Data Acquisition Applications Research Articles

Magneto acoustic emission technique: A review of methodology, applications, and future prospects in non-destructive testing

The magneto acoustic emission (MAE) technique is an emerging non-destructive testing (NDT) method that has shown potential for defect detection in the bulk of material, proven vital for the safety and integrity of the material. The MAE technique has shown promising results among modern non-destructive testing techniques for ferromagnetic materials. Although MAE technique is a long-established NDT method, it has the potential for further development. This paper provides an in-depth review of MAE signals' physical origin and characteristics, compares them with their counterpart, Magneto Barkhausen Noise (MBN), and explores data acquisition methods and applications, including specific case studies in material defect detection. The research gaps in the MAE technique are discussed, and the paper explores potential developments, highlighting future perspectives and possible improvements.

Smart Water Management System Using IOT based Sensors

The advancement of technology in the field of Internet of Things (IoT) have grown rapidly over the past years in the field of water management in various context. The work aims to contribute to general process flow of a smart city management specifically in the field of the residential water management system. As well to design a smart water management system for the residential areas that can be implemented not only to a single residential area but to multiple residential areas. This is achieved through implementing sensor-based water flow meter to replace current mechanical water flow gauge. It is as well addressed the need of a common platform to store data that are currently proposed through an additional data acquisition application. Lastly, the work proposes an affordable prototype to measure water flow rate using IOT based sensors to be implemented at large scale and promising usability testing result.

Development and Implementation of an FPGA-Embedded Multimedia Remote Monitoring System for Information Technology Server Room Management

Conventional data acquisition systems face challenges in achieving high acquisition speeds and rapid storage of large data volumes using microcontrollers. In contrast, field-programmable gate arrays (FPGAs) offer numerous advantages, including high clock frequencies, minimal internal delays, fast operational speeds, abundant internal RAM resources, and simplified control of complex peripheral circuits. This study presents the design of an FPGA-based multimedia remote monitoring system for information technology server rooms. The proposed system utilizes an FPGA as the primary controller and incorporates environmental sensors, electrical energy sensors, carbon monoxide sensors, smoke sensors, and A/D converter modules to monitor multiple locations within the server room. Simultaneously, the FPGA transmits the collected data from each monitoring point via a serial port to an LCD serial screen for display. An alarm is triggered if any environmental anomalies are detected, indicating abnormal statuses. Additionally, the system employs the fast Fourier transform algorithm and butterfly operations to derive voltage and current AC quantities, while utilizing relative temperature differences to identify equipment faults within the server room. The system is evaluated in terms of functionality, user-friendliness, and reliability. Experimental results demonstrate that all performance measures align with expectations, fulfilling the initial design objectives and highlighting the potential applicability and relevance of FPGA technology in the monitoring field. In addition, a comparison between FPGA and traditional microcontroller systems is performed, showcasing the superior processing speed and performance of the FPGA-based system. This comparative analysis further validates the advantages of using FPGA technology in high-speed data acquisition and monitoring applications.

A Versatile Board for Event-Driven Data Acquisition.

Event-driven data acquisition is used to capture information from fast transient phenomena typically requiring a high sampling speed. This is an important requirement in the ITER Neutral Beam Test Facility for the development of one of the heating systems of the ITER nuclear fusion experiment. The Red Pitaya board has been chosen for this project because of its versatility and low cost. Versatility is provided by the hosted Zynq System on Chip (SoC), which allows full configuration of the module architecture and the OpenSource architecture of Red Pitaya. Price is an important factor, because the boards are installed in a hostile environment where devices can be damaged by EMI and radiation. A flexible solution for event-driven data acquisition has been developed in the Zynq SoC and interfaced to the Linux-based embedded ARM processor. It has been successfully adopted in a variety of data acquisition applications in the test facility.

