The growing demand for services and the rapid deployment of virtualized network functions (VNFs) pose significant challenges for achieving low-latency and energy-efficient orchestration in modern edge-core network infrastructures. To address these challenges, this study proposes a Digital Twin (DT)-empowered Deep Reinforcement Learning framework for intelligent VNF migration that jointly minimizes average end-to-end (E2E) delay and energy consumption. By formulating the VNF migration problem as a Markov Decision Process and utilizing the Advantage Actor-Critic model, the proposed framework enables adaptive and real-time migration decisions. A key innovation of the proposed framework is the integration of a DT module composed of a multi-task Variational Autoencoder and a multi-task Long Short-Term Memory network. This combination collectively simulates environment dynamics and generates high-quality synthetic experiences, significantly enhancing training efficiency and accelerating policy convergence. Simulation results demonstrate substantial performance gains, such as significant reductions in both average E2E delay and energy consumption, thereby establishing new benchmarks for intelligent VNF migration in edge-core networks.

The next generation networks offers significant potential to advance Intelligent Transportation Systems (ITS), particularly through the integration of Digital Twins (DTs). However, ensuring the uninterrupted operation of DTs through efficient computing resource management remains an open challenge. This paper introduces a distributed computing architecture that integrates DTs and Mobile Edge Computing (MEC) within a software-defined vehicular networking framework to enable intelligent, low-latency transportation services. A network aware scalable collaborative task provisioning algorithm is developed to train an autonomous agent, which is evaluated using a realistic connected autonomous vehicle (CAV) traffic simulation. The proposed framework significantly enhances the robustness and scalability of DT operations by reducing synchronization errors to as low as 7% while achieving up to 99.5% utilization of edge computing resources.

In recent years, there has been an observable proliferation of travel services available to travelers. Consequently, a shift has occurred in the manner in which travelers plan their journeys. Historically, travelers depended on travel guides and literature as primary sources of information. In the contemporary context, however, travelers are increasingly turning to emotional and experiential data to inform their travel planning decisions. The objective of this study is to propose travel destinations that are congruent with travelers’ preferences and emotional states. In this study, we propose the EmoTABI that extends the TABIMAE Mind Search System introducing an atmosphere emotion extraction.This system utilizes image data to discern travelers’ emotions, identify objects, and assess the prevailing atmosphere of a given location. The findings from the demonstration of this system indicate its potential for predicting the success of proposed tourist destinations in terms of their emotional appeal to travelers.

Six-generation mobile systems aim to support stringent end-to-end service-level agreements for diverse user applications simultaneously. This paper introduces novel multi-domain computation-aware resource slicing orchestration that jointly manages in-network communications, computation, and caching storage resources for multi-domain networking. It automatically programs wireless access, edge cloud, and regional/central cloud infrastructure to enable wireless-optical network virtualization. Specifically, a mobile virtual network operator's long-term profit maximization problem and two subproblems are formulated to slice wired and wireless infrastructure resources and assign user requests and contents to slices. Accordingly, a reinforcement learning-based slicing with greedy pre-caching is proposed, which automatically allocates in-network resources for dynamic wireless connectivity and supports real-time inferring with minimal user request signaling. Numerical results show that our solutions provide superior performance from both user and infrastructure perspectives, with 20% improved operator profits, 17% enhanced service provisioning rates, and 80% reduced delay when simultaneously serving augmented reality and large language model's quality demands. This innovation exploits a generalized rein-forcement learning approach to minimize the signaling overheads and computation complexity while agilely adapting to practical converged networks, thus benchmarking AI-driven multi-domain network slicing development.

