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Signal Acquisition and Processing for Measurement and Testing
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Signal Acquisition and Processing for Measurement and Testing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 8684

Special Issue Editors

Prof. Dr. Yijiu Zhao

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: signal acquisition and transformation; statistical signal processing; embedded system design
Prof. Dr. Yindong Xiao

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: integrated circuits; testing technology; testing equipments
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mingjiang Shi

E-Mail Website
Guest Editor
School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
Interests: weak signal detection; machine learning; nondestructive testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to the application of signal acquisition and processing in measurement and testing fields. The signal acquisition technique aims to acquire the analog signal, and it is governed by the Shannon sampling theorem. However, the rate of signal is usually high, and it brings a great challenge to the signal acquisition techniques. Therefore, the alternative signal acquisition techniques, such as sub-Nyquist sampling, compressive sampling etc., would be potential solutions to acquire the high speed signal. In this Special Issue, we will focus on the signal sampling methods and the corresponding processing techniques. The scope of submitted papers may encompass: (1) sampling models with mathematical explanations, particularly welcome will be models that could be implemented in circuitry. (2) signal reconstruction, representation and estimation; (3) design, circuitry implementation.

Prof. Dr. Yijiu Zhao
Dr. Yindong Xiao
Prof. Dr. Mingjiang Shi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sampling methods
  • sub-Nyquist sampling
  • signal reconstruction
  • wideband signal acquisition
  • waveform synthesis techniques
  • compressive sampling
  • spectral estimation
  • spectrum sensing

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Published Papers (6 papers)

