Jongan Park

The improved data encryption standard (DES) algorithm

The cryptosystem which is most used throughout the world for protecting information is the Data Encryption Standard (DES) which was announced by the National Bureau of Standard (NBS). The DES must be stronger than the other cryptosystems in its security. But, because the process time required for cryptanalysis has lessened, and because hardware technique has developed rapidly, the DES may be attacked by various kinds of cryptanalysis using parallel process. It may be especially vulnerable to attack by differential cryptanalysis. Therefore, the DES will require strengthening to ensure cryptographic security in the days to come. This paper proposes design of a DES-like cryptosystem called the Improved-DES. The Improved-DES is a new algorithm. We show that the Improved-DES is stronger than the DES against differential cryptanalysis for cryptographic security. We will divide one data block (96 bits) into 3 sub-blocks of 32 bits and then perform different f functions on each of the 3 sub-blocks, and then increase the S/sub 1/-S/sub 8/ of the S-boxes to S/sub 1/-S/sub 16/, satisfying the strict avalanche criterion (SAC: p/sub i, j/) and the correlation coefficient (p/sub i, j/). Finally we will increase the key length to 112 bits. The analysis will show that the unicity distance (UD) in the Improved-DES is increased more than the DESs UD.

Secure Multipath Routing Scheme for Mobile Ad Hoc Network

Mobile ad hoc networks (MANETs) are collections of autonomous mobile nodes with links that are made or broken in an arbitrary way. Due to frequent node and link failures, multipath MANET is preferred than single-path MANET in many applications. Multipath routing schemes are used for achieving various goals such as robustness, reliability, and load balancing. However, before they can be successfully deployed several security threats must be addressed. Due to lack of fixed infrastructure, security in ad-hoc routing is challenging task, especially in multipath MANET. In this paper, we propose a robust multipath routing scheme for MANET and also security mechanism for such a routing scheme. We discuss security analysis for our scheme. And we conduct simulation to evaluate the cost of the proposed secure multipath routing scheme and present some preliminary results.

Automatic hepatic tumor segmentation using statistical optimal threshold

This paper proposes an automatic hepatic tumor segmentation method of a computed tomography (CT) image using statistical optimal threshold. The liver structure is first segmented using histogram transformation, multi-modal threshold, maximum a posteriori decision, and binary morphological filtering. Hepatic vessels are removed from the liver because hepatic vessels are not related to tumor segmentation. Statistical optimal threshold is calculated by a transformed mixture probability density and minimum total probability error. Then a hepatic tumor is segmented using the optimal threshold value. In order to test the proposed method, 262 slices from 10 patients were selected. Experimental results show that the proposed method is very useful for diagnosis of the normal and abnormal liver.

Efficient liver segmentation based on the spine

The first significant process for liver diagnosis of the computed tomography is to segment the liver structure from other abdominal organs. In this paper, we propose an efficient liver segmentation algorithm using the spine as a reference point without the reference image and training data. A multi-modal threshold method based on piecewise linear interpolation extracts ranges of regions of interest. Spine segmentation is performed to find the reference point providing geometrical coordinates. C-class maximum a posteriori decision using the reference point selects the liver region. Then binary morphological filtering is processed to provide better segmentation and boundary smoothing. In order to evaluate automatically segmented results of the proposed algorithm, the area error rate and rotational binary region projection matching method are applied. Evaluation results suggest proposed liver segmentation has strong similarity performance as the manual method of a medical doctor.

Travel Ontology for Intelligent Recommendation System

Nowadays, travel information is increasing to appeal the tourists on the web. Although there are numerous information provided on the web, the user gets puzzled in finding accurate information. In order to solve these web problems, the concept of semantic web comes into existence to have communication between human and computer.In this paper, we propose intelligent recommendation system based on Jeju travel ontology. The proposed system can recommend the tourist more intelligent information using properties, relationships of travel ontology. Next, the system is responsible for finding personalized attractions and plotting location of traveler on the AlMap.

Efficient Image Retrieval Using Adaptive Segmentation of HSV Color Space

This paper presents an efficient image retrieval system using adaptive segmentation of hue, saturation and value (HSV) color space. We classify the image into n number of areas based on different selected ranges of hue and value, then each area is partitioned into m number of segments based on the number of pixels it contains, the area which has more pixels will be partitioned into more segments and the area which has less pixels will be partitioned into less number of segments. This is used as a feature vector. Retrieval system outputs the image with a high matching factor. A small demonstration system has been tested and shows superior performance compared with the simple color based retrieval systems.

Automatic liver segmentation of contrast enhanced CT images based on histogram processing

Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has a problem to segregate the liver structure because of similar gray-level values of neighboring organs in the abdomen. In this paper, an automatic liver segmentation method using histogram processing is proposed for overcoming randomness of CE-CT images and removing other abdominal organs. Forty CE-CT slices of ten patients were selected to evaluate the proposed method. As the evaluation measure, the normalized average area and area error rate were used. From the results of experiments, liver segmentation using histogram process has similar performance as the manual method by medical doctor.

Sum of Values of Local Histograms for Image Retrieval

CBIR makes a wide use of histogram based methods for image indexing. Histograms describe the global intensity distribution of images. They are very easy to compute and are insensitive to small changes in object translations and rotations. However, they are not robust to large appearance changes, and they might give similar results for different kinds of images if the distributions of colors are same in the images. Our research focuses mainly on the image bins (histogram value divisions by frequency) separation technique followed by calculating the sum of values, and using them as image local features. At first, the histogram is first calculated for an image. After that, it is subdivided into sixteen equal bins and the sum of local values is calculated and stored. We have tested the proposed algorithm on a large database of images.

CBIR Based on Adaptive Segmentation of HSV Color Space

Proposed algorithm is based on color information using HSV color space. Histogram search characterizes an image by its color distribution, or histogram but the drawback of a global histogram representation is that information about object location, shape, and texture is discarded. Thus local histogram is used for extracting the maximum color occurrence from each segment. Before extracting the maximum color from each segment the input image is adaptively segmented. Different quantization of hue and saturation are used for partitioning the image into different number of segments. Finally minkowski metric is used for feature vector comparison. Web based image retrieval demo system is built to make it easy to test the retrieval performance and to expedite further algorithm investigation

Classification of Feature Set Using K-means Clustering from Histogram Refinement Method

In this paper, we propose to use K-means clustering for the classification of feature set obtained from the histogram refinement method. Histogram refinement provides a set of features for proposed for Content Based Image Retrieval (CBIR). Standard histograms, because of their efficiency and insensitivity to small changes, are widely used for content based image retrieval. But the main disadvantage of histograms is that many images of different appearances can have similar histograms because histograms provide coarse characterization of an image. Hence histogram refinement method further refines the histogram by splitting the pixels in a given bucket into several classes based on color coherence vectors. Several features are calculated for each of the cluster and these features are further classified using the K-means clustering.