The rapid evolution of Industry 4.0 has led to the emergence of smart factories, where multirobot system autonomously operates to enhance productivity, reduce operational costs, and improve system adaptability. However, maintaining reliable and efficient network operations in these dynamic and complex environments requires advanced automation mechanisms. This study presents a zero-touch network platform that integrates a hierarchical Open Radio Access Network (O-RAN) architecture, enabling the seamless incorporation of advanced machine learning algorithms and dynamic management of communication and computational resources, while ensuring uninterrupted connectivity with multirobot system. Leveraging this adaptability, the platform utilizes federated deep reinforcement learning (FedDRL) to enable distributed decision-making across multiple learning agents, facilitating the adaptive parameter reconfiguration of transmitters (i.e., multirobot system) to optimize long-term system throughput and transmission energy efficiency. Simulation results demonstrate that within the proposed O-RAN-enabled zero-touch network platform, FedDRL achieves a 12% increase in system throughput, a 32% improvement in normalized average transmission energy efficiency, and a 28% reduction in average transmission energy consumption compared to baseline methods such as independent DRL.

The need for prioritized multi-tenant quality of service (QoS) management in emerging mobile edge systems is particularly critical for high-throughput next generation networks. Current traffic engineering tools rely on network administrator driven, complex functions embedded in closed, proprietary infrastructures, which significantly restrict design flexibility, scalability, and adaptability. This study addresses these challenges by proposing a software-defined networking (SDN) based dynamic QoS provisioning scheme, powered by a digital twin (DT) of networks. By separating the control and data planes, the scheme enables automated traffic management through SDN programmability and data-driven decision-making. It incorporates few-shot learning to dynamically identify and prioritize multi-tenant network traffic utilizing flow statistics from SDN. The proposed QoS provisioning mechanism allocates sufficient bandwidth to high-priority flows while optimizing the remaining bandwidth for lower-priority traffic. Performance evaluations show that the model achieves up to 98% accuracy in identifying the priority of previously unseen traffic flows. Hardware-in-the-loop (HiL) simulations further validate the scheme's effectiveness in meeting multi-tenant QoS requirements, offering a robust and scalable solution for traffic prioritization in SDN based edge networks.

The integration of 5G terrestrial networks with Low Earth Orbit (LEO) satellites has the potential to provide seamless global connectivity and enhanced service quality, particularly in regions with limited terrestrial infrastructure such as rural areas. Furthermore, the incorporation of Multi-connectivity (MC) enables user equipment (UEs) to maintain simultaneous connections with both terrestrial 5G base stations and LEO satellites, improving system reliability. However, the high mobility of LEO satellites and the dynamic behavior of UEs present significant challenges, particularly in handover decision-making which can adversely impact system throughput, and quality of service (QoS). To address these challenges, we propose a novel deep reinforcement learning-based approach that integrates online Random Ensemble Mixture and Dual Experience Replay into a Dueling Double Deep Q-Network architecture. This proposed scheme intelligently optimizes handover decisions in MC-enabled terrestrial-satellite networks, improving decision accuracy in highly dynamic scenarios. Simulation results demonstrate substantial gains in system throughput, reduced system delay, average handover reduction, and increased transmission success probability, setting new performance benchmarks for integrated terrestrial-satellite networks while adhering to diverse QoS requirements.

The distributed and disaggregated architecture of next-generation (NextG) networks, including 6G has sparked growing interest in federated learning (FL) as a strategy for enabling privacy-preserving collaborative network traffic analysis at the edge. However, FL encounters significant challenges due to data heterogeneity driven by diverse data distributions across edge nodes, and the scarcity of labeled data further worsened by the time-intensive process of data labeling. Although a few studies have addressed these challenges in network traffic analysis tasks using Multi-Task Learning (MTL), existing approaches pre-dominantly focus on single-task FL, centralized model solutions and overlook the integration of MTL in NextG networks. To bridge this gap, we propose O-FedMTL, a novel framework that combines FL with MTL to enable cooperative traffic analysis within an Open Radio Access Network (O-RAN) environment in NextG networks. MTL enhances FL by mitigating the issues of data heterogeneity and labeled data scarcity through shared knowledge derived from multiple interconnected traffic analysis tasks, i.e., traffic classification, flow duration analysis, and bandwidth estimation. Additionally, MTL offers significant benefits by reducing energy consumption and computation costs at the edge through the simultaneous processing of these tasks within a single model. Extensive experimental results demonstrate that O-FedMTL achieves the target global accuracy for traffic classification, flow duration analysis, and bandwidth estimation with 20, 12, and 23 fewer global communication rounds, respectively, compared to the baseline federated averaging. Additionally, O-FedMTL reduces computation costs by 43% compared to the baseline-combined.

