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This paper introduces the implementation of a new device for measuring deformations at the surface layers of bituminous pavement. Using wireless technology, rechargeable remotely, low cost, and easily positioned in a layer by coring after pavement construction, this sensor makes it possible to obtain measurements of the deformation when a vehicle passes by. The development of the wireless sensor is presented as well as its advantages and limitations. It was then tested in the laboratory under a hydraulic press and in situ using a full-scale test of the mobile load simulator (MLS10 type). This system allows simple measurement, gives reliable results, and could be a useful device for the structural monitoring of pavement structures.

RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13% and 86.76% for 1 and 3 data samples.

Resonators are passive time-invariant components that do not produce a frequency shift. However, they respond to an excitation signal close to resonance with an oscillation at their natural frequencies with exponentially decreasing amplitudes. If resonators are connected to antennas, they form purely passive sensors that can be read remotely. In this work, we model the external excitation of a resonator with different excitation signals and its subsequent decay characteristics analytically as well as numerically. The analytical modeling explains the properties of the resonator during transient response and decay behavior. The analytical modeling clarifies how natural oscillations are generated in a linear time-invariant system, even if their spectrum was not included in the stimulation spectrum. In addition, it enables the readout signals to be optimized in terms of duration and bandwidth.

A compact wireless near-field hydrogen gas sensor is proposed, which detects leaking hydrogen near its source to achieve fast responses and high reliability. A semiconductor-type sensing element is implemented in the sensor, which can provide a significant response in 100 ms when stimulated by pure hydrogen. The overall response time is shortened by orders of magnitude compared to conventional sensors according to simulation results, which will be within 200 ms, compared with over 25 s for spatial concentration sensors under the worst conditions. Over 1 year maintenance intervals are enabled by wireless design based on the Bluetooth low energy protocol. The average energy consumption during a single alarm process is 153 μJ/s. The whole sensor is integrated on a 20 × 26 mm circuit board for compact use.

In order to further study the application of energy acquisition model, based on the relevant theory of adaptive dynamic programming, the wireless sensor is tracked by modifying the energy acquisition model, and the relevant data and parameters are quantitatively analyzed according to the test results. Finally, the parameters of different samples are tracked and identified. The results show that the evaluation analysis curve with four different parameters can be divided into two stages according to the different variation trends: fluctuation stage and stable stage. It indicates that the increase of time can effectively improve the stability corresponding to the convergence of neural network. The trajectory under the three different parameter states shows a general trend of rapid decline at first and then tends to be stable. There is a certain disturbance in the rapid decline of the curve, but when the time is between 1 and 2, it corresponds to the drop of the curve, and the curve gradually flattens after rapid decline. Parameters in the adaptive dynamic programming model are shown as follows: parameter W decreases slowly to the lowest point and then rises slowly with the increase of iteration steps; The proportion of parameter W 1 in the model shows a trend of slow increase. Relevant research can provide theoretical basis for the application of energy acquisition model based on adaptive dynamic programming and analysis in other fields. [ABSTRACT FROM AUTHOR]

This paper uses patterned structure to study the efficiency improvement of the practical application of BIM technology, and innovates the patterned structure method based on this. The research focus of this paper is to build a new assembly component library based on BIM model and algorithm optimization, improve its use and management efficiency, and use the core technology to design the simulation analysis model of BIM in construction, and give timely feedback of experimental information. Analyze the existing assembly structure design process, design standards and design methods, propose a BIM-based assembly structure design method, change the design idea from the whole to the component elements, and use the design method for prefabricated components. Taking the W prefabricated component library as a starting point, select the prefabricated components of the component library for assembly design to form a BIM model with a complete structure. Adjust and optimize the BIM model, and finally form a reasonable design plan. This BIM type of product includes production, transportation and assembly construction. Prefabricated buildings have obvious advantages, its comprehensive benefits are higher than other buildings, and the technical content is relatively high, through innovative prefabricated buildings, the modernization level of the building can be improved. Therefore, on the basis of in-depth investigation and study of the development status of China's construction industry, this paper studies the impact of different factors on engineering costs, including engineering schemes, engineering construction modes, etc., based on a large number of actual engineering operations and application cases, so as to achieve the purpose of reducing cost costs, improving project income, and improving project quality. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor convergence and uneven energy consumption of heed routing protocol, this paper proposes an opfh routing protocol based on options clustering algorithm for the first time. Firstly, the protocol divides the network into several top-level groups by using options clustering algorithm, then elects each top-level group at the same time, and designs and develops the medical intelligent system through wireless sensor. The medical intelligent system developed in this paper not only facilitates the completion of rehabilitation work, but also facilitates the management of hospital rehabilitation, greatly reducing manpower and material resources. It plays an important role in the treatment of schizophrenia. However, patients with schizophrenia have high inner sensitivity, often accompanied by anxiety and depression. Nurses need to take the initiative to communicate with patients, understand and meet the needs of patients, and use the system to manage the negative emotions of patients, so as to improve the confidence and compliance of patients. In order to improve the treatment satisfaction of patients, the large-scale data set without marked target task was used for domain transfer learning of small-scale data set. In this paper, the research of wireless sensor is applied to medical intelligent system, so as to promote the vigorous development of medical intelligent system. [ABSTRACT FROM AUTHOR]

