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Sustained clinical and hemodynamic benefit after revascularization for chronic limb-threatening ischemia (CLTI) is needed to resolve symptoms and prevent limb loss. We sought to compare rates of clinical and hemodynamic failure as well as resolution of initial and prevention of recurrent CLTI after endovascular (ENDO) vs bypass (OPEN) revascularization in the Best-Endovascular-versus-best-Surgical-Therapy-in-patients-with-CLTI (BEST-CLI) trial. As planned secondary analyses of the BEST-CLI trial, we examined the rates of (1) clinical failure (a composite of all-cause death, above-ankle amputation, major reintervention, and degradation of WIfI stage); (2) hemodynamic failure (a composite of above-ankle amputation, major and minor reintervention to maintain index limb patency, failure to an initial increase or a subsequent decrease in ankle brachial index of 0.15 or toe brachial index of 0.10, and radiographic evidence of treatment stenosis or occlusion); (3) time to resolution of presenting CLTI symptoms; and (4) incidence of recurrent CLTI. Time-to-event analyses were performed by intention-to-treat assignment in both trial cohorts (cohort 1: suitable single segment great saphenous vein [SSGSV], N = 1434; cohort 2: lacking suitable SSGSV, N = 396), and multivariate stratified Cox regression models were created. In cohort 1, there was a significant difference in time to clinical failure (log-rank P <.001), hemodynamic failure (log-rank P <.001), and resolution of presenting symptoms (log-rank P =.009) in favor of OPEN. In cohort 2, there was a significantly lower rate of hemodynamic failure (log-rank P =.006) favoring OPEN, and no significant difference in time to clinical failure or resolution of presenting symptoms. Multivariate analysis revealed that assignment to OPEN was associated with a significantly lower risk of clinical and hemodynamic failure in both cohorts and a significantly higher likelihood of resolving initial and preventing recurrent CLTI symptoms in cohort 1, including after adjustment for key baseline patient covariates (end-stage renal disease [ESRD], prior revascularization, smoking, diabetes, age >80 years, WIfI stage, tissue loss, and infrapopliteal disease). Factors independently associated with clinical failure included age >80 years in cohort 1 and ESRD across both cohorts. ESRD was associated with hemodynamic failure in cohort 1. Factors associated with slower resolution of presenting symptoms included diabetes in cohort 1 and WIfI stage in cohort 2. Durable clinical and hemodynamic benefit after revascularization for CLTI is important to avoid persistent and recurrent CLTI, reinterventions, and limb loss. When compared with ENDO, initial treatment with OPEN surgical bypass, particularly with available saphenous vein, is associated with improved clinical and hemodynamic outcomes and enhanced resolution of CLTI symptoms. [ABSTRACT FROM AUTHOR]

Time-synchronised data streams from bio-loggers are becoming increasingly important for analysing and interpreting intricate animal behaviour including split-second decision making, group dynamics, and collective responses to environmental conditions. With the increased use of AI-based approaches for behaviour classification, time synchronisation between recording systems is becoming an essential challenge. Current solutions in bio-logging rely on manually removing time errors during post processing, which is complex and typically does not achieve sub-second timing accuracies. We first introduce an error model to quantify time errors, then optimise three wireless methods for automated onboard time (re)synchronisation on bio-loggers (GPS, WiFi, proximity messages). The methods can be combined as required and, when coupled with a state-of-the-art real time clock, facilitate accurate time annotations for all types of bio-logging data without need for post processing. We analyse time accuracy of our optimised methods in stationary tests and in a case study on 99 Egyptian fruit bats (Rousettus aegyptiacus). Based on the results, we offer recommendations for projects that require high time synchrony. During stationary tests, our low power synchronisation methods achieved median time accuracies of 2.72 / 0.43 ms (GPS / WiFi), compared to UTC time, and relative median time accuracies of 5 ms between tags (wireless proximity messages). In our case study with bats, we achieved a median relative time accuracy of 40 ms between tags throughout the entire 10-day duration of tag deployment. Using only one automated resynchronisation per day, permanent UTC time accuracies of ≤ 185 ms can be guaranteed in 95% of cases over a wide temperature range between 0 and 50 °C. Accurate timekeeping required a minimal battery capacity, operating in the nano- to microwatt range. Time measurements on bio-loggers, similar to other forms of sensor-derived data, are prone to errors and so far received little scientific attention. Our combinable methods offer a means to quantify time errors and autonomously correct them at the source (i.e., on bio-loggers). This approach facilitates sub-second comparisons of simultaneously recorded time series data across multiple individuals and off-animal devices such as cameras or weather stations. Through automated resynchronisations on bio-loggers, long-term sub-second accurate timestamps become feasible, even for life-time studies on animals. We contend that our methods have potential to greatly enhance the quality of ecological data, thereby improving scientific conclusions. [ABSTRACT FROM AUTHOR]

