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It is essential to monitor the radiation levels to which the general population may be exposed and compare them to limits established by national and international standards. This is especially true in residential environments, where there is a consolidated use of microwave ovens and Wi-Fi access points, in addition to the short distances devices can be from the user, as well as prolonged periods of user exposure. The objective of this work is to measure the exposure levels for daily Wi-Fi and microwave oven use scenarios in residential environments, to compare measured levels with the standards and to verify possible relation with the characteristics of the equipment under analysis. We use the Narda NBM-520 to support our measurements, the same equipment the National Telecommunication Agency (ANATEL) uses for Brazil's Non-ionizing Radiation (NIR) inspection. This work presents measurements in 40 Wi-Fi access points in which the highest exposure level was $ 4.66\,{\rm V}/{\rm m} $ 4.66V/m (7.64 % of the limit), verified for the 2.4 GHz network (the so-called Situation 1). This work also presents measurements in 50 microwave ovens. It was observed that 72% of them had the highest level of exposure for the position in front of the door. [ABSTRACT FROM AUTHOR]

\nPlain Language SummaryDue to the increase in data rate in mobile communication and the widespread use of mobile internet, electromagnetic communication systems are increasing daily. This situation causes increases in the use of more mobile communication devices and environmental non-ionizing Electromagnetic Field (EMF) levels. The rise of bee deaths and colony losses in beekeeping parallel to the increase of the EMF sources cause the concept of “electromagnetic pollution” to be considered among the reasons. Therefore, studying the effects of non-ionizing Electromagnetic Radiation (EMR) on the health of living things is one of the most significant issues today. The bees determine their direction with the Earth’s magnetic field. Electromagnetic signals emitted by GSM base stations, etc. may affect the direction-finding capabilities of honey bees and constitute a stress factor. In this study, the aim was to determine the effect of EMF on honey bees and honey yield. Honey bee colonies were used, obtained from the same farm in the Trabzon region, and equalized in all respects. Moreover, these colonies were divided into five groups randomly as experiments and control groups. The experiment hives were exposed to the EMF in the frequency band of the Wi-Fi signals (2.4 GHz) and the high-voltage line (50 hz). The control hives are located far away from the EMR sources. The study was repeated in the second year to confirm the results. During the investigation, some physiological and behavioural effects of bees, such as aggressiveness, brood area, etc. were determined based on EMR exposure. Studying the effects of non-ionizing Electromagnetic Radiation (EMR) on the health of living things is one of the most significant issues today.In this study, the aim was to determine the effect of EMF on honey bees and honey yield.Honey bee colonies were used, obtained from the same farm in the Trabzon region, and equalized in all respects.During the investigation, some physiological and behavioural effects of bees, such as aggressiveness, brood area, etc. were determined based on EMR exposure.Studying the effects of non-ionizing Electromagnetic Radiation (EMR) on the health of living things is one of the most significant issues today.In this study, the aim was to determine the effect of EMF on honey bees and honey yield.Honey bee colonies were used, obtained from the same farm in the Trabzon region, and equalized in all respects.During the investigation, some physiological and behavioural effects of bees, such as aggressiveness, brood area, etc. were determined based on EMR exposure. [ABSTRACT FROM AUTHOR]

A Multi-Channel Man-in-the-Middle (MC-MitM) attack is an advanced form of MitM attack, characterized by its ability to manipulate encrypted wireless communications between the Access Point (AP) and clients within a WiFi network. MC-MitM attacks can target any Wi-Fi client, regardless of the authentication method used with the AP. Notable examples of such attacks include Key Reinstallation Attacks and FragAttacks, which have impacted millions of WiFi systems worldwide, especially those involving Internet of Things devices. Current defense mechanisms are inadequate against these attacks due to interoperability challenges and the need for modifications to devices or protocols within the targeted Wi-Fi networks. This paper introduces a distributed and cooperative signature-based wireless intrusion detection mechanism designed for online passive monitoring to detect malicious traffic patterns during MC-MitM attacks in any environment, from apartments and houses to large areas like hotels, offices or industrial sites. We implemented the proposed framework on Raspberry Pis and evaluated it in real-world settings. Our evaluation demonstrates that this framework can effectively identify MC-MitM attacks with an average accuracy of 98% when deployed across different locations within our experimental testbed. [ABSTRACT FROM AUTHOR]

