Wireless Transmission Research Articles

Enhancement of Operational Safety in Marine Cargo Cranes on a Container Ship Through the Application of Authenticated Wi-Fi Based Wireless Data Transmission from Multiple Sensors

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.

Adaptive Wireless Image Transmission Transformer Architecture for Image Transmission and Reconstruction

The advancement of 6G (6th Generation Mobile Networks) communication technology has posed challenges for traditional communication network architectures in meeting the demands for communication efficiency and quality. Semantic communication technology, characterized by its “understand before transmit” approach, has emerged as a pivotal technology driving the progress of 6G due to its ability to enhance communication efficiency and quality. The Wireless Image Transmission Transformer (WITT) model, which operates as a semantic communication system leveraging vision transformer technology for the transmission of semantic images, has shown efficacy in transmitting input images through processes of feature extraction and channel adaptation. This study introduces an advanced channel adaptive module that is informed by deep learning methodologies and the adaptive modulation principles of the Variational Information Bottleneck (VIB). This innovation enhances the original WITT model, resulting in the development of the Adaptive Wireless Image Transmission Transformer (ADWITT) architecture. Comprehensive experimental results have unequivocally shown that the transmission performance of the ADWITT architecture substantially surpasses that of the conventional WITT (Wavelet Image Transmission Technique) model, particularly in scenarios characterized by harsh and detrimental channel conditions. These findings underscore the robustness and adaptability of the ADWITT approach, which is poised to improve the field of image transmission by offering superior performance and resilience in environments where traditional methods falter.

A Wireless Bi-Directional Brain–Computer Interface Supporting Both Bluetooth and Wi-Fi Transmission

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.

Optimized beamforming vector and antenna selection using autoencoder with Bayesian RNN and hybrid heuristic algorithm for MIMO systems

SummaryWireless communication systems depend on channel state information (CSI). However, the CSI collection is a significant barrier to achieving high performance. Present wireless communication models mainly rely on many antennas and related signal dispensation for increasing effectiveness. User mobility, the usage of greater frequencies, and a better number of antennas contribute to beamforming and beam management in these networks. Therefore, an improved hybrid heuristic algorithm is proposed here for providing optimized Joint beam forming and antenna selection (JBAS) in a multi‐input multi‐output (MIMO) model. The major novelty of the research work is to jointly perform the beam forming and antenna selection in MIMO that effectively enhanced the performance of the wireless transmission system. The major intention of this paper is to optimize the beamforming vectors and antennae by using the designed algorithm, where the spectral efficiency gets maximized. Initially, the data is collected by varying the number of users, locations, and sampling points, which is optimally selected by developing the hybrid reptile search‐based lion optimization algorithm (HRS‐LOA). Once it is collected, the relevant CSI is considered. This state of information is fed as input to the Auto Encoder with Bayesian Recurrent Neural Network (AE‐BRNN). Finally, through the network, the optimized value of the beamforming vector and antenna is obtained, which is useful to increase the sum rate/spectral efficiency. From the numerical results, the recommended model attained a spectral efficiency of 30 bits/s/Hz, which is higher than the BWO‐AE‐BRNN with 13%, RSO‐AE‐BRNN with 16%, RSA‐AE‐BRNN with 17%, and LA‐AE‐BRNN with 40% respectively.

Demonstration of 400 Gbit/s/λ wireless transmission for photonic-assisted SISO integration system at 220 GHz

Demonstration of 400 Gbit/s/λ wireless transmission for photonic-assisted SISO integration system at 220 GHz

Self-Powered, Flexible, Wireless and Intelligent Human Health Management System Based on Natural Recyclable Materials.

Combining wearable sensors with modern technologies such as internet of things and big data to monitor or intervene in obesity-induced chronic diseases, such as obstructive sleep apnea, type II diabetes, cardiovascular diseases, and Alzheimer's disease, is of great significance to the self-health management of human beings. This study designed a loofah-conducting graphite four friction layer enhanced triboelectric nanogenerator (LG-TENG) and developed a health management system for human motion recognition and early warning of sleep breathing abnormalities. By uniformly spraying and depositing conductive graphite on the surface of the loofah and the elastic film cross-interlocking bending structure design, the signal strength of the LG-TENG has been improved by 390%. The stable output signal is still maintained after 1500 s of continuous operation at a frequency of 2 Hz. LG-TENG can realize accurate motion analysis by muscle contraction state. Combining different deep learning models resulted in 98.1% accuracy in recognizing seven categories of displacement speeds for an individual and 96.46% accuracy in recognizing seven categories of displacement speeds for three individuals. In addition, the sleep breathing monitoring early warning system was developed by integrating Bluetooth wireless transmission and upper computer analysis technology. This system aims to analyze and provide real-time warnings for sleep-breathing abnormalities. This research promotes an innovation of TENG technology based on the advantages of natural materials, recyclability and low cost. It offers new ideas for self-health management and scientific exercise for obese people, showing a broad application prospect.

