The robust reduced order observer for a class of discrete-time Lipschitz nonlinear systems with external disturbance is proposed. It is shown that the proposed observer design can suppress the effect on the estimation error of external disturbance up to the prescribed level. Also, linear matrix inequalities are used to represent sufficient conditions on the existence of the proposed observer. Moreover, the maximum admissible Lipschitz constant of the proposed design is obtained for a given disturbance attenuation level. Finally, an illustrative example is given to verify the effectiveness of the proposed design.

This paper describes non-reference objective quality evaluation for noise-reduced speech. First, a subjective test is conducted in accordance with ITU-T Rec. P.835 to obtain the speech quality, the noise quality, and the overall quality of noise-reduced speech. Based on the results, we then propose an overall quality estimation model. The unique point of the proposed model is that the estimation of the overall quality is done only using the previously estimated speech quality and noise quality, in contrast to conventional models, which utilize the acoustical features extracted. Finally, we propose a non-reference objective quality evaluation method using the proposed model. The results of an experiment with different noise reduction algorithms and noise types confirmed that the proposed method gives more accurate estimates of the overall quality compared with the method described in ITU-T Rec. P.563.

This paper presents a Reduced-reference based video-quality estimation method suitable for individual end-user quality monitoring of IPTV services. With the proposed method, the activity values for individual given-size pixel blocks of an original video are transmitted to end-user terminals. At the end-user terminals, the video quality of a received video is estimated on the basis of the activity-difference between the original video and the received video. Psychovisual weightings and video-quality score adjustments for fatal degradations are applied to improve estimation accuracy. In addition, low-bit-rate transmission is achieved by using temporal sub-sampling and by transmitting only the lower six bits of each activity value. The proposed method achieves accurate video quality estimation using only low-bit-rate original video information (15 kbps for SDTV). The correlation coefficient between actual subjective video quality and estimated quality is 0.901 with 15 kbps side information. The proposed method does not need computationally demanding spatial and gain-and-offset registrations. Therefore, it is suitable for real-time video-quality monitoring in IPTV services.

This paper presents a robust reduced order observer for a class of Lipschitz nonlinear systems with external disturbance. Sufficient conditions on the existence of the proposed observer are characterized by linear matrix inequalities. It is also shown that the proposed observer design can reduce the effect on the estimation error of external disturbance up to the prescribed level. Finally, a numerical example is provided to verify the proposed design method.

Image quality assessment method is a methodology that measures the difference of quality between the reference image and its distorted one. In this paper, we propose a novel reduced-reference (RR) quality assessment method for JPEG-2000 compressed images, which exploits the statistical characteristics of context information extracted through partial entropy decoding or decoding. These statistical features obtained in the process of JPEG-2000 encoding are transmitted to the receiver as side information and used to estimate the quality of images transmitted over various noisy channels at the decompression side. In the framework of JPEG-2000, the context of a current coefficient is determined depending on the pattern of the significance and/or the sign of its neighbors in three bit-plane coding passes and four coding modes. As the context information represents the local property of images, it can efficiently describe textured pattern and edge orientation. The quality of transmitted images is measured by the difference of entropy of context information between received and original images. Moreover, the proposed quality assessment method can directly process the images in the JPEG-2000 compressed domain without full decompression. Therefore, our proposed can accelerate the work of assessing image quality. Through simulations, we demonstrate that our method achieves fairly good performance in terms of the quality measurement accuracy as well as the computational complexity.

The multistage Wiener filter (MWF) outperforms the full rank Wiener filter in low sample support environments. However, the MWF adaptive process should be stopped at an optimum stage to get the best performance. There are two methods to stop the MWF adaptive process. One method is to calculate until the final full-stage, and the second method is to terminate at r-stage less than full-stage. The computational load is smaller in the latter method, however, a performance degradation is caused by an additional or subtractive stage calculation. Therefore, it is very important for the r-stage calculation to stop an adaptive process at the optimum stage. In this paper, we propose a simple method based on a cross-correlation coefficient to stop the MWF adaptive process. Because its coefficient is calculated by the MWF forward recursion, the optimum stage is determined automatically and additional calculations are avoided. The performance was evaluated by simulation examples, demonstrating the superiority of the proposed method.

