Data Processing Functions Research Articles

Innovative Design of Marine Resources Database System Based on Fuzzy Cluster Analysis

ABSTRACT Lian, Y., 2018. Innovative design of marine resources database system based on fuzzy cluster analysis. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 662–667. Coconut Creek (Florida), ISSN 0749-0208. Traditional marine resources database system has performance issue in aspects of CPU, memory, index, therefore, this paper proposes a kind of innovation design method of marine resources database system based on fuzzy clustering analysis. Firstly, on the basis of collecting the marine capture yield over the years, the statistical information of fishing effort data, the biological parameter of main commercial fish and the research literature bibliography of fishery resources, we sort out and add data, and build the innovative database of marine resources, and then use the fuzzy clustering analysis method to realize the function of data processing and analysis. Finally, we use basic language programming to give...

Investigation on Public Opinion of People Towards Regional Sustainable Development Policies Implementation in the Smolyan Region

Investigation on the public opinion of people in the Smolyan region focuses on determining the public awareness of sustainable development (SD) and the policies related to it, on the one hand, as well as finding out if the people from this region take actions, corresponding to SD, without associating them with it, on the other hand. The article also contributes to revealing the barriers which arise with the implementation of SD, being laid down in the Regional Strategy for Development of the Smolyan Region for 2014-2025. The research toolkit includes a survey (on-line), using Google’s functions of data collection and processing. To come to the most adequate and applicable conclusions on mountain regional development, the territorial scope of the study is based on the idea that a "region" is considered to be a mountainous territorial system within an area that includes all mountainous municipalities according to all typologies.

Risk assessment of water inrush from aquifers underlying the Qiuji coal mine in China

With the continuous increase in mining depth, mine-confined water accidents occur more frequently, which seriously threaten the safety of employees. Taking Qiuji coal mine in eastern China as an example, this paper introduces a novel method for evaluating high confined water hazard in coal seam floor. The no. 11 coal seam in Qiuji coal mine has been threatened by the underlying karst water. The water pressure of the aquifer, the aquiclude thickness, the coal seam depth and thickness, the brittle-rock thickness of the coal seam floor, and the geological structure are the important factors to control the floor water inrush. The risk of water inrush from the floor of 11 coal seam in Qiuji coal mine was evaluated using the improved analytic hierarchy process vulnerability index (IAHP-VI) method. The IAHP-VI method used the improved analytic hierarchy process to derive the weights of six factors and establish the vulnerability index model. Consequently, through data processing and spatial superposition function of GIS, the zoning map of risk assessment for high confined water hazard on the floor of the study area was obtained. The IAHP-VI method was compared to traditional water inrush coefficient method, which demonstrated the advantages of IAHP-VI method. The IAHP-VI method was more suitable for risk assessment of floor water inrush under complex geological conditions.

MS-Helios: a Circos wrapper to visualize multi-omic datasets

BackgroundAdvances in high-resolution mass spectrometry facilitate the identification of hundreds of metabolites, thousands of proteins and their post-translational modifications. This remarkable progress poses a challenge to data analysis and visualization, requiring methods to reduce dimensionality and represent the data in a compact way. To provide a more holistic view, we recently introduced circular proteome maps (CPMs). However, the CPM construction requires prior data transformation and extensive knowledge of the Perl-based tool, Circos.ResultsWe present MS-Helios, an easy to use command line tool with multiple built-in data processing functions, allowing non-expert users to construct CPMs or in general terms circular plots with a non-genomic basis. MS-Helios automatically generates data and configuration files to create high quality and publishable circular plots with Circos. We showcase the software on large-scale multi-omic datasets to visualize global trends and/or to contextualize specific features.ConclusionsMS-Helios provides the means to easily map and visualize multi-omic data in a comprehensive way. The software, datasets, source code, and tutorial are available at https://sourceforge.net/projects/ms-helios/.

Geoinformation Sharing System for East and Southeast Asia Using SDI, OGC Web Services and FOSS

This paper presents an innovative, cost-effective and efficient web-based sharing system for geoscience information among the countries in East and Southeast Asia. The information system’s main components are organized following the Spatial Data Infrastructure (SDI) model. It also uses the Open Geospatial Consortium (OGC) Web Map Service (WMS) and Web Processing Service (WPS) for online spatial data rendition and processing, respectively. Free and Open Source Software (FOSS) are also used for other important spatial data processing functions. The geospatial contents of the information system are stored and managed using the open source software PostgreSQL and its PostGIS extension. The information system makes spatial data sharing more efficient and organized by formulating a hierarchical information content classification scheme and the option for the users to put up their own customized WebGIS portals. These give users the option to show their chosen geospatial contents on their own web-based platform. The information system also makes spatial data sharing more flexible by providing data owners the choice to determine the data access privileges for each user or group of users. It also provides module for processing, rendering and sharing Japan’s ASTER satellite images. The information system is called GSi for Geoinformation Sharing Infrastructure for East and Southeast Asia. The URL of the web-based information system is https://ccop-gsi.org/main/index.html.