This paper will address a theme that highlights the role of an electrical equipment in the electrical industry, focusing on the process of soldering components onto the surface of printed circuit boards. The choice of the theme "Reflow Oven with Wi-Fi Connectivity" was driven by the desire to create a system with practical and applicative outcomes. The aim is to develop equipment that facilitates the soldering process of Surface Mounted Device (SMD) components. Such an automated system is valuable in the production of small batches of equipment, such as electronic modules for prototypes or various applications. The equipment or electronic module aims to depict a Printed Circuit Board (PCB) on which both Surface Mounted Devices (SMD) and Through Hole Technology (THT) components are mounted. Throughout the paper, the implementation of an automated system capable of carrying out the soldering process for SMD components will be discussed. [ABSTRACT FROM AUTHOR]

Pre-A*- Lattices in which there is another associative binary operation that is compatible with the Pre-A*- lattice operations are identified as lattice ordered semigroups. This manuscript deals with the notion of lattice ordered semigroups in Pre-A*_Algebras and their characterizations are attained. Lattice theory and Lattice reduction math tools and their extensive and adaptive properties and methods are widely used in wireless systems like Wi-Fi, IoT etc. Automation or logical reasoning plays a crucial role in real world applications like machines (washing machine, television, refrigerator etc.), technology (wireless, Wi-Fi, Bluetooth etc.) and Robots etc. From an analytical point of view reasoning includes Boolean logic and its application. [ABSTRACT FROM AUTHOR]

The article presents the design and investigation of a lotus-shaped triple-tuned wideband monopole-antenna for wireless communication services (WCS), satellite radio, and weather RADAR applications. The novel lotus-shaped antenna structure contains seven arms developed using arithmetic operations and loaded with SRR-quadruplets. Further, the effect of ring orientation position changes is analysed. Seven arms increase the electrical length of the patch which yields an improvement in broad bandwidth. The parasitic loading of an antenna with SRR quadruplets changes the nature of the antenna to triple-tuned at 2.04 GHz, 2.27 GHz, and 2.90 GHz with enhanced -10dB fractional bandwidth (FBW) 57.19% from initially designed single band (2.06-3.12 GHz) antenna. A wideband is achieved by reduced compressed ground. The proposed antenna achieves a peak gain of 5.2dBi and an efficiency of 92%. The RLC equivalent circuit of the designed antenna is presented for compatibility at low-frequency operations. The ADS and HFSS-generated electrical equivalent circuit reflection coefficients are found in excellent agreements and they validate the antenna. The wideband nature of the antenna from 1.61GHz to 3.24 GHz makes this antenna suitable for WCS, satellite digital audio radio services (SDARS), and weather RADAR applications. The modified antenna structure and quadruplet-SRR loading offer additional 5G bands, GSM, and PCS applications. [ABSTRACT FROM AUTHOR]

The use of wireless technology in common marine engineering applications as a medium for data transaction in measurement and control systems, is not as popular as it should be. This article aims to demonstrate the advantages of using wireless technology in maritime engineering applications through a proposed Wi-Fi based wireless system dedicated to performance and safety monitoring in marine cargo cranes. The system is based on some concepts that were suggested in the earlier literature to perform authenticated data transmission from multiple sensors through using both the ESP-NOW protocol and the WebSerial remote serial monitor. The introduced system will be integrated with an already installed system in order to render the means for implementing effective principles in automation and control engineering, such as functional safety and predictive maintenance. Additionally, this article will highlight the economic efficiency of adopting wireless technology instead of cabling as a medium for data transaction in measurement and control systems in marine engineering applications such as cargo cranes. [ABSTRACT FROM AUTHOR]

