Wireless Data Research Articles

Evaluating basic chest pass skills in the 21st century using chest pass automatic board 2.0

Introduction: This research develops an automated tool to measure the chest pass ability in basketball. We developed this measurement to reduce human error in assessing the chest pass ability in basketball. Objective: The product refines the previous version of the automatic chest pass board by adding a feature for automatic result calculation and utilizing wireless technology. Methodology: This study utilized the research and development approach. A total of 42 participative students were involved in this research. Results: The results of the first test showed a significance value of Sig. (2-tailed) = 0.047 (p < 0.05). In the second test, the significance value obtained was Sig. (2-tailed) = 0.008 (p < 0.05). Both tests showed significant differences. The results of three prototype reliability tests showed very good reliability (ICC = 0.990; 0.987 and 0.920) with a 95% confidence interval. The Bland-Altman plot analysis shows that there is a good level of agreement for this prototype. There is no measurement bias in chest passes using either the automatic board or the usual methods. This is because both instruments are very reliable. Discussion: The inventions that make it easier and faster to evaluate chest pass test results include the use of vibration sensors, wireless data transmission, a computer program, and a database that directly obtains measurement results. Conclusions: The prototype of the automatic chest pass board 2.0 is a reliable and stable potential substitute for measuring the chest pass ability of each individual in basketball games.

Wearable gesture control design for unmanned aerial vehicle based on multi-sensor fusion

Abstract Traditional bulky and complex control devices such as remote control and ground station cannot meet the requirement of fast and flexible control of unmanned aerial vehicles (UAVs) in complex environments. Therefore, a data glove based on multi-sensor fusion is designed in this paper. In order to achieve the goal of gesture control of UAVs, the method can accurately recognize various gestures and convert them into corresponding UAV control commands. First, the wireless data glove fuses flexible fiber optic sensors and inertial sensors to construct a gesture dataset. Then, the trained neural network model is deployed to the STM32 microcontroller-based data glove for real-time gesture recognition, in which the convolutional neural network-Attention mechanism (CNN-Attention) network is used for static gesture recognition, and the convolutional neural network-bidirectional long and short-term memory (CNN-Bi-LSTM) network is used for dynamic gesture recognition. Finally, the gestures are converted into control commands and sent to the vehicle terminal to control the UAV. Through the UAV simulation test on the simulation platform, the average recognition accuracy of 32 static gestures reaches 99.7%, and the average recognition accuracy of 13 dynamic gestures reaches 99.9%, which indicates that the system’s gesture recognition effect is perfect. The task test in the scene constructed in the real environment shows that the UAV can respond to the gestures quickly, and the method proposed in this paper can realize the real-time stable control of the UAV on the terminal side.

Vialess heterogeneous skin patch for multimodal monitoring and stimulation

System-level wearable electronics require to be flexible to ensure conformal contact with the skin, but they also need to integrate rigid and bulky functional components to achieve system-level functionality. As one of integration methods, folding integration offers simplified processing and enhanced functionality through rigid-soft region separation, but so far, it has mainly been applied to modality of electrical sensing and stimulation. This paper introduces a vialess heterogeneous skin patch with multi modalities that separates the soft region and strain-robust region through folded structure. Our system includes electrical and optical modalities for hemodynamic and cardiovascular monitoring, and a force-electrically driven micropump for drug delivery. Each modality is demonstrated through on-demand drug delivery, flexible waveguide-based PPG monitoring, and ECG and body movement monitoring. Wireless data transmission and real-time measurement validate the feedback operation for multi-modalities. This engineered closed-loop platform offers the possibility for broad applications, including cardiovascular monitoring and chronic disease management.

