Exploring edible bird nest's potential in mitigating Wi-Fi's impact on male reproductive health.

Purpose: This study aimed to evaluate the protective effects of edible bird nest (EBN) against the detrimental impact of Wi-Fi on male reproductive health. Specifically, it examines whether EBN can mitigate Wi-Fi-induced changes in male reproductive hormones, estrogen receptors (ER), spermatogenesis, and sperm parameters. Methods: Thirty-six adult male rats were divided into six groups (n = 6): Control, Control EBN, Control E2, Wi-Fi, Wi-Fi+EBN, and Wi-Fi+E2. Control EBN and Wi-Fi+EBN groups received 250 mg/kg/day EBN, while Control E2 and Wi-Fi+E2 groups received 12 µg/kg/day E2 for 10 days. Wi-Fi exposure and EBN supplementation lasted eight weeks. Assessments included organ weight, hormone levels (FSH, LH, testosterone, and E2), ERα/ERß mRNA and protein expression, spermatogenic markers (c-KIT and SCF), and sperm quality. Results: Wi-Fi exposure led to decreased FSH, testosterone, ERα mRNA, and sperm quality (concentration, motility, and viability). EBN supplementation restored serum FSH and testosterone levels, increased serum LH levels, and the testosterone/E2 ratio, and normalized mRNA ERα expression. Additionally, EBN increased sperm concentration in Wi-Fi-exposed rats without affecting motility or viability. Conclusions: EBN plays a crucial role in regulating male reproductive hormones and spermatogenesis, leading to improved sperm concentration. This could notably benefit men experiencing oligospermia due to excessive Wi-Fi exposure.

Design of a Compact Multiband Monopole Antenna with MIMO Mutual Coupling Reduction.

In this article, the authors present the design of a compact multiband monopole antenna measuring 30 × 10 × 1.6 mm3, which is aimed at optimizing performance across various communication bands, with a particular focus on Wi-Fi and sub-6G bands. These bands include the 2.4 GHz band, the 3.5 GHz band, and the 5-6 GHz band, ensuring versatility in practical applications. Another important point is that this paper demonstrates effective methods for reducing mutual coupling through two meander slits on the common ground, resembling a defected ground structure (DGS) between two antenna elements. This approach achieves mutual coupling suppression from -6.5 dB and -9 dB to -26 dB and -13 dB at 2.46 GHz and 3.47 GHz, respectively. Simulated and measured results are in good agreement, demonstrating significant improvements in isolation and overall multiple-input multiple-output (MIMO) antenna system performance. This research proposes a compact multiband monopole antenna and demonstrates a method to suppress coupling in multiband antennas, making them suitable for internet of things (IoT) sensor devices and Wi-Fi infrastructure systems.

Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments.

Wi-Fi fingerprint-based indoor localization methods are effective in static environments but encounter challenges in dynamic, real-world scenarios due to evolving fingerprint patterns and feature spaces. This study investigates the temporal variations in signal strength over a 25-month period to enhance adaptive long-term Wi-Fi localization. Key aspects explored include the significance of signal features, the effects of sampling fluctuations, and overall accuracy measured by mean absolute error. Techniques such as mean-based feature selection, principal component analysis (PCA), and functional discriminant analysis (FDA) were employed to analyze signal features. The proposed algorithm, Ada-LT IP, which incorporates data reduction and transfer learning, shows improved accuracy compared to state-of-the-art methods evaluated in the study. Additionally, the study addresses multicollinearity through PCA and covariance analysis, revealing a reduction in computational complexity and enhanced accuracy for the proposed method, thereby providing valuable insights for improving adaptive long-term Wi-Fi indoor localization systems.

Wi-Fi Fingerprint Indoor Localization by Semi-Supervised Generative Adversarial Network.

