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This paper proposes a nature-inspired spider web-shaped ultra-high frequency (UHF) radio frequency identification (RFID) reader antenna and battery-free sensor-based system for healthcare applications. This antenna design consists of eight concentric decagons of various sizes and five straight microstrip lines.These lines are connected to the ground using 50 Ω resistors from both ends, except for one microstrip line that is reserved for connecting a feeding port. The reader antenna design features fairly strong and uniform electric and magnetic field characteristics. It also exhibits wideband characteristics, covering whole UHF RFID band (860–960 MHz) and providing a tag reading volume of 200 × 200 × 20 mm 3 . Additionally, it has low gain characteristics, which are necessary for the majority of nearfield applications to prevent the misreading of other tags. Moreover, the current distribution in this design is symmetric throughout the structure, effectively resolving orientation sensitivity issues commonly encountered in low-cost linearly polarized tag antennas. The measurement results show that the reader antenna can read medicine pills tagged using low-cost passive/battery-free RFID tags, tagged expensive jewelry, intervenes solution, and blood bags positioned in various orientations. As a result, the proposed reader antenna-based system is a strong contender for near-field RFID, healthcare, and IoT applications. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Statistical analysis is frequently used to determine how manufacturing tolerances or operating condition uncertainties affect system performance. Surrogate is one of the accelerating ways in engineering tolerance quantification to analyze uncertainty with an acceptable computational burden rather than costly traditional methods such as Monte Carlo simulation. Compared with more complicated surrogates such as the Gaussian process, or Radial Basis Function (RBF), the Polynomial Regression (PR) provides simpler formulations yet acceptable outcomes. However, PR with the common least-squares method needs to be more accurate and flexible for approximating nonlinear and nonconvex models. In this study, a new approach is proposed to enhance the accuracy and approximation power of PR in dealing with uncertainty quantification in engineering tolerances. For this purpose, first, by computing the differences between training sample points and a reference point (e.g., nominal design), we employ certain linear and exponential basis functions to transform an original variable design into new transformed variables. A second adjustment is made to calculate the bias between the true simulation model and the surrogate’s approximated response. To demonstrate the effectiveness of the proposed PR approach, we provide comparison results between conventional and proposed surrogates employing four practical problems with geometric fabrication tolerances such as three-bar truss design, welded beam design, and trajectory planning of two-link and three-link (two and three degrees of freedom) robot manipulator. The obtained results prove the preference of the proposed approach over conventional PR by improving the approximation accuracy of the model with significantly lower prediction errors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper aims the development of a new reduced-cost algorithm for a multi-objective robust transmitter placement under uncertainty. Toward this end, we propose a new hybrid Kriging/Grey Wolf Optimizer (GWO) approach combined with robust design optimization to estimate the set of Pareto frontier by searching robustness as well as accuracy (lower objective function) in a design space. We consider minimization of the energy power consumption for transmitting as well as maximization of signal coverage in a multi-objective robust optimization model. The reliability of the model to control signal overlap for multiple transmitting antennas is also provided. To smooth computational cost, the proposed method instead of evaluating all receiver test points in each optimization iteration approximates signal coverages using Kriging interpolation to obtain optimal transmitter positions. The results demonstrate the utility and the efficiency of the proposed method in rendering the robust optimal design and analyzing the sensitivity of the transmitter placement problem under practically less-expensive computational efforts (350% and 320% less than computational time elapsed using standalone GWO and NSGAII respectively). [ABSTRACT FROM AUTHOR]

