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Vital signs are important indicators to evaluate the health status of patients. Channel state information (CSI) can sense the displacement of the chest wall caused by cardiorespiratory activity in a non-contact manner. Due to the influence of clutter, DC components, and respiratory harmonics, it is difficult to detect reliable heartbeat signals. To address this problem, this paper proposes a robust and novel method for simultaneously extracting breath and heartbeat signals using software defined radios (SDR). Specifically, we model and analyze the signal and propose singular value decomposition (SVD)-based clutter suppression method to enhance the vital sign signals. The DC is estimated and compensated by the circle fitting method. Then, the heartbeat signal and respiratory signal are obtained by the modified variational modal decomposition (VMD). The experimental results demonstrate that the proposed method can accurately separate the respiratory signal and the heartbeat signal from the filtered signal. The Bland-Altman analysis shows that the proposed system is in good agreement with the medical sensors. In addition, the proposed system can accurately measure the heart rate variability (HRV) within 0.5m. In summary, our system can be used as a preferred contactless alternative to traditional contact medical sensors, which can provide advanced patient-centered healthcare solutions.

For this article, a low-cost, compact, and flexible inkjet-printed electromagnetic sensor was investigated for its chemical and biomedical applications. The investigated sensor design was used to estimate variations in the concentration of chemicals (ethanol and methanol) and biochemicals (hydrocortisone—a chemical derivative of cortisol, a biomarker of stress and cardiovascular effects). The proposed design’s sensitivity was further improved by carefully choosing the frequency range (0.5–4 GHz), so that the analyzed samples showed approximately linear variations in their dielectric properties. The dielectric properties were measured using a vector network analyzer (VNA) and an Agilent 85070E Dielectric Probe Kit. The sensor design had a resonant frequency at 2.2 GHz when investigated without samples, and a consistent shift in resonant frequency was observed, with variation in the concentrations of the investigated chemicals. The sensitivity of the designed sensor is decent and is comparable to its non-flexible counterparts. Furthermore, the simulation and measured results were in agreement and were comparable to similar investigated sensor prototypes based on non-flexible Rogers substrates (Rogers RO4003C) and Rogers Droid/RT 5880), demonstrating true potential for chemical, biomedical applications, and healthcare.

Imaging from optical coherence tomography (OCT) is widely used for detecting retinal diseases, localization of intra-retinal boundaries, etc. It is, however, degraded by speckle noise. Deep learning models can aid with denoising, allowing clinicians to clearly diagnose retinal diseases. Deep learning models can be considered as an end-to-end framework. We selected denoising studies that used deep learning models with retinal OCT imagery. Each study was quality-assessed through image quality metrics (including the peak signal-to-noise ratio—PSNR, contrast-to-noise ratio—CNR, and structural similarity index metric—SSIM). Meta-analysis could not be performed due to heterogeneity in the methods of the studies and measurements of their performance. Multiple databases (including Medline via PubMed, Google Scholar, Scopus, Embase) and a repository (ArXiv) were screened for publications published after 2010, without any limitation on language. From the 95 potential studies identified, a total of 41 were evaluated thoroughly. Fifty-four of these studies were excluded after full text assessment depending on whether deep learning (DL) was utilized or the dataset and results were not effectively explained. Numerous types of OCT images are mentioned in this review consisting of public retinal image datasets utilized purposefully for denoising OCT images (n = 37) and the Optic Nerve Head (ONH) (n = 4). A wide range of image quality metrics was used; PSNR and SNR that ranged between 8 and 156 dB. The minority of studies (n = 8) showed a low risk of bias in all domains. Studies utilizing ONH images produced either a PSNR or SNR value varying from 8.1 to 25.7 dB, and that of public retinal datasets was 26.4 to 158.6 dB. Further analysis on denoising models was not possible due to discrepancies in reporting that did not allow useful pooling. An increasing number of studies have investigated denoising retinal OCT images using deep learning, with a range of architectures being implemented. The reported increase in image quality metrics seems promising, while study and reporting quality are currently low.