Wi-Fi fingerprint indoor localization uses Wi-Fi signal strength measurements obtained from a number of access points. This method needs manual data collection across a positioning area and an annotation process to label locations to the measurement sets. To reduce the cost and effort, this paper proposes a Wi-Fi Semi-Supervised Generative Adversarial Network (SSGAN), which produces artificial but realistic trainable fingerprint data. The Wi-Fi SSGAN is based on a deep learning, which is extended from GAN in a semi-supervised learning manner. It is designed to create location-labeled Wi-Fi fingerprint data, which is different to unlabeled data generation by a normal GAN. Also, the proposed Wi-Fi SSGAN network includes a positioning model, so it does not need a external positioning method. When the Wi-Fi SSGAN is applied to a multi-story landmark localization, the experimental results demonstrate a 35% more accurate performance in comparison to a standard supervised deep neural network.

Driving Wi-Fi IoT Sensors by a Hybrid Magneto-Mechano-Electric Energy Generator Extracting a Power of over 50 mW.

Energy harvesting technology is mainly used as a power source for driving Internet of Things (IoT) devices. However, the output power of conventional harvesting devices are limited, suitable only for low-power-consumption IoT sensors based on Bluetooth communication. In contrast to Bluetooth, wireless fidelity (Wi-Fi) communication offers superior real-time monitoring and transmission capabilities, but requires more power in the range of hundreds of milliwatts or higher. Therefore, the hybridization of three energy conversion devices, namely, piezoelectric magneto-mechano-electric (MME) generator, electromagnetic (EM) induction coil, and triboelectric nanogenerator (TENG) is proposed as a standalone power source for Wi-Fi communication sensors. By integrating these three mechanisms, the hybrid MME energy harvester can achieve an output power exceeding 50 mW at the second harmonic resonance condition under the alternating current (AC) magnetic field of 10 Oe. Furthermore, it can successfully drive the Wi-Fi sensor, enabling continuous real-time monitoring without the degradation of charged power in a supercapacitor. These results highlight that energy harvesting technology is not limited to low-power devices but can also be applied to Wi-Fi communication sensors and beyond.

Combining 5G New Radio, Wi-Fi, and LiFi for Industry 4.0: Performance Evaluation.

Fifth-generation mobile networks (5G) are designed to support enhanced Mobile Broadband, Ultra-Reliable Low-Latency Communications, and massive Machine-Type Communications. To meet these diverse needs, 5G uses technologies like network softwarization, network slicing, and artificial intelligence. Multi-connectivity is crucial for boosting mobile device performance by using different Wireless Access Technologies (WATs) simultaneously, enhancing throughput, reducing latency, and improving reliability. This paper presents a multi-connectivity testbed from the 5G-CLARITY project for performance evaluation. MultiPath TCP (MPTCP) was employed to enable mobile devices to send data through various WATs simultaneously. A new MPTCP scheduler was developed, allowing operators to better control traffic distribution across different technologies and maximize aggregated throughput. Our proposal mitigates the impact of limitations on one path affecting others, avoiding the Head-of-Line blocking problem. Performance was tested with real equipment using 5GNR, Wi-Fi, and LiFi -complementary WATs in the 5G-CLARITY project-in both static and dynamic scenarios. The results demonstrate that the proposed scheduler can manage the traffic distribution across different WATs and achieve the combined capacities of these technologies, approximately 1.4 Gbps in our tests, outperforming the other MPTCP schedulers. Recovery times after interruptions, such as coverage loss in one technology, were also measured, with values ranging from 400 to 500 ms.

Multi-Fingerprints Indoor Localization for Variable Spatial Environments: A Naive Bayesian Approach.

Fingerprint-based indoor localization has been a hot research topic. However, the current fingerprint-based indoor localization approaches still rely on a single fingerprint database, where the average level of data at reference points is used as the fingerprint representation. In variable environmental conditions, the variations in signals caused by changes in the environmental states introduce significant deviations between the average level and the actual fingerprint characteristics. This deviation leads to a mismatch between the constructed fingerprint database and the real-world conditions, thereby affecting the effectiveness of fingerprint matching. Meanwhile, the sharp noise interference caused by uncertainties such as personnel movement has a significant interference on the creation of the fingerprint database and fingerprint matching in online stage. Examination of the sampling data after denoising with Robust Principal Component Analysis (RPCA) revealed distinct multi-fingerprint characteristics with clear boundaries at certain access points. Based on these observations, the concept of constructing a fingerprint database using multiple fingerprints is introduced and its feasibility is explored. Additionally, a multi-fingerprint solution based on naive Bayes classification is proposed to accurately represent fingerprint characteristics under different environmental conditions. This method is based on the online stage fingerprints. The corresponding state space is selected using the naive Bayes classifier, enabling the selection of an appropriate fingerprint database for matching. Through simulations and empirical evaluations, the proposed multi-fingerprints construction scheme consistently outperforms the traditional single-fingerprint database in terms of positioning accuracy across all tested localization algorithms.