DomoticX (Smart Home Automation System)

DomoticX is a cutting-edge home automation system designed to intelligently control and monitor household appliances. Leveraging ESP32 microcontrollers alongside Rain Maker, IR remote, menu switches, relay modules, and TSOP IR, the system ensures seamless operation in manual and automated modes. It features six relays for light, bulb, and door open/close control, four relays for speed adjustments, and a secure door open and close function, offering comprehensive home management. Wireless data transmission enables real-time monitoring and control via intuitive interfaces. DomoticX enhances energy efficiency, user convenience, and scalability, embodying the future of smart home ecosystems through IoT-driven solutions

Undersea Capacitive Coupled Simultaneous Wireless Power and Data Transfer for Multiload Applications

Undersea Capacitive Coupled Simultaneous Wireless Power and Data Transfer for Multiload Applications

Wireless Power, Drive, and Data Transfer for Ultrasonic Motors

Wireless Power, Drive, and Data Transfer for Ultrasonic Motors

Application of Wireless Data Collection Driven by Intelligent Sensors in Bridge Collision Warning

Application of Wireless Data Collection Driven by Intelligent Sensors in Bridge Collision Warning

Multimodal data visualization method for digital twin campus construction

ABSTRACT University campuses, as distinctive yet commonplace urban environments, face similar challenges of high population density and efficient management. Integrating digital twins into smart campus construction is essential for optimizing campus management. Multimodal data integration and visualization are key to building a digital twin campus. Traditional visualization methods often lack effective inter-data relationships, limiting their capacity to support robust multimodal data integration and create a multidimensional, real-time, comprehensive management view. Therefore, this paper proposes a tailored approach for multimodal data integration and visualization in digital twin campus construction. First, a comprehensive analysis of multimodal campus data identifies spatiotemporal associations among datasets. Subsequently, visualization strategies are developed based on these data characteristics and associations, enabling the multidimensional integration of 3D models, video surveillance, and wireless sensor data. Finally, a prototype system is implemented and evaluated through a case study. The results demonstrate that the proposed method efficiently integrates 2D maps, surveillance videos, and sensor data to create a dynamic, interactive, and panoramic campus view, achieving rendering efficiency exceeding 60 frames per second. Moreover, this adaptable framework can be applied to various campus or geographic contexts, offering critical technical support for multimodal visualization in IoT and big data environments.

An IoT Prototype for Water Quality Monitoring System with Case Study in NPIC Campus

Water is one of the natural resources that may be found in the environment. It is the most essential element of life. All living things, including humans, depend on water to survive, as do other species. Regretfully, water contamination has become a significant worldwide issue because of industrialization and irresponsible consumption. Moreover, low-quality water is poisonous to entire ecosystems due to its hazardous chemical and microbial contents. The issue is made much more dangerous by the lack of accessible tools for monitoring water quality other than costly laboratory tests. In this study, we developed an integrated system to measure water quality using the Internet of Things. Among the sensors, the system makes use of are Turbidity, pH (potential of hydrogen), TDS (Total Dissolved Solids), and water temperature. The embedded system is involved in this project as well. It will help with the establishment of wireless data transmission and sensor device detection. We employed LoRa technology alongside a cellular network ensuring effective communication. Firebase was utilized as the backend platform to securely store and manage the sensor data. The information can be tracked via a mobile or online application. Based on the sensor data, the water environment's quality was assessed and problems with the water's quality were anticipated to prevent the spread of contamination.

Soft Wireless Passive Chipless Sensors for Biological Applications: A Review.

Soft wireless passive sensors have been applied in biological, engineering, and other fields due to their advantages in powerless supply and remote data transmission. External information is obtained by soft wireless passive sensors via the external coils based on electromagnetic induction. The purpose of this review paper is to outline the biological applications of soft wireless passive chipless sensors and provide a classification of wireless passive sensors and an overall explanation of the main work. Three kinds of soft wireless sensors, soft wireless passive LC-resonant sensors, soft wireless radio frequency (RF) sensors, and soft wireless surface acoustic wave (SAW) sensors, are introduced with their working principles, equitant circuits, and biological applications. Soft wireless passive sensors with integrated LC-resonant units are applied to physical quantity measurements for denoting the mapping relationship between the frequency resonance and the monitored object. Utilizing the electromagnetic field principle, RF sensors enable wireless measurements and data exchange of physical parameters. SAW sensors with piezoelectric substrates are applied to physical parameter monitoring using guided waves in monitoring objects. Soft wireless passive sensors aim to monitor biological health without an external power supply or wired data communication, which would bring increased convenience to the lives of the people who use them.

