Data Processing Speed Research Articles

Emotion Recognition by Audio Signals as one of the Ways to Combat Phone Fraud

The relevance of the study is dictated by the current state in the field of telephone fraud. According to research conducted by Kaspersky Lab, the share of users who encountered various unwanted spam calls in the spring of 2022 was at the level of 71%. The subject of the research is machine learning and deep learning technologies for determining emotions by the timbre of the voice. The authors consider in detail such aspects as: the creation of a marked-up dataset; the conversion of WAV audio format into a numerical form convenient for fast processing; machine learning methods for solving the problem of multiclass classification; the construction and optimization of neural network architecture to determine emotions in real time. A special contribution to the study of the topic is that the authors implemented a fast method of conversion sound formats into numerical coefficients, which significantly increased the speed of data processing, practically without sacrificing their informativeness. As a result, the models were trained by machine learning algorithms quickly and efficiently. It should be particularly noted that the architecture of a convolutional neural network was modeled, which allowed to obtain the quality of model training up to 98%. The model turned out to be lightweight and was taken as the basis for training the model to determine emotions in real time. The results of the real-time operation of the model were comparable with the results of the trained model. The developed algorithms can be implemented in the work of mobile operators or banks in the fight against telephone fraud. The article was prepared as part of the state assignment of the Government of the Russian Federation to the Financial University for 2022 on the topic "Models and methods of text recognition in anti-telephone fraud systems" (VTK-GZ-PI-30-2022).

High speed data transmission method of substation intelligent auxiliary monitoring system

In order to better ensure the effective operation of substation intelligent auxiliary monitoring system, aiming at the problems of slow data transmission speed and poor data processing security of current monitoring system, a high-speed data transmission method of the substation intelligent auxiliary monitoring system is proposed. By constructing the data processing platform of substation intelligent auxiliary monitoring system, the data transmission algorithm of auxiliary monitoring system is optimized, reasonably select the optimal path of data transmission and conduct security encryption processing, so as to improve the high-speed data transmission efficiency of substation intelligent auxiliary monitoring system. Finally, the experiment shows that the high-speed data transmission method of the substation intelligent auxiliary monitoring system has higher practicability in the process of practical application and fully meets the research requirements.

Analisis Penerapan Metode Scrum Pada Pengembangan Sistem Informasi Akuntansi Koperasi

Koperasi are one of the drivers of the economy in Indonesia. But in the operations of the koperasi, there are many problems such as lack of information, unrecorded deposits, the amount of credit that exceeds capacity, and lack of accuracy in accounting calculations. The study used the object of the employee koperasi of SWCU (Satya Wacana Christian University) Salatiga by conducting an observation process on the ongoing koperasi business system. The design of a koperasi accounting information system using a web platform with the basic model of the Scrum Process. The Scrum process model is a software development management framework with iterative and incremental characteristics and properties. The koperasi accounting information system that is built based on Scrum can reduce previous processing errors and can increase the speed and accuracy of data processing so that in the short term it can improve koperasi performance. System performance is tested by black box testing method to ensure all features can work properly. The test results showed that the system built can help reducing problems occurred in the operational process of koperasi, particularly in the accounting process. The results show that the system is more efficient to reduce problems occurred in the operational process of koperasi, reduce time in transaction processing, increase accuracy in the accounting process, and make it easier for users to produce reports with high validity.

Load Balancing Cloud Storage Data Distribution Strategy of Internet of Things Terminal Nodes considering Access Cost.

With the rapid development of Internet of Things (IoT) technology, IoT terminal nodes are facing many challenges in data storage, distribution, and data management. In particular, in the IoT terminal nodes considering access cost, the corresponding data distribution and storage are professional, complex, and miscellaneous. Based on the abovementioned current situation, this article innovatively proposes a complex sensor data placement algorithm based on the cloud storage distribution of IoT terminal nodes. Under this algorithm, the accurate division of IoT data I/O methods is realized through reasonable configuration. Through the adaptive sensing algorithm, while fully considering the access cost of the algorithm, the performance of the IoT data storage system is further optimized. In the corresponding terminal node load balancing problem, this article innovatively proposes the terminal node data sorting and distribution algorithm through the node data. The sorting and distribution algorithm realizes the precise segmentation of the IoT data to be processed, thereby realizing the improvement of data reading and processing speed. Based on the proposed algorithm, this article designs a load balancing cloud storage data distribution optimization system of IoT terminal nodes considering access cost and carries out experimental verification in a real environment. The experimental results show that the data pattern division accuracy corresponding to the proposed distribution strategy is improved to 97.13% and the corresponding data access efficiency is improved to 98.3%, compared with the traditional distribution strategy. Therefore, the data distribution strategy proposed in this article has obvious performance advantages and further promotion value.

