Image Processing Algorithm Design of Agricultural Internet of Things Platform Based on Big Data Analysis

Decision letter: Neural markers of predictive coding under perceptual uncertainty revealed with Hierarchical Frequency Tagging

PurposeThe purpose of this paper is to conduct a big data analysis of public library operations and services of two cities in two countries by using the Chernoff face method.Design/methodology/approachThe study is designed to evaluate library services by analyzing the Chernoff face. Big data on public libraries in London and Seoul were collected, respectively, from Chartered Institute of Public Finance and Accountancy and the Korean government’s website for drawing a Chernoff face. The association of variables and human facial features was decided by survey. Although limited in its capacity to handle a large number of variables (eight were analyzed in this study) the Chernoff face method does readily allow for the comparison of a large number of instances of analysis. A total of 58 Chernoff faces were drawn from the formatted data by using the R programming language.FindingsThe study reveals that most of the local governments in London perform better than those of Seoul. This consequence is due to the fact that local governments in London operate more libraries, invest more budgets, allocate more staff and hold more collections than local governments in Seoul. This administration resulted in more use of libraries in London than Seoul. The study validates the benefit of using the Chernoff face method for big data analysis of library services.Practical implicationsThe Chernoff face method for big data analysis offers a new evaluation technique for library services and provides insights that may not be as readily apparent and discernible using more traditional analytical methods.Originality/valueThis study is the first to use the Chernoff face method for big data analysis of library services in library and information research.

Frequent and accurate estimation of forest structure parameters, such as number of trees per hectare or total height, are mandatory for sustainable forest management. Unmanned aircraft system (UAS) equipped with inexpensive sensors can be used to monitor and measure forest structure. The detailed information provided by the UAS allows tree level forest inventory. However, tree identification depends on a variety of parameters defining the image processing and tree segmentation algorithms. The objective of our study was to identify parameter combinations that accurately delineated trees and their heights. We evaluated the impact of different tree segmentation and point cloud generation algorithms on forest inventory from imagery collected with a UAS over a mature Douglas-fir plantation forest. We processed the images with two commonly used commercial software packages, Agisoft PhotoScan and Pix4Dmapper, both implementing image processing algorithms called Structure from Motion. For each software we generated photogrammetric point clouds by varying the parameters defining the implementation. We segmented individual trees and heights using three tree algorithms: Variable Window Filter, Graph-Theoretical, and Watershed Segmentation. We assessed the impact of image processing algorithms on forest inventory by comparing the estimated trees with trees manually identified from the point clouds. We found that the type of tree segmentation and image processing algorithms have a significant effect in accurately identifying trees. For tree height estimation, we found strong evidence that image processing algorithms had significant effects, whereas tree segmentation algorithms did not significantly affect tree height estimation.These findings may be of interest to others that are using high-resolution spatial imagery to estimate forest inventory parameters.

In the case of the global energy crisis and the higher sound of energy saving and emission reduction, how to take effective management measures of ship energy efficiency to achieve the goal of energy saving and emission reduction, put forward a new challenge for the development of shipping technology. The application of big data technology provides a new idea for the research of ship energy efficiency optimization management. The energy efficiency management level of the operating ship can be improved by the analysis and mining of the big data. In this paper, a big data analysis platform for ship energy efficiency management based on the widely used Hadoop platform architecture is designed. Afterward, due to the huge amount of involved data on the energy efficiency management which has exceeded the processing ability of traditional solutions, the big data analysis method is used to achieve the route division according to environmental factors, thus to lay the foundation for speed optimization in different segments of a route. Finally, a simple decision-making method of optimal engine speed based on the result of route division is proposed, which could improve ship energy efficiency and hence reduce CO2 emission.

A comparative study between two types of tool wear monitoring systems for milling processes is introduced in this paper. The suggested sensory fusion approach includes the implementation of an infrared camera, in addition to force, vibration, sound and acoustic emission sensors. The majority of the research work available in literature and industry focuses on using one dimensional signals, such as force, vibration. Two dimensional data, such as infrared and visual images, are limited in literature in relation to machining operations. This work compares between one dimensional and two dimensional data for the development of a tool condition monitoring system for milling processes. The paper presents a comparative study between the performance of signal and image processing algorithms using neural networks. Fourier Transformation and Wavelets analysis are used to process one dimensional and Two dimensional data respectively. The results indicate that two dimensional data obtained from infrared images has significant capability in comparison to one dimensional data for the detection of tool wear for the selected image and signal processing algorithms.

