Bridging digital and electromagnetic realms for enhanced wireless communication: current space.

Recently, Tie Jun Cui and team members introduced innovative macroscopic and statistical models for digital coding metasurfaces, bridging the digital and electromagnetic realms and quantifying information loss for enhanced wireless communication system design. This is a highlight of it.

Highly Sensitive Terahertz Metasurface Based on Electromagnetically Induced Transparency-Like Resonance in Detection of Skin Cancer Cells.

Terahertz (THz) metasurfaces based on high Q-factor electromagnetically induced transparency-like (EIT-like) resonances are promising for biological sensing. Despite this potential, they have not often been investigated for practical differentiation between cancerous and healthy cells. The present methodology relies mainly on refractive index sensing, while factors of transmission magnitude and Q-factor offer significant information about the tumors. To address this limitation and improve sensitivity, we fabricated a THz EIT-like metasurface based on asymmetric resonators on an ultra-thin and flexible dielectric substrate. Bright-dark modes coupling at 1.96 THz was experimentally verified, and numerical results and theoretical analysis were presented. An enhanced theoretical sensitivity of 550 GHz/RIU was achieved for a sample with a thickness of 13 µm due to the ultra-thin substrate and novel design. A two-layer skin model was generated whereby keratinocyte cell lines were cultured on a base of collagen. When NEB1-shPTCH (basal cell carcinoma (BCC)) were switched out for NEB1-shCON cell lines (healthy) and when BCC's density was raised from 1 × 105 to 2.5 × 105, a frequency shift of 40 and 20 GHz were observed, respectively. A combined sensing analysis characterizes different cell lines. The findings may open new opportunities for early cancer detection with a fast, less-complicated, and inexpensive method.

RIS-assisted near-field localization using practical phase shift model.

Our research focuses on examining the problem of localizing user equipment (UE) in the uplink scenario using reconfigurable intelligent surfaces (RIS) based lens. We carry out a thorough analysis of the Fisher information matrix (FIM) and assess the influence of various RIS-based lens configurations using an actual RIS phase-dependent amplitude variations model. Furthermore, to reduce the complexity of the maximum likelihood (ML) estimator, a simple localization algorithm-based angular expansion is presented. Simulation results show superior localization performance when prior location information is available for directional and positional channel configurations. The position error bound (PEB) and the root mean square error (RMSE) are studied to evaluate the localization accuracy of the user utilizing the realistic RIS phase-dependent amplitude model in the near-field region. Furthermore, the achievable data rate is obtained in the same region using the realistic RIS phase-dependent amplitude model. It is noticed that adopting the actual RIS phase-dependent amplitude model under the near-field channel increases the localization error and degrades the data rate performance for amplitude value less than one so, the unity assumption of the RIS phase shift model used widely in the literature is inaccurate.

A comprehensive multimodal dataset for contactless lip reading and acoustic analysis.

Small-scale motion detection using non-invasive remote sensing techniques has recently garnered significant interest in the field of speech recognition. Our dataset paper aims to facilitate the enhancement and restoration of speech information from diverse data sources for speakers. In this paper, we introduce a novel multimodal dataset based on Radio Frequency, visual, text, audio, laser and lip landmark information, also called RVTALL. Specifically, the dataset consists of 7.5 GHz Channel Impulse Response (CIR) data from ultra-wideband (UWB) radars, 77 GHz frequency modulated continuous wave (FMCW) data from millimeter wave (mmWave) radar, visual and audio information, lip landmarks and laser data, offering a unique multimodal approach to speech recognition research. Meanwhile, a depth camera is adopted to record the landmarks of the subject's lip and voice. Approximately 400 minutes of annotated speech profiles are provided, which are collected from 20 participants speaking 5 vowels, 15 words, and 16 sentences. The dataset has been validated and has potential for the investigation of lip reading and multimodal speech recognition.

Reconfigurable absorptive and polarization conversion metasurface consistent for wide angles of incidence.

In this paper, a single-layer reconfigurable reflective metasurface is presented. The proposed metasurface operates at 5.4 GHz and can achieve either absorption or cross-polarization conversion corresponding at two different diode biasing states. The reflective metasurface acts as an absorber for an incident wave when the diodes are forward-biased. Similarly, it changes the polarization state of the reflected wave for a linearly polarized incident wave when the diodes are reverse-biased. The proposed structure maintains the aforementioned performance characteristics for oblique incidence, up to 60° compared to the perpendicular incidence. The proposed metasurface can achieve linear to linear polarization conversion with polarization conversion ratio (PCR) > 95% and absorption, with absorption ratio (AR) > 80% in the same frequency band just by reconfiguring the state of the PIN diodes.

Dual-sense slot-based CP MIMO antenna with polarization bandwidth reconfigurability.