With the evolution of modern telecommunications, the fronthaul network has become an indispensable component of the infrastructure, particularly in the context of Cloud Ra-dio Access Networks. Nevertheless, fronthaul networks still face significant challenges such as power outages, particularly when a disaster, like an earthquake or severe weather event, occurs. Damage to power supply facilities may cause operational failures while communication is one of the most crucial needs in the disaster area to make rescue operations more effective. Specialized fibers that can deliver electrical power can help mitigate this problem. However, power losses may be extremely large over distances and there is no flexibility after the installation of the fibers. The network topology design that reduces capital and operational costs while satisfying power constraints is an important problem. In this paper, we focus on network topology design in an urban area by taking into account both fiber and power costs. We then propose integer linear programming and fast algorithms based on methods for single-facility location problems. The results demonstrate that multiple approaches help to achieve the optimal design, with our proposed method standing out due to its efficiency in finding feasible solutions, scalability, and ≈656x reduction in execution time.

In the realms of the internet of vehicles (IoV) and intelligent transportation systems (ITS), software defined vehicular networks (SDVN) and edge computing (EC) have emerged as promising technologies for enhancing road traffic efficiency. However, the increasing number of connected autonomous vehicles (CAVs) and EC-based applications presents multi-domain challenges such as inefficient traffic flow due to poor CAV coordination and flow-table overflow in SDVN from increased connectivity and limited ternary content addressable memory (TCAM) capacity. To address these, we focus on a data-driven approach using virtualization technologies like digital twin (DT) to leverage real-time data and simulations. We introduce a DT design and propose two data-driven solutions: a centralized decision support framework to improve traffic efficiency by reducing waiting times at roundabouts and an approach to minimize flow-table overflow and flow re-installation by optimizing flow-entry lifespan in SDVN. Simulation results show the decision support framework reduces average waiting times by 22% compared to human-driven vehicles, even with a CAV penetration rate of 40%. Additionally, the proposed optimization of flow-table space usage demonstrates a 50% reduction in flow-table space requirements, even with 100% penetration of connected vehicles.

C-band enabled Elastic optical networks (EONs) have been one of the most deployed optical network solutions in the world. However, as traffic demands continue to increase, capacity exhaustion is inevitable. There are two major technologies, namely, multiband elastic optical networks (MB-EONs) and space division multiplexed elastic optical networks (SDM-EONs) that can enhance capacity. Each technology offers a tradeoff between better capacity and deployment overhead which directly affects the network performance. Considering the different characteristics of these two technologies, we present our proposed strategy, Progressive Optics Deployment and Integration for Growing Yields (PRODIGY), to gradually migrate the current C-band EONs. PRODIGY uses various proactive measures, inspired by Swiss Cheese Model, to make the network robust for handling network traffic peaks and ensure that the service level agreement is met. We present a detailed comparison of our proposed strategy with customized baseline strategies, and demonstrate the superiority of our proposed approach.

The availability of a variety of Internet-based services for tourists has drastically changed the way travel plans are made. This has indicated that instead of travel planning from traditional travel information (articles and guidebooks), it is starting from emotional information linked to photographs and other imagery information. In this study, in order to provide an "EmoTABI experience," a travel to feel EMOI, we have developed to analyze image of interest, extract a color emotion derived from the color information of image and object emotion related to objects in the image, respectively, and search for optimal sightseeing spots match the mental value of before travel, TABIMAE. Using this developed the TABIMAE mind search system, recommendations for tourist destinations were provided for Tokyo, Osaka, and Nasu Town, Tochigi Prefecture. Furthermore, we carried out a questionnaire survey to confirm the validity of the system. As a result, we confirmed that the results obtained from this system were valid. In this paper, the TABIMAE mind search system is overall and reported to discuss the obtained results through the prototype system.

