Efficient Data Acquisition Research Articles

Novel Surveillance View: A Novel Benchmark and View-Optimized Framework for Pedestrian Detection from UAV Perspectives

To address the issues of insufficient samples, limited scene diversity, missing perspectives, and low resolution in existing UAV-based pedestrian detection datasets, this paper proposes a novel UAV-based pedestrian detection benchmark dataset named the Novel Surveillance View (NSV). This dataset encompasses diverse scenes and pedestrian information captured from multiple perspectives, and introduces an innovative data mining approach that leverages tracking and optical flow information. This approach significantly improves data acquisition efficiency while ensuring annotation quality. Furthermore, an improved pedestrian detection method is proposed to overcome the performance degradation caused by significant perspective changes in top-down UAV views. Firstly, the View-Agnostic Decomposition (VAD) module decouples features into perspective-dependent and perspective-independent branches to enhance the model’s generalization ability to perspective variations. Secondly, the Deformable Conv-BN-SiLU (DCBS) module dynamically adjusts the receptive field shape to better adapt to the geometric deformations of pedestrians. Finally, the Context-Aware Pyramid Spatial Attention (CPSA) module integrates multi-scale features with attention mechanisms to address the challenge of drastic target scale variations. The experimental results demonstrate that the proposed method improves the mean Average Precision (mAP) by 9% on the NSV dataset, thereby validating that the approach effectively enhances pedestrian detection accuracy from UAV perspectives by optimizing perspective features.

Quantitative evaluation of surface crack depth on notched bars with laser-infrared detection technology

ABSTRACT To achieve non-destructive detection of crack depth in the process of notching and cracking of metal bars in low-stress cropping, a quantitative detection method for crack depth of notched bars based on infrared thermography technology under laser excitation is proposed in this paper. Combined with physical experiments, a simulation model of the temperature distribution of the laser-excited bar is established for efficient data acquisition, and the temperature curves of the bar surface under laser excitation are analysed from the perspective of space and time. On this basis, the study selects the feature parameters of the three types of defects on the surface of the metal bar, namely notch, surface crack and crack of notch bottom. A backpropagation (BP) neural network model is established for crack depth by dividing the crack into two types of a notch with crack and unnotched crack, and compared with other common prediction models. The results show that the selected features can accurately characterise the cracks in this BP neural network model, and the detected error in crack depth assessment is less than 3%. Performance metrics are established to evaluate the model, which has good reliability under different noises.

Deformation Detection Method for Substation Noise Barrier Column Based on Deep Learning and Digital Image Technology

The dynamic identification of the deformation of a noise barrier column is of great significance to the monitoring of its health. At the same time, the maximum stress of the column is an important indicator for the evaluation of its health status. Traditional contact displacement monitoring installs sensors on the structure, requires a lot of wiring and data acquisition equipment, and establishes a relatively independent and stable displacement reference system. Affected by the environment, wear, and material aging, the efficiency and reliability of data acquisition are reduced. A monitoring method based on digital image has the advantages of non-contact monitoring, high precision, and strong reliability. The existing DIC detection methods are limited by processor performance and image resolution, which are difficult to apply to engineering detection. In this paper, a structural displacement identification method based on convolutional neural networks (CNNs) and DIC technology is proposed. In this method, the data set is formed according to the column displacement cloud image obtained by DIC analysis, and the data set is enhanced by data normalization and region division. Through the analysis of the number of network layers and learning rate, the model design of the deep learning network is carried out. The high-speed camera image results of the test are introduced and identified by the static loading test of the equal-scale sound barrier. The results show that the structural displacement identification method based on CNN and DIC technology can accurately identify the displacement change in the structure, which greatly improves the efficiency of image displacement calculation using DIC technology.

Optimized Airborne Millimeter-Wave InSAR for Complex Mountain Terrain Mapping.

The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system's potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy.

UHPLC-TIMS-PASEF®-MS for Lipidomics: From Theory to Practice.

Trapped ion mobility spectrometry (TIMS) using parallel accumulation serial fragmentation (PASEF®) is an advanced analytical technique that offers several advantages in mass spectrometry (MS)-based lipidomics. TIMS provides an additional dimension of separation to mass spectrometry and accurate collision cross-section (CCS) measurements for ions, aiding in the structural characterization of molecules. This is especially valuable in lipidomics for identifying and distinguishing isomeric or structurally similar compounds. On the other hand, PASEF technology allows for fast and efficient data acquisition by accumulating ions in parallel and then serially fragmenting them. This accelerates the analysis process and improves throughput, making it suitable for high-throughput applications. Moreover, the combination of TIMS and PASEF reduces co-elution and ion coalescence issues, leading to cleaner and more interpretable mass spectra. This results in higher data quality and more confident identifications. In this chapter, a data-dependent TIMS-PASEF® workflow for lipidomics analysis is presented.

