A novel error concealment method using a Kalman filter is presented in this paper. In order to successfully utilize the Kalman filter, its state transition and observation models that are suitable for the video error concealment are newly defined as follows. The state transition model represents the video decoding process by a motion-compensated prediction. Furthermore, the new observation model that represents an image blurring process is defined, and calculation of the Kalman gain becomes possible. The problem of the traditional methods is solved by using the Kalman filter in the proposed method, and accurate reconstruction of corrupted video frames is achieved. Consequently, an effective error concealment method using the Kalman filter is realized. Experimental results showed that the proposed method has better performance than that of traditional methods.

Communication-free Petri nets provide a net semantics for Basic Parallel Processes, which form a subclass of Milner's Calculus of Communicating Systems (CCS) a process calculus for the description and algebraic manipulation of concurrent communicating systems. It is known that the reachability problem for communication-free Petri nets is NP-complete. Lacking the synchronization mechanism, the expressive power of communication-free Petri nets is somewhat limited. It is therefore importance to see whether the power of communication-free Petri nets can be enhanced without sacrificing their analytical capabilities. As a first step towards this line of research, in this paper our main concern is to investigate, from the decidability/complexity viewpoint, the reachability problem for a number of variants of communication-free Petri nets, including communication-free Petri nets augmented with 'static priorities,' 'dynamic priorities,' 'states,' 'inhibitor arcs,' and 'timing constraints.'

The observed phenomena in actual sound and electromagnetic environment are inevitably contaminated by the background noise of arbitrary distribution type. Therefore, in order to evaluate sound and electromagnetic environment, it is necessary to establish some signal processing methods to remove the undesirable effects of the background noise. In this paper, we propose noise cancellation methods for estimating a specific signal with the existence of background noise of non-Gaussian distribution from two viewpoins of static and dynamic signal processing. By applying the well-known least mean squared method for the moment statistics with several orders, practical methods for estimating the specific signal are derived. The effectiveness of the proposed theoretical methods is experimentally confirmed by applying them to estimation problems in actual sound and magnetic field environment.

This letter corrects some errors on a previous letter concerning the derivation of the covariance matrix of phase noise. This derivation doesn't affect the results of the previous letter.

By use of the shadow theory developed recently, this paper deals with the transverse electric (TE) wave diffraction by a perfectly conductive periodic array of rectangular grooves. A set of equations for scattering factors and mode factors are derived and solved numerically. In terms of the scattering factors, diffraction amplitudes and diffraction efficiencies are calculated and shown in figures. It is demonstrated that diffraction efficiencies become discontinuous at an incident wave number where the incident wave is switched from a propagating wave to an evanescent one, whereas scattering factors and diffraction amplitudes are continuous even at such an incident wave number.

For statistical language model training, target domain matched corpora are required. However, training corpora sometimes include both target domain matched and unmatched sentences. In such a case, training set selection is effective for both reducing model size and improving model performance. In this paper, training set selection method for statistical language model training is described. The method provides two advantages for training a language model. One is its capacity to improve the language model performance, and the other is its capacity to reduce computational loads for the language model. The method has four steps. 1) Sentence clustering is applied to all available corpora. 2) Language models are trained on each cluster. 3) Perplexity on the development set is calculated using the language models. 4) For the final language model training, we use the clusters whose language models yield low perplexities. The experimental results indicate that the language model trained on the data selected by our method gives lower perplexity on an open test set than a language model trained on all available corpora.

We have proposed and successfully demonstrated a two step method for localizing defects on an LSI chip. The first step is the same as a conventional laser-SQUID (L-SQUID) imaging where a SQUID and a laser beam are fixed during LSI chip scanning. The second step is a new L-SQUID imaging where a laser beam is stayed at the point, located in the first step results, during SQUID scanning. In the second step, a SQUID size (Aeff) and the distance between the SQUID and the LSI chip (ΔZ) are key factors limiting spatial resolution. In order to improve the spatial resolution, we have developed a micro-SQUID and the vacuum chamber housing both the micro-SQUID and the LSI chip. The Aeff of the micro-SQUID is a thousand of that of a conventional SQUID. The minimum value of ΔZ was successfully reduced to 25 µm by setting both the micro-SQUID and an LSI chip in the same vacuum chamber. The spatial resolution in the second step was shown to be 53 µm. Demonstration of actual complicated defects localization was succeeded, and this result suggests that the two step localization method is useful for LSI failure analysis.

Liquid-phase detection of biological targets utilizing magnetic marker and superconducting quantum interference device (SQUID) magnetometer is shown. In this method, magnetic markers are coupled to the biological targets, and the binding reaction between them is detected by measuring the magnetic signal from the bound markers. Detection can be done in the liquid phase, i.e., we can detect only the bound markers even in the presence of unbound (free) markers. Since the detection principle is based on the different magnetic properties between the free and bound markers, we clarified the Brownian relaxation of the free markers and the Neel relaxation of the bound markers. Usefulness of the present method is demonstrated from the detection of the biological targets, such as biotin-coated polymer beads, IgE and Candida albicans.