Although DX initiatives have been mandated since the 2010s, the design of digital information systems can be influenced by business and technology aspects and have a significant impact on the attractiveness of services, but there is not enough design science research in this area. In addition, the design of information systems often consists of multiple people with diverse capabilities, and methods that effectively utilize diverse perspectives are needed to design attractive solutions. The authors are conducting research on the application of support systems in engineering (CDSS) to methodologies that support design (conceptual design) in the information systems field. Based on the results of applying CDSS to the digital field, this paper defines a new CDSS methodology that incorporates new "storytelling" and "digital business strategy" in the problem understanding phase to derive solutions to human and economic requirements. We will also incorporate World Cafe as a team discussion method into the storytelling and define CDSS for DX application design process.

Due to the rapid globalization of engineering fields and studies, engineering students are required to present their research in English at an international level. However, many engineering students feel anxiety for speaking in English; thus, proper training is necessary for them by providing a simulated conference environment. In this study, the authors develop a prototype of virtual reality (VR) training material for engineering students to practice and experience difficult communication situations in English at an international conference. The effects of the material were investigated by measuring the changes in the hemoglobin (Hb) concentration of the frontal cortex and heart rates of subjects who participated in the VR training and evaluation sessions with an English conversation trainer. The physiological data measurements obtained from the subjects before and after the VR training showed that they were more relaxed when responding to difficult English questions at a mocked question-and-answer (Q&A) session after the training. The result indicates that the VR training material can reduce the anxiety of engineering students who need to give a presentation in English, and this type of learning material is expected to be effectively used from an educational standpoint.

Elastic optical networks (EONs) have emerged as attractive candidates to satisfy the dramatic growth of demand in 5G and cloud applications. EONs promise to provide high spectrum utilization due to flexibility in resource assignment. In translucent EONs, the spectrum efficiency can be further improved by deploying regenerators. Because of their extremely high flexibility, developing efficient mechanisms and strategies to ensure the survivability of translucent EONs is a challenging problem. In this paper, we consider disaster mitigation in translucent EONs. We propose a new approach to disaster management by introducing the concept of mitigation zone, which identifies a region surrounding the disaster zone wherein lightpaths may be reconfigured with degraded service (with a penalty) in order to improve overall performance. We formulate an integer linear program (ILP) to minimize the penalty due to service degradation after a disaster, and present a heuristic algorithm named Disaster Management Algorithm with Mitigation Awareness and 3R regenerators (DRAMA+). Simulation results demonstrate that the proposed algorithms have a better performance in terms of total penalty and blocking ratio than conventional disaster recovery algorithms.

Elastic Optical Networks (EONs) have become a promising solution to satisfy the dramatic growth of bandwidth demand due to 5G and cloud applications. Due to the flexibility of resource allocation, EONs provide high spectrum utilization efficiency, and because of this, developing efficient policies to ensure the survivability of EONs is a challenging problem. A well-designed disaster management plan is needed to prevent data loss during network failures and large-scale disasters. The bottleneck problem caused by disabled parts of the network causes difficulties for disaster recovery. Depending on the disaster, even traffic that may be far away from the disaster may be impacted by it. In this paper, we propose a new approach to disaster management using machine learning to facilitate efficient recovery. In addition to traffic immediately affected by the disaster, all traffic which is “close to” the disaster is re-routed and re-assigned with possibly degraded service, while requests “far from” the disaster are left unaffected. A deep reinforcement learning disaster recovery algorithm with mitigation awareness (DeepDRAMA) is proposed for recovery. A novel deep reinforcement learning agent is designed and trained for the agent to select the appropriate level of service degradation for re-assigned traffic. Simulation results show the performance improvement with DeepDRAMA.