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Research

21 pages, 10351 KiB  
Article
TSViT: A Time Series Vision Transformer for Fault Diagnosis of Rotating Machinery
by Shouhua Zhang, Jiehan Zhou, Xue Ma, Susanna Pirttikangas and Chunsheng Yang
Appl. Sci. 2024, 14(23), 10781; https://doi.org/10.3390/app142310781 - 21 Nov 2024
Cited by 1 | Viewed by 1494
Abstract
Efficient and accurate fault diagnosis of rotating machinery is extremely important. Fault diagnosis methods using vibration signals based on convolutional neural networks (CNNs) have become increasingly mature. They often struggle with capturing the temporal dynamics of vibration signals. To overcome this, the application [...] Read more.
Efficient and accurate fault diagnosis of rotating machinery is extremely important. Fault diagnosis methods using vibration signals based on convolutional neural networks (CNNs) have become increasingly mature. They often struggle with capturing the temporal dynamics of vibration signals. To overcome this, the application of Transformer-based Vision Transformer (ViT) methods to fault diagnosis is gaining attraction. Nonetheless, these methods typically require extensive preprocessing, which increases computational complexity, potentially reducing the efficiency of the diagnosis process. Addressing this gap, this paper presents the Time Series Vision Transformer (TSViT), tailored for effective fault diagnosis. The TSViT incorporates a convolutional layer to extract local features from vibration signals alongside a transformer encoder to discern long-term temporal patterns. A thorough experimental comparison of three diverse datasets demonstrates the TSViT’s effectiveness and adaptability. Moreover, the paper delves into the influence of hyperparameter tuning on the model’s performance, computational demand, and parameter count. Remarkably, the TSViT achieves an unprecedented 100% average accuracy on two of the test sets and 99.99% on the other, showcasing its exceptional fault diagnosis capabilities for rotating machinery. The implementation of this model will bring significant economic benefits. Full article
(This article belongs to the Special Issue Signal Acquisition and Processing for Measurement and Testing)
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14 pages, 8406 KiB  
Article
A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
by Lin Hu, Baoqi Zhao and Guangji Wang
Appl. Sci. 2024, 14(17), 7954; https://doi.org/10.3390/app14177954 - 6 Sep 2024
Cited by 2 | Viewed by 1178
Abstract
With the rapid development of the Internet of Things (IoT) technology, the number and types of devices accessing the Internet are increasing, leading to increased network security problems such as hacker attacks and botnets. Usually, these attacks are related to the type of [...] Read more.
With the rapid development of the Internet of Things (IoT) technology, the number and types of devices accessing the Internet are increasing, leading to increased network security problems such as hacker attacks and botnets. Usually, these attacks are related to the type of device, and the risk can be effectively reduced if the type of network device can be efficiently identified and controlled. The traditional network device identification method uses active detection technology to obtain information about the device and match it with a manually defined fingerprint database to achieve network device identification. This method impacts the smoothness of the network and requires the manual establishment of fingerprint libraries, which imposes a large labor cost but only achieves a low identification efficiency. The traditional machine learning method only considers the information of individual packets; it does not consider the timing relationship between packets, and the recognition effect is poor. Based on the above research, in this paper, we considered the packet temporal relationship, proposed the TCN model of the Inception structure, extracted the packet temporal relationship, and designed a multi-head self-attention mechanism to fuse the features to generate device fingerprints for device identification. Experiments were conducted on the publicly available UNSW dataset, and the results showed that this method achieved notable improvements compared to the traditional machine learning method, with F1 reaching 96.76%. Full article
(This article belongs to the Special Issue Signal Acquisition and Processing for Measurement and Testing)
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18 pages, 1129 KiB  
Article
Research on Clock Synchronization of Data Acquisition Based on NoC
by Chaoyong Meng, Chuanpei Xu and Jiafeng Liao
Appl. Sci. 2024, 14(11), 4838; https://doi.org/10.3390/app14114838 - 3 Jun 2024
Cited by 1 | Viewed by 1168
Abstract
Data acquisition based on network-on-chip (NoC) technology is a high-sampling-rate data acquisition scheme using low-sampling-rate analog–digital conversion (ADC) chips. It has the characteristics of multi-task parallel communication, being global asynchronous, local synchronous clock distribution, high throughput, low transmission latency, and strong scalability. High-speed [...] Read more.
Data acquisition based on network-on-chip (NoC) technology is a high-sampling-rate data acquisition scheme using low-sampling-rate analog–digital conversion (ADC) chips. It has the characteristics of multi-task parallel communication, being global asynchronous, local synchronous clock distribution, high throughput, low transmission latency, and strong scalability. High-speed data acquisition is realized through the combination of an on-chip network and time-interleaved data acquisition technology. In the time-interleaved sampling technique, the precision of clock synchronization directly affects the precision of sampling. Based on the proposed NOC data acquisition scheme, an improved White Rabbit clock synchronization protocol is applied to high-speed data acquisition to achieve high-precision synchronization of multi-channel time-interleaved sampling clocks. Firstly, the offset of the master clock and slave clock is determined by the PTP protocol, and the offset is corrected to achieve rough synchronization between the master clock and slave clock. Secondly, a digital dual-mixer time difference (DDMTD) is used to measure the phases of the master and slave clocks. After that, the phase of the slave clock is corrected through the dynamic phase-shift function of the clock’s phase-locked loop (PLL). Finally, according to the simulation results in Modelsim, the average absolute error of a TI-ADC sampling clock can be less than 20 ps. Full article
