scholarly journals Joint Representation and Recognition for Ship-Radiated Noise Based on Multimodal Deep Learning

2019 ◽  
Vol 7 (11) ◽  
pp. 380
Author(s):  
Fei Yuan ◽  
Xiaoquan Ke ◽  
En Cheng
Keyword(s):  
Deep Learning ◽  
Recognition Performance ◽  
Visual Observation ◽  
Experimental Results ◽  
Visual Modality ◽  
Traditional Methods ◽  
Radiated Noise ◽  
Noise Data ◽  
Joint Representation ◽  
Single Modality

Ship recognition based on ship-radiated noise is one of the most important and challenging subjects in underwater acoustic signal processing. The recognition methods for ship-radiated noise recognition include traditional methods and deep learning (DL) methods. Developing from the DL methods and inspired by audio–video speech recognition (AVSR), the paper further introduces multimodal deep learning (multimodal-DL) methods for the recognition of ship-radiated noise. In this paper, ship-radiated noise (acoustics modality) and visual observation of the ships (visual modality) are two different modalities that the multimodal-DL methods model on. The paper specially designs a multimodal-DL framework, the multimodal convolutional neural networks (multimodal-CNNs) for the recognition of ship-radiated noise. Then the paper proposes a strategy based on canonical correlation analysis (CCA-based strategy) to build a joint representation and recognition on the two different single-modality (acoustics modality and visual modality). The multimodal-CNNs and the CCA-based strategy are tested on real ship-radiated noise data recorded. Experimental results show that, using the CCA-based strategy, strong-discriminative information can be built from weak-discriminative information provided from a single-modality. Experimental results also show that as long as any one of the single-modalities can provide information for the recognition, the multimodal-DL methods can have a much better multiclass recognition performance than the DL methods. The paper also discusses the advantages and superiorities of the multimodal-Dl methods over the traditional methods for ship-radiated noise recognition.

scholarly journals Research on Recognition Method of COVID-19 Images Based on Deep Learning

2020 ◽  
Author(s):  
dongshen ji ◽  
yanzhong zhao ◽  
zhujun zhang ◽  
qianchuan zhao
Keyword(s):  
Deep Learning ◽  
Image Recognition ◽  
Feature Fusion ◽  
Recognition Accuracy ◽  
Recognition Performance ◽  
Small Sample ◽  
Experimental Results ◽  
Ct Image ◽  
Recognition Method ◽  
Sample Recognition

In view of the large demand for new coronary pneumonia covid19 image recognition samples,the recognition accuracy is not ideal.In this paper,a new coronary pneumonia positive image recognition method proposed based on small sample recognition. First, the CT image pictures are preprocessed, and the pictures are converted into the picture formats which are required for transfer learning. Secondly, perform small-sample image enhancement and expansion on the converted picture, such as miscut transformation, random rotation and translation, etc.. Then, multiple migration models are used to extract features and then perform feature fusion. Finally,the model is adjusted by fine-tuning.Then train the model to obtain experimental results. The experimental results show that our method has excellent recognition performance in the recognition of new coronary pneumonia images,even with only a small number of CT image samples.

scholarly journals 2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

2021 ◽  
Author(s):  
Wei Jia ◽  
Wei Xia ◽  
Yang Zhao ◽  
Hai Min ◽  
Yan-Xiang Chen
Keyword(s):  
Deep Learning ◽  
Recognition Performance ◽  
Research Direction ◽  
Palmprint Recognition ◽  
Neural Architecture ◽  
Development Direction ◽  
Vein Recognition ◽  
Palm Vein ◽  
2D And 3D ◽  
Important Research Direction

AbstractPalmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition and have achieved impressive results. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

scholarly journals PyConvU-Net: a lightweight and multiscale network for biomedical image segmentation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Changyong Li ◽  
Yongxian Fan ◽  
Xiaodong Cai
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
State Of The Art ◽  
Experimental Results ◽  
Actual Situation ◽  
Controlled Experiments ◽  
Biomedical Image ◽  
Segmentation Methods ◽  
Art Performance

Abstract Background With the development of deep learning (DL), more and more methods based on deep learning are proposed and achieve state-of-the-art performance in biomedical image segmentation. However, these methods are usually complex and require the support of powerful computing resources. According to the actual situation, it is impractical that we use huge computing resources in clinical situations. Thus, it is significant to develop accurate DL based biomedical image segmentation methods which depend on resources-constraint computing. Results A lightweight and multiscale network called PyConvU-Net is proposed to potentially work with low-resources computing. Through strictly controlled experiments, PyConvU-Net predictions have a good performance on three biomedical image segmentation tasks with the fewest parameters. Conclusions Our experimental results preliminarily demonstrate the potential of proposed PyConvU-Net in biomedical image segmentation with resources-constraint computing.

scholarly journals Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2595
Author(s):  
Balakrishnan Ramalingam ◽  
Abdullah Aamir Hayat ◽  
Mohan Rajesh Elara ◽  
Braulio Félix Gómez ◽  
Lim Yi ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
High Accuracy ◽  
Experimental Results ◽  
Mobile Mapping ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Reconfigurable Robot ◽  
Nvidia Gpu ◽  
Inspection Task

The pavement inspection task, which mainly includes crack and garbage detection, is essential and carried out frequently. The human-based or dedicated system approach for inspection can be easily carried out by integrating with the pavement sweeping machines. This work proposes a deep learning-based pavement inspection framework for self-reconfigurable robot named Panthera. Semantic segmentation framework SegNet was adopted to segment the pavement region from other objects. Deep Convolutional Neural Network (DCNN) based object detection is used to detect and localize pavement defects and garbage. Furthermore, Mobile Mapping System (MMS) was adopted for the geotagging of the defects. The proposed system was implemented and tested with the Panthera robot having NVIDIA GPU cards. The experimental results showed that the proposed technique identifies the pavement defects and litters or garbage detection with high accuracy. The experimental results on the crack and garbage detection are presented. It is found that the proposed technique is suitable for deployment in real-time for garbage detection and, eventually, sweeping or cleaning tasks.

