Continuous Data Processing Research Articles

Inverse Modeling for Filters Using a Regularized Deep Neural Network Approach

Extracting coupling matrix from given ${S}$ -parameters can be viewed as an inverse problem for microwave filters, which is of importance for filter design and tuning. In this letter, a regularized deep belief network (R-DBN) is proposed to handle this inverse modeling problem. The training of an R-DBN consists of two steps. First, in unsupervised training, to accommodate the characteristics of input data, this model is constructed with a series of traditional restricted Boltzmann machines (RBMs), which are equipped with a continuous version of transfer function for continuous data processing. In addition, this training can provide suitable weights and bias for the following step. Second, in supervised training, Bayesian regularization is employed to increase modeling ability and prevent overfitting. Two experiments with different simulation environments are illustrated, and the calibration results show high accuracy and robustness in a more intelligent way using this method.

A Distance-Window Approach for the Continuous Processing of Spatial Data Streams

Real-time and continuous processing of citywide spatial data is an essential requirement of smart cities to guarantee the delivery of basic life necessities to its residents and to maintain law and order. To support real-time continuous processing of data streams, continuous queries (CQs) are used. CQs utilize windows to split the unbounded data streams into finite sets or windows. Existing stream processing engines either support time-based or count-based windows. However, these are not much useful for the spatial streams containing the trajectories of moving objects. Hence, this paper presents a distance-window based approach for the processing of spatial data streams, where the unbounded streams can be split with respect to the trajectory length. Since the window operation involves repeated computation, this work presents two incremental distance-based window approaches to avoid the repetition. A detailed experimental evaluation is presented to prove the effectiveness of the proposed incremental distance-based windows.

Trends in development of technologies at hospitality business in the conditions of economy digitalization

This article discusses trends in development of technologies at hospitality business in the conditions of economy digitalization. At the current time economy digitalization of the Russian Federation significantly affects hospitality industry, in particular, hotel automation associated with the use of modern technologies that improves the quality of customer service. This research focuses on such technologies as artificial intelligence, blockchain, augmented reality technologies, cloud technologies, analysis of speech scripts, face recognition technologies, voice assistants, and many others.The results of the study show that despite the fact that hotel business is successfully mastering modern digital technologies, only fulfillment of continuous data collection, processing, analysis and aggregation will allow the hospitality industry management to achieve one of their main goals, which is to better understand their customers and interact with them.

Processing streams in a monitoring cloud cluster

The creation of monitoring clusters based on cloud computing technologies is a promising direction for the development of systems for continuous monitoring of objects for various purposes in the web space. Hadoop web-programming environment is the technological basis for the development of algorithmic and software solutions for the synthesis of monitoring clusters, including information security and information counteraction systems. The International Telecommunication Union’ (ITU) recommendations Y. 3510 present the requirements for cloud infrastructure that require monitoring the performance of deployed applications based on the collection of real-world statistics. Often, computing resources of monitoring clusters of cloud data centers are allocated for continuous parallel processing of high-speed streaming data, which imposes new requirements to monitoring technologies, necessitating the creation and research of new models of parallel computing. The need to use service monitoring plays an important role in the cloud computing industry, especially for SLA/QoS assessment, as the application or service may experience problems even if the virtual machines on which the work is taking place appear to be operational. This requires to study the methodological possibilities of organization to study of parallel processing high-speed streaming services with the processing of huge amounts of bit data, and, simultaneously, to estimate the necessary computational resource. In the conditions of high dynamics of changes in the bit rate of information generation from the source, a model of the bit rate of Discretized Stream (DStream) formation is proposed, which has a common application. Based on the poly-burst nature of the bit rate model, a model of group content traffic of any sources of different services processed in the cloud cluster was created. The obtained results made it possible to develop mathematical models of parallel DStreams from sources processed in a cloud cluster via Hadoop technology using the micro-batch architecture of the Spark Streaming module. These models take into account the flow of requests for maintenance from sources of different services, on the one hand, and, on the other hand, the needs of services in bit rate, taking into account the multichannel traffic of sources of various services. At the same time, analytical relations are obtained to calculate the required performance of the Hadoop cluster at a given value of the probability of batch loss.