Analysis of Cardiovascular Disease Angiography Process Based on Rough Set and Internet of Things.

The angiography image enhancement technology has the potential to enhance the vascular structure in the image while suppressing the background and nonvascular structures simultaneously. This technology has the ability to enhance the result as close to the real structure of blood vessels as possible. Angiographic image processing is one of the essential contents in the field of medical image processing and analysis. However, the existing cardiovascular angiography schemes suffer from various issues. In this paper, the detection process of cardiovascular angiography is studied by combining the Internet of Things and rough set technology. Firstly, this paper designs the architecture design of the cardiovascular angiography process combined with the Internet of Things technology. Secondly, this paper uses a rough set algorithm to optimize the background noise and boundary shrinkage because of the sensitivity of the contrast background noise and boundary shrinkage. Simulation results verified the applicability and efficiency of the proposed model in the cardiovascular angiography scheme. The model has been optimized during implementation. Compared with the traditional algorithm, the same image data processing speed is significantly improved to ensure the enhancement effect.

Convergence of Stereo Vision-Based Multimodal YOLOs for Faster Detection爋f Potholes

Road potholes can cause serious social issues, such as unexpected damages to vehicles and traffic accidents. For efficient road management, technologies that quickly find potholes are required, and thus researches on such technologies have been conducted actively. The three-dimensional (3D) reconstruction method has relatively high accuracy and can be used in practice but it has limited application owing to its long data processing time and high sensor maintenance cost. The two-dimensional (2D) vision method has the advantage of inexpensive and easy application of sensor. Recently, although the 2D vision method using the convolutional neural network (CNN) has shown improved pothole detection performance and adaptability, large amount of data is required to sufficiently train the CNN. Therefore, we propose a method to improve the learning performance of CNN-based object detection model by artificially generating synthetic data similar to a pothole and enhancing the learning data. Additionally, to make the defective areas appear more contrasting, the transformed disparity map (TDM) was calculated using stereo-vision cameras, and the detection performance of the model was further improved through the late fusion with RGB (Red, Green, Blue) images. Consequently, through the convergence of multimodal You Only Look Once (YOLO) frameworks trained by RGB images and TDMs respectively, the detection performance was enhanced by 10.7% compared with that when using only RGB. Further, the superiority of the proposed method was confirmed by showing that the data processing speed was two times faster than the existing 3D reconstruction method.

Random and Sequence Workload for Web-Scale Architecture for NFS, GlusterFS and MooseFS Performance Enhancement

The problem in the data storage method that can support the data processing speed in the network is one of the key problems in big data. As computing speed increases and cluster size increases, I/O and network processes related to intensive data usage cannot keep up with the growth rate and data processing speed. Data processing applications will experience latency issues from long I/O. Distributed data storage systems can use Web scale technology to assist centralized data storage in a computing environment to meet the needs of data science. By analyzing several distributed data storage models, namely NFS, GlusterFS and MooseFS, a distributed data storage method is proposed. The parameters used in this study are transfer rate, IOPS and CPU resource usage. Through testing the sequential and random reading and writing of data, it is found that GlusterFS has faster performance and the best performance for sequential and random data reading when using 64k block data storage. MooseFS uses 64k power storage blocks to obtain the best performance in random data read operations. Using 32k data storage blocks, NFS achieves the best results in random writes. The performance of a distributed data storage system may be affected by the size of the data storage block. Using a larger data storage block can achieve faster performance in data transmission and performing operations on data.

