This paper proposes an adaptive algorithm for identifying unknown systems containing nonlinear amplitude characteristics. Usually, the nonlinearity is so small as to be negligible. However, in low cost systems, such as acoustic echo canceller using a small loudspeaker, the nonlinearity deteriorates the performance of the identification. Several methods preventing the deterioration, polynomial or Volterra series approximations, have been hence proposed and studied. However, the conventional methods require high processing cost. In this paper, we propose a method approximating the nonlinear characteristics with a piecewise linear curve and show using computer simulations that the performance can be extremely improved. The proposed method can also reduce the processing cost to only about twice that of the linear adaptive filter system.

In this paper, we propose a distance-based howling canceller with high speech quality. We have developed a distance-based howling canceller that uses only distance information by noticing the property that howling occurs according to the distance between a loudspeaker and a microphone. This method estimates the distance by transmitting a pilot signal from the loudspeaker to the microphone. Multiple frequency candidates for each howling are computed from the estimated distance and eliminated by cascading notch filters that have nulls at them. However degradation of speech quality occurs at the howling canceller output. The first cause is a shot noise occurrence at the beginning and end of the pilot signal transmission due to the discontinuous change of the amplitude. We thus develop a new pilot signal that is robust against ambient noises. We can then reduce the shot noise effect by taking the amplitude small. The second one is a speech degradation caused from overlapped stopbands of the notch filters. We thus derive a condition on the bandwidths so that stopbands do not overlap, and propose an adaptive bandwidth scheme which changes the bandwidth according to the distance.

This paper presents methods for controlling the intensity of emotional expressions and speaking styles of an arbitrary speaker's synthetic speech by using a small amount of his/her speech data in HMM-based speech synthesis. Model adaptation approaches are introduced into the style control technique based on the multiple-regression hidden semi-Markov model (MRHSMM). Two different approaches are proposed for training a target speaker's MRHSMMs. The first one is MRHSMM-based model adaptation in which the pretrained MRHSMM is adapted to the target speaker's model. For this purpose, we formulate the MLLR adaptation algorithm for the MRHSMM. The second method utilizes simultaneous adaptation of speaker and style from an average voice model to obtain the target speaker's style-dependent HSMMs which are used for the initialization of the MRHSMM. From the result of subjective evaluation using adaptation data of 50 sentences of each style, we show that the proposed methods outperform the conventional speaker-dependent model training when using the same size of speech data of the target speaker.

We have developed a high-temperature superconducting (HTS) filter with narrow bandwidth characteristic for receiver of weather radar in order to reduce interference between adjacent radar channels. To realize a filter with which a narrow bandwidth and low insertion loss are compatible, resonators with high unloaded Q (Qu) value are required. Hairpin microstrip resonators with 1.5 times wavelength were adopted to suppress the radiation loss and achieve a high Qu value. The developed HTS filter has 8-pole quasi-elliptic function response for sharp skirt characteristic. The measured frequency response of the developed filter shows center frequency of 5370 MHz, insertion loss of 2.04 dB and maximum return loss of 15 dB, which agrees with the designed responses.

When we watch television or computer monitor under a certain viewing condition, we partially adapt to the display and partially to the ambient light. As an illumination level and chromaticity change, the eye's subjective white point changes between the display's white point and the ambient light's white point. In this paper, we propose a model that could predict the white point under a mixed adaptation condition including display and illuminant. Finally we verify this model by experimental results.

In this paper, we propose a subcarrier resource allocation algorithm for managing the video quality degradation for multiuser orthogonal frequency division multiplex (OFDM) systems. The proposed algorithm exploits the unequal importance existing in different picture types for video coding and the diversity of subcarriers for multiuser systems. A model-based performance metric is first derived considering the error concealment and error propagation properties of the H.264 video coding structure. Based on the information on video quality enhancement existing in a packet to be transmitted, we propose the distortion management algorithm for balancing the subcarriers and power usages for each user and minimizing the overall video quality degradation. In the simulation results, the proposed algorithm demonstrates a more gradual video quality degradation for different numbers of users compared with other resource allocation schemes.

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.

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].

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.