This paper considers the protection of translucent elastic optical networks (EONs) through p-cycles. Such networks improve spectrum efficiency by employing regenerators and using advanced modulation formats for transmission. P-cycles provide fast restoration and high protection efficiency, and have been studied for conventional fixed-grid WDM networks as well as EONs. In this paper, we consider the design and selection of p- cycles for translucent EONs with 3R regenerators in a network. We propose two novel link-protection p-cycle evaluation methods in translucent EONs: individual p-cycle selection and p-cycle set selection. Based on these two metrics, Traffic Independent P-cycle Selection with 3R regenerator (TIPS-3R) and Traffic-Oriented P-cycle Selection with 3R regenerator (TOPS-3R), are designed to find the best set of p-cycles under a given 3R regenerator placement. We evaluate our algorithms using both static traffic and dynamic traffic. Simulation results indicate that the proposed algorithms have a lower spectrum usage and lower blocking ratio compared with baseline algorithms.

The rapid growth of Internet traffic has caused researchers continue to seek new ways to increase fiber bandwidth and spectrum utilization efficiency through elastic optical networking (EON). The advantage of EON comes from the fine-grained grid, which allows traffic demands to be better matched through a flexible allocation of fiber bandwidth. To further increase capacity, multiple fibers per link will be desired. Conventional optical crossconnects (OXCs) that use wavelength selective switches (WSSs) to switch the slots of a lightpath from input fibers to output fibers do not scale well. A more scalable and cost-effective node architecture called a flexible wavebanding crossconnect (FLEX) has been proposed recently. The FLEX architecture considerably reduces the cost of the crossconnect while introducing a small performance penalty in the form of reduced switching flexibility. In order to alleviate the limited switching capability, a cost-function-pluggable auxiliary layered-graph framework has also been proposed recently to solve the routing, fiber, waveband, and spectrum assignment (RFBSA) problem in multi-fiber EON with FLEX nodes. In this paper, we address the following problem. Given a budget in terms of the number of available WSSs for the network, determine the number and placements of FLEX nodes, and solve the RFBSA problem jointly in order to optimize network performance. We present an integer linear programming formulation, and propose a heuristic algorithm to solve this joint problem. The results show that our heuristic algorithm achieves good network performance, as measured by the average maximum spectrum usage (MSU), while saving significant hardware costs.

The dramatic growth of Internet traffic brings challenges for optical network designers. There have been a number of advances recently in increasing fiber bandwidth and spectrum utilization efficiency through elastic optical networking (EON). In EON, a flexible and more fine-grained grid than conventional approaches is employed, and this allows allocated fiber bandwidth to better match traffic demands. Despite these advances, imminent fiber capacity exhaustion means that multiple fibers per link will be inevitable. In an effort to reduce the complexity of optical crossconnects, a flexible wavebanding crossconnect has been proposed recently. Elastic networking and flexible wavebanding introduce a new problem, namely, the routing, fiber, waveband, and spectrum assignment (RFBSA) problem. In this work, we propose new cost functions that are pluggable into an auxiliary layered-graph framework to solve the RFBSA problem with different objectives. We focus on minimizing the maximum spectrum usage for a set of traffic demands, and show that our approach outperforms traditional approaches.

In this paper, we propose a machine-learning-based novel dynamic performance control method for routers that supports several performance levels. The method utilizes support vector machine (SVM) to determine performance level change points. We utilize a traffic normalization technique with a corresponding performance threshold that allows us to apply the same SVM to different traffic volumes. This technique shortens the learning process for control. Several experiments on real Internet traffic data sequences prove that the method yields higher energy efficiency than conventional methods, that is, our previously proposed frequency decomposition-based method and a conventional traffic prediction method based on auto-regressive moving average model with optimal parameter values given by Akaike's information criterion. We also evaluate the impact of several key parameters such as traffic measurement interval and the number of packet processing engines. The results clarify the proper ranges of parameter values that attain significant power reductions while keeping packet loss to acceptable levels.