The dynamic spectrum management of cognitive technology can greatly increase the utilization of network spectrum, but while bringing convenience to our daily lives, this new technology will also greatly increase the consumption of nodes. At present, the short video industry is growing rapidly with the development of smart phones. Among them, the emergence of online popular music singing teaching platforms (mainly software for shooting short videos with music as the background) has brought significant changes to people's lifestyles. We can learn from the three-stage theory of flow experience to design and explore the method of plan implementation, and propose a targeted interaction design strategy for each flow characteristic, so that the overall design strategy becomes more practical and practical. In the design of this paper, the principle of cognitive wireless sensor network is applied in the technical aspect and the principle of interactive design is applied in the product design. When a user reaches a state of flow while using the online popular music singing teaching platform, it can be evaluated that the user experience of the app is the best. The research on the interaction design of the online pop music singing teaching platform under the flow experience is intended to propose an interactive design strategy that can guide the online pop music singing teaching platform to better create a flow experience for users. [ABSTRACT FROM AUTHOR]

The rise of Internet of Things (IoT) technology has moved the digital world in a new direction and is considered the third wave of the information industry. To meet the current growing demand for food, the agricultural industry should adopt updated technologies and smart agriculture based on the IoT which will strongly enable farmers to reduce waste and increase productivity. This research presents a novel system for the application of IoT technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time with a high speed, while the mobile phone app uses the cloud platform as a monitoring center. A low power consumption is specified in the hardware and software, and a modular power supply and time-saving algorithm are adopted to improve the energy effectiveness of the nodes. Meanwhile, a novel soil information prediction strategy was explored based on the deep Q network (DQN) reinforcement learning algorithm. Following the weighted combination of a bidirectional long short-term memory, online sequential extreme learning machine, and parallel extreme machine learning, the DQN Bi-OS-P prediction model was obtained. The proposed data acquisition system achieved a long-term stable and reliable collection of time-series soil data with equal intervals and provided an accurate dataset for the precise diagnosis of soil information. The RMSE, MAE, and MAPE of the DQN Bi-OS-P were all reduced, and the R2 was improved by 0.1% when compared to other methods. This research successfully implemented the smart soil system and experimentally showed that the time error between the value displayed on the mobile phone app and its exact acquisition moment was no more than 3 s, proving that mobile applications can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the Internet of Things. [ABSTRACT FROM AUTHOR]