(This article belongs to the Special Issue Signal Acquisition and Processing for Measurement and Testing)
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16 pages, 7082 KiB  
Article
Research on an Alpha Navigation Signal Detection Method Based on Multichannel Orthogonal Correlation
by Wenhe Yan, Shifeng Li, Xinze Ma, Yuhang Song, Jiangbin Yuan and Yu Hua
Appl. Sci. 2024, 14(9), 3620; https://doi.org/10.3390/app14093620 - 25 Apr 2024
Viewed by 1233
Abstract
The Alpha navigation system is the only operating radio system based on very-low-frequency (VLF) signals that can be used to research VLF navigation, timing, and ionospheric characteristics. The detection of the Alpha navigation signal is the key step in the Alpha receiver; however, [...] Read more.
The Alpha navigation system is the only operating radio system based on very-low-frequency (VLF) signals that can be used to research VLF navigation, timing, and ionospheric characteristics. The detection of the Alpha navigation signal is the key step in the Alpha receiver; however, the received Alpha navigation signal is susceptible to noise and mutual interference, which deteriorates signal detection performance. This paper presents a multichannel orthogonal correlation method for Alpha navigation signal detection. Once the three frequency signals of the Alpha navigation system are obtained using a notch filter, station identification is realized using a multichannel orthogonal correlation method and signal format. The selection of key parameters and the detection performance under noise and mutual interference are analyzed. This method’s detection probability exceeds 90% when the signal-to-noise ratio (SNR) is greater than −10 dB. The influence of mutual interference on the signal correlation peak is less than 1% when the signal-to-interference ratio (SIR) of the mutual interference is greater than −28 dB. The proposed method is verified using an actual signal collected using an Alpha receiver. The results show that an Alpha signal can be detected at an extremely low SNR. This method has strong practicability and satisfies the application requirements of an Alpha receiver. Full article
(This article belongs to the Special Issue Signal Acquisition and Processing for Measurement and Testing)
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16 pages, 7113 KiB  
Article
Effect of Face Masks on Automatic Speech Recognition Accuracy for Mandarin
by Xiaoya Li, Ke Ni and Yu Huang
Appl. Sci. 2024, 14(8), 3273; https://doi.org/10.3390/app14083273 - 12 Apr 2024
Cited by 1 | Viewed by 1118
Abstract
Automatic speech recognition (ASR) has been widely used to realize daily human–machine interactions. Face masks have become everyday wear in our post-pandemic life, and speech through masks may have impaired the ASR. This study explored the effects of different kinds of face masks [...] Read more.
Automatic speech recognition (ASR) has been widely used to realize daily human–machine interactions. Face masks have become everyday wear in our post-pandemic life, and speech through masks may have impaired the ASR. This study explored the effects of different kinds of face masks (e.g., surgical mask, KN95 mask, and cloth mask) on the Mandarin word accuracy of two ASR systems with or without noises. A mouth simulator was used to play speech audio with or without wearing a mask. Acoustic signals were recorded at distances of 0.2 m and 0.6 m. Recordings were mixed with two noises at a signal-to-noise ratio of +3 dB: restaurant noise and speech-shaped noise. Results showed that masks did not affect ASR accuracy without noise. Under noises, masks did not significantly influence ASR accuracy at 0.2 m but had significant effects at 0.6 m. The activated-carbon mask had the most significant impact on ASR accuracy at 0.6 m, reducing the accuracy by 18.5 percentage points compared to that without a mask, whereas the cloth mask had the least effect on ASR accuracy at 0.6 m, reducing the accuracy by 0.9 percentage points. The acoustic attenuation of masks on the high-frequency band at around 3.15 kHz of the speech signal attributed to the effects of masks on ASR accuracy. When training ASR models, it may be important to consider mask robustness. Full article
(This article belongs to the Special Issue Signal Acquisition and Processing for Measurement and Testing)
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15 pages, 5694 KiB  
Article
Estimation of Impact Loads Transmitted to Vibro-Ripper Housing Using Transfer Path Analysis
by Daeji Kim, Hyune-Jun Park, Joo-Young Oh, Jung-Woo Cho, Jintai Chung and Changheon Song
Appl. Sci. 2023, 13(19), 10990; https://doi.org/10.3390/app131910990 - 5 Oct 2023
Viewed by 1807
Abstract
This study aimed to estimate impact loads delivered to vibro-ripper housing through link modules, together with loads transmitted in various directions. Housing vibration resulting from impact loads generated during the operating condition and frequency response functions were assessed by vibration and modal experiments, [...] Read more.
This study aimed to estimate impact loads delivered to vibro-ripper housing through link modules, together with loads transmitted in various directions. Housing vibration resulting from impact loads generated during the operating condition and frequency response functions were assessed by vibration and modal experiments, respectively. Vibration data and transfer functions were applied in a transfer path analysis (TPA) model to analyze the quantified impact loads transmitted through the key components of the vibro-ripper to its housing. Impact loads derived by TPA for different housing parts were compared with those in the tooth derived from load-cell measurements, validating the TPA method. As a result of the verification, the impact load calculated by the TPA method was 193.7 kN, whereas that from the striking force measured by the load cell was 220 kN, a difference of 12.3%. The results of this study may be important input values for numerical analysis in equipment design and can be used as key data for structural safety evaluation and optimization. In summary, this paper introduces the vibration-based TPA method and considers its applicability to construction machinery exposed to impact vibration and loads. Full article
(This article belongs to the Special Issue Signal Acquisition and Processing for Measurement and Testing)
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