Real-time Module Research Articles

Innovative Evaluation Method of Government Physical Education Training Quality Based on Fuzzy Mathematics

Current sports training quality evaluation methods cannot accurately detect the integrity of training data, resulting in low accuracy of training quality evaluation. Considering the function of the government in providing public sports services and the role of the government in coordinating the sports training of all parties, this paper puts forward an innovative evaluation method of government physical education training quality based on fuzzy mathematics. First of all, the real-time data on sports training sensor signal is obtained by using SiCN (Silicon Carbonitride) sensor, and the real-time detection circuit and data amplification module are designed. Based on the core hardware for instant data detection of sports training sensor signal, a fuzzy support vector machine model is established to detect the integrity of government sports innovation training data, reconstruct the track of sports training action, and thus construct an innovative evaluation model of government sports training quality based on heredity and support vector machine. Experimental results show that compared with existing methods, the proposed method has higher accuracy in data integrity detection, better reconstruction effect in sports training action, with evaluation results reaching more than 90 points.

Dynamic characteristics of drought conditions during the growth of summer maize

Abstract This study aimed at investigating the applicability of a SWAT (Soil and Water Assessment Tool) model in understanding the effects of drought on summer maize. A real-time irrigation module was developed for the downstream irrigation area of the Yellow River to estimate the real-time irrigation of crops. By further simulating the dynamic evolution process of soil moisture content, a dynamic drought evaluation model of summer maize was established, and the relative soil moisture was set as the evaluation index to assess and analyze the dynamic variation of drought evolution during the growth of summer maize. The results showed that the improved SWAT model has strong applicability. During the growth of summer maize, the variation trend of drought is consistent with that of natural precipitation. Moreover, drought mainly occurs during the sowing-seedling and seedling-jointing stages, and the average frequency is 84.8 and 78.3%, respectively. Moderate drought is most likely to occur during the growth of summer maize and occurs mainly during the sowing-seedling and seedling-jointing stages, and the occurrence frequency is 55.3 and 32.6%, respectively. Extra-severe drought has the greatest impact, mainly in the jointing-tasseling, tasseling-milking and milking-maturity stages, and the occurrence frequency is 17.4, 15.2 and 10.9%, respectively.

Cataluminescence System Coupled with Vacuum Desorption-Sampling Methodology for Real-Time Ozone Sensing during the Self-Decomposition Process on Functional Boron Nitride.

The treatment and detection of ozone have been widely studied in recent decades with respect to toxicity and contamination, while the measurement method of ozone is relatively toneless. Fortunately, a new concept of the cataluminescence (CTL) sensor provides a scheme of real-time ozone sensing in a tiny system. Here, a novel CTL sensor system was specially developed with silica-hydroxyl functional boron nitride as the sensing material for rapid and sensitive ozone detection. Coupled with the construction of a pulse vacuum static sampling system, ozone on the surface of sensing material can be desorbed rapidly and can step into the next detection circulation in a few seconds. Based on the strong emission initiated by the transient of reactive oxygen species (ROS) including singlet oxygen, a trioxide group, and an oxygen radical, the detection limit of ozone could be optimized to be as low as 51.2 ppb. Besides, the sensor system exhibited remarkable anti-interference performance in which humidity changes and common VOCs do not disturb or weakly disturb ozone sensing, and the CTL mechanism of the multistep degradation process was further discussed on the basis of multiple pieces of experimental evidence and a DFT transient calculation. A real-time degradation-sensing module was further attached to the system to realize the functions of ozone decomposition and real-time monitoring.

