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BY 4.0 license Open Access Published by De Gruyter Open Access August 31, 2023

Exploration on the application of electronic information technology in signal processing based on big data

  • Li Liu EMAIL logo
From the journal Open Computer Science


Mobile phones are the most commonly used electronic devices in people’s daily life. The image, voice, and other information in these devices need to be processed through signal transmission. The role of signal processing is to process the acquired information in a certain way to get the final result. In order to ensure that the whole processing program can work normally, it is necessary to implement good control to achieve the desired effect. However, with the continuous progress and development of science and technology, its requirements are becoming increasingly strict. The traditional signal processing method is unreliable, has poor real time, and has error-prone characteristics, which can no longer meet the accuracy requirements of current information acquisition equipment. Therefore, people begin to study more complex and precise information processing methods and apply these algorithms to various advanced electronic devices to achieve better results. From the perspective of big data, electronic information technology is generated and developed based on massive data processing. It not only has a strong storage function but also has strong computing power and a wide range of application scenarios. It has strong applicability in real life. In this article, the signal to be processed was divided into several wavelet components in different frequency ranges by empirical mode decomposition technology, and then the signal was denoised by combining three wavelet denoising methods to obtain noise data with good signal-to-noise ratio and high classification accuracy. Finally, the corresponding feature information was extracted according to the signal-receiving model to improve the system recognition rate. This article compared the traditional signal processing methods with the signal processing approaches from the perspective of electronic information technology. The results showed that the processing method had a high computing speed and could better solve the problem of detection performance degradation caused by interference. User satisfaction had also increased by 2.87%, which showed that signal processing based on big data and information processing technology had broad application prospects in communication systems. The core of open computer science is to build a unified, efficient, and scalable computing platform based on massive data processing and use signal processing and computer technology to manage and optimize the scheduling of information resources to better meet various business needs.

1 Introduction

With the continuous expansion of mobile communication networks and the increasing demand for various services, people have increasingly high requirements for data transmission quality. In particular, on the communication link, signal processing often has the defects of large data, small information capacity, and time extension, leading to low system operation efficiency. Therefore, improving the efficiency of signal transmission has become a very urgent and difficult problem.

Signal processing has always been the focus of attention. Purwins et al. provided an overview of the latest deep learning technology used in audio signal processing, which covers prominent application fields, including audio recognition, synthesis, and conversion, and identified the key issues and future trends in the application of deep learning in audio signal processing [1]. Xu et al. reviewed the latest progress of photonic RF signal processors based on the microcomb, including real-time delay, a reconfigurable filter, a Hilbert transformer, a differentiator, and a channeler, and discussed the powerful potential of the function and integration of optical microcomb in RF photonics applications [2]. Ortega et al. outlined the core idea of graph signal processing and its connection with traditional digital signal processing (DSP) and analyzed the application of signal processing in sensor network data processing and analysis, biological data, and image processing and machine learning (ML), which provided a new idea for signal processing based on image information in the future [3]. Monga et al. analyzed several key signal processing algorithms in deep learning, including convolutional neural network and adaptive filtering, and proposed a new, improved method combined with the idea of local optimization, which reduced the computational complexity and improved the training efficiency under the premise of ensuring a high recognition rate [4]. Zhong et al. summarized the important application and development of DSP in short-distance communication systems and analyzed the existing problems and future development trends of wireless communication technology, which provided a reference for realizing low power consumption and low-cost, short-range wireless access [5]. Petrović et al. investigated the differences in the difficulty degree and scores of students in learning content when using DSP for online formative assessment, which provided reference for improving students’ learning effect and problem-solving ability [6]. The main task of signal processing is to convert the input signal into an electrical signal and then convert the digital signal into sound or image information.

