Invariant 2D object recognition using the wavelet transform and structured neural networks

1999 ◽  
Author(s):  
Mahmoud I. Khalil ◽  
Mohamed M. Bayoumi
Keyword(s):  
Neural Networks ◽  
Wavelet Transform ◽  
Object Recognition

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

scholarly journals Human and bird detection and classification based on Doppler radar spectrograms and vision images using convolutional neural networks

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110105
Author(s):  
Jnana Sai Abhishek Varma Gokaraju ◽  
Weon Keun Song ◽  
Min-Ho Ka ◽  
Somyot Kaitwanidvilai
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Networks ◽  
Doppler Radar ◽  
Autonomous Vehicle ◽  
Sampling Point ◽  
Body Segment ◽  
Kinematic Modeling ◽  
Deep Convolutional Neural Networks ◽  
Kinematic Models

The study investigated object detection and classification based on both Doppler radar spectrograms and vision images using two deep convolutional neural networks. The kinematic models for a walking human and a bird flapping its wings were incorporated into MATLAB simulations to create data sets. The dynamic simulator identified the final position of each ellipsoidal body segment taking its rotational motion into consideration in addition to its bulk motion at each sampling point to describe its specific motion naturally. The total motion induced a micro-Doppler effect and created a micro-Doppler signature that varied in response to changes in the input parameters, such as varying body segment size, velocity, and radar location. Micro-Doppler signature identification of the radar signals returned from the target objects that were animated by the simulator required kinematic modeling based on a short-time Fourier transform analysis of the signals. Both You Only Look Once V3 and Inception V3 were used for the detection and classification of the objects with different red, green, blue colors on black or white backgrounds. The results suggested that clear micro-Doppler signature image-based object recognition could be achieved in low-visibility conditions. This feasibility study demonstrated the application possibility of Doppler radar to autonomous vehicle driving as a backup sensor for cameras in darkness. In this study, the first successful attempt of animated kinematic models and their synchronized radar spectrograms to object recognition was made.

scholarly journals RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A Survey

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 43110-43136 ◽  
Author(s):  
Mingliang Gao ◽  
Jun Jiang ◽  
Guofeng Zou ◽  
Vijay John ◽  
Zheng Liu
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Networks

A solution to the tag-assignment problem for neural networks

1989 ◽  
Vol 12 (3) ◽  
pp. 381-397 ◽  
Author(s):  
Gary W. Strong ◽  
Bruce A. Whitehead
Keyword(s):  
Neural Networks ◽  
Object Recognition ◽  
Assignment Problem ◽  
Model Object ◽  
Parallel Processes ◽  
Illusory Conjunctions ◽  
Feature Processing ◽  
Human Object ◽  
Processing Activity ◽  
Normal Perception

AbstractPurely parallel neural networks can model object recognition in brief displays – the same conditions under which illusory conjunctions (the incorrect combination of features into perceived objects in a stimulus array) have been demonstrated empirically (Treisman 1986; Treisman & Gelade 1980). Correcting errors of illusory conjunction is the “tag-assignment” problem for a purely parallel processor: the problem of assigning a spatial tag to nonspatial features, feature combinations, and objects. This problem must be solved to model human object recognition over a longer time scale. Our model simulates both the parallel processes that may underlie illusory conjunctions and the serial processes that may solve the tag-assignment problem in normal perception. One component of the model extracts pooled features and another provides attentional tags that correct illusory conjunctions. Our approach addresses two questions: (i) How can objects be identified from simultaneously attended features in a parallel, distributed representation? (ii) How can the spatial selectional requirements of such an attentional process be met by a separation of pathways for spatial and nonspatial processing? Our analysis of these questions yields a neurally plausible simulation of tag assignment based on synchronizing feature processing activity in a spatial focus of attention.

Sign in / Sign up

Export Citation Format

Share Document