In the modern world of human–computer interaction, notable advancements in human identification have been achieved across fields like healthcare, academia, security, etc. Despite these advancements, challenges remain, particularly in scenarios with poor lighting, occlusion, or non-line-of-sight. To overcome these limitations, the utilization of radio frequency (RF) wireless signals, particularly wireless fidelity (WiFi), has been considered an innovative solution in recent research studies. By analyzing WiFi signal fluctuations caused by human presence, researchers have developed machine learning (ML) models that significantly improve identification accuracy. This paper conducts a comprehensive survey of recent advances and practical implementations of WiFi-based human identification. Furthermore, it covers the ML models used for human identification, system overviews, and detailed WiFi-based human identification methods. It also includes system evaluation, discussion, and future trends related to human identification. Finally, we conclude by examining the limitations of the research and discussing how researchers can shift their attention toward shaping the future trajectory of human identification through wireless signals. [ABSTRACT FROM AUTHOR]

The Society for Vascular Surgery (SVS) Wound, Ischemia, and foot Infection (WIfI) classification system aims to risk stratify patients with chronic limb-threatening ischemia (CLTI), predicting both amputation rates and the need for revascularization. However, real-world use of the system and whether it predicts outcomes accurately after open revascularization and peripheral interventions is unclear. Therefore, we sought to determine the adoption of the WIfI classification system within a contemporary statewide collaborative as well as the impact of patient factor, and WIfI risk assessment on short- and long-term outcomes. Using data from a large statewide collaborative, we identified patients with CLTI undergoing open surgical revascularization or peripheral vascular intervention (PVI) between 2016 and 2022. The primary exposure was preoperative clinical WIfI stage. Patients were categorized according to the SVS Lower Extremity Threatened Limb Classification System into clinical WIfI stages 1, 2, 3, or 4. The primary outcomes were 30-day and 1-year amputation and mortality rates. Multivariable logistic regression was performed to estimate the association of WIfI stage on postrevascularization outcomes. In the cohort of 17,417 patients, 83.4% (n = 14,529) had WIfI stage documented. PVIs were performed on 57.6% of patients, and 42.4% underwent an open surgical revascularization. Of the patients, 49.5% were classified as stage 1, 19.3% stage 2, 12.8% stage 3, and 18.3% of patients met stage 4 criteria. Stage 3 and 4 patients had higher rates of diabetes, congestive heart failure, and renal failure, and were less likely to be current or former smokers. One-half of stage 3 patients underwent open surgical revascularization, whereas stage 1 patients were most likely to have received a PVI (64%). As WIfI stage increased from 1 to 4, 1-year mortality increased from 12% to 21% (P <.001), 30-day amputation rates increased from 5% to 38% (P <.001), and 1-year amputation rates increased from 15% to 55% (P <.001). Finally, patients who did not have WIfI scores classified had significantly higher 30-day and 1-year mortality rates, as well as higher 30-day and 1-year amputation rates. The SVS WIfI clinical stage is significantly associated with 1-year amputation rates in patients with CLTI after lower extremity revascularization. Because nearly 55% of stage 4 patients require a major amputation within 1 year of intervention, this finding study supports use of the WIfI classification system in clinical decision-making for patients with CLTI. [ABSTRACT FROM AUTHOR]

Abstract Time-synchronised data streams from bio-loggers are becoming increasingly important for analysing and interpreting intricate animal behaviour including split-second decision making, group dynamics, and collective responses to environmental conditions. With the increased use of AI-based approaches for behaviour classification, time synchronisation between recording systems is becoming an essential challenge. Current solutions in bio-logging rely on manually removing time errors during post processing, which is complex and typically does not achieve sub-second timing accuracies. We first introduce an error model to quantify time errors, then optimise three wireless methods for automated onboard time (re)synchronisation on bio-loggers (GPS, WiFi, proximity messages). The methods can be combined as required and, when coupled with a state-of-the-art real time clock, facilitate accurate time annotations for all types of bio-logging data without need for post processing. We analyse time accuracy of our optimised methods in stationary tests and in a case study on 99 Egyptian fruit bats (Rousettus aegyptiacus). Based on the results, we offer recommendations for projects that require high time synchrony. During stationary tests, our low power synchronisation methods achieved median time accuracies of 2.72 / 0.43 ms (GPS / WiFi), compared to UTC time, and relative median time accuracies of 5 ms between tags (wireless proximity messages). In our case study with bats, we achieved a median relative time accuracy of 40 ms between tags throughout the entire 10-day duration of tag deployment. Using only one automated resynchronisation per day, permanent UTC time accuracies of ≤ 185 ms can be guaranteed in 95% of cases over a wide temperature range between 0 and 50 °C. Accurate timekeeping required a minimal battery capacity, operating in the nano- to microwatt range. Time measurements on bio-loggers, similar to other forms of sensor-derived data, are prone to errors and so far received little scientific attention. Our combinable methods offer a means to quantify time errors and autonomously correct them at the source (i.e., on bio-loggers). This approach facilitates sub-second comparisons of simultaneously recorded time series data across multiple individuals and off-animal devices such as cameras or weather stations. Through automated resynchronisations on bio-loggers, long-term sub-second accurate timestamps become feasible, even for life-time studies on animals. We contend that our methods have potential to greatly enhance the quality of ecological data, thereby improving scientific conclusions.

