Convolutional neural network for single-sensor acoustic localization of a transiting broadband source in very shallow water

2019 ◽  
Vol 146 (6) ◽  
pp. 4687-4698 ◽  
Author(s):  
Eric L. Ferguson ◽  
Stefan B. Williams ◽  
Craig T. Jin
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Shallow Water ◽  
Acoustic Localization ◽  
Single Sensor

scholarly journals Defect Segmentation in Concrete Structures Combining Registered Infrared and Visible Images: A Comparative Experimental Study

2021 ◽  
Vol 8 (1) ◽  
pp. 29
Author(s):  
Sandra Pozzer ◽  
Marcos Paulo Vieira de Souza ◽  
Bata Hena ◽  
Reza Khoshkbary Rezayiye ◽  
Setayesh Hesam ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Study ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Concrete Structures ◽  
Semantic Segmentation ◽  
Imaging Modalities ◽  
Visible Images ◽  
Single Sensor ◽  
Infrared Cameras

This study investigates the semantic segmentation of common concrete defects when using different imaging modalities. One pre-trained Convolutional Neural Network (CNN) model was trained via transfer learning and tested to detect concrete defect indications, such as cracks, spalling, and internal voids. The model’s performance was compared using datasets of visible, thermal, and fused images. The data were collected from four different concrete structures and built using four infrared cameras that have different sensitivities and resolutions, with imaging campaigns conducted during autumn, summer, and winter periods. Although specific defects can be detected in monomodal images, the results demonstrate that a larger number of defect classes can be accurately detected using multimodal fused images with the same viewpoint and resolution of the single-sensor image.

scholarly journals Command Recognition Using Binarized Convolutional Neural Network with Voice and Radar Sensors for Human-Vehicle Interaction

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3906
Author(s):  
Seunghyun Oh ◽  
Chanhee Bae ◽  
Jaechan Cho ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Continuous Wave ◽  
Low Complexity ◽  
Cluttered Environments ◽  
Radar Sensors ◽  
Wave Radar ◽  
Single Voice ◽  
Neural Network Algorithm ◽  
Single Sensor

Recently, as technology has advanced, the use of in-vehicle infotainment systems has increased, providing many functions. However, if the driver’s attention is diverted to control these systems, it can cause a fatal accident, and thus human–vehicle interaction is becoming more important. Therefore, in this paper, we propose a human–vehicle interaction system to reduce driver distraction during driving. We used voice and continuous-wave radar sensors that require low complexity for application to vehicle environments as resource-constrained platforms. The proposed system applies sensor fusion techniques to improve the limit of single-sensor monitoring. In addition, we used a binarized convolutional neural network algorithm, which significantly reduces the computational workload of the convolutional neural network in command classification. As a result of performance evaluation in noisy and cluttered environments, the proposed system showed a recognition accuracy of 96.4%, an improvement of 7.6% compared to a single voice sensor-based system, and 9.0% compared to a single radar sensor-based system.

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Sign in / Sign up

Export Citation Format

Share Document