On Radio k – Chromatic Number for Mycielski of Some Graphs

Objectives: In radio networks, the main difficulty is managing the radio spectrum, assigning radio frequencies to transmitters optimally without any interferences. This study aims to find the smallest span of Mycielski of some graphs, the maximum color assigned to any node is called span. Methods : This study focused on the problem of reducing interference by modeling it with a radio k - coloring problem on graphs. Where transmitters are modeled as nodes in a graph, with edges connecting nodes that represent transmitters in close proximity to one another. For a graph , with node set , edge set and an integer , a radio k - coloring of is a function satisfying the condition for any two nodes and , where is the distance between and in . The radio k - coloring of , is the maximum color assigned to any node of and it is denoted by . The radio k -chromatic number of is the minimum value of taken over all radio k - coloring of and it is denoted by . Findings: This study obtained the radio k-chromatic number for Mycielski of some graphs for and 3 Novelty: To solve the channel assignment problem in radio transmitters, the interference graph is developed, and the channel assignment has been converted into a graph coloring. Reducing the interference by a radio k - coloring problem will motivate many researchers to find the radio k - coloring in various graphs. Keywords: Radio k – Coloring, Mycielski graph, Double Star graph, Triple Star graph, Sunlet graph, Helm graph and Closed Helm graph

Winter torpor patterns of tricolored bats (Perimyotis subflavus) in the southeastern United States

Abstract The Tricolored Bat (Perimyotis subflavus) has suffered significant population declines in much of its geographic range due to white-nose syndrome (WNS). Our aim was to describe the torpor patterns of tricolored bats within the southeastern United States to further our understanding of their winter ecology and potential susceptibility to WNS in currently unimpacted areas based on data from bats in both a WNS-affected hibernaculum and an unaffected hibernaculum. We placed temperature-sensitive radio transmitters on tricolored bats in a site in northwestern South Carolina that was WNS-positive and another site in northwestern Florida that was WNS-negative, and determined torpid and arousal skin temperatures (TSK), torpor bout duration (TBD), and arousal duration (AD) during 3 winters. Mean hibernacula temperature (TH) and vapor pressure deficit (VPDH) in both hibernacula were within the range of optimal Pseudogymnoascus destructans (Pd) growth (TH = 12.7 to 13.6 °C, VPDH = 0.028 to 0.059 kPA). Mean torpid TSK was 15.7 ± 0.1 °C and mean TBD was 4.1 ± 0.2 days across sites. Sex was the best predictor of TBD with males having significantly longer TBD than females. Torpid TSK was positively related to TH and negatively related to VPDH. Because the TSK of a bat was similar to the optimal growth temperatures of Pd, our findings suggest that even though winters in the southeastern United States are more benign than in other parts of the Tricolored Bat range, the warmer temperatures in southern hibernacula may provide optimal conditions for the growth of Pd. Greater understanding of the physiological responses of tricolored bats in hibernacula across their range will provide important data on the potential for WNS morbidity and mortality in unaffected areas and allow for a better distribution of resources for prevention and treatment of WNS.

High-performance waterproof flexible thermoelectric generators for self-powered electronics

Flexible and wearable thermoelectric generators (TEG) have emerged as promising energy sources for wearable health-monitoring devices. However, common thermoelectric generators are difficult to be applied to a wide range of application scenarios due to their poor durability, low output power, and susceptibility to human sweat or underwater environments. Here, we propose a waterproof wearable TEG with a gap structure. Through structural optimization and full-coverage elastomer encapsulation, the wearable TEG has a maximum power density of 4.38 mW∙cm−2 at a temperature difference (ΔT) of 30 K and exhibits good stability under aqueous environments, which is capable of supplying power to electronic devices even under submerged conditions. The internal resistance of wearable TEG changes by less than 10 % at 50 % stretch or 900 degrees of torsion, showing good flexibility and mechanical stability. Based on this, we designed an energy harvesting and management system to help wearable TEG collect and utilize energy on demand, and successfully implemented wireless transmission of human skin temperature data for underwater environments and real-time electrocardiograms (ECG) tests for health detection in air environments. This research provides a reliable method for powering wearable electronic devices using thermoelectric generators.

