Recently, there are so many researches on Autonomous Distributed Manufacturing Systems (ADMSs), where cooperation among agents is used to solve problems, such as the scheduling problem and the vehicle routing problem. We target ADMSs where an ADMS consists of two sub-systems: a Production System (PS) and an Autonomous Transportation System (ATS). This paper discusses an on-line Tasks Assignment and Routing Problem (TARP) for ATSs under conditions of given production schedule and finite buffer capacity. The TARP results in a constrained version of the Pickup and Delivery Problem with Time Windows (PDPTW), and this paper gives a mathematical formulation of the problem. This paper, also, proposes a cooperative algorithm to obtain suboptimal solutions in which no deadlocks and buffer overflows occur. By computational experiments, we will examine the effectiveness of the proposed algorithm. Computational experiments show that the proposed algorithm is able to obtain efficient and deadlock-free routes even though the buffer capacity is less.

This paper presents a Fault-Tolerant Object Group (FTOG) model that provides the group management service and the fault-tolerance service for consistency maintenance and state transparency. Through Intelligent Home Network Simulator, we verify that FTOG model supports both of reliability and the stability of the distributed system.

In our previous work, the hyper H∞ filter is developed for tracking of unknown time-varying systems. Additionally, a fast algorithm, called the fast H∞ filter, of the hyper H∞ filter is derived on condition that the observation matrix has a shifting property. This algorithm has a computational complexity of O(N) where N is the dimension of the state vector. However, there still remains a possibility of deriving alternative forms of the hyper H∞ filter. In this work, a fast J-unitary form of the hyper H∞ filter is derived, providing a new H∞ fast algorithm, called the J-fast H∞ filter. The J-fast H∞ filter possesses a computational complexity of O(N), and the resulting algorithm is very amenable to parallel processing. The validity and performance of the derived algorithm are confirmed by computer simulations.

This paper presents a new technique for automatically synthesizing human walking motion. In the technique, a set of fundamental motion units called motion primitives is defined and each primitive is modeled statistically from motion capture data using a hidden semi-Markov model (HSMM), which is a hidden Markov model (HMM) with explicit state duration probability distributions. The mean parameter for the probability distribution function of HSMM is assumed to be given by a function of factors that control the walking pace and stride length, and a training algorithm, called factor adaptive training, is derived based on the EM algorithm. A parameter generation algorithm from motion primitive HSMMs with given control factors is also described. Experimental results for generating walking motion are presented when the walking pace and stride length are changed. The results show that the proposing technique can generate smooth and realistic motion, which are not included in the motion capture data, without the need for smoothing or interpolation.

We are developing a helper robot that carries out tasks ordered by the user through speech. The robot needs a vision system to recognize the objects appearing in the orders. It is, however, difficult to realize vision systems that can work in various conditions. Thus, we have proposed to use the human user's assistance through speech. When the vision system cannot achieve a task, the robot makes a speech to the user so that the natural response by the user can give helpful information for its vision system. Our previous system assumes that it can segment images without failure. However, if there are occluded objects and/or objects composed of multicolor parts, segmentation failures cannot be avoided. This paper presents an extended system that tries to recover from segmentation failures using photometric invariance. If the system is not sure about segmentation results, the system asks the user by appropriate expressions depending on the invariant values. Experimental results show the usefulness of the system.

A robust joint symbol timing and fractional frequency offset estimator for OFDM systems in multipath fading channels is proposed based on cyclic shifting and autocorrelation properties of PN codes. A new timing metric is also introduced by considering the delay spread to improve the robustness of the estimator in the multipath fading channels.

A sampling frequency offset estimation scheme for MB-OFDM UWB systems is proposed based on technical specification and multi-band utilization of the MB-OFDM. An estimation scheme using simple weighting factor based on the received signal power of each sub-channel is also introduced to efficiently combine estimates obtained from all the sub-carriers and to improve the estimation performance.

The conventional decentralized supervisory control architectures for discrete event systems assume that default control of controllable events is static. In this paper, we propose a new decentralized supervisory control architecture using dynamic default control of controllable events. We present necessary and sufficient conditions for the existence of a decentralized supervisor in the proposed architecture. Then, we give an example of a language that is achieved in the proposed architecture, but not in the conventional architectures using static default control.

In the letter, the fast one-dimensional (1-D) and two-dimensional (2-D) algorithms for realizing low-complexity 44 discrete cosine transform (DCT) for H.264 applications are developed. Through applying matrix utilizations with Kronecker product and direct sum, the efficient fast 2-D 44 DCT algorithm can be developed from the proposed fast 1-D 44 DCT algorithm by matrix decompositions. The fast 1-D and 2-D low-complexity 44 DCT algorithms requires fewer multiplications and additions than other fast DCT algorithms. Owing to regular modularity, the proposed fast algorithms can achieve real-time H.264 video signal processing with VLSI implementation.

