Data Acquisition Module Research Articles

Application of Scene Perception and Object Detection Technology Based on Computer Vision in Autonomous Driving for Public Transportation Route Optimization

The aim of this article is to discuss the use of computer vision and GIS-based scene perception for autonomous driving in route planning for public transportation. Blending computer vision with GIS information, the design dynamically alters routes based on the environments dynamic variations, such as traffic volume, surface, and obstruction. The system design comprises data acquisition modules, computer vision engine, GIS connectivity, and adaptive route optimization algorithm. Results from virtual cities reveal significant savings in travel time, energy use and flexibility compared to fixed-route approaches. Furthermore, the system works well and offers high detection performance in all kinds of environments. We describe how it might benefit urban transport infrastructure by describing how AVs with real-time vision can optimize routes to speed up time, eliminate delays, and increase safety. Future studies will focus on computing burden and performance under adverse weather conditions, assessing how new technologies like 5G and IoT could provide scalability. This research helps build safer, smarter and more adaptive public transport in cities.

Integrating neural network in advanced flow sensors for enhanced fluid flow characterization in conduit using carbon black

The proposed work involves the use of a carbon black coated sensor to measure the flow rate in a circular pipe and to monitor the pressure developed therein using a novel system with the change in electrical resistance. The carbon black coated sensor was made using the dip coating method in various proportions such as 5 wt%, 10 wt%, 15 wt%, and 20 wt%. The pressure measurements were taken using the working model and the data acquisition module. When a liquid flows through the pipe, it causes a change in the electrical resistance of the carbon black coating, which is then detected by the electrodes connected to the sensor. The actual water flow rate is determined using the general equation and the electrical resistance is also noted simultaneously. A mathematical model was developed to monitor the flow in real time between the actual flow rate and the electrical resistance. It was found that 5 wt% carbon black has the highest sensitivity of 3.06 compared to the other proportions 10 wt%, 15 wt%, and 20 wt% which are 2.24, 1.71, and 1.15 respectively. The sensor with 5 wt% carbon black was further used to calculate the predicted water flow rate using the regression model. The obtained R2 value is 0.98, which confirms the strong correlation between the actual water flow rate and the calculated water flow rate. Then the prediction modeling is performed using the neural network, and the obtained data points were trained and tested. With a prediction error of less than 6%, the proposed approach provides exponentially cost-effective monitoring of the liquid flow rate.

Design and realization of self-tuned absorber control system

Abstract A self-tuning vibration absorber is capable of adjusting its intrinsic frequency in real time to track the excitation frequency and achieve effective vibration control over a wide frequency range. In this study, a self-tuning vibration absorber control system based on STM32 is proposed. The system acquires vibration signals using MPU6050 sensors, calculates the vibration frequency in real time, and controls the motor rotation to achieve self-tuning vibration absorber control. The system employs the STM32F407ZGT6 as the core control chip and is designed in terms of the overall scheme, hardware circuit, and software. For hardware, the system uses the STM32F407 as the control core and the MPU6050 sensor to obtain vibration signals while controlling motor rotation using the TMC2209 motor driver chip. Keil μVision software is applied for program writing and debugging. The system mainly includes modules for data acquisition, signal processing, control algorithm, and drive output. The signal processing module employs the fast Fourier transform (FFT) algorithm, which can achieve fast frequency calculation and vibration frequency tracking. Finally, experiments verify the system’s feasibility and effectiveness. The experimental results demonstrate that the system can accurately control the self-tuning vibration absorber, with excellent control performance and practicality.