Synergetic enhancement of moisture-electric generation through interfacial evaporation and active electrode

Ion migration-based moisture-electric generator holds the open-circuit voltage to power portable electronics, the Internet of Things, and wireless transmission. However, most devices still encounter challenges with the attainment of high-density power generation in continuous mode. Here, we introduce a sustainable and high-power density ion-selective bipolar moisture-electric generator that relies on Au-Al electrodes, capillary water supply, and interfacial evaporation. The device generates electricity by exploiting the salinity gradient between the capillary waterways in the photothermal and waste heat layers. This process is synergized by electrochemical reactions of the electrodes, which propel the migration of cations and anions through ion-selective bipolar hydrogels toward the intermediate waterway. It demonstrates a short-circuit current density of 52.2 A m−2 and a power density of up to 33.8 W m−2 over 0.5 cm × 0.5 cm electrodes. Connecting 13 devices in series in darkness successfully illuminates an LED lamp with a rated power of 1 W together with an operating voltage of 2.0–2.8 V. This work offers an off-grid, environmentally friendly, and affordable solution for high-density moisture power generation.

Resource optimization of multi-UAV assisted communication system based on user scheduling

In order to improve the transmission rate of the multi-user mobile communication downlink wireless transmission system, a resource optimization method for multi-unmanned aerial vehicle (UAV) assisted communication system based on user scheduling and trajectory optimization is proposed. The proposed method establishes an optimization problem with the maximization of the total multi-user throughput as the criterion under the constraints of user scheduling, total energy consumption of UAVs and user service quality requirements. In order to solve this non-convex problem, the original non-convex problem is decomposed into three easy-to-handle non-convex sub-problems by using the block coordinate descent method (BCD). The sub-problems are transformed and solved by introducing slack variables, first-order Taylor expressions, continuous convex approximation (SCA) and other methods, and then alternately iteratively optimized to obtain an approximate suboptimal solution to the original non-convex problem. Simulation results show that the proposed algorithm can effectively improve the total system throughput and has good convergence in both single and multi-UAV communication systems.

A Rapid Adaptation Approach for Dynamic Air-Writing Recognition Using Wearable Wristbands with Self-Supervised Contrastive Learning

HighlightsUtilizing self-supervised learning, the proposed wearable wristband with a four-channel sensing array and wireless transmission module is developed for tracking air-writing and dynamic gestures.The model can learn prior features from unlabeled signals of random wrist movements, significantly reducing the dependency on extensive labeled data for training.The wristband system rapidly adapts to multiple scenarios after fine-tuning using few-shot data, enhancing user interaction through natural and intuitive communication.

5G network performance using novel MIMO mixed FSO/MMW communication systems under pointing errors effect: test analysis using image transmission

Abstract Millimeter waves (MMW) enable high data rates over short distances; free-space optical (FSO) provides high-capacity optical wireless transmissions; and multiple-input multiple-output (MIMO) maximizes antenna utilization. These are the three leading technologies that work harmoniously to deliver 5G’s superior performance. This collaborative effort results in 5G’s ability to offer high connection capacities, low latency, and rapid speeds, creating new opportunities for industrial and internet of things applications. In this paper, the proposed system combines FSO links and MMW radio frequency (RF) links to enhance the performance of mixed FSO/RF systems. FSO experiences turbulence and pointing errors (PE), while MMW undergoes fading. Using both FSO and MMW provides diversity against channel fading. Furthermore, both the FSO and MMW links utilize MIMO technology to combat fading through spatial diversity. The FSO link is modeled with a Gamma-Gamma Turbulence model and PE. The MMW link is modeled with Rayleigh fading. By combining these two types of links with MIMO, the system can achieve improved performance compared to using either FSO or MMW alone. The dual nature of the system provides robustness against different types of channels fading effects. Overall, the goal is to enhance performance by capitalizing on the complementary channel characteristics of optical and RF links and exploiting MIMO’s spatial diversity benefits. In the present work, closed-form formulas accounting for the influence of PE are generated for the bit error rate (BER). This article also explores the MIMO mixed FSO/MMW system with multiple modulation techniques under various turbulence situations. The suggested system is ultimately verified by transmitting images with suitable fixed BER.