Multimedia content represents a significant portion of the traffic in computer networks, and COVID-19 has only made this portion bigger, as it now represents an even more significant part of the traffic. This overhead can, however, be reduced when many users access the same content. In this context, Wi-Fi, which is the most popular Radio Access Technology, introduced the Group Addressed Transmission Service (GATS) with the amendment IEEE 802.11aa. GATS defines a set of policies aiming to make multicast traffic more robust and efficient. However, Wi-Fi is constantly evolving, and as it improves and greater bandwidths and data rates become available, it is necessary to reevaluate the behavior of mechanisms introduced in past amendments. This is also the case with GATS, whose policies have different behaviors and adapt better to different channel conditions. These policies have been evaluated in the past on High Throughput networks. Still, none of the evaluations provided insights into the behavior of GATS policies in Very-High Throughput (VHT) physical layers in a realistic manner. This is extremely relevant as a greater available bandwidth can impact the decisions of the GATS policy configuration. Thus, in this work, we present an evaluation of the IEEE 802.11aa amendment with a VHT physical layer in a realistic scenario that uses Minstrel as a rate adaptation algorithm simulated in NS-3. [ABSTRACT FROM AUTHOR]

Wireless neural signal transmission is essential for both neuroscience research and neural disorder therapies. However, conventional wireless systems are often constrained by low sampling rates, limited channel counts, and their support of only a single transmission mode. Here, we developed a wireless bi-directional brain–computer interface system featuring dual transmission modes. This system supports both low-power Bluetooth transmission and high-sampling-rate Wi-Fi transmission, providing flexibility for various application scenarios. The Bluetooth mode, with a maximum sampling rate of 14.4 kS/s, is well suited for detecting low-frequency signals, as demonstrated by both in vitro recordings of signals from 10 to 50 Hz and in vivo recordings of 16-channel local field potentials in mice. More importantly, the Wi-Fi mode, offering a maximum sampling rate of 56.8 kS/s, is optimized for recording high-frequency signals. This capability was validated through in vitro recordings of signals from 500 to 2000 Hz and in vivo recordings of single-neuron spike firings with amplitudes reaching hundreds of microvolts and high signal-to-noise ratios. Additionally, the system incorporates a wireless stimulation function capable of delivering current pulses up to 2.55 mA, with adjustable pulse width and polarity. Overall, this dual-mode system provides an efficient and flexible solution for both neural recording and stimulation applications. [ABSTRACT FROM AUTHOR]

The study assessed radiation power absorption in the 1.5 to 5 GHz range for Wi-Fi, LTE, and AM modulation, using three Vivaldi antennas in an anechoic chamber with a phantom arm made of animal bone, ballistic gel, and pigskin. Wi-Fi showed the highest absorption levels, but these values (0.168 mW and 0.050 mW) were below ICNIRP safety limits, indicating no significant risk to human health.

In recent years microstrip patch antennas having minute size is an exciting topic for many researchers and design engineers. In this paper a novel miniaturized microstrip patch antenna for Wi-Fi applications is proposed. The concept of defected ground plane (DGS) is used for achieving miniaturization. The prototype is designed and analyzed by CST Microwave Studio. Low cost FR-4 substrate having thickness of 0.8 mm is used for the design of the antenna. The antenna is having compact size of 16 × 17 mm2. The simulated results demonstrate that the antenna has bandwidth of 2.02% (49 MHz) with -41.46 dB reflection coefficient at resonating frequency. The antenna has bidirectional radiation pattern. The proposed design provides a size reduction of 75.46% in comparison to conventional patch. The proposed antenna is having compact size and is low cost which makes it a suitable candidate for 2.4 GHz Wi-Fi band.

Approximately 20% of primary energy consumed in the U.S. is attributed to HVAC use. Ideally, HVAC operation would be driven by actual building occupancy, but lack of reliable occupancy information often results in the use of conservative static schedules. This disparity is even more pronounced in a college campus, where the function of each space differs by building (classrooms, offices, libraries) and the class schedules change frequently -- every semester, day of week, and hour. While several research papers propose the use of counts of the Wi-Fi connections (e.g., phones, computers) as a proxy for occupancy, few real-world implementationsexist. This paper describes the development and deployment of an open-sourceWi-Fi-to-Occupancy software library in 65 buildings of a college campus, and the plannedintegration with the building energy management and control system at the building scale. Overa year of Wi-Fi data was gathered into distinct academic periods, including fall and springsemester, academic breaks, and summer sessions. Patterns such as students moving between classrooms, closing laptops before exams, etc., can be visualized from the data. Approximating occupancy from Wi-Fi data presents challenges which we address in this project -- for example, identifying static devices, or estimating the ratio of devices per person. Utilizing real-time occupancy data to inform optimal HVAC schedules and ventilation rates creates the potential to identify and reduce energy waste. Other potential applications include forecasting occupancy, and using Wi-Fi data to predict peak demands. Finally, the paper discusses how to easily scale these tools to other buildings.