The wireless capture of analog differential signals from fully passive (battery-less) sensors is technically challenging but it can allow for the seamless capture of differential biosignals such as an electrocardiogram (ECG). This paper presents a novel design for the wireless capture of analog differential signals using a novel conjugate coil pair for a wireless resistive analog passive (WRAP) ECG sensor. Furthermore, we integrate this sensor with a new type of dry electrode, namely conductive polymer polypyrrole (PPy)-coated patterned vertical carbon nanotube (pvCNT) electrodes. The proposed circuit uses dual-gate depletion-mode MOSFETs to convert the differential biopotential signals to correlated drain-source resistance changes and the conjugate coil wirelessly transmits the differences of the two input signals. The circuit rejects (17.24 dB) common mode signals and passing only differential signals. We have integrated this novel design with our previously reported PPy-coated pvCNT dry ECG electrodes, fabricated on a stainless steel substrate with a diameter of 10 mm, which provided a zero-power (battery-less) ECG capture system for long duration monitoring. The scanner transmits an RF carrier signal at 8.37 MHz. The proposed ECG WRAP sensor uses only two complementary biopotential amplifier circuits, each of which has a single-depletion MOSFET. The amplitude-modulated RF signal is envelope-detected, filtered, amplified, and transmitted to a computer for signal processing. ECG signals are collected using this WRAP sensor and compared with a commercial counterpart. Due to the battery-less nature of the ECG WRAP sensor, it has the potential to be a body-worn electronic circuit patch with dry pvCNT electrodes that stably operate for a long period of time. [ABSTRACT FROM AUTHOR]

Real-time environmental data acquisition and monitoring is a significant aspect of IoT-enabled farming to overcome the constraints in present day's farming that includes regular monitoring of agricultural fields and adjacent weather-related information. Real-time monitoring can be achieved by measuring various parameters such as humidity, pressure, temperature and location data using sensors. The humidity, pressure and temperature data help in environmental monitoring of the farming zone, and the latitude and longitude data enable specific location-based farming. The measured parameters are to be communicated to the primary users efficiently in real time. This work showcases the concept of IoT-enabled farming in line with agriculture 4.0 where a hardware module consisting of a Raspberry Pi, SenseHat and low-cost, compact GPS receiver is implemented for agricultural applications. This idea would be useful for cost-effective IoT research, application development and for data recording in harsh and constrained environmental conditions with advantages of compact size and low power consumptions. The module design has a dimension of 20 × 11cm2 and has a temperature accuracy of ± 2 °C, humidity in the 20–80% RH range with an accuracy ± 4.5%, pressure sensor with 260–1260 hPa absolute range with ± 0.1 hPa under normal conditions, and the GPS sensor has an accuracy of 2.5 m. The proposed system is made Wi-Fi enabled to acquire data in the server for the primary users. [ABSTRACT FROM AUTHOR]

The existence of abnormal breakpoint data leads to poor channel balance in wireless sensor networks (WSN). To enhance the communication quality of WSNs, a method for positioning abnormal breakpoint data in WSNs on the basis of signal reconstruction is studied. The WSN signal is collected using compressed sensing theory; the common part of the associated data set is mined by exchanging common information among the cluster head nodes, and the independent parts are updated within each cluster head node. To solve the non-convergence problem in the distributed computing, the approximate term is introduced into the optimization objective function to make the sub-optimization problem strictly convex. And the decompressed sensing signal reconstruction problem is addressed by the alternating direction multiplier method to realize the distributed signal reconstruction of WSNs. Based on the reconstructed WSN signal, the abnormal breakpoint data is located according to the characteristic information of the cross-power spectrum. The proposed method can accurately acquire and reconstruct the signal, reduce the bit error rate during signal transmission, and enhance the communication quality of the experimental object. [ABSTRACT FROM AUTHOR]

