Design and Research of Efficiency Data Acquisition and Analysis Platform Based on Android

In this paper a method for efficient data acquisition from sensor network with large number of nodes is proposed. A specific design allows simple device connection in daisy-chain configuration, thus removing addressing problems and allowing synchronized sampling design using just 4 wires. Several limitations are considered to provide mathematical model for estimation of maximum number of sensor nodes. An experimental setup was designed which demonstrated theoretical ability to acquire data from up to 200 sensors with 50 Hz sampling rate using conventional low-cost hardware, however, more advanced setup could allow to increase maximum number of sensors several times. The wiring structure of the network as well as hardware is specifically designed for use in smart garments or similar applications that require sensor integration in compact environment and low-power, real-time performance.

A computer-based data acquisition and analysis system was developed customized to EMG measurement and analysis requirements. The system was designed as an assistive tool for researchers studying human electrophysiology and kinesiology. A user-friendly interface characterized by a great adaptability and compatibility with other similar existing software packages was implemented, enabling multichannel acquisition, data storage and analysis. The application integrates the most frequent processing techniques required by EMG researchers and analysts, as well as a number of advanced signal processing techniques adjusted to the features of the EMG signal. The system comprises flexible signal analysis and display modules, forming a fully operational tool that can be easily configured to serve any similar purposed measurement requirements.

Electronic health records (EHRs) are an increasingly important source of information for healthcare professionals and researchers. However, EHRs are often fragmented, unstructured, and difficult to analyze due to the heterogeneity of the data sources and the sheer volume of information. Knowledge graphs have emerged as a powerful tool for capturing and representing complex relationships within large datasets. In this study, we explore the use of knowledge graphs to capture and represent complex relationships within EHRs. Specifically, we address the following research question: Can a knowledge graph created using the MIMIC III dataset and GraphDB effectively capture semantic relationships within EHRs and enable more efficient and accurate data analysis? We map the MIMIC III dataset to an ontology using text refinement and Protege; then, we create a knowledge graph using GraphDB and use SPARQL queries to retrieve and analyze information from the graph. Our results demonstrate that knowledge graphs can effectively capture semantic relationships within EHRs, enabling more efficient and accurate data analysis. We provide examples of how our implementation can be used to analyze patient outcomes and identify potential risk factors. Our results demonstrate that knowledge graphs are an effective tool for capturing semantic relationships within EHRs, enabling a more efficient and accurate data analysis. Our implementation provides valuable insights into patient outcomes and potential risk factors, contributing to the growing body of literature on the use of knowledge graphs in healthcare. In particular, our study highlights the potential of knowledge graphs to support decision-making and improve patient outcomes by enabling a more comprehensive and holistic analysis of EHR data. Overall, our research contributes to a better understanding of the value of knowledge graphs in healthcare and lays the foundation for further research in this area.

The performance of two quality control (QC) tests for aerodynamic particle size distributions (APSD) of orally inhaled drug products (OIPs) is compared. One of the tests is based on the fine particle dose (FPD) metric currently expected by the European regulators. The other test, called efficient data analysis (EDA), uses the ratio of large particle mass to small particle mass (LPM/SPM), along with impactor sized mass (ISM), to detect changes in APSD for QC purposes. The comparison is based on analysis of APSD data from four products (two different pressurized metered dose inhalers (MDIs) and two dry powder inhalers (DPIs)). It is demonstrated that in each case, EDA is able to detect shifts and abnormalities that FPD misses. The lack of sensitivity on the part of FPD is due to its "aggregate" nature, since FPD is a univariate measure of all particles less than about 5μm aerodynamic diameter, and shifts or changes within the range encompassed by this metric may go undetected. EDA is thus shown to be superior to FPD for routine control of OIP quality. This finding augments previously reported superiority of EDA compared with impactor stage groupings (favored by US regulators) for incorrect rejections (type I errors) when incorrect acceptances (type II errors) were adjusted to the same probability for both approaches. EDA is therefore proposed as a method of choice for routine quality control of OIPs in both European and US regulatory environments.