Low-cost photogrammetry solutions for surveying confined underground spaces: testing the traditional set-up against 360° camera on Tombs of the Kings archaeological site

Abstract. This study explores low-cost photogrammetry solutions for surveying confined underground spaces, focusing on Tomb 7 at the UNESCO World Heritage Site, Tombs of the Kings in Paphos, Cyprus. The research, part of the ENGINEER project, compares traditional photogrammetric methods using frame cameras against a 360° multi-lens camera. The aim is to identify reliable, low-cost methods for 3D documentation of archaeological sites, which can be used for structural analysis and systematic monitoring.Three photogrammetric acquisition methodologies were tested: handheld with frame camera, standard with frame camera, and relaxed with 360° camera. The study evaluates the accuracy of these acquisition methods by comparing dense point clouds generated from each dataset against a reference dataset obtained via terrestrial laser scanning (TLS). Metrics such as cloud-to-cloud distance, roughness, and point cloud density were used for comparison.Results indicate that while the 360° camera offers ease of use and high data density, it also introduces more noise and variability. Traditional methods, though more time-consuming, provide more consistent and accurate results. The findings suggest that combining both approaches could optimize data quality and acquisition efficiency, making the 360° multi-lens camera a viable low-cost photogrammetry option for heritage documentation.

Design and implementation of a scalable and high-throughput EEG acquisition and analysis system

Recent advances in neuroscience, neuromorphic intelligence, and brain–computer interface (BCI) technologies have created a need for fast, efficient, and convenient electroencephalogram (EEG) data acquisition systems. However, the existing equipment was limited in its flexibility, restricting non-invasive studies to research or medical settings. To address this issue, low-cost, compact EEG acquisition devices have been developed, allowing for frequent and flexible brain data acquisition in various scenarios. This paper introduces a scalable and high-throughput EEG signal acquisition and analysis system based on field-programmable gate array (FPGA) technology. The proposed system offers electrode scalability, on-chip computing, and optional wireless functionality extension. These features are achieved through the design of a highly scalable underlying EEG acquisition module and an FPGA central module that enables software-defined high-throughput expansion and high-speed data exchange between software and hardware. The paper presents two implementation cases that demonstrate the potential of the proposed system. The first case introduces a wearable wireless EEG system, enabling the deployment of effective and user-friendly steady-state visual evoked potential (SSVEP)-BCI applications in consumer-grade scenarios. The second case integrates an FPGA central module with multiple basic EEG acquisition modules to construct a high-throughput BCI system for cost-effective and real-time EEG data acquisition and processing. This configuration allows for flexible deployment in research and clinical applications, including attention index, SSVEP, motor imagery (MI), and emotion recognition. This combination further demonstrates the potential of scalable EEG systems and emphasizes the need for further integration or chipization. These implementations validate the feasibility of compact and efficient EEG devices and highlight the promising applications of scalable BCI system in various fields.

Применение беспилотных летательных аппаратов в горнодобывающей промышленности

Unmanned aerial vehicles have been actively used in the mining industry over the past decade to perform various tasks ranging from mineral exploration to reclamation of industrial areas. The study focuses on the tasks of the aeromagnetic survey, mine surveying and monitoring of tailings dumps. Using unmanned aerial vehicles offers the advantages of increased efficiency, reduced operational costs and risks, as well as improved quality of real-time data. Examples are presented of the successful use of unmanned aerial vehicles to create digital terrain models, to map geological structures and to monitor the condition of slopes and mine workings. High accuracy and efficiency of data acquisition using modern photogrammetry technologies and software are noted, which helps to optimize the planning procedures and ensure a higher level of operational safety. Despite a significant progress in using unmanned aerial vehicles, there is a need to further explore their application in more complex tasks, such as integration of digital model data into production processes and development of methods to be used in underground conditions.