Proportional fair bandwidth allocation in packet switches is a fundamental issue to provide quality of service (QoS) support in IP networks. In input-queued switches, packet-mode scheduling delivers all the segments of a packet contiguously from the input port to the output port, thus greatly simplifying the design of packet reassembly modules and yielding performance advantage over cell-mode scheduling under certain conditions [1]. One of the important issues of packet-mode scheduling is how to achieve fair bandwidth allocation among flows with different packet sizes. This paper presents an algorithm called packet-mode fair scheduling (pFS) that guarantees each flow a bandwidth proportional to its reservation regardless of the packet size distribution and the system load. Simulations show that our approach achieves good fairness as well as high throughput and low packet delay. Compared to algorithms without fairness mechanism, pFS yields significant performance improvement in terms of average packet delay when the traffic is heterogeneous. A hardware implementation is presented to show that the proposed algorithm has low complexity and the computation can be completed in a single clock cycle, which makes pFS applicable to high-speed switches.

Given the limited channel capacity in wireless LANs, it is important to achieve high throughput and good fairness through medium access control (MAC) schemes. Although many schemes have been proposed to enhance throughput or fairness of the original IEEE 802.11 standard, they either fail to consider both throughput and fairness, or to do so with complicated algorithms. In this paper, we propose a new MAC scheme that dynamically optimizes each active node's backoff process. The key idea is to enable each node to adjust its Contention Window (CW) to approach the optimal one that will maximize the throughput. Meanwhile, when the network enters into steady state in saturated case, i.e., under heavy traffic load, all the nodes will maintain approximately identical CWs, which guarantees fair share of the channel among all nodes. A distinguishing feature of this scheme is the use of an index called average channel idle interval for optimizing the backoff process without estimating the number of active nodes in networks. We show through theoretical analysis that the average channel ideal interval can represent current network traffic load and indicate the optimal CW. Moreover, since it can be obtained through direct measurement, our scheme eliminates the need for complicated estimation of the number of active nodes as required in previous schemes, which makes our schemes simpler and more reliable when network traffic changes frequently. Through simulation comparison with previous schemes, we show that our scheme can greatly improve the throughput no matter the network is in saturated or non-saturated case, while maintaining good fairness.

This paper proposes two different packet schedulers for IEEE 802.16e type time division duplex - orthogonal frequency division multiple access (TDD-OFDMA), which are the weighted fair scheduling (WFS) and the throughput guarantee scheduling (TGS). The performance of proposed schedulers is compared to those of some of conventional schedulers such as round robin (RR), proportional fair (PF), fast fair throughput (FFTH), and fair throughput (FTH) in terms of service coverage, effective throughput and fairness at 64 kbps and 128 kbps minimum user throughput requirements. For a relatively smaller throughput (64 kbps) requirement, the proposed schedulers increase the number of users per sector within 95% service coverage while satisfying the 1xEV-DV fairness criterion. For a relatively larger throughput (128 kbps) requirement, the proposed schedulers provide higher coverage than the PF scheduler while maintaining the same effective aggregate throughput.

This paper highlights some of our recent research efforts in designing and evaluating life-like characters that are capable of entertaining affective and social communication with human users. The key novelty of our approach is the use of human physiological information: first, as a method to evaluate the effect of life-like character behavior on a moment-to-moment basis, and second, as an input modality for a new generation of interface agents that we call 'physiologically perceptive' life-like characters. By exploiting the stream of primarily involuntary human responses, such as autonomic nervous system activity or eye movements, those characters are expected to respond to users' affective and social needs in a truly sensitive, and hence effective, friendly, and beneficial way.

An accurate audio-visual speech corpus is inevitable for talking-heads research. This paper presents our audio-visual speech corpus collection and proposes a head-movement normalization method and a facial motion generation method. The audio-visual corpus contains speech data, movie data on faces, and positions and movements of facial organs. The corpus consists of Japanese phoneme-balanced sentences uttered by a female native speaker. An accurate facial capture is realized by using an optical motion-capture system. We captured high-resolution 3D data by arranging many markers on the speaker's face. In addition, we propose a method of acquiring the facial movements and removing head movements by using affine transformation for computing displacements of pure facial organs. Finally, in order to easily create facial animation from this motion data, we propose a technique assigning the captured data to the facial polygon model. Evaluation results demonstrate the effectiveness of the proposed facial motion generation method and show the relationship between the number of markers and errors.

