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Gu HQ, Liu XX, Xu L, Zhang YJ, Lu ZM. Period Estimation of Spread Spectrum Codes Based on ResNet. SENSORS (BASEL, SWITZERLAND) 2023; 23:7002. [PMID: 37571785 PMCID: PMC10422606 DOI: 10.3390/s23157002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 08/02/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023]
Abstract
In order to more effectively monitor and interfere with enemy signals, it is particularly important to accurately and efficiently identify the intercepted signals and estimate their parameters in the increasingly complex electromagnetic environment. Therefore, in non-cooperative situations, it is of great practical significance to study how to accurately detect direct sequence spread spectrum (DSSS) signals in real time and estimate their parameters. The traditional time-delay correlation algorithm encounters the challenges such as peak energy leakage and false peak interference. As an alternative, this paper introduces a Pseudo-Noise (PN) code period estimation method utilizing a one-dimensional (1D) convolutional neural network based on the residual network (CNN-ResNet). This method transforms the problem of spread spectrum code period estimation into a multi-classification problem of spread spectrum code length estimation. Firstly, the In-phase/Quadrature(I/Q) two-way of the received DSSS signals is directly input into the CNN-ResNet model, which will automatically learn the characteristics of the DSSS signal with different PN code lengths and then estimate the PN code length. Simulation experiments are conducted using a data set with DSSS signals ranging from -20 to 10 dB in terms of signal-to-noise ratios (SNRs). Upon training and verifying the model using BPSK modulation, it is then put to the test with QPSK-modulated signals, and the estimation performance was analyzed through metrics such as loss function, accuracy rate, recall rate, and confusion matrix. The results demonstrate that the 1D CNN-ResNet proposed in this paper is capable of effectively estimating the PN code period of the non-cooperative DSSS signal, exhibiting robust generalization abilities.
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Affiliation(s)
- Han-Qing Gu
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.-Q.G.); (X.-X.L.); (L.X.)
| | - Xia-Xia Liu
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.-Q.G.); (X.-X.L.); (L.X.)
| | - Lu Xu
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.-Q.G.); (X.-X.L.); (L.X.)
| | - Yi-Jia Zhang
- School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China; (H.-Q.G.); (X.-X.L.); (L.X.)
| | - Zhe-Ming Lu
- School of Aeronautics and Astronautics, Zhejiang University, Hangzhou 310027, China
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Paramasivam K, Sindha MMR, Balakrishnan SB. KNN-Based Machine Learning Classifier Used on Deep Learned Spatial Motion Features for Human Action Recognition. ENTROPY (BASEL, SWITZERLAND) 2023; 25:844. [PMID: 37372188 DOI: 10.3390/e25060844] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/04/2023] [Accepted: 05/09/2023] [Indexed: 06/29/2023]
Abstract
Human action recognition is an essential process in surveillance video analysis, which is used to understand the behavior of people to ensure safety. Most of the existing methods for HAR use computationally heavy networks such as 3D CNN and two-stream networks. To alleviate the challenges in the implementation and training of 3D deep learning networks, which have more parameters, a customized lightweight directed acyclic graph-based residual 2D CNN with fewer parameters was designed from scratch and named HARNet. A novel pipeline for the construction of spatial motion data from raw video input is presented for the latent representation learning of human actions. The constructed input is fed to the network for simultaneous operation over spatial and motion information in a single stream, and the latent representation learned at the fully connected layer is extracted and fed to the conventional machine learning classifiers for action recognition. The proposed work was empirically verified, and the experimental results were compared with those for existing methods. The results show that the proposed method outperforms state-of-the-art (SOTA) methods with a percentage improvement of 2.75% on UCF101, 10.94% on HMDB51, and 0.18% on the KTH dataset.