This paper proposes two algorithms for Voice Activity Detection (VAD) based on sparse representation in spectro-temporal domain. The first algorithm was made using two-dimensional STRF (Spectro-Temporal Response Field) space based on sparse representation. Dictionaries with different atomic sizes and two dictionary learning methods were investigated in this approach. This algorithm revealed good results at high SNRs (signal-to-noise ratio). The second algorithm, whose approach is more complicated, suggests a speech detector using the sparse representation in four-dimensional STRF space. Due to the large volume of STRF's four-dimensional space, this space was divided into cubes, with dictionaries made for each cube separately by NMF (non-negative matrix factorization) learning algorithm. Simulation results were presented to illustrate the effectiveness of our new VAD algorithms. The results revealed that the achieved performance was 90.11% and 91.75% under -5 dB SNR in white and car noise respectively, outperforming most of the state-of-the-art VAD algorithms.

The study of consumer behavior at all stages of marketing is related to Big Data analysis. The purpose of the study is to structure information on assessing consumer behavior, which relates to Big Data on the components of marketing max, to form methods for analyzing this information and basic results that will contribute to sustainable business development in the conditions of information and technological transformations. During the research, methods of synthesis, system analysis, comparison and systematization were used to track trends in the use of Big Data analysis in a system for assessing consumer behavior. The result of the study is the systematization of information data, methods of Big Data analysis for assessing consumer behavior. The proposed tools for assessing consumer behavior based on Big Data technologies make it possible to improve the marketing strategy for enterprise development and form a mechanism for strategic marketing management. Further research may concern generalizing the methodology for using Big Data for different categories of consumers.

Recently, e-education around the world is developing rapidly and the main problem is the timely provision of students with quality educational information. A significant impetus for this is the global epidemic of covid-19. The problem of implementing e-education cannot be solved without analysing the large flow of information coming into the information environment of e-education from participants in the educational process – students, teachers, administration, etc. In this environment, there are a large number of different types of data, both structured and unstructured, which are difficult to process by traditional statistical methods. The aim of the study is to show that the development and implementation of successful e-learning systems requires the use of new technologies that would allow the storage and processing of large data streams. Large amounts of disk space are required to store large data. It is shown that to solve this problem it is expedient to use cluster technology NAS (Network Area Storage), which allows to store information of educational institutions on NAS - servers and to have access to them from the Internet. To process and personalize Big Data in the e-learning environment, it is proposed to use technologies MapReduce, Hadoop, NoSQL and others. The article provides examples of the use of these technologies in the cloud environment. These technologies in e-learning make it possible to achieve flexibility, scalability, accessibility, security, confidentiality and ease of use of educational information. Another important problem of e-learning is the discovery of new, sometimes hidden, relationships in big data, new knowledge (data mining), which can be used to improve the educational process and increase the efficiency of its management. To classify electronic educational resources, identify patterns (patterns) of students with similar psychological, behavioural and intellectual characteristics, the development of individualized curricula in the article it is proposed to use methods of big data analysis. The article shows that to date, many software applications have been developed for big data mining. These software products can be used for classification, clustering, regression and network analysis of educational information. The application of these methods in e-education will allow teachers to receive timely information about students, to respond quickly to any changes in the learning process, to make timely changes to educational content. The obtained results of the research are offered to be used for development of recommendations at creation of electronic courses in higher and secondary educational institutions of Ukraine.

Objectives: The rapid change and emergence of the technology are happening more than a decade. The current world has become almost completely a digital and computing world. The objective of this paper is to describe real impact of Big Data analytics techniques in this digital world to obtain valuable knowledge. The essential goal of this paper was to examine about the big data analytics methods and techniques. It has reviewed six of the techniques that are used by the big data researchers to pick up bits of knowledge by processing and analyzing the big data in limited time. The development of these techniques will help us to analyze the big data more effectively and subsequently delivering more productive and gainful outcomes. Methods/Statistical Analysis: We reviewed different analytics techniques for text, audio, video, and social media data, as well as predictive analytics. This paper describes the six Analytics Techniques to obtain valuable knowledge in limited time. Findings: The six analytics techniques which is discussed in this paper is very important to obtain valuable information from big data in limited time, because now a days data is growing at very fast rate. Application/Improvements: This paper presented an overview of most commonly use big data analytics techniques. Keywords: Big Data, Data, Digital, Information, Knowledge, Technology

A unidirectional microelectromechanical system (MEMS) microphone single module for handsfree and voice recognition systems in automobiles is presented. Because in-cabin noise and temperature variation affect the reduction in the handsfree and voice recognition system performance, noise suppression and increasing the signal-to-noise ratio (SNR) of the microphone are required. In this study, a capacitive MEMS microphone module with a high SNR and a unidirectional characteristic is achieved by designing the structure, package, and module of the MEMS microphone. To improve the SNR, the microphone is developed using a slit-edged membrane. The slit structure is designed to release the residual stress of the membrane to achieve improved sensitivity and SNR. The unidirectional characteristic of the microphone enables suppression of noise signals from undesired directions. The directional characteristic of the microphone is realized by attaching a porous SU-8 filter to delay the time to one of the two acoustic ports on the package. Tests on the proposed unidirectional MEMS microphone package and module show that an SNR of 62.4 dB and a front-back ratio of 27.1 dB are achieved.