A NURBS Curve Interpolator Based on Double-Step Signal and Finite Impulse Response Filters

Abstract In order to solve the problem that the traditional trajectory planning method cannot flexibly control the velocities and the arrival time of critical points in the trajectory, this paper proposes a non-uniform rational B-spline interpolator based on double-step signal and finite impulse response filters. The interpolator consists of two modules: a pre-processing module and a real-time interpolation module. To reduce the contour error, first, the pre-processing module scans the curve for critical points and sets their velocities according to the chord error. Then, the curve is split, and for each segment, the length and inverse length functions are calculated to eliminate the length error and enhance the real-time performance. Moreover, to control the arrival time and smooth the velocity, the real-time interpolation module generates the trajectory by processing the double-step signal with three finite impulse response filters, which satisfies the constraints of velocity, acceleration, jerk, and displacement. Simulation results indicate that the proposed interpolator can accurately reach the critical points at the set arrival time with smooth velocity and low chord error, and experimental results on a SCARA robot show that the proposed interpolator is feasible and effective.

Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques.

The human-computer interaction has become inevitable in digital world. HCI helps humans to incorporate technology to resolve even their day-to-day problems. The main objective of the paper is to utilize HCI in Intelligent Transportation Systems. In India, the most common and convenient mode of transportation is the buses. Every state government provides the bus transportation facility to all routes at an affordable cost. The main difficulty faced by the passengers (humans) is lack of information about bus numbers available for the particular route and Estimated Time of Arrival (ETA) of the buses. There may be different reasons for the bus delay. These include heavy traffic, breakdowns, and bad weather conditions. The passengers waiting in the bus stops are neither aware of the delay nor the bus arrival time. These issues can be resolved by providing an HCI-based web/mobile application for the passengers to track their bus locations in real time. They can also check the Estimated Time of Arrival (ETA) of a particular bus, calculated using machine learning techniques by considering the impacts of environmental dynamics, and other factors like traffic density and weather conditions and track their bus locations in real time. This can be achieved by developing a real-time bus management system for the benefit of passengers, bus drivers, and bus managers. This system can effectively address the problems related to bus timing transparency and arrival time forecasting. The buses are equipped with real-time vehicle tracking module containing Raspberry Pi, GPS, and GSM. The traffic density in the current location of the bus and weather data are some of the factors used for the ETA prediction using the Support Vector Regression algorithm. The model showed RMSE of 27 seconds when tested. The model is performing well when compared with other models.

A 245 GHz Real-Time Wideband Wireless Communication Link with 30 Gbps Data Rate

This paper presents a 245 GHz wireless communications system with a data rate of 30 Giga bits per second (Gbps) at a 1.2 m distance, which proves the potential for future high-speed communications beyond 5G technology. The system consists of low-complexity and real-time base-band modules to provide the high-speed wideband signal processing capability. Multi-channel base-band signals are combined and converted to 15.65 ± 6.25 GHz wideband intermediate frequency (IF) signals. A novel 245 GHz waveguide bandpass filter (BPF) with low loss and high selectivity is designed and applied to a terahertz (THz) front-end for image rejection and noise suppression. Configuration of the base-band, IF, and THz front-end modules is also given in detail. The 245 GHz wireless communication link is demonstrated over a distance of 1.2 m.

Real-Time Measurement of Moisture Content of Paddy Rice Based on Microstrip Microwave Sensor Assisted by Machine Learning Strategies

Moisture content is extremely imoprtant to the processes of storage, packaging, and transportation of grains. In this study, a portable moisture measuring device was developed based on microwave microstrip sensors. The device is composed of three parts: a microwave circuit module, a real-time measurement module, and software to display the results. This work proposes an improvement measure by optimizing the thickness of paddy rice samples (8–13 cm) and adding the ambient temperatures and the moisture contents (13.66–27.02% w.b.) at a 3.00 GHz frequency. A random forest, decision tree, k-nearest neighbor, and support vector machine were applied to predict the moisture content in the paddy rice. Microwave characteristics, phase shift, and temperature compensation were selected as the input variables to the prediction models, which have achieved high accuracy. Among those prediction models, the random forest model yielded the best performance with highest accuracy and stability (R2 = 0.99, RMSE = 0.28, MAE = 0.26). The device showed a relatively stable performance (the maximum average absolute error was 0.55%, the minimum absolute error was 0.17%, the mean standard deviation was 0.18%, the maximum standard deviation was 0.41%, and the minimum standard deviation was 0.08%) within the moisture content range of 13–30%. The instrument has the advantages of real-time, simple structure, convenient operation, low cost, and portability. This work is expected to provide an important reference for the real-time in situ measurement of agricultural products, and to be of great significance for the development of intelligent agricultural equipment.