Since the last decade radio frequency (RF) sensing has emerged as an alternative in‐home monitoring and tracking technology because of its inherent device‐free and privacy‐preserved features over camera and wearable technologies. In this work, contrary to conventional radar‐based sensing and WiFi‐based setups which require line‐of‐sight (LoS) to process and collect the RF signals for monitoring, it is shown for the first time in literature the potential of using RF sensing for vitals, that is, heartbeat detection in the non‐line‐of‐sight (NLoS) using an in‐house‐developed reconfigurable intelligent surface (RIS), that is, wireless on the walls. The developed RIS is the world's first prototype which consists of “4096 elements”. The unit cell elements are individually controlled via PIN diodes, 1‐bit, operating at 3.75 GHz specified for the fifth‐generation (5G) network. Near‐field beam steering is demonstrated, with a switching speed of 15 ms. For the first time in the literature its is demonstrated that RIS can assist in the detection of microactivities including the human heartbeat in NLoS indoor complex environment, a limitation with the existing RF sensing technologies to date. In addition, it is experimentally verified that the RIS can potentially reduce the e‐field magnitude at the user's by regulating the gain at the user terminal.

This paper presents a novel dielectricless floating patch antenna structure fed with an empty substrate integrated waveguide (ESIW). The main premise is to eliminate the dielectric from the patch design, which is equivalent to have an air-dielectric and leads to the necessity of a proper 3D printed plastic support to fasten the patch in the air above the ESIW, which is used as the feeding waveguide through a slot under the aperture-coupled approximation. A single-element antenna prototype has been studied, designed, optimized and manufactured in order to achieve excellent bandwidth, directivity, gain and beamwidth performance at 28 GHz. The main advantages of this prototype are a low fabrication cost, low losses, a low profile, high integration capability and removal of bulky dielectric material. On the whole, this prototype is a new suitable solution to be used in millimeter-wave 5G applications and beyond compared to the state-of-the-art.

A low-profile and broad steerable patch array antenna is presented. The improvement of the multi-directional functions from the broadside is very limited for the patch array antenna. In this work, the number of directional beams is enhanced by 40% using a novel approach, inter-parasitic element expansion technique and adopted only four PIN diode switches. Results in simulation and measurement have verified that the patch parasitic array antenna is capable -of generating seven beam patterns directed towards −52°, −30°, −10°, 0°, +10°, +30°, and +52° at the xz-plane. Applying the inter-parasitic element spacing’s optimization and minimizing the switching circuitry using four RF PIN diodes on the parasitic elements have contributed to the gain achievement of more than 7 dBi.

Switched filter banks find widespread application in frequency-hopping radar systems and communication networks with multiple operating frequencies, especially in situations demanding elevated filter element isolation. In this paper, the design and implementation of a highly isolated switchable narrow-bandpass filter bank architecture using hairpin microstrip topology is presented. The filter bank has four discrete bandpass filters with passbands of 2.0–2.2 GHz, 2.3–2.5 GHz, 3.1–3.3 GHz, and 3.9–4.1 GHz. These filters span the radar S-frequency band (2.0–4.0 GHz). In order to switch between channels with a switching speed of nanoseconds, low-loss and highly isolated SP4T switches are implemented. Advanced design system (ADS) software is used to design the various filter functionalities, and the entire system is tested on a vector network analyzer (VNA). The proposed architecture makes it much easier to put the filter bank into practice and switch it to the desired frequency, which is useful for radar receiver applications.

Considering the ongoing climate transformations, the appropriate and reliable phenotyping information of plant leaves is quite significant for early detection of disease, yield improvement. In real-life digital agricultural environment, the real-time prediction and identification of living plants leaves has immensely grown in recent years. Hence, cost-effective and automated and timely detection of plans species is vital for sustainable agriculture. This paper presents a novel, non-invasive method aiming to establish a feasible, and viable technique for the precise identification and observation of altering behaviour of plants species at cellular level for four consecutive days by integrating machine learning (ML) and THz with a swissto12 materials characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz. For this purpose, measurements observations data of seven various living plants leaves were determined and incorporate three different ML algorithms such as random forest (RF), support vector machine, (SVM), and K-nearest neighbour (KNN). The results demonstrated that RF exhibited higher accuracy of 98.87% followed by KNN and SVM with an accuracy of 94.64% and 89.67%, respectively, for precise detection of different leaves by observing their morphological features. In addition, RF outperformed other classifiers for determination of water-stressed leaves and having an accuracy of 99.42%. It is envisioned that proposed study can be proven beneficial and vital in digital agriculture technology for the timely detection of plants species to significantly help in mitigate yield and economic losses and improve crops quality.