Ensuring the security of Wi-Fi networks is a top priority and an ongoing research topic for academic and industry research groups. This work investigates the impact of class distribution in benchmark datasets on the detection performance of machine learning (ML)-driven wireless intrusion detection systems (WIDS). More specifically, we aim to address a critical question not yet explored in the literature: To what extent does class distribution influence WIDS performance? In pursuit of answers, we consider a multiclass problem and utilize several resampling schemes, including oversampling, undersampling, and both under- and oversampling, exploring their impingement on the classification results in terms of standard metrics. We evaluate a variety of ML models, both traditional and deep ones, and contrast their performance against those yielded when the algorithms are trained on the original imbalanced dataset. The key finding is that training on the original imbalanced dataset significantly reduces false positives vis-á-vis a balanced dataset created by any resampling technique. This reduction in terms of macro-averaged false positive rate is particularly noteworthy, approximately 139 and 43 times lower false positives compared to the worst and best resampling scheme, respectively. However, while resampled balanced datasets can lead to fewer false negatives (missed attacks), the improvement is less pronounced, at about 1.2 times lower relative to the imbalanced dataset. [ABSTRACT FROM AUTHOR]

This article is a continuation of a review whose first part analysed the works on the effect of radio frequency electromagnetic fields (RF EMF) on the central nervous system (CNS) in vitro (changes in the action potential, cell and myelin sheath morphology, as well as in the permeability of the blood–brain barrier (using cultures of nerve cells only); in addition, it presented various approaches to studying the effects of RF EMF and pointed out difficulties of systematizing the experimental data. The present article dwells on the morpho-histological changes in CNS structures under RF EMF exposure in young animals, since it allows us to give an indirect assessment of possible negative consequences of RF EMF exposure for children and adolescents as the cohort most vulnerable to any environmental factors. Morphological and histological changes in CNS structures (cerebral cortex, brainstem, cerebellum, auditory system, etc.) as well as changing electroencephalographic parameters were analysed. A bulk of data on the morphological and histological changes in the hippocampus was considered separately. In addition, the paper presents an analysis of changes in the biometric parameters of experimental animals under chronic exposure to RF EMF and its effect on cell viability (including nerve cell apoptosis and autophagy). Thus, having a reliable corpus of modern experimental studies proving the seriousness of the problem of the effects of electromagnetic fields on the nervous system in children and adolescents is important in the context of ever-increasing electromagnetic pollution, primarily from electromagnetic fields produced by cellular networks.

Significant technological progress in the field of wireless devices that were primarily intended for military purposes, has resulted in their common manipulation by the general population. Wi-Fi, mobile phones, and other modern devices offer many advantages to their users. On the other hand, their excessive usage creates an environmental burden, also known as electrosmog. The objective of our current study was the observation of the Wi-Fi radiation effect on the histo-logical structure of the organs in the 9-day-old chicken embryo. On day 9 of incubation, the embryological material was routinely processed for preparation of hematoxylin-eosin, picrosirius red and periodic acid Schiff stained histological sections. Radiation with a frequency of 2.4 GHz and average power density of 300 µW.m−2 applied during the entire development up to the 9th embryonic day did not fundamentally affect general organogenesis. However, in the parenchyma of organs such as the liver, spleen, lungs, kidneys, and gonads, as well as in the developing mesenchyme, obvious vascular congestion of the blood vessels of different caliber was observed. Also, an increase in collagen and glycosaminoglycans production in the cartilaginous matrix and perichondrium of the future bone skeleton as well as developing connective tissue was noted. Although these morphological changes were just subtle, they point to the Wi-Fi radiation’s ability to influence the histogenesis of the individual.