Energy harvesting (EH) sources require the tracking of their maximum power point (MPP) to ensure that maximum energy is captured. This tracking process, performed by an MPP tracker (MPPT), is performed by periodically measuring the EH transducer's output at a given sampling rate. The harvested power as a function of the sampling parameters has been analyzed in a few works, but the power gain achieved with respect to the case of a much slower sampling rate than the EH source's frequency has not been assessed so far. In this work, simple expressions are obtained that predict this gain assuming a Thévenin equivalent for the EH transducer. It is shown that the power gain depends on the relationship between the square of AC to DC open circuit voltage of the EH transducer. On the other hand, it is proven that harvested power increases, using a suitable constant signal for the MPP voltage instead of tracking the MPP at a low sampling rate. Experimental results confirmed the theoretical predictions. First, a function generator with a series resistor of 1 kΩ was used, emulating a generic Thévenin equivalent EH. Three waveform types were used (sinus, square, and triangular) with a DC voltage of 2.5 V and AC rms voltage of 0.83 V. A commercial MPPT with a fixed sampling rate of 3 Hz was used and the frequency of the waveforms was changed from 50 mHz to 50 Hz, thus effectively emulating different sampling rates. Experimental power gains of 11.1%, 20.7%, and 7.43% were, respectively, achieved for the sinus, square, and triangular waves, mainly agreeing with the theoretical predicted ones. Then, experimental tests were carried out with a wave energy converter (WEC) embedded into a drifter and attached to a linear shaker, with a sinus excitation frequency of 2 Hz and peak-to-peak amplitude of 0.4 g, in order to emulate the drifter's movement under a sea environment. The WEC provided a sinus-like waveform. In this case, another commercial MPPT with a sampling period of 16 s was used for generating a slow sampling rate, whereas a custom MPPT with a sampling rate of 60 Hz was used for generating a high sampling rate. A power gain around 20% was achieved in this case, also agreeing with the predicted gain. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor detection effect, high rate of missed detection and high rate of false detection in traditional methods, an anomaly detection method for wireless sensor networks based on improved GM model is proposed. The multilateral measurement method is used to locate the nodes in the wireless sensor network, and the state tracking of the running track of the located nodes is carried out. According to the tracking results, the self similarity between nodes is measured by Hurst index. Based on the measurement results, the improved GM model is used to predict the abnormal nodes. The abnormal values of the wireless sensor network are calculated by the distance between adjacent points, and the state of the current node is judged, this completes the anomaly detection of wireless sensor networks. The experimental results show that the proposed method is effective in anomaly detection of wireless sensor networks, and the rate of missed detection and false detection is low.

The need to ensure the longevity of Wireless Sensor Networks (WSNs) and secure their communication has spurred various researchers to come up with various WSN models. Prime among the methods for extending the life span of WSNs is the clustering of Wireless Sensors (WS), which reduces the workload of WS and thereby reduces its power consumption. However, a drastic reduction in the power consumption of the sensors when multicore sensors are used in combination with sensors clustering has not been well explored. Therefore, this work proposes a WSN model that employs clustering of multicore WS. The existing Elliptic Curve Cryptographic (ECC) algorithm is optimized for parallel execution of the encryption/decryption processes and security against primitive attacks. The Elliptic Curve Diffie-Helman (ECDH) was used for the key exchange algorithm, and the Elliptic Curve Digital Signature Algorithm (ECDSA) was used to authenticate the communicating nodes. Security analysis of the model and comparative performance analysis with the existing ones were demonstrated. The security analysis results reveal that the proposed model meets the security requirements and resists various security attacks. Additionally, the projected model is scalable, energy-conservative, and supports data freshness. The results of comparative performance analysis show that the proposed WSN model can efficiently leverage multiprocessors and/or many cores for quicker execution and conserves power usage.

Control of structures in order to prevent serious damage to structures has become a prominent issue in earthquake engineering. In traditional structural control systems, wires were used to communicate between sensors, actuators, controllers and integrate the entire system as a single network. Using of wireless sensors for measurement, communication and control in structures has increased with the advancement of technology. The use of this technology has attracted the attention of engineers due to the reduction of installation costs and flexible system. In this article, the benchmark nonlinear three-story structure with magnetorheological damper (MR) equipped by wireless sensor network is investigated. The seismically excited building is combined with a wireless sensor network simulated by TOSSIM in the MATLAB 2018 simulator environment. For real network simulation, noise and wireless signals collected from a real structure have been used. In this network, a time division multiple access protocol (TDMA) is used. This protocol assigns a specific time to each sensor to send information. The benchmark structure is trained by a fuzzy-genetic controller. Then, the structure equipped with wire and wireless sensors was evaluated under the various earthquakes. Examining the evaluation criteria and time history charts shows the proper performance of the wireless system in reducing structural responses. At the same time, the wireless system has caused a slight increase in the average displacement and acceleration of floors compared to the wired mode. [ABSTRACT FROM AUTHOR]