In this letter, a compact, planar circularly polarized (CP) sub-GHz slot-based multiple-input-multiple-output (MIMO) antenna with dual sense CP along with polarization bandwidth reconfigurability is presented. The pentagonal reactively loaded slot is fed by two folded tapered feedlines to achieve CP. The antenna offers left-hand-circular polarization (RHCP) with the as well as right hand circular polarization (LHCP). The antenna exhibit linearly polarization (LP) by exciting two ports simultaneously. Moreover, the antenna CP resonance can be reconfigured by varying the capacitance of the varactor diode. The antenna has a wide -10 dB operating frequency band from 578-929 MHz. while the axial ratio (AR) bandwidth ranges from 490-810 MHz. Moreover, the two elements MIMO are optimized and placed on compact dimensions 100 × 100 × 0.76 mm3 to realize pattern diversity. The antenna's key characteristics are compact size, wide-band sub-GHz operation, dual sense CP, polarization bandwidth reconfigurability and good MIMO performance. Thus, it is a suitable candidate to be utilized in CubeSats applications in sub-GHz bands.

Configuring reconfigurable intelligent surfaces using a practical codebook approach.

It is proven that the scattering, reflection, and refraction properties of electromagnetic signals can be adapted and managed by using reconfigurable intelligent surfaces (RISs). In this paper, we have investigated the performance of a single-input-single-output (SISO) wideband system in terms of achievable data rate by optimizing the phases of RIS elements and performing a fair power allocation for each subcarrier over the entire bandwidth. A new beamforming codebook is developed from which the maximizing signal-to-noise (SNR) configuration is selected. The channel state information (CSI) along with the selected maximizing SNR configuration is then used by the proposed power algorithm to obtain the optimal configuration of the RIS. To validate our proposed method, it is compared with state-of-the-art semidefinite relaxation (SDR) scheme in terms of performance, complexity and run-time consumption. Our method shows dramatically lower computational complexity than the SDR method and achieves an order of 2.5 increase in the achievable data rate with an optimized RIS compared with an un-configured surface.

Flexible Antenna with Circular/Linear Polarization for Wideband Biomedical Wireless Communication.

A wideband low-profile radiating G-shaped strip on a flexible substrate is proposed to operate as biomedical antenna for off-body communication. The antenna is designed to produce circular polarization over the frequency range 5-6 GHz to communicate with WiMAX/WLAN antennas. Furthermore, it is designed to produce linear polarization over the frequency range 6-19 GHz for communication with the on-body biosensor antennas. It is shown that an inverted G-shaped strip produces circular polarization (CP) of the opposite sense to that produced by G-shaped strip over the frequency range 5-6 GHz. The antenna design is explained and its performance is investigated through simulation, as well as experimental measurements. This antenna can be viewed as composed of a semicircular strip terminated with a horizontal extension at its lower end and terminated with a small circular patch through a corner-shaped strip extension at its upper end to form the shape of "G" or inverted "G". The purpose of the corner-shaped extension and the circular patch termination is to match the antenna impedance to 50 Ω over the entire frequency band (5-19 GHz) and to improve the circular polarization over the frequency band (5-6 GHz). To be fabricated on only one face of the flexible dielectric substrate, the antenna is fed through a co-planar waveguide (CPW). The antenna and the CPW dimensions are optimized to obtain the most optimal performance regarding the impedance matching bandwidth, 3dB Axial Ratio (AR) bandwidth, radiation efficiency, and maximum gain. The results show that the achieved 3dB-AR bandwidth is 18% (5-6 GHz). Thus, the proposed antenna covers the 5 GHz frequency band of the WiMAX/WLAN applications within its 3dB-AR frequency band. Furthermore, the impedance matching bandwidth is 117% (5-19 GHz) which enables low-power communication with the on-body sensors over this wide range of the frequency. The maximum gain and radiation efficiency are 5.37 dBi and 98%, respectively. The overall antenna dimensions are 25 × 27 × 0.13 mm3 and the bandwidth-dimension ratio (BDR) is 1733.

Acoustic Lung Imaging Utilized in Continual Assessment of Patients with Obstructed Airway: A Systematic Review.

Smart respiratory therapy is enabled by continual assessment of lung functions. This systematic review provides an overview of the suitability of equipment-to-patient acoustic imaging in continual assessment of lung conditions. The literature search was conducted using Scopus, PubMed, ScienceDirect, Web of Science, SciELO Preprints, and Google Scholar. Fifteen studies remained for additional examination after the screening process. Two imaging modalities, lung ultrasound (LUS) and vibration imaging response (VRI), were identified. The most common outcome obtained from eleven studies was positive observations of changes to the geographical lung area, sound energy, or both, while positive observation of lung consolidation was reported in the remaining four studies. Two different modalities of lung assessment were used in eight studies, with one study comparing VRI against chest X-ray, one study comparing VRI with LUS, two studies comparing LUS to chest X-ray, and four studies comparing LUS in contrast to computed tomography. Our findings indicate that the acoustic imaging approach could assess and provide regional information on lung function. No technology has been shown to be better than another for measuring obstructed airways; hence, more research is required on acoustic imaging in detecting obstructed airways regionally in the application of enabling smart therapy.