Recent advancements in occupancy and indoor environmental monitoring have encouraged the development of innovative solutions. This paper presents a novel approach to room occupancy detection using Wi-Fi probe requests and machine learning techniques. We propose a methodology that splits occupancy detection into two distinct subtasks: personnel presence detection, where the model predicts whether someone is present in the room, and occupancy level detection, which estimates the number of occupants on a six-level scale (ranging from 1 person to up to 25 people) based on probe requests. To achieve this, we evaluated three types of neural networks: CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). Our experimental results show that the GRU model exhibits superior performance in both tasks. For personnel presence detection, the GRU model achieves an accuracy of 91.8%, outperforming the CNN and LSTM models with accuracies of 88.7% and 63.8%, respectively. This demonstrates the effectiveness of GRU in discerning room occupancy. Furthermore, for occupancy level detection, the GRU model achieves an accuracy of 75.1%, surpassing the CNN and LSTM models with accuracies of 47.1% and 52.8%, respectively. This research contributes to the field of occupancy detection by providing a cost-effective solution that utilizes existing Wi-Fi infrastructure and demonstrates the potential of machine learning techniques in accurately classifying room occupancy.

Electromagnetic waves are one of the tensions around agricultural plants, which have recently been widely used due to the development of telecommunication technology. Therefore, a field experiment was conducted during the years 2020-2021 at the Research Farm of Tarbiat Modares University to investigate the effect of WiFi electromagnetic wave (WEW) on the seed germination and growth characteristics of camelina. Seeds were exposed to Wi-Fi electromagnetic radiation at 15 cm (ER 15) and 25 cm (ER 25) cm from the modem for 24 hours (Pre-sowing treatment). The results showed that the germination percentage of seeds treated with WEW decreased by 24%. and this decrease was observed among growth factors such as hypocotyl length (20-43%), number of siliques per plant (56%), thousand seed weight (43%). Conversely, the root length, plant height, seed per silique and dry plant weight were increased by 24.6, 60.9, 10.5 and 56.3% under WEW treatment, respectively. Overall, this study showed that grain yield was greatly affected by electromagnetic waves and increased by about 23.45%. In general, among all the growth parameters, the correlation of the GY was positive and significant with DPW (r=0.735*), PH (r=0.669*) and SPS (r=0.659*). This result highlights the necessity for a better understanding of the mechanisms of electromagnetic waves in crops to help better seedling establishment.

To effectively manage the escalating traffic, attention is drawn to the coexistence mechanism of cellular and Wi-Fi networks which offload the cellular traffic from the licensed band to the unlicensed band. For the coexistence of 5G new radio-unlicensed and Wi-Fi, we tackle the energy efficiency maximization problem by sequentially determining the duty cycle and transmit power through the use of distributed deep Q-network (DQN) techniques. When utilizing the existing unlicensed band, the proposed method minimizes the impact on Wi-Fi networks while achieving optimal energy efficiency. Simulations validate the superior energy efficiency of the proposed coexistence mechanism over various benchmark methods.

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With the surge in mobile data traffic, leveraging Wi-Fi has become pivotal to alleviate the strain on LTE networks. An essential challenge lies in selecting the optimal Wi-Fi access point during Handover (HO), impacting Quality of Experience (QoE) and connection stability. This study introduces an innovative approach to enhance VoIP call Quality of Service (QoS) during HOs from LTE to Wi-Fi. Our contribution integrates two Mean Opinion Score (MOS) prediction models, multilayer perceptron (MLP) and recurrent neural networks (LSTMs) into the software-defined network controller (SDN), optimized by the Grey Wolf Optimizer (GWO). This solution provides accurate predictions for the SDN to forward mobile devices (MEs) and traffic to the nearest optimal Wi-Fi access points. Simulation results affirm the effectiveness of our approach. The LSTM-based approach performs well in the long run and provides better stability in terms of QoS parameters, especially when the metric of choice for mobile users is based on the Signal-to-Noise and Interference Ratio (SNIR), rather than the Received Signal Strength (RSSI) indicator. These results explore opportunities for the real-world deployment of the implemented models, particularly in urban areas experiencing cellular network congestion. [ABSTRACT FROM AUTHOR]