Design of Smart Home Implementation Within IoT Natural Language Interface

To process continuous sensor data in Internet of Things (IoT) environments, this study optimizes queries using multiple MJoin operators. To achieve efficient storage management, it classifies and reduces data using a support vector machine (SVM) classification algorithm. A global shared query execution technique was used to optimize multiple MJoin queries. By comparing each kernel function of the SVM classification algorithm, the system's performance was evaluated through experiments according to the selected optimal kernel function and changes in sliding window size. Furthermore, to implement a smart home system that can actively respond to users, classified and reduced sensor data were utilized to enable the intelligent control of devices inside the home. The sensor data (e.g., temperature, humidity, gas) used to recognize the current conditions of an IoT-based smart home system and corresponding date data were classified into decision trees, and the system was designed using five sensors to intelligently control priorities such as ventilation, temperature, and fire and intrusion detection. The experiments demonstrated that the multiple MJoin technique yields high improvements in performance with relatively few searches. In this study, the sigmoid was selected as the optimal kernel function for the SVM classification algorithm. According to the SVM classification algorithm results, based on changes in the sliding window size, the average error rate was 2.42%, the reduction result was 17.58%, and the classification accuracy was 85.94%. According to the comparison of the classification performance of SVM and other algorithms, the SVM classification algorithm exhibited a minimum 9% better classification performance. Thus, compared to existing home systems, this algorithm is expected to increase system efficiency and convenience by enabling the configuration of a more intelligent environment according to the user's characteristics or requirements.

Лес в колористической картине мира К.Г. Паустовского

In the work, on extensive factual material of the works included in K.G. Paustovsky’s Collected Works, on the example of the lexeme forest functioning, correlating with a wide range of attributes and predicates, a significant fragment of K.G. Paustovsky’s language picture of the world is reconstructed. Considering the importance of the coloristic component for the writer’s worldview, it seems appropriate to identify the specifics of the forest individual author’s color per-ception and visualization. Key findings: 1) the coloristic spectrum used by the writer to visualize the forest is established; 2) the dominants of this spectrum are identified; 3) the functional poten-tial of the color terms is identified; 4) the determinism of the forest coloristic characteristics is de-termined; 5) the correlation of national and individual author’s in color visualization of the forest is established; 6) the specifics of the author’s color style of writing is described, all this in complex contribute to holistic reconstruction of the writer’s coloristic picture of the world. The theoretical basis of the study are the key provisions of linguistics of color, the methodological basis – a set of methods used at different stages of work, including the continuous sampling method, descriptive-analytical, contextual, and quantitative data processing.

Variation of Suspended Particles in the Bottom Layer of the East China Sea with Data from Seafloor Observatory.

The in situ scattering and transmissometry laser (LISST-100X), equipped with an acoustic wave and current (AWAC) meter and conductivity, temperature, and depth (CTD) instruments over the seabed in the East China Sea, was used to monitor the variation in suspended particles in the bottom sea layer, including particle size distribution (PSD) and volume concentration. The power law approximation was tested to describe the variability in PSD based on the field data. The results show that the power law was robust in processing continuous data, accompanied with the same optimal reference particle size (~63 μm) and little change in the corresponding exponent (~3.4) in both periods. Suspended particles were divided into three types: macroflocs (>133 μm), microflocs (36–133 μm), and single grains (<36 μm). Particle sizes were coarse during the two seasons, with macroflocs representing more than 60% of all the suspended particles, especially in February, when the particle size spectra were usually open-ended. Results from the harmonic analysis method indicate that tidal-induced resuspension and advection are the major reasons for the diurnal dynamics of sediments. Due to the tidal asymmetry in the region, we only found one mode in volume concentration at the moment of maximum velocity. However, the ratios of macroflocs were bimodal, with maximum floods and ebbs in one tidal cycle in February, when the higher mode at the maximum ebbs may be contributed to by the flocculation of finer particles considering the decreasing ratios of finer particles. Due to the enhanced stratification and the clean barrier built up by the Taiwan Warm Current in the southeast corner, the significant daily variation in suspended particles observed in February weakened in September. The influence of waves was uncertain, although the correlation coefficient between significant wave height and volume concentration was about 64% in February.