Lower-dimensional transformations in similar sequence matching show different performance characteristics depending on the type of time-series data. In this paper we propose a hybrid approach that exploits multiple transformations at a time in a single hybrid index. This hybrid approach has advantages of exploiting the similar effect of using multiple transformations and reducing the index maintenance overhead. For this, we first propose a new notion of hybrid lower-dimensional transformation that extracts various features using different transformations. We next define the hybrid distance to compute the distance between the hybrid transformed points. We then formally prove that the hybrid approach performs similar sequence matching correctly. We also present the index building and similar sequence matching algorithms based on the hybrid transformation and distance. Experimental results show that our hybrid approach outperforms the single transformation-based approach.

Image restoration based on Bayesian estimation in most previous studies has assumed that the noise accumulated in an image was independent for each pixel. However, when we take optical effects into account, it is reasonable to expect spatial correlation in the superimposed noise. In this paper, we discuss the restoration of images distorted by noise which is spatially correlated with translational symmetry in the realm of probabilistic processing. First, we assume that the original image can be produced by a Gaussian model based on only a nearest-neighbor effect and that the noise superimposed at each pixel is produced by a Gaussian model having spatial correlation characterized by translational symmetry. With this model, we can use Fourier transformation to calculate system characteristics such as the restoration error and also minimize the restoration error when the hyperparameters of the probabilistic model used in the restoration process coincides with those used in the formation process. We also discuss the characteristics of image restoration distorted by spatially correlated noise using a natural image. In addition, we estimate the hyperparameters using the maximum marginal likelihood and restore an image distorted by spatially correlated noise to evaluate this method of image restoration.

Nonnegative matrix factorization (NMF) and its extensions such as Nonnegative Tensor Factorization (NTF) have become prominent techniques for blind sources separation (BSS), analysis of image databases, data mining and other information retrieval and clustering applications. In this paper we propose a family of efficient algorithms for NMF/NTF, as well as sparse nonnegative coding and representation, that has many potential applications in computational neuroscience, multi-sensory processing, compressed sensing and multidimensional data analysis. We have developed a class of optimized local algorithms which are referred to as Hierarchical Alternating Least Squares (HALS) algorithms. For these purposes, we have performed sequential constrained minimization on a set of squared Euclidean distances. We then extend this approach to robust cost functions using the alpha and beta divergences and derive flexible update rules. Our algorithms are locally stable and work well for NMF-based blind source separation (BSS) not only for the over-determined case but also for an under-determined (over-complete) case (i.e., for a system which has less sensors than sources) if data are sufficiently sparse. The NMF learning rules are extended and generalized for N-th order nonnegative tensor factorization (NTF). Moreover, these algorithms can be tuned to different noise statistics by adjusting a single parameter. Extensive experimental results confirm the accuracy and computational performance of the developed algorithms, especially, with usage of multi-layer hierarchical NMF approach [3].

We propose an asymmetric fragile watermarking technique that uses a number theoretic transform (NTT). Signature data is extracted from a watermarked image by determining correlation functions that are computed using the NTT. The effectiveness of the proposed method is evaluated by simulated detection of altering.

MPLS-based path technology shows promise as a means of realizing reliable IP networks. Real-time services such as VoIP and video-conference supplied through a multi-domain MPLS network must be able to guarantee end-to-end QoS of the inter-domain paths. Thus, it is important to allocate an appropriate QoS class to the inter-domain paths in each domain traversed by the inter-domain paths. Because each domain has its own policy for QoS class allocation, it is necessary to adaptively allocate the optimum QoS class based on estimation of the QoS class allocation policies in other domains. This paper proposes two kinds of adaptive QoS class allocation schemes, assuming that the arriving inter-domain path requests include the number of downstream domains traversed by the inter-domain paths and the remaining QoS value toward the destination nodes. First, a measurement-based scheme, based on measurement of the loss rates of inter-domain paths in the downstream domains, is proposed. This scheme estimates the QoS class allocation policies in the downstream domains, using the measured loss rates of path requests. Second, a state-dependent type scheme, based on measurement of the arrival rates of path requests in addition to the loss rates of paths in the downstream domains, is also proposed. This scheme allows an appropriate QoS class to be allocated according to the domain state. This paper proposes an application of the Markov decision theory to the modeling of state-dependent type scheme. The performances of the proposed schemes are evaluated and compared with those of the other less complicated non-adaptive schemes using a computer simulation. The results of the comparison reveal that the proposed schemes can adaptively increase the number of inter-domain paths accommodated in the considered domain, even when the QoS class allocation policies change in the other domains and the arrival pattern of path requests varies in the considered domain.

Slit-Mura defect is a notorious yield flaw of color filters. In this study, an innovative non-contact in-line optical inspection method is developed to detect low contrast slit Mura through quantitative measurements by a spectrometer. Using the features of either thickness or chromaticity profiles across a slit Mura, a thickness difference from 21 nm to 41 nm of color filters can be differentiated accurately. Thus, the quality of color filters can be accessed in-line during the manufacturing process TFT-LCDs.