During these years, we have been focusing on developing ultra high-data-rate wireless access systems for future wireless multimedia communications. One of such kind of systems is called DPC-OF/TDMA (dynamic parameter controlled orthogonal frequency and time division multiple access) which targets at beyond 100 Mbps data rate. In order to support higher data rates, e.g., several hundreds of Mbps or even Gbps for future wireless multimedia applications (e.g., streaming video and file transfer), it is necessary to enhance DPC-OF/TDMA system based on MIMO-OFDM (multiple-input multiple-output orthogonal frequency division multiplexing) platform. In this paper, we propose an enhanced DPC-OF/TDMA system based on Multi-Layer MIMO-OFDM scheme which combines both diversity and multiplexing in order to exploit potentials of both techniques. The performance investigation shows the proposed scheme has better performance than its counterpart based on full-multiplexing MIMO-OFDM scheme. In addition to the Exhaustive Detection (EXD) scheme which applies the same detection algorithm on each subcarrier independently, we propose the Reduced-Complexity Detection (RCD) scheme. The complexity reduction is achieved by exploiting the suboptimal Layer Detection Order and subcarrier correlation. The simulation results show that huge complexity can be reduced with very small performance loss, by using the proposed detection scheme. For example, 60.7% complexity can be cut off with only 1.1 dB performance loss for the 88 enhanced DPC-OF/TDMA system.

Reduced-complexity maximum a posteriori probability (MAP) signal detection is a promising technique for multiple-input multiple-output space division multiplexing (MIMO-SDM) transmission. These detectors avoid exhaustive searches for all possible transmitted symbol vectors by generating a set of candidate symbol vectors. One problem with the reduced-complexity MAP detectors when used in conjunction with soft input decoders is the inaccuracy of log likelihood ratio (LLR) values since they are computed from a handful of candidate symbol vectors, which degrades the subsequent decoding process. To rectify this weakness, this paper proposes an LLR computation scheme for reduced complexity MAP detectors. The unique feature of the proposed scheme is that it utilizes the statistical property of the MIMO channel metric to narrow down further the number of candidate symbol vectors. Toward this goal, metric selection is performed to select only a statistically "good" portion of the candidate symbol vectors. Computer simulation results show that the proposed LLR computation scheme is more effective than the existing schemes especially when the number of candidate symbol vectors becomes smaller in reduced-complexity MAP detection.

A reduced-sample-rate (RSR) sigma-delta-pipeline (SDP) analog-to-digital converter architecture suitable for high-resolution and high-speed applications with low oversampling ratios (OSR) is presented. The proposed architecture employs a class of high-order noise transfer function (NTF) with a novel pole-zero locations. A design methodology is developed to reach the optimum NTF. The optimum NTF determines the location of the non-zero poles improving the stability of the loop and implementing the reduced-sample-rate structure, simultaneously. Unity gain signal transfer function to mitigate the analog circuit imperfections, simplified analog implementation with reduced number of operational transconductance amplifiers (OTAs), and novel, aggressive yet stable NTF with high out of band gain to achieve larger peak signal-to-noise ratio (SNR) are the main features of the proposed NTF and ADC architecture. To verify the usefulness of the proposed architecture, NTF, and design methodology, two different cases are investigated. Simulation results show that with a 4th-order modulator, designed making use of the proposed approach, the maximum SNDR of 115 dB and 124.1 dB can be achieved with only OSR of 8, and 16 respectively.

This paper presents a wavelet-based approach for the design of the finite impulse response (FIR) notch filter with controlled null width. The M-band P-regular wavelet filters are employed to constitute the null space of the derivative constraint matrix. Taking advantage of the vanishing moment property of the wavelet filters, the proposed method controls the null width of the notch filter by adjusting the regularity of the employed wavelet filters. Besides, the selection of large number of bands of the wavelet filters can effectively reduce the minimum mean square error and thus improve the performance of the notch filter. Computer simulations show that, in addition to possessing lower computational complexity, the proposed reduced-rank method has similar frequency response compared to those of the full-rank-based techniques.

In unidentifiable models, the Bayes estimation has the advantage of generalization performance over the maximum likelihood estimation. However, accurate approximation of the posterior distribution requires huge computational costs. In this paper, we consider an alternative approximation method, which we call a subspace Bayes approach. A subspace Bayes approach is an empirical Bayes approach where a part of the parameters are regarded as hyperparameters. Consequently, in some three-layer models, this approach requires much less computational costs than Markov chain Monte Carlo methods. We show that, in three-layer linear neural networks, a subspace Bayes approach is asymptotically equivalent to a positive-part James-Stein type shrinkage estimation, and theoretically clarify its generalization error and training error. We also discuss the domination over the maximum likelihood estimation and the relation to the variational Bayes approach.

To take intercarrier interference (ICI) attributed to time variations of the channel into consideration, the time- and frequency-selective (doubly-selective) channel is parameterized by a finite parameter model. By capitalizing on the finite parameter model to approximate the doubly-selective channel, a Kalman filter is developed for channel estimation. The ICI suppressing, reduced-complexity Viterbi-type Maximum Likelihood (RML) equalizer is incorporated into the Kalman filter for recursive channel tracking and equalization to improve the system performance. An enhancement in the channel tracking ability is validated by theoretical analysis, and a significant improvement in BER performance using the channel estimates obtained by the recursive channel estimation method is verified by Monte-Carlo simulations.