Hypersensitivity to man-made electromagnetic fields (EHS) correlates with immune responsivity to oxidative stress: a case report.

There is increasing evidence that exposure to weak electromagnetic fields (EMFs) generated by modern telecommunications or household appliances has physiological consequences, including reports of electromagnetic field hypersensitivity (EHS) leading to adverse health effects. Although symptoms can be serious, no underlying mechanism for EHS is known and there is no general cure or effective therapy. Here, we present the case study of a self-reported EHS patient whose symptoms include severe headaches, generalized fatigue, cardiac arrhythmia, attention and memory deficit, and generalized systemic pain within minutes of exposure to telecommunications (Wifi, cellular phones), high tension lines and electronic devices. Tests for cerebral, cardiovascular, and other physiological anomalies proved negative, as did serological tests for inflammation, allergies, infections, auto-immune conditions, and hormonal imbalance. However, further investigation revealed deficits in cellular anti-oxidants and increased radical scavenging enzymes, indicative of systemic oxidative stress. Significantly, there was a large increase in circulating antibodies for oxidized Low-Density Lipoprotein (LDLox), byproducts of oxidative stress accumulating in membranes of vascular cells. Because a known primary effect of EMF exposure is to increase the concentration of cellular oxidants, we propose that pathology in this patient may be causally related to a resulting increase in LDLox synthesis. This in turn could trigger an exaggerated auto-immune response consistent with EHS symptoms. This case report thereby provides a testable mechanistic framework for EHS pathology with therapeutic implications for this debilitating and poorly understood condition.

A high-dimensional, multi-transceiver channel state information dataset for enhanced human activity recognition.

Human Activity Recognition (HAR) has emerged as a critical research area due to its extensive applications in various real-world domains. Numerous CSI-based datasets have been established to support the development and evaluation of advanced HAR algorithms. However, existing CSI-based HAR datasets are frequently limited by a dearth of complexity and diversity in the activities represented, hindering the design of robust HAR models. These limitations typically manifest as a narrow focus on a limited range of activities or the exclusion of factors influencing real-world CSI measurements. Consequently, the scarcity of diverse training data can impede the development of efficient HAR systems. To address the limitations of existing datasets, this paper introduces a novel dataset that captures spatial diversity through multiple transceiver orientations over a high dimensional space encompassing a large number of subcarriers. The dataset incorporates a wider range of real-world factors including extensive activity range, a spectrum of human movements (encompassing both micro-and macro-movements), variations in body composition, and diverse environmental conditions (noise and interference). The experiment is performed in a controlled laboratory environment with dimensions of 5 m (width) × 8 m (length) × 3 m (height) to capture CSI measurements for various human activities. Four ESP32-S3-DevKitC-1 devices, configured as transceiver pairs with unique Media Access Control (MAC) addresses, collect CSI data according to the Wi-Fi IEEE 802.11n standard. Mounted on tripods at a height of 1.5 m, the transmitter devices (powered by external power banks) positioned at north and east send multiple Wi-Fi beacons to their respective receivers (connected to laptops via USB for data collection) located at south and west. To capture multi-perspective CSI data, all six participants sequentially performed designated activities while standing in the centre of the tripod arrangement for 5 s per sample. The system collected approximately 300-450 packets per sample for approximately 1200 samples per activity, capturing CSI information across the 166 subcarriers employed in the Wi-Fi IEEE 802.11n standard. By leveraging the richness of this dataset, HAR researchers can develop more robust and generalizable CSI-based HAR models. Compared to traditional HAR approaches, these CSI-based models hold the promise of significantly enhanced accuracy and robustness when deployed in real-world scenarios. This stems from their ability to capture the nuanced dynamics of human movement through the analysis of wireless channel characteristic from different spatial variations (utilizing two-diagonal ESP32 transceivers configuration) with higher degree of dimensionality (166 subcarriers).

