode (like radar).We tackle the fundamental time aspect based on Wi-Fi first, but leave the other three as challenges for exploration. For each of the three aspects, we present our proposals along with verification experiments, while putting forward challenges along with potential solutions. In particular, the protocol aspect discusses how ISACoT can be generalized to other IoT protocols beyond Wi-Fi.

Detection and Prediction of Sleep Disorders by Covert Bed-Integrated RF Sensors.

Respiratory disturbances during sleep are a prevalent health condition that affects a large adult population. The gold standard to evaluate sleep disorders including apnea is overnight polysomnography, which requires a trained technician for live monitoring and post-processing scoring. Currently, the disorder events can hardly be predicted using the respiratory waveforms preceding the events. The objective of this paper is to develop an autonomous system to detect and predict respiratory events reliably based on real-time covert sensing. A bed-integrated radio-frequency (RF) sensor by near-field coherent sensing (NCS) was employed to retrieve continuous respiratory waveforms without user's awareness. Overnight recordings were collected from 27 patients in the Weill Cornell Center for Sleep Medicine. We extracted respiratory features to feed into the random-forest machine learning model for disorder detection and prediction. The technician annotation, derived from observation by polysomnography, was used as the ground truth during the supervised learning. Apneic event detection achieved a sensitivity and specificity up to 88.6% and 89.0% for k-fold validation, and 83.1% and 91.6% for subject-independent validation. Prediction of forthcoming apneic events could be made up to 90 s in advance. Apneic event prediction achieved a sensitivity and specificity up to 81.3% and 82.1% for k-fold validation, and 80.5% and 82.4% for subject-independent validation. The most important features for event detection and prediction can be assessed in the learning model. A bed-integrated RF sensor can covertly and reliably detect and predict apneic events. Predictive warning of the sleep disorders in advance can intervene serious apnea, especially for infants, servicemen, and patients with chronic conditions.

RF-CM

Radio-Frequency (RF) based human activity recognition (HAR) enables many attractive applications such as smart home, health monitoring, and virtual reality (VR). Among multiple RF sensors, mmWave radar is emerging as a new trend due to its fine-grained sensing capability. However, laborious data collection and labeling processes are required when employing a radar-based sensing system in a new environment. To this end, we propose RF-CM, a general cross-modal human activity recognition framework. The key enabler is to leverage the knowledge learned from a massive WiFi dataset to build a radar-based HAR system with limited radar samples. It can significantly reduce the overhead of training data collection. In addition, RF-CM can work well regardless of the deployment setups of WiFi and mmWave radar, such as performing environments, users' characteristics, and device deployment. RF-CM achieves this by first capturing the activity-related variation patterns through data processing schemes. It then employs a convolution neural network-based feature extraction module to extract the high-dimensional features to be fed into the activity recognition module. Finally, RF-CM takes the generalization knowledge from WiFi networks as guide labels to supervise the training of the radar model, thus enabling a few-shot radar-based HAR system. We evaluate RF-CM by applying it to two HAR applications, fine-grained American sign language recognition (WiFi-cross-radar) and coarse-grained gesture recognition (WiFi-cross-RFID). The accuracy improvement of over 10% in both applications demonstrates the effectiveness of RF-CM. This cross-modal ability allows RF-CM to support more cross-modal applications.

Study on the Use of Industry 4.0 technology for the Textile Industry Sustainable Supply Chain Process

This paper studies a sustainable closed-loop supply chain progress for the textile industry based on the Industry 4.0 technologies to facilitate data-driven decision-making and ensure sustainability, which is premised on the ability of the IoTs to capture real-time information from the different stages of the supply chain through the use of radio frequency identification tags and sensors based data. Block-chain and Big Data technologies application in the closed-loop supply chain will support tracking and enable supply chain traceability and provide secure means of recording information in the supply chain progress according to the findings in this paper. It implicates the Industry 4.0 technologies will facilitate sustainable supply chain process in the textile industry as it helps in data collection, analysis and information transmission.

Detection of Sodium Ion Imbalance in Human Body Fluids Using an Improved RF Sensor