Electrokinetically propelled digital pendulum for seismic alert

An Electrostatic Induction Pendulum (EIP) has been designed and developed for the first time in the innovative construction and maintenance of earthquake prewarning systems to address the existed issues of massive funding required, incomplete distribution even as well as the lack of coverage in remote or sparsely populated areas. This innovative EIP includes a pendulum and a static-electricity induced sensor which could detect subtle variations in electrical potential during motion. The sensor offers crucial insights into the vibration, damping, and resonance traits of the EIP, with the minimum detectable vibration intensity of 0.17 m/s2. Simultaneously, by introducing external vibrations varied with frequencies, the inherent frequency of the EIP can correspondingly be modified subtly leading energy distribution to be weakened, which lay a groundwork for earthquake monitoring and early warning. Furthermore, an earthquake monitoring and early warning system aided by EIP, data acquisition module, and machine learning is engineered for real-time analysis and decision-making. Once an earthquake occurs, the system automatically sends alerts to relevant departments or individuals to take protective measures in advance. This cost-effective, easily portable, and widely distributed earthquake monitoring solution complements existing systems and can be easily deployed even in remote areas.

Advancing Sustainable Mobility: A Data Acquisition System for Light Vehicles and Active Mobility

Active mobility and light vehicles, such as e-bikes, are gaining increasing attention as sustainable transportation alternatives to internal combustion solutions. In this context, collecting comprehensive data on environmental conditions, vehicle performance, and user interaction is crucial for improving system efficiency and user experience. This paper presents a data acquisition system designed to collect data from multiple sensor platforms. The architecture is optimized to maintain low power consumption and operate within limited computational resources, making it suitable for real-time data acquisition on light vehicles. To achieve this, a data acquisition module was developed using a single-board computer integrated with a custom shield, which also captures data related to the assistance of an e-bike motor through a wireless interface. The paper provides an in-depth discussion of the architecture and software development, along with a detailed overview of the sensors used. A demonstrator was created to verify the system architecture idea and prove the potentialities of the system overall. The demonstrator has been qualified by professional and semi-professional riders in the framework of the Giro-E, a cyclist event which took place in May 2024, on the same roads of the Giro d’Italia. Finally, some preliminary analyses on the data acquired are provided to show the performance of the system, particularly in reconstructing the user behavior, the environmental parameters, and the type of road.

Design and Implementation of Touch Display Accuracy Optimization Software Based on Deep Learning

This paper mainly introduces the touch display accuracy optimization software based on convolutional neural network to enhance the touch accuracy of the smart board system. Convolutional neural networks are used to train touch data, which enables the software to automatically correct the position of touch points and reduce touch bias. The system architecture includes a data acquisition module, a deep learning model training module, and a touch accuracy optimization module, which is expected to be applied to more fields with the continuous application and development of technology. On the whole, the launch of the software provides a new technical way to realize the smart board book system.

A deep learning GNSS spoofing and jamming detection method with dual-frequency C/N0 heatmap

Abstract The Global Navigation Satellite System (GNSS) is vulnerable to interference due to the open signal structure and low signal strength, posing a significant threat to the billions of terminals worldwide that rely on GNSS receivers for precise Positioning, Navigation, and Timing (PNT) services. In this paper, we propose a cloud-edge framwork for GNSS spoofing and jamming monitoring, comprising the data acquisition module, GNSS monitoring module, detecting and reporting module. In this framwork, we design a Deep Learning (DL) method for detecting GNSS interference through Dual-frequency Carrier-to-Noise density ratio (C/N0) heatmaps (DD-C/N0). This method involves extracting and correlating features from C/N0 heatmaps of visible navigation satellites operating in the GPS L1 and L2 frequency bands, allowing the identification of anomalous patterns. A U-BLOX receiver was utilized to capture the GNSS satellite signals, while commercial jammers and Software-Defined Radio (SDR) HackRF One kits were employed to simulate the interference sources. Experimental results demonstrate that the proposed method achieves significantly higher performance, with an accuracy of 99% and 98% on the public dataset and real-time testing data, compared to unsupervised, semi-supervised, and supervised detectors that rely solely on single-channel data (L1 frequency band). Integrated with the DD-C/N0 method, the online GNSS monitoring system will be improved and deployed to automate spoofing and jamming detection tasks in the next step.