Towards a container-based architecture for CMS data acquisition

The CMS data acquisition (DAQ) is implemented as a service-oriented architecture where DAQ applications, as well as general applications such as monitoring and error reporting, are run as self-contained services. The task of deployment and operation of services is achieved by using several heterogeneous facilities, custom configuration data and scripts in several languages. In this work, we restructure the existing system into a homogeneous, scalable cloud architecture adopting a uniform paradigm, where all applications are orchestrated in a uniform environment with standardized facilities. In this new paradigm DAQ applications are organized as groups of containers and the required software is packaged into container images. Automation of all aspects of coordinating and managing containers is provided by the Kubernetes environment, where a set of physical and virtual machines is unified in a single pool of compute resources. We demonstrate that a container-based cloud architecture provides an acrossthe-board solution that can be applied for DAQ in CMS. We show strengths and advantages of running DAQ applications in a container infrastructure as compared to a traditional application model.

Applications of deep neural networks in exploration seismology: A technical survey

Exploration seismology uses reflected and refracted seismic waves, emitted from a controlled (active) source into the ground, and recorded by an array of seismic sensors (receivers) to image the subsurface geologic structures. These seismic images are the main resources for energy and resource exploration and scientific investigation of the crust and upper mantle. We survey recent advances in applications of machine-learning methods, more specifically deep neural networks (DNNs), in exploration seismology. We provide a technically oriented review of DNN applications for seismic data acquisition; data preprocessing tasks such as interpolation/extrapolation, denoising, first-break picking, velocity picking, and seismic migration; data processing tasks such as geologic and structural interpretations; and data modeling tasks such as the inference of subsurface structures and lithologic and petrophysical properties. DNNs have entered almost every sector of exploration seismology. They have outperformed many traditional algorithms for the automation of seismic data acquisition, data preprocessing, data processing, interpretations, and data modeling tasks. However, despite the impressive performances of DNN-based approaches, the out-of-distribution generalization and interpretability of these models remain challenging. To overcome these challenges, incorporating domain knowledge into the DNNs is a promising path and a focus of current deep-learning research in seismology.

Artificial intelligence in psychiatry research, diagnosis, and therapy.

Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.

The Current Situation and Countermeasures of Chinese Enterprise Management under Big Data

With the rapid development of science and technology, big data is becoming an important resource for enterprise management. The purpose of this paper is to analyse the current situation of Chinese enterprise management under big data, conduct cause analysis, and propose corresponding countermeasures. By reviewing and analysing relevant literature, we find that in the era of big data, Chinese enterprises face challenges in data acquisition, data processing and data application, data security privacy and protection. Then, we analyse the causes from four aspects: data dispersion and fragmentation, data quality issues, data privacy and security issues, and technology and talent shortage. Finally, in order to make full use of the opportunities that big data brings to enterprises, Chinese enterprises need to establish a sound data strategy, strengthen data security and protection, build a data-driven decision-making system, strengthen data sharing and cooperation, continuously innovate and optimise their business models, strengthen data governance, broaden the field of data applications, and cultivate high-quality data analytics talents. These countermeasures will help Chinese enterprises better cope with the challenges posed by the era of big data and achieve sustained innovation and development.

General purpose digital twin framework using digital shadow and distributed system concepts

Digital twin (DT) is an emerging concept in the Industry 4.0 era. It integrates intelligence into industrial processes. The broadness of DT’s concept allows for multiple definitions and designs to be labeled as DTs. However, general-purpose DTs are still out of reach due to the lack of consensus, tool dependency, and the focus on system integration. In this paper, a general-purpose DT framework is proposed by utilizing distributed system concepts. Data acquisition, system modeling, and intelligence applications are modularized in order to enable framework generality. Digital shadow (DS) is implemented as a core component of DT, which provides a real-time virtual replica of its physical counterpart. DT applications utilize DS models for recommending intelligent decisions. A systematic modeling approach is designed by differentiating between DS models and DT applications. The proposed approach defines the model flow based on its roles and objectives. The proposed DT framework is applied to a semiconductor production use case. For this system, the proposed DT provides insights into the production system’s behavior and estimates product completion time. Operators dispatch work-in-progress products using DT recommendations based on the equipment setup and the production system’s status.