Suitability of a commercial low-cost biologging system for monitoring movement, behaviour and heart rate of grazing dairy cows

This study explored the suitability of a commercial biologging system incorporating GPS and heart rate (HR) sensors to monitor grazing cattle’s movement, behaviour and heart rate. We preliminarily tested the GPS accuracy with stationary tests and then monitored six dairy cows grazing in an alpine summer pasture for 20 days and nights. We trained a random forest model on direct observations to infer cows’ behaviours (resting, grazing, walking) from GPS movement data. We associated each GPS position with the HR (beats per minute - bpm) mean and maximum-minimum difference in the 120–second interval preceding its acquisition. The GPS sensor showed high accuracy (positioning error lower than 2 m in open sky-view and 3 m under tree canopy cover) and efficiency of position acquisition of 95% after excluding outlier positions. The efficiency of HR data acquisition was lower, peaking at 77% during daytime activity and dropping to 50% during night-time resting. The HR mean and the maximum-minimum difference were lower during resting and at night and higher during grazing, walking, and daytime. They also increased with slope and Temperature Humidity Index (THI). This study indicates that this commercial biologging system is suitable for short-term monitoring of animals’ movement, behaviour and physiological responses to varying pasture and climatic conditions, offering insights for livestock management in alpine summer pastures.

Seamless Science: Lifting Experimental Mechanical Testing Lab Data to an Interoperable Semantic Representation

The scientific landscape is undergoing rapid transformations with the advent of the digital age which revolutionizes research methodologies. In materials science and engineering, an adoption of modern data management techniques is desirable to maximize the efficiency and accessibility of research efforts. Traditional practices in testing laboratories are usually inadequate for efficient data acquisition and utilization as they lead to local storage and difficulty in publication and correlation with other results. Electronic laboratory notebooks (ELNs) are promising prospects in this respect. Semantic concepts and ontologies enhance interoperability by standardizing experimental data representation. An in‐laboratory pipeline seamlessly integrating an ELN with transformation scripts to convert experimental into interoperable data in a machine‐actionable format is created in this study as a proof of concept. Tensile test results and the corresponding tensile test ontology are used exemplary. Linking ELN data to semantic concepts enriches the stored information while improving interpretability and reusability. Involving undergraduate students builds a bridge between theory and practice during their training and promotes their digital skills. This study underscores the potential of ELNs and knowledge representations as beneficial means toward improved data management practices that enhance collaborative research and education while ensuring compatibility with evolving standards and technologies.

Miniaturized Shear Testing: In-Plane and Through-Thickness Characterization of Plywood.

This study addresses the challenges associated with conventional plywood shear testing by introducing a novel miniaturized shear test method. This approach utilizes a controlled router toolpath for precise sample fabrication, enabling efficient material use and data acquisition. Miniaturized samples, designed with double shear zones, were tested for τxy, τxz, and τyz configurations using a universal testing machine. Results revealed a mean ultimate shear strength ranging from 5.6 MPa to 7.3 MPa and a mean shear modulus ranging from 0.039 GPa to 0.095 GPa, confirming the orthotropic nature of plywood. The resulting shear behavior was determined with stress-strain curves correlated with failure patterns. The miniaturized tests effectively captured the material's heterogeneous behavior, particularly at smaller scales, and demonstrated consistent load-bearing capacity even after substantial stress reduction, suggesting suitability for bracing applications. This method allows for increased sample sizes, facilitating robust data collection for developing and validating finite element models. Future work will focus on evaluating the scalability of the observed orthotropic behavior and data scatter at larger scales and assessing the potential for this method to replace conventional full-scale plywood shear testing.

High-density gas target at the LHCb experiment

The recently installed internal gas target at LHCb presents exceptional opportunities for an extensive physics program for heavy-ion, hadron, spin, and astroparticle physics. A storage cell placed in the LHC primary vacuum, an advanced Gas Feed System, the availability of multi-TeV proton and ion beams, and the recent upgrade of the LHCb detector make this project unique worldwide. In this paper, we outline the main components of the system, the physics prospects it offers, and the hardware challenges encountered during its implementation. The commissioning phase has yielded promising results, demonstrating that fixed-target collisions can occur concurrently with the collider mode without compromising efficient data acquisition and high-quality reconstruction of beam-gas and beam-beam interactions. Published by the American Physical Society 2024

Cascaded compressed-sensing single-pixel camera for high-dimensional optical imaging

Single-pixel detectors are popular devices in optical sciences because of their fast temporal response, high sensitivity, and low cost. However, when being used for imaging, they face a fundamental challenge in acquiring high-dimensional information of an optical field because they are essentially zero-dimensional sensors and measure only the light intensity. To address this problem, we developed a cascaded compressed-sensing single-pixel camera, which decomposes the measurement into multiple stages, sequentially reducing the dimensionality of the data from a high-dimensional space to zero dimension. This measurement scheme allows us to exploit the compressibility of a natural scene in multiple domains, leading to highly efficient data acquisition. We demonstrated our method in several demanding applications, including enabling tunable single-pixel full-waveform hyperspectral light detection and ranging (LIDAR) for the first time.