This paper is concerned with timing synchronization of high rates UWB signals operating in a dense multipath environment, where access must tackle inter-frame interference (IFI), inter-symbol interference (ISI) and even multi-user interference (MUI). A training-based joint timing and channel estimation scheme is proposed, which is resilient to IFI, ISI, MUI and pulse distortion. A low-complexity detection scheme similar to transmit-reference (TR) scheme comes out as a by-product. For saving the training symbols, we further develop an extended decision-directed (DD) scheme. A lower bound on the probability of correct detection is derived which agrees well with the simulated result for moderate to high SNR values. The results show that the proposed algorithm achieves a significant performance gain in terms of mean square error and bit error rate in comparison to the "timing with dirty templates" (TDT) algorithms.

In this report, we propose a high-performance pipelined VLSI architecture of the LMS adaptive filter derived by a cut-set retiming technique. The proposed architecture has a peculiar pipelined form with 3 adaptation delays, and the FIR filter portion has a peculiar class of the transposed form providing a minimum output latency and coefficient delay. Both the delays, the adaptation delay and coefficient delay, are compensated by a look-ahead conversion. A new high-speed 4-input and 2-output CSA type adder with a small hardware is employed. The proposed architecture can achieve a good convergence property, high-sampling rate, minimum output latency, small hardware, and lower power dissipation, simultaneously, and is very suitable to implement on the VLSI.

Image matching is an important area of research in the field of artificial intelligence, machine vision and visual navigation. This paper presents a new image matching scheme suitable for visual navigation. In this scheme, gray scale images are sliced and quantized to form sub-band binary images. The information in the binary images is then signaturized to form a vector space and the signatures are sorted as per significance. These sorted signatures are then normalized to transform the represented image pictorial features in a rotation and scale invariant form. For the image matching these two vector spaces from both the images are compared in the transformed domain. This comparison yields efficient results directly in the image spatial domain avoiding the need of image inverse transformation. As compared to the conventional correlation, this comparison avoids the wide range of square error calculations all over the image. In fact, it directly guides the solution to converge towards the estimate given by the adaptive prediction for a high speed performance in an aerial video sequence. A four dimensional solution population scheme has also been presented with a matching confidence factor. This factor helps in terminating the iterations when the essential matching conditions have been achieved. The proposed scheme gives robust and fast results for normal, scaled and rotated templates. Speed comparison with older techniques shows the computational viability of this new technique and its much lesser dependence on image size. The method also shows noise immunity at 30 dB AWGN and impulsive noise.

Resource search is a fundamental problem in large-scale and highly dynamic Peer-to-Peer (P2P) systems. Unstructured search approaches are widely used because of their flexibility and robustness. However, such approaches incur high communication cost. The index-dissemination-based search is a kind of efficient unstructured search approach. We investigate such approaches with respect to minimize the system communication cost. Based on a dynamic system model that peers continuously leave and join, we solve two problems. One problem is how to efficiently disseminate and maintain a given number of indices. Another is to determine the optimal number of indices for each resource object of a given popularity. Finally, we propose an optimized index dissemination scheme which is fully decentralized and self-adaptive. A remarkable advantage is that the scheme yields no additional communication cost to achieve the self-adaptive feature.

Although each application has its own quality of service (QoS) requirements, the resource allocation for multiclass services has not been studied adequately in multiuser orthogonal frequency division multiplexing (OFDM) systems. In this paper, a total transmit power minimization problem for downlink transmission is examined while satisfying multiclass services consisting of different data rates and target bit-error rates (BER). Lagrangian relaxation is used to find an optimal subcarrier allocation criterion in the context of subcarrier time-sharing by all users. We suggest an iterative algorithm using this criterion to find the upper and lower bounds of optimal power consumption. We also propose a prioritized subcarrier allocation (PSA) algorithm that provides low computation cost and performance very close to that of the iterative algorithm. The PSA algorithm employs subcarrier selection order (SSO) in order to decide which user takes its best subcarrier first over other users. The SSO is determined by the data rates, channel gain, and target BER of each user. The proposed algorithms are simulated in various QoS parameters and the fading channel model. Furthermore, resource allocation is performed not only subcarrier by subcarrier but also frequency block by frequency block (comprises several subcarriers). These extensive simulation environments provide a more complete assessment of the proposed algorithms. Simulation results show that the proposed algorithms significantly outperform existing algorithms in terms of total transmit power consumption.