Recognition and Semantic Information Extraction for Map Based on Deep Learning

Abstract. Geospatial information contained in maps plays an important role in geographic information data acquisition, map understanding, intelligent mapping and other applications. In terms of map recognition and geospatial information extraction from maps, traditional methods that heavily rely on human or human-computer interaction for semantic recognition can no longer meet the real-time needs. In this paper, we first analysed the composition and characteristics of maps, and then systematically illustrated the semantic understanding methods of map image recognition, target detection of geographic features and semantic segmentation of geographic features based on deep learning architecture, which is crucial to intelligent map recognition and mapping.

DAIKON: A Data Acquisition, Integration, and Knowledge Capture Web Application for Target-Based Drug Discovery.

Primitive data organization practices struggle to deliver at the scale and consistency required to meet multidisciplinary collaborations in drug discovery. For effective data sharing and coordination, a unified platform that can collect and analyze scientific information is essential. We present DAIKON, an open-source framework that integrates targets, screens, hits, and manages projects within a target-based drug discovery portfolio. Its knowledge capture components enable teams to record subsequent molecules as their properties improve, facilitate team collaboration through discussion threads, and include modules that visually illustrate the progress of each target as it advances through the pipeline. It serves as a repository for scientists sourcing data from Mycobrowser, UniProt, PDB. The goal is to globalize several variations of the drug-discovery program without compromising local aspects of specific workflows. DAIKON is modularized by abstracting the database and creating separate layers for entities, business logic, infrastructure, APIs, and frontend, with each tier allowing for extensions. Using Docker, the framework is packaged into two solutions: daikon-server-core and daikon-client. Organizations may deploy the project to on-premises servers or VPC. Active-Directory/SSO is supported for user administration. End users can access the application with a web browser. Currently, DAIKON is implemented in the TB Drug Accelerator program (TBDA).

Adaptation of motor control to musculoskeletal pain: Theories for the sensorimotor interactions involved

BACKGROUND: There are reports in the literature showing that the pain alters motor control, whether in static or dynamic conditions. VIEW OF THE PAST: Over the past decades theories have been proposed on how and in what conditions the pain affects motor control. To date, changes in movement control have been identified at both peripheral and central levels, which potentially leads to the emergence of compensatory lesions in the medium and long term. CURRENT STATE: The current state of the art on the understanding of how pain alters movement control has enabled the emergence of preventive exercise protocols and treatment of movement disorders generated by the presence of pain. FUTURE PERSPECTIVE: The application of new data acquisition and analysis technologies will allow the development of effective exercise protocols for pain management.

Agnostic CH-DT Technique for SCADA Network High-Dimensional Data-Aware Intrusion Detection System

The pervasiveness in the industrial internet of things (IIoT) due to the application of supervisory control and data acquisition (SCADA) has led to the growth of heterogeneous sensor data, thereby increasing the risk of intrusions and attacks. The existence and effect of intruders and their innovative attack techniques are on the rise. Existing intrusion detection systems (IDS) tend to be computationally expensive with this form of data due to the presence of noise. In real-time domains, available methods lag, necessitating additional research into effective feature extraction schemes, which is fundamental in machine learning (ML) for time exigency. This study, in a comparative analysis of some feature selection techniques (FS), proposes a combination of an efficient ML classifier and an agnostic feature selection (FS) scheme for attack detection and classification in a real-time SCADA network. The flexibility and interoperability of the proposed approach resolve the computational complexity of vulnerability detection schemes while reducing false alarm rates (FAR) and overall model execution time. With the view of an online preprocessing, the proposed technique is phased thus: (i) data preparatory consisting of data cleansing and normalization followed by (ii) the combination of a pre-pruned decision tree (DT) algorithm and an agnostic Chi-square FS approach built to obtain an optimal subset of data features for efficient IDS. (iii) Evaluation of proposed agnostic DT-CH and other FS candidates for anomaly detection.