Coconut Leaf Nitrogen Measurement using Different Vegetative Indices and Multispectral Images

To ensure sustainable agricultural practices, it is crucial to monitor the health of coconut trees. Managing the shape and size of their canopies effectively poses significant challenges due to the absence of advanced technological solutions. This industry and economy can benefit greatly from the integration of multispectral imaging, which offers innovative ways to assess and improve tree health and productivity. By deploying Unmanned Aerial Vehicles (UAV), critical vegetation indexes such as Normalized Difference Vegetative Index (NDVI), Normalized Difference Red Edge (NDRE), Chlorophyll Index (CI) green, and Chlorophyll Index red edge derived from multispectral images are cost-effective, high-resolution, and facilitating continuous plant monitoring. This study is aimed to monitor leaf nitrogen content in coconut plantation using multispectral UAV-based approach in Farm at Faculty of Agriculture, Mapalana, Kamburupitiya. The correlations were identified between UAV captured images and SPAD values, as well as soil nitrogen percentage using ground truth data which were gathered through targeted explorations aimed to optimizing data acquisition efficiency. Data processing revealed strong correlations between crop and soil variables and specific vegetation index, including NDVI, NDRE CI green and CI red edge. The NDVI demonstrated strong association with soil nitrogen content, while NDRE and CI green showed significant correlations with SPAD value with R-squared values of 71.3 % and 73.4 %, respectively. The R-Squared values for the soil nitrogen content with NDVI was 57.2 %. These findings highlight that NDRE and CI green are more effective indicators for measuring nitrogen concentration of coconut leaves. Monitoring of vegetation indices using Utilizing UAV-based monitoring of vegetation indices can greatly enhance site specific management in coconut plantation, promoting sustainable agricultural practices.

Enhancing IoT data acquisition efficiency via FPGA-based implementation with OpenCL framework

The increasing demand for real-time data processing in Internet of Things (IoT) applications necessitates the development of efficient and flexible data acquisition systems capable of receiving and processing data from various sensor types. In conjunction with OpenCL, field-programmable gate arrays (FPGAs) have recently emerged as powerful platforms for accelerating data-intensive tasks. This study explored the implementation of an FPGA for data acquisition using OpenCL, aiming to design and implement an efficient data acquisition system tailored for IoT applications. Utilizing OpenCL for FPGA-based data acquisition offers several advantages that contribute to system efficiency, particularly in hardware interfaces between FPGA and external devices used in IoT applications. OpenCL abstracts the complexity of the FPGA hardware interface to external DDR memory for storing temporary data and a communication interface to the host CPU for transferring the collected data and enabling remote access, enabling developers to focus on algorithm design and functionality. To enable data reading from an external analog-to-digital converter (ADC) chip for IoT applications, we developed a component module that utilizes the Avalon-streaming interface and can stream the data to the OpenCL kernel. An experiment was conducted to demonstrate the performance of our proposed design. According to the findings of the experiments, a data acquisition implementation based on an FPGA and OpenCL can simultaneously read analog signals via a multichannel ADC. The proposed design provides a foundation for designing efficient data acquisition solutions, addressing the increasing needs of FPGA-based data acquisition in various IoT environments.

Intelligence data acquisition based on embedded system in Chinese cuisine cooker (CCICR V1.0)

The Chinese cooking process, intricate and diverse, encompasses a wide range of dishes, generating substantial data that often poses significant challenges for automation. To address these challenges, this paper introduces a versatile, highly reliable, user-friendly, and intelligent data recorder specifically designed for Chinese cuisine-the Chinese Cuisine Intelligent Cooker Recorder-V1.0 (CCICR-V1.0). This device is intended to record data generated during the cooking process and provide essential support for intelligent cooking robot systems. The core of CCICR-V1.0 is a STM32 microcontroller, equipped with sensors that monitor temperature, pressure, and attitude. These sensors facilitate the intelligent collection of data related to dishes, ingredients, and the movements of cookware throughout the cooking process. To ensure efficient data acquisition and storage, CCICR-V1.0 employs a multi-task data buffering storage method that effectively allocates CPU resources. NAND Flash is used as the storage medium, guaranteeing secure storage, management, and preservation of high-frequency, multi-channel data. Experimental results demonstrate the effectiveness of CCICR-V1.0 in achieving multi-channel, high-frequency data acquisition and storage in complex environments. This leads to an increase in automation efficiency of 89.9%. The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. Furthermore, the non-contact infrared temperature measurement module exhibits impressive performance with a maximum absolute error of 0.258∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ C$$\\end{document} and a maximum relative error of 0.88%. Notably, this measurement module has achieved a cost reduction of approximately 91.7% and an accuracy improvement of around 40%. Additionally, the response time of CCICR-V1.0 is less than 3 ms. With its ability to effectively capture data related to Chinese cooking, CCICR-V1.0 holds commercial value for widespread adoption and application in the field of intelligent cooking.