As automatic test technology advances, the number of programmable instruments in a single test system increases. Traditional wired communication methods have a limited range and involve complex cable layouts. Single-function wireless converters provide a viable alternative, but they have limitations. These include complicated configuration, issues with multi-system collaboration, and data blocking. This paper proposes a wireless ad hoc network platform for multi-bus instruments based on a low-cost ESP-12H WiFi module. The platform supports GPIB, RS232, RS485, and CAN bus interface instrument access. It features easy configuration, ad hoc networking, and self-repairing capabilities. A relay multi-hop network with a tree topology expands capacity and coverage. Additionally, a dynamic window-receiving mode and an improved multi-priority queue ensure data transmission integrity. The experimental results show that the platform's networking time is less than 10 s, and the coverage range reaches 50 m in complex indoor environments. It also shows good stability when running for a long time. However, due to hardware and software design limitations, the actual upload speeds fall short of the theoretical values. For example, RS232 and RS485 are about 10% slower than the theoretical values, and GPIB is about 80% slower. Further optimization is required in the future. [ABSTRACT FROM AUTHOR]

Advances in network technology are increasingly developing. Due to the human need for fast, easy and comfortable access to technology. So there is competition between network device manufacturers to innovate in creating new, compact technology, especially after the emergence of AI technology which has been trying to be embedded in various devices recently. Mash technology is nothing new in the networking world. However, problems that often occur with Mesh devices include complicated installation and requiring different types of devices and sometimes with different brands there is quite noticeable latency. Whole Home Mash (WHM) is a technology made by TP-LINK which is embedded in DECO series devices which have various contemporary features such as WiFi 6, OFDMA and MU-MIMO which can facilitate device connections in accessing the internet. What is focused on in this research is the WiFi or wirelessbased Mesh feature which makes this device very easy to install and the settings are supported by an App that can be accessed from the user's cellphone. Based on the Quality of Service (QoS) assessment criteria which consist of: Throughput, Delay, Jitter and Packet Loss, each of which gets a score of 4 in the index which means it is in the very good category. The results show that in 4 times with different conditions and distances it met criteria 4 (very good) in Throughput, Delay, Jitter but experienced a decrease in Packet Loss with the "moderate" criteria for data sizes above 400 MB. In short, this device meets index 4 of the four criteria above and can be a reference in modeling a WiFi-based network in the future. [ABSTRACT FROM AUTHOR]

WiFi fingerprint-based positioning is a method for indoor localization with the advent of widespread deployment of WiFi and the Internet of Things. However, single WiFi fingerprint positioning has the problems of mismatch, unstable signal strength and limited accuracy. Aiming to address these issues, this paper proposes the fusion algorithm combining WiFi and pedestrian dead reckoning (PDR). Firstly, the particle swarm optimization (PSO) model is utilized to optimize the weighted k-nearest neighbors (WKNN) in the WiFi part. Additionally, the artemisinin optimization (AO) algorithm is used to optimize the particle filter (PF) to improve the fusion effect of the WiFi and PDR. Finally, to thoroughly validate the localization performance of the proposed algorithm, we designed experiments involving two scenarios with four smartphone gestures: calling, dangling, handheld, and pocketed. The experimental results unequivocally indicate that the positioning error of AO-PSO-PF algorithm is lower than that of other algorithms including PDR, WiFi, PF, APF, and FPF. The average positioning errors for the two experiments are 0.95 m and 1.42 m, respectively. [ABSTRACT FROM AUTHOR]

For over forty years, TCP has been the main protocol for transporting data on the Internet. To improve congestion control algorithms (CCAs), delay bounding algorithms such as Vegas, FAST, BBR, PCC, and Copa have been developed. However, despite being designed to ensure fairness between data flows, these CCAs can still lead to unfairness and, in some cases, even cause data flow starvation in WiFi networks under certain conditions. We propose a new CCA switching solution that works with existing TCP and WiFi standards. This solution is offline and uses Deep Reinforcement Learning (DRL) trained on features such as noncongestive delay variations to predict and prevent extreme unfairness and starvation. Our DRL-driven approach allows for dynamic and efficient CCA switching. We have tested our design preliminarily in realistic datasets, ensuring that they support both fairness and efficiency over WiFi networks, which requires further investigation and extensive evaluation before online deployment. [ABSTRACT FROM AUTHOR]

In large indoor environments, accurate positioning and tracking of people and autonomous equipment have become essential requirements. The application of increasingly automated moving transportation units in large indoor spaces demands a precise knowledge of their positions, for both efficiency and safety reasons. Moreover, satellite-based Global Positioning System (GPS) signals are likely to be unusable in deep indoor spaces, and technologies like WiFi and Bluetooth are susceptible to signal noise and fading effects. For these reasons, a hybrid approach that employs at least two different signal typologies proved to be more effective, resilient, robust, and accurate in determining localization in indoor environments. This paper proposes an improved hybrid technique that implements fingerprinting-based indoor positioning using Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points and Wireless Sensor Network (WSN) technology. Six signals were recorded on a regular grid of anchor points covering the research surface. For optimization purposes, appropriate raw signal weighing was applied in accordance with previous research on the same data. The novel approach in this work consisted of performing a virtual tessellation of the considered indoor surface with a regular set of tiles encompassing the whole area. The optimization process was focused on varying the size of the tiles as well as their relative position concerning the signal acquisition grid, with the goal of minimizing the average distance error based on tile identification accuracy. The optimization process was conducted using a standard Quantum Particle Swarm Optimization (QPSO), while the position error estimate for each tile configuration was performed using a 3-layer Multilayer Perceptron (MLP) neural network. These experimental results showed a 16% reduction in the positioning error when a suitable tile configuration was calculated in the optimization process. Our final achieved value of 0.611 m of location incertitude shows a sensible improvement compared to our previous results. [ABSTRACT FROM AUTHOR]