Electronic information technology is widely used in the field of signal processing. Gasulla introduced the core and implementation of a multi-purpose silicon photonic signal processor, which aimed to process data at high speed and use several different types of transistors to form an array to improve performance and efficiency [7]. Patole et al. summarized all aspects of automotive radar signal processing technology, including waveform design, possible radar architecture, estimation algorithm, and adaptive processing in complex environments, to provide accurate and reliable data under various harsh conditions [8]. Nakamura et al. studied gravity induction and signal conversion in plant gravity and found that signal processing can locate the signal source under certain conditions. They proposed a target tracking system based on signal conversion, with high positioning accuracy and good noise resistance. It can meet the needs of large-scale applications and broaden the scope of application of existing signal positioning systems [9]. Xu et al. conducted hierarchical recursive signal processing modeling based on multi frequency signals of discrete measurement data. The multi-scale method was used for hierarchical processing, and the genetic algorithm and particle swarm optimization algorithm were used to optimize the model parameters and to improve the system identification accuracy [10]. Shao et al. proposed a multi signal processing fault diagnosis method, which can learn from multiple types of sensor signals at the same time to obtain robust performance and finally achieve accurate identification of induction motor faults [11]. Lu et al. used ML methods for high-speed channel modeling for signal integrity analysis. By converting complex communication network models into simple, easy-to-understand algorithms, higher throughput and transmission delay performance was achieved, and user experience was enhanced [12]. Signal processing based on big data and electronic information technology can take advantage of the massive amount of data to improve data processing capabilities, simplify the hardware structure, and reduce costs.

As one of the most popular research directions in the field of artificial intelligence (AI), signal processing has been applied in a wide range of fields with the continuous improvement of the theory and practice level of related disciplines in recent years. It has become one of the frontier courses supported by many scientific research institutions. At the same time, because of many defects in traditional algorithms in dealing with complex environments, large-scale parallel computing cannot be realized. For this reason, this article proposes a distributed parallel real-time data processing method based on big data and electronic information technology. By integrating different types and quantities of tasks into a whole, these tasks are effectively scheduled and managed to improve the system operation efficiency.

2 Electronic information technology and signal processing

2.1 General application of electronic information technology

With the continuous improvement of the social economy and science and technology, people’s demand for information is growing. In particular, with the rapid development of modern industrial technology, human beings are entering the information age with computers as the core, which requires all walks of life to accelerate the pace of digital development. Electronic information technology is also known as the “information industry.” With the advantages of high speed and large capacity, it has become the key means to achieve these goals [13]. It has penetrated various fields and been widely used, such as transportation, communication, electricity, etc., and has brought great convenience to the production and operation of enterprises and people’s work and life. The extensive application and development of electronic information technology have greatly enriched the demand for information resources in people’s life and work. All walks of life also attach great importance to the development and application of this technology, which is shown in Figure 1.

Figure 1 
                  General application of electronic information technology.
Figure 1

General application of electronic information technology.

In the process of university library management, electronic information technology can expand the choice of information dissemination channels and enhance the flexibility of the construction and utilization of library resources. Functions such as online search methods, website content updates, and user access services can be used to improve book borrowing efficiency, teaching, and research level. Modern enterprise management also increasingly uses information technology to achieve various work objectives in production and operation activities. For example, establishing an enterprise resource planning system, a financial management system, a human resource management system, etc., enables enterprises to allocate human resources, material resources, and financial resources more reasonably to better provide customers with more perfect personalized services. Moreover, a reasonable resource planning mechanism can be designed according to the needs to enable each task to be fully and efficiently executed to reduce the impact caused by system failures and ensure the normal operation of the entire network. Modern management based on electronic information technology not only optimizes and upgrades traditional management work but also transforms knowledge into productivity through the effective use of information technology, which greatly improves the management efficiency and economic benefits of enterprises [14,15]. The application of electronic information technology can effectively reduce operating costs and improve management efficiency. Therefore, in the current situation, this advanced concept should be actively developed and promoted so that people can make deeper development and utilization of information resources and make integration and interoperability between different types and functions of information systems, which greatly enhances the role played by the whole society in the process of informatization.

