2981642
Placement of wavelength converters for minimal wavelength usage in WDM networks
An important goal of the design of WDM (wavelength division multiplexing) networks is to use less wavelengths to serve more communication needs. According to the wavelength conflict rule, we know that the number of wavelengths required in a WDM network is at least equal to the maximal number of channels over a fiber (called maximal link load) in the network. By placing wavelength converters at some nodes in the network, the number of wavelengths needed can be made equal to the maximal link load. In this paper we study the problem of placing the minimal number of converters in a network to achieve that the number of wavelengths in use is equal to the maximal link load. For duplex communication channels, we prove that an optimal solution can be obtained in polynomial-time. For unidirectional communication channels, which was proved to be NP-complete, we develop a set of lemmas which lead to an efficient approximation algorithm whose approximation ratio is two.
15961891
On the optimal placement of wavelength converters in WDM networks
An important goal of the design of wavelength division multiplexing (WDM) networks is to use less wavelengths to serve more communication needs. According to the wavelength conflict rule, we know that the number of wavelengths required in a WDM network is at least equal to the maximal number of channels over a fiber (called maximal link load) in the network. By placing wavelength converters at some nodes in the network, the number of wavelengths needed can be made equal to the maximal link load. In this paper, we study the problem of placing the minimal number of converters in a network to achieve that the number of wavelengths in use is equal to the maximal link load. For duplex communication channels, we prove that the optimal solution can be obtained in polynomial-time. For unidirectional communication channels, which was proved to be NP-complete, we develop a set of lemmas which lead to a simple approximation algorithm whose theoretically guaranteed performance ratio is at most two.
15872873
Reversible Data Hiding for 3D Point Cloud Model
This paper proposes a reversible data hiding scheme for 3D point cloud model. It exploits the high correlation among neighbor vertices to embed data. It starts with creating a set of 8-neighbor vertices clusters with randomly selected seed vertices. Then an 8-point integer DCT is performed on these clusters and an efficient highest frequency coefficient modification technique is employed to modulate the watermark bit. In data extraction, the modified clusters are retrieved first, and other operations are the inverse process of the data hiding. The original model can be perfectly recovered if it is intact. Our scheme is suitable for some specific applications where content accuracy of the original model must be guaranteed. Moreover, the method can be used for 3D point cloud model authentication, e.g., hiding a hash sequence.
8641332
Magnetic diffusion: Scalability, reliability, and QoS of data dissemination mechanisms for wireless sensor networks
Envisioning a new generation of sensor network applications in healthcare and workplace safety, we seek mechanisms that provide timely and reliable transmissions of mission-critical data. Inspired by the physics in magnetism, we propose a simple diffusion-based data dissemination mechanism, referred to as the magnetic diffusion (MD). In that, the data sink, functioning like the magnet, propagates the magnetic charge to set up the magnetic field. Under the influence of the magnetic field, the sensor data, functioning like the metallic nails, are attracted towards the sink. We compare MD to the state-of-the-art mechanisms and find that MD: (1) performs the best in timely delivery of data, (2) achieves high data reliability in the presence of network dynamics, and yet (3) works as energy efficiently as the state of the art. These suggest that MD is an effective data dissemination solution to the mission-critical applications.
45740416
Automated Information Mining on Multimedia TV News Archives
This paper addresses an integrated information mining techniques for multimedia TV-news archive. The utilizes techniques from the fields of acoustic, image, and video analysis, for information retrieval on news story title, newsman and scene identification. The goal is to construct a compact yet meaningful abstraction of broadcast news video, allowing users to browse through large amounts of data in a non-linear fashion with flexibility and efficiency. By using acoustic analysis, the system can classify video into news versus commercials, with 90% accuracy on a data set of 400 hours TV-news recorded off the air from July 2003 to August of 2004. By applying speaker identification and/or image detection techniques, each news stories can be segmented with an accuracy of 96%. On screen captions or subtitles are recognized by OCR techniques to produce the text title of each news stories. The extracted title words can be used to link or to navigate more related News contents on the WWW. In cooperation with facial and scene analysis and recognition techniques, OCR results can provide users with multimodality query for specific news stories. Some experimental results are presented and discussed for the system reliability and performance evaluation and comparison.
13572740
Image Texture Segmentation with Ant Colony Systems
A new scheme for texture segmentation based on ant colony systems (ACS) is proposed in this paper. Texture segmentation is one of the important branches in image pattern recognition, which provides usefulness in many applications. Until now, how to find an effective way for accomplishing texture segmentation in practical applications is still a major task. In this paper, we employ wavelet coefficients and characteristics of different subbands to serve as the basis of characteristic vectors, and we use three feature-extraction elements, namely, the extrema, entropy, and energy, to compose the characteristic vector. To alleviate segmentation fragments caused from the information in high frequency bands of texture images, we integrate the fourth element, the mean variance, into the characteristic vector. Finally, we use ACS to find a trade-off between texture segmentation and fragments. Simulation results demonstrate the effectiveness and practicability of the proposed algorithm
12033624
Computing motif correlations in proteins
Protein motifs, which are specific regions and conserved regions, are found by comparing multiple protein sequences. These conserved regions in general play an important role in protein functions and protein folds, for example, for their binding properties or enzymatic activities. The aim here is to find the existence correlations of protein motifs. The knowledge of protein motif/domain sharing should be important in shedding new light on the biologic functions of proteins and offering a basis in analyzing the evolution in the human genome or other genomes. The protein sequences used here are obtained from the PIR-NREF database and the protein motifs are retrieved from the PROSITE database. We apply data mining approach to discover the occurrence correlations of motif in protein sequences. The correlation of motifs mined can be used in evolution analyses and protein structure prediction. We discuss the latter, i.e., protein structure prediction in this study. The correlations mined are stored and maintained in a database system. The database is now available at http://bioinfo.csie.ncu.edu.tw/ProMotif/.