A novel coarse granular routing scheme for elastic optical networks is proposed in this paper together with a node architecture and network design algorithm. The proposed scheme bundles optical paths to be routed together and each bundle of paths, named flexible waveband, is routed as an entity. Path bundling is done by a small port count wavelength-selective switch (WSS) while flexible waveband routing is done by the other optical switches, i.e., two stage routing. The proposed network design algorithm resolves the routing and frequency slot assignment problem while considering specific constraints imposed by the routing scheme. Numerical experiments on several topologies confirm that the routing performance degradation caused by the coarse granular routing is small while the number of WSSs is substantially reduced.

Large scale optical cross-connects (OXCs) are required due to the increasing traffic demands. Currently, wavelength-selective switches (WSS) are utilized to create the OXCs. However, the port count of commercially available WSSs is limited. To achieve high port counts in OXCs, the existing WSS-based approach is to cascade WSSs, which results in a square order increment in the number of required WSSs. To save the hardware costs in terms of number of WSSs, two novel OXC architectures utilizing the waveband switching technique have been proposed. In this paper, we conduct a detailed comparison among the conventional cascading architecture and the two new architectures. We propose algorithms to accommodate dynamic traffic demands for all architectures and compare the blocking rates. The results show that the blocking rates of the novel architectures are very small and close to that of the cascading architecture, while the novel architectures have much less node complexity in terms of hardware requirement.

In conceptual design for an attractive product, the visualization of Kansei value as requirements and the realization of its value for creating product ideas are desired. Therefore, the Idea Creation Support System (ICSS)-for an idea creation with Kando understanding process through Word Of Mouth (WOM) effectiveness—has been developed. ICSS has consisted of two understanding processes, i.e., problem and Kando understanding processes. However, ICSS does not include shape and layout understanding process—which is grasping the imagined topology of mind as requirement—which is most important process as for mechanical products. In this study, we propose the shape and layout understanding method which consisted of SIMP method as a topology optimization and Kansei information understanding of topology using Electroencephalography (EEG) through Brain Machine Interface (BMI), and also discuss the system configuration of ICSS with shape and layout understanding method.

In computer vision, Iterative Closest Point (ICP) has been a key tool for registration algorithms, a fundamental task in computer vision. However, ICP based registration algorithms always face with local minima problem and pre-aligned pointsets are the must to guarantee correct convergence. Pre-alignment used to be carried out by our human in some mesh processing softwares. This paper provides a solution for initialization problem for registering two 3D surfaces under L 2 error using ICP algorithm. Our algorithm uses a combination between Nested Annealing (NA) and ICP in which NA is used as global optimization search engine to find the global minima with a novel approach of using point based boundary searching. The algorithm uses ICP to derive local minima as well as local minima error. The integration between ICP and NA is successfully implemented and coded into a program which inputs two range image and outputs the transformation matrix between them at high accuracy and success rate.

A new strategy of Adaptive Plan System with Genetic Algorithm (APGA) is proposed to reduce a large amount of calculation cost and to improve stability in convergence to an optimal solution for multi-peak optimization problems with multi-dimensions. This is an approach that combines the global search ability of Genetic Algorithm (GA) and the local search ability of Adaptive Plan (AP). The APGA differs from GAs in handling design variable vectors (DVs). GAs generally encode DVs into genes and handle them through GA operators. However, the APGA encodes control variable vectors (CVs) of AP, which searches for local optimum, into its genes. CVs determine the global behavior of AP, and DVs are handled by AP in the optimization process of APGA. In this paper, we introduce a new approach for Adaptive Plan System of swarm intelligent using Particle Swarm Optimization (PSO) with Genetic Algorithm (PSO-APGA) to solve a huge scale optimization problem, and to improve the convergence towards the optimal solution. The PSO-APGA is applied to several benchmark functions with multi-dimensions to evaluate its performance.We confirmed satisfactory performance through various benchmark tests.