Unsupervised Learning Used in Automatic Detection and Classification of Ambient‐Noise Recordings from a Large‐N Array

AbstractWe present a method for automatic detection and classification of seismic events from continuous ambient‐noise (AN) recordings using an unsupervised machine‐learning (ML) approach. We combine classic and recently developed array‐processing techniques with ML enabling the use of unsupervised techniques in the routine processing of continuous data. We test our method on a dataset from a large‐number (large‐N) array, which was deployed over the Kylylahti underground mine (Finland), and show the potential to automatically process and cluster the volumes of AN data. Automatic sorting of detected events into different classes allows faster data analysis and facilitates the selection of desired parts of the wavefield for imaging (e.g., using seismic interferometry) and monitoring. First, using array‐processing techniques, we obtain directivity, location, velocity, and frequency representations of AN data. Next, we transform these representations into vector‐shaped matrices. The transformed data are input into a clustering algorithm (called k‐means) to define groups of similar events, and optimization methods are used to obtain the optimal number of clusters (called elbow and silhouette tests). We use these techniques to obtain the optimal number of classes that characterize the AN recordings and consequently assign the proper class membership (cluster) to each data sample. For the Kylylahti AN, the unsupervised clustering produced 40 clusters. After visual inspection of events belonging to different clusters that were quality controlled by the silhouette method, we confirm the reliability of 10 clusters with a prediction accuracy higher than 90%. The obtained division into separate seismic‐event classes proves the feasibility of the unsupervised ML approach to advance the automation of processing and the utilization of array AN data. Our workflow is very flexible and can be easily adapted for other input features and classification algorithms.

IoT and Big Data Technologies for Monitoring and Processing Real-Time Healthcare Data

Recent advances in pervasive technologies, such as wireless, ad hoc networks, and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as the Internet of Things (IoT). The IoT is seen as an enabler to the development of intelligent and context-aware services and applications. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data technologies and data analytics have been recently proposed for timely analyzing information (i.e., data, events) streams. The main aim is to make users' life more comfortable according to their locations, current requirements, and ongoing activities. In this article, combining IoT techniques and Big data technologies into a holistic platform for continuous and real-time health-care data monitoring and processing is introduced. Real-testing experiments have been conducted and results are reported to show the usefulness of this platform in a real-case scenario.

A version of Organization of Multithread Processing of Confidential Data on the Basis of Cellular Automata

Рurpose of research is to develop a multithread processing system based on an encryption algorithm using cellular automata and to study statistical performance indicators depending on the hardware components and the size of the input block, and to develop recommendations for improving the cryptostrength of the method. Methods . A mathematical model of the encryption method using a floating window based on cellular automata was considered [3]. To study the speed of confidential data processing, there was developed a variant of the organization of the structure of the software module with an extended block of setting parameters that determine the dimension of the matrix, the line of activation of the bit neighborhood of the processed elements, the number of parallel computations (threads) and the rule of expansion of the boundary elements of the matrix. A method for the development of the dependence curve of the processing time and inital parameters that can be applied both to process individual files and continuous network subscriber data flow, is proposed. Results. The cryptographic module implementing the encryption method on the basis of cellular automata, which specific feature is a multithread mode of operation and dynamic control of the block of initial parameters, was developed. Recommendations for setting the neighborhood of the active elements of the matrix and the number of threads taking into account the architecture of the CPU were formulated. Experimental studies were conducted to confirm the completeness and correctness of the proposed solutions. The expediency of using high-speed hard disk drives and saving the results of encryption in asynchronous segmented mode with working thread-bind results was revealed. Conclusion. The proposed version of the organization of the confidential data processing system in the form of a software module, taking into account the features of the hardware, allows optimization of the processing speed, and the compliance with the recommendations for the expansion of the neighborhood in the block transformation can improve the cryptographic algorithm based on cellular automata with a floating window.

Enhancing Survey Quality: Continuous Data Processing Systems

Abstract Producers of large government-sponsored surveys regularly use Computer-Assisted Interviewing (CAI) software to design data collection instruments, monitor fieldwork operations, and evaluate data quality. When used in conjunction with responsive survey designs, last-minute modifications to problems in the field are quickly addressed. Complementing this strategy, but little discussed, is the need to implement similar changes in the post data collection stage of the survey data life cycle. We describe a continuous data processing system where completed interviews are carefully examined as soon as they are collected; editing, recode, and imputation programs are applied using CAI tools; and the results are reviewed to correct problematic cases. The goal: provide higher quality data and shorten the time between the conclusion of data collection and the appearance of public use data files.

IoTSim-Stream: Modelling stream graph application in cloud simulation

In the era of big data, the high velocity of data imposes the demand for processing such data in real-time to gain real-time insights. Various real-time big data platforms/services (i.e. Apache Storm, Amazon Kinesis) allow to develop real-time big data applications to process continuous data to get incremental results. Composing those applications to form a workflow that is designed to accomplish certain goal is the becoming more important nowadays. However, given the current need of composing those applications into data pipelines forming stream workflow applications (aka stream graph applications) to support decision making, a simulation toolkit is required to simulate the behaviour of this graph application in Cloud computing environment. Therefore, in this paper, we propose an IoT Simulator for Stream processing on the big data (named IoTSim-Stream) that offers an environment to model complex stream graph applications in Multicloud environment, where the large-scale simulation-based studies can be conducted to evaluate and analyse these applications. The experimental results show that IoTSim-Stream is effective in modelling and simulating different structures of complex stream graph applications with excellent performance and scalability.