Research on target classification method for dense matching point cloud based on improved random forest algorithm

Aiming at the problems of low accuracy and low efficiency of traditional point cloud target classification methods, this paper designs a new classification method based on improved random forest algorithm. Bagging is combined with random subspace to form a subset of feature training at random, so that the generalisation ability of random forest algorithm can be increased while the data processing speed can be accelerated to avoid overfitting phenomenon. On the basis of extracting geometric features of coloured point clouds, the optimal feature subset for classification is determined, and then the dense matching point clouds are classified using the improved random forest algorithm. Experimental results show that the classification error rate of this method is less than 1%, the average classification process takes only 83.995 s, and the VIM value is all over 0.1, indicating that this method can effectively improve the classification effect of dense matching point cloud targets.

Research and application of tongue and face diagnosis based on deep learning.

ObjectiveTo explore the technical research and application characteristics of deep learning in tongue-facial diagnosis.MethodsThrough summarizing the merits and demerits of current image processing techniques used in the traditional medical tongue and face diagnosis, the research status of deep learning in tongue image preprocessing, segmentation, and classification was analyzed and reviewed, and the algorithm was compared and verified with the real tongue and face image. Images of the face and tongue used for diagnosis in conventional medicine were systematically reviewed, from acquisition and pre-processing to segmentation, classification, algorithm comparison, result from analysis, and application.ResultsDeep learning improved the speed and accuracy of tongue and face diagnostic image data processing. Among them, the average intersection ratio of U-net and Seg-net models exceeded 0.98, and the segmentation speed ranged from 54 to 58 ms.ConclusionThere is no unified standard for lingual-facial diagnosis objectification in terms of image acquisition conditions and image processing methods, thus further research is indispensable. It is feasible to use the images acquired by mobile in the field of medical image analysis by reducing the influence of environmental and other factors on the quality of lingual-facial diagnosis images and improving the efficiency of image processing.

Implementing the Lightweight mCrypton Algorithm in Hardware using FPGA

The use of lightweight cryptography might be beneficial for low-power devices such as RFID tags, nodes in the Internet of Things, and nodes in wireless sensor network nodes. When developing a lightweight strategy, it is essential to take into consideration a cryptographic algorithm’s data storage requirements, processing speed, throughput, and energy consumption. Taking everything into consideration, the findings of this research provide a contemporary foundation for the effective lightweight cryptographic technology known as mCrypton. The restoration not only reduces the overall size of the common rooms but also boosts the efficiency of those spaces. FPGA boards manufactured by Xilinx, namely the Spartan 3, were used in the construction of the recommended design by ISE. The results of the simulation show that the proposed architecture is capable of functioning at a frequency of 302 MHz, accomplishing a amount of 646 Mbps, also accomplishing an efficacy of 1.7 Mbps/slice while using a entire of 0.089 W of power. Because of this, the proposed layout achieves superior results than those of its contemporaries in terms of the amount of space used, the energy consumption, the performance, and the throughput.

Research on Financial Risk Assessment Based on Artificial Intelligence

The effective application of artificial intelligence (AI) models in various fields in the field of financial risk can improve the speed of data processing, deepen the degree of data analysis and reduce the labor cost, thus effectively improving the efficiency of financial risk control. The application of AI in the field of financial risk management puts forward new requirements for the system setup and operation mode of financial supervision. With the rapid growth ofcomputer and network technology, the increase of market transaction frequency, the diversification of data sources and the development and application of big data, it brings new challenges to financial risk management based on massive data. Based on this, this paper analyzes the role of AI in promoting the reform and growth ofthe financial industry, and puts forward some countermeasures for rational use of AI in the field of financial risk management.

Thermal Wave Variation Anticipation Under Minute Scale Time-Advance With Low-Pass NGD Digital Circuit

Over the emerging industry 4.0 era, the building control system performance depends on the development of smart sensor technology. Nowadays, the building control engineers challenge on the design of high-capacity smart sensor susceptible to operate with high speed of data processing. In this context, this paper introduces a pioneer research work on the negative group delay (NGD) circuit original application for room temperature anticipation useful for smart-building control. The real-time anticipation of sensor data by using a low-pass (LP) NGD digital circuit is studied. This approach enables minimizing the latency time for optimizing control action. The unfamiliar LP-NGD digital circuit design method and theoretical formulation are described for anticipating thermal wave experimentation in real-time. The digital circuit equation coefficients are computed regarding the time-advance of anticipated thermal completely arbitrary waveform signal. The main interest of using the NGD method-based for thermal wave anticipation regarding temperature variation from heater and freezer control is demonstrated. The anticipation feasibility is illustrated from the minute scale time-advance LP-NGD digital circuit implemented on the STM32® microcontroller unit. The NGD characterization is performed from frequency domain analysis and cosine waveform pulse transient test. Then, the -30 seconds to -10 seconds real-time-advance of temperature variation is verified by calculation and experimentation. The present study result opens a promising NGD method application for the advanced fault control of a future industrial system by anticipating system failures.