To improve the calculation cost and the convergence to optimal solutions for multi-peak optimization problems with multiple dimensions, we propose a new evolutionary algorithm, which is an Adaptive Plan system with Genetic Algorithm (APGA). This is an approach that combines the global search ability of a GA and an Adaptive Plan with excellent local search ability. The APGA differs from GAs in how it handles design variable vectors. GAs generally encode design variable vectors into genes, and handle them through GA operations. However, the APGA encodes the control variable vectors of the Adaptive Plan, which searches for local minima, into its genes. The control variable vectors determine the global behavior of the AP, and design variable vectors are handled by the AP in the optimization process of the APGA. In this paper, the Variable Neighborhood range Control (VNC), which changes a neighborhood range based on an individual's situation—fitness, is introduced into the APGA to dramatically improve the convergence up to the optimal solution. The APGA/VNC is applied to some benchmark functions to evaluate its performance. We confirmed satisfactory performance through these various benchmark tests.

In the present paper, we present a study focusing on the improvement of the operation of finger pointers, which are promising with respect to their potential for use as pointing devices in home environments. We developed a basic finger pointer and aimed at solving the following problems related to finger pointers: (1) instability and (2) time lag. For this purpose, the concept of prediction based on gray theory was applied to the finger pointer, and the relevant requirements were examined. The effectiveness of the proposed method was assessed through subjective evaluation, and the results indicated that all of the evaluated items were characterized by increased stability brought by the proposed technique. Using the proposed finger pointer together with the gray theory, we constructed a three-dimensional information presentation program whose operability was evaluated by using it as an electronic photo album, and the superiority of the proposed system was confirmed.

We propose a quasi-dynamic network design algorithm considering traffic growth in multi-layered photonic networks that consist of electrical paths and optical paths. The quasi-dynamic network design proposed herein leaves existing paths unchanged in order to minimize service disruption due to path rerouting as well as human error. Simulations verify that the network cost strongly depends on the network design algorithms adopted and the holding terms of service traffic. We, then, introduce a new parameter that represents expected service holding terms. The parameter is shown to represent effectively the network cost trend versus the number of the incremental designs. Furthermore, lower network cost is obtained by taking advantage of the network design algorithms that consider service holding times. This study is the first to address the criteria in designing future multi-layered photonic networks that offer cost-effective expansion while accommodating different services.

We propose a hierarchical optical path network design algorithm. In order to efficiently accommodate wavelength paths in each waveband path, we define a source-destination Cartesian product space that allows the 'closeness' among wavelength paths to be assessed. By grouping 'close' wavelength paths, found by searching for clusters in the space, we iteratively create waveband paths that efficiently accommodate the wavelength paths. Numerical experiments demonstrate that the proposed algorithm offers lower total network cost than the conventional algorithms. The results also show that the hierarchical optical path network is effective even when traffic demand is relatively small.

This paper investigates a 3D reconstruction of the building by detecting the vanishing points from the interlaced video sequence. It is theoretically possible to reconstruct a 3D model of the building from a single picture. However, the accuracy of reconstruction is not enough with a single picture because of the deterioration of the picture, such as noise or dim etc. Increasing the number of pictures, accuracy of the reconstructed model will become higher. It is also possible to apply this method to video sequences. There are deteriorations caused by interlace scanning in addition to noise in each video image. Our preliminary results show that from information of the many pictures, this method can reconstruct precise 3D structure. The addition average values that are obtained from the 50 pictures or more have been settled within 2% of the actual value.

In this paper, we propose a Wavelet-based prediction method of the Internet traffic volume. By introducing maximal overlap formulation of the Haar wavelet, the proposed method is free from so-called boundary condition, which arises from processing delay of analysis filters of the wavelet transform and refrains from full utilization of information on recent input signals. The proposed method is based on a vector autoregressive model so as to introduce inter-scale correlation of Wavelet coefficient series at different scales.