The MicroTCA.4 Fast Control and Processing Board for Generic Control and Data Acquisition Applications in HEP Experiments

The MicroTCA.4 Fast Control and Processing board (u4FCP) is an FPGA-based double-width MicroTCA.4 compatible Advanced Mezzanine Card (AMC) for generic control and data acquisition applications in High-Energy Physics (HEP) experiments. Built around the Xilinx Kintex UltraScale+ FPGA, the u4FCP provides users with a platform which has access to on-board FPGA Mezzanine Card (FMC) sockets with an array of configurable I/O and high-speed links up to 100 Gbps. In addition, the rear transition module (RTM) allows separation of processing and I/O functionality with the Kintex -7 FPGA and more FMC sockets. The increased processing capacity and area on the RTM allow a significant increase in the amount of FPGA logic and I/O connections. The on-going R&D effort carried out on applications in HEP experiments is presented.

IDADET: Iterative Double-Sided Auction-Based Data-Energy Transaction Ecosystem in Internet of Vehicles

In the era of big data, the unprecedented growth of data has been regarded as an important asset and the commercial application of data acquisition markets has emerged accordingly. With the advancement of vehicle manufacturing and sensor technologies, a large amount of data can be collected and stored in electric vehicles (EV), making the data acquisition scenario gradually extend to the Internet of Vehicles (IoV), and thus the corresponding operational rules and economic feasibility need to be fully investigated there. In this paper, we focus on a general IoV-oriented data acquisition market that consists of a data center, multiple EVs, multiple roadside units (RSUs), and a market operator (broker), with the objective of social welfare maximization (SWM) by identifying the optimal data task allocation. However, due to the inherent information asymmetry and fragmentation in such a market, it is not feasible to solve the SWM problem directly. To this end, we propose an iterative double-sided auction (IDA) mechanism, which leverages the self-interested feature of RSUs and EVs to decompose the SWM problem, enabling every participant to make decisions in a distributed manner under the broker’s coordination. A complete set of operational rules covering the data task allocation, bidding, payment, and reimbursement are elaborately designed to achieve SWM, and energy is adopted as the pricing “currency”, such that an IDA-based Data-Energy Transaction (IDADET) ecosystem is established in IoV. We verify the economic feasibility of the proposed IDADET ecosystem by showing its convergence and desirable properties of individual rationality, budget balance, incentive compatibility, and economic efficiency. In addition, considering the psychological effects of practical market participants, we make amendments to the operational rules of the IDADET ecosystem from the behavioral economics perspective, aiming to ensure its long-term well-functioning. Extensive numerical results are presented to show the performance of the IDADET ecosystem and demonstrate its advantages in terms of economic properties, operational feasibility, fast convergence, and market social welfare.

Using FPGA-Based AMC Carrier Boards for FMC to Implement Intelligent Data Acquisition Applications in MTCA Systems Using OpenCL

The Micro Telecommunications Computing Architecture (MTCA) standard is widely used in developing advanced data acquisition and processing solutions in the big physics community. The number of applications implemented using commercial Advanced Mezzanine Cards (AMC) using AMD-Xilinx and Intel® Field Programmable Gate Array (FPGA) systems on chips is growing due to the flexibility and scalability of these reconfigurable hardware devices and their suitability to implement intelligent applications using artificial intelligence and machine learning techniques. The paper presents the specific design methodologies for hardware acceleration proposed by both FPGA manufacturers. Comparative results are obtained from two different software/hardware setups using two different AMCs, one based on Intel® FPGA Arria 10 and another based on Xilinx ZynqMP. The paper illustrates the process of how to modify the board support package, required by the hardware acceleration methodology, to implement the JESD204B and Low-Voltage Differential Signaling (LVDS) interfaces with the FPGA Mezzanine Card (FMC) modules containing the ADCs, to prepare the AMC cards to implement such kind of applications. The data acquisition and processing implementation inside these reference designs, with both languages OpenCL and High-Level Synthesis (HLS), is described. An important feature, needed for many applications in the big physics field, is the interface with the Experimental Physics and Industrial Control System (EPICS) software framework using the ITER Nominal Device Support framework, which is briefly described.