2.2 Signal processing process and law

Signal processing is a new subject after the development of computer technology to a certain stage, and it is also an important part of modern communication technology. It has strong processing ability, high anti-interference ability, and a wide range of applications in practical projects. Particularly, in the aspects of communication systems and radar systems, its performance directly affects the safety and reliability of the whole system. At the same time, with the continuous development of computer science and technology, and the increasing requirements of various electronic equipment for signal bandwidth, signal processing is also developing toward high speed and high capacity. The process and rules of signal processing in practical applications are shown in Figure 2.

Figure 2 
                  Signal processing process and law.
Figure 2

Signal processing process and law.

Signal processing generally includes data preprocessing, feature extraction and classification, filtering, pattern recognition, and state estimation. Data preprocessing is mainly used to discretize and compress the original data into binary information of a certain length. Feature extraction and recognition are mainly realized by ML technology. On this basis, the difference between different types of models can be used to establish prediction models. The filtering process is used to eliminate the influence of noise and improve the signal-to-noise ratio. The pattern recognition method is used to describe the interaction between unknown objects in the system, and the recognition method mainly adopts the neural network method and fuzzy inference theory. State estimation is applied to fault detection in various environments, such as fire or traffic accidents. By providing accurate and reliable parameters, fault diagnosis becomes simple and effective. With the development of signal acquisition and analysis equipment with the continuous improvement of computer software and hardware, the hardware performance is getting better, and the software functions are becoming more abundant, which causes a qualitative leap in signal processing [16].

The research directions in signal processing mainly include image segmentation, voice detection, video classification, pattern recognition, image enhancement, moving object tracking, etc. Its application rules include recognition and location of distance variation characteristics between different objects in various complex environments. For example, the larger the signal waveform, the stronger the signal-to-noise ratio; the smaller the pulse width, the weaker the relationship between amplitude and phase; the lower the pulse power, the more uniform the energy distribution in the spectrum. Different types of signals have different characteristics, which determine that the signal strength has a certain degree of randomness and uncertainty. This would bring great difficulties to all kinds of signal processing. In practical applications, selecting appropriate filters or other processing methods according to the required information is often necessary to achieve specific functions.

In practical application, a large part of the equipment is processed by the signal processor. There are two common signal transmission modes: one is an analog signal, such as telephone; the other is a digital signal, such as a TV, radio, etc. The operating principles and functions of these two types of signals are different, which are shown in Table 1.

Table 1

Comparison of the principles and functions of analog and digital signals

Analog signals Digital signals
Power supply method Powered by various power supplies of different frequencies Data from a computer or other device output
Circuits Soft switching devices and integrated circuits DSP or AI chips
Price High Low
Advantages Stronger anti-interference ability Higher signal-to-noise ratio

It can be seen from Table 1 that the circuits commonly used in analog communication systems mainly include soft switching devices and integrated circuits. Their operating voltages are low, so they have high anti-interference capabilities, but relatively high prices. Digital communication systems generally use digital DSP or AI chips. Its working voltage is high, so it has a higher signal-to-noise ratio, and the cost is relatively low. In terms of power supply, analog signals are powered by power supplies with different frequencies, while digital signals come from the data output by computers or other devices, so this information can be converted into electrical signals through DSP. In addition, analog communication systems have many advantages over digital communication systems. It can realize the digital function and adapt to high-speed data transmission requirements. It can also realize synchronous detection of multiple users’ sending and receiving signals at the same time, which can effectively improve the traffic and transmission speed. At present, digital technology has been widely used in life, making human beings enter a new information age.

2.3 Signal processing process based on big data and electronic information technology

With the increasing demand for information acquisition and transmission, signals must be obtained more accurately and reliably from multiple sources. However, traditional methods require a lot of computation to achieve these goals, leading to high computing costs. Therefore, developing a real-time operating system based on big data electronic information technology is necessary to provide users with more convenient services. This article proposed a new real-time data signal processing system based on the current development of distributed data processing technology, which is applied to network data transmission to meet the requirements of real time and security. The system is shown in Figure 3.

Figure 3 
                  Signal processing based on big data and electronic information technology.
Figure 3

Signal processing based on big data and electronic information technology.