Digital world meets urban planet – new prospects for evidence-based urban studies arising from joint exploitation of big earth data, information technology and shared knowledge

ABSTRACTThe digital transformation taking place in all areas of life has led to a massive increase in digital data – in particular, related to the places where and the ways how we live. To facilitate an exploration of the new opportunities arising from this development the Urban Thematic Exploitation Platform (U-TEP) has been set-up. This enabling instrument represents a virtual environment that combines open access to multi-source data repositories with dedicated data processing, analysis and visualisation functionalities. Moreover, it includes mechanisms for the development and sharing of technology and knowledge. After an introduction of the underlying methodical concept, this paper introduces four selected use cases that were carried out on the basis of U-TEP: two technology-driven applications implemented by users from the remote sensing and software engineering community (generation of cloud-free mosaics, processing of drone data) and two examples related to concrete use scenarios defined by planners and decision makers (data analytics related to global urbanization, monitoring of regional land-use dynamics). The experiences from U-TEP’s pre-operations phase show that the system can effectively support the derivation of new data, facts and empirical evidence that helps scientists and decision-makers to implement improved strategies for sustainable urban development.

Research and implementation of a universal workflow model to evaluate the soil fertility based on OGC Web Service

ABSTRACTA method based on workflow technology and Open Geospatial Consortium (OGC) specification is proposed to establish a universal workflow conceptual model in the network environment. In this paper, the soil fertility evaluation conceptual model was developed as an evaluation method of soil fertility by analyzing the GIS-based fertility evaluation method and extracting the dynamic variable to verify the feasibility model. This validation process involves determining the instantiation of the conceptual model. The proposed conceptual model achieved the following goals. All data acquisition and processing functions were packaged into an OGC-compliant service model; these service models were organized into a processing chain in a certain order on the workflow platform by Petri-Net; the fertility evaluation was realized on the workflow platform by calling the processing chain. Results showed that processing functions and data can be shared in the network environment, and the network workflow model can be realized by the workflow technology. The successful implementation of fertility evaluation proved the feasibility of the network-based universal workflow conceptual model. In addition, the flexibility of our modeling method is demonstrated by reconstructing the workflow model.

BRANT: A Versatile and Extendable Resting-State fMRI Toolkit.

Data processing toolboxes for resting-state functional MRI (rs-fMRI) have provided us with a variety of functions and user friendly graphic user interfaces (GUIs). However, many toolboxes only cover a certain range of functions, and use exclusively designed GUIs. To facilitate data processing and alleviate the burden of manually drawing GUIs, we have developed a versatile and extendable MATLAB-based toolbox, BRANT (BRAinNetome fmri Toolkit), with a wide range of rs-fMRI data processing functions and code-generated GUIs. During the implementation, we have also empowered the toolbox with parallel computing techniques, efficient file handling methods for compressed file format, and one-line scripting. In BRANT, users can find rs-fMRI batch processing functions for preprocessing, brain spontaneous activity analysis, functional connectivity analysis, complex network analysis, statistical analysis, and results visualization, while developers can quickly publish scripts with code-generated GUIs.

Assessment of water inrush risk using the principal component logistic regression model in the Pandao coal mine, China

In order to improve the accuracy of floor water inrush assessment, the risk prediction model of floor water inrush was established by combining the principal component logistic regression analysis (PCLRA) and GIS spatial geographic analysis. In this paper, the geological data of Pandao coal mine was taken as the engineering background. First of all, main controlling factors of floor water inrush were determined and quantified. Next, PCLRA was used to determine the weight of each factor and establish the mathematical model for predicting the floor water inrush. And then, GIS’s spatial analysis and data processing function was used to draw related single factor thematic maps. Related thematic maps were weighted superposed to draw a floor water inrush zoning map based on PCLRA mathematical model. The study areas were divided into five levels by Jenks optimization method and vulnerability index initial model. And the corresponding threshold range was determined. The results show that (1) the high sensitivity factors in floor failure depth were added to evaluate the water inrush, and the fault fractal dimension was used to replace the fault structure related factors, and the main controlling factors of floor water inrush are more comprehensive; (2) the fitting degree of PCLRA model is high and the test accuracy is 83.3%; (3) the prediction results were well fitted to the actual position of water inrush (three water inrush points are located in the dangerous area, and two water inrush points are located in the relatively dangerous area).