A hybrid sparrow optimization Kriging model and its application in geological modeling

With the proposal of intelligent mines, the demand for drilling is increasing daily. Therefore, it is particularly crucial to gather more geological data by interpolation of limited drilling data for subsequent three-dimensional geological modeling. In this paper, a hybrid sparrow optimization Kriging model (HSSA), in which chaos theory and Levy flight are integrated into the initial population update algorithm of the sparrow algorithm and the location update algorithm of the entrants, is proposed. Next, the golden sine optimization algorithm is introduced into the reconnaissance and early warning mechanism of the sparrow algorithm to further improve the accuracy and local escape ability. By the correlation optimization of the original sparrow algorithm, the speed and accuracy of swarm intelligence optimization are further improved. In addition, the model solves the parameters of the variation function of the ordinary Kriging interpolation and reduces the generation error of the formation data interpolation. The results of relevant experiments show that the hybrid sparrow optimization Kriging model improves the accuracy and convergence speed compared with other swarm intelligence algorithms and that the accuracy of this model is improved by 8.4% compared with the original Kriging interpolation algorithm. Based on the hybrid sparrow optimization Kriging model, we propose a three-dimensional stratigraphic model for the Yangchangwan Coal Mine, which provides further support for mining operations and three-dimensional stratigraphic research in this area. The accuracy and applicability of the hybrid sparrow optimization Kriging model are further explained using a case study with the stratigraphic model data in the Yangchangwan Coal Mine. HSSA with significant potential for applications in industries such as coal mining and geological exploration. In these fields, the efficient acquisition, processing, and modeling of stratigraphic data are critical for enhancing geological interpretation and optimizing operational workflows.

Development of decision support methods for efficient maintenance of urban wastewater systems

The sanitation network is a vital urban infrastructure that requires systematic and co-ordinated activities and practices to realize lowest life cycle cost. The sustainable management of this asset is based on the efficient acquisition of diagnostic data from the sanitation network in order to analyze the defects of each section, then to assess their condition to prioritize and consider the effective mode of maintenance of sections deemed failing. In this perspective, knowledge of the sanitation networks is essential for reliable classification of defects and effective maintenance. A numerical model based on weighted criticality is developed to evaluate and prioritize the defects in an urban sanitation network. It is based on the combination of two methods: Fuzzy Analytic Hierarchy Process (F-AHP) and Weighted Product Model (WPM). A case study of a real sanitation network in Bejaia city (Algeria), consisting mainly of non-visitable pipes evaluated from television inspections, is used to illustrate and validate the proposed model. The results show that the weighting of criticality parameters has a considerable influence on the classification of defects in a sanitation network. The weighted criticality results obtained in the present case are significantly better than those obtained using a method based on the notion of classical criticality. This clearly indicates the relevance of the proposed numerical model.

Efficient Training Data Acquisition Technique for Deep Learning Networks in Radar Applications

In the field of radar, deep learning techniques have shown considerably superior performance over traditional classifiers in detecting and classifying targets. However, acquiring sufficient training data for deep learning applications is often challenging and time consuming. In this study, we propose a technique for acquiring training data efficiently using a combination of synthesized data and measured background data. We utilized graphics processing unit (GPU)-based physical optics methods to obtain the backscattered field of moving targets. We then generated a virtual dataset by mixing the synthesized target signal with the background signal real data. Subsequently, we trained a convolutional neural network using the virtual dataset to identify three different classes—Bird, Drone, and Background—from a range-Doppler map. When tested using the measurement data, the trained model achieved an accuracy of over 90%, demonstrating the effectiveness of the proposed method in acquiring training data for radar-based deep learning applications.

GoFish: a foray into open-source, aquatic behavioral automation.

As the most species-rich vertebrate group, fish provide an array of opportunities to investigate the link between ecological interactions and the evolution of behavior and cognition, yet, as an animal model, they are relatively underutilized in studies of comparative cognition. To address this gap, we developed a fully automated platform for behavioral experiments in aquatic species, GoFish. GoFish includes closed-loop control of task contingencies using real-time video tracking, presentation of visual stimuli, automatic food reward dispensers, and built-in data acquisition. The hardware is relatively inexpensive and accessible, and all software components of the platform are open-source. GoFish facilitates experimental automation, allowing for customization of high-throughput protocols and the efficient acquisition of rich behavioral data. We hope this platform proves to be a useful tool for the research community, facilitating refined, reproducible behavioral experiments on aquatic species in comparative cognition, behavioral ecology, and neuroscience.