Enabling Safer Crosswalks with State-of-the-Art Vehicle-to-Everything (V2X) Technology

Vehicle-to-Everything (V2X) communication is a technology that enables vehicles to communicate with other vehicles (V2V), infrastructure (V2I), cyclists, and pedestrians (V2P). V2X employs antenna technology that allows omnidirectional wireless data transmission between all nodes in the transportation ecosystem. This has strong implications for improving pedestrian safety, reducing traffic congestion, and enabling smart city applications. One area where V2X can have a tremendous impact is at pedestrian crosswalks. Currently, these are dangerous zones where vehicle-pedestrian collisions occur frequently due to blind spots, distracted driving/walking, and unclear right-of-way. V2X aims to eliminate these collisions by allowing vehicles and pedestrian smartphones to continuously share their real-time locations, trajectories, and analytics. This seamless connectivity is enabled by V2X antennas embedded in cars and mobile devices. The primary goal of this research is to increase the safety of pedestrians on and near crosswalks positioned on roadways. The Altair FEKO 2022.1 software is used in this study to create a symmetrical V2X communication scenario with intersecting highways. Cars, pedestrians, roads, and traffic lights are arranged in the Altair WallMan Computer-Aided Design (CAD) software. The Shooting Bouncing Ray (SBR+) solver in Altair ProMan is computed on a CAD-modelled database to simulate the power received at key prediction points on crosswalks in the path of each vehicle. Intelligent Ray Tracing (IRT) is utilized to animate the scene with moving cars and pedestrians over a three-second time interval while simultaneously counting the number of propagation pathways and rays used at each instant. At each prediction time instant and for every prediction point, power received is measured in decibel-milliwatts (dBm). The computed simulation results are analysed at 5.8 GHz, 6 GHz, and 28 GHz.

Sensing Technologies for Outdoor/Indoor Farming.

To face the increasing requirement for grains as the global population continues to grow, improving both crop yield and quality has become essential. Plant health directly impacts crop quality and yield, making the development of plant health-monitoring technologies essential. Variable sensing technologies for outdoor/indoor farming based on different working principles have emerged as important tools for monitoring plants and their microclimates. These technologies can detect factors such as plant water content, volatile organic compounds (VOCs), and hormones released by plants, as well as environmental conditions like humidity, temperature, wind speed, and light intensity. To achieve comprehensive plant health monitoring for multidimensional assessment, multimodal sensors have been developed. Non-invasive monitoring approaches are also gaining attention, leveraging biocompatible and flexible sensors for plant monitoring without interference with its natural growth. Furthermore, wireless data transmission is crucial for real-time monitoring and efficient farm management. Reliable power supplies for these systems are vital to ensure continuous operation. By combining wearable sensors with intelligent data analysis and remote monitoring, modern agriculture can achieve refined management, resource optimization, and sustainable production, offering innovative solutions to global food security and environmental challenges.

Cluster-based Data Sharing for Web 3.0 in Intelligent Transportation Systems

Intelligent transportation system (ITS) applications are dependent on secure and robust wireless data sharing among vehicles and roadside units (RSUs). Multiple types of data are shared among the ITS devices which include safety information, road services, web based information retrieval and task computation. Web 3.0 offers a decentralized, distributed and secure data sharing mechanism for ITSs. Allocation of wireless channel resources are critical to enable an efficient ITS system. In this paper, a novel data sharing technique for Web 3.0 based ITS is presented that relies on an intelligent clustering algorithm. In the first step, the proposed technique uses a K-means algorithm to find groups of vehicles with similar speeds. In the second step, each cluster is assigned an RSU which has the highest average data rate with all vehicles in the cluster. This is achieved by using a stable matching technique so that there is no contention and each cluster is assigned a separate RSU. The algorithm periodically updates the clusters and RSU allocation for web data sharing between vehicles and RSUs. Simulation results show that the proposed clustering-based data sharing technique improves sum-rate by 20% and reduces network delay by 23%.