Design and Implementation of Data Analysis System of Social Network

To solve the problem that data collection efficiency of social network analysis technology based on single-mode network structure is low and cannot meet the problem of data information processing of large-scale social network, the social network data analysis system based on the network node centrality theory is proposed. The naive Bayesian classification algorithm is used to improve the traditional data mining mode. The traditional social network data analysis technology is compared with the social network data analysis system proposed in this research from four aspects: multi-platform network data processing response time, data acquisition and processing accuracy, social network data feature condition introduction rate, and data feature condition introduction accuracy. The accuracy of data collection and processing in the traditional social network data analysis system is 89.26%, the response time of multi-platform network data processing is 7.32s, the introduction rate of data feature conditions is 82.65%, and the accuracy rate of data feature conditions is 78.88%. The data processing accuracy of the data analysis system proposed in this research based on the network node centrality theory is 98.99%, the response time of multi-platform network data processing is 1.35s, the data feature condition introduction rate is 97.91%, and the data feature condition introduction accuracy rate is 97.39%. The social network data analysis system proposed in this research is significantly better than the traditional social network data analysis system in all aspects, and its data processing performance is very good especially in multi-platform social network. The results show that the social network data analysis system based on network centrality theory proposed in this research is feasible and can meet the daily application and research requirements of social network data collection, storage, analysis and visual display, which has the advantages of fast data processing speed, high accuracy and wide range of data storage.

Data Integrity Audit Based on Data Blinding for Cloud and Fog Environment

Cloud-fog computing is a novel computing model that expands the functionality of cloud computing, which provides various services through fog nodes. The issue of traditional data integrity auditing are low data security, slow data processing speed and low communication efficiency. To solve these problems, this paper proposes a data integrity audit scheme based on data blinding. This scheme uses the edge devices in the transmission node to establish a fog computing layer between the cloud service provider and the data owner to reduce transmission delay. The subordinate distribution relationship and weight between fog nodes dynamically allocate the optimal path and transmit the data to reduce transmission delay. At the same time, a blind factor is added to the integrity audit in the evidence generation process to avoid data leakage. This paper gives a security model and security proof based on computational Diffie-Hellman (CDH) assumptions. The experimental results show that the fog computing layer and blind factor are introduced into the data integrity audit process, which can reduce the data communication delay effectively and improve the security of data audit.

A Spectral–Spatial Change Detection Method Based on Simplified 3-D Convolutional Autoencoder for Multitemporal Hyperspectral Images

Change detection for multitemporal hyperspectral images (HSIs) has always been a research hotspot of remote sensing. However, most current detection methods only use spectral information or spatial information separately, and there are many false detection areas in the detection results. Besides, the feature extraction method based on neural networks needs a huge amount of training samples, but collecting labeled training samples for change detection tasks is difficult. Therefore, this letter proposes a hyperspectral change detection method based on a simplified 3-D convolutional autoencoder (S3DCAECD). First, the framework is based on deep unsupervised autoencoder (AE), which can extract deep spectral–spatial features from bitemporal images without the need for prior information. Second, by adding a 3-D convolution kernel and eliminating the pooling layer, the structure of 3-D convolutional AE is simplified, which can reduce spectral redundancy and improve data processing speed. Finally, a softmax classifier with a 2-D convolutional layer added is used to obtain the detection result, and only a few label samples are needed to train the classifier. Three HSIs’ experimental results indicate that the accuracy of the S3DCAECD is more than 95% on three experimental datasets and it has better detection results than several commonly used methods.