Palm Vein Recognition Based on Independent Component Analysis

2013 ◽  
Vol 333-335 ◽  
pp. 1106-1109
Author(s):  
Wei Wu
Keyword(s):  
Pattern Recognition ◽  
Independent Component Analysis ◽  
Euclidean Distance ◽  
Recognition Performance ◽  
Component Analysis ◽  
Independent Component ◽  
Experimental Results ◽  
Vein Pattern ◽  
Vein Recognition ◽  
Palm Vein

Palm vein pattern recognition is one of the newest biometric techniques researched today. This paper proposes project the palm vein image matrix based on independent component analysis directly, then calculates the Euclidean distance of the projection matrix, seeks the nearest distance for classification. The experiment has been done in a self-build palm vein database. Experimental results show that the algorithm of independent component analysis is suitable for palm vein recognition and the recognition performance is practical.

A Radiated-Noise Simulator for Underwater Target Based on Vector Time Series

2014 ◽  
Vol 644-650 ◽  
pp. 4023-4026
Author(s):  
Yang Ju ◽  
Xin Yong Wang
Keyword(s):  
Time Series ◽  
Confidence Interval ◽  
Time Series Model ◽  
Experimental Results ◽  
Small Probability ◽  
Radiated Noise ◽  
True Value ◽  
Underwater Target ◽  
Vector Time Series

The vector time series model for simulating the underwater target radiated-noise is developed in this paper. Experimental results show that the true value lying outside the confidence interval would be a small probability event.

Wavelets as activation functions in Neural Networks

2021 ◽  
pp. 1-11
Author(s):  
Oscar Herrera ◽  
Belém Priego
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
High Performance ◽  
Research Area ◽  
Experimental Results ◽  
Activation Functions ◽  
Hyperbolic Tangent ◽  
Open Research ◽  
Bounded Functions

Traditionally, a few activation functions have been considered in neural networks, including bounded functions such as threshold, sigmoidal and hyperbolic-tangent, as well as unbounded ReLU, GELU, and Soft-plus, among other functions for deep learning, but the search for new activation functions still being an open research area. In this paper, wavelets are reconsidered as activation functions in neural networks and the performance of Gaussian family wavelets (first, second and third derivatives) are studied together with other functions available in Keras-Tensorflow. Experimental results show how the combination of these activation functions can improve the performance and supports the idea of extending the list of activation functions to wavelets which can be available in high performance platforms.

The Development of an Identification Photo Booth System based on a Deep Learning Automatic Image Capturing Method

2020 ◽  
Author(s):  
Yu-Xiang Zhao ◽  
Yi-Zeng Hsieh ◽  
Shih-Syun Lin
Keyword(s):  
Deep Learning ◽  
Experimental Results ◽  
Automatic Annotation ◽  
Learning Method ◽  
Facial Region ◽  
Facial Landmarks ◽  
Image Capturing ◽  
The Face ◽  
Facial Contours

With advances in technology, photo booths equipped with automatic capturing systems have gradually replaced the identification (ID) photo service provided by photography studios, thereby enabling consumers to save a considerable amount of time and money. Common automatic capturing systems employ text and voice instructions to guide users in capturing their ID photos; however, the capturing results may not conform to ID photo specifications. To address this issue, this study proposes an ID photo capturing algorithm that can automatically detect facial contours and adjust the size of captured images. The authors adopted a deep learning method (You Only Look Once) to detect the face and applied a semi-automatic annotation technique of facial landmarks to find the lip and chin regions from the facial region. In the experiments, subjects were seated at various distances and heights for testing the performance of the proposed algorithm. The experimental results show that the proposed algorithm can effectively and accurately capture ID photos that satisfy the required specifications.

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

scholarly journals Research on the Application of Artificial Intelligence Machine Learning Technology in Improving the Accuracy of Engineering Image Processing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042007
Author(s):  
Xiaowen Liu ◽  
Juncheng Lei
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Deep Learning ◽  
Image Recognition ◽  
Recognition Performance ◽  
Color Space ◽  
Gaussian Model ◽  
Image Feature ◽  
Layer By Layer ◽  
Image Information

Abstract Image recognition technology mainly includes image feature extraction and classification recognition. Feature extraction is the key link, which determines whether the recognition performance is good or bad. Deep learning builds a model by building a hierarchical model structure like the human brain, extracting features layer by layer from the data. Applying deep learning to image recognition can further improve the accuracy of image recognition. Based on the idea of clustering, this article establishes a multi-mix Gaussian model for engineering image information in RGB color space through offline learning and expectation-maximization algorithms, to obtain a multi-mix cluster representation of engineering image information. Then use the sparse Gaussian machine learning model on the YCrCb color space to quickly learn the distribution of engineering images online, and design an engineering image recognizer based on multi-color space information.

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