Current Issue : January-March Volume : 2024 Issue Number : 1 Articles : 5 Articles
This paper is based on a project titled underwater acoustic communication in which communication is performed between transmitter and receiver side underwater using water as a channel; data are is transmitted through a piezo transducer underwater, which are then be received by a receiver, i.e., a wireless hydrophone. Signal processing and analysis are performed on the received wireless signals. Data reception and propagation are important parts on the receiver side, which involve conditioning and processing of the received signal. Morse code is used to detect the signals and processed data, which are then analyzed using MATLAB simulation software....
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 onmassive data processing and use signal processing and computer technology tomanage and optimize the scheduling of information resources to better meet various business needs....
Mine microseismic signal denoising is a basic and crucial link in microseismic data processing, which influences the accuracy and reliability of the monitoring system, and is of great significance with regard to safety during mining. Therefore, this study introduces a deep learning method to improve the mapping function and sparsity of signals in the time-frequency domain and constructs a denoising framework based on a deep convolutional autoencoder to address the denoising problem of mine microseismic signals. First, all noisy microseismic signals are normalized to ensure the nonlinear expression ability of the constructed denoising framework. Then, the normalized signals are transformed into the time-frequency domain using the short-time Fourier transform (STFT), and the real and imaginary parts of time-frequency coefficients serve as the input of the deep convolutional autoencoder to output the masks of the effective and noise signals. Next, these masks are applied to the time-frequency coefficients of the noisy microseismic signals, and the time-frequency coefficients of the potentially effective and noise signals are estimated. Finally, inverse STFT is used to transform these time-frequency coefficients to the time domain to obtain the final denoised effective and noise signals. The constructed framework automatically learns rich features from synthetic data to separate the effective and noise signals, thereby achieving the purpose of fast and automatic denoising. The experimental results show that compared with the wavelet threshold and ensemble empirical mode decomposition, the denoising framework considerably improves the signal-to-noise ratio of mine microseismic signals with less waveform distortion. Moreover, it can achieve a better denoising effect efficiently even in the case of a low SNR, which has obvious advantages.The constructed denoising framework is suitable for microseismic monitoring signals of various mine dynamic disasters and provides strong technical support for intelligent monitoring and early warning concerning production risks in mines....
This research aims to propose a comprehensive simulation and implementation methodology for LFM (Linear Frequency Modulated) Radar Signal Processing and its application, using digital signal processing techniques on the DSP Starter Kit (DSK) 6713 board. The motivation behind this study is to develop control software based on MATLAB R14 and SIMULINK to model various system software tasks, including detection, A/D conversion, Fast Fourier Transform (FFT), modulation, accumulation, decision-making, and target detection. The simulations are categorized into two groups: ideal beat frequency and parameterized beat frequency. We introduce several important terminologies for consideration, including pulse compression, SNR, matched filter, Doppler effect, and more. The use of real-time data exchange (RTDX) will facilitate the generation of input data and enable real-time calculations for outputs, leading to the creation of machine code for the DSP chip. This process aims to ensure data verification calculations and enhance the credibility and performance of the proposed methodology. By conducting thorough simulations, verification, and practical testing, the study demonstrates the satisfactory credibility and performance of the developed method. Using this research, we aim to contribute to the advancement of LFM Radar Signal Processing and enable its efficient implementation using digital signal processing techniques on the DSP Starter Kit (DSK) 6713 board....
Roadheader is important large equipment in coal mining. The roadheader has a higher failure rate due to its harsh working environment and high working intensity. In this paper, we proposed a fault diagnosis method based on reference manifold (RM) learning by using the vibration signals of roadheader in the actual production process. First, health and fault vibration signals were extracted from a large number of field data. The abovementioned signals were analyzed by time domain and wavelet packet energy analysis and got the characteristic parameters of the signal which can form the characteristic parameter sets. RM method can reduce the dimension of the characteristic parameters, and the projection of different characteristic parameters was obtained. Finally, the health parameters and fault parameters of different characteristic parameters were segmented by linear discriminant analysis (LDA). It could get the different segment area range of characteristic parameters for health signals and fault signals. This method provides a set of fault analysis ideas and methods for equipment working under complex working conditions and improves the theoretical basis for fault type analysis....
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