Real-time exposure air monitoring is essential to protect the respiratory health of the Malaysian traffic police. However, the data from monitoring stations have been inadequate to provide accurate information about their exposure. This report describes the conceptual design of a wireless exposure indicator system, and then evaluates the field performance of the system by collocation. The study tested the accuracy of particulate matter size 2.5 (PM2.5), carbon monoxide (CO), and nitrogen dioxide (NO2) by comparing the measurements from the prototype with the measurements from reference instruments. The field testing found that the data tested were significantly correlated with each other (PM2.5-rs = 0.207, p = 0.019; NO2-rs = 0.576, p = 0.02 and CO-rs = 0.545, p = 0.04). The prototype proved to be successful as it can compute and transmit real-time monitoring data on the level of exposure to harmful air. [ABSTRACT FROM AUTHOR]

With low-power gadgets proliferating, the development of a small, effective rectenna is crucial for wirelessly energizing devices. A simple circular patch with a partial ground plane for RF-energy harvesting at ISM (2.45 GHz) band is proposed in this work. The simulated antenna resonates at 2.45 GHz with an input impedance of 50 Ω and a gain of 2.38 dBi. An L-section matching a circuit with a voltage doubler is proposed to provide excellent RF-to-DC transformation efficiency at low power input. The proposed rectenna is fabricated and the results show that the return loss and realized gain have good characteristics at the ISM band with 52% of RF-to-DC transformation efficiency, with an input of 0 dBm power. The projected rectenna is apt for power-up low sensor nodes in wireless sensor applications. [ABSTRACT FROM AUTHOR]

Wireless monitoring sensor gradually replaces wired equipment for data support in petrochemical industry production. This paper presented a design of a thermoelectric generator (TEG) system-based petrochemical pipeline waste heat recovery for wireless sensor. The TEG is assembled using an easy-to-modify structure that can be mechanically adjusted to different pipelines. Besides, we designed a new zero-power, micro-vibration, and easy-to-install heat sink for TEG applied in high-temperature petrochemical pipelines. The proposed TEG prototype has been fabricated and tested. The experimental results show that the system proposed in this work can supply enough energy to sustain the operation of a wireless sensor node that measures petrochemical fluid temperature and pressure for precision industrial production applications. The minimum output power is 24.7 mW at the hot side of thermoelectric generation (TEG) temperature of 100◦C −190◦C. This work presents a promising, cost-effective new approach to the conversion of waste heat into electricity for self-powered sensor networks. [ABSTRACT FROM AUTHOR]

This paper constructs a low-resolution model for face recognition and sports training actions based on wireless sensors. The model obtains the distribution of the information size in the face image by calculating the image entropy value, and assigns different weights according to the size of the information to perform face recognition calculation, so that the original module-based algorithm is simply based on image segmentation into one based on entropy. The size of the value is divided into blocks, which solves the problem of computational quantification of category information. In the test stage, the traditional orthogonal matching pursuit algorithm is used to solve the coding coefficients, and the excellent classification and recognition results are obtained by calculating the intra-class matrix of the face image and the inter-class matrix of the sports training action image. Methods that perform well on classification problems further improve face recognition rates. The specific processing process is to add Gaussian noise, salt and pepper noise to the input face image and reduce the size of the face image in the input image, so that the improved algorithms are improved. The experimental results show that the high-efficiency resolution sensing technology is used to learn the sports training actions corresponding to the two modalities, and the matrix coefficient between the obtained high-resolution modal and low-resolution modal images reaches 0.971, and the iteration rate is improved by 71.5%, effectively promoting the high recognition rate of faces and actions. [ABSTRACT FROM AUTHOR]

A planetary gearbox is more complex in structure and motion than a gearbox with a fixed shaft, making it difficult to monitor and make a fault diagnosis in practice. Components must be frequently inspected to avoid excessive wear, but there is no simple way to directly measure wear. The most direct method is to log vibration and temperature signals using external sensors. Wireless sensors offer more space advantages than a wired one, so this study developed a measurement system that features a three-axis MEMS accelerometer, temperature sensing and wireless modules that are integrated into a planetary gearbox. Along with the system, a virtual instrument (VI) utilizing graphics programming language LabVIEW was developed to acquire and display data time and frequency domains to detect the gear's faults. To determine the root cause of vibrations in a planetary gearbox, determine the vibration signal model of amplitude modulation (AM) and frequency modulation (FM) due to gear damage and derive the characteristic frequencies of vibrations for a planetary gearbox, the characteristic frequencies and AMFM modulation were summarized in closed form. Different degrees of each gear damage were then detected in the planetary gearbox. The vibration signal model was validated by experiments to indicate the sideband around the gear meshing frequency and its feasibility for fault diagnosis of a planetary gearbox with the wireless embedded sensor. [ABSTRACT FROM AUTHOR]