Based on the signal processing of big data and electronic information technology, real-time monitoring of human posture under the conditions of moving objects and environments is realized. First, in terms of hardware design, DSP is used to complete the development of the data collector, which provides necessary hardware platform support for the subsequent algorithm. Second, the corresponding software module is designed, including the preprocessing of the electrical signals received from the sensor, such as measuring speed, acceleration, and other parameters. Appropriate data processing methods are selected according to different working conditions, such as the single threshold method, maximum likelihood method, etc. Finally, the simulation environment and model base are used to build an experimental platform to achieve data fusion and obtain more accurate results. In order to improve the measurement accuracy, advanced DSP must be used to provide the required information. Electronic information processing technology based on big data is a new information technology means and application form. It can not only improve system performance but also change human lifestyle to a large extent. Its combination with the concept of deep learning can also enable people to better understand the nature of complex problems and the ways to solve practical problems, so it has become a new direction for the future development of today’s scientific and technological revolution [4].

Signal processing based on big data and electronic information technology also has the following characteristics: first, with the rapid increase of computing and data volume, massive information processing becomes increasingly difficult. Second, multi-sensor fusion is the main development direction in the future, so it needs a large number of high-computing-power equipment to support it. Third, multi-tasking concurrent operation requires fast parallel processing speed and can meet the rapid response requirements. Fourth, a high-performance computing platform becomes the future development trend. These characteristics determine that the signal processing system with big data as the core must have powerful computing power and rich functional resources to be applied to various scenarios to achieve real-time analysis and management of environmental information.

2.4 Open computer science and engineering index

With the development of computer technology and communication technology, people can conduct real-time monitoring through computers to achieve effective monitoring of the on-site environment and production operation. The open computer science and engineering index is a tool to provide literature retrieval services for researchers in computer science and other related disciplines. It can establish a global database system for information sharing, resource sharing, interoperability, and mutual complementation to help students achieve their goals through online communication and help, thus promoting the development of information technology and accelerating communication and cooperation among all sectors of society [17]. Its specific functions are shown in Figure 4.

Figure 4 
                  The role of open computer science and engineering index in signal processing.
Figure 4

The role of open computer science and engineering index in signal processing.

The application of open computer science and engineering index in the field of signal processing technology is of great significance to the preprocessing, storage, and retrieval of various data. The first is the filtering of sound signals. First, the position of the sound source is extracted by analyzing the sound frequency, then the required signal noise ratio is calculated according to this distance, and the optimal sampling time is determined. Finally, the optimal method is selected to realize the noise reduction function. The second is the detection of light intensity. Multispectral image sensors are used to obtain the light intensity values of various parts of the human body, and then feature extraction is carried out in combination with the movement speed and posture changes of the target. At the same time, it can also convert the collected image into video or audio stream in real time and transmit it to the computer for segmentation and recognition of the target. The third is to process visual information. The texture features of different colors can be used to judge whether the object is occluded or the boundary is blurred. This method can quickly and accurately complete the identification task between people in complex environments. It can also maintain good performance under poor lighting conditions and has strong robustness.

3 Application model of electronic information technology in signal processing

3.1 Empirical mode decomposition (EMD)

EMD is a relatively new numerical calculation method. It can not only be used to analyze the response characteristics and motion laws of nonlinear systems under various loads but also be used to solve discrete variable problems. The signal processing process is based on this idea.

By taking the original signal q(a) as the signal to be processed, the local maximum point and the minimum point of the signal are selected. Through cubic spline interpolation, the maximum and minimum values obtained are connected to form the upper and lower envelopes so that all data points of the signal are between the two envelopes. The mean value w ( a ) of the upper and lower envelopes is calculated as follows.

By subtracting w ( a ) from the signal q ( a ) to be processed, the following can be obtained:

(1) Q 1 ( a ) = q ( a ) w ( a ) .

P 1 ( a ) is defined as a basic vector decomposed from the signal, which satisfies the following conditions:

(2) P 1 ( a ) = Q 1 ( a ) ,

(3) M 1 ( a ) = q ( a ) P 1 ( a ) .