In this work, a novel quad-band rectifier circuit is introduced for RF energy harvesting and Internet of Things (IoT) applications. The proposed rectifier operates in the Wi-Fi frequency band and can supply low-power sensors and systems used in IoT services. The circuit operates at 2.4 , 3.5 , 5, and 5.8 GHz. The proposed RF-to-DC rectifier is designed based on Delon theory and Greinacher topology on an RT/Duroid 5880 substrate. The results show that our proposed circuit can harvest RF energy from the environment, providing maximum power conversion efficiency (PCE) greater than 81% when the output load is 0.511 kΩ and the input power is 12 dBm. In this work, we provide a comprehensive design framework for an affordable RF-to-DC rectifier. Our circuit performs better than similar designs in the literature. This rectifier could be integrated into an IoT node to harvest RF energy, thereby proving a green energy source. The IoT node can operate at various frequencies. [ABSTRACT FROM AUTHOR]

Recently, the use of fingerprinting has been proposed for positioning using the Wi-Fi RTT estimations gathered by IEEE 802.11mc devices. Wi-Fi RTT poses a challenge on scalability due to the location-specific traffic injected in the network, which may limit the data traffic transmissions of other Wi-Fi users. In this respect, fingerprinting has been regarded as a promising scalable technique, compared to multilateration. While coupling other metrics should bring relief to the system, reducing the number of APs to which RTT measurements are requested alleviates the burden in specific cells. But how far may we go? This paper assesses several methods aimed at reducing the Wi-Fi RTT overhead while preserving the precision of the calculated position. The use of the Wi-Fi RTT standard deviation is assessed for the first time, being especially useful when the number of RTT procedures is minimized. The application of clustering can also improve position estimates while leveraging bandwidth for other users' purposes. [ABSTRACT FROM AUTHOR]

This research explores the concept of Worldwide one network (WON), a hypothetical ultra-large scale ad-hoc wireless network characterized by its non-hierarchical, open, scalable, homogeneous, and autopoiesis nature. The primary objectives are to address challenges in network formation, individual node unique addressing, and network management. This paper proposes a novel addressing mechanism named 'Cubid', which utilizes geo-coordinates as the primary identifier for network nodes with 1 m resolution and aims for at least 512 unique node addresses per cubic meter space on Earth. Unique three-dimensional address space, received signal strength based trilateration for network formation, address negotiation, and the use of Cubid as a MAC address to bypass traditional Layer 2–3 Internet Protocol activities are few of the differentiator aspects involved in this research work. Preliminary tests of this hypothetical network yield in practical viability of identifying network node's geographical coordinates with an accuracy of 3 m without GPS devices, and corresponding simulations results in an average frame delivery time of 27 ms over a 100-hop, varying hop length network path. These findings indicate that WON could serve as a viable alternative communication network, especially when substantial infrastructure-based networks, such as the Internet fails. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We study a most recent mechanism for reserving channel resources to deliver high-priority traffic in Wi-Fi networks, Restricted Target Wake Time (R-TWT). We develop an analytical model of a Wi-Fi network using the R-TWT mechanism, which allows us to estimate the throughput of users transmitting low-priority traffic, depending on the R-TWT reservation period, restrictions on the size of transmitted frames and modulation and coding schemes used. The model shows that the dependence of the user throughput on the R-TWT period is not monotonic, and a slight increase in the R-TWT period can lead to both an increase and a significant drop in the user throughput. [ABSTRACT FROM AUTHOR]