The Internet of Things (IoT) offers enhanced connectivity so that any system, being, or process can be reached from anywhere at any time by perpetual surveillance, which results in very large and complex data sets, i.e., Big Data. Despite numerous advantages, IoT technology comes with some unavoidable drawbacks. Considering the number of devices to be added to the current electromagnetic spectrum, it is a fact that wireless communications will severely suffer and eventually become inoperable. Furthermore, as wireless devices are equipped with limited capacity batteries, frequent replenishments and/or maintenance will be needed. However, this is neither practical nor achievable due to the excessive number of devices envisioned by the IoT paradigm. Here, the unification of Energy Harvesting (EH) and Cognitive Radio (CR) stands highly promising to alleviate the current drawbacks, enabling more efficient data generation, acquisition, and analysis. This chapter outlines a new vision, namely Internet of Energy Harvesting Cognitive Radios (IoEH-CRs), to take the IoT-enabled Big Data paradigm a step further. It discusses the basics of the EH-assisted spectrum-aware communications and their implications for the IoT, as well as the challenges posed by the unification of these techniques. An operational framework together with node and network architectures is also presented.

Motivation: To accelerate the acquisition time of 2D spiral cine DENSE MRI for strain imaging. Goal(s): To develop a golden-angle-ordered spiral cine DENSE sequence and a compressed sensing MRI reconstruction method and investigate the performance retrospectively undersampled datasets. Approach: We used a compressed sensing model with constraints in the spatial domain and also along the cardiac phase domain for image reconstruction. We compared the image quality and the strain measurements from data acquired with linear ordering and golden-angle ordering. Results: We demonstrated 1.5-fold accelerated 2D cine DENSE MRI. The proposed work may facilitate efficient DENSE data acquisition for strain imaging in clinical settings. Impact: The proposed work may facilitate efficient DENSE data acquisition for strain imaging in clinical settings.

Ultrashort pulses imply ultrabroad spectra, as a consequence of the transform limit. Hence, femtosecond time resolution is in trade-off with meV resolution spectroscopy. Further, beamline experiments will enormously profit from preparation, optimization and data replication at home-lab plasma -based sources, where beamtime limitations do not apply. We highlight the potential of such synergy among large and compact XUV sources, and how migrating advanced high-resolution spectroscopy to benchtop setups implies more efficient data acquisition and processing methods.

With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although such data offers super opportunities for economic analysis, its low-quality, high-dimensionality and huge-volume pose great challenges on efficient analysis of economic big data. The existing methods have primarily analyzed economic data from the perspective of econometrics, which involves limited indicators and demands prior knowledge of economists. When embracing large varieties of economic factors, these methods tend to yield unsatisfactory performance. To address the challenges, this paper presents a new framework for efficient analysis of high-dimensional economic big data based on innovative distributed feature selection. Specifically, the framework combines the methods of economic feature selection and econometric model construction to reveal the hidden patterns for economic development. The functionality rests on three pillars: (i) novel data pre-processing techniques to prepare high-quality economic data, (ii) an innovative distributed feature identification solution to locate important and representative economic indicators from multidimensional data sets, and (iii) new econometric models to capture the hidden patterns for economic development. The experimental results on the economic data collected in Dalian, China, demonstrate that our proposed framework and methods have superior performance in analyzing enormous economic data.

Industry 4.0 (I4.0) is a multidisciplinary engineering principle combing the IoT (Internet of things), big data, and cloud computing to cope with the dynamic changing industry. In this paper, the DDS (data distribution service) communication protocol was employed to implement a cloud platform for data acquisition from various sensors on a precision legacy machine tool including an accelerometer and sound, temperature, brightness, and humidity sensors. The sensor signals were acquired using Raspberry Pi as the edge device, then published to the cloud using the DDS application, and stored in the MySQL database. Using the Django web server, the acquired sensor signals could be shown in real time on the webpage via a combination of MQTT and Node-RED. In addition, the motion displacement of the machine tool detected by the encoder could be recorded through the edge device for further performance examination. With the proposed DDS cloud platform, it is demonstrated that a legacy machine can enable sensing and communication abilities such that the development of a smart machine is achievable for future I4.0 application.