Development of a Data Overflow Protection System for Super-Kamiokande to Maximize Data from Nearby Supernovae

Abstract Neutrinos from very nearby supernovae, such as Betelgeuse, are expected to generate more than ten million events over 10 s in Super-Kamokande (SK). At such large event rates, the buffers of the SK analog-to-digital conversion board (QBEE) will overflow, causing random loss of data that are critical for understanding the dynamics of the supernova explosion mechanism. In order to solve this problem, two new data-acquisition (DAQ) modules were developed to aid in the observation of very nearby supernovae. The first of these, the SN module, is designed to save only the number of hit photomultiplier tubes during a supernova burst and the second, the Veto module, prescales the high-rate neutrino events to prevent the QBEE from overflowing based on information from the SN module. In the event of a very nearby supernova, these modules allow SK to reconstruct the time evolution of the neutrino event rate from beginning to end using both QBEE and SN module data. This paper presents the development and testing of these modules together with an analysis of supernova-like data generated with a flashing laser diode. We demonstrate that the Veto module successfully prevents DAQ overflows for Betelgeuse-like supernovae as well as the long-term stability of the new modules. During normal running the Veto module is found to issue DAQ vetos a few times per month resulting in a total dead-time less than 1 ms, and does not influence ordinary operations. Additionally, using simulation data we find that supernovae closer than 800 pc will trigger the Veto module, resulting in a prescaling of the observed neutrino data.

Forecasting Solar Photovoltaic Power Production: A Comprehensive Review and Innovative Data-Driven Modeling Framework

The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. The systematic and integrating framework comprises three main phases carried out by seven main comprehensive modules for addressing numerous practical difficulties of the prediction task: phase I handles the aspects related to data acquisition (module 1) and manipulation (module 2) in preparation for the development of the prediction scheme; phase II tackles the aspects associated with the development of the prediction model (module 3) and the assessment of its accuracy (module 4), including the quantification of the uncertainty (module 5); and phase III evolves towards enhancing the prediction accuracy by incorporating aspects of context change detection (module 6) and incremental learning when new data become available (module 7). This framework adeptly addresses all facets of solar PV power production prediction, bridging existing gaps and offering a comprehensive solution to inherent challenges. By seamlessly integrating these elements, our approach stands as a robust and versatile tool for enhancing the precision of solar PV power prediction in real-world applications.

Embedded System Based on Task Interval Data Interaction Model

In the Internet of Things era, more intelligent systems can communicate with each other. Embedded system combined with network communication applications has become the basis for Internet of Things research. The programmable logic unit designed by ARM architecture has great advantages in running speed, power control and so on. In the paper, to solve the problems of common embedded MCU control resources occupying a large amount of memory and the slow speed of building engineering simulation model, it is necessary to use the queuing connection algorithm model to directly input the timing physical characteristics of the code stream in the embedded system to calculate and match the timing physical characteristics of the output code stream. The optimization algorithm of DTF-MARTE to detect the probability of timing deviation is used in the paper. It is to detect the problem of inaccurate timing information in the demand. We compare the expected physical characteristics of the timing sequence and obtain the timing deviation probability of the output data stream. The model developed in this paper has the characteristic of dynamic reconfiguration of the task interval. The design of monotonically decreasing data tasks can be realized, and the reconfigured task modules are used for interacting the data buffer area and dynamically reconstructing the instruction overhead and transmission. We analyze the performance comparison between the proposed model and the traditional communication connection model. It proves that the proposed model can further improve the priority queue and guide the data flow. According to that method, the problem of asynchronous spatial data interaction by controlling and combining different communication modes in a large scene can be solved. Data interaction can be triggered at a fixed time, and mutual interference of randomly triggered wireless communication and data acquisition modules can be avoided. It can solve the problem of insufficient computing power when future embedded devices need massive data encryption in the Internet of Things era, and provide a new way of thinking for fast, safe and efficient implementation.