Design of Miniaturized and Wideband Four-Port MIMO Antenna Pair for WiFi.

A miniaturized and wideband four-port multiple-input multiple-output (MIMO) antenna pair for Wi-Fi mobile terminals application is proposed. The proposed antenna pair consists of four multi-branch antenna elements arranged orthogonally, with an overall size of 40 × 40 × 3.5 mm3 and each antenna element size of 15.2 × 3.5 mm × 0.8 mm3. The performance of the proposed antenna shows the advantages of a wide frequency band, low mutual coupling, high efficiency, and a compact structure. The wideband characteristics of the antenna elements are achieved through multi-mode resonance. The suppression of coupling is accomplished by strategically positioning the four compact antenna elements to ensure their maximum radiation directions are orthogonal, thus eliminating the need for an additional decoupling structure. In this paper, the proposed antenna is optimized in terms of the parameters then simulated and measured. The simulated results illustrate that an impedance bandwidth of the antenna is about 15% (5.06~5.88 GHz) with S11 < -10 dB, excellent port isolation exceeds 20 dB between all ports, a high radiation efficiency ranges from 51.2% to 89.9%, the maximum gain is 4.5 dBi, and the ECCs are less than 0.04. The measured results show that the -10 dB impedance bandwidth of the antenna is about 13% (5.13~5.80 GHz), the isolation between the antenna elements is better than 21 dB, the radiation efficiency ranges from 51.8% to 92.3%, the maximum gain is 5.3 dBi, and the ECCs are less than 0.05. The proposed four-port MIMO antenna works on the 5G LTE band 46 and Wi-Fi 6E operating bands. As a mobile terminal antenna, the proposed design scheme demonstrates excellent performance and applicability, fulfilling the requirements for 5G mobile terminal applications.

Effect of Electromagnetic Radiation of Wi-Fi Router on Thyroid Gland and the Possible Protective Role of Combined Vitamin C and Zinc Administration in Adult Male Albino Rats.

Introduction: Electromagnetic radiation (EMR) is widely used nowadays in various fields due to rapid expansion of technology and affects different organs such as endocrine glands. Antioxidants protect the cells and act as a free radical scavenger. Aim of Work: The aim of the study was to clarify the effect of EMR emitted from Wi-Fi router on the thyroid gland of adult male albino rats and the possible protective role of combined Vitamin C and zinc. Materials and Methods: Thirty adult male albino rats were divided into three groups: Group I (control group), Group II (received combined Vitamin C and Zinc in one tablet called IMMUNO-MASH), and Group III (experimental groups). Group III was divided into two subgroups (A and B) according to the duration of exposure: 6 h and 24 h/day. Each of these groups was divided into two equal subgroups. One was exposed only to EMR while the other was exposed to EMR and received combined Vitamin C and zinc. All rats were weighed at the beginning and at the end of the experiment. The thyroid gland was prepared for general histological, anti-calcitonin immunostaining, and ultrastructural study. Furthermore, measurement of total serum T3, T4, and thyroid-stimulating hormone (TSH) hormone levels and quantitative analysis of immunoreactive C-cells were done. Then, statistical analysis was done on the number of immunoreactive C-cells, data of the body weight, and the hormonal levels. Results: A highly significant increase in the body weight in subgroups exposed to EMR for 24 h/day was observed. Furthermore, they showed a highly significant decline in T3 and T4 levels together with a highly significant increase in TSH level. With increasing period of exposure, there was a variable degree of deterioration in the form of congestion and dilatation of blood vessels, cellular infiltration, follicular disintegration, vacuolar degeneration, and desquamated follicular cells in the colloid. The C-cells showed a significant increase in the mean number compared with the control group. Ultrastructural analysis of follicular cells revealed colloid droplets, deteriorations in rough endoplasmic reticulum, degenerating nuclei, and swollen mitochondria according to the dose of exposure. There was apparent improvement with the use of combined Vitamin C and zinc. Conclusion: Wi-Fi radiation has a very serious effect on thyroid gland morphology and activity. Moreover, experimentally induced hypothyroidism by radiation resulted in increased C-cell number. Combined Vitamin C and zinc could have a protective role against this tissue damage.