RFID Occupied Advanced Framework for Specialized Vehicle Access, Operating System, and Wireless Data Transmission Without Human Intervention

RFID tags are tiny electronic devices that include a distinctive code that can be detected by a reader that supports RFID. The string of characters is used for recognizing and monitoring the object to which the tag is connected. RFID tags interact with RFID readers using radio frequency. While the tag comes into close proximity to the scanner, that device sends a radio signal that triggers the tag. The tag then responds by sending its unique identity. RFID pinpointing ability is verified experimentally in two different techniques, one utilizing radar and the other a photosensitive transition. The initial phase is intended to test if the continuously changing location provided from RFID tags equals the precise position recorded by sonar. It is also excellent for getting into parking lots, private spaces, professional parking spots, and offices quickly and easily. The implementation of RFID will bring a revolutionary transformation in the authorization, administration, correspondence, computerization, and security systems for specialized vehicle Access, Operating System, and Wireless Data Transmission without Human Intervention using an internet based wireless communication system. RFID vehicular registration is an authentication system which employs RFID readers to give authorization for automobiles. The technique normally consists of installing a reader with RFID technology at the entrance/exit entrance to an enclosed or a limited region. RFID scanning can deliver efficient techniques for transportation administration, making the process straightforward, dependable, as well as secure.

Effect of cell structures on electrical degradation of GaAs laser power convertors after 1 MeV electron irradiation and structure-optimization for improving radiation resistance

Laser wireless power transfer (LWPT) technology holds significant promise for wireless power transmission in space, necessitating that high-efficiency GaAs laser power convertors (LPCs) have strong tolerance to high-energy particle radiation. Therefore, the degradation characteristics of GaAs LPCs with different architectures under 1 MeV electron irradiation were investigated. Experimental and simulation results demonstrate that LPCs with thicker bottom cells suffer from significantly more electrical degradation. The degradation is primarily due to a reduction in electron concentrations in the base of the bottom cells, which is considerably less pronounced as the thickness of the bottom cells decreases. Based on these analyses, thinning the thickness and optimizing the doping profile for the bottom cells are proposed to improve the radiation resistance of the LPCs. Simulations show that the electrical degradation of the optimized four-junction LPCs is notably less than that of the original four-junction LPCs under the same irradiation conditions, indicating that the proposed strategies effectively enhance the radiation resistance of the LPCs.

Identifying Tampered Radio-Frequency Transmissions in LoRa Networks Using Machine Learning.

Long-range networks, renowned for their long-range, low-power communication capabilities, form the backbone of many Internet of Things systems, enabling efficient and reliable data transmission. However, detecting tampered frequency signals poses a considerable challenge due to the vulnerability of LoRa devices to radio-frequency interference and signal manipulation, which can undermine both data integrity and security. This paper presents an innovative method for identifying tampered radio frequency transmissions by employing five sophisticated anomaly detection algorithms-Local Outlier Factor, Isolation Forest, Variational Autoencoder, traditional Autoencoder, and Principal Component Analysis within the framework of a LoRa-based Internet of Things network structure. The novelty of this work lies in applying image-based tampered frequency techniques with these algorithms, offering a new perspective on securing LoRa transmissions. We generated a dataset of over 26,000 images derived from real-world experiments with both normal and manipulated frequency signals by splitting video recordings of LoRa transmissions into frames to thoroughly assess the performance of each algorithm. Our results demonstrate that Local Outlier Factor achieved the highest accuracy of 97.78%, followed by Variational Autoencoder, traditional Autoencoder and Principal Component Analysis at 97.27%, and Isolation Forest at 84.49%. These findings highlight the effectiveness of these methods in detecting tampered frequencies, underscoring their potential for enhancing the reliability and security of LoRa networks.

Smartphone application-based intervention to lower blood pressure: a systematic review and meta-analysis.

Nowadays, the mHealth market is flooded with smartphone applications (apps) lacking validation for blood pressure (BP)-lowering effects and BP measurement accuracy. This systematic review for Guidelines for BP control using digital technologies of the Japanese Society of Hypertension aimed to assess the validation studies of apps. We searched eligible studies in Ovid MEDLINE, Cochrane Library, and Ichushi, focusing on randomized controlled trials and observational studies comparing the effects of smartphone app-based interventions with non-digital healthcare. Random effects models of meta-analysis were employed to estimate the pooled effects of mean BP change and 95% confidence intervals (CIs). Out of 7385 studies screened, 76 studies with 46,459 participants were included. The interventions were significantly associated with a reduction in office systolic and diastolic BP at six months (systolic BP, -2.76 mmHg, 95% CI: -3.94 to -1.58; diastolic BP, -1.23 mmHg, -1.80 to -0.67). Normotensives saw a significant reduction in office systolic BP at three-month (-4.44 mmHg, -6.96 to -1.92), diminishing afterward (six-month, 0.86 mmHg, -2.81 to 4.52; twelve-month, 0.86 mmHg, -2.81 to 4.52). Conversely, hypertensive participants experienced a significant reduction in office systolic BP at both three- and six-month (three-month, -7.71 mmHg, -10.63 to -4.79; six-month, -1.88 mmHg, -3.41 to -0.35), albeit with limited evidence thereafter. A larger BP reduction was observed among participants using apps with wireless transmission of BP measurements (P = 0.047 for interaction), while there was no clear difference in BP reduction according to the presence of other functions. Smartphone app-based interventions may hold the potential to improve BP levels.