By decomposing M 1 ( a ) as a new signal to be processed, m basic pattern vectors can be obtained, and they are named P 1 ( a ) , P 2 ( a ) , , P m ( a ) . By decomposing the original signal q ( a ) into the basic vector form, the following can be obtained:

(4) q ( a ) = n = 1 m P 1 ( a ) + M m ( a ) .

Basic mode components are obtained by EMD. In signal processing, high-frequency basic mode components are usually discarded, and the remaining mode components are added to achieve the denoising effect.

3.2 Wavelet denoising

The root mean square error mode adopted is

(5) σ = ( u v ) 2 n .

The signal-to-noise ratio formula is

(6) S / N = 10 × log n u 2 n / ( u v ) 2 n .

The smoothness index is

(7) r = n 1 ( u n + 1 x n ) 2 n 1 ( v n + 1 v n ) 2 ,

where u is the value of filtered echo signal, v is the value of original echo signal, and n is the number of sampling points.

3.3 Received signal model

After the signal sent by the signal source is transmitted through the channel, Gaussian white noise is added. The band signal model received by the ith sensor sub node is

(8) Q i ( a ) = r i ( a ) + t i ( a ) ,

where r i ( a ) represents the modulated signal, and t i ( a ) represents white noise.

(9) r i ( a ) = S p n e j ( 2 π f a + θ i ) h ( a n T r ) ,

where S p is the average power of the received signal, θ i is the carrier phase deviation of the signal received by the ith sensor; h ( a ) is the pulse shaping function, and T r is the symbol period.

(10) Q i ( n ) ¯ = Q i ( n ) 1 S d i = 1 S d Q i ( n ) 2 ,

where S d represents the length of the signal sampling sequence, and n represents the sampling signal point.

4 Signal processing contrast experiment based on big data electronic information technology

4.1 Experimental methods

Twenty computer majors were randomly selected from a university to conduct a questionnaire survey, and they were divided into two groups: A and B. Group A used the original signal processing system for training and testing, and Group B used the signal processing system based on electronic information technology. It was known that Group A and Group B had the same professional level, and each group had ten students. A 4-week comparative experiment was conducted on them in terms of operation speed, system performance, and satisfaction, and experimental data were recorded and analyzed.

4.2 Data analysis

4.2.1 Operation speed

The weekly changes in signal processing speed of the two groups were counted. The results are shown in Figure 5.

Figure 5 
                     Comparison of computing speed between the two groups.
Figure 5

Comparison of computing speed between the two groups.

It can be seen from Figure 5 that the curve of Group A was distributed below Group B, and Group B showed a straight upward trend. On the contrary, it can be seen that the speed of Group A increased and then decreased significantly. The system was unstable, and the curve fluctuated greatly; after 4 weeks of adjustment, it still did not reach the ideal state, so the overall operation speed of Group B was much higher than that of Group A. In the process of traditional signal processing, students mainly rely on manual methods to complete information processing. Under the environment of electronic information technology, because of its strong flexibility and real-time characteristics, it has become a very effective and efficient method, which provides strong support for improving the data processing ability of computer science professionals with large amounts of calculation and tight time.

4.2.2 System performance

The system performance of the two groups of processing systems was compared in terms of capacity, energy consumption, and accuracy, and the set score was 1–10 points. The score results are shown in Figure 6.

Figure 6 
                     Comparison of signal processor capacity between two groups.
Figure 6

Comparison of signal processor capacity between two groups.

According to Figure 6, the data performance of Group B was better than that of Group A in these three aspects. In terms of accuracy, the difference between the two was slightly small, which showed that the accuracy of both was worth affirming. In terms of energy consumption, the gap between the two groups was the largest, which indicated that under the promotion of electronic information technology, DSP technology is gradually changing the way people use electric energy and information transmission equipment and plays a key role in improving energy efficiency.

4.2.3 Satisfaction

The change of satisfaction of the two groups during the 4 weeks was investigated. The results are shown in Figure 7.

Figure 7 
                     Comparison of satisfaction between two groups.
Figure 7

Comparison of satisfaction between two groups.