A novel radio frequency (RF) sensor is designed, developed, and tested to determine sodium ion imbalance in human body fluids. The proposed sensor consists of a complementary split ring resonators (CSRR)-based structure with the sensing region surrounded by air vias to improve the confinement of the E-field within the sensing region. The proposed RF sensor is designed using the full wave EM simulator, the computer simulation technology (CST) Microwave Studio, and fabricated on Rogers 6002 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${{\rm{\varepsilon }}}_{\mathrm{r}}$</tex-math></inline-formula> = 2.2) having a thickness of 3.75 mm. The various parameters of the designed sensor are optimized for improved performance, and its equivalent circuit model is developed using the ADS software. The proposed sensor confines the field in the sensing area and the test fluid is injected into the sensing region through a specially designed 1.5 mm fluidic channel made across the width of the substrate. This kind of channel embedded in the substrate limits the quantity of fluid interacting with the sensing region thereby facilitating testing of Simulated Body Fluid (SBF) possessing even high-value permittivity in the range of 80–100. The SBF in the present situation is primarily being used to mimic human blood plasma to detect Na <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> concentrations in a lab environment. The developed RF sensor prototype is successfully utilized to determine the Sodium ion constituent variation in the body fluid, which is an important aspect as Na <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> concentration affects the metabolic functioning in the body. The measured sensitivity for Na <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> ions in the body fluid is recorded to be 0.03 dB/(mmol/dm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) for the fabricated RF sensor.

RF-Protect

In the last decade, both academia and industry have relied on FMCWradar based radio-frequency (RF) sensors to enable through-wall human tracking. These sensors capture reflections from human bodies to track occupancy of rooms [1], motion patterns of occupants [1,2], their daily activities [3], and their health metrics [4, 5]. Recently, Google has incorporated high frequency FMCW-based sensing into their smart home devices [6, 7], and Amazon received an FCC waiver [8] to conduct testing for the same.

Radio-Frequency Near-Field Sensor Design for Minuscule Internal Motion

We present the improved design of a radio-frequency (RF) near-field sensor for minuscule internal motion inside structures and small animals. The design tradeoffs of the antenna placement and transceiver configuration are explored experimentally by a structural setup of the fluid stirrer. The sensor performance is benchmarked in various antenna configurations and quadrature baseband sampling settings. An optimized antenna pair holder design is then applied to a continuous touchless recording for the respiration and heartbeat of nestling and adult tree swallows noninvasively. Furthermore, we also built a low-power portable all-in-one RF sensor printed circuit board (PCB) using the system parameters from previous experiments. A 40-min experiment on a conscious and free-to-move 13-day-old nestling tree swallow for respiration monitoring was successfully conducted.

A feasibility study using radio-frequency sensors to collect respiratory metrics in patients with chronic obstructive pulmonary disease.

Background: The symptom of dyspnea is commonly encountered in patients with underlying serious illness and can lead to distress and poor quality of life. In patients with chronic obstructive lung disease (COPD), the prevalence is reported in up to 95% of patients. With the growth in sensor technologies, continuous monitoring of respiratory metrics provides an opportunity to better understand the relationship between patient-reported dyspnea and objective respiratory measures. Aims: To assess the feasibility of implementing a radio-frequency (RF) sensor in patients with COPD and describe the relationship between dyspnea and respiratory metrics in patients with COPD when compared to healthy controls. Methods: A prospective cohort study was conducted to collect data on dyspnea scores and respiratory metrics in patients with COPD and healthy controls while conducting a walking test using a wearable RF sensor. Results: Of the 12 COPD patients and 15 healthy controls recruited, all participants completed the modified incremental shuttle walking test while wearing the RF sensor; there was no attrition. For every one-point increase in the dyspnea score, there was a mean 1.94 increase in the respiratory rate per minute in the COPD group as compared to a 1.09 increase in respiratory rate in the healthy control group. Conclusion: Preliminary data demonstrate the potential of using the RF sensors to track respiratory metrics in COPD patients and healthy adults. As this technology develops, it shows considerable promise and could provide significant implications regarding the use of non-invasive continuous monitoring for patients with lung disease.

Non-Invasive Assessment of Lung Water Content Using Chest Patch RF Sensors: A Computer Study Using NIH Patients CT Scan Database and AI Classification Algorithms

Chronic heart failure, pulmonary hypertension, acute respiratory distress syndrome (ARDS), coronavirus disease (COVID), and kidney failure are leading causes of death in the U.S. and across the globe. The cornerstone for managing these diseases is assessing patients’ volume fluid status in lungs. Available methods for measuring fluid accumulation in lungs are either expensive and invasive, thus unsuitable for continuous monitoring, or inaccurate and unreliable. With the recent COVID-19 epidemic, the development of a non-invasive, affordable, and accurate method for assessing lung water content in patients became utmost priority for controlling these widespread respiratory related diseases. In this paper, we propose a novel approach for non-invasive assessment of lung water content in patients. The assessment includes quantitative baseline assessment of fluid accumulation in lungs (normal, moderate edema, edema), as well as continuous monitoring of changes in lung water content. The proposed method is based on using a pair of chest patch radio frequency (RF) sensors and measuring the scattering parameters (S-parameters) of a 915-MHz signal transmitted into the body. To conduct an extensive computational study and validate our results, we utilize a National Institute of Health (NIH) database of computerized tomography (CT) scans of lungs in a diverse population of patients. An automatic workflow is proposed to convert CT scan images to three-dimensional lung objects in High-Frequency Simulation Software and obtain the S-parameters of the lungs at different water levels. Then a personalized machine learning model is developed to assess lung water status based on patient attributes and S-parameter measurements. Decision trees are chosen as our models for the superior accuracy and interpretability. Important patient attributes are identified for lung water assessment. A “cluster-then-predict” approach is adopted, where we cluster the patients based on their ages and fat thickness and train a decision tree for each cluster, resulting in simpler and more interpretable decision trees with improved accuracy. The developed machine learning models achieve areas under the receiver operating characteristic curve of 0.719 and 0.756 for 115 male and 119 female patients, respectively. These results suggest that the proposed “Chest Patch” RF sensors and machine learning models present a promising approach for non-invasive monitoring of patients with respiratory diseases.