With the onset of interactive digital data broadcasting, a user-friendly pointing device for controlling home use displays such as digital HDTVs is desirable. A linger pointer that does not use any artificial devices in the hand is a suitable candidate. This paper describes the basic design and usability of the finger pointer. To obtain a degree of freedom in the operation, we propose a finger pointer used in the crooked elbow position. In the experiments, we detect the position of the fingertip with a stereo camera and examine whether there is a datum point on the body that has sufficient accuracy for our purpose. Then, the psychological plane where the display is mapped by the finger is measured. The performance of the finger pointer is evaluated in accordance with IS09241-9. Moreover, we examine the subjective evaluation test and a comparison test using the mouse. The experimental results show the finger pointer to be a promising device for domestic use.

In this paper, we propose a method that recovers a smooth high-resolution image from several blurred and roughly quantized low-resolution images. For compensation of the quantization effect we introduce a measurement of smoothness originally used for suppression of block noises in a JPEG compressed image [Schultz & Stevenson '94]. With a simple operator that approximates to the convex projection onto constraint set defined for each quantized image [Hasegawa et al. '05], we propose a method that minimizes these cost functions, which are smooth convex functions, over the intersection of all constraint sets, i.e. the set of all images satisfying all quantization constraints simultaneously, by using hybrid steepest descent method [Yamada & Ogura '04]. Finally in the numerical example we compare images derived by the proposed method, POCS based conventional method, and generalized proposed method minimizing smoothed total variation and energy of output of Laplacian

We present a novel adaptive super-resolution of videos based on an embedded constraint version of adaptive projected subgradient method (Yamada & Ogura, Numerical Functional Analysis and Optimization, vol 25, no.7&8, p.593-617, 2004). The super-resolution image recovery problem is formulated as an estimation of linear time-varying systems, which is a modified version of (Elad & Feuer, IEEE Trans. on Image Proc., vol.8, no.3, p.387-395, 1999). Our method efficiently improves the estimation accuracy by simple iterative operations which can be processed on parallel systems. Robustness to additive noise as well as inaccurate estimation of degradation parameters, is realized by incorporating stochastic information of the noise.

An efficient scheme is presented to the color super-resolution problem for recovery of a color high-resolution image with knowledge of multiple Bayer filtered low-resolution images. To recover a visually natural high-resolution image, we restrict fairly smooth initial candidates to all images satisfying all bounds imposed on the several nonsmooth convex color total variations as well as a non-smooth convex inter cross correlation measure among color channels. In the proposed scheme, the data-fidelity is optimized in a systematic way, over all initial candidates, with the hybrid steepest descent method for quasi-nonexpansive mappings [Yamada & Ogura 2004], by minimizing successively an weighted average of pure mean square errors between the low-resolution transforms of the high-resolution estimate and the multiple low-resolution images. Numerical examples show that the proposed scheme recovers visually natural high resolution images by resolving the tradeoff between noise suppression and edge preservation of the recovered image while keeping fair inter channel cross correlation among color channels.

In this paper, we present a novel iterative MPEG super-resolution method based on an embedded constraint version of adaptive projected subgradient method [Yamada & Ogura 2003]. We propose an efficient operator that approximates convex projection onto a set characterizing framewise quantization, whereas a conventional method can only handle a convex projection defined for each DCT coefficient of a frame. By using the operator, the proposed method generates a sequence that efficiently approaches to a solution of super-resolution problem defined in terms of quantization error of MPEG compression.

In this paper, we propose a novel higher order time-frequency distribution (GDH) for discrete time signals. This distribution is defined over the original discrete time-frequency grids through a delicate discretization of an equivalent expression of a higher order distribution, for continuous time signals, in [Fonollosa & Nikias 1993]. We also present a constructive design method, for the kernel of the GDH, by which the distribution satisfies (i) the alias free condition as well as (ii) the marginal conditions. A numerical example shows that the proposed distribution reasonably suppresses the artifacts which are observed severely in a simple higher order generalization of the Wigner distribution.