Eye contact is an effective means of controlling human communication, such as in starting communication. It seems that we can make eye contact if we simply look at each other. However, this alone does not establish eye contact. Both parties also need to be aware of being watched by the other. We propose a method of bidirectional eye contact satisfying these conditions for human-robot communication. When a human wants to start communication with a robot, he/she watches the robot. If it finds a human looking at it, the robot turns to him/her, changing its facial expressions to let him/her know its awareness of his/her gaze. When the robot wants to initiate communication with a particular person, it moves its body and face toward him/her and changes its facial expressions to make the person notice its gaze. We show several experimental results to prove the effectiveness of this method. Moreover, we present a robot that can recognize hand gestures after making eye contact with the human to show the usefulness of eye contact as a means of controlling communication.

This paper describes a pattern classifier for detecting frontal-view faces via learning a decision boundary. The proposed classifier consists of two major parts for improving classification accuracy: the implicit modeling of both the face and the near-face classes resulting in an extended discriminative feature set, and the subsequent composite Support Vector Machines (SVMs) for speeding up the classification. For the extended discriminative feature set, Principal Component Analysis (PCA) or Independent Component Analysis (ICA) is performed for the face and near-face classes separately. The projections and distances to the two different subspaces are complementary, which significantly enhances classification accuracy of SVM. Multiple nonlinear SVMs are trained for the local facial feature spaces considering the general multi-modal characteristic of the face space. Each component SVM has a simpler boundary than that of a single SVM for the whole face space. The most appropriate component SVM is selected by a gating mechanism based on clustering. The classification by utilizing one of the multiple SVMs guarantees good generalization performance and speeds up face detection. The proposed classifier is finally implemented to work in real-time by cascading a boosting based face detector.

In this paper, we propose a new measurement-based fair call admission control policy for heterogeneous packet radio networks. The objectives of the new admission policy are to 1) reduce computation overhead and rapidly re-allocate resources to keep desired Quality of Service (QoS) demands, 2) relieve hot-spot cell by reducing the number of handoff calls from neighboring cells, 3) fairly admit voice and data traffic to prevent declined service degradation. With numerical analysis, we evaluate QoS constraints and inter-cell as well as inter-service fairness by using queuing model, which is described by a two-dimensional continuous-time Markov chain. Finally, we validate our model's accuracy by comparing results of analytical model with ones of event-driven simulation.

In this letter, we propose a protocol sensitive random early detection algorithm for active queue management to improve fairness between TCP and UDP flows and to reduce delay time with small overheads. The algorithm classifies the packets into responsive and unresponsive flows, and applies the RED algorithm individually to each classified group. Using ns-2 simulations, we showed the effectiveness of the proposed PSRED algorithm compared with several well-known AQM schemes, such as RED and RED-PD algorithms.

Though block-based motion estimation techniques are primarily designed for video coding applications, they are increasingly being used in other video analysis applications due to their simplicity and ease of implementation. The major drawback associated with these techniques is that noises, in the form of false motion vectors, cannot be avoided while capturing block motion vectors. Similar noises may further be introduced when the technique of global motion compensation is applied to obtain true object motion from video sequences where both the camera and object motions are present. This paper presents a new technique for capturing large number of true object motion vectors by eliminating the false motion vector fields, for use in the application of object motion based video indexing and retrieval applications. Experimental results prove that our proposed technique significantly increases the percentage of retained true object motion vectors while eliminating all false motion vectors for variety of standard and non-standard video sequences.

This paper presents Dynamic Attention Map by Ising model for face detection. In general, a face detector can not know where faces there are and how many faces there are in advance. Therefore, the face detector must search the whole regions on the image and requires much computational time. To speed up the search, the information obtained at previous search points should be used effectively. In order to use the likelihood of face obtained at previous search points effectively, Ising model is adopted to face detection. Ising model has the two-state spins; "up" and "down". The state of a spin is updated by depending on the neighboring spins and an external magnetic field. Ising spins are assigned to "face" and "non-face" states of face detection. In addition, the measured likelihood of face is integrated into the energy function of Ising model as the external magnetic field. It is confirmed that face candidates would be reduced effectively by spin flip dynamics. To improve the search performance further, the single level Ising search method is extended to the multilevel Ising search. The interactions between two layers which are characterized by the renormalization group method is used to reduce the face candidates. The effectiveness of the multilevel Ising search method is also confirmed by the comparison with the single level Ising search method.