Design of advanced control experimental platform for main steam pressure based on Matlab

In the field of process control, MATLAB is used for system simulation because of its powerful data processing functions. The subject of this thesis is to optimize PID parameters based on various optimization algorithms. The purpose of cascade PID controller is to control the main steam pressure, simulate the control system by MATLAB and connect Simulink with GUI interface. The parameters are set in the interface, and all the parameters are input and displayed in the GUI interface, which makes the user clear and convenient to optimize the operation interface and get the optimal value. The result of Simulink simulation was optimized and modified to achieve a complete control platform.

On-Chip Enzymatic Microbiofuel Cell-Powered Integrated Circuits

In situ energy harvesting is a major limitation for the development of implantable biomedical digital devices. Considerable efforts have been made to improve Glucose/O2 Enzymatic Biofuel Cells (EBFCs), able to generate electrical power in the physiological environment, in order to match the demands of biomedical electronics [1]. A key advance was the development of redox hydrogels. They permit efficient connection of high amounts of oxidoreductases to electrodes, leading to easily miniaturizable BFCs with power densities in the range of 100 μW.cm-2 suitable for application [2]. However, their open circuit potentials still fall below the requirements of conventional electronics. On the other hand, the power and voltage demands have decreased thanks to tremendous progress in low-power electronics [3], rendering devices compatible with the performance of EBFCs. Here we demonstrate that common data processing functions such as binary counting can be powered by a glucose/O2 micro-EBFC directly integrated on an Application Specific Integrated Circuit (ASIC) at the die level. An EBFC based on Glucose Oxidase and Bilirubin Oxidase hydrogels immobilized on 400 μm diameter electrodes was designed on the Si chip of the 4-bit ripple-counter. The system could be continuously powered during several hours from a single 50 μL droplet of aerated 5mM glucose solution. 1] S. Cosnier, A. Le Goff, M. Holzinger, Electrochem. Comm., 2014, 38, 19-23. [2] N. Mano, F. Mao, A. Heller, JACS, 2003, 125, 6588-6594. [3] A. Chandrakasan, N. Verma, D.C. Daly, Annual Rev. Biomed. Eng., 2008, 10, 247-274.

A Strong Entropy Power Inequality

When one of the random summands is Gaussian, we sharpen the entropy power inequality (EPI) in terms of the strong data processing function for Gaussian channels. Among other consequences, this ‘strong’ EPI generalizes the vector extension of Costa’s EPI to non-Gaussian channels in a precise sense. This leads to a new reverse EPI and, as a corollary, sharpens Stam’s uncertainty principle relating entropy power and Fisher information (or, equivalently, Gross’ logarithmic Sobolev inequality). Applications to network information theory are also given, including a short self-contained proof of the rate region for the two-encoder quadratic Gaussian source coding problem and a new outer bound for the one-sided Gaussian interference channel.

Map Reduce for big data processing based on traffic aware partition and aggregation

Big data refers to data sets whose volume is 500+ terabytes of data per day. The velocity makes it difficult to capture, manage, process and analyze 2 million records per day. Another characteristics of big data is variability which makes it difficult to identify the reason for losses in i.e., images, audio, video, sensor data and log files etc., Hadoop can be used to analyze this huge amount of data using Hadoop an approximate early result for executing the job partially becomes available for the user even before completion of job which reduce the response time. In Layers 3 Traffic aware clustering programming model is used for processing big data which includes the data processing function map by sort and reducing techniques. The implementation of the layers three traffic aware clustering method will be on the top of Hadoop which is partitioned into HDFS fixed sized blocks and generates intermediate output as a collection of pairs. The conventional hash function method is used for partitioning intermediate data among reduced task but it is not traffic efficient. In this paper to reduce network traffic cost, a Map Reduce task is done by designing data partition and aggregator that can reduce task merged traffic from multiple map tasks. The proposed algorithm is more efficient to reduce response time and the simulation results have showed proposal can reduce network traffic.

Implementation of UWB indoor location and distance measurement based on TOF algorithm

In order to achieve indoor positioning, based on the distance positioning algorithm is essential, it designs a company using DecaWave DW1000 chip and STM32 embedded control chip using the TOF algorithm and ranging system, indoor location in wireless sensor network. The whole system is divided into hardware and software. The hardware part includes anchor nodes and node labels, set up node using DW1000 chip and STM32 chip combination, the software using C language to write the STM32 location algorithm and control information controller, complete data acquisition, transmission and processing functions, in order to get the node label and the anchor node distance.