BackgroundIn integrated PET/MR hybrid imaging the evaluation of PET performance characteristics according to the NEMA standard NU 2–2007 is challenging because of incomplete MR-based attenuation correction (AC) for phantom imaging. In this study, a strategy for CT-based AC of the NEMA image quality (IQ) phantom is assessed. The method is systematically evaluated in NEMA IQ phantom measurements on an integrated PET/MR system.MethodsNEMA IQ measurements were performed on the integrated 3.0 Tesla PET/MR hybrid system (Biograph mMR, Siemens Healthcare). AC of the NEMA IQ phantom was realized by an MR-based and by a CT-based method. The suggested CT-based AC uses a template μ-map of the NEMA IQ phantom and a phantom holder for exact repositioning of the phantom on the systems patient table. The PET image quality parameters contrast recovery, background variability, and signal-to-noise ratio (SNR) were determined and compared for both phantom AC methods. Reconstruction parameters of an iterative 3D OP-OSEM reconstruction were optimized for highest lesion SNR in NEMA IQ phantom imaging.ResultsUsing a CT-based NEMA IQ phantom μ-map on the PET/MR system is straightforward and allowed performing accurate NEMA IQ measurements on the hybrid system. MR-based AC was determined to be insufficient for PET quantification in the tested NEMA IQ phantom because only photon attenuation caused by the MR-visible phantom filling but not the phantom housing is considered. Using the suggested CT-based AC, the highest SNR in this phantom experiment for small lesions (<= 13 mm) was obtained with 3 iterations, 21 subsets and 4 mm Gaussian filtering.ConclusionThis study suggests CT-based AC for the NEMA IQ phantom when performing PET NEMA IQ measurements on an integrated PET/MR hybrid system. The superiority of CT-based AC for this phantom is demonstrated by comparison to measurements using MR-based AC. Furthermore, optimized PET image reconstruction parameters are provided for the highest lesion SNR in NEMA IQ phantom measurements.

With the rapid development of Internet and big data, the method of directing the film and television through big data analysis has attracted more and more attention. The visual analysis of data is an important method of big data analysis. The demand for big data in film industry is becoming larger and larger. The development trend of the industry relies on visualization for film and television data analysis. The basic concept of big data, the theory of big data visualization analysis and the film evaluation research base on the film expert evaluation system are presented in this paper.

“Big data” has become a well-deserved focus in the industry in the past year. Especially with the development of new SNS network, the surge of video traffic and the emergence of photo sharing needs, big data grows more rapidly in the fertile soil. The era of big data has come, we can use big data for all aspects of analysis, so as to provide us with more abundant information resources. This paper takes Chinese drama as the research object and analyzes the characteristics of Chinese drama with big data analysis method, so as to summarize the characteristics of Chinese drama and contribute to the development of Chinese drama. The big data analysis method adopted in this paper is to use crawler software and SPSS Modeler to conduct data mining and modeling and obtain the corresponding feature word cloud. The research of this paper shows that the unique performance style and expression technique of Chinese drama make the characteristics of drama obvious and have the characteristics of generality, freehand brushwork and theatricality. According to these characteristics, relevant laws and regulations can be issued at the national or government level to promote the development of Chinese drama and enable the broad masses of people to have a deeper understanding of Chinese drama.

This paper considers the optimal transmit precoder design for MIMO systems operating with antenna mutual coupling at high signal to noise ratios (SNR). The design problem is formulated as a convex optimization problem with two separate power constraints: a constraint on the available power imposed on a per-amplifier basis, and a sum constraint on the allowable radiated power. To gain insights into the mutual coupling effect and to simplify the optimization problem, sufficient and necessary conditions on the power constraints are derived, under which the dual-constraint problem reduces to a single-constraint problem with a closed-form solution. An efficient suboptimal algorithm is also proposed for the dual constraint problem by relaxing the per-amplifier power constraint to sum power constraint. One interesting conclusion of our analysis is that, with mutual coupling, isotropic inputs are not optimal in general at high SNR. Our proposed optimal precoder design is shown to yield significant improvements compared to conventional techniques which are designed based on one power constraint only.

The reasonable use of water resources has become an important issue for the sustainable development of humanity in the future. Many researches focus on groundwater quality inspection, but not groundwater quality assessment. This paper aims to study groundwater quality evaluation models based on multi-scale fuzzy comprehensive evaluation and big data analysis methods. We combine coarse-grained multi-scale fuzzy entropy and fuzzy comprehensive evaluation method to establish a groundwater quality evaluation model based on big data environment. The evaluation of groundwater samples from 327 test points in Huangpu District, Xuhui District, Hongkou District, and Putuo District of Shanghai was conducted. The results show that the overall condition of Shanghai groundwater is better, and more than 94% of samples qualified as drinking water sources. The method presented in this paper not only guarantees that the coarse-grained data on all scales are consistent with the length of the original data, but also avoids the phenomenon of data loss, which greatly improves the accuracy of subsequent algorithms.