High dimensionality carrierless amplitude phase modulation technique for radio over fiber system

Advanced modulation formats such as carrierless amplitude phase (CAP) modulation technique is one of the solutions to increase flexibility and high bit rates to support multi-level and multi-dimensional modulations with the absence of sinusoidal carrier. Recent work are focussing on the 2D CAP-64 QAM Radio-over-Fiber (RoF) system but no extension of higher dimensions is reported. This thesis expands the area of CAP modulation technique and RoF system. The work described in this thesis is devoted to the investigation of 1.25 GSa/s sampling rate for multi-level and multi-dimensional CAP in point-to-point (P2P) and RoF system at 3 km single-mode fiber (SMF). Another advanced modulation format which is known as discrete multitone (DMT) is compared with CAP modulation in order to observe the performance in different modulation schemes. The 4QAM-DMT and 16QAM-DMT at different number of subcarriers are carried out in this propagation. Based on the results, the transmission performance in terms of BER and received optical power for RoF transmission are degraded to almost 3 dB when comparing to 3 km SMF transmission. These are caused by the wireless power loss and impairment effects. The bit rate and spectral efficiency can be increased with the increasing number of levels, and may decreased once the number of dimensions is increased due to the higher up-sampling factor. However, the additional dimensions can be used to support multiple service applications. Therefore, it can be concluded that CAP has better performance as compared to DMT in terms of higher spectral efficiency and data rate. To conclude, the results presented in this thesis exhibit high feasibility of CAP modulation in the increasing number of dimensions and levels. Thus, CAP has the potential to be utilized in multiple service allocations for different number of users

A Fast Vertical Edge Detection Algorithm for Car License Plate Detection

Recently, License Plate Detection (LPD) has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them worked under restricted conditions, such as fixed illumination, stationary background, and high resolution images. LPD plays an important role in Car License Plate Recognition (CLPR) system because it affects the system's accuracy. This thesis aims to propose a fast vertical edge detector using Vertical Edge Detection Algorithm (VEDA) and to build a Car License Plate Detection (CLPD) method. Pre-processing step is performed in order to enhance and initialize the input image for the next steps. This step is divided into three processes: First, the color image conversion to a gray scale image. Second, an adaptive thresholding is used in order to constitute a binarized image. Third, Unwanted Lines Elimination Algorithm (ULEA) is used in order to enhance the image. The next step is to extract the vertical edges from the 352x288 resolution image by using VEDA. This algorithm is based on the contrast between the values in the binarized image. VEDA is proposed in order to enhance the CLPD method computation time. After the vertical edges have been extracted by VEDA, a morphological operation is used to highlight the vertical details in the image. Next, candidate regions are extracted. Finally, the license plate area is detected. This thesis shows that VEDA is faster than Sobel operator; the results reveal that VEDA is faster than Sobel by about 5-9 times, this range depends on the image resolution. The proposed CLPD method can efficiently detect the license plate area. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 from 643 images are successfully detected. The average accuracy of car license plate detection is 90%. For more evaluation and comparison purposes, the proposed CLPD method is compared with a similar Malaysian license plate detection method, which is CAR Plate Extraction Technology (CARPET). This comparison reveled that the CLPD method is more efficient than CARPET and also has more detection rate

Car license plate detection method for Malaysian plates-styles by using a web camera

Recently, license plate detection has been used in many applications especially in transportation systems. Many methods have been proposed in order to detect license plates, but most of them work under restricted conditions such as fixed illumination, stationary background, and high resolution images. License plate detection plays an important role in car license plate recognition systems because it affects the accuracy and processing time of the system. This work aims to build a Car License Plate Detection (CLPD) system at a lower cost of its hardware devices and with less complexity of algorithms' design, and then compare its performance with the local CAR Plate Extraction Technology (CARPET). As Malaysian plates have special design and they differ from other international plates, this work tries to compare two likely-design methods. The images are taken using a web camera for both the systems. One of the most important contributions in this paper is that the proposed CLPD method uses Vertical Edge Detection Algorithm (VEDA) to extract the vertical edges of plates. The proposed CLPD method can work to detect the region of car license plates. The method shows the total time of processing one 352x288 image is 47.7 ms, and it meets the requirement of real time processing. Under the experiment datasets, which were taken from real scenes, 579 out of 643 images were successfully detected. Meanwhile, the average accuracy of locating car license plate was 90%. In this work, a comparison between CARPET and the proposed CLPD method for the same tested images was done in terms of detection rate and efficiency. The results indicated that the detection rate was 92% and 84% for the CLPD method and CARPET, respectively. The results also showed that the CLPD method could work using dark images to detect license plates, whereas CARPET had failed to do so

MINHLP: Module to Identify New Hampshire License Plates

A license plate, referred to simply as a plate or vehicle registration plate, is a small plastic or metal plate attached to a motor vehicle for official identification purposes. Most governments require a registration plate to be attached to both the front and rear of a vehicle, although certain jurisdictions or vehicle types, such as motorcycles, require only one plate, which is usually attached to the rear of the vehicle. We present analysis of Automatic License Plate Recognition (ALPR) of New Hampshire (NH) plates using open source products. This thesis contains an implementation of a demonstrated model and analysis of the results. In this paper, OpenCV (computer vision library) and Tesseract (open source optical character reader) is presented as a core intelligent infrastructure. The thesis explains the mathematical principles and algorithms used for number plate detection, processes of proper characters segmentation, normalization and recognition. A description of the challenges involved in detecting and reading license plate in NH, previous studies done by others and the strategies adopted to solve them is also given