Real-Time Definite Sequence Modulation Based Spectral Broadening Scheme for High-Power Narrow-Linewidth Fiber Laser

High-power narrow-linewidth continuous fiber lasers have wide applications in the fields of power synthesis, nonlinear frequency conversion, and industrial processing. The output power of kW or even MW can be achieved by adopting spectral synthesis. However, in the power amplification process, the nonlinear effect represented by the stimulated Brillouin scattering (SBS) effect limits the further increase of the laser power. And the accompanying self-pulsing effect poses a threat to the safety of the laser system. A high-order phase modulation scheme using a definite time sequence has been proposed to realize the controllable spectral broadening of the seed source, so as to suppress the SBS effect and improve the output power in the amplification stage. In this paper, we demonstrate a real-time definite time sequence generation scheme by using an integrated field-programmable gate array (FPGA) and digital-to-analog converter (DAC). Combined with a single-stage phase modulator, the broadened bandwidth up to 30 GHz and controllable spectral shape can be achieved by adjusting the output waveform of the DAC directly. The generation principles and waveform control methods of real-time sequence are introduced in detail. Proven by a master oscillator power amplifier (MOPA) structure, the laser output power has increased by 30% under the extended bandwidth of 10 GHz compared to the cascaded white noise modulation case. This scheme provides a compact real-time control module for spectral broadening, which lays the foundation for implementing low-cost, compact, and standardized high-power narrow-linewidth fiber lasers.

A Hybrid Encryption Scheme for Quantum Secure Video Conferencing Combined with Blockchain

Traditional video conference systems depend largely on computational complexity to ensure system security, but with the development of high-performance computers, the existing encryption system will be seriously threatened. To solve this problem, a hybrid encryption scheme for quantum secure video conferencing combined with blockchain is proposed in this study. In the system solution architecture, first, the quantum key distribution network is embedded in the classic network; then, the “classical + quantum” hybrid encryption scheme is designed according to the secret level required for the video conference content. Besides, the real-time monitoring module of the quantum key distribution network is designed to ensure that users can check the running state of the network at any time. Meeting minutes can be shared by combining with blockchain. In order to quickly query meeting minutes, a cache-efficient query method based on B+ tree is proposed. The experimental results show that compared with the traditional video conference system, the quantum secure video conference system sufficiently integrates the technical advantages of the quantum key distribution to resist the security threats such as channel eavesdropping and high-performance computational attacks while ensuring the stable operation of the classic system, thus providing a video conference system with a higher security level. Meanwhile, the query time cost of blockchain with different lengths is tested, and the query efficiency of the proposed method is 3.15-times higher than the original query efficiency of blockchain.

Adaptive Control with Switched Reference Models: Application to Learn-to-Fly

Learn-to-Fly (L2F) is a new framework that aims to replace the traditional iterative development paradigm for aerial vehicles with a combination of real-time aerodynamic modeling, guidance, and learning control. To ensure safe learning of the vehicle dynamics on the fly, this paper presents an adaptive control ()-based scheme, which actively estimates and compensates for the discrepancy between the intermediately learned dynamics and the actual dynamics. First, to incorporate the periodic update of the learned model within the L2F framework, this paper extends the architecture to handle a switched reference system subject to unknown time-varying parameters and disturbances. The paper also includes analysis of both transient and steady-state performance of the architecture in the presence of nonzero initialization error for the state predictor. Second, the paper presents how the proposed scheme is integrated into the L2F framework, including its interaction with the baseline controller and the real-time modeling module. Finally, flight tests on an unmanned aerial vehicle validate the efficacy of the proposed control and learning scheme.