For this article, a low-cost, compact, and flexible inkjet-printed electromagnetic sensor was investigated for its chemical and biomedical applications. The investigated sensor design was used to estimate variations in the concentration of chemicals (ethanol and methanol) and biochemicals (hydrocortisone—a chemical derivative of cortisol, a biomarker of stress and cardiovascular effects). The proposed design's sensitivity was further improved by carefully choosing the frequency range (0.5–4 GHz), so that the analyzed samples showed approximately linear variations in their dielectric properties. The dielectric properties were measured using a vector network analyzer (VNA) and an Agilent 85070E Dielectric Probe Kit. The sensor design had a resonant frequency at 2.2 GHz when investigated without samples, and a consistent shift in resonant frequency was observed, with variation in the concentrations of the investigated chemicals. The sensitivity of the designed sensor is decent and is comparable to its non-flexible counterparts. Furthermore, the simulation and measured results were in agreement and were comparable to similar investigated sensor prototypes based on non-flexible Rogers substrates (Rogers RO4003C) and Rogers Droid/RT 5880), demonstrating true potential for chemical, biomedical applications, and healthcare. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) is an emerging imaging tool in healthcare with common applications in ophthalmology for the detection of retinal diseases and in dentistry for the early detection of tooth decay. Speckle noise is ubiquitous in OCT images, which can hinder diagnosis by clinicians. In this paper, a region-based, deep learning framework for the detection of anomalies is proposed for OCT-acquired images. The core of the framework is Transformer-Enhanced Detection (TED), which includes attention gates (AGs) to ensure focus is placed on the foreground while identifying and removing noise artifacts as anomalies. TED was designed to detect the different types of anomalies commonly present in OCT images for diagnostic purposes and thus aid clinical interpretation. Extensive quantitative evaluations were performed to measure the performance of TED against current, widely known, deep learning detection algorithms. Three different datasets were tested: two dental and one CT (hosting scans of lung nodules, livers, etc.). The results showed that the approach verifiably detected tooth decay and numerous lesions across two modalities, achieving superior performance compared to several well-known algorithms. The proposed method improved the accuracy of detection by 16–22% and the Intersection over Union (IOU) by 10% for both dentistry datasets. For the CT dataset, the performance metrics were similarly improved by 9% and 20%, respectively.

Mild cognitive impairment (MCI) is a condition characterized by impairment in a single cognitive domain or mild deficit in several cognitive domains. MCI patients are at increased risk of progression to dementia with almost 50% of MCI patients developing dementia within five years. Early detection can play an important role in early intervention, prevention, and appropriate treatments. In this study, we examined heart rate variability (HRV) as a novel physiological biomarker for identifying individuals at higher risk of MCI. We investigated if measuring HRV using non-invasive sensors might offer reliable, non-invasive techniques to distinguish MCI patients from healthy controls. Twenty-one MCI patients were recruited to examine this possibility. HRV was assessed using CorSense wearable device. HRV indices were analyzed and compared in rest between MCI and healthy controls. The significance of difference of numerical data between two groups was assessed using parametric unpaired t-test or non-parametric Wilcoxon rank sum test based on the fulfilment of unpaired t-test assumptions. Multiple linear regression models were performed to assess the association between individual HRV parameter with the cognitive status adjusting for gender and age. Time-domain parameters i.e., the standard deviation of NN intervals (SDNN), and the root mean square of successive differences between normal heartbeats (RMSSD) were significantly lower in MCI patients compared with healthy controls. Prediction accuracy for the logistic regression using 10-fold cross-validation was 76.5%, Specificity was 0.8571, while sensitivity was 0.8095. Our study demonstrated that healthy participants have higher HRV indices compared to older adults with MCI using non-invasive biosensors technologies. Our results are of clinical importance in terms of showing the possibility that MCI of older people can be predicted using only HRV PPG-based data.

Abstract This paper presents a multi-functional bi-anisotropic metasurface having ultra-wide out of band transmission characteristics. The proposed metasurface is comprised of 90° rotated T-shaped configuration yielding greater than or equal to 50% out-of-band transmission from above L- to X-band. Moreover, this metasurface achieves a maximum of 99% out-of-band transmission at lower frequency bands (i.e., L-band). The simultaneous absorptive and controlled reflection functionalities are achieved at 15.028 to 15.164 GHz along with polarization-insensitive and angular stable properties. The proposed metasurface yields state-of-the-art features compared to already published papers and has broader scope for Fabry Perot cavity, Radar cross-section (RCS) reduction, electromagnetic compatibility and interference (EMC/I) shielding, selective multi-frequency bolometers, ultrathin wave trapping filters, sensors and beam-splitters in the microwave domain.