In this research, a quad‐band sickle‐shaped MIMO antenna of compact size 32 × 32 mm2 (0.28λ0 × 0.28λ0 at lower resonant frequency 2.6 GHz) has been proposed using partial ground and tilted isolation stub at an angle of 45°. It covers 2.40 to 2.80 GHz frequency range with bandwidth (BW) of 15.38% (0.40 GHz) which is applicable for Wi‐Fi application in first band and in second band it covers from 4.48 to 5.75 GHz frequency range with BW of 24.83% (1.27 GHz) for WLAN application. In addition, the third band starts from 9.75 to 10.43 GHz frequency range with BW of 6.74% (0.68 GHz) for the X‐band whereas in fourth band it covers from 13.07 to 15.87 GHz frequency range with BW of 19.38% (2.8 GHz) frequency, which is applicable on Ku‐band application. Moreover, the isolation is < −15 dB in the entire resonating frequency band which is an acceptable limit for reduction of mutual coupling. ECC value is <0.5 in the entire operating band of frequency. The diversity gain of the proposed MIMO antenna is approximately 9.96 dB that shows the antenna exhibits good diversity property. The channel capacity loss value is <0.4 bps/s/Hz in the entire resonating band. In view of all the diversity parameters, the proposed MIMO structure fulfills all the requirements of the MIMO antenna. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Agriculture plays a pivotal role in the economy of most countries, serving as a primary source of livelihood and sustenance. In the case of India, it occupies a substantial portion of the nation's land. This article proposes the integration of IoT (Internet of Things) and an automated irrigation system with ML/DL (Machine Learning and Deep Learning) to revolutionize agriculture. The implementation of crop monitoring through sensors not only eases the burden on farmers but also enhances crop productivity. The system, at its core, monitors crucial field parameters such as soil moisture, temperature, and humidity. Given the increasing importance of efficient water management in agriculture, this study outlines an automated irrigation system that leverages cloud computing and IoT to curtail water consumption. Its primary objective is to gather and consolidate data from diverse sources, including data generated by sensors and IoT devices. This centralized data storage approach facilitates seamless data integration from various locations and devices. Through the application of algorithms and dataset analysis, the study determines that the cultivation of "Spider" plants is more favorable when compared to other plant species. Notably, the Random Forest classifier emerged as the most accurate, achieving an impressive 94.77% accuracy rate in this project. In essence, this research endeavors to propel agriculture into a technology-driven and sustainable future, optimizing water usage and improving crop yield. [ABSTRACT FROM AUTHOR]

Electromagnetic waves are one of the tensions around agricultural plants, which have recently been widely used due to the development of telecommunication technology. Therefore, a field experiment was conducted during the years 2020-2021 at the Research Farm of Tarbiat Modares University to investigate the effect of WiFi electromagnetic wave (WEW) on the seed germination and growth characteristics of camelina. Seeds were exposed to Wi-Fi electromagnetic radiation at 15 cm (ER 15) and 25 cm (ER 25) cm from the modem for 24 hours (Pre-sowing treatment). The results showed that the germination percentage of seeds treated with WEW decreased by 24%. and this decrease was observed among growth factors such as hypocotyl length (20-43%), number of siliques per plant (56%), thousand seed weight (43%). Conversely, the root length, plant height, seed per silique and dry plant weight were increased by 24.6, 60.9, 10.5 and 56.3% under WEW treatment, respectively. Overall, this study showed that grain yield was greatly affected by electromagnetic waves and increased by about 23.45%. In general, among all the growth parameters, the correlation of the GY was positive and significant with DPW (r=0.735*), PH (r=0.669*) and SPS (r=0.659*). This result highlights the necessity for a better understanding of the mechanisms of electromagnetic waves in crops to help better seedling establishment. [ABSTRACT FROM AUTHOR]

Recent advancements in occupancy and indoor environmental monitoring have encouraged the development of innovative solutions. This paper presents a novel approach to room occupancy detection using Wi-Fi probe requests and machine learning techniques. We propose a methodology that splits occupancy detection into two distinct sub-tasks: personnel presence detection, where the model predicts whether someone is present in the room, and occupancy level detection, which estimates the number of occupants on a six-level scale (ranging from 1 person to up to 25 people) based on probe requests. To achieve this, we evaluated three types of neural networks: CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). Our experimental results show that the GRU model exhibits superior performance in both tasks. For personnel presence detection, the GRU model achieves an accuracy of 91.8%, outperforming the CNN and LSTM models with accuracies of 88.7% and 63.8%, respectively. This demonstrates the effectiveness of GRU in discerning room occupancy. Furthermore, for occupancy level detection, the GRU model achieves an accuracy of 75.1%, surpassing the CNN and LSTM models with accuracies of 47.1% and 52.8%, respectively. This research contributes to the field of occupancy detection by providing a cost-effective solution that utilizes existing Wi-Fi infrastructure and demonstrates the potential of machine learning techniques in accurately classifying room occupancy. [ABSTRACT FROM AUTHOR]