Real-Time Monitoring of Gas Fields: Prototype at Pt Gamma Energi Pratama Bogor

PT Gamma Energi Pratama is a company engaged in the instrumentation sector. One of the projects undertaken by PT Gamma Energi Pratama is on an oil and natural gas drilling site. The oil drilling process involves a tool called a Gas Jack Compressor originating from the United States. At first, the technician there used the local panel that came with the compressor. But procuring spare parts takes a long time. At the same time, the needs in the field demand to be met immediately. Therefore, the Programmable Logic Controller (PLC) was chosen as a special microcontroller device that can access the compressor via the Modbus Protocol. PLCs can also be connected to Supervisory Control and Data Acquisition (SCADA) applications via Ethernet. This solution monitors data from sensor readings installed on the Gas Jack Compressor. The system is already running with its use only on the local scope. For the development of the system so that monitoring can be carried out in real-time and online, it needs to be linked to flow control devices, database systems, and interfaces for data visualization. Thus, monitoring gas fields can be done in real-time online.

Data Acquisition System of Temperature Measurement Using LabVIEW Application

Data Acquisition (DAQ) is widely used in Research and Development, Quality Control, Testing. Data acquisition (DAQ) is a process in which raw transducer signals are converted to computer readable data so that computers can store and manipulate that data. Temperature control system mainly the measuring and even to achieve stable or desired value have big challenge and importance in all sector of industry. This article tries to summarize the recent advancements in data acquisition technology and also explains the utility of LabVIEW software in DAQ applications related to temperature measurement.

Emerging 0D, 1D, 2D, and 3D nanostructures for efficient point-of-care biosensing

The recent COVID-19 infection outbreak has raised the demand for rapid, highly sensitive POC biosensing technology for intelligent health and wellness. In this direction, efforts are being made to explore high-performance nano-systems for developing novel sensing technologies capable of functioning at point-of-care (POC) applications for quick diagnosis, data acquisition, and disease management. A combination of nanostructures [i.e., 0D (nanoparticles & quantum dots), 1D (nanorods, nanofibers, nanopillars, & nanowires), 2D (nanosheets, nanoplates, nanopores) & 3D nanomaterials (nanocomposites and complex hierarchical structures)], biosensing prototype, and micro-electronics makes biosensing suitable for early diagnosis, detection & prevention of life-threatening diseases. However, a knowledge gap associated with the potential of 0D, 1D, 2D, and 3D nanostructures for the design and development of efficient POC sensing is yet to be explored carefully and critically. With this focus, this review highlights the latest engineered 0D, 1D, 2D, and 3D nanomaterials for developing next-generation miniaturized, portable POC biosensors development to achieve high sensitivity with potential integration with the internet of medical things (IoMT, for miniaturization and data collection, security, and sharing), artificial intelligence (AI, for desired analytics), etc. for better diagnosis and disease management at the personalized level.

Antigallbladder Carcinoma Activity Analysis of a New Nanometer Drug Delivery System Based on Data Acquisition.

In order to solve the problem of the application of data acquisition in the new nanometer drug delivery system of microscope, a research of antigallbladder carcinoma activity analysis was proposed. Gallbladder carcinoma (GBC) is a common malignant tumor in biliary tract diseases. Due to the lack of specific clinical manifestations in the early stage, GBC has the shortcomings of the hidden onset, the difficult diagnosis, and the high misdiagnosis rate. GBC ranks in the top position among the most common tumors of the digestive system worldwide. The preoperative diagnosis rate is low, and the incidence of accidental gallbladder carcinoma is gradually increasing. Domestic gallbladder carcinoma related to cholecystectomy is not sensitive to radiotherapy and chemotherapy. Surgical resection is still the only effective method for the treatment of accidental gallbladder carcinoma.