It can be seen from Figure 7 that the satisfaction of Group A was higher than that of Group B in the first week. However, with the increase in time, the satisfaction of Group B began to show a linear upward trend, while that of Group A gradually declined and finally stabilized at 90–95%. The satisfaction of Group B reached 98.3% in the fourth week. After calculation, the satisfaction of Group B increased by 2.87% compared with that of Group A. The optimized signal processing process can be well adapted to the characteristics of high environmental uncertainty, to define parameters accurately, and multifactor comprehensive evaluation in complex situations and has robustness to a certain extent.

Based on the aforementioned three aspects, the operation speed, system performance, and satisfaction of signal processing under the two forms were compared. For the convenience of statistics, the satisfaction data shall be converted into figures within 10. The data of Group A was about 2.75, and that of Group B was about 7.5; the satisfaction data of Group A was about 9.33, and that of Group B was about 9.62. The comparison results are shown in Figure 8.

Figure 8 
                     Overall comparison results between the two groups.
Figure 8

Overall comparison results between the two groups.

It can be seen from Figure 8 that the data performance of Group B was better than that of Group A in the aforementioned three aspects. In terms of satisfaction, there was no significant difference between the two groups. However, in terms of operation speed and system performance, Group B had a faster improvement than Group A. Therefore, compared with the original signal processing scheme, the optimized signal processing scheme not only improved the signal waveform quality but also reduced the impact of noise on signal-to-noise ratio, bit error rate, and other indicators. It gave the whole system good anti-interference ability, high performance price ratio, and strong scalability, which provided a theoretical basis for the follow-up system research.

5 Conclusions

As the basis of modern industrial control, the performance of signal processing systems is directly related to the quality, efficiency, and cost of the entire industrial production process. With the continuous development of computer technology, signal processing based on big data electronic information technology would become a new technology application mode and gradually replace the traditional simulation equipment and methods to better realize the acquisition, processing, and analysis of signals. Based on the concept of electronic information technology, this article analyzed the advantages and development trends of DSP in the field of signal processing. According to the characteristics of different types of noise produced by DSP chips when processing signals, various noise reduction schemes for DSP were proposed. It can improve the signal-to-noise ratio of the signal processing process, and then reduce the power consumption of the digital circuit to improve the running speed of the signal processor and achieve higher computing power.

  1. Funding information: This study did not receive any funding in any form.

  2. Author contributions: The author contributed equally to this study.

  3. Conflict of interest: The author declares that there is no conflict of interest with any financial organizations regarding the material reported in this manuscript.

  4. Ethical approval: Ethical approval for this study and written informed consent from the participants of the study were not required in accordance with local legislation and national guidelines.

  5. Data availability statement: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.


[1] H. Purwins, B. Li, T. Virtanen, J. Schlüter, S. Y. Chang, and T. Sainath, “Deep learning for audio signal processing,” IEEE J. Sel. Top. Signal. Process., vol. 13, no. 2, pp. 206–219, 2019.10.1109/JSTSP.2019.2908700Search in Google Scholar

[2] X. Xu, M. Tan, J. Wu, R. Morandotti, A. Mitchell, and D. J. Moss, “Microcomb-based photonic RF signal processing,” IEEE Photonics Technol. Lett., vol. 31, no. 23, pp. 1854–1857, 2019.10.1109/LPT.2019.2940497Search in Google Scholar

[3] A. Ortega, P. Frossard, J. Kovačević, J. M. Moura, and P. Vandergheynst, “Graph signal processing: Overview, challenges, and applications,” Proc. IEEE, vol. 106, no. 5, pp. 808–828, 2018.10.1109/JPROC.2018.2820126Search in Google Scholar

[4] V. Monga, Y. Li, and Y. C. Eldar, “Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing,” IEEE Signal. Process. Mag., vol. 38, no. 2, pp. 18–44, 2021.10.1109/MSP.2020.3016905Search in Google Scholar