Radio SLAM: A Review on Radio-Based Simultaneous Localization and Mapping

Simultaneous localization and mapping (SLAM) algorithm has enabled the automation of mobile robots in unknown environments. It enables the robot to navigate through an unknown trajectory by employing sensors that provide measurements to infer the surrounding environment and use this information to localize the robot. Sensor technology plays a key role in defining the quality of measurements as it affects the overall performance of SLAM. While visual sensors, like cameras, can capture rich features of the environment, they, however, fail to work in low-light conditions. On the other hand, radio frequency sensors are invariant to light conditions, however, high-frequency signals such as millimeter wave (mm-wave) are prone to severe channel attenuation, therefore, they are suitable for short-range indoor applications. Despite the high localization accuracy that the mm-wave frequency band has to offer, its shortcomings have limited the amount of research work carried out to enhance the performance of SLAM. Therefore, this paper aims to provide an overview of the recent developments in radio SLAM, with a specific focus on mm-wave enabled localization and SLAM methods. However, some notable research work based on other radio frequency sensors has also been discussed. In addition, we highlight the role of deep learning-based methods for localization and identify some of the key challenges in data-driven implementation.

Enhanced Traffic Management for Emergency Vehicle Information Transmission using Wireless Sensor Networks

Considerable research has been conducted in over a decade on traffic management systems that utilize Wireless Sensor Networks (WSNs) to mitigate congestion and prioritize emergency vehicles. The traffic management is becoming a wide interesting area for both academic and industrial researchers. The real-time traffic management is a dynamic scheme and is very challenging to provide an accurate signalling time and priority for a specific vehicle. This paper introduces a novel emergency vehicle information passing system that utilizes Radio Frequency (RF) sensors. This research study presents an innovative system for transmitting emergency vehicle information, which makes use of Radio Frequency (RF) sensors. The system effectively transmits crucial data, such as vehicle ID, approaching direction, mileage driven, and destination time. By doing so, it enables other vehicles to allocate appropriate space and facilitate smoother passage for emergency vehicles. The main intention of this approach is to improve the communication speed among the nodes and to reduce the response time. The communication among the nodes is done with different frequencies to enhance the method's effectiveness. We also propose a priority-based MAC (PMAC), which guarantees a slot allocation for emergency message transmission in the network. The effectiveness of the proposed approach is assessed through simulation using NS-2. The findings emphasize the effectiveness of the RF sensor when it comes to its ability to respond quickly and showcasing the PMAC's capability to reduce end-to-end delay.

Broadband radio frequency signal measurement based on quantum compression sensing

With the rapid development of radio frequency technology such as radar, electronic warfare and 5G communication, the measurement and real-time spectrum characterization of broadband radio frequency signals become increasingly important. The traditional radio frequency signal real-time measurement technology is limited by the sampling rate of analog-to-digital converter and the ability to process digital signals, and encounters the problems of narrow measurement band, large data volume, and susceptibility to electromagnetic interference. This work is to study a radio frequency signal measurement technology based on quantum compression sensing, which uses integrated electro-optical crystal as radio frequency sensor, and constructs a compression sensing machine by modulating the photon wave function of the measured radio frequency signal to realize the compression measurement of broadband radio frequency signal, significantly improving the spectrum sensing bandwidth. The experiment demonstrates the long-term spectrum monitoring of power frequency and intermediate frequency high voltage signals, and the real-time spectrum measurement of high frequency radio frequency signals. Under the Fourier limit spectrum resolution, the real-time spectrum analysis bandwidth of GHz magnitude is realized, and the data compression rate reaches 1.7×10&lt;sup&gt;–5&lt;/sup&gt;, which can meet the needs of 5G wireless communication, cognitive radio and other applications for broadband radio frequency signal spectrum measurement, and provide a new technical path for developing the next-generation broadband spectrum sensing technology.