Tuition Single Classification using Decision Tree Method and C4.5

Tuition Single (UKT in Indonesia) is an operational cost measurement standard for universities in Indonesia which is applied by the government since 2013. This standard is enforced for every public university students who pass entrance university test. The problem with this standard is still found that there are students with tuition single category which does not fit with their condition, thus resulting in the incriminating of students with difficult economic situations. The usage of the decision tree and C4.5 algorithm for classification is able to process continuous data and build model efficiently with a large number of data. The input variables for tuition single classification are the income of the financier, the status of the children within the family, the taxable value of land and building, and the ownership of the vehicle. This work uses a multiclass variable. The output variables are group 1 to group 7. The accuracy of the system is 80.25% where the groups with the most students are group 3 and group 7. The decision tree built is also quite big considering the number of the variables and the variation of data used in each variable.

SURROGATES: Virtual OBUs to Foster 5G Vehicular Services

Virtualization technologies are key enablers of softwarized 5G networks, and their usage in the vehicular domain can provide flexibility and reliability in real deployments, where mobility and processing needs may be an issue. Next-generation vehicular services, such as the ones in the area of urban mobility and, in general, those interconnecting on-board sensors, require continuous data gathering and processing, but current architectures are stratified in two-tier solutions in which data is collected by on-board units (OBU) and sent to cloud servers. In this line, intermediate cache and processing layers are needed in order to cover quasi-ubiquitous data-gathering needs of vehicles in scenarios of smart cities/roads considering vehicles as moving sensors. The SURROGATES solution presented in this paper proposes to virtualize vehicle OBUs and create a novel Multi-Access Edge Computing (MEC) layer with the aim of offloading processing from the vehicle and serving data-access requests. This deals with potential disconnection periods of vehicles, saves radio resources when accessing the physical OBU and improves data processing performance. A proof of concept has been implemented using OpenStack and Open Source MANO to virtualize resources and gather data from in-vehicle sensors, and a final traffic monitoring service has been implemented to validate the proposal. Performance results reveal a speedup of more than 50% in the data request resolution, with consequently great savings of network resources in the wireless segment. Thus, this work opens a novel path regarding the virtualization of end-devices in the Intelligent Transportation Systems (ITS) ecosystem.

Research on Power Wireless Private Network Planning Technology Considering Propagation Model Correction Part One Theory Fundament

The development of power wireless private network planning work has very important guiding significance for realizing wireless network coverage in the target area and optimizing construction investment. However, the power wireless private network has the characteristics of limited base station location, fixed service terminal location, and access priority. The existing wireless public network planning and location strategy is not applicable to the background of the power wireless network. This paper analyzes the power wireless private network access service, and gives the overall plan process. Before establishing the modified model, some calibration requirements, continuous wave(CW) test steps and data processing are put forward.

АНАЛИЗ СВЯЗИ ПОКАЗАТЕЛЕЙ ТУРБУЛЕНТНОСТИ И ВАРИАБЕЛЬНОСТИ СЕРДЕЧНОГО РИТМА У ЛИЦ С РАЗНЫМ УРОВНЕМ ДАВЛЕНИЯ

Aim. The article deals with factor and regression analysis of heart rate variability and heart rate turbulence during Holter monitoring in persons with various blood pressure. Materials and methods. 68 persons aged 56.21 ± 1.06 participated in the study (48.5% female participants). We analyzed 24-hour ECG recordings obtained with the help of a Holter monitor and hardware software complex ‘Kardiotekhnika-04’ (INCART). The results obtained were interpreted using a standard diagnostic scheme proposed by the Russian society of cardiology in 2013. We also assessed heart rate turbulence and heart rate variability using a standard procedure. We used the methods of descriptive statistics to process continuous numerical data. We studied continuous and interval variables with the help of factor analysis and used the data obtained to conduct regression analysis. Results. The analysis revealed that the variables studied depend on each other, while the most important factor is, obviously, arterial blood pressure. Conclusion. The results obtained prove that the main reason of heart rate variability and heart rate turbulence is an increase in arterial blood pressure.

