This paper proposes a high quality tone mapping technique using Logarithmic Mapping. This technique will improve contrast in very dark scenes. The main focus of this paper is to study and analysis of encoding and decoding of HDR images using Reinhard Operator. The application is deployed and tested by the IDL Language.

A common method to create high dynamic range (HDR) images is to combine several different exposures of the same scene. In this approach, the use of higher ISO settings will reduce exposure times, and thereby the total capture time which minimize ghosting artifacts. However, exposures taken at high sensitivity settings tend to be noisy, which is further amplified by the HDR creation algorithm. We present a robust and efficient technique to significantly reduce noise in an HDR image even when its constituent exposures are taken at very high ISO settings. The method does not introduce blur or other artifacts, and leverages the wealth of information available in a sequence of aligned exposures. The resulting image is again applied to FFT to obtain more effective HDR image.

Electronic noses have been designed as instruments for the identification of gases,odours and vapours. An Electronic noses consists of a large array of chemical sensors with associated signal conditioning and pattern recognition techniques. Electronic noses instruments consists a sensor array which results in moderately overlapping sensitivity such that each sensor reacts to a range of vapours slightly than to a specific one. Signals obtained from the sensor arrays need to be translated by pattern recognition methods such as artificial neural networks (ANNs). The Electronic noses are used for the discrimination of gases ,volatile organic compounds(VOC's) to solve a range of problems in food and beverages industry, chemical and biological hazards,environmental monitoring, process monitoring , flavours, fragrances and cosmetics productions, safety, security and military use. In this review paper,we explain the working principle of Electronic nose technology and present various applications of Electronic noses in wide areas.

License Plate recognition is the major application in traffic systems. This Paper is to develop an Automatic License Plate recognition System which will achieve better localization and segmentation results than the existing systems. Simple and efficient algorithm is used in this process. Hybrid Binarization technique are used to better Localization and segmentation result. It is a very important subject in license plate recognition. After extracting all license plates from an image, all characters in a license plate have to be segmented at the character segmentation stage of license plate recognition. If the obtained character segment is good, character recognition will be more accurate. The final stage completed by Template matching technique used for character extraction. At this stage, all characters have to be recognized. This stage is the most important one in license plate recognition. It has a substantial effect on success. This Paper is to develop an advanced process which can do search, localization, segmentation and character recognition at a time from a group of images given. And simultaneously it is to extract the required data off Localization and character recognition, and then store in the database.

In this paper Proposes the technique of fibonacci coding and lossless Compression based encryption.Lossless compression of encrypted sources,such as images can be achieved through fibonacci coding.For the encrypted real world images, the key is to improve the compression efficiency and exploiting the source dependency.

This paper presents anomaly detection using Artificial Immune System (AIS). The AIS mimics the human immune system. The statistical data are generated from the benchmark DARPA 99 training dataset using tcpstat tool. From the statistical data antibodies (i.e. detector set) are generated using enhanced random generation method to detect the attacks. By using the detector set, the DARPA 99 testing dataset records are tested to identify abnormal unusual behavior (anomalies). This algorithm determines the detection rate and false alarm rate. The results of experimental study obtained using Various Similarity Measures (VSM) are presented in this paper. It is found that the performance of the Euclidean distance measure is best when compared to other similarity measures. Also this method is implemented using KDDCUP 99 dataset which contains four classes of attacks. Detection Rate and False Alarm rate are calculated for each attack class. The results are compared with existing methods.