On the optimal integration of legacy and SDWN communication nodes in emergency scenarios

AbstractSpontaneous networks may need to establish connections with already-in-place devices and their resources, irrespectively of their capabilities, for example, in emergency scenarios. Interoperability and integration are a key issue when combining multiple wireless networks and heterogeneous devices. To address this issue, this article extends the SDN concept, and presents a new hybrid network architecture that combines legacy devices in a wireless data plane, therefore increasing the range, and making the network more efficient, dynamic and programmable. In this article, we propose an optimized adaptive routing approach in a network architecture that joins SDN-capable devices with legacy devices for emergency scenarios. This solution takes into consideration the impact that a flow can have in the network, therefore minimizing its channel interference, and to a certain degree, balancing the load in the network’s devices. In this work, the importance of the network and service parameters is optimized through machine learning for each flow’s data rate, and then feed into the routing approach to determine the best hybrid legacy and SDN-based path. Results show that the new architecture can successfully integrate the legacy devices in the wireless software defined data plane, and utilize them to increase the performance. The combination of delivery ratio and delay are always relevant to every data rate. The link stability is more relevant for lower data rates, while the break probability is more relevant for higher data rates. Moreover, the number of hops and possible interference combined have a high importance in every data rates, and the nodes’ speed knowledge can have a good impact on the network performance.

WSN intrusion detection method using improved spatiotemporal ResNet and GAN

Abstract A network intrusion detection method that integrates improved spatiotemporal residual network and generative adversarial network (GAN) in a big data environment is proposed to address the issues of poor feature extraction and significant impact from data imbalance in most existing intrusion detection methods. First, GANs are used for wireless sensor network data resampling to generate new sample sets, thereby overcoming the impact of data imbalance. Then, an improved spatiotemporal residual network model is designed, in which the spatial and temporal features of the data are extracted and fused through multi-scale one-dimensional convolution modules and gated loop unit modules, and identity maps are added based on the idea of residual networks to avoid network degradation and other issues. Finally, the resampled samples are input into the improved spatiotemporal residual network model to output the intrusion detection results of the network. Based on the NSL-KDD, UNSW-NB15, and CICIDS2017 datasets, experimental analysis is conducted on the proposed method. The results showed that its accuracy on the three datasets is 99.62, 83.98, and 99.86%, respectively, which are superior to other comparative methods.

Fabric‐Based Stretchable and Breathable Backscattered Monitoring System

AbstractThe demand for wearable monitoring devices in contemporary medicine has significantly increased, especially in dynamic environments where traditional bulky equipment is impractical. Conventional flexible wearable devices or systems suffer from limited air and moisture permeability, lack of stretchability, and high power consumption, which restrict their long‐term usage and comfort. Herein, a stretchable and breathable backscattered monitoring system (SBBMS) is introduced, integrated with a fabric substrate. To address the challenges associated with fabric substrate system fabrication and encapsulation, a printing‐cutting‐transfer technology is proposed. This method enables the creation of unique, low‐cost, high‐precision, and robust circuit routing and electronic devices on fabric, maintaining high compatibility with commercial surface mounting technology while minimizing sacrifices in breathability. Additionally, a backscatter communication mechanism is designed and implemented to achieve wireless data transmission, which significantly reduces power consumption. Combined with energy management technology and hydrogel batteries, the SBBMS receives safe, multi‐source, and eco‐friendly energy support. Furthermore, through meticulous design, all modules—including the antenna, circuit, and battery—are made stretchable, providing the system with excellent strain‐resistive performance. The approach paves the way for the development of breathable, high‐performance, and highly integrated fabric‐based wearable systems, catering to specific user groups such as athletes, soldiers, and pilots.

