The convergence speed of the conventional adaptive