In this letter, we propose a 2-D receiver structure for multicarrier code division multiple access (MC-CDMA) systems with the reduced-rank multistage Wiener filter. Due to the fast convergence property of the reduced-rank processing, it outperforms MMSE-based receivers with the classical Wiener solution, which is estimated by using a limited number of samples.

In Japanese, there is frequent alternation between CV morae and moraic geminate consonants. In this study, we analyzed the phonemic environments of consonant gemination (CG) using the "Corpus of Spontaneous Japanese (CSJ)." The results revealed that the environment in which gemination occurs is, to some extent, parallel to that of vowel devoicing. However, there are two crucial differences. One difference is that the CG tends to occur in a /kVk/ environment, whereas such is not the case for vowel devoicing. The second difference is that when the preceding consonant is /r/, gemination occurs, but not vowel devoicing. These observations suggest that the mechanism leading to CG differs from that which leads to vowel devoicing.

A new moment computation technique for general lumped R(L)C interconnect circuits with multiple resistor loops is proposed. Using the concept of tearing, a lumped R(L)C network can be partitioned into a spanning tree and several resistor links. The contributions of network moments from each tree and the corresponding links can be determined independently. By combining the conventional moment computation algorithms and the reduced ordered binary decision diagram (ROBDD), the proposed method can compute system moments efficiently. Experimental results have demonstrate that the proposed method can indeed obtain accurate moments and is more efficient than the conventional approach.

In this paper, an adaptive bound reduced-form genetic algorithm (ABRGA) to tune the control points of B-spline neural networks is proposed. It is developed not only to search for the optimal control points but also to adaptively tune the bounds of the control points of the B-spline neural networks by enlarging the search space of the control points. To improve the searching speed of the reduced-form genetic algorithm (RGA), the ABRGA is derived, in which better bounds of control points of B-spline neural networks are determined and paralleled with the optimal control points searched. It is shown that better efficiency is obtained if the bounds of control points are adjusted properly for the RGA-based B-spline neural networks.

This study proposes a 3 dB branch-line coupler using radial stubs to achieve reduced coupler size and simplified stub arrangement. As the electrical lengths of stubs used here are less than 90at center frequency, a method of comparing input impedances to obtain radial stubs that are equivalent to straight stubs is discussed. The frequency characteristics of the proposed coupler are derived by combining classical transmission line theory with the computed data of radial stub input impedances. The methods presented here increase possibilities for realizing reduced branch-line couplers by means of stub design. Experimental results agree well with theoretical results except for slight differences in the high frequency region.

Reduced-state sequence estimation (RSSE) for trellis-coded modulation (TCM) in cyclic prefixed single carrier (CPSC) with minimum mean-square error-linear equalization (MMSE-LE) on frequency-selective Rayleigh fading channels is proposed. The Viterbi algorithm (VA) is used to search for the best path through the reduced-state trellis combined equalization and TCM decoding. Computer simulations confirm the symbol error probability of the proposed scheme.

This paper describes a sparse realization of passive reduced-order interconnect models via PRIMA to provide the SPICE compatible models. It is demonstrated that, if the SPICE models are directly realized so that the reduced-order equations obtained via PRIMA are stamped into the MNA matrix, the simulations of networks containing the macromodels become computationally inefficient when size of the reduced-order equations is relatively large. This is due to dense coefficient matrices of the reduced-order equations resulting from congruent transformations in PRIMA. To overcome this disadvantage, we propose a sparse realization of the reduced-order models. Since the expression is equivalent to the reduced-order equations, the passivity of the SPICE models generated is also guaranteed. Computational efficiency on SPICE is demonstrated in performing the transient analysis of circuits containing the proposed macromodels.

This paper presents a novel wavelet compression technique to increase compression of images. Based on zerotree entropy coding method, this technique initially uses only two symbols (significant and zerotree) to compress image data for each level. Additionally, sign bit is used for newly significant coefficients to indicate them being positive or negative. Contrary to isolated zero symbols used in conventional zerotree algorithms, the proposed algorithm changes them to significant coefficients and saves its location, they are then treated just like other significant coefficients. This is done to decrease number of symbols and hence, decrease number of bits to represent the symbols used. In the end, algorithm indicates isolated zero coordinates that are used to change the value back to original during reconstruction. Noticeably high compression ratio is achieved for most of the images, without changing image quality.