Automatic Roadblock Identification Algorithm for Unmanned Vehicles Based on Binocular Vision

In order to improve the accuracy of automatic obstacle recognition algorithm for driverless vehicles, an automatic obstacle recognition algorithm for driverless vehicles based on binocular vision is constructed. Firstly, the relevant parameters of the camera are calibrated around the new car coordinate system to determine the corresponding obstacle position of the vehicle. At the same time, the three-dimensional coordinates of obstacle points are obtained by binocular matching method. Then, the left and right cameras are used to capture the feature points of obstacles in the image to realize the recognition of obstacles. Finally, the experimental results show that for obstacle 1, the recognition error of the algorithm is 0.03 m; for obstacle 2, the recognition error is 0.02 m; for obstacle 3, the recognition error is 0.01 m. The algorithm has small recognition error. The vehicle coordinate system is added in the camera calibration process, which can accurately measure the relative position information between the vehicle and the obstacle.

Research on Basketball Teaching Network Course Resource Recommendation Method Based on Deep Learning Algorithm

To solve the problem of invalid resource recommendation data and poor recommendation effect in basketball teaching network course resource recommendation, a basketball teaching network course resource recommendation method based on a deep learning algorithm is proposed. The objective function is applied to eliminate the noise in the basketball teaching network course resource data. The prominent characteristics of basketball teaching network curriculum resources are extracted using a kernel function and combined into a feature set. A convolution neural network (CNN) was employed to realize the basketball teaching network curriculum resources recommendation model. The model was assessed in terms of computation time and recognition error. To validate the performance, the proposed model was compared with two well-known recommendation models such as the learning resource recommendation method based on transfer learning and the personalized learning resource recommendation method based on three-dimensional feature collaborative domination. Experimental results show that the proposed model achieved the lowest computation time of 15 s and recommendation error less than 0.4% as compared with the existing model.

Effect of anisotropy coeficient on error rates of linear discriminant functions

Paper deals with statistical classification of spatial data as a part of widely applicable statistical approach to pattern recognition. Error rates in supervised classification of Gaussian random field observation into one of two populations specified by different constant means and common stationary geometric anisotropic covariance are considered. Formula for the exact Bayesian error rate is derived. The influence of the ratio of anisotropy to the error rates is evaluated numerically for the case of complete parametric certainty.

Multi-Sensor Information Ensemble-Based Automatic Parking System for Vehicle Parallel/Nonparallel Initial State

The goal of automatic parking system is to accomplish the vehicle parking to the specified space automatically. It mainly includes parking space recognition, parking space matching, and trajectory generation. It has been developed enormously, but it is still a challenging work due to parking space recognition error and trajectory generation for vehicle nonparallel initial state with parking space. In this study, the authors propose multi-sensor information ensemble for parking space recognition and adaptive trajectory generation method, which is also robust to vehicle nonparallel initial state. Both simulation and real vehicle experiments are conducted to prove that the proposed method can improve the automatic parking system performance.

Optical pattern generator for efficient bio-data encoding in a photonic sequence comparison architecture

In this study, optical technology is considered as SA issues' solution with the potential ability to increase the speed, overcome memory-limitation, reduce power consumption, and increase output accuracy. So we examine the effect of bio-data encoding and the creation of input images on the pattern-recognition error-rate at the output of optical Vander-lugt correlator. Moreover, we present a genetic algorithm-based coding approach, named as GAC, to minimize output noises of cross-correlating data. As a case study, we adopt the proposed coding approach within a correlation-based optical architecture for counting k-mers in a DNA string. As verified by the simulations on Salmonella whole-genome, we can improve sensitivity and speed more than 86% and 81%, respectively, compared to BLAST by using coding set generated by GAC method fed to the proposed optical correlator system. Moreover, we present a comprehensive report on the impact of 1D and 2D cross-correlation approaches, as-well-as various coding parameters on the output noise, which motivate the system designers to customize the coding sets within the optical setup.

A systematic approach for English education model based on the neural network algorithm

This paper algorithms based on neural network model designed for English education, to develop a model education system with artificial intelligence, summarized the dimensions were can be used for data analysis related indicators. These indicators include not only the contents of the learning behavior, test behavior, cooperation behavior and resource search behavior and other human-computer interaction behavior data, also includes demographic background information, learning ability, learning attitude, and other characteristic data that affect the learning effect. We tried to collect relevant indicators to the maximum extent. An audiovisual fusion method based on Convolutional Neural Network (CNN) is proposed. The independent CNN structure is used to realize independent modeling of audiovisual perception and asynchronous information transmission and obtain the description of audiovisual parallel data in the high-dimensional feature space. Following the shared fully connected structure, it is possible to model the long-term dependence of audiovisual parallel data in a higher dimension. Experiments show that the AVSR system built using a CNN-based audiovisual fusion method can achieve a significant performance improvement, and its recognition error rate is relatively reduced by about 15%. The speech recognition system trained with the cross-domain adaptive method can obtain a significant performance improvement, and its recognition error rate is more than 10% lower than that of the baseline system..