With the rise of various emerging fraud crimes, criminals using communication devices (e.g., mobile phones) to defraud victims has become the mainstream trend in global crime. Therefore, how to effectively locate criminals has become the main challenge for law enforcement agencies (LEAs). Considering that the current Long Term Evolution (LTE) positioning technology is costly and not easily popularized, this study uses WiFi signals for 3D spatial positioning. By utilizing the inverse relationship between WiFi signal strength and distance, a conversion equation between signal strength and distance is constructed. Then, using four observation points for triangulation on the target point, the research results show that WiFi signals can successfully complete positioning. However, weaker signals are prone to causing distance errors. This study proposes multiple conditional positioning equations for different signal limits, which can effectively improve positioning accuracy. The results of this study can provide LEAs with a theoretical basis for the future development of WiFi signal-based positioning. [ABSTRACT FROM AUTHOR]

Proprietary communication technologies for time-critical communication in industrial environments are being gradually replaced by Time-sensitive Networking (TSN)-enabled Ethernet. Furthermore, attempts have been made to bring TSN features into wireless networks so that the flexibility of wireless networks can be utilized, and the end-to-end timings for Time-Triggered (TT) streams can be guaranteed. Given a mixed wired-wireless network, the scheduling problem should be solved for a set of TT stream requests. In this paper, we formulate the no-wait scheduling problem for mixed wired-wireless networks as a Mixed Integer Linear Programming (MILP) model with the objective of minimizing the flowspan. We also propose a relaxation of the original MILP in the form of a 2-stage MILP formulation. Next, a scalable approach based on the greedy heuristic is proposed to solve the problem for realistic-size networks. Evaluation results show that the greedy heuristic is suitable for realistic problem sizes where the MILP-based approach is found to be practically infeasible. Furthermore, the impact of wireless requests on the performance of the greedy heuristic is reported. [ABSTRACT FROM AUTHOR]

Enclosed public spaces are hotspots for airborne disease transmission. To measure and maintain indoor air quality in terms of airborne transmission, an open source, low cost and distributed array of particulate matter sensors was developed and named Dynamic Aerosol Transport for Indoor Ventilation, or DATIV, system. This system can use multiple particulate matter sensors (PMSs) simultaneously and can be remotely controlled using a Raspberry Pi-based operating system. The data acquisition system can be easily operated using the GUI within any common browser installed on a remote device such as a PC or smartphone with a corresponding IP address. The software architecture and validation measurements are presented together with possible future developments. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the emerging development of single-antenna wireless respiration detection that can be integrated into IoT devices with a single transceiver chain. However, existing single-antenna-based solutions are all limited by the short sensing range within 2-4 m due to noise interference, which makes them difficult to be adopted in most room-scale scenarios. To deal with this dilemma, we propose a room-scale, noise-resistance, and accurate respiration monitoring system, named Wi-Cyclops,1 which captures CSI changes induced by respiratory movements only via one antenna on commercial WiFi devices. To push the limits of effective sensing distance, we innovatively supply a new perspective to review the CSI samples along the sub-carrier dimension. From this dimension, we find that the interrelationship between sub-carriers with different timestamps still shows a high correlation even though the SNR decreases. Based on that, we analyze the noise characteristics along the sub-carrier dimension and correspondingly design a series of denoising schemes. Specifically, we carefully design a PCA-based denoising method to filter out ambient noises. After that, considering the low distribution densities of the AGC-induced noise, we then remove it by optimizing the DBSCAN denoising method with the K-Means-based adaptive radius search. Extensive experiments demonstrate that our system can work effectively in three typical family scenarios. Wi-Cyclops can achieve 98% accuracy even when the person is 7 m away from the transceiver pair. Compared with the start-of-art single-antenna-based approaches in real scenarios, Wi-Cyclops can improve the sensing range from 3 m to 7 m, which can meet the requirements of room-scale respiration monitoring. Additionally, to show the high compatibility with smart home devices, Wi-Cyclops is deployed on seven commercial IoT devices and still achieves a low average absolute error with 0.41 bpm. [ABSTRACT FROM AUTHOR]

