This paper introduces the design of an Optical Switching Module (OSM) for our newly proposed Gigabit Ethernet Optical Switched Access Network (GE-OSAN) architecture that uses the Multi-Point Control Protocol (MPCP), defined in IEEE 802.3ah. We outline the GE-OSAN architecture to clarify OSM's role in the network. We offer an OSM configuration that has the basic functions needed to realize downstream and upstream high-speed data services in GE-OSAN. We clarify the OSM optical switching time that allows GE-OSAN to achieve the same throughput as GE-PON. Our survey of currently available optical switches identifies the optical packet switches that can meet this switching time requirement. We evaluate OSM insertion loss with these switches. We propose an OSM configuration that has a regeneration function as well as the basic ones to realize wider network configurations that can lead to a reduction in overall system costs. In addition, we present OSM configurations that have broadcast and multicast functions as well as the basic ones so that GE-OSAN can support broadcast and multicast video services to equal and exceed GE-PON.

This paper proposes a new optical access network architecture that differs from those of conventional Point-to-Point (PP) and Passive Optical Networks (PON). The proposed architecture, Optical Switched Access Network (OSAN), uses Optical Switching Modules (OSMs) that connect an Optical Line Terminal (OLT) to Optical Network Units (ONUs) in a virtual point to point configuration so that it offers the merits of both PP and PON while overcoming their demerits. Each OSM optically switches packets of variable length one by one under electrical control. To allow the elimination of optical buffers from OSM, OSAN uses the Multi-Point Control Protocol (MPCP) defined in IEEE 802.3ah. We evaluate the transmission distances between OLT and ONUs, and consider a network synchronization scheme and discovery mechanism that supports MPCP.

As part of ISO standards on public-key encryption, Shoup introduced the framework of KEM (Key Encapsulation Mechanism), and DEM (Data Encapsulation Mechanism), for formalizing and realizing one-directional hybrid encryption; KEM is a formalization of asymmetric encryption specified for key distribution, which DEM is a formalization of symmetric encryption. This paper investigates a more general hybrid protocol, secure channel, that uses KEM and DEM, while KEM supports distribution of a session key and DEM, along with the session key, is used for multiple bi-directional encrypted transactions in a session. This paper shows that KEM, which is semantically secure against adaptively chosen ciphertext attacks (IND-CCA2), and DEM, which is semantically secure against adaptively chosen plaintext/ciphertext attacks (IND-P2-C2), along with secure signatures and ideal certification authority are sufficient to realize a universally composable (UC) secure channel. To obtain the main result, this paper also shows several equivalence results: UC KEM, IND-CCA2 KEM and NM-CCA2 (non-malleable against CCA2) KEM are equivalent, and UC DEM, IND-P2-C2 DEM and NM-P2-C2 DEM are equivalent.

This paper presents a color demosaicing method by introducing iterative asymmetric average interpolation. Missing primary colors on a Bayer pattern color filter array (CFA) are estimated by an asymmetric average interpolation where less intensity variation is assumed to be of stronger significance, before sharpness of an initial estimate is further improved by an iterative procedure. The iteration is implemented by an observation process followed by a restoration process. The former is modeled by blurring followed by CFA sampling and the latter is completely as same as the color demosaicing method initially applied. Experimental results have shown a favorable performance in terms of PSNR and visual appearance, in particular, in sharpness recovery.

In this paper, a new Mixed Integer Linear Programming algorithm is proposed to resolve the light-tree routing and wavelength assignment with wavelength continuity constraints. The node in our system is limited branching and power-efficient multicast capable OXC. Numerical results are given and discussed to show the efficiency of our algorithm.

This paper presents an edge alignment method for stitching images when they have large displacements and light changes. First, without building any correspondences, the proposed method predicts all possible translation solutions by examining the consistency between edge positions. Then, the best solution can be obtained from the set of possible translations by a verification process. The proposed method has better capabilities to stitch images when they have large light changes and displacements. Since the method doesn't require building any correspondences or involve any optimization process, it performs more efficiently than other correlation techniques like feature-matching or phase-correlation approaches. Due to its simplicity and efficiency, different images can be very quickly aligned (less than 0.1 seconds) with the proposed method. Experimental results are provided to verify the superiority of the proposed method.

