scholarly journals A Review of Object Visual Detection for Intelligent Vehicles

2021 ◽  
pp. 1-10
Author(s):  
Sirin Kumar Singh ◽  
Keyword(s):  
Neural Network ◽  
Review Paper ◽  
Visual Detection ◽  
Item Recognition ◽  
Intelligent Vehicles ◽  
High Quality ◽  
Survey Paper ◽  
Optical Flow Method ◽  
Short Presentation ◽  
Item Location

This paper contains the details of different object detection (OD) techniques, object identification's relationship with video investigation, and picture understanding, it has pulled in much exploration consideration as of late. Customary item identification strategies are based on high-quality highlights and shallow teachable models. This survey paper presents one such strategy which is named as Optical Flow method (OFM). This strategy is discovered to be stronger and more effective for moving item recognition and the equivalent has been appeared by an investigation in this review paper. Applying optical stream to a picture gives stream vectors of the focuses comparing to the moving items. Next piece of denoting the necessary moving object of interest checks to the post-preparing. Post handling is the real commitment of the review paper for moving item identification issues. Their presentation effectively deteriorates by developing complex troupes which join numerous low-level picture highlights with significant level set-ting from object indicators and scene classifiers. With the fast advancement in profound learning, all the more useful assets, which can learn semantic, significant level, further highlights, are acquainted with address the issues existing in customary designs. These models carry on contrastingly in network design, preparing system, and advancement work, and so on in this review paper, we give an audit on profound learning-based item location systems. Our survey starts with a short presentation on the historical backdrop of profound learning and its agent device, in particular, Convolutional Neural Network (CNN) and region-based convolutional neural networks (R-CNN).

scholarly journals A Review of Automatic Driving System by Recognizing Road Signs Using Digital Image Processing

2021 ◽  
Vol 2 (2) ◽  
pp. 1-9
Author(s):  
Afroj Alam ◽  
Keyword(s):  
Review Paper ◽  
Item Recognition ◽  
High Quality ◽  
Driving System ◽  
Survey Paper ◽  
Automatic Driving ◽  
Road Signs ◽  
Optical Flow Method ◽  
Short Presentation ◽  
Item Location

In this review, the paper furnishes object identification's relationship with video investigation and picture understanding, it has pulled in much exploration consideration as of late. Customary item identification strategies are based on high-quality highlights and shallow teachable models. This survey paper presents one such strategy which is named as Optical Flow method. This strategy is discovered to be stronger and more effective for moving item recognition and the equivalent has been appeared by an investigation in this review paper. Applying optical stream to a picture gives stream vectors of the focus-es comparing to the moving items. Next piece of denoting the necessary moving object of interest checks to the post preparation. Post handling is the real commitment of the review paper for moving item identification issues. Their presentation effectively deteriorates by developing complex troupes which join numerous low-level picture highlights with significant level setting from object indicators and scene classifiers. With the fast advancement in profound learning, all the more useful assets, which can learn semantic, significant level, further highlights, are acquainted with address the issues existing in customary designs. These models carry on contrastingly in network design, preparing system, and advancement work, and so on In this review paper, we give an audit on pro-found learning-based item location systems. Our survey starts with a short presentation on the historical backdrop of profound learning and its agent device, in particular Convolutional Neural Network (CNN).

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals McStas (ii): An overview of components, their use, and advice for user contributions

2021 ◽  
Vol 23 (1) ◽  
pp. 7-27
Author(s):  
Peter Kjær Willendrup ◽  
Kim Lefmann
Keyword(s):  
Review Paper ◽  
Time Of Flight ◽  
Physical Models ◽  
High Quality ◽  
Physical Elements ◽  
Main Component ◽  
Continuous Source ◽  
Component Class

A key element of the success of McStas is the component layer where users and developers alike are contributing to the description of new physical models and features. In McStas, components realise all physical elements of the simulated instrument from source via optics and samples to detector. In this second review paper of the McStas package, we present an overview of the component classes in McStas: sources, monitors, optics, samples, misc, and contrib. Within each component class we give thorough examples of high-quality components, including their algorithms and example use. We present two example instruments, one for a continuous source and one for a time-of-flight source, that together demonstrate the use of the main component classes. Finally, we give tips and instructions that will allow the reader to write good components and elucidate the pathway of contributing new components to McStas.