Remote Drowsiness Detection Based on the mmWave FMCW Radar

Drowsiness can lead to inefficiency and major disasters; thus it is important to address it in both academia and industry. Despite multiple types of research in this field, a nonintrusive classifier system for detecting drowsiness in real-time under a natural environment without specific stimulation is lacking. This study develops a real-time drowsiness detection system using a 77 GHz millimeter-wave (mmWave) frequency-modulated continuous wave radar. Specifically, firstly, a real-time mmWave processing module is proposed, which can adaptively suppress both stationary and non-stationary clutters. Secondly, a feature extraction module based on a hybrid of handcrafted and machine learning (ML) features is propsoed to obtain a holistic view of mmWave-based vital signals, in which ML features represent linear and temporal changes. Thirdly, a drowsiness classification model is proposed based on feature fusion and extreme gradient boosting algorithms, thus classifying a user’s state into two categories: non-drowsy and drowsy. To confirm the proposed system’s performance, it is validated on a self-collected dataset (n = 28). The experimental results show the following (i) the accuracy of heart rate evaluation is 96.4%, and (ii) based on 10-fold cross-validation, the proposed system gains a detection accuracy of 82.9% and outperforms the state-of-the-art approaches.

Design of a Resident Physical Fitness Data Monitoring System Based on the Sensor and Fuzzy Algorithm

This article first introduces the working principle and research progress of motion sensors and analyzes the advantages and disadvantages of motion tracking sensors to provide a reference for in-depth research on motion sensing technology. By combining the fuzzy judgment method to identify the kinematic parameters, this paper establishes the statistical identification of features to predict the kinematic parameters and uses the fuzzy identification method to model the human behavioral characteristics. At the same time, the article designs a set of real-time data monitoring system for athletes' real-time monitoring behavior under actual sports conditions, which is used to obtain residents' sports data. Then, based on such requirements, the overall system architecture was designed to integrate the data acquisition module, data storage and processing module, and real-time data presentation module. The motion monitor can initiate a real-time monitoring request for a specific system group, the server can monitor individual groups to add real-time individual motion status, and the system will issue an alert if the system monitors a potentially dangerous situation.

Stacking ensemble based fault diagnosis approach for improved operation of photovoltaic arrays

ABSTRACT The operation of photovoltaic (PV) systems, like any other system, in fault free environment ensures maximum performance. Hence, accurate and timely fault identification of PV system demands greater importance. Present investigation proposes a stacking ensemble-based PV array fault diagnosis method, which is integrated with automatic feature engineering and selection technique, handling of imbalanced dataset for unbiased classification. The proposed ensemble utilizes decision tree (DT), random forest (RF), extra trees (EXT), extreme gradient boosting machine (XGBoost) as base learners, and light gradient boosting machine (LightGBM) as both base and meta-learner. To validate the proposed technique, a test system of 4.8kWp capacity has been built using the MATLAB/Simulink environment incorporating one-year real-time irradiance and module temperature data. Irradiance, module temperature, voltage, current, and power are collected as primary raw data which are then concocted using the autofeat python library for automatic feature engineering and selection. Subsequently, the proposed fault diagnosis strategy is built in python 3.8.5 using the engineered and class label balanced dataset, prepared using the synthetic minority over-sampling technique (SMOTE). Results demonstrate promising performance of the proposed ensemble technique with 97.04% accuracy in classifying the different faults in the PV array, which accounts for approximately 1–3% improvement over other individual machine learning (ML) models.

Development of a distributed high-speed data acquisition and monitoring system based on a special data packet format for HUST RF negative ion source

A distributed high-speed data acquisition and monitoring system for the RF negative ion source at Huazhong University of Science and Technology (HUST) is developed, which consists of data acquisition, data forwarding and data processing. Firstly, the data acquisition modules sample physical signals at high speed and upload the sampling data with corresponding absolute-time labels over UDP, which builds the time correlation among different signals. And a special data packet format is proposed for the data upload, which is convenient for packing or parsing a fixed-length packet, especially when the span of the time labels in a packet crosses an absolute second. The data forwarding modules then receive the UDP messages and distribute their data packets to the real-time display module and the data storage modules by PUB/SUB-pattern message queue of ZeroMQ. As for the data storage, a scheme combining the file server and MySQL database is adopted to increase the storage rate and facilitate the data query. The test results show that the loss rate of the data packets is within the range of 0–5% and the storage rate is higher than 20 Mbps, both acceptable for the HUST RF negative ion source.