Background: Presenting symptoms of COVID-19 patients are unusual compared with many other illnesses. Blood pressure, heart rate, and respiratory rate may stay within acceptable ranges as the disease progresses. Consequently, intermittent monitoring does not detect deterioration as it is happening. We investigated whether continuously monitoring heart rate and respiratory rate enables earlier detection of deterioration compared with intermittent monitoring, or introduces any risks. Methods: When available, patients admitted to a COVID-19 ward received a wireless wearable sensor which continuously measured heart rate and respiratory rate. Two intensive care unit (ICU) physicians independently assessed sensor data, indicating when an intervention might be necessary (alarms). A third ICU physician independently extracted clinical events from the electronic medical record (EMR events). The primary outcome was the number of true alarms. Secondary outcomes included the time difference between true alarms and EMR events, interrater agreement for the alarms, and severity of EMR events that were not detected. Results: In clinical practice, 48 (EMR) events occurred. None of the 4 ICU admissions were detected with the sensor. Of the 62 sensor events, 13 were true alarms (also EMR events). Of these, two were related to rapid response team calls. The true alarms were detected 39 min (SD = 113) before EMR events, on average. Interrater agreement was 10%. Severity of the 38 non-detected events was similar to the severity of 10 detected events. Conclusion: Continuously monitoring heart rate and respiratory rate does not reliably detect deterioration in COVID-19 patients when assessed by ICU physicians. [ABSTRACT FROM AUTHOR]

In this paper, a new method for the wireless detection of liquid level is proposed by integrating a capacitive IDC-sensing element with a passive three-port RFID-sensing architecture. The sensing element transduces changes in the liquid level to corresponding fringe-capacitance variations, which alters the phase of the RFID backscattered signal. Variation in capacitance also changes the resonance magnitude of the sensing element, which is associated with a high phase transition. This change in the reactive phase is used as a sensing parameter by the RFID architecture for liquid-level detection. Practical measurements were conducted in a real-world scenario by placing the sensor at a distance of approximately 2 m (with a maximum range of about 7 m) from the RFID reader. The results show that the sensor node offers a high sensitivity of 2.15°/mm to the liquid-level variation. Additionally, the sensor can be used within or outside the container for the accurate measurement of conductive- or non-conductive-type liquids due to the use of polyethylene coating on the sensitive element. The proposed sensor increases the reliability of the current level sensors by eliminating the internal power source as well as complex signal-processing circuits, and it offers real-time response, linearity, high sensitivity, and excellent repeatability, which are suitable for widespread deployment of sensor node applications. [ABSTRACT FROM AUTHOR]

This article presents the development of a model center using agricultural intelligent center technology. The goal of this research is: 1. To develop a wireless sensor network. 2. To be a source of learning on the use of sensor technology in agriculture. For local and nearby farmers Using the Sufficiency Economy Learning Center, according to King's Science. The Rajamangala University of Technology Suvarnabhumi is a research area. With the problems faced in farming today. It found that the world's climate change whether it's drought. Rains leave ranges and toxic airborne particulate matter caused by farming to match current problem conditions. The researchers then designed a two-part system: 1. Node Moisture Sensor that measures soil moisture and commands the opening - It also controls on-off with a manual switch. Wind speed and wind direction sensors, light intensity sensors, temperature, and humidity sensors, and Particulate Matters Sensor 1.0, 2.5, 10 with environmental reports within the growing area via Wi-F signals to (Sever) Raspberry Pi record real-time data. Every 30 seconds According to research, node moisture sensors can measure soil moisture and record results, and the station measures the environment within the growing area via a Wi-F signal to (Sever) Raspberry Pi. Rainfall values measured by local rainfall sensors measuring up to 35.3 mm are within the threshold of heavy rain. The maximum wind speed measured is 8.5 km/h, the maximum temperature of 35.8 degrees Celsius, and the maximum humidity of 99.9 percent, the light intensity is up to 58,002 Lux, and the Final Particles, with pm 1.0 up to 40.1 microns, PM 2.5 up to 51.3 microns and PM 10 up to 63.5 microns. Apply agriculture to 50 interested farmers after receiving knowledge transfer of smart sensor technology. The expansion has resulted in 3 farmers and will continue to expand in the future. Promote the use of agricultural technology. Intensifying communities and supporting global climate change. [ABSTRACT FROM AUTHOR]