DATIV-Remote Enhancement of Smart Aerosol Measurement System Using Raspberry Pi-Based Distributed Sensors.

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.

The impact of revascularization strategy on clinical failure, hemodynamic failure, and chronic limb-threatening ischemia symptoms in the BEST-CLI Trial.

OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Assessing the influence of rural residence and economic distress on lower extremity risk stratification among diabetic foot ulcer patients utilizing the Wound, Ischemia, and Foot Infection (WIfI) classification system.

OBJECTIVE: Diabetic foot ulcers (DFU) are a major sequela of uncontrolled diabetes with a high risk of adverse outcomes. Poor DFU outcomes disproportionately impact patients living in rural and economically distressed communities with lack of access to consistent, quality care. This study aimed to analyze the risk of geographic and economic disparities, including rural status and county economic distress, on the disease burden of DFU at presentation utilizing the SVS WIfI classification system. METHODS: We conducted a retrospective review of 454 patients diagnosed with a DFU from 2011 to 2020 at a single institution's inpatient and outpatient wound care service. Patients >18 years old, with type II diabetes mellitus, and diabetic foot ulcer were included. RESULTS: ANCOVA analyses showed rural patients had significantly higher WIfI composite scores (F(1,451) = 9.61, p = .002), grades of wound (F(1,439) = 11.03, p = .001), and ischemia (F(1,380) = 12.574, p = .001) compared to the urban patients. Patients that resided in at-risk economic counties had significantly higher overall WIfI composite scores (F(2,448) = 3.31, p = .037) than patients who lived in transitional economic counties, and higher foot infection grading (F(2,440) = 3.02, p = .05) compared to patients who lived in distressed economic counties. DFU patients who resided in distressed economic counties presented with higher individual grades of ischemia (F(2, 377) = 3.14, p = .04) than patients in transitional economic counties. Chi-Square analyses demonstrated patients who resided in urban counties were significantly more likely to present with grade 1 wounds (χ2(3) = 9.86, p = .02) and grade 0 ischemia (χ2(3) = 16.18, p = .001) compared to patients in rural areas. Economically distressed patients presented with significantly less grade 0 ischemia compared to patients in transitional economic counties (χ2(6) = 17.48, p = .008). CONCLUSIONS: Our findings are the first to demonstrate the impact of geographic and economic disparities on the disease burden of DFU at presentation utilizing the SVS WIfI classification system. This may indicate need for improved multidisciplinary primary care prevention strategies with vascular specialists in these communities to mitigate worsening DFU and promote early intervention.

On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database.

Large-scale multi-building and multi-floor indoor localization has recently been the focus of intense research in indoor localization based on Wi-Fi fingerprinting. Although significant progress has been made in developing indoor localization algorithms, few studies are dedicated to the critical issues of using existing and constructing new Wi-Fi fingerprint databases, especially for large-scale multi-building and multi-floor indoor localization. In this paper, we first identify the challenges in using and constructing Wi-Fi fingerprint databases for large-scale multi-building and multi-floor indoor localization and then provide our recommendations for those challenges based on a case study of the UJIIndoorLoc database, which is the most popular publicly available Wi-Fi fingerprint multi-building and multi-floor database. Through the case study, we investigate its statistical characteristics with a focus on the three aspects of (1) the properties of detected wireless access points, (2) the number, distribution and quality of labels, and (3) the composition of the database records. We then identify potential issues and ways to address them using the UJIIndoorLoc database. Based on the results from the case study, we not only provide valuable insights on the use of existing databases but also give important directions for the design and construction of new databases for large-scale multi-building and multi-floor indoor localization in the future.

Daily Living Activity Recognition with Frequency-Shift WiFi Backscatter Tags.

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.

Device-Free Wireless Sensing for Gesture Recognition Based on Complementary CSI Amplitude and Phase.