Light fidelity (LiFi) and wireless fidelity (WiFi) can be applied with the same network under the different constraints, which is suitable for COVID-19 surveillance in hospitals. The LiFi network is a high-capacity and security platform. A COVID-19 surveillance system using LiFi is proposed, which consists of two switching modes: communication and surveillance. Firstly, the communication targets are to accommodate the electromagnetic interference (EMI) immunity and high-capacity and security data transmission, where secondly the COVID-19 surveillance can be applied. In operation, the up and downlink system uses a metamaterial antenna embedded by Mach Zehnder interferometer (MZI). An antenna consists of silver bars embedded at the microring center with two-phase modulators at its sides. The entangled source namely a dark soliton is applied to form the transmission, where the information security based on quantum cryptography can be managed. By using the suitable parameters, the whispering gallery modes (WGMs) are generated and the up and downlink nodes are formed. The input information is multiplexed with time to form the multiplexed signals, where the big data transmission (40 Pbit s - 1 ) can be employed. By using the surveillance mode, the plasmonic antenna can be applied for temperature and electric force sensors, which can offer the disinfectant spray and temperature sensor for COVID-19 applications. The optimum plasma force sensitivity is 0.16 N kg−1 mW−1. The center frequencies of 191.48 THz and 199.41 THz are obtained for uplink and downlink antennas, respectively. The optimum temperature sensitivity is 0.05 rads−1 °C−1. In conclusion, the novelty of proposed work is that the integrated sensor circuits are employed for COVID-19 surveillance in the hospital. The fuzzy-based system is designed for critical patient monitoring alert using this surveillance and management inside the hospital for COVID-19 patients. [ABSTRACT FROM AUTHOR]

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To enable continuous, mobile health monitoring, body-worn sensors need to offer comparable performance to clinical devices in a lightweight, unobtrusive package. This work presents a complete versatile wireless electrophysiology data acquisition system (weDAQ) that is demonstrated for in-ear electroencephalography (EEG) and other on-body electrophysiology with user-generic dry-contact electrodes made from standard printed circuit boards (PCBs). Each weDAQ device provides 16 recording channels, driven right leg (DRL), a 3-axis accelerometer, local data storage, and adaptable data transmission modes. The weDAQ wireless interface supports deployment of a body area network (BAN) capable of aggregating various biosignal streams over multiple worn devices simultaneously, on the 802.11n WiFi protocol. Each channel resolves biopotentials ranging over 5 orders of magnitude with a noise level of 0.52 μVrms over a 1000-Hz bandwidth, and a peak SNDR of 119 dB and CMRR of 111 dB at 2 ksps. The device leverages in-band impedance scanning and an input multiplexer to dynamically select good skin contacting electrodes for reference and sensing channels. In-ear and forehead EEG measurements taken from subjects captured modulation of alpha brain activity, electrooculogram (EOG) characteristic eye movements, and electromyogram (EMG) from jaw muscles. Simultaneous ECG and EMG measurements were demonstrated on multiple, freely-moving subjects in their natural office environment during periods of rest and exercise. The small footprint, performance, and configurability of the open-source weDAQ platform and scalable PCB electrodes presented, aim to provide the biosensing community greater experimental flexibility and lower the barrier to entry for new health monitoring research.

The Internet of Things (IoT) is a growing network of interconnected devices used in transportation, finance, public services, healthcare, smart cities, surveillance, and agriculture. IoT devices are increasingly integrated into mobile assets like trains, cars, and airplanes. Among the IoT components, wearable sensors are expected to reach three billion by 2050, becoming more common in smart environments like buildings, campuses, and healthcare facilities. A notable IoT application is the smart campus for educational purposes. Timely notifications are essential in critical scenarios. IoT devices gather and relay important information in real time to individuals with special needs via mobile applications and connected devices, aiding health-monitoring and decision-making. Ensuring IoT connectivity with end users requires long-range communication, low power consumption, and cost-effectiveness. The LPWAN is a promising technology for meeting these needs, offering a low cost, long range, and minimal power use. Despite their potential, mobile IoT and LPWANs in healthcare, especially for emergency response systems, have not received adequate research attention. Our study evaluated an LPWAN-based emergency response system for visually impaired individuals on the Hazara University campus in Mansehra, Pakistan. Experiments showed that the LPWAN technology is reliable, with 98% reliability, and suitable for implementing emergency response systems in smart campus environments. [ABSTRACT FROM AUTHOR]

To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and backscatter communication, are preferred due to their minimal privacy intrusion, reduced physical burden, and low maintenance costs. However, these methods face challenges, including environmental dependence, proximity limitations between the device and the user, and untested accuracy amidst various radio obstacles such as furniture, appliances, walls, and other radio waves. In this paper, we propose a frequency-shift backscatter tag-based in-home activity recognition method and test its feasibility in a near-real residential setting. Consisting of simple components such as antennas and switches, these tags facilitate ultra-low power consumption and demonstrate robustness against environmental noise because a context corresponding to a tag can be obtained by only observing frequency shifts. We implemented a sensing system consisting of SD-WiFi, a software-defined WiFi AP, and physical switches on backscatter tags tailored for detecting the movements of daily objects. Our experiments demonstrate that frequency shifts by tags can be detected within a 2 m range with 72% accuracy under the line of sight (LoS) conditions and achieve a 96.0% accuracy (F-score) in recognizing seven typical daily living activities with an appropriate receiver/transmitter layout. Furthermore, in an additional experiment, we confirmed that increasing the number of overlaying packets enables frequency shift-detection even without LoS at distances of 3–5 m. [ABSTRACT FROM AUTHOR]

