Hyochang Ahn

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.

Performance analysis of object recognition and tracking for the use of surveillance system

Recent research on large data processing have been actively carried out in the name of cloud computing. Video surveillance system must handle larger amounts of data in real time. Video surveillance systems in a cloud computing environment constantly need to handle larger amounts of data in order to recognize and track an object. The system requires a technique which can handle larger amounts of data in order to recognize and track an object by extracting the feature of the object. However, most object tracking approaches based on feature matching have a problem, showing high computational complexity and/or weak robustness in various environments. This paper proposes a robust object recognition and tracking method, which uses an advanced feature matching for use on real time environment. Our algorithm recognizes an object using invariant features, and reduces the dimension of a feature descriptor to deal with the problems. The experimental result shows that our method is faster and more robust than the traditional methods, as well as the proposed method that can detect and track a moving object accurately in various environments.

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.

A low complexity image stitching using advanced feature matching in mobile environments

This paper is a contribution to the special issue on Mobile Communication Systems selected topic from the SDPM and it is coordinated by Sangyeob Oh, K. Chung, Supratip Ghose.

Implementation of Image Descriptor Based on SURF and DCD

Image retrieval is one of the most exciting and fastest growing research areas in the field of multimedia technology. In this paper, we present and implement an image descriptor that extracts the feature of image using combination of SURF (Speed Up Robust Feature) and DCD (Dominant Color Descriptor) scheme. To evaluate the performance of implemented algorithm, we assess the performance of simulation in terms of average precision on two image database commonly used. The result shows that the proposed approach obtain an enough results which are applicable to mobile environments.

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 Retrieval Using Image and Audio Features in Video Stream

Recently, information has changed to the development of the information communication network and multimedia technology. The multimedia technology is used as application techniques including the storage, playback, compression, and so on. Also it has changed rapidly and extensively over the world. Therefore numerous search schemes such as the video retrieval, speech retrieval are required. Current video retrieval methods have various problems as following redundancy of data and the inconsistency of a keyword, and infringement of copyright. In this paper, we propose implementation of novel scheme that carried out efficient retrievals for audio signal and image in video stream as rapidly changing generation of information communication.

Advanced face recognition and verification in mobile platforms

Purpose – This paper holds a big advantage to enable to recognize faces, regardless of time and place. Also this provides an independent performance of smart phone, because of its process by a computer of third party not by that of the mobile device. In addition, it is desirable to minimize the expensive operations in mobile device with constraint computational power (i.e. battery consumption). Thus, the authors exclude the process of transmission failed from the input device. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the authors have proposed a new face detection and verification algorithm, based on skin color detection to enable extracting the face region from color images of the mobile phone. And then extracted the facial feature as eigenface, verified whether or not the identity of users is right, applied support vector machine to the region of detected face. Findings – The experimental results for two datasets show that the proposed method achieves slightly ...

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.