Path guidance &amp; emergency helping system via Radio Frequency (RF) concepts

This project proposes an area detection system using Radio Frequency (RF) technology to monitor forest areas for illegal logging or encroachment activities. The system consists of a network of RF sensors deployed throughout the forest to detect any movement or changes in the forest's environment. The data collected by the sensors is transmitted wirelessly to a central monitoring station, where it is analyzed using machine learning algorithms. The proposed system aims to provide an effective and efficient solution for monitoring forest areas and preventing illegal activities that may damage the environment

A Flexible Tuned Radio-Frequency Planar Resonant Loop for Noninvasive Hydration Sensing

In this work, a planar radio-frequency sensor based on a flexible polyimide substrate has been developed to monitor the water content changes noninvasively and efficiently in a human body or tissues. The sensor is based on the detection of electromagnetic resonance that is susceptible to dielectric property changes by water content variations. The planar loop resonator tuned with a metal pad features improved resonance, compact size, and flexibility to conform to a curved surface. Designs and experiments to continuously monitor human hydration processes <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> have been demonstrated. The recorded data shows distinct trends when a person becomes hydrated from a dehydrated state. Discrete and continuous measurements are compared with the simulations conducted by using documented, generalized human skin permittivity properties. Discrepancies between the measurements and simulations are investigated and verified with directly measured skin permittivities. Measurements variations are also investigated. With the advantages of being compact, flexible, and planar, the sensor can be integrated into a wearable on the human forearm. Additionally, the sensor has been used to demonstrate its ability to detect water content changes in a phantom made of ground pork. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex vivo</i> results indicate the sensitivity and consequential variations in practical scenarios. The promising results show great potential for human body monitoring and also general applications in agriculture and the food processing industry from the demonstration of the sensing principle in the pork phantom.

Printed Wireless Sensing Devices using Radio Frequency Communication

Sensing technology in the Internet-of-Things (IoT) era is one of the key elements in the next industrial revolution. Especially, wireless sensors enable monitoring of various physical and chemical changes through wireless communication in applications like robotics, wearable devices, and biomedical and environmental sensing. Radio Frequency (RF) wireless sensors fabricated by printing methods with conductive materials have been recently spotlighted thanks to lots of benefits that they can provide like a simple operation, cost-effectiveness, customization, etc. Here, we focus on recent studies dedicated to the advances in various RF sensing technologies fabricated by cutting-edge printing methodologies for the realization of desired properties. By capturing recent developments in wireless sensor designs, sensing mechanisms, sensing performance, and printing materials, this Spotlight foresees the future direction of wireless RF sensors printed by industry-proven technologies in different applications.

Development of RF-Photonic System for Automatic Targets’ Nonlinear Rotational/Flapping/Gliding Signatures Imaging Applications

Nowadays, developing the radio frequency (RF)-photonic sensors suitable for several Defence and civil applications is emerging due to its various unique advantages. In today’s scientific world, the involvement of low radar cross-section (RCS) airspace targets, such as unman aerial vehicles, drones, mini-helicopters, ornithopters, bionic birds, etc., is more for the purpose of airspace traffic management/guidance, materials delivery, legal/illegal surveillance and Defence/offense applications. The detection and recognition of such targets using the conventional RF sensors (which process only the main Doppler) is almost impossible. Therefore, extracting the distinctive micro-Doppler (m-D) signatures of these low-RCS targets and using them to image/differentiate/recognize their postures become significant which is the main contribution in this paper. A C-band continuous wave RF-photonic sensor is developed and different low-RCS targets: 2/3 blades rotating propeller system (drones), cone like structure (warhead) and a bionic-bird (spy-bird); are operated in front of it. The different nonlinear postures, of these targets, covered in our experimental measurements are m-D extraction of a slowly moving propeller system, detection of back-and-forth movements of a rotational propeller system, discerning the moving and static rotational propeller systems, simultaneous extraction of m-D signatures of a rotational and moving targets and recognition of flapping/gliding motions of bionic-bird. The analysis and recognition of all these postures using the experimentally generated m-D signatures are described.

Optimization of the RFID network in IoT through Hybrid Genetic Algorithmz

Several cutting-edge technologies, like as Radio Frequency Identification (RFID) optimization and sensors, are enabling the Internet of Things (IoT). Using a unique code connected with tags, this radio frequency identification technology allows the sensor to read from a distance without requiring sight contact. Optimal tag coverage, cost efficiency, and quality of service needs are all important considerations when deploying RFID networks. We investigated optimization approaches for RFID in Internet of Things (IoT) devices in this paper. RFID optimization saves processing time, hardware costs, and improves efficiency. Random search strategies based on Artificial Intelligence (AI) are more suited than deterministic methods that are difficult to solve in polynomial time. In this paper, we present a hybrid algorithm that combines PSO and GA features to improve the efficiency of an IoT network system.

Multi-Controller Model for Improving the Performance of IoT Networks

Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.