An adaptive algorithm to optimize the packet size in wireless mobile networks with Gauss-Markov mobility is presented. The proposed control algorithm conducts adaptive packet size control for mobile terminals that experience relatively fast changing channel conditions, which could be caused by fast mobility or other rapidly changing interference conditions. Due to the fast changing channel conditions, the packet size controller uses short channel history for channel status estimation and takes advantage of a pre-calculated probability density function (PDF) of the distance of the mobile nodes in the estimation process. The packet size is adapted to maximize the communication performance through automatic repeat request (ARQ). The adaptive packet size controlling algorithm is based on an estimation of the channel error rate and the link statistics obtained from the mobility pattern. It was found that the distribution of the link distance among mobile nodes following the Markov-Gauss mobility pattern in a circular communication range well fits the Beta PDF. By adapting the Beta PDF from the mobility pattern, the results show that it is possible to estimate the channel condition more accurately and thereby improve the throughput and utilization performance in rapidly changing wireless mobile networking systems.

The purpose of this study is the real-time estimation of Doppler spectral moments for precipitation in the presence of ground clutter overlap. The proposed method is a frequency domain approach that uses a Gaussian model both to remove clutter spectrum and to estimate weather spectrum. The main advantage of this method is that it does not use processes like several fitting procedures and enables to estimate profiles of precipitation in a short processing time. Therefore this method is efficient for real-time radar observation with high range and time resolution. The performance of this method is evaluated based on simulation data and the observation data acquired by the Ku-band broad band radar (BBR) [1].

The block based motion estimation technique is adopted by various video coding standards to reduce the temporal redundancy in video sequences. The core of that technique is the search algorithm implemented to find the location of the best matched block. Indeed, the full search algorithm is the most straightforward and optimal but computationally demanding search algorithm. Consequently, many fast and suboptimal search algorithms have been proposed. Reduction of the number of location being searched is the approach used to decrease the computational load of full search. In this paper, hybridization between an adaptive search algorithm and the full search algorithm is proposed. The adaptive search algorithm benefits from the correlation within spatial and temporal adjacent blocks. At the same time, a feature domain based matching criteria is used to reduce the complexity resulting from applying the pixel based conventional criteria. It is shown that the proposed algorithm produces good quality performance and requires less computational time compared with popular block matching algorithms.

This paper proposes a Inter-symbol Interference (ISI) suppressing method For Orthogonal Frequency Division Multiplexing (OFDM) system. In the environments where the delay spread exceeds the guard interval, the occurrence of ISI results in a degradation of system performance. A receiving method based on the Equivalent Transmission Path (ETP) model is proposed for Single-Input Multiple-Output (SIMO) OFDM system. Compared to the receiving scheme using Maximal Ratio Combining (MRC) method, the proposed one shows better performance in suppressing errors due to ISI. The paper also points out that the ETP-based model can be used for detecting the desired signal in the multi-user OFDM system. Simulation results of the system performance show the effectiveness of the proposed method over the conventional OFDM system in suppressing ISI.

In this paper, we propose an Adaptation Decision-Taking Engine (ADTE) that targets the delivery of scalable video content in mobile usage environments. Our ADTE design relies on an objective perceptual quality metric in order to achieve video adaptation according to human visual perception, thus allowing to maximize the Quality of Service (QoS). To describe the characteristics of a particular usage environment, as well as the properties of the scalable video content, MPEG-21 Digital Item Adaptation (DIA) is used. Our experimental results show that the proposed ADTE design provides video content with a higher subjective quality than an ADTE using the conventional maximum-bit-allocation method.

In this letter, we first investigate the bias of Doppler shift estimator based on autocorrelation function (ACF). Then we derive a signal-to-noise ratio (SNR) independent condition for Doppler shift estimation and achieve this condition by a adaptive process. Moreover, we present theoretical analysis about the convergency of our adaptive Doppler shift estimator, and derive a close-form expression for its mean square error (MSE). We verify the proposed estimator by computer simulation, the results of which are in agreement with the analysis, i.e., the proposed method achieves a good SNR-independent performance in a wide range of velocities and SNRs.