Abstract This paper presents a block-chain enabled inkjet-printed ultrahigh frequency radiofrequency identification (UHF RFID) system for the supply chain management, traceability and authentication of hard to tag bottled consumer products containing fluids such as water, oil, juice, and wine. In this context, we propose a novel low-cost, compact inkjet-printed UHF RFID tag antenna design for liquid bottles, with 2.5 m read range improvement over existing designs along with robust performance on different liquid bottle products. The tag antenna is based on a nested slot-based configuration that achieves good impedance matching around high permittivity surfaces. The tag was designed and optimized using the characteristic mode analysis. Moreover, the proposed RFID tag was commercially tested for tagging and billing of liquid bottle products in a conveyer belt and smart refrigerator for automatic billing applications. With the help of block-chain based product tracking and a mobile application, we demonstrate a real-time, secure and smart supply chain process in which items can be monitored using the proposed RFID technology. We believe the standalone system presented in this paper can be deployed to create smart contracts that benefit both the suppliers and consumers through the development of trust. Furthermore, the proposed system will paves the way towards authentic and contact-less delivery of food, drinks and medicine in recent Corona virus pandemic.

Abstract In this paper, a flexible bianisotropic metasurface possessing omega-type coupling is presented. The designed metasurface behaves differently when excited from either forward (port 1) or back (port 2) sides. It provides an absorption of 99.46% at 15.1 Gigahertz (GHz), when illuminated from port 1, whereas, on simultaneous illumination from port 2, it behaves like a partially reflective surface (PRS). Furthermore, the presented metasurface not only acts as an in-band absorptive surface (port 1) and partially reflective surface (port 2), but it also provides 97% out-of-band transmission at 7.8 GHz. The response of the presented metasurface remains the same for both transverse Electric (TE) and transverse magnetic (TM) polarized wave or any arbitrary linearly polarized wave. Additionally, the response of the metasurface is angularly stable for any oblique incidence up to 45º. The proposed ultrathin flexible metasurface with absorption, partial reflection and out-of-band transmission properties can be used in the Fabry Perrot cavity antenna for gain enhancement with radar cross-section (RCS) reduction both for passband and stop-band filtering, and conformal antenna applications.

The main goal of this manuscript is to provide an extensive literature review and analysis of certain biomarkers, which are frequently used to identify stress, anxiety, and other emotions, leading to potential solutions for the monitoring of mental wellness using wearable technologies. It is possible to see the impacts of several biomarkers in detecting stress levels and their effectiveness with an investigation into the literature on this subject. Biofeedback training has demonstrated some psychological effects, such as a reduction in anxiety and self-control enhancement. This survey demonstrates backed up by evidence that wearable devices are assistive in providing health and mental wellness solutions. Because physical activity tracing would reduce the stress stressors, which affect the subject’s body, therefore, it would also affect the mental activity and would lead to a reduction in cognitive mental load.

A compact, conformal, all-textile wearable antenna is proposed in this paper for the 2.45 GHz ISM (Industrial, Scientific and Medical) band. The integrated design consists of a monopole radiator backed by a 2 × 1 Electromagnetic Band Gap (EBG) array, resulting in a small form factor suitable for wristband applications. An EBG unit cell is optimized to work in the desired operating band, the results of which are further explored to achieve bandwidth maximization via floating EBG ground. A monopole radiator is made to work in association with the EBG layer to produce the resonance in the ISM band with plausible radiation characteristics. The fabricated design is tested for free space performance analysis and subjected to human body loading. The proposed antenna design achieves bandwidth of 2.39 GHz to 2.54 GHz with a compact footprint of 35.4 × 82.4 mm2. The experimental investigations reveal that the reported design adequately retains its performance while operating in close proximity to human beings. The presented Specific Absorption Rate (SAR) analysis reveals 0.297 W/kg calculated at 0.5 W input power, which certifies that the proposed antenna is safe for use in wearable devices.