Sang-Burm Rhee

Simulation of a RSSI-Based Indoor Localization System Using Wireless Sensor Network

In recent years, with advance in wireless communication technology, sensing technology, micro-electronics technology and embedded system, wireless sensor network (WSN) can be used for various application areas, such as home health care, military and industry. The localization service which provides the location information of mobile user is one of important service provided by WSN, because many applications need to locate the source of incoming measurements. The current localization algorithm in WSN can be divided into two categories: range-based and range-free schemes. Most of the range-based localization method proposed use of the received signal strength indication (RSSI) to make an estimation of the distance between transmitter and receiver. The localization algorithm used the values of RSSI received from the cluster included the mobile node to minimize frequency of the diffraction, reflection, and attenuation. Closer nodes to the router from mobile node are given more weight and location of the node is determined the center of gravity of router nodes. Our localization algorithm used the values of RSSI received from the cluster included the mobile node and used weighted centroid method in which closer nodes to the router from mobile node are given more weight and location of the node is determined the center of gravity of router nodes. CC2420 RF module and ATmega128L MCU are used to be the hardware platform of nodes on the location experiment is designed and implemented in this paper.

Content-Based Image Retrieval Using Wavelet Spatial-Color and Gabor Normalized Texture in Multi-resolution Database

Image retrieval is one of the most exciting and fastest growing research areas in the field of multimedia technology. This paper proposes a new efficient image retrieval method that applies a weighted combination of color and texture to the wavelet transform, based on spatial-color and second order statistics, respectively. To evaluate the performance of the proposed algorithm, we assess the simulations performance in terms of average precision and Fscore using several image databases, and perform comparative analysis with existing methods such as MPEG-7. The experimental results show that the proposed approach significantly improves the effectiveness of image retrieval. The proposed descriptor is particularly useful for multi-resolution image search and retrieval.

Robust audio watermarking in wavelet domain using pseudorandom sequences

In this paper, we proposed digital watermarking algorithms for high quality audio to improve robustness of embedded watermark by adding spread spectrum. The watermark is embedded in each audio frame by adding a perceptually-shaped pseudo-random sequence. The proposed method realized digital audio watermarking technique that audience cannot perceive as a noise by inserting the watermark with using the psychoacoustic model.

Fast Image Stitching Based on Improved SURF Algorithm Using Meaningful Features

Recently, we can easily create high resolution images with digital cameras for high-performance and make use them at variety fields. Especially, the image stitching method which adjusts couple of images has been researched. Image stitching can be used for military purposes such as satellites and reconnaissance aircraft, and computer vision such as medical image and the map. In this paper, we have proposed fast image stitching based on improved SURF algorithm using meaningful features in the process of images matching after extracting features from scenery image. The features are extracted in each image to find out corresponding points. At this time, the meaningful features can be searched by removing the error, such as noise, in extracted features. And these features are used for corresponding points on image matching. The total processing time of image stitching is improved due to the reduced time in searching out corresponding points. In our results, the processing time of feature matching and image stitching is faster than previous algorithms, and also that method can make natural-looking stitched image.

Efficient Photo Image Retrieval System Based on Combination of Smart Sensing and Visual Descriptor

In this paper, we propose a novel efficient photo image retrieval method that automatically indexes for the searching of relevant images using a combination of geo-coded information and content-based visual features. A photo image is labeled with its GPS (Global Positioning System) coordinates at the moment of capture, and the label leads to generating a geo-spatial index with three elements of latitude, longitude and image view direction. Then, content-based visual features are extracted, and combined with the geo-spatial information for indexing and retrieving the photo images. For users querying process, the proposed method adopts two steps as a progressive approach, filtering the relevant subset prior to using a content-based ranking function. To evaluate the performance of the proposed algorithm, we assess the simulation performance in terms of average precision and F-score using a natural photo collection. Comparing the proposed approach to search using visual feature alone, an improvement of 20.8%...

Image Gradient Detection with Hough Transform

In this paper, we propose gradient detection system of the vertical straight line using the Hough transform after extracting from the color images. The input color images can be used to a method for detecting a gradient from only the luminance component of an image. In edge detection, traditional method of the mask of vertical edge detection can detect with the one direction of the image change but proposed method can detect bi-direction. Since the Hough transform is required much time for processing, our method can be improved processing time through experimentally calculating valid pixel.

Efficient Image Stitching Using Fast Feature Descriptor Extraction and Matching

Recently, the field of computer vision has been actively researched through digital image which can be easily generated as the development and expansion of digital camera technology. Especially, research that extracts and utilizes the feature in image has been actively carried out. The image stitching is a method that creates the high resolution image using features extract and match. Image stitching can be widely used in military and medical purposes as well as in variety fields of real life. In this paper, we have proposed efficient image stitching method using fast feature descriptor extraction and matching based on SURF algorithm. It can be accurately, and quickly found matching point by reduction of dimension of feature descriptor. The feature descriptor is generated by classifying of unnecessary minutiae in extracted features. To reduce the computational time and efficient match feature, we have reduced dimension of the descriptor and expanded orientation window. In our results, the processing time of feature matching and image stitching are faster than previous algorithms, and also that method can make natural-looking stitched image.

Efficient Image Retrieval Using Advanced Clustering SURF

Image retrieval is one of the most exciting and fastest growing research areas in the field of multimedia technology. In this paper, we propose and implement a new image retrieval method that extracts the feature of image using efficient clustering for SURF (Speed Up Robust Feature) scheme, applicable to mobile environments. Since SURF works only on gray-scale images, our clustering SURF is combined with well-known dominant color descriptor to improve the performance of the system. Also, we calculate the feature vector to remove an unnecessary feature, which is located in the sparse region, not to be clustered in final target features. To evaluate the performance of the proposed algorithm, we assess the simulations performance in terms of average precision on two image databases commonly used. Based on the average precision from all queries, 86% and 79% of all relevant images were retrieved. The result shows that the proposed approach obtains an enough results which are applicable to mobile environments.

A RSSI-Based Approach for Localization of Wireless Sensor Network in Indoor

The interest in sensor network has increased as it moved into Ubiquitous Computing era recently. Especially various researches using localization have done lively. In this paper, it abstracts location information by using the RSSI value received from the cluster which a mobile node belongs to in order to minimize diffraction, reflection, and diminution phenomena of frequency. It judges the location of node by the center of gravity of received router nodes by giving them a higher weight to closer router nodes. It proposes a method that uses beacon scheduling method to extract location information through low-power sensor networking by converting to low-consumption power mode unless it is for communication.

Research of Object Recognition and Tracking Based on Feature Matching

Object recognition and tracking are very important task in several computer vision applications in our life. Most of feature matching approaches have problems which are high computational complexity and weak robustness in various environments. In this paper, we proposed a low complexity and robust object recognition and tracking using advanced feature matching for real time environment. Our algorithm recognizes object using invariant features and reduces dimension of feature descriptor to deal with the problems. Our experiments demonstrate that our work is more fast and robust than the traditional methods and can track object accurately in various environments.