RTSDM: A Real-Time Semantic Dense Mapping System for UAVs

Intelligent drones or flying robots play a significant role in serving our society in applications such as rescue, inspection, agriculture, etc. Understanding the scene of the surroundings is an essential capability for further autonomous tasks. Intuitively, knowing the self-location of the UAV and creating a semantic 3D map is significant for fully autonomous tasks. However, integrating simultaneous localization, 3D reconstruction, and semantic segmentation together is a huge challenge for power-limited systems such as UAVs. To address this, we propose a real-time semantic mapping system that can help a power-limited UAV system to understand its location and surroundings. The proposed approach includes a modified visual SLAM with the direct method to accelerate the computationally intensive feature matching process and a real-time semantic segmentation module at the back end. The semantic module runs a lightweight network, BiSeNetV2, and performs segmentation only at key frames from the front-end SLAM task. Considering fast navigation and the on-board memory resources, we provide a real-time dense-map-building module to generate an OctoMap with the segmented semantic map. The proposed system is verified in real-time experiments on a UAV platform with a Jetson TX2 as the computation unit. A frame rate of around 12 Hz, with a semantic segmentation accuracy of around 89% demonstrates that our proposed system is computationally efficient while providing sufficient information for fully autonomous tasks such as rescue, inspection, etc.

Research on Pattern Extraction Method of Underwater Acoustic Signal Based on Linear Array

Underwater acoustic signal is an important reference data for marine information research. The research and application of underwater acoustic signal have been widely concerned and valued by countries and enterprises. With the needs of modern military development and the development of marine industry, the research and application of underwater acoustic signal will develop faster and faster. In order to better understand marine information, it is necessary to collect seawater acoustic signal data. Aiming at the purpose of recording underwater acoustic signals placed in the ocean for a long time, this study innovates the calibration and recording of large dynamic range of long-time underwater acoustic signals, improves the circuit setting methods such as receiving, amplification, and sampling, designs a large dynamic series of long-time underwater acoustic signal recording device, and adopts the linear array extraction method, so that it can monitor the underwater acoustic biological sound under the condition of low power. It can also monitor the blasting sound of offshore engineering. Hardware circuit design mainly includes main control chip selection, amplification circuit design, filter circuit design, analog-to-digital conversion circuit design, storage circuit design, and some auxiliary circuit design. The fourth chapter introduces the software development process of large dynamic range underwater acoustic signal recorder and mainly introduces the system development tool, system clock working method, real-time clock module working method, underwater acoustic signal acquisition method, data storage scheme design, and the use of FatFs file system. The underwater acoustic signal data is stored on a MicroSD in the form of TXT file; linear array extraction method is used for feature extraction. Compared to other methods, the transformer will suppress DC and low-frequency interference signals, thus achieving high-pass filtering characteristics. Finally, the performance and experimental results of the whole underwater acoustic signal recording device are analyzed. After testing, the underwater acoustic signal recording device designed in this paper works stably and can record underwater acoustic signals with large dynamic range for a long time in low-power mode.

Course Teaching Design and Practice Analysis by Intelligent Electronic Technology under Big Data

The exploration expects to improve the teaching quality of the electronic technology distance course. The teaching management system of the electronic technology network course is constructed based on a flipped classroom. Firstly, the idea of the flipped classroom is analyzed, and the electronic technology course is designed. Secondly, the teaching management system of the electronic technology network course is established, and the remote experiment module is discussed. Finally, the questionnaire survey is made to investigate the use effect of the system. The results show that about 79% of the students are satisfied with the system. About 80% think it allows them to focus on the teaching process. Only 1.3% think that this system does not work for their learning. This shows that the system can help students improve their learning efficiency. However, the main defect is the lack of a real-time feedback module for users on the application effect of the system. In a word, the exploration provides a reference for developing and designing the follow-up network course teaching system.