A Study on the Intergrated Module Development Device of CAN/LIN Communication Control Device

As the number of electronic control devices mounted in the vehicle increases, communication standards between electronic control devices are evolving. In particular, the recently released new car is equipped with various types of advanced driver assistance systems that receive and process information from camera sensors and radar, so more data must be exchanged quickly and safely. LIN and CAN are currently the most widely used in-vehicle communication standards, but their maximum communication speed is slow at 40 Kbit/s and 1 Mbit/s, respectively. CAN communication controllers and LIN communication controllers are essential when testing functions and performance after manufacturing in-vehicle devices. Since the CAN communication controller and the LIN communication controller are produced and sold as separate products, manufacturing costs of inspection equipment increase, and data processing speed decreases due to repeated data generation and transmission in the control PC. That is, there is a problem of incorrectly recognizing data loss due to data bottleneck as a defect in the device to be inspected. An integrated module was developed to solve this problem and software was developed in data transmission.

Keberhasilan Manajemen Penjualan Lasser Work Service Zigzag di Semarang Menggunakan Java (Netbeans 7.3)

This research examines the success of Sales Information System Management at Zigzag Lasser Work Service, Semarang, Indonesia. The proposed system is a computerized information system where data can be managed and integrated properly and built with Java programming (Netbeans 7.3) based on localhost. With the proposed Sales Information System, it is able to produce fast, accurate and timely information to facilitate processing of product data, order data, payment data, delivery data or delivery of goods. Sales reports and income reports are generated automatically with integrated processing so as to produce reports that are accurate, fast and can be justified. The speed of data processing and minimizing the occurrence of human error can help management reduce costs in order to apply economic principles to achieve maximum profit.

OVERVIEW OF OPTIMIZATION METHODS FOR PRODUCTIVITY OF THE ETL PROCES

One of the important aspects in management and acceleration of processes, operations in databases and data warehouses is ETL processes, the process of extracting, transforming and loading data. These processes without optimizing, a realization data warehouse project is costly, complex, and time-consuming. This paper provides an overview and research of methods for optimizing the performance of ETL processes; that the most important indicator of ETL system's operation is the time and speed of data processing is shown. The issues of the generalized structure of ETL process flows are considered, the architecture of ETL process optimization is proposed, and the main methods of parallel data processing in ETL systems are presented, those methods can improve its performance. The most relevant today of the problem is performance of ETL processes for data warehouses is considered in detail.

Attosecond electron motion control in dielectric

Attosecond science capitalizes on the extreme nonlinearity of strong fields—driven by few-cycle pulses—to attain attosecond temporal resolution and give access to the electron motion dynamics of matter in real time. Here we measured the relative electronic delay response of a dielectric system triggered by a strong field of few-cycle pulses to be of the order of a few hundred attoseconds. Moreover, we exploited the electronic response following the strong driver field to demonstrate all-optical light-field-sampling methodology with attosecond resolution. This methodology provides a direct connection between the driver field and induced ultrafast dynamics in matter. Also, we demonstrate control of electron motion in a dielectric using synthesized light waveforms. This on-demand control of electron motion paves the way for establishing long-anticipated ultrafast switches and quantum electronics. This advancement promises to increase the limiting speed of data processing and information encoding to rates that exceed one petabit per second, opening a new realm of information technology.

Research on Automation of Test Skill Evaluation Based on Fuzzy Controller

The fuzzy control system is composed of an integral part of modern intelligent automatic control, can control of precision is difficult to directly establish the mathematical model of the intelligent control system object data for effective fuzzy control, so fuzzy has wide application field, considering the basic features of fuzzy control arithmetic, more close to the parallel processing of the way of fuzzy data stream, so fuzzy control is more suitable for implementation by a special control circuit, not only can not only improve the speed of data processing, and it also can improve the control system running stability. With the improvement of programming logic and programmable ability of FPGA, FPGA has the necessary ability to implement ASIC directly and meet the requirements of system programming and design on chip. It has become the development trend of FPGA. The fuzzy analog controller can be used as a digital analog controller of a DC/DC fuzzy converter. In the absence of Matlab/Simulinkc environment, a DC/DC fuzzy converter can be modeled by using Maplecs toolbox. The simulation test results firstly verify the practical feasibility of the fuzzy analog controller. Then PLXILINXXC3S500EFPGA chip is used to realize the fuzzy simulation controller, and good simulation results are obtained.