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Power Electronics Converters Applications And Design

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Preface 1. Introduction 2. Transfer functions 3. The extra element theorem 4. The N extra element theorem 5. Electronic negative feedback 6. High-frequency and microwave circuits 7. Passive filters 8. PWM switching DC-to-DC converters.

Fast analytical techniques for electrical and electronic circuits: PWM switching dc-to-dc converters

Presents different approaches to discrimination and classification problems from a statistical perspective. Provides computer projects concentrating on the most widely used and important algorithms, numerical examples, and theoretical questions reinforce to further develop the ideas introduced in the text.

Discrimination and Classification.

The process of detecting vehicles’ license plates, along with recognizing the characters inside them, has always been a challenging issue due to various conditions. These conditions include different weather and illumination, inevitable data acquisition noises, and some other challenging scenarios like the demand for real-time performance in state-of-the-art Intelligent Transportation Systems (ITS) applications. This paper proposes a method for vehicle License Plates Detection (LPD) and Character Recognition (CR) as a unified application that presents significant accuracy and real-time performance. The mentioned system is designed for Iranian vehicle license plates, which have the characteristics of different resolution and layouts, scarce digits/characters, various background colors, and different font sizes. In this regard, the system uses a separate fine-tuned You Only Look Once (YOLO) version 3 platform for each of the mentioned phases and extracts Persian characters from input images in two automatic steps. For training and testing stages, a wide range of vehicle images in different challenging and straightforward conditions have been collected from practical systems installed as surveillance applications. Experimental results show an end-to-end accuracy of 95.05% on 5719 images. The test data included both color and grayscale images containing the vehicles with different distances and shooting angles with various brightness and resolution. Additionally, the system can perform the LPD and CR tasks in an average of 119.73 milliseconds for real life data, which illustrates a real-time performance for the system and usable applicability. The system is fully automated, and no pre-processing, calibration or configuration procedures are needed.

A Robust Deep Learning Approach for Automatic Iranian Vehicle License Plate Detection and Recognition for Surveillance Systems

Video-based vehicle speed measurement systems are known as effective applications for Intelligent Transportation Systems (ITS) due to their great development capabilities and low costs. These systems utilize camera outputs to apply video processing techniques and extract the desired information. This paper presents a new vehicle speed measurement approach based on motion detection. Contrary to featurebased methods that need visual features of the vehicles like license-plate or windshield, the proposed method is able to estimate vehicle’s speed by analyzing its motion parameters inside a pre-defined Region of Interest (ROI) with specified dimensions. This capability provides realtime computing and performs better than feature-based approaches. The proposed method consists of three primary modules including vehicle detection, tracking, and speed measurement. Each moving object is detected as it enters the ROI by the means of Mixture-of-Gaussian background subtraction method. Then by applying morphology transforms, the distinct parts of these objects turn into unified filled shapes and some defined filtration functions leave behind only the objects with the highest possibility of being a vehicle. Detected vehicles are then tracked using blob tracking algorithm and their displacement among sequential frames are calculated for final speed measurement module. The outputs of the system include the vehicle’s image, its corresponding speed, and detection time. Experimental results show that the proposed approach has an acceptable accuracy in comparison with current speed measurement systems.

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Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

Video-based Intelligent Transportation Systems (V-ITS) can play an important role in developing a wide range of applications in transportation field. These systems use the outputs of video cameras to extract desired information by the means of various Artificial Intelligence techniques. Considering impressive advantages of applying Deep Neural Networks (DNNs) in different fields of object detection and classification, these methods have attracted a huge attention among researchers in recent years. In this regard, Convolutional Neural Networks (CNNs) as an important class of DNNs have been used for visual imagery goals in a wide variety of applications such as image recognition and video analysis, and even made their way through ITS applications. One of the most important steps of V-ITS applications is the process of vehicle detection in video frames and the high accuracy rate in this step can provide applicable data for other complementary modules such as vehicle tracking and classification. In this paper, a robust method to detect vehicles in video frames based on CNNs is proposed which provides an almost real-time performance and impressive accuracy. To overcome the challenges of building a precise vehicle detection model from still images, we have transformed the main architecture of a pre-trained ResNet-50 residual network to Faster Region-based Convolutional Neural Network (Faster R-CNN). Experimental results show that the system's sensitivity factor is 0.985 and it needs an average of 74 milliseconds to detect vehicles in real condition data. Consequently, our method can provide acceptable results in vehicle detection in terms of accuracy and execution time.

A Robust Vehicle Detection Approach based on Faster R-CNN Algorithm

The most popular second-order statistical texture features are derived from the co-occurrence matrix, which has been proposed by Haralick. However, the computation of both matrix and extracting texture features are very time consuming. In order to improve the performance of co-occurrence matrices and texture feature extraction algorithms, we propose an architecture on FPGA platform. In the proposed architecture, first, the co-occurrence matrix is computed then all thirteen texture features are calculated in parallel using computed co-occurrence matrix. We have implemented the proposed architecture on Virtex 5 fx130T-3 FPGA device. Our experimental results show that a speedup of 421[× yields over a software implementation on Intel Core i7 2.0 GHz processor. In order to improve much more performance on textures, we have reduced the computation of 13 texture features to 3 texture features using ranking of Haralick's features. The performance improvement is 484×.

High performance implementation of texture features extraction algorithms using FPGA architecture

Statistical texture features extraction algorithms can be classified into first, second, and higher order. The difference between these classes is that the first-order statistics estimate properties of individual image pixel values, while in the second and higher order statistics estimate properties of two or more image pixel values occurring at specific locations relative to each other. The most popular second order statistical texture features are derived from the co-occurrence matrix, which has been proposed by Haralick. However, the computation of both matrix and extracting texture features are very time consuming. In this paper we improve the performance of those algorithms using FPGA implementations. Our experimental results show that the computational time of co-occurrence matrix is 6.74 times faster than the computational time of extracting thirteen texture features for an image size 128×128 and 8bit gray level. Furthermore whole execution of both algorithms is almost 214x faster than the software implementations.

Alireza Akoushideh

Papers

Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

A Robust Vehicle Detection Approach based on Faster R-CNN Algorithm

High performance implementation of texture features extraction algorithms using FPGA architecture

Accelerating Texture Features Extraction Algorithms Using FPGA Architecture

Facial expression recognition using a combination of enhanced local binary pattern and pyramid histogram of oriented gradients features extraction