The long-term stability of n-type single-walled carbon nanotubes (SWCNTs) in air makes all-carbon thermoelectric generators (TEGs) viable. To increase the performance of TEGs, we developed a dual-type flexible-film thermoelectric generator (DFTEG). The vacuum filtering was used to form p- and n-type SWCNT films from ethanol-based dispersion and water-based solutions with cationic surfactant, respectively. DFTEGs were fabricated as follows: strip-shaped p- and n-type SWCNT films were attached on the top and back sides of a polyimide substrate, respectively, and were connected alternately in series by bending copper tapes on the edge of the polyimide substrate. The thermoelectric performance was measured after attaching the DFTEG outside a beaker full of water, where the water surface reached the center of the DFTEG. For a 10 mm long film and 15 p-n pairs, the DFTEG had an output voltage of 40 mV and a maximum power of 891 nW at a temperature difference of 25 K. The measured thermoelectric performance was significantly higher than that of the single-type TEG for almost the same SWCNT films. This result demonstrates that thermoelectric performance can be improved by using DFTEGs that are fabricated with optimum structural designs. [ABSTRACT FROM AUTHOR]

Modern systems of intelligent sensors commonly use radio data transmission. Hand movement acquisition with the use of inertial sensors requires the processing and transmission of a relatively large amount of data, which may be associated with a significant load on the network structure. Network traffic limitation, without losing the quality of monitoring parameters from the sensor system, is therefore important for the functioning of the radio network which integrates both the teletransmission sensor system and the data acquisition server. The paper presents a wearable solution for hand movement acquisition, which uses data transmission in the Wi-Fi standard and contains 16 MEMS (Micro Electro Mechanical System) sensors. An adaptive algorithm to control radio data transmission for the sensor system has been proposed. The algorithm implemented in the embedded system controls the change of the frame length, the length of the transmission frame and the frequency of its sending, which reduces the load on the network router. The use of the algorithm makes it possible to reduce the power consumption by the sensor system by up to 19.9% and to limit the number of data transferred by up to about 91.6%, without losing the quality of the monitored signal. The data analysis showed no statistically significant differences (p > 0.05) between the signal reconstructed from the complete data and processed by the algorithm. [ABSTRACT FROM AUTHOR]

Early identification of infants at risk of neurodevelopmental delay is an essential public health aim. Such a diagnosis allows early interventions for infants that maximally take advantage of the neural plasticity in the developing brain. Using standardized physiological developmental tests, such as the assessment of neurophysiological response to environmental events using cardiac orienting responses (CORs), is a promising and effective approach for early recognition of neurodevelopmental delay. Previous CORs have been collected on children using large bulky equipment that would not be feasible for widespread screening in routine clinical visits. We developed a portable wireless electrocardiogram (ECG) system along with a custom application for IOS tablets that, in tandem, can extract CORs with sufficient physiologic and timing accuracy to reflect the well-characterized ECG response to both auditory and visual stimuli. The sensor described here serves as an initial step in determining the extent to which COR tools are cost-effective for the early screening of children to determine who is at risk of developing neurocognitive deficits and may benefit from early interventions. We demonstrated that our approach, based on a wireless heartbeat sensor system and a custom mobile application for stimulus display and data recording, is sufficient to capture CORs from infants. The COR monitoring approach described here with mobile technology is an example of a desired standardized physiologic assessment that is a cost-and-time efficient, scalable method for early recognition of neurodevelopmental delay. [ABSTRACT FROM AUTHOR]

