International Journal of Computer and Applications

A new night-time lane detection system and its accompanying framework are presented in this paper. The accompanying framework consists of an automated ground truth process and systematic storage of captured videos that will be used for training and testing. The proposed Advanced Lane Detector 2.0 (ALD 2.0) is an improvement over the ALD 1.0 or Layered Approach with integration of pixel remapping, outlier removal, and prediction with tracking. Additionally, a novel procedure to generate the ground truth data for lane marker locations is also proposed. The procedure consists of an original process called time slicing, which provides the user with unique visualization of the captured video and enables quick generation of ground truth information. Finally, the setup and implementation of a database hosting lane detection videos and standardized data sets for testing are also described. The ALD 2.0 is evaluated by means of the user-created annotations accompanying the videos. Finally, the planned improvements and remaining work are addressed.

A Novel Lane Detection System With Efficient Ground Truth Generation

IEEE transactions on systems, man and cybernetics. Part B, Cybernetics

The development of wireless sensor networks, such as researchers Advanced Driver Assistance Systems (ADAS) requires the ability to analyze the road scene just like a human does. Road scene analysis is an essential, complex, and challenging task and it consists of: road detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehicle's heading direction) and obstacle detection (which is mainly based on localizing possible obstacles on the vehicle's path). The detection of the road borders, the estimation of the road geometry, and the localization of the vehicle are essential tasks in this context since they are required for the lateral and longitudinal control of the vehicle. Within this field, on-board vision has been widely used since it has many advantages (higher resolution, low power consumption, low cost, easy aesthetic integration, and nonintrusive nature) over other active sensors such as RADAR or LIDAR. At first glance the problem of detecting the road geometry from visual information seems simple and early works in this field were quickly rewarded with promising results. However, the large variety of scenarios and the high rates of success demanded by the industry have kept the lane detection research work alive. In this article a comprehensive review of vision-based road detection systems vision is presented.

Keeping the vehicle on the road: A survey on on-road lane detection systems

Speech and audio signal processing: processing and perception of speech and music [Book Review]

Speech recognition is a major topic in speech signal processing. Speech recognition is considered as one of the most popular and reliable biometric technologies used in automatic personal identification systems. Speech recognition systems are used for variety of applications such as multimedia browsing tool, access centre, security and finance. It allows people work in active environment to use computer. For a reliable and high accuracy of speech recognition, simple and efficient representation methods are required. In this paper, the zero crossing extraction and the energy level detection are applied to the recorded speech signal for voiced/unvoiced area detection. The detected voiced signals are applied for segmentation. Further, the MFCC method is applied to all of the segmented windows. The extracted MFCC data are further used as inputs for neural network training.

Mel-frequency cepstral coefficient analysis in speech recognition

Lane detection is an essential component of autonomous mobile robot applications. Any lane detection method has to deal with the varying conditions of the lane and surrounding that the robot would encounter while moving. Lane detection procedure can provide estimates for the position and orientation of the robot within the lane and also can provide a reference system for locating other obstacles in the path of the robot. In this paper we present a method for lane detection in video frames of a camera mounted on top of the mobile robot. Given video input from the camera, the gradient of the current lane in the near field of view are automatically detected. Randomized Hough Transform is used for extracting parametric curves from the images acquired. A priori knowledge of the lane position is assumed for better accuracy of lane detection.

Fast lane detection with Randomized Hough Transform

In this paper, we derive an implicit finite difference approximation equation of the one-dimensional linear time fractional diffusion equations, based on the Caputo's time fractional derivative. Then this approximation equation leads the corresponding system of linear equation, which is large scale and sparse. Due to the characteristics of the coefficient matrix, we use the Accelerated Over-Relaxation (AOR) iterative method for solving the generated linear system. One example of the problem is presented to illustrate the effectiveness of AOR method. The numerical results of this study show that the proposed iterative method is superior compared with the existing one weighted parameter iterative method.

https://iopscience.iop.org/article/10.1088/1742-6596/495/1/012032/pdf

Implicit finite difference solution for time-fractional diffusion equations using AOR method

This paper presents our study on a simulation of path planning for indoor robot that relies on the use of Laplace’s equation to constrain the generation of Harmonic Potential Fields (HPF). The computation of HPF requires immense amount of computing resources, particularly when the size of environment is large. In the past, fast iterative methods that apply the use of half-sweep iteration and block technique are suggested. In this study, faster iterative method known as Four Point-Explicit Decoupled Group Successive Over relaxation via 9-Point Laplacian (4-EDGSOR-9L) is introduced. Essentially, the 4-EDGSOR-9L is actually a variant of block SOR iterative method based on four points that employs half-sweep iteration and utilizes 9-Point Laplacian discretization scheme. Once the HPF is obtained, the standard Gradient Descent Search (GDS) technique is performed for path tracing to the goal point.

Azali Saudi

Papers

Mel-frequency cepstral coefficient analysis in speech recognition

Fast lane detection with Randomized Hough Transform

Implicit finite difference solution for time-fractional diffusion equations using AOR method

Path planning simulation using harmonic potential fields through four point-edgsor method via 9-point laplacian

Robot Path Planning Using Four Point-Explicit Group Via Nine-Point Laplacian (4EG9L) Iterative Method