Fog Computing: An Overview of Big IoT Data Analytics

A huge amount of data, generated by Internet of Things (IoT), is growing up exponentially based on nonstop operational states. Those IoT devices are generating an avalanche of information that is disruptive for predictable data processing and analytics functionality, which is perfectly handled by the cloud before explosion growth of IoT. Fog computing structure confronts those disruptions, with powerful complement functionality of cloud framework, based on deployment of micro clouds (fog nodes) at proximity edge of data sources. Particularly big IoT data analytics by fog computing structure is on emerging phase and requires extensive research to produce more proficient knowledge and smart decisions. This survey summarizes the fog challenges and opportunities in the context of big IoT data analytics on fog networking. In addition, it emphasizes that the key characteristics in some proposed research works make the fog computing a suitable platform for new proliferating IoT devices, services, and applications. Most significant fog applications (e.g., health care monitoring, smart cities, connected vehicles, and smart grid) will be discussed here to create a well‐organized green computing paradigm to support the next generation of IoT applications.

Data Preprocessing Based on MFL Inspection

It’s necessary for detected ferromagnetic material to do fault diagnosis to ensure it to operate safely, lengthen its service life, prevent leakage and environment pollution. Now, magnetic flux leakage (MFL) inspection method is widely recognized as one of the most effective nondestructive testing methods for ferromagnetic material. In this paper, data preprocessing and data processing are the main research content. Data preprocessing makes data neater, which lays a solid foundation for accurate and fast defect localization, feature extraction and life prediction. Furthermore, data preprocessing and image rendering implement the data processing function rapidly and precisely and greatly reduce the difficulty of data processing.

Access Control with Delegated Authorization Policy Evaluation for Data-Driven Microservice Workflows

Microservices offer a compelling competitive advantage for building data flow systems as a choreography of self-contained data endpoints that each implement a specific data processing functionality. Such a ‘single responsibility principle’ design makes them well suited for constructing scalable and flexible data integration and real-time data flow applications. In this paper, we investigate microservice based data processing workflows from a security point of view, i.e., (1) how to constrain data processing workflows with respect to dynamic authorization policies granting or denying access to certain microservice results depending on the flow of the data; (2) how to let multiple microservices contribute to a collective data-driven authorization decision and (3) how to put adequate measures in place such that the data within each individual microservice is protected against illegitimate access from unauthorized users or other microservices. Due to this multifold objective, enforcing access control on the data endpoints to prevent information leakage or preserve one’s privacy becomes far more challenging, as authorization policies can have dependencies and decision outcomes cross-cutting data in multiple microservices. To address this challenge, we present and evaluate a workflow-oriented authorization framework that enforces authorization policies in a decentralized manner and where the delegated policy evaluation leverages feature toggles that are managed at runtime by software circuit breakers to secure the distributed data processing workflows. The benefit of our solution is that, on the one hand, authorization policies restrict access to the data endpoints of the microservices, and on the other hand, microservices can safely rely on other data endpoints to collectively evaluate cross-cutting access control decisions without having to rely on a shared storage backend holding all the necessary information for the policy evaluation.

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.

Big Data Management: What to Keep from the Past to Face Future Challenges?

The emergence of new hardware architectures, and the continuous production of data open new challenges for data management. It is no longer pertinent to reason with respect to a predefined set of resources (i.e., computing, storage and main memory). Instead, it is necessary to design data processing algorithms and processes considering unlimited resources via the “pay-as-you-go” model. According to this model, resources provision must consider the economic cost of the processes versus the use and parallel exploitation of available computing resources. In consequence, new methodologies, algorithms and tools for querying, deploying and programming data management functions have to be provided in scalable and elastic architectures that can cope with the characteristics of Big Data aware systems (intelligent systems, decision making, virtual environments, smart cities, drug personalization). These functions, must respect QoS properties (e.g., security, reliability, fault tolerance, dynamic evolution and adaptability) and behavior properties (e.g., transactional execution) according to application requirements. Mature and novel system architectures propose models and mechanisms for adding these properties to new efficient data management and processing functions delivered as services. This paper gives an overview of the different architectures in which efficient data management functions can be delivered for addressing Big Data processing challenges.

Eddy-Current Sensor for a Tachometer

Abstract The speed of a rotor or rotors of a turbine engine is important information on its operation. Due to the specific conditions, in which the measurement is taken in a turbine engine, which in particular applies to small engines that are becoming increasingly popular in manned and unmanned aviation, the most universal detector is the detector using the phenomenon of eddy currents. This article presents the developed detector system solution. The correct operation of a detector is conditional upon the possibility to detect the presence of a blade in the detection field in a reliable manner, while maintaining the resistance to interference. The developed system is resistant to changes in parameters due to the use of a differential system consisting of two detector coils connected in a bridge circuit, a rectifier circuit with filters acting as a demodulator and a comparator. The system works with a microprocessor performing data processing functions. The executed functional model was tested in a laboratory, which enabled to determine its characteristics in several configurations and to confirm its correct operation.