Development of a high-efficiency gaseous elemental radioiodine generator.

This study aims to develop a high-efficiency gaseous elemental radioactive iodine generator. To observe the yield of the generator over time, a real-time measurement module was assembled with NaI(Tl) gamma detectors. The Taguchi method was employed to optimize the process parameters and improve the performance of the generator. According to the experimental results, the optimum process parameters were 3.6 l/min for the flow rate of the carrier gas, 0.2 mol/l for the concentration of the NaI reactant solution, and 70 °C for the water bath temperature. The most influential factor was the concentration of the NaI reactant solution. With these optimized process parameters, the yield of the generator was 83% ± 1% (k = 1) in 5 min and reached a plateau (93% ± 1%) in 20 ± 2 min (k = 1).

Design and test of intelligent inspection and monitoring system for cotton bale storage based on RFID

To solve the inspection problems in cotton storage, as well as the need for environmental monitoring in the process of modern cotton bale storage, an intelligent inspection and temperature and humidity intelligent monitoring system based on RFID cotton bale was developed by adopting RFID (Radio Frequency Identification) technology, wireless temperature and humidity real-time monitoring technology and handheld terminal intelligent inspection technology. The system was composed of RFID positioning inspection module and temperature and humidity real-time monitoring and transmission module. The artificial neural network (ANN) based on the particle swarm optimization (PSO) algorithm was used to process the monitoring data of the system by Gaussian filtering, and an accurate classification model of RSSI and label position was established. The test results showed that: Through the comparative analysis of the RFID indoor positioning algorithm, the positioning error of the PSO-ANN algorithm was small. In the actual cotton bale warehouse test, the relative error of positioning and monitoring for RFID cotton bale intelligent inspection and monitoring system was less than 6.7%, which effectively improved the working efficiency of inspection personnel and the security of cotton bale storage. The relative error of temperature and humidity was less than 8% and less than 7%, which could display the temperature and humidity information in real time and meet the real-time demand. This study improved the management personnel's effective positioning and inspection of the cotton bale, prevented the loss of cotton bale, reduced the deterioration probability of cotton bale, and effectively improved the storage management level of the cotton bale. It was of great practical significance to realize the networking, automation, and intelligence of cotton bale storage management.

Career Interview Readiness in Virtual Reality (CIRVR): A Platform for Simulated Interview Training for Autistic Individuals and Their Employers

Employment outcomes for autistic 1 individuals are often poorer relative to their neurotypical (NT) peers, resulting in a greater need for other forms of financial and social support. While a great deal of work has focused on developing interventions for autistic children, relatively less attention has been paid to directly addressing the employment challenges faced by autistic adults. One key impediment to autistic individuals securing employment is the job interview. Autistic individuals often experience anxiety in interview situations, particularly with open-ended questions and unexpected interruptions. They also exhibit atypical gaze patterns that may be perceived as, but not necessarily indicative of, disinterest or inattention. In response, we developed a closed-loop adaptive virtual reality (VR)–based job interview training platform, which we have named Career Interview Readiness in VR (CIRVR). CIRVR is designed to provide an engaging, adaptive, and individualized experience to practice and refine interviewing skills in a less anxiety-inducing virtual context. CIRVR contains a real-time physiology-based stress detection module, as well as a real-time gaze detection module, to permit individualized adaptation. We also present the first prototype of the CIRVR Dashboard, which provides visualizations of data to help autistic individuals as well as potential employers and job coaches make sense of the data gathered from interview sessions. We conducted a feasibility study with 9 autistic and 8 NT individuals to assess the preliminary usability and feasibility of CIRVR. Results showed differences in perceived usability of the system between autistic and NT participants, and higher levels of stress in autistic individuals during interviews. Participants across both groups reported satisfaction with CIRVR and the structure of the interview. These findings and feedback will support future work in improving CIRVR’s features in hopes for it to be a valuable tool to support autistic job candidates as well as their potential employers.