Effective resource allocation is a crucial aspect in improving the performance of visible light communication (VLC)/hybrid WiFi networks. This study presents a dynamic resource allocation algorithm for multiuser scenarios. A fuzzy logic technique is employed to select the network resources with higher scores for communication. In this sense, to ensure fairness in the hybrid VLC-WiFi network, an enhanced proportional fairness (PF) algorithm is utilized. The algorithm considers the access delay and the distance to the access point as users move, striking a reasonable balance between fair allocation of user resources and maximizing system resource utilization. Therefore, to enhance the user experience in real-time scheduling, a compensation factor is introduced to compensate users with higher latency, increasing their priority. A thorough analysis is provided for the throughput, latency, utility value, and fairness models of the VLC-WiFi heterogeneous network system. The algorithm proposed in this paper is simulated and compared with four traditional algorithms, namely the Round-Robin algorithm, maximum carrier to interference ratio (Max C/I) algorithm, modified largest weighted delay first algorithm, and PF algorithm, under different metrics. Simulation results show that the proposed method is effective, and the improved algorithm yields better fairness and higher throughput compared to the conventional algorithm. As the number of frames increases, the fairness index of the proposed algorithm increases the fastest and gradually converges to 1. The throughput of this method is significantly greater than that of previous algorithms, reaching a stable rate of roughly 1.3 Mbps. The suggested algorithm is approximately 150% higher than the throughput achieved by conventional PF algorithms. The algorithm improves user satisfaction in different regions, and the utility value remains around 0.975. The packet loss rate is approximately 75% lower than traditional typical algorithms, and the average latency is approximately 60% lower. Simulation results show that the proposed algorithm has good application prospects in heterogeneous networks. [ABSTRACT FROM AUTHOR]

In the context of Industry 4.0, industrial production equipment needs to communicate through the industrial internet to improve the intelligence of industrial production. This requires the current communication network to have the ability of large-scale equipment access, multiple communication protocols/heterogeneous systems interoperability, and end-to-end deterministic low-latency transmission. Time-sensitive network (TSN), as a new generation of deterministic Ethernet communication technology, is the main development direction of time-critical communication technology applied in industrial environments, and Wi-Fi technology has become the main way of wireless access for users due to its advantages of high portability and mobility. Therefore, accessing WiFi in the TSN is a major development direction of the current industrial internet. In this paper, we model the scheduling problem of TSN and WiFi converged networks and propose a scheme based on a greedy strategy distributed estimation algorithm (GE) to solve the scheduling problem. Compared with the integer linear programming (ILP) algorithm and the Tabu algorithm, the algorithm implemented in this paper outperforms the other algorithms in being able to adapt to a variety of different scenarios and in scheduling optimization efficiency, especially when the amount of traffic to be deployed is large. [ABSTRACT FROM AUTHOR]

A Hybrid LiFi and WiFi network (HLWNet) integrates the rapid data transmission capabilities of Light Fidelity (LiFi) with the extensive connectivity provided by Wireless Fidelity (WiFi), resulting in significant benefits for wireless data transmissions in the designated area. However, the challenge of decision-making during the handover process in HLWNet is made more complex due to the specific characteristics of electromagnetic signals' line-of-sight transmission, resulting in a greater level of intricacy compared to previous heterogeneous networks. This research work addresses the problem of handover decisions in the Hybrid LiFi and WiFi networks and treats it as a binary classification problem. Consequently, it proposes a handover method based on a deep neural network (DNN). The comprehensive handover scheme incorporates two sets of neural networks (ANN and DNN) that utilize input factors such as channel quality and the mobility of users to enable informed decisions during handovers. Following training with labeled datasets, the neural-network-based handover approach achieves an accuracy rate exceeding 95%. A comparative analysis of the proposed scheme against the benchmark reveals that the proposed method considerably increases user throughput by approximately 18.58% to 38.5% while reducing the handover rate by approximately 55.21% to 67.15% compared to the benchmark artificial neural network (ANN); moreover, the proposed method demonstrates robustness in the face of variations in user mobility and channel conditions. [ABSTRACT FROM AUTHOR]

WiFi-based indoor positioning technology has gradually become a hot research topic in the field of indoor positioning, but the development of this technology has been facing the challenge of susceptibility to environmental interference. Therefore, in this paper, the kernel function method (KFM) with stronger interference resistance is used for positioning, and the adaptive σ algorithm is proposed for the time-consuming and laborious problem of manual parameter tuning, which incorporates the ideas of cross-validation and iteration. In addition, too many wireless access points (APs) mean higher computational cost and longer positioning time, so it is necessary to choose reasonable APs for positioning. In this paper, we use the random forest (RF) algorithm to assess the importance of APs and filter out a small number of APs with high importance. Considering the obvious differences in the WiFi signals received on different floors, a system framework for positioning by floors based on WiFi fingerprints is proposed. In the offline phase, the fingerprint library is first divided according to floors, and then perform separately AP selection and parameter tuning for each sub-fingerprint library. In the online phase, support vector machine is used to discriminate the floors first, and then KFM is used for planar positioning. Experiments are conducted on the public dataset, and the results show that the proposed algorithm has higher positioning accuracy, more robustness, and less time-consuming compared to several common algorithms. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research focuses on a comprehensive analysis of the energy efficiency of the Renesas DA16200 microcontroller. The investigation adopts a comparative approach, directly contrasting the power consumption of the DA16200 with the widely used ESP8266 controller under identical operating conditions. The primary metric employed to assess energy efficiency is average battery life. Additionally, a detailed examination of current consumption is conducted across various operational modes, encompassing active states like data exchange, reception, and transmission, as well as low-power sleep mode. This analysis extends beyond simply measuring peak current draw. Transient current profiles are captured, providing time-resolved insights into how current consumption fluctuates throughout different operational phases. This granular data enables a deeper understanding of the microcontrollers' energy utilization patterns. Furthermore, the research explores and evaluates techniques for minimizing energy consumption specifically in the ESP8266. These findings are then juxtaposed against the inherent energy-saving features of the DA16200 microcontroller. To facilitate a precise and verifiable comparison, a custom test bench accommodating both the DA16200 and ESP8266 is designed and implemented. This controlled environment ensures consistency in operating conditions and minimizes external variables that could influence the results. The culmination of this research is the presentation of a comprehensive analysis, detailing the comparative energy consumption profiles of the studied microcontrollers. This data forms the foundation for objectively evaluating their suitability for various low-power the Internet of Things applications.