scholarly journals CIST: An Improved ISAR Imaging Method Using Convolution Neural Network

2020 ◽  
Vol 12 (16) ◽  
pp. 2641
Author(s):  
Shunjun Wei ◽  
Jiadian Liang ◽  
Mou Wang ◽  
Xiangfeng Zeng ◽  
Jun Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Computational Cost ◽  
Convolution Neural Network ◽  
Imaging Method ◽  
Sampled Data ◽  
High Quality ◽  
Imaging Methods ◽  
Adaptive Parameter ◽  
Imaging Results ◽  
Isar Imaging

Compressive sensing (CS) has been widely utilized in inverse synthetic aperture radar (ISAR) imaging, since ISAR measured data are generally non-completed in cross-range direction, and CS-based imaging methods can obtain high-quality imaging results using under-sampled data. However, the traditional CS-based methods need to pre-define parameters and sparse transforms, which are tough to be hand-crafted. Besides, these methods usually require heavy computational cost with large matrices operation. In this paper, inspired by the adaptive parameter learning and rapidly reconstruction of convolution neural network (CNN), a novel imaging method, called convolution iterative shrinkage-thresholding (CIST) network, is proposed for ISAR efficient sparse imaging. CIST is capable of learning optimal parameters and sparse transforms throughout the CNN training process, instead of being manually defined. Specifically, CIST replaces the linear sparse transform with non-linear convolution operations. This new transform and essential parameters are learnable end-to-end across the iterations, which increases the flexibility and robustness of CIST. When compared with the traditional state-of-the-art CS imaging methods, both simulation and experimental results demonstrate that the proposed CIST-based ISAR imaging method can obtain imaging results of high quality, while maintaining high computational efficiency. CIST-based ISAR imaging is tens of times faster than other methods.

scholarly journals The Use of Neural Networks in High-Energy Physics

1993 ◽  
Vol 5 (4) ◽  
pp. 505-549 ◽  
Author(s):  
Bruce Denby
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
High Energy Physics ◽  
High Energy ◽  
High Quality ◽  
Network Algorithms ◽  
The Past ◽  
The Neural Network ◽  
Energy Physics

In the past few years a wide variety of applications of neural networks to pattern recognition in experimental high-energy physics has appeared. The neural network solutions are in general of high quality, and, in a number of cases, are superior to those obtained using "traditional'' methods. But neural networks are of particular interest in high-energy physics for another reason as well: much of the pattern recognition must be performed online, that is, in a few microseconds or less. The inherent parallelism of neural network algorithms, and the ability to implement them as very fast hardware devices, may make them an ideal technology for this application.

Using BP Neural Network to Predict the Sinter Comprehensive Performance: TFe and Fuel Consumption

2013 ◽  
Vol 771 ◽  
pp. 213-216
Author(s):  
Wei Chen ◽  
Bao Xiang Wang ◽  
Ying Chen ◽  
Hui Juan Zhang ◽  
Xing Li
Keyword(s):  
Neural Network ◽  
Chemical Composition ◽  
Fuel Consumption ◽  
Bp Neural Network ◽  
Raw Material ◽  
High Rate ◽  
Production Data ◽  
Practical Application ◽  
High Quality ◽  
Comprehensive Performance

The principal objective of blast furnace is to produce high quality molten iron at a high rate with a low consumption. It is very important to control sinter chemical composition and comprehensive performance. This is because the sinter is the main raw material for ironmaking. In this paper, a predictive system for sinter chemical composition TFe and the solid fuel consumption was established based on BP neural network, which was trained by actual production data. The MATLAB m file editor was used to write code directly in this paper. Practical application shows the applications of the system not only can reduce the work difficulty of technical personnel, but also can improve the hit ratio of production index and the productivity.

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