We define a d-matched digraph and propose a recursive procedure for designing an optimal d-matched digraph without bidirectional edges. The digraph represents an optimal highly structured system which is a special class of self-diagnosable systems and identifies all of the faulty units independently and locally in O(|E|) time complexity. The procedure is straightforward and gives a system flexible in network connections. Hence the procedure is applicable to real systems such as the Internet or cooperative robotic systems which change their topology dynamically.

System-level diagnosis is a very important technique for identifying faulty processors in a system with a large number of processors. Processors can test other processors, and then output the test results. The aim of diagnosis is to determine correctly the faulty/fault-free status of all processors. The adaptive diagnosis have been studied in order to perform diagnosis more efficiently. In this paper, we present adaptive diagnosis algorithms for a system modeled by butterfly networks. Our algorithms identify all faulty nodes in butterfly networks with the optimal number of tests. Then, we design another algorithm for diagnosis with very small constant number of rounds.

We consider diagnosability of butterfly networks under the comparison approach proposed by Maeng and Malek. Sengupta and Dahbura discussed characterization of diagnosable systems under the comparison approach, and designed a polynomial time algorithm to identify the faulty processors. However, for a general system, it is not algorithmically easy to determine its diagnosability. This paper proposes two comparison schemes for generating syndromes on butterfly networks, and determine the diagnosability of the network.

This paper describes a method of age-group classification of young males based on their facial images. The facial shapes of males and females are mostly formed by age 20 and 15, respectively. Our study only considered young males as they have a longer period during which facial shape is a determining factor in age estimation. Age classification was carried out using artificial neural networks. We employed 440 facial images in our experiment, composed of 4 different photographic images taken at ages 12, 15, 18 and 22 of 110 young males. Two methods of age classification were used, each employing different features extracted from the facial images, namely, "mosaic features" and "KL features. " As a result, we obtained about an 80% successful classification rate using mosaic features, and a slightly lower rate using KL features. We also analyzed the connection weights between the hidden and input layers of the trained networks, and examined facial features characteristic to each age group.

Browsing is an important function supporting efficient access to relevant information in video archives. In this paper, we present PanoramaExcerpts -- a video browsing interface that shows a catalogue of two types of video icons: panoramic and keyframe icons. A panoramic icon is automatically synthesized from a video segment taken with camera pan or tilt using a camera parameter estimation technique. One keyframe icon is extracted for each shot to supplement the panoramic icons. A panoramic icon represents the entire visible contents of a scene extended with a camera pan or tilt, which is difficult to represent using a single keyframe. A graphical representation, called camera-work trajectory, is also proposed to show the direction and the speed of camera operation. For the automatic generation of PanoramaExcerpts, we propose an approach to integrate the following: (a) a shot-change detection method; (b) a method for locating segments that contain smooth camera operations; (c) a layout method for packing icons in a space-efficient manner. In this paper, we mainly describe (b) and (c) with experimental results.

We describe a theoretically optimal algorithm for computing the homography between two images. First, we derive a theoretical accuracy bound based on a mathematical model of image noise and do simulation to confirm that our renormalization technique effectively attains that bound. Then, we apply our technique to mosaicing of images with small overlaps. By using real images, we show how our algorithm reduces the instability of the image mapping.

Given a set of still images taken from a hand-held camera, we present a fast method for mosaicing them into a single blended picture. We design time- and memory- efficient still image mosaicing algorithms based on geometric point feature matchings that can handle both arbitrary rotations and large zoom factors. We discuss extensions of the methodology to related problems like the recovering of the epipolar geometry for 3d reconstruction and object recognition tasks.

System level diagnosis that can identify the faulty units in the system was introduced by Preparata, Metze, and Chien. In this area, the fundamental problem is to decide the diagnosability of given networks. We study the diagnosability of networks represented by the cartesian product. Our result is the optimal one with respect to the restriction of degrees of vertices of graphs that represent the networks.

An individual identification system is developed. In this system, we unify profile curve identification and full face image identification to obtain more successful recognition rate. In profile cruve identification process, the P-type Fourier descriptor is made use of. In full face image identification process, mosaic density values are made use of. A combination of the two processes shows higher recognition rates than those obtained by each single process.