By integrating sensing capability into wireless communication, wireless sensing technology has become a promising contactless and non-line-of-sight sensing paradigm to explore the dynamic characteristics of channel state information (CSI) for recognizing human behaviors. In this paper, we develop an effective device-free human gesture recognition (HGR) system based on WiFi wireless sensing technology in which the complementary CSI amplitude and phase of communication link are jointly exploited. To improve the quality of collected CSI, a linear transform-based data processing method is first used to eliminate the phase offset and noise and to reduce the impact of multi-path effects. Then, six different time and frequency domain features are chosen for both amplitude and phase, including the mean, variance, root mean square, interquartile range, energy entropy and power spectral entropy, and a feature selection algorithm to remove irrelevant and redundant features is proposed based on filtering and principal component analysis methods, resulting in the construction of a feature subspace to distinguish different gestures. On this basis, a support vector machine-based stacking algorithm is proposed for gesture classification based on the selected and complementary amplitude and phase features. Lastly, we conduct experiments under a practical scenario with one transmitter and receiver. The results demonstrate that the average accuracy of the proposed HGR system is 98.3% and that the F1-score is over 97%.

Design and Evaluation of a Low-Power Wide-Area Network (LPWAN)-Based Emergency Response System for Individuals with Special Needs in Smart Buildings.

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.

Loom: A Modular Open-Source Approach to Rapidly Produce Sensor, Actuator, Datalogger Systems.

In the face of rising population, erratic climate, resource depletion, and increased exposure to natural hazards, environmental monitoring is increasingly important. Satellite data form most of our observations of Earth. On-the-ground observations based on in situ sensor systems are crucial for these remote measurements to be dependable. Providing open-source options to rapidly prototype environmental datalogging systems allows quick advancement of research and monitoring programs. This paper introduces Loom, a development environment for low-power Arduino-programmable microcontrollers. Loom accommodates a range of integrated components including sensors, various datalogging formats, internet connectivity (including Wi-Fi and 4G Long Term Evolution (LTE)), radio telemetry, timing mechanisms, debugging information, and power conservation functions. Additionally, Loom includes unique applications for science, technology, engineering, and mathematics (STEM) education. By establishing modular, reconfigurable, and extensible functionality across components, Loom reduces development time for prototyping new systems. Bug fixes and optimizations achieved in one project benefit all projects that use Loom, enhancing efficiency. Although not a one-size-fits-all solution, this approach has empowered a small group of developers to support larger multidisciplinary teams designing diverse environmental sensing applications for water, soil, atmosphere, agriculture, environmental hazards, scientific monitoring, and education. This paper not only outlines the system design but also discusses alternative approaches explored and key decision points in Loom's development.

Contention-Less Multi-Link Synchronous Transmission for Throughput Enhancement and Heterogeneous Fairness in Wi-Fi 7.

Multi-link operation (MLO) is a new and essential mechanism of IEEE 802.11be Extremely High Throughput (Wi-Fi 7) that can increase throughput and decrease latency in Wireless Local Area Networks (WLANs). The MLO enables a Multi-Link Device (MLD) to perform Simultaneous Transmission and Reception (STR) in different frequency bands. However, not all MLDs can support STR due to cross-link or in-device coexistence interference, while an STR-unable MLD (NSTR-MLD) can transmit multiple frames simultaneously in more than two links. This study focuses on the problems when NSTR-MLDs share a link with Single-Link Devices (SLDs). We propose a Contention-Less Synchronous Transmission (CLST) mechanism to improve fairness between NSTR-MLDs and SLDs while increasing the total network throughput. The proposed mechanism classifies links as MLD Dominant Links (MDLs) and Heterogeneous Coexistence Links (HCLs). In the proposed mechanism, an NSTR-MLD obtains a Synchronous Transmission Token (STT) through a virtual channel contention in the HCL but does not actually transmit a frame in the HCL, which is compensated for by a synchronous transmission triggered in the MDL. Moreover, the CLST mechanism allows additional subsequent transmissions up to the accumulated STT without further contention. Extensive simulation results confirm the outstanding performance of the CLST mechanism in terms of total throughput and fairness compared to existing synchronous transmission mechanisms.