Real-time hybrid test method for floating wind turbines: Focusing on the aerodynamic load identification

Since the development of floating wind turbine (FWT) shows a rapid trend towards larger capacity and larger rotor size, the traditional full-model basin test method has encountered limits. Especially the balance between the wind generation system (WGS) size and scale ratio of the model FWT system. Under such circumstances, the hybrid model test method provides the possibility to improve the FWT model test. In the hybrid model test method, the original physical model system is divided into physical subsystem and numerical subsystem, and the data acquisition and process module between the 2 subsystems plays an important role. In this paper, a framework of real-time hybrid test (RTHT) system is setup firstly, which combines physical model wind turbine and motion platform. The damping modification to the corresponding numerical code is applied to improve the motion calculation accuracy. Delay implementation is employed to avoid motion divergence. Then a simulation loop is setup in numerical environment to study the identification of aerodynamic load. The influence of identification accuracy to the RTHT result is analyzed. Lastly, the dual-accelerometer method of aerodynamic load identification is employed in the proposed RTHT system. Decay tests and irregular wave only tests are carried out to validate the aerodynamic load identification method. The capability and potential of the RTHT method of floating wind turbine model test is preliminary proved in the work of this paper.

Based on Embedded-RAM Smart Home

This design is based on the FS-MP1A development board, using the STM32MP157A processor. The smart home system includes a door access module, data acquisition and monitoring module, device control module, weather forecast module, and Baidu Cloud interaction module. The door access module mainly uses the sqlite3 database to store residents’ login information and implements password login for unlocking. The monitoring system primarily uses the V4L2 driver for the OV5640 camera, sets the video capture format to YUYN, converts the YUYN format video raw frames to RGB format, and then converts the RGB data to QImage for display on the screen. Environmental monitoring mainly involves detecting indoor light intensity, temperature, and humidity. The device control module mainly controls the on/off of LED lights, activates the buzzer, and regulates the fan speed. The weather forecast module mainly forecasts the weather conditions of the local city and displays the weather information on the screen. The Baidu Cloud interaction module mainly facilitates information exchange and controls devices such as fans, lights, and the buzzer.

Performance evaluation of Yonsei Single-photon Emission Computed Tomography (YSECT) for partial-defect inspection within PWR-type spent nuclear fuel

As the amount of power generated by nuclear power plants increases, the importance of managing Spent Nuclear Fuel (SNF) to prevent proliferation increases as well. In a previous study, we optimally designed the gamma emission tomography instrument named Yonsei Single-photon Emission Computed Tomography version 2 (YSECT.v.2) based on Monte Carlo (MC) simulation for inspection and detection of defects in SNF. The objective of the current study was to fabricate a prototype YSECT.v.2 instrument and to evaluate its performance. The YSECT.v.2 instrument consists of scintillator-based detection modules, a multi-channel data acquisition (DAQ) module, a heat reduction module, and a waterproof housing. Based on the experimental results for a mock-up of fresh nuclear fuel rods, the spatial resolution of the prototype YSECT.v.2 instrument was determined to be 10.8 mm. In the experiment with 137Cs test sources, a high-quality image could be obtained with the exclusion of the pixel value below the threshold. For a 6 × 6 array, the spatial resolution for 137Cs was analyzed to be 7.2 mm. Considering these results, we expected that source distribution could be distinguishable using the YSECT.v.2. In the future, further experimentation will validate the performance of the fabricated instrument for a mock-up of SNF rods in water.