Breath rate is an important vital sign for monitoring the well-being and detecting underlying illnesses related to the respiratory system. The spirometer is widely used for evaluating the health of the respiratory system by measuring lung capacity and respiration rate (RR). Although this test yields accurate results, however, it is significantly inconvenient and invasive, as the test subject must breathe through a tube inserted in the mouth. Therefore, this device is not suitable for continuous monitoring of respiration. In this article, a novel contactless wearable breath rate measurement system is presented. The proposed sensor is based on a spiral resonator (SR) tag printed on a thin flexible textile substrate. The sensor exploits the body expansion and contraction during respiration to provide an estimate of the breathing frequency. This movement is measured by a microstrip probing loop that acts as the antenna of the reader. The reader operates at one frequency; the resonant frequency of the tag, and the sensing principle relies on the near-field coupling between the probe and the resonating tag at this frequency. The proposed sensor is characterized with its simple and lightweight design, can be easily integrated in garments, and its capability of providing continuous monitoring of the breathing rate. The proposed sensor has been tested through experimental measurements, and the results matched measurements from a reference ground-truth breath rate sensor based on a nasal cannula equipped with a thermistor. The solution proposed in this work provides a novel contactless technique for RR estimation. [ABSTRACT FROM AUTHOR]

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In this article, a methodology for wireless and passive pressure measurement systems has been presented based on the principle of inductive-capacitive (LC) sensing. In the proposed method, two flexible carbon composite materials: Velostat and carbon conductive foam have been chosen as the pressure sensing element. Both the materials have the property of piezoresistivity and show the change in resistivity with applied pressure. In the designed sensor, the LC circuit has been modified into an LCR tank circuit with a variable resistor as a sensing element. During the measurement, pressure has been monitored and measured using a reader coil, connected with the impedance analyzer. The change in resistance in Velostat or conductive foam with applied pressure, changes the input impedance, quality factor, and resonance frequency. The proposed measurement system has shown an innovative technique based on an LC pressure measurement system with a resistive sensing element. Both the sensing materials have been taken separately and individual experiments have been performed. The method has been tested and evaluated experimentally, which also analyzes the two carbon-composite-based materials for this type of wireless pressure sensor. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is applicable in all IoT applications, thus it has many advancements. However, it has many drawbacks like localization, link failure, and so on. In addition, the reduction of received signal strength (RSS) often causes path loss, while transferring the data when the path is lost then it drops the packets. To address this problem, the current research aimed to develop a novel grey wolf ant lion recurrent (GWALR) localization model in WSN to find the location of each unknown node. Moreover, the fitness function of GWALR is utilized to track the location of each node. The key focus of this proposed model is to find the location of unknown nodes and to improve the RSS by reducing the localization error. In addition, the model that attained high RSS measure has better data broadcasting rate. Finally, the performance of the proposed approach is compared with existing works and attained better accuracy and reduced error rate. Thus the outcome of the proposed model proved the efficiency of the proposed work by gaining maximum throughput ratio as 7000bps, data broadcasting rate as 99%, accuracy 99.8% and reduced error rate as 1.4%. [ABSTRACT FROM AUTHOR]

• A self-powered wireless sensing system based on TENG is developed. • The system utilizes negative resistance resonator as transmitter. • The system demonstrates excellent stability and sensing precision. • Practicability for monitoring gas pressure is validated. Triboelectric nanogenerators (TENG) based self-powered instantaneous wireless sensing systems have attracted much attention for applications. However, such sensing systems typically use passive inductor capacitor (LC) resonators with low-quality factor, Q , as the transmitter, and have very low sensing precision and accuracy due to the tremendously short sensing signals duration of 10–50 μs and very short sensing distance. Here, we proposed a TENG-based self-powered wireless sensing system with extremely high precision and signal stability by using a negative resistance LC resonator. The introduction of negative resistance greatly increased the system's Q -factor from 102 to 2.6 × 105, and significantly extended the signal duration of up to 1 ms. These allowed the sensor system to achieve a frequency precision and accuracy down to 50 Hz with a fluctuation of less than 0.05 % for a sensing distance of 50 cm, both of them are 40 times better than those of conventional ones (2 kHz, 2 %). A self-powered wireless gas pressure sensing system was developed, showing a high theoretical sensing precision of 2.4 Pa, demonstrated its great application prospects. [ABSTRACT FROM AUTHOR]