The development of ultra-dense heterogeneous networks (HetNets) will cause a significant rise in energy consumption with large-scale base station (BS) deployments, requiring cellular networks to be more energy efficient to reduce operational expense and promote sustainability. Cell switching is an effective method to achieve the energy efficiency goals, but traditional heuristic cell switching algorithms are computationally demanding with limited generalization abilities for ultra-dense HetNet applications, motivating the usage of machine learning techniques for adaptive cell switching. Graph neural networks (GNNs) are powerful deep learning models with strong generalization abilities but receive little attention for cell switching. This paper proposes a GNN-based cell switching solution (GBCSS) that has a smaller computational complexity than existing heuristic algorithms. The presented performance evaluation uses the Milan telecommunication dataset based on real-world call detail records, comparing GBCSS with a traditional exhaustive search (ES) algorithm, a state-of-the-art learning-based algorithm, and the baseline without cell switching. Results indicate that GBCSS achieves a 10.41% energy efficiency gain when compared with the baseline and achieves 75.76% of the optimal performance obtained with ES algorithm. The results also demonstrate GBCSS' significant scalability and generalization abilities to differing load conditions and the number of BSs, suggesting this approach is well-suited to ultra-dense HetNet deployment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this paper, the cell-free massive multiple input multiple output (MIMO) network is affected by the pilot contamination phenomenon when a large number of users and a small number of available pilots exists, the quality of service (QoS) will deteriorate due to the low accuracy of the channel estimation because some of users will use the same pilot. Therefore, we address this problem by presenting two novel schemes of pilot assignment and pilot power control design based on the matching technique for the uplink of cell-free massive MIMO systems to maximize spectral efficiency. We first formulate an assignment optimization problem in order to find the best possible pilot sequence to be used by utilizing genetic algorithm (GA) and then propose a Hungarian matching algorithm to solve this formulated problem. Regarding the power control design, we formulate a minimum-weighted assignment problem to assign pilot power control coefficients to the estimated channel's minimum mean-squared error by considering the access point (AP) selection. Then, we also propose the Hungarian algorithm to solve this problem. Simulation results show that our proposed schemes outperform the state-of-the-art techniques concerning both the pilot assignment and the pilot power control design by achieving a 15% improvement in the spectral efficiency. Finally, the computational complexity analysis is provided for the proposed schemes compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The problem of Lip-reading has become an important research challenge in recent years. The goal is to recognise speech from lip movements. Most of the Lip-reading technologies developed so far are camera-based, which require video recording of the target. However, these technologies have well-known limitations of occlusion and ambient lighting with serious privacy concerns. Furthermore, vision-based technologies are not useful for multi-modal hearing aids in the coronavirus (COVID-19) environment, where face masks have become a norm. This paper aims to solve the fundamental limitations of camera-based systems by proposing a radio frequency (RF) based Lip-reading framework, having an ability to read lips under face masks. The framework employs Wi-Fi and radar technologies as enablers of RF sensing based Lip-reading. A dataset comprising of vowels A, E, I, O, U and empty (static/closed lips) is collected using both technologies, with a face mask. The collected data is used to train machine learning (ML) and deep learning (DL) models. A high classification accuracy of 95% is achieved on the Wi-Fi data utilising neural network (NN) models. Moreover, similar accuracy is achieved by VGG16 deep learning model on the collected radar-based dataset. This study aims to solve the fundamental limitations of camera-based systems by proposing a contactless and privacy-preserving radio frequency-based lip-reading framework, which can recognise lip movements under face masks. [ABSTRACT FROM AUTHOR]

This paper presents a millimeter-wave direction of arrival estimation (DoA) technique powered by dynamic aperture optimization. The frequency-diverse medium in this work is a lens-loaded oversized mmWave cavity that hosts quasi-random wave-chaotic radiation modes. The presence of the lens is shown to confine the radiation within the field of view and improve the gain of each radiation mode; hence, enhancing the accuracy of the DoA estimation. It is also shown, for the first time, that a lens loaded-cavity can be transformed into a lens-loaded dynamic aperture by introducing a mechanically controlled mode-mixing mechanism inside the cavity. This work also proposes a way of optimizing this lens-loaded dynamic aperture by exploiting the mode mixing mechanism governed by a machine learning-assisted evolutionary algorithm. The concept is verified by a series of extensive simulations of the dynamic aperture states obtained via the machine learning-assisted evolutionary optimization technique. The simulation results show a 25 % improvement in the conditioning for the DoA estimation using the proposed technique. [ABSTRACT FROM AUTHOR]

Climate change, conventional agricultural management practices, and increasing water scarcity pose a major threat to agricultural production and biodiversity as well as environmental sustainability. Climate-smart agriculture (CSA) is recognized as an efficient, sustainable, and feasible agricultural system that plays a vital role in addressing the potential impacts of climate change in Pakistan. First-hand information was collected from 450 farm households in 24 villages from Okara, Sahiwal, and Khanewal irrigation divisions, having various wheat-based cropping systems of Pakistan. This includes rice–wheat (RW), maize-wheat (MW), and cotton-wheat (CW) cropping systems in the Lower Bari Doab Canal (LBDC) irrigation system. This study estimated and compared the sustainability and efficiency analysis of CSA and conventional agricultural practices. This study also estimated the impact of water-smart practices of the CSA, technical training, and groundwater quality on agricultural production by using production function and bootstrap truncated regression. The findings of this study revealed that adopters of CSA of the wheat-based cropping systems have higher economic benefits and improved resource use efficiencies compared to the conventional farmers. The findings of the study also revealed the increased efficiency of CSA adopters over other two systems in CW cropping system. The water-smart practices of CSA, access to credit, technical training, use of groundwater of varying quality, and other inputs also showed variations in the agricultural production and resource use efficiency. It has been concluded that farmers can earn more profit, save inputs (such as water), and increase their production by adopting water-smart practices of CSA. Hence, the government and other relevant institutions should devise and implement policies that adequately addressed the importance and enhance the use of water-smart practices of CSA in Punjab and beyond. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors' knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being. [ABSTRACT FROM AUTHOR]