Delamination detection by Multi-Level Wavelet Processing of Continuous Scanning Laser Doppler Vibrometry data

A novel non-destructive testing procedure for delamination detection based on the exploitation of the simultaneous time and spatial sampling provided by Continuous Scanning Laser Doppler Vibrometry (CSLDV) and the feature extraction capability of Multi-Level wavelet-based processing is presented in this paper. The processing procedure consists in a multi-step approach. Once the optimal mother-wavelet is selected as the one maximizing the Energy to Shannon Entropy Ratio criterion among the mother-wavelet space, a pruning operation aiming at identifying the best combination of nodes inside the full-binary tree given by Wavelet Packet Decomposition (WPD) is performed. The pruning algorithm exploits, in double step way, a measure of the randomness of the point pattern distribution on the damage map space with an analysis of the energy concentration of the wavelet coefficients on those nodes provided by the first pruning operation. A combination of the point pattern distributions provided by each node of the ensemble node set from the pruning algorithm allows for setting a Damage Reliability Index associated to the final damage map. The effectiveness of the whole approach is proven on both simulated and real test cases. A sensitivity analysis related to the influence of noise on the CSLDV signal provided to the algorithm is also discussed, showing that the processing developed is robust enough to measurement noise. The method is promising: damages are well identified on different materials and for different damage-structure varieties.

On the use of IoT and Big Data Technologies for Real-time Monitoring and Data Processing

Recent advances in pervasive technologies, such as wireless ad hoc networks and wearable sensor devices, allow the connection of everyday things to the Internet, commonly denoted as Internet of Things (IoT). IoT is seen as an enabler to the development of intelligent and context-aware services and applications. These services could dynamically react to the environment changes and users’ preferences. The main aim is to make users’ life more comfortable according to their locations, current requirements, and ongoing activities. However, handling dynamic and frequent context changes is a difficult task without a real-time event/data acquisition and processing platform. Big data and IoT technologies have been recently proposed for timely analysing information (i.e., data, events) streams. In this paper, we propose to combine IoT techniques with Big data technologies into a holistic platform for continuous and real-time data monitoring and processing. Preliminary experiments have been conducted and results are reported to show the usefulness of this platform in a real-case scenario.

An efficient reversible privacy-preserving data mining technology over data streams.

With the popularity of smart handheld devices and the emergence of cloud computing, users and companies can save various data, which may contain private data, to the cloud. Topics relating to data security have therefore received much attention. This study focuses on data stream environments and uses the concept of a sliding window to design a reversible privacy-preserving technology to process continuous data in real time, known as a continuous reversible privacy-preserving (CRP) algorithm. Data with CRP algorithm protection can be accurately recovered through a data recovery process. In addition, by using an embedded watermark, the integrity of the data can be verified. The results from the experiments show that, compared to existing algorithms, CRP is better at preserving knowledge and is more effective in terms of reducing information loss and privacy disclosure risk. In addition, it takes far less time for CRP to process continuous data than existing algorithms. As a result, CRP is confirmed as suitable for data stream environments and fulfills the requirements of being lightweight and energy-efficient for smart handheld devices.

Implementing the ElarmS Earthquake Early Warning Algorithm on the Israeli Seismic Network

Earthquake early warning systems (EEWS) are being operated and tested increasingly around the globe in recent years. Following the Israeli government’s decision to build an EEWS in Israel, and as the Californian EEWS (ShakeAlert) moves toward its operational phase, we demonstrate implementation of one of its three algorithms, ElarmS, to the Israel region. We provide new tools and approaches for implementing and assessing ElarmS outside of California. The main challenges of this research are to identify, verify, and adjust the embedded location‐dependent parameters in ElarmS to the Israeli region, utilizing an unoptimized seismic network and low seismicity rate. To this end, we run ElarmS in three different modes: (1) historical playbacks, (2) real‐time continuous data processing, and (3) simulated data playbacks. These modes enable us to overcome the limitations of low seismicity rates in the region and evaluate the performance of ElarmS with the network that is currently available. We use historical playbacks to adjust the magnitude estimation equations of ElarmS. We then analyze real‐time processing results and provide detailed analysis of two significant events in the region ( M D 5.5 and 4.4). Finally, we provide the first case of how to use synthetic data to evaluate the performance of ElarmS. We find that alert times are mostly affected by the network geometry and also by data delays. Alerts are typically issued within 80 ms after the arrival of the required four P ‐wave triggers data to the system. Magnitude estimations are reliable for events with M D>3.5 within 100 km of the Israeli network using a locally adjusted magnitude relation equation.