License plate recognition is a fully automated real time technique that has been widely used for identification, theft control and security validation of vehicles. For recognition and extraction of desired regions of the number plate of the vehicle, different algorithms are used. An image processing technology based on license plate recognition (LPR) that is being used to identify vehicles, using neural networks and image co-relation was developed by K. Yilmaz [2]. In this paper, a different novel approach has been presented to increase the quality of the image and to enhance the results for extracting license plate from dull and low intensity images. In the previous technique the recognition rate (percentage of image recognized) reached was 96.64% [2], but now using multithresholding and neural pattern recognition (NPR) techniques together with artificial neural networks, a higher recognition rate of 98.40% has been achieved. Certain problems related to neural networks in the previous research methodology such as blobs extraction, segmentation and character recognition, that inhibit complete extraction of features from number plate of the vehicle were analyzed in this approach. The proposed technique helps to improve the quality of the images and detect the characters or digits of the number plate with a better recognition rate.

In this paper, design of linear phase FIR digital differentiators is investigated using convex optimization. The problem of differentiator design is first described in terms of convex optimization with different optimization variables' options, taken one at a time. The method is then used to design first order low pass differentiators and results are compared with Salesnick's technique and Parks McClellan algorithm. The designed FIR low pass differentiator has improvement in transition width and flexibility to optimize different parameters. The concept of low pass differentiation is further generalized to fractional order differentiators. Fractional order differentiators are designed by using minmax technique on mean square error. Design examples demonstrate easy design procedure and flexibility in the process as well as improvement over existing fractional order differentiators in terms of mean square error in passband. Finally, fractional order differentiators are designed and used for texture enhancement of color images. Better texture enhancement than existing filtering approaches is established based on average gradient and entropy values. General TermsFIR Filter, Fractional order differentiator, Low pass differentiator, Full band differentiator, Image Enhancement.

In this paper design of non-recursive higher order low pass digital differentiators satisfying given specifications is investigated. The concept of low pass differentiation is further generalized to higher order differentiators. A formula is derived using Fourier integral to compute impulse response coefficients of the differentiator. The equation is then used to design first order differentiators and results are compared with Salesnick's technique. The proposed FIR low pass differentiator has improvement in transition width and flexibility to choose cutoff frequency. The same technique has been demonstrated for second order design according to provided specifications. This method is used in the design of second order low pass differentiator for QRS detection in ECG. It is shown that the proposed implementation has low hardware and software complexity as compared to existing second derivative based techniques of QRS detection, giving advantage in optimization of current real time ECG systems.