A sub 1 GHz ultra miniaturized folded dipole patch antenna for biomedical applications.

A miniaturized folded dipole patch antenna (FDPA) design for biomedical applications operating at sub 1 GHz (434 MHz) band is presented. Antenna is fabricated on FR-4 substrate material having dimensions of 16.40 mm [Formula: see text] 8.60 mm [Formula: see text] 1.52 mm (0.023[Formula: see text] [Formula: see text] 0.012[Formula: see text] [Formula: see text] 0.002[Formula: see text]). Indirect feed coupling is applied through two parallel strips at bottom layer of the substrate. The antenna size is reduced by 83% through lumped inductor placed at the center path of the radiating FDPA, suitable for biomedical (implantable) applications and hyperthermia. Moreover, Impedance matching is achieved without using any Balun transformer or any other complex matching network. The proposed antenna provides an impedance bandwidth of 6 MHz (431-437 MHz) below - 10 dB and a gain of - 31 dB at 434 MHz. The designed antenna is also placed on a human body model to evaluate its performance for hyperthermia through Specific Absorption Rate (SAR), Effective Field Size (EFS), and penetration depth (PD).

RIS-Enabled Secret Key Generation for Secured Vehicular Communication in the Presence of Denial-of-Service Attacks.

Wireless communication has become an integral part of modern vehicles. However, securing the information exchanged between interconnected terminals poses a significant challenge. Effective security solutions should be computationally inexpensive, ultra-reliable, and capable of operating in any wireless propagation environment. Physical layer secret key generation has emerged as a promising technique, which leverages the inherent randomness of wireless-channel responses in amplitude and phase to generate high-entropy symmetric shared keys. The sensitivity of the channel-phase responses to the distance between network terminals makes this technique a viable solution for secure vehicular communication, given the dynamic behavior of these terminals. However, the practical implementation of this technique in vehicular communication is hindered by fluctuations in the communication link between line-of-sight (LoS) and non-line-of-sight (NLoS) conditions. This study introduces a key-generation approach that uses a reconfigurable intelligent surface (RIS) to secure message exchange in vehicular communication. The RIS improves the performance of key extraction in scenarios with low signal-to-noise ratios (SNRs) and NLoS conditions. Additionally, it enhances the network's security against denial-of-service (DoS) attacks. In this context, we propose an efficient RIS configuration optimization technique that reinforces the signals received from legitimate users and weakens the signals from potential adversaries. The effectiveness of the proposed scheme is evaluated through practical implementation using a 1-bit RIS with 64×64 elements and software-defined radios operating within the 5G frequency band. The results demonstrate improved key-extraction performance and increased resistance to DoS attacks. The hardware implementation of the proposed approach further validated its effectiveness in enhancing key-extraction performance in terms of the key generation and mismatch rates, while reducing the effect of the DoS attacks on the network.

Enhancing System Performance through Objective Feature Scoring of Multiple Persons' Breathing Using Non-Contact RF Approach.

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system's performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system's performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.

Dental caries detection using a semi-supervised learning approach.

Early diagnosis of dental caries progression can prevent invasive treatment and enable preventive treatment. In this regard, dental radiography is a widely used tool to capture dental visuals that are used for the detection and diagnosis of caries. Different deep learning (DL) techniques have been used to automatically analyse dental images for caries detection. However, most of these techniques require large-scale annotated data to train DL models. On the other hand, in clinical settings, such medical images are scarcely available and annotations are costly and time-consuming. To this end, we present an efficient self-training-based method for caries detection and segmentation that leverages a small set of labelled images for training the teacher model and a large collection of unlabelled images for training the student model. We also propose to use centroid cropped images of the caries region and different augmentation techniques for the training of self-supervised models that provide computational and performance gains as compared to fully supervised learning and standard self-supervised learning methods. We present a fully labelled dental radiographic dataset of 141 images that are used for the evaluation of baseline and proposed models. Our proposed self-supervised learning strategy has provided performance improvement of approximately 6% and 3% in terms of average pixel accuracy and mean intersection over union, respectively as compared to standard self-supervised learning. Data and code will be made available to facilitate future research.

Contactless WiFi Sensing and Monitoring for Future Healthcare - Emerging Trends, Challenges, and Opportunities.

WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users' bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the technology and discusses its use-cases from a perspective that considers hardware, advanced signal processing, and data acquisition.

Multi-Person Breathing Detection With Switching Antenna Array Based on WiFi Signal.

WiFi sensing, an emerging sensing technology, has been widely used in vital sign monitoring. However, most respiration monitoring studies have focused on single-person tasks. In this paper, we propose a multi-person breathing sensing system based on WiFi signals. Specifically, we use radio frequency (RF) switch to extend the antennas to form switching antenna array. A reference channel is introduced in the receiver, which is connected to the transmitter by cable and attenuator. The phase offset introduced by asynchronous transceiver devices can be eliminated by using the ratio of the channel frequency response (CFR) between the antenna array and the reference channel. In order to realize multi-person breathing perception, we use beamforming technology to conduct two-dimensional scanning of the whole scene. After eliminating static clutter, we combine frequency domain and angle of arrival (AOA) domain analysis to construct the AOA and frequency (AOA-FREQ) spectrogram. Finally, the respiratory frequency and position of each target are obtained by clustering. Experimental results show that the proposed system can not only estimate the direction and respiration rate of multi-person, but also monitor abnormal respiration in multi-person scenarios. The proposed low-cost, non-contact, rapid multi-person respiratory detection technology can meet the requirements of long-term home health monitoring.

Evaluation of deep learning models in contactless human motion detection system for next generation healthcare.

Recent decades have witnessed the growing importance of human motion detection systems based on artificial intelligence (AI). The growing interest in human motion detection systems is the advantages of automation in the monitoring of patients remotely and giving warnings to doctors promptly. Currently, wearable devices are frequently used for human motion detection systems. However, such devices have several limitations, such as the elderly not wearing devices due to lack of comfort or forgetfulness and/or battery limitations. To overcome the problems of wearable devices, we propose an AI-driven human motion detection system (deep learning-based system) using channel state information (CSI) extracted from Radio Frequency (RF) signals. The main contribution of this paper is to improve the performance of the deep learning models through techniques, including structure modification and dimension reduction of the original data. In this work, We firstly collected the CSI data with the center frequency 5.32 GHz and implemented the structure of the basic deep learning network in our previous work. After that, we changed the basic deep learning network by increasing the depth, increasing the width, adapting some advanced network structures, and reducing dimensions. After finishing those modifications, we observed the results and analyzed how to further improve the deep learning performance of this contactless AI-enabled human motion detection system. It can be found that reducing the dimension of the original data can work better than modifying the structure of the deep learning model.

Intelligent Reflecting Surface-Based Non-LOS Human Activity Recognition for Next-Generation 6G-Enabled Healthcare System.

Human activity monitoring is a fascinating area of research to support autonomous living in the aged and disabled community. Cameras, sensors, wearables, and non-contact microwave sensing have all been suggested in the past as methods for identifying distinct human activities. Microwave sensing is an approach that has lately attracted much interest since it has the potential to address privacy problems caused by cameras and discomfort caused by wearables, especially in the healthcare domain. A fundamental drawback of the current microwave sensing methods such as radar is non-line-of-sight and multi-floor environments. They need precise and regulated conditions to detect activity with high precision. In this paper, we have utilised the publicly available online database based on the intelligent reflecting surface (IRS) system developed at the Communications, Sensing and Imaging group at the University of Glasgow, UK (references 39 and 40). The IRS system works better in the multi-floor and non-line-of-sight environments. This work for the first time uses algorithms such as support vector machine Bagging and Decision Tree on the publicly available IRS data and achieves better accuracy when a subset of the available data is considered along specific human activities. Additionally, the work also considers the processing time taken by the classier in training stage when exposed to the IRS data which was not previously explored.

Low-profile Button Sensor Antenna Design for Wireless Medical Body Area Networks.

A button sensor antenna (BSA) for wireless medical body area networks (WMBAN) is presented, which works through the IEEE 802.11b/g/n standard. Due to strong interaction between the sensor antenna and the body, a new robust system is designed with a small footprint that can serve on- and off-body healthcare applications. The measured and simulated results are matched well. The design offers a wide range of omnidirectional radiation patterns in free space, with a reflection coefficient (S11) of -29.30 (-30.97) dB in the lower (upper) bands. S11 reaches up to -23.07 (-27.07) dB and -30.76 (-31.12) dB on the body chest and arm, respectively. The Specific Absorption Rate (SAR) values are below the regulatory limitations for both 1-gram (1.6 W/Kg) and 10-gram tissues (2.0 W/Kg). Experimental tests of the read range validate the results of a maximum coverage range of 40 meters. Clinical Relevance- WMBAN technology allows for continuous monitoring and analysis of patient health data to improve the quality of healthcare services.