In modern technologies the virtual equipment techniques can greatly help the fault diagnosis of the gear box in the sense that they can share the hardware and software recourses and compromise a self test system. In this paper the Labview was first used to set up the data acquisition and analysis platform. The data pre-processing software was designed including resonance demodulation and synchronizing average in the time domain, which can provide the analyzed data for the post-diagnosis. Then the BP neural network model was utilized for the fault diagnosis. The experiment results showed that the combination of these two processing methods can help reject the noise signals, thus expose the fault attributes and greatly reduce the false diagnosis.

BackgroundThe primary objective of the Critical Path for Alzheimer’s Disease (CPAD) consortium, a pre‐competitive consortium of the Critical Path Institute, is to promote, support, and manage pre‐competitive data and knowledge sharing from Alzheimer disease (AD) drug development stakeholders. Since 2019, CPAD has embarked on a new data sharing initiative to acquire fluid and imaging biomarker‐rich data sources for development of novel tools to quantify Alzheimer’s Disease progression across the AD continuum.MethodPatient‐level data from contemporary Phase2/Phase3 AD clinical trials and observational studies is acquired through formal data contribution agreements within CPAD, curated in collaboration with data contributors, standardized to common structure and terminology, and input into a relational data model (RDM) tool to generate analysis subsets. Data standardization is being accomplished using Clinical Data Interchange Standards Consortium (CDISC) standards. The RDM was developed using the open‐source software R and converts CDISC structure to analysis ready files for quantitative analyses through user defined primary keys, i.e. unique identifying variables for extracting information. The RDM includes a front‐end data interrogator that provides users with versatile capabilities to visualize and tabulate the data.ResultAs of October 2020, CPAD’s database (https://codr.c‐path.org/) contains 41 studies, standardized to the AD CDISC standards, of which three datasets were used to generate analysis subsets. Four additional datasets were recently acquired and are being standardized. Execution of Data Contribution Agreements (DCAs) is in progress for two contemporary datasets rich in key fluid and imaging biomarkers. Nine additional prioritized datasets are targeted for acquisition. The RDM is currently being used to transform the CDISC standardized data into analysis ready subsets containing patient demographics (age, sex), fluid biomarker information (Aβ 40/42), and clinical outcome assessments (Clinical Dementia Rating Scale – Sum of Boxes).ConclusionThe data acquisition and analysis platform developed is stimulating pre‐competitive efforts to generate quantitative tools for advancing AD drug development. Previously developed tools, including a regulatory endorsed clinical trial simulation tool, help optimize trial design and advance drug development in AD.

Architecture of intercomponent interaction of a microprocessor-powered sustainable relay protection system based on open standards IoT

To ensure reliability and safety, embedded prognostics are essential for ultrasonic motor, which is a key component of spacecraft. Although many big-data algorithms have been proposed for prognostics and health management (PHM), no suitable embedded prognostic platform is found for the ultrasonic motor. In this paper, a big data acquisition and processing platform is proposed. The platform is composed of a high-speed signal acquisition board, an edge computing module and a display screen. As the key part, the high-speed signal acquisition board is discussed in detail. Moreover, this platform has been tested with an ultrasonic motor. The results show that the acquired waveform is quite clear, which indicates this platform can successfully be used for prognostics of ultrasonic motors.

The waterborne sector faces nowadays significant challenges due to several environmental, financial and other concerns. Such challenges may be addressed, among others, by optimizing voyage plans, and diagnosing as early as possible engine failures that may lead to performance degradation. These two procedures require a continuous stream of information acquired from a number of diverse sensors. Frequently, beside central storage and transmission of such information, it is necessary to provide an agile and robust infrastructure which will allow local storage and ad hoc input and manipulation of information, via a user-friendly interface. Our proposed intelligent data acquisition and transmission platform provides an efficient, scalable and low-cost approach.

Novel method for industrial sewage outfall detection: Water pollution monitoring based on web crawler and remote sensing interpretation techniques