[5] K. Zhong, X. Zhou, J. Huo, C. Yu, C. Lu, and A. P. Lau, “Digital signal processing for short-reach optical communications: A review of current technologies and future trends,” J. Lightwave Technol., vol. 36, no. 2, pp. 377–400, 2018.10.1109/JLT.2018.2793881Search in Google Scholar

[6] J. Petrović, P. Pale, and B. Jeren, “Online formative assessments in a digital signal processing course: Effects of feedback type and content difficulty on students learning achievements,” Educ. Inf. Technol., vol. 22, no. 6, pp. 3047–3061, 2017.10.1007/s10639-016-9571-0Search in Google Scholar

[7] I. Gasulla, “Multipurpose silicon photonics signal processor core,” Nat. Commun., vol. 8, no. 1, pp. 1–9, 2017.10.1038/s41467-017-00714-1Search in Google Scholar PubMed PubMed Central

[8] S. M. Patole, M. Torlak, D. Wang, and M. Ali, “Automotive radars: A review of signal processing techniques,” IEEE Signal. Process. Mag., vol. 34, no. 2, pp. 22–35, 2017.10.1109/MSP.2016.2628914Search in Google Scholar

[9] M. Nakamura, T. Nishimura, and M. T. Morita, “Gravity sensing and signal conversion in plant gravitropism,” J. Exp. Bot., vol. 70, no. 14, pp. 3495–3506, 2019.10.1093/jxb/erz158Search in Google Scholar PubMed

[10] L. Xu, F. Chen, F. Ding, A. Alsaedi, and T. Hayat, “Hierarchical recursive signal modeling for multifrequency signals based on discrete measured data,” Int. J. Adapt. Control. Signal. Process., vol. 35, no. 5, pp. 676–693, 2021.10.1002/acs.3221Search in Google Scholar

[11] S. Shao, R. Yan, Y. Lu, P. Wang, and R. X. Gao, “DCNN-based multi-signal induction motor fault diagnosis,” IEEE Trans. Instrum. Meas., vol. 69, no. 6, pp. 2658–2669, 2019.10.1109/TIM.2019.2925247Search in Google Scholar

[12] T. Lu, J. Sun, K. Wu, and Z. Yang, “High-speed channel modeling with machine learning methods for signal integrity analysis,” IEEE Trans. Electromagn. Compat., vol. 60, no. 6, pp. 1957–1964, 2018.10.1109/TEMC.2017.2784833Search in Google Scholar

[13] J. Chen, Z. Lv, and H. Song, “Design of personnel big data management system based on blockchain,” Future Gener. Comput. Syst., vol. 101, pp. 1122–1129, 2019.10.1016/j.future.2019.07.037Search in Google Scholar

[14] F. Hu, X. Xi, and Y. Zhang, “Influencing mechanism of reverse knowledge spillover on investment enterprises’ technological progress: An empirical examination of Chinese firms,” Technol. Forecast. Soc. Change, vol. 169, p. 120797, 2021.10.1016/j.techfore.2021.120797Search in Google Scholar

[15] L. Li and J. Zhang, “Research and analysis of an enterprise E-commerce marketing system under the big data environment,” J. Organ. End. User Comput., vol. 33, no. 6, pp. 1–19, 2021.10.4018/JOEUC.20211101.oa15Search in Google Scholar

[16] P. Ghamisi, N. Yokoya, J. Li, W. Liao, S. Liu, J. Plaza, et al., “Advances in hyperspectral image and signal processing: A comprehensive overview of the state of the art,” IEEE Geosci. Remote. Sens. Mag., vol. 5, no. 4, pp. 37–78, 2017.10.1109/MGRS.2017.2762087Search in Google Scholar

[17] Q. Hao, B. Barnes, E. Wright, and R. M. Branch, “The influence of achievement goals on online help seeking of computer science students.” Br. J. Educ. Technol., vol. 48, no. 6, pp. 1273–1283, 2017.10.1111/bjet.12499Search in Google Scholar

Received: 2022-11-25
Revised: 2023-02-19
Accepted: 2023-03-16
Published Online: 2023-08-31

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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