Vehicle license plate detection and recognition

"December 2013.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis supervisor: Dr. Zhihai He.In this work, we develop a license plate detection method using a SVM (Support Vector Machine) classifier with HOG (Histogram of Oriented Gradients) features. The system performs window searching at different scales and analyzes the HOG feature using a SVM and locates their bounding boxes using a Mean Shift method. Edge information is used to accelerate the time consuming scanning process. Our license plate detection results show that this method is relatively insensitive to variations in illumination, license plate patterns, camera perspective and background variations. We tested our method on 200 real life images, captured on Chinese highways under different weather conditions and lighting conditions. And we achieved a detection rate of 100%. After detecting license plates, alignment is then performed on the plate candidates. Conceptually, this alignment method searches neighbors of the bounding box detected, and finds the optimum edge position where the outside regions are very different from the inside regions of the license plate, from color's perspective in RGB space. This method accurately aligns the bounding box to the edges of the plate so that the subsequent license plate segmentation and recognition can be performed accurately and reliably. The system performs license plate segmentation using global alignment on the binary license plate. A global model depending on the layout of license plates is proposed to segment the plates. This model searches for the optimum position where the characters are all segmented but not chopped into pieces. At last, the characters are recognized by another SVM classifier, with a feature size of 576, including raw features, vertical and horizontal scanning features. Our character recognition results show that 99% of the digits are successfully recognized, while the letters achieve an recognition rate of 95%. The license plate recognition system was then incorporated into an embedded system for parallel computing. Several TS7250 and an auxiliary board are used to simulIncludes bibliographical references (pages 67-73)

Automatic License Plate Recognition System using Histogram Graph Algorithm

Character recognition is one of the most important applications of image processing. In last couple of decades, the numbers of vehicles have been increasing day by day. With this increase, it is becoming difficult to keep track of each vehicle for the purpose of law enforcement and traffic management. Thus, we need that type of system, which is capable of providing appropriate solutions to the traffic issues and hence this Automatic License Plate Recognition System is developed using Histogram graph algorithm in MATLAB. In which image is captured from camera and converted into Gray scale image for preprocessing .After conversion, dilation process is applied on image. After dilation horizontal and vertical edge processing has been done and passed these histograms through low pass filter. After filtration, image is segmented and license plate is extracted. For detection each character separately from detected license plate smearing algorithm is used

Automatic Car Registration Plate Recognition Using the Hough Transform

The development of automatic car registration plate recognition systems will provide greater efficiency for vehicle monitoring in automatic access control, and will avoid the need to equip vehicles with special RF tags for identification since all vehicles possess a unique registration plate. Thus this study is an attempt to introduce an automatic car registration plate recognition system based on identifying the plate characters by using the Hough transform. However, the proposed recognition system can be used in conjunction with a tag system for higher security access control. The automatic registration plate recognition could also have considerable potential in a wide range of applications especially in the identification of vehicle-based offences and with law enforcement. Recent advances in computer vision technology and the falling price of the related devices has contributed in making it practical to build an automatic, registration plate recognition systems. There have been a number of Optical Character Recognition (OCR) techniques, which have been used in the recognition of car registration plate characters. These systems include the character details matching process (Lotufo, et al. 1990), BAM (Bi-directional Associative Memories) neural network (Fahmy 1994) neural network (Tindall, 1995) and cross correlation pattern matching character matching techniques (Cornelli, et al. 1995). All of these systems recognized the characters by matching the full image of every character with a character\u27s template database which requires considerable processing time and large memory for the database. The purpose of this study is to explore the potential for using Hough transform (Hough 1962) in vehicle registration plate recognition. The OCR technique used in this project is unlike the other systems where the character recognition was based on matching the character\u27s full image; However the OCR technique in this system used Hough transform to identify the characters, where the recognition of a character is based on matching its identification array to the database. To validate the research, a car registration plate recognition system was developed to locate the registration plate from the full image of a vehicle and then extrar.t the plate characters by using image processing techniques. A Hough transform algorithm was applied to every character within the registration plate image to produce an identification array for these characters, and the plate characters were recognized by matching their identification array to the database. The system has been applied to a number of video recorded car images to recognize their registration plates. The rate of correctly recognized characters was 82.7% of the extracted characters, but improvement can be granted by using a faster digital camera and taking some precautions in the registration plate frames. However, the research indicated that the optical character recognition technique used in the study is an efficient and simple algorithm to identify characters, without requiring a relatively large processing memory

Image Type Water Meter Character Recognition Based on Embedded DSP

In the paper, we combined DSP processor with image processing algorithm and studied the method of water meter character recognition. We collected water meter image through camera at a fixed angle, and the projection method is used to recognize those digital images. The experiment results show that the method can recognize the meter characters accurately and artificial meter reading is replaced by automatic digital recognition, which improves working efficiency