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Affiliation(s)
- Kalaivani Paramasivam
- Department of Electronics and Communication Engineering, Government College of Engineering, Bodinayakanur 625582, Tamilnadu, India
| | - Mohamed Mansoor Roomi Sindha
- Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai 625015, Tamilnadu, India
| | - Sathya Bama Balakrishnan
- Department of Electronics and Communication Engineering, Thiagarajar College of Engineering, Madurai 625015, Tamilnadu, India
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Lin X, Hong D, Zhang D, Huang M, Yu H. Detecting Proximal Caries on Periapical Radiographs Using Convolutional Neural Networks with Different Training Strategies on Small Datasets. Diagnostics (Basel) 2022; 12:diagnostics12051047. [PMID: 35626203 PMCID: PMC9139265 DOI: 10.3390/diagnostics12051047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/14/2022] [Accepted: 04/19/2022] [Indexed: 02/05/2023] Open
Abstract
The present study aimed to evaluate the performance of convolutional neural networks (CNNs) that were trained with small datasets using different strategies in the detection of proximal caries at different levels of severity on periapical radiographs. Small datasets containing 800 periapical radiographs were randomly categorized into a training and validation dataset (n = 600) and a test dataset (n = 200). A pretrained Cifar-10Net CNN was used in the present study. Different training strategies were used to train the CNN model independently; these strategies were defined as image recognition (IR), edge extraction (EE), and image segmentation (IS). Different metrics, such as sensitivity and area under the receiver operating characteristic curve (AUC), for the trained CNN and human observers were analysed to evaluate the performance in detecting proximal caries. IR, EE, and IS recognition modes and human eyes achieved AUCs of 0.805, 0.860, 0.549, and 0.767, respectively, with the EE recognition mode having the highest values (p all < 0.05). The EE recognition mode was significantly more sensitive in detecting both enamel and dentin caries than human eyes (p all < 0.05). The CNN trained with the EE strategy, the best performer in the present study, showed potential utility in detecting proximal caries on periapical radiographs when using small datasets.
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Affiliation(s)
- Xiujiao Lin
- Fujian Provincial Engineering Research Center of Oral Biomaterial, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350005, China; (X.L.); (D.H.)
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350005, China
| | - Dengwei Hong
- Fujian Provincial Engineering Research Center of Oral Biomaterial, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350005, China; (X.L.); (D.H.)
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350005, China
| | - Dong Zhang
- College of Computer and Data Science, Fuzhou University, Fuzhou 350025, China; (D.Z.); (M.H.)
| | - Mingyi Huang
- College of Computer and Data Science, Fuzhou University, Fuzhou 350025, China; (D.Z.); (M.H.)
| | - Hao Yu
- Fujian Provincial Engineering Research Center of Oral Biomaterial, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350005, China; (X.L.); (D.H.)
- Department of Prosthodontics, School and Hospital of Stomatology, Fujian Medical University, Fuzhou 350005, China
- Department of Applied Prosthodontics, Graduate School of Biomedical Sciences, Nagasaki University, Nagasaki 852-8521, Japan
- Correspondence:
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An Automated Recognition of Work Activity in Industrial Manufacturing Using Convolutional Neural Networks. ELECTRONICS 2021. [DOI: 10.3390/electronics10232946] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The automated assessment and analysis of employee activity in a manufacturing enterprise, operating in accordance with the concept of Industry 4.0, is essential for a quick and precise diagnosis of work quality, especially in the process of training a new employee. In the case of industrial solutions, many approaches involving the recognition and detection of work activity are based on Convolutional Neural Networks (CNNs). Despite the wide use of CNNs, it is difficult to find solutions supporting the automated checking of work activities performed by trained employees. We propose a novel framework for the automatic generation of workplace instructions and real-time recognition of worker activities. The proposed method integrates CNN, CNN Support Vector Machine (SVM), CNN Region-Based CNN (Yolov3 Tiny) for recognizing and checking the completed work tasks. First, video recordings of the work process are analyzed and reference video frames corresponding to work activity stages are determined. Next, work-related features and objects are determined using CNN with SVM (achieving 94% accuracy) and Yolov3 Tiny network based on the characteristics of the reference frames. Additionally, matching matrix between the reference frames and the test frames using mean absolute error (MAE) as a measure of errors between paired observations was built. Finally, the practical usefulness of the proposed approach by applying the method for supporting the automatic training of new employees and checking the correctness of their work done on solid fuel boiler equipment in a manufacturing company was demonstrated. The developed information system can be integrated with other Industry 4.0 technologies introduced within an enterprise.
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