This research proposed and verified a novel method in realizing end-fire radial line slot array (RLSA) antennas. This method involved the use of high beamsquint values in the design of slot pairs, which aimed to shift the antenna’s beam toward the end-fire direction. Furthermore, identical slot pairs were also placed in the antenna’s background to further squint the beam in the end-fire direction. By using this method, forty multibeam endfire RLSA antennas were modeled and simulated to determine the most efficient model to be fabricated. The accuracy of the simulations was confirmed through measurements taken from the fabricated prototype, which demonstrate good agreement with the simulation results and confirm the validity of the proposed method. The result showed that it is possible to design four end-fire beam antennas with a gain of 8 dBi, directions of 0°, 90°, 180°, and 270° in the azimuth direction, and a beamwidth of about 20°. The antenna also showed low reflection and bandwidth of about 500 MHz, which is suitable for Wi-Fi applications. [ABSTRACT FROM AUTHOR]

This paper introduces a Patron Counting and Analysis (PCA) system that leverages Wi-Fi-connection data to monitor space utilization and analyze visitor patterns in academic libraries. The PCA system offers real-time crowding information to the public and a comprehensive visitor analysis dashboard for library administrators. The system's development was driven by the need for occupancy restrictions during the pandemic, ensuring a spacious environment for library visitors as well as balancing between efficient utilization and adhering to social distancing regulations. Traditional methods of patron behavior performance and library spatial analysis, such as manual head counting or card-swiping systems, often incur additional costs for labor, hardware installation, or software subscription. The PCA system, however, utilizes existing Wi-Fi-connection data, providing a cost-effective solution to represent patron demographics and spatial usage. Limitations may arise when patrons do not carry Wi-Fi-enabled devices or during periods of low Wi-Fi service functionality. Implemented in Node.js and integrated with Python Flask framework and related libraries, the PCA system was piloted at the King Library in Miami University, successfully demonstrating a high validity compared to manually collected data. It filters out noise and redundancy, visualizes the occupancy index meter in real time, and generates statistical reports by linking user IDs with demographic information. The PCA system's reliability was validated through manually head counting data collected at the King Library in Miami University, establishing it as a reliable tool for library space management and patron analysis. [ABSTRACT FROM AUTHOR]

In this paper, we propose a printed circuit board (PCB)-based planar antenna pair, operating at 2.4 GHz and 5.0 GHz frequency bands, respectively, for dual-band routers. The antennas are both rectangular and consist of twisted radiating elements and microstrips etched on an FR4 dielectric substrate. Etching slots on the radiating elements and adjusting the serpentine microstrips influence surface current distribution and therefore effectively reduce antenna size and enhance antenna gain. The proposed antenna features a compact size compared to general router antennas and demonstrates high gain characteristics compared to dipole antennas. In the 2.3–2.5 GHz band, the simulated S11 of the 2.4 GHz antenna was lower than −10 dB, while the gain was 3.9 dBi at 2.4 GHz. In the 5.1–5.9 GHz band, the simulated S11 of the 5.0 GHz antenna was lower than −10 dB, and the gain was greater than 4.8 dBi. The proposed antenna has potential for application to router antennas.

Global Positioning System (GPS) is a famous technology around the world in identifying the real time precise location of any object with the assistance of satellites. The most common application of GPS is the use of outdoor maps. GPS offers efficient, scalable and cost-effective location services. However, this technology is not reliable when the position is in an indoor environment. The signal is very weak or totally lost due to signal attenuation and multipath effects. Among the indoor positioning technologies, WLAN is the most convenient and cost effective. In recent research, machine learning algorithms have become popular and utilized in wireless indoor positioning to achieve better performance. In this paper, different machine learning algorithms are employed to classify different positions in the real-world environment (e.g., Ixora Apartment - House and Multimedia University Malacca – FIST building). Received Signal Strength Indication (RSSI) is collected at each reference point. This data is then used to train the model with hyperparameter tuning. Based on the experiment result, Random Forest achieved 82% accuracy in Ixora Apartment and 84% accuracy in one of the buildings in Multimedia University Malacca. These results outperformed the other models, i.e., K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).