Design and Implementation of a Car’s Black Box System using Arduino

A black box system (BBS) in a car is crucial for recording and analyzing critical data to enhance safety, investigate accidents, and improve vehicle performance. This research presents a BBS developed using Arduino for cars, aimed at using the power of modern technology for comprehensive data capture and analysis in vehicular contexts. The BBS, or Event Data Recorder (EDR), is an essential component for enhancing road safety, accident analysis, and overall vehicle performance evaluation. The proposed system uses Arduino, a versatile and cost-effective microcontroller platform, to create a robust and customizable solution. It integrates various sensors and data acquisition modules to collect critical data points, including speed, acceleration, GPS coordinates, engine performance, and vehicle diagnostics. The architecture of the system and its smooth integration into automobiles are described in this article through detailed hardware and software design. Data retrieval and analysis are made possible by the system's user-friendly interface, which helps with fleet management, driver behaviour analysis, and accident investigation. This paper addresses the importance of data privacy and security while highlighting technological improvements. It proposes measures to ensure that personal data is managed responsibly and in accordance with legal requirements. In conclusion, a major advancement in improving road safety and vehicle monitoring has been made with the integration of Arduino technology into the car's BBS. Considering data security and privacy, this system provides users with an extensive set of facts to enable them to make well-informed decisions.

Computer network big data detection based on internet of things technology

In order to solve the problems of traditional network security intrusion detection system in detection accuracy and missed detection rate, this paper proposes a computer network data detection based on Internet of things technology. This paper designs an intrusion detection system based on data clustering analysis algorithm. The hardware part of the intrusion detection system is composed of six parts: detection module, adaptive module, control management module, risk early warning module, control access module and data acquisition module. Each module is closely connected and updated with the rule base and database in real time; In the software flow design of the system, based on the data clustering analysis algorithm, the collected original data are classified and processed, and the Mings distance is determined, revealing the difference between normal data and abnormal data, and finally realizing the accurate detection of cloud computing network intrusion behavior. The experimental results show that the proposed intrusion detection system has a higher detection accuracy for malicious data, and the average missed detection rate can be controlled below 0.3 %. Conclusionthe detection system designed in this paper has more advantages in stability performance.

Research on the influence of solar radiation fuzzy adaptive system on the wet and hot environment in greenhouse

Chinese solar greenhouse (CSG) is an artificially constructed agricultural facility that provides a suitable environment for crop growth. However, the imbalanced temperature and humidity phenomenon occurring in greenhouse during summer greatly affects crop yield. This article proposes the application of solar radiation fuzzy adaptive system in greenhouse to address this issue. The design process includes hardware and algorithm design. The hardware part involves designing the travel distance of the curtain machine and the film winding machine, laying out the temperature and humidity sensors, and designing the control cabinet. The algorithm is optimized based on fuzzy control theory, data acquisition module consists of temperature and humidity sensors, executive components include the curtain machine and the film winding machine, and the data transmission, storage and feedback module consists of the control cabinet and the cloud-based database. These form an adaptive system, which will be subjected to a 10-day comparative experiment. The experimental results showed that in region 3, the peak value of temperature reduction in the third layer during the daytime was 26.2 °C, and the humidity increased by a maximum of 31.5 %. In region 2, the temperature increased by a maximum of 4.2 °C during the nighttime in the second layer, and the humidity decreased by 4.1 °C. Introduction of adaptive systems can play an important role in maintaining microclimate temperature and humidity in summer.

Online Real-Time Monitoring System of A Structural Steel Railway Bridge Using Wireless Smart Sensors

In the transportation network, railway bridges are crucial for the transfer of both passengers and commodities. Railway bridges require continuous monitoring to observe their performance. A structural health monitoring system is one method for assessing the viability of a railway bridge structure. The functioning of railroad bridge structures has been extensively observed using wireless technology. This research aims to implement smart wireless sensors for monitoring the structural health of the railway bridge online in real-time during operation. Many sensor kinds were installed on the railway bridge, including strain gauges, accelerometers, linear variable displacement transducers, and proximity sensors. Geometric modeling and numerical simulation were performed to find critical frame locations on the railway bridge where the instrumentation sensors would be placed. In this study, MONITA® is employed for data acquisition modules. The MONITA® system consists of a combination of hardware and software that functions to retrieve, send, store, and process data. This paper describes the result of the establishment of this method to comprehend the performance of the steel railway bridge structure in real-time via the human-machine interface display dashboard. As a result, the monitoring system can appropriately be used to assess a structural railway bridge in real-time. This study may be helpful to practicing engineers and researchers in future studies of steel railway bridge evaluation. This could be a useful reference for future studies in implementing such systems as the railway bridge early warning system technique in detecting bridge damage.