Experimental studies of electroencephalograms of an operator located in conditions of electromagnetic noise radiation in the WiFi range were carried out. Electroencephalograms were recorded in standard leads Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fpz, Fz, Cz, Pz, Oz. The quantitative parameters of the operator's emotional state, expressed by the power spectral density of the rhythmic components of the brain, as well as such information parameters as sample entropy, fractal dimension, Lempel-Ziv complexity, averaged for 10 subjects were analyzed. It has been shown that when exposed to radiation, the operator experiences depression. It has been shown that the trend of changes in the parameters of the spectral power density of theta, alpha, gamma rhythms, fractal dimension, Lempel-Ziv complexity, sample entropy in most leads of electroencephalograms coincides with the trend of changes in these parameters presented in the scientific literature in depression. It has been established that the operator experiences fear, which is determined by an increase in the fractal dimension parameter of electroencephalograms by no more than 0.4% relative to the background

Telecom operators are under pressure to provide more spectrum resources to meet the increasing demand for high-throughput mobile communications. To achieve this, they have focused on unlicensed frequency bands, but the coexistence of 5G signals with other communication systems on these bands poses a challenge. To address this, beamforming technology has been introduced to reduce interference in space and enable independent demodulation of 5G and Wi-Fi signals. This paper implements a 5G base station (BS) prototype with an eight-antenna beamforming hardware transmitter, which concentrates the transmitted energy on individual 5G receivers while reducing the signal’s impact on Wi-Fi receivers. Angle of arrival estimation and power control mechanisms are also integrated to enhance the efficiency of the solution. To verify the system coexistence, software-defined radio is used to realize 5G and Wi-Fi receivers. The proposed solution is demonstrated via over-the-air experiments to perform reliable transmission to multiple 5G receivers without interrupting Wi-Fi traffic on the same frequency band. The beamformer can also adjust its spatial profile and power level through environment-sensing functions built into the BS prototype. The proposed power control method delivers improved 5G transmitter and Wi-Fi transmitter energy performance by 50% and 51%, respectively.

Precise positioning will play a key role in future 5G/6G services. The upcoming location-based services drive the necessity of high-precision positioning to indoors. In fingerprinting, which is the most commonly used location algorithm indoors, comprehensive radio maps are essential for a precise localization service and highly influence on the result of the final position of the user. A Machine Learning (ML) algorithm that supports missing reference points information when maps are incomplete that are used during the training phase may improve the robustness and reliability of the localization service. In this work, we compare the performance of the classical fingerprinting technique and different Decision Tree Regressor (DTR)-based algorithms that are Decision Tree Adaboost (DTA), Linear Tree Adaboost (LTA) and Random Forest (RF). The experiments were carried out with real 5G and WiFi data in an indoor scenario to test the performance of the techniques. Additionally, we demonstrate the benefits of fusion of technologies when positioning with radio maps. Finally, an evaluation of the robustness from the different methods was carried out when missing information in radio maps during the training phase.

In the modern world of human–computer interaction, notable advancements in human identification have been achieved across fields like healthcare, academia, security, etc. Despite these advancements, challenges remain, particularly in scenarios with poor lighting, occlusion, or non-line-of-sight. To overcome these limitations, the utilization of radio frequency (RF) wireless signals, particularly wireless fidelity (WiFi), has been considered an innovative solution in recent research studies. By analyzing WiFi signal fluctuations caused by human presence, researchers have developed machine learning (ML) models that significantly improve identification accuracy. This paper conducts a comprehensive survey of recent advances and practical implementations of WiFi-based human identification. Furthermore, it covers the ML models used for human identification, system overviews, and detailed WiFi-based human identification methods. It also includes system evaluation, discussion, and future trends related to human identification. Finally, we conclude by examining the limitations of the research and discussing how researchers can shift their attention toward shaping the future trajectory of human identification through wireless signals.