Traditional agricultural practices, extensive use of inputs, and abrupt changes in climate have been of great concern to agriculture production around the world, especially in developing countries. Therefore, it is very vital to adopt and expand Climate-Smart agricultural (CSA) practices. By the cross-sectional data of 350 cotton farmers from major cotton-growing districts of Punjab Pakistan, adoption of CSA practices such as irrigation and soil and crop management practices is evaluated, and factors which affect farmer adoption decision and its impact on poverty, income, and yield are estimated by using logistic regression and propensity score matching (PSM) respectively. The results found that education, access to credit, tubewell ownership, farming experience, and access to extension services positively influenced farmers' adoption behavior. Further, PSM results revealed that adoption of CSA practices is economical, financially, environmentally desirable, and pro-poor. According to these findings, ultimately adoption would help in reducing the negative impact of climate change on the cotton crop by ensuring profits, removing the barriers in the adoption, disseminating the information about CSA, and strictly enforcing the regulations for CSA. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the rapid restoration of indigenous hydrocarbon degraders after chemical oxidation, in order to enhance the subsequent biodegradation of crude oil in soils. Materials and methods: The Fe-SOM (2231 mg kg−1) and H2O2 were added into the oil-contaminated soil (total petroleum hydrocarbon (TPH): 16064 mg kg−1) for chemical oxidation. Then, the all and partial alkane re-biodegradation reagents were added for re-biodegradation experiments. Calculated and analyzed C-source (DOC and TPH) and N-source ( NH 4 + − N , NO 3 − − N , NO 2 − − N ) consumption and the population of hydrocarbon degraders in each period. Results and discussion: After chemical oxidation, the total biodegradation of all alkanes (re-biodegradation degree: 100%) with reagent for all alkane re-biodegradation in the late stage was higher than that in the early stage. Further study found that DOC was the main C-source and NH 4 + − N was the main N-source in the early stage, while TPH and nitrate were the main C&N-sources in the late stage of re-biodegradation. In other words, the C&N-sources changed over to TPH and nitrate in the late stage. After re-biodegradation stage, increasing biodegradation (from 644 mg kg−1 in the 1st stage to 842 mg kg−1 in the 3rd stage) was observed. Conclusions: The results showed that re-biodegradation of all alkanes could recover growth of indigenous hydrocarbon degraders and continuously increase biodegradation of all alkanes after chemical oxidation. The C&N-sources changed from DOC and NH 4 + − N to all alkanes and NO 3 − − N during re-biodegradation stage was the key factor. [ABSTRACT FROM AUTHOR]

Ultra-reliable and low-latency communication (URLLC) is one of the most important scenarios in forthcoming fifth generation (5G) cellular networks to ensure timely exchange of information and realize real-time wireless control. In URLLC, timely information update needs to be guaranteed since control performance, e.g., control cost and stability, is directly determined by timely control information update. In this paper, we introduce an effective age of information (EAoI) to evaluate the timeliness of information update in control process. We consider the control process with two phases: sensor to controller phase and controller to actuator phase. We adopt first-generate-first-serve (FGFS) M/M/1/1 → M/M/1/2 and FGFS M/M/1/1* → M/M/1/2* tandem queuing models to represent control process and we use finite-state Markov chains to describe control information updates. By studying state transitions, we calculate the average EAoI for both tandem queuing models. More importantly, we analyze throughput of wireless control systems and its relationship with average EAoI, which provides a guideline for URLLC system design in real-time feedback control systems. Simulation results show the advantage of using EAoI. [ABSTRACT FROM AUTHOR]

In this research article, a multiband circular polarization selective (CPS) metasurface is presented. A reciprocal bi-layered metasurface is designed by introducing the chirality in the structure. The top layer of the proposed metasurface is composed of circular split-ring resonator with a cross shape structure inside it. The same structure is printed on the bottom side of the proposed metasurface by rotating it at an angle of 90° to achieve chirality in the structure. The proposed metasurface is able to add CPS surface capability between 5.18 and 5.23 GHz for y-polarized incident wave. For the frequency band of 5.18–5.23 GHz, the transmission goes up to − 4 dB, while the polarization extinction ratio (PER) reaches up to − 27.4 dB at 5.2 GHz. Similarly, for x-polarized incident wave, three strategic CPS operating bands are achieved within the frequency ranges of 10.64–10.82 GHz, 12.25–12.47 GHz, and 14.42–14.67 GHz. The maximum PER of 47.16 dB has been achieved for the 14.42–14.67 GHz frequency band at 14.53 GHz. Furthermore, the response of the metasurface does not vary against oblique incidences up to 45°. The simple structure, angular stability, multiband and miniaturized size make this metasurface an outstanding applicant for polarization conversion and biomedical applications. [ABSTRACT FROM AUTHOR]