Summary of Wireless Charging Technology for Electric Vehicles

The rapid popularity of electric vehicles has promoted the rapid development of wireless charging technology. This paper reviews the current research progress and application status of wireless charging technology for electric vehicles. Inductive coupling, magnetic resonance coupling and non-directional radiofrequency radiation, three main wireless power transmission methods, including their principles and advantages, are introduced. In addition, the practical application of the technique, such as near field application and far field application, is also discussed. Finally, the development direction of wireless charging technology for electric vehicles in the future is looked forward, including the key technical paths of optimizing system design, improving energy transmission efficiency and reducing system cost. In summary, this paper aims to provide comprehensive theoretical support and practical guidance for the research and practice of wireless charging technology for electric vehicles.

Performance Analysis and Prediction of 5G Round-Trip Time Based on the VMD-LSTM Method

With the increasing level of industrial informatization, massive industrial data require real-time and high-fidelity wireless transmission. Although some industrial wireless network protocols have been designed over the last few decades, most of them have limited coverage and narrow bandwidth. They cannot always ensure the certainty of information transmission, making it especially difficult to meet the requirements of low latency in industrial manufacturing fields. The 5G technology is characterized by a high transmission rate and low latency; therefore, it has good prospects in industrial applications. To apply 5G technology to factory environments with low latency requirements for data transmission, in this study, we analyze the statistical performance of the round-trip time (RTT) in a 5G-R15 communication system. The results indicate that the average value of 5G RTT is about 11 ms, which is less than the 25 ms of WIA-FA. We then consider 5G RTT data as a group of time series, utilizing the augmented Dickey–Fuller (ADF) test method to analyze the stability of the RTT data. We conclude that the RTT data are non-stationary. Therefore, firstly, the original 5G RTT series are subjected to first-order differencing to obtain differential sequences with stronger stationarity. Then, a time series analysis-based variational mode decomposition–long short-term memory (VMD-LSTM) method is proposed to separately predict each differential sequence. Finally, the predicted results are subjected to inverse difference to obtain the predicted value of 5G RTT, and a predictive error of 4.481% indicates that the method performs better than LSTM and other methods. The prediction results could be used to evaluate network performance based on business requirements, reduce the impact of instruction packet loss, and improve the robustness of control algorithms. The proposed early warning accuracy metrics for control issues can also be used to indicate when to retrain the model and to indicate the setting of the control cycle. The field of industrial control, especially in the manufacturing industry, which requires low latency, will benefit from this analysis. It should be noted that the above analysis and prediction methods are also applicable to the R16 and R17 versions.

Application and Research of Intelligent Multi-source Information Fusion Technology in Firefighting Equipment

As modern firefighting environments become increasingly complex, traditional firefighter equipment and operational methods are no longer adequate to address the high-risk challenges of fire rescue operations. Fire scenes are characterized by complex and hazardous environmental factors, with firefighters facing multiple threats such as extreme temperatures, dense smoke, and toxic gases. Therefore, ensuring the safety and efficiency of firefighters is of paramount importance. This study integrates multiple sensor technologies to enable real-time monitoring of firefighters' physiological status, posture changes, and fire scene conditions. The system incorporates ErgoLAB wireless ECG and PPG sensors, the BWT61CL posture sensor, and the MAG14mini infrared camera, which are capable of simultaneously capturing key physiological data and heat distribution in the fire scene. To ensure data accuracy and real-time performance, the study employs efficient signal preprocessing techniques, including noise reduction, baseline correction, and time synchronization. In addition, wireless transmission technology and multimodal data fusion algorithms are utilized to comprehensively analyze the firefighters' status and fire scene conditions. This approach significantly enhances the precision of firefighter safety monitoring and operational efficiency, demonstrating substantial innovation and practical value.