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The work details the development of a two-port arc-shaped Multiple-Input-Multiple-Output (MIMO) antenna with enhanced isolation characteristics. The proposed dual-port MIMO provides a wide impedance bandwidth of 3.28–5.93 GHz and a maximum isolation of 27 dB between elements in the operating frequency band. A reduced ground plane is utilized to obtain the wideband characteristics for the proposed antenna. The major applications covered by the antenna include sub-6 GHz: n77/n78/n79 and WiFi-5 bands. The important MIMO parameters such as Envelope Correlation Coefficient (ECC), Mean Effective Gain (MEG), Diversity Gain (DG) and Total Active Reflection Coefficient (TARC) are presented to estimate the performance of the developed antenna in the MIMO environment. [ABSTRACT FROM AUTHOR]

The imperative for sustainable energy production has necessitated the significant expansion of renewable energy sources, particularly photovoltaic (PV) systems. The utilization of real-time monitoring and data analysis is imperative to enhance the efficiency and performance of photovoltaic systems. This abstract presents developing and deploying a wireless monitoring system for a photovoltaic system. The system utilizes a Raspberry Pi device connected to a WiFi network and an SD card for data storage to enable remote monitoring and management of PV systems. The proposed monitoring system comprises a Raspberry Pi equipped with sensors to measure various parameters such as voltage, current, temperature, and the ambient conditions of the solar panels; the monitoring system can be remotely accessible through the wireless capabilities of the Raspberry Pi, which are activated by establishing a connection to an existing WiFi network. The proposed configuration facilitates the placement of the monitoring station in any desired location, hence eliminating the requirement for intricate wiring connections. These real-time data enable solar system managers to quickly identify anomalies, anticipate breakdowns, and optimise energy production. The paper presents a wireless monitoring system with a cost-effective and scalable solution for monitoring photovoltaic systems. [ABSTRACT FROM AUTHOR]

With the widespread use of wireless communication technologies and IoT applications, researchers are developing approaches that utilize WiFi signals for indoor location determination. In this study, indoor positioning process based on heuristic optimization-based methods was performed by creating weighted visibility matrices of access points based on WiFi signal strength (RSSI) values. In the proposed method, the PSO and GA approaches determine the position of the mobile user using a common fitness function based on the visibility weight matrices. The proposed method has been tested on a virtual scenario where position ranges based on RSSI ranges are determined. Both heuristic optimization methods are compared according to different criteria and the positioning process is performed with a maximum error of 3m for the GA based method and a maximum of 1.5m for the PSO based method. [ABSTRACT FROM AUTHOR]

High bit rate modulation is an essential aspect of next-generation WiFi and plays a crucial role in achieving high data rates and increased network capacity. high-bit rate modulation uses advanced modulation schemes, such as 4096-QAM, to increase the maximum number of bits sent over a particular frequency band. Using nonlinear amplifiers in wireless communication systems will significantly improve the performance of high bit rate modulation techniques, such as 4096-QAM. Nonlinear amplifiers can cause a range of impairments, including intermodulation, harmonic distortion, and amplitude compression, which can affect the transmitted signal quality. It is essential to minimize the effects of these impairments through proper design and optimization of the amplifier to ensure the highest quality possible of the transmitted signal. In this paper, M-QAM with nonlinear amplifier system design and implanted with MATLAB Simulink where in terms of Transmitted and Received spectrum signal and constellation diagram and calculated error vector magnitude (EVM) and modulation error ratio (MER) measurements for 1024, 2048, and 4096-QAM that used in WiFi 6, WiFi 6E, WiFi 7, respectively. [ABSTRACT FROM AUTHOR]

Diabetes mellitus reportedly affects 5.6% of the Scottish population, a number that is increasing by -0.1% annually. In Scotland, 4.7% of people with diabetes will experience foot ulceration in their lives, with 0.5% undergoing lower-limb amputation (Scottish Diabetes Data Group, [SDDG] 2018). It is estimated that care costs for people with diabetes account for £1 billion of the £13 billion total spend of NHS Scotland (Colhoun and McKnight, 2020). The main causes of diabetic foot disease leading to amputation are peripheral arterial disease (PAD) in combination with damage to small nerves and blood vessels secondary to hyperglycaemia (Weledji and Fokam, 2014). Many healthcare professionals are involved in the management of PAD and chronic limb-threatening ischaemia, and variability in practice patterns and access to services is high, which contributes to a disparity in assessment, treatments and clinical outcomes. The aim of the study was to assess whether the use of the WIFI classification system is feasible in busy, specialised podiatry wound care clinical settings. [ABSTRACT FROM AUTHOR]

For over forty years, TCP has been the main protocol for transporting data on the Internet. To improve congestion control algorithms (CCAs), delay bounding algorithms such as Vegas, FAST, BBR, PCC, and Copa have been developed. However, despite being designed to ensure fairness between data flows, these CCAs can still lead to unfairness and, in some cases, even cause data flow starvation in WiFi networks under certain conditions. We propose a new CCA switching solution that works with existing TCP and WiFi standards. This solution is offline and uses Deep Reinforcement Learning (DRL) trained on features such as noncongestive delay variations to predict and prevent extreme unfairness and starvation. Our DRL-driven approach allows for dynamic and efficient CCA switching. We have tested our design preliminarily in realistic datasets, ensuring that they support both fairness and efficiency over WiFi networks, which requires further investigation and extensive evaluation before online deployment.