The integration of multihop relays with orthogonal frequency-division multiple access (OFDMA) cellular infrastructures can meet the growing demands for better coverage and higher throughput. Resource allocation in the OFDMA two-hop relay system is more complex than that in the conventional single-hop OFDMA system. With time division between transmissions from the base station (BS) and those from relay stations (RSs), fixed partitioning of the BS subframe and RS subframes can not adapt to various traffic demands. Moreover, single-hop scheduling algorithms can not be used directly in the two-hop system. Therefore, we propose a semi-distributed algorithm called ASP to adjust the length of every subframe adaptively, and suggest two ways to extend single-hop scheduling algorithms into multihop scenarios: link-based and end-to-end approaches. Simulation results indicate that the ASP algorithm increases system utilization and fairness. The max carrier-to-interference ratio (Max C/I) and proportional fairness (PF) scheduling algorithms extended using the end-to-end approach obtain higher throughput than those using the link-based approach, but at the expense of more overhead for information exchange between the BS and RSs. The resource allocation scheme using ASP and end-to-end PF scheduling achieves a tradeoff between system throughput maximization and fairness.

We address the issue of MIMO channel estimation with the aid of a priori temporal correlation statistics of the channel as well as the spatial correlation. The temporal correlations are incorporated to the estimation scheme by assuming the Gauss-Markov channel model. Under the MMSE criteria, the Kalman filter performs an iterative optimal estimation. To take advantage of the enhanced estimation capability, we focus on the problem of channel estimation from a partial channel measurement in the MIMO antenna selection system. We discuss the optimal training sequence design, and also the optimal antenna subset selection for channel measurement based on the statistics. In a highly correlated channel, the estimation works even when the measurements from some antenna elements are omitted at each fading block.

In this paper, we propose a cross-layer design of packet level cooperation for wireless LANs that support rate adaptation. While keeping compatibility with legacy wireless LANs, distributed control of multi-hop packet transmission is enabled without pre-negotiation of routing or pairing. These features are provided by prioritization scheme based on IEEE 802.11e EDCF in which we set the parameters according to the measured link condition at each terminal. Relaying packets with high transmission rate makes much efficient use of radio resource, and it leads not only to improve performance of the total system, but also to overcome the fairness issue known in rate adaptation, where the terminals with good link conditions cannot gain enough resource because of the time consumed by low data rate transmission. These advantages are confirmed through computer simulations considering packet error rate at each transmission which is assumed by receiving power calculated from the distance between source, relay, and destination terminals. Furthermore, we also discuss about the fairness between cooperation-enabled and legacy terminals when they coexist in the same system, in order to make gradual deployment feasible.

As a candidate for the transmission technology of next generation mobile communication systems, time-domain spreading MC-CDMA systems have begun to attract much attention. In these systems, data and pilot symbols are spread in the time domain and code-multiplexed. To combat fading issues, we need to conduct channel estimation by using the code-multiplexed pilot symbols. Especially in next generation systems, frequency bands higher than those of current systems, which raise the maximum Doppler frequency, are expected to be used, so that a more powerful channel estimation method is expected. Considering this, we propose a channel estimation method for highly accurate channel estimation; it is a combination of a two-dimensional channel estimation method and an impulse response-based channel estimation method. We evaluate the proposed method by computer simulations.

Optical burst switching (OBS) is a promising approach for the realization of future flexible high-speed optical networks. In particular, a multicast forwarding method for optical bursts is important if an efficient high-speed grid computing network is to be realized. In OBS networks, the number of burst replicas generated at each node is strongly restricted due to optical power impairment of multicast bursts. Moreover, unrestricted replication of multicast bursts at each OBS node may not be advantageous because an increase in the number of multicast bursts within the network causes more frequent deflection forwarding of both multicast and unicast bursts. This paper proposes an efficient hop-by-hop multicast forwarding method for optical bursts, where idle output ports are selected based on scores simply calculated using a routing table that each OBS node holds. This method can mitigate increases in loss rate and transfer delay of multicast bursts, even if the number of burst replicas generated at each OBS node is strongly restricted. Moreover, this method can efficiently mitigate an increase in the number of multicast bursts within the network by avoiding unnecessary replication of multicast bursts at each OBS node. Simulation results show that the proposed method can actually mitigate degradation of the loss rate and transfer delay for multicast bursts under the restricted number of burst replicas at each OBS node. Moreover, when the arrival rate of multicast bursts is large relative to that of unicast bursts, the proposed method is able to improve the loss rates of both multicast and unicast bursts by switching the forwarding method for the multicast bursts to the simple unicast forwarding method without burst replication.

In this letter, a closed form approximation for the average sum rate of random beamforming is derived. It provides a near-exact approximation for arbitrary numbers of beams, users, and received SNR. The approximation is also applied to an average-sense multimode random beamforming scheme which optimizes the number of random beams without any type of instantaneous channel information. The proposed scheme shows better sum rate performance than random beamforming as well as an existing dual mode random beamforming scheme based on instantaneous channel information, while the number of feedback bits for beam index is reduced compared to random beamforming.