Modular multi-channel high voltage arbitrary waveform generator and imaging setup for dielectric elastomer actuator characterisation

Various applications require multi-channel high-voltage sources for their control, e.g. electrostatic adhesion, electrophoresis and artificial muscles such as piezoelectric, hydraulically amplified self-healing electrostatic(HASEL) and dielectric elastomer actuators(DEAs). Further, the ability to simultaneously monitor the state of the actuators either with images, or voltage and current sensing is crucial to characterise their behaviour. In this work, we present the design of a versatile characterisation setup, capable of generating eight HV (15 kV) arbitrary waveforms(rise time of 8 ms and fall time of 80 ms for 60 MΩ load), while synchronously monitoring voltage and current, and record high-speed (120 fps) video. The setup ensures modularity and customisability by consisting of three independent modules: (1) The imaging module includes a Raspberry Pi and a Pi Camera; (2) A 3.3 V analogue interface 16-bit resolution data acquisition module on a PCB that accommodates a microcontroller board, two 8-channel analogue-to-digital converters, and an 8-channel digital-to-analogue converter; (3) Up to 8 DC-to-HVDC converter boards powered by 12 V DC, with 3.3 V analogue interface.

Ground Wave Propagation Measurement using DAQ Module and LabVIEW

Utilizing a data acquisition (DAQ) module and LABVIEW programming requires to develop in earth science/ engineering and equivalent, especially for educational purposes. This study implemented a geophone and an accelerometer to measure ground wave propagation with a study case of soil surface. Its measurement method consists of two modes. Firstly, the geophone is close to a wave source. Then, its position is changed by the accelerometer. The DAQ converts the detected signals from both sensors and then the LABVIEW interface processes and displays the outputs on the computer. The system can sense and reconstruct waveforms with a steady sampling frequency from 5 to 50 kHz. Also, it can automatically calculate a wave velocity by identifying the rising or falling edges of the wave signal and counting its arrival time within the distance between two sensors. As a result, it produces a better result at an interval of 0.2-0.6 m with a computed wave velocity of 72.531 m/s on average, even though a correction variable should be appended to the outcomes, amplified by two.

Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring.

The increasing popularity of pigs has prompted farmers to increase pig production to meet the growing demand. However, while the number of pigs is increasing, that of farm workers has been declining, making it challenging to perform various farm tasks, the most important among them being managing the pigs' health and welfare. This study proposes a pattern mining-based pig behavior analysis system to provide visualized information and behavioral patterns, assisting farmers in effectively monitoring and assessing pigs' health and welfare. The system consists of four modules: (1) data acquisition module for collecting pigs video; (2) detection and tracking module for localizing and uniquely identifying pigs, using tracking information to crop pig images; (3) pig behavior recognition module for recognizing pig behaviors from sequences of cropped images; and (4) pig behavior analysis module for providing visualized information and behavioral patterns to effectively help farmers understand and manage pigs. In the second module, we utilize ByteTrack, which comprises YOLOx as the detector and the BYTE algorithm as the tracker, while MnasNet and LSTM serve as appearance features and temporal information extractors in the third module. The experimental results show that the system achieved a multi-object tracking accuracy of 0.971 for tracking and an F1 score of 0.931 for behavior recognition, while also highlighting the effectiveness of visualization and pattern mining in helping farmers comprehend and manage pigs' health and welfare.