ДЕРЕКТЕРДІ КӨРІНЕТІН ЖАРЫҚ АРҚЫЛЫ БЕРУГЕ БОЛАТЫН МОДУЛЯЦИЯ ӘДІСТЕРІН ЗЕРТТЕУ

The emergence of new directions based on the optical range in the field of communications is explained by existing problems in organizing modern wireless infrastructure. Currently, the radio frequency band widely used for data transmission is overloaded and cannot meet the rapidly growing traffic and demand for wireless data services. Additional implementation of wireless systems based on the use of optical range can help solve existing problems. Visible light communication, as an integral part of optical wireless communication, can realize the requirements for data transmission speed in the network, and reduce the load on the bandwidth of existing wireless networks. The article discusses modulation methods in a wireless optical communication network. Modulation characteristics that are important for system performance are studied. For types of pulse modulation, the power spectral density is calculated and studied to obtain time waveforms with a given signal-to-noise ratio. A model of an ideal transmission and reception system for OOC-NRZ in the presence of additive white Gaussian noise is constructed, which is suitable for indoor visible light data transmission systems. Signal shapes and the influence of instrumental and external noise were studied. Calculated values of the bit error probability based on experimental data are given. The proposed simulation model can be used in the design of real transmitting and receiving devices for a visible light communication system.

Using digitalisation for a real-word implementation of an early warning system for water leakages

ABSTRACT A timely detection and localisation of leakages is of highest interest to reduce water losses. As stated in the literature, most leakage detection and localisation methods consider unrealistic assumptions and are rarely assessed under real-world conditions. To overcome these limitations, an early warning system for new leakages is implemented in a real-world case study. Therefore, the case study is equipped with digital water meters at customer sites to monitor household water demands at 15-min resolution. These data are subject to the European General Data Protection Regulation, which requires the active customers consent (and therefore some refusals), and are also subject to data loss during transmission due to the use of wireless data communication. The incomplete data (costumer refusals and transmission failures) were then used for the early warning system, including data-based detection and model-based localisation, and tested on 10 engineered leakage events. Thereby, small leakages up to a size of 0.4 l/s could be detected reliably even under high demand fluctuations. Additionally, the search area for the fine search on site could be reduced to 10–40% of the entire district metered area for leakage sizes between 0.1 and 2.0 l/s also under these real-world boundary conditions.

Design and verification of a novel self-powered sensing system for intelligent bearings

Abstract This paper presents the development of a novel self-powered sensing system for roller bearing NU208. The system was designed based on an analysis of the bearing’s structural parameters and service condition monitoring signals. Through the selection of appropriate hardware and the design of a suitable circuitry, the power supply circuit, the main controller circuit, the parameter monitoring circuit, and the wireless data transmission circuit have been constructed. Additionally, the wireless data receiving module program and the upper computer software interface program have been developed. The upper computer software interface enables the real-time display of bearing vibration, cage speed, inner ring speed, and temperature signals. The self-powered sensing system is integrated and encapsulated within the roller bearing NU208, and its performance is verified on a small bearing test bench. The test results are precise and the data accuracy and output frequency meet the anticipated requirements. The self-powered sensing system developed in this paper fulfills the criteria for intelligent bearings that do not require an external power source or monitoring signals via cable transmission. It has significant scientific value and engineering significance for the study of intelligent bearings’ autonomous decision-making and self-regulation.

Robust Magnetoelectric Backscatter Communication System for Bioelectronic Implants.

Wireless communication technologies for bioelectronic implants enable remote monitoring for diagnosis and adaptive therapeutic intervention without the constraints of wired connections. However, wireless data uplink from millimeter-scale devices deep in the body struggles to achieve low power consumption while maintaining large misalignment tolerances. Here, we report a passive wireless backscatter communication system based on magnetoelectric transducers that consumes less than 0.3 pJ/bit and achieves less than 1E-6 bit error rate at a distance of 55 mm while tolerating a misalignment of 10 mm. Using this robust data uplink, we designed a wireless cardiac sensing node that can transmit electrocardiogram signals from the beating heart surface of a porcine model to a custom external transceiver using the magnetoelectric backscatter uplink. This reliable, near-zero-power communication method provides opportunities for next-generation bioelectronics to feature real-time physiological monitoring and closed-loop therapies while maintaining a small form factor and low power consumption.