Construction of critical systems (e.g. disaster/emergency management systems) demands extra efforts from the developers as compared to non-critical systems. A critical system may be comprised of hardware communication infrastructure and the management system software. Many of hardware failures or in-capabilities can be handled by the resilient software components. The management software's main tasks are to continuously collect data (from distributed sensors), to predict possible threats, and to intimate the right authorities for timely action to avoid/reduce the damages of the threats. For the construction of such systems, by reusing existing reliable software components, a verifiable software composition mechanism is highly desired. In this paper, we select a component model (EX-MAN) from the component-based development approaches for providing pre-defined exogenous connectors. In this paper, at the methodological level, we define an approach to verify the correctness of exogenous connectors. To evaluate our approach, we design an emergency management system in EX-MAN and implement the system in our tool for EX-MAN. [ABSTRACT FROM AUTHOR]

The understanding of temperature trends in high elevation mountain areas is an integral part of climate change research and it is critical for assessing the impacts of climate change on water resources including glacier melt, degradation of soils, and active layer thickness. In this study, climate changes were analyzed based on trends in air temperature variables (Tmax, Tmin, Tmean), and Diurnal Temperature Range (DTR) as well as elevation-dependent warming at annual and seasonal scales in the Headwaters of Yangtze River (HWYZ), Qinghai Tibetan Plateau. The Base Period (1965-2014) was split into two subperiods; Period-I (1965-1989) and Period-II (1990- 2014) and the analysis was constrained over two subbasins; Zhimenda and Tuotuohe. Increasing trends were found in absolute changes in temperature variables during Period-II as compared to Period-I. Tmax, Tmin, and Tmean had significant increasing trends for both sub-basins. The highest significant trends in annual time scale were observed in Tmin (1.15°C decade−1) in Tuotuohe and 0.98°C decade−1 in Zhimenda sub-basins. In Period-II, only the winter season had the highest magnitudes of Tmax and Tmin 0.58°C decade−1 and 1.26°C decade−1 in Tuotuohe subbasin, respectively. Elevation dependent warming analysis revealed that Tmax, Tmin and Tmean trend magnitudes increase with the increase of elevations in the middle reaches (4000 m to 4400 m) of the HWYZ during Period-II annually. The increasing trend magnitude during Period-II, for Tmax, is 1.77, 0.92, and 1.31°C decade−1, for Tmin 1.20, 1.32 and 1.59°C decade−1, for Tmean 1.51, 1.10 and 1.51°C decade−1 at elevations of 4066 m, 4175 m and 4415 m respectively in the winter season. Tmean increases during the spring season for > 3681 m elevations during Period-II, with no particular relation with elevation dependency for other variables. During the summer season in Period- II, Tmax, Tmin, Tmean increases with the increase of elevations (3681 m to 4415 m) in the middle reaches of HWYZ. Elevation dependent warming (EDW), the study concluded that magnitudes of Tmin are increasing significantly after the 1990s as compared to Tmax in the HWYZ. It is concluded that the climate of the HWYZ is getting warmer in both sub-basins and the rate of warming was more evident after the 1990s. The outcomes of the study provide an essential insight into climate change in the region and would be a primary index to select and design research scenarios to explore the impacts of climate change on water resources. [ABSTRACT FROM AUTHOR]

Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-022-21490-z, published online 22 October 2022 The original version of this Article omitted an affiliation for Shahid Mumtaz. The original article can be found online at https://doi.org/10.1038/s41598-022-21490-z. [Extracted from the article]

In this paper we analyze the energy efficiency of single-hop, multi-hop, cooperative selective decode-and-forward, cooperative incremental decode-and-forward, and even the combination of cooperative and non-cooperative schemes, in wireless sensor networks composed of several nodes. We assume that, as the sensor nodes can experience either non line-of-sight or some line-of-sight conditions, the Nakagami-m fading distribution is used to model the wireless environment. The energy efficiency analysis is constrained by a target outage probability and an end-to-end throughput. Our results show that in most scenarios cooperative incremental schemes are more energy efficient than the other methods. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1038/s41598-021-97930-z. The original version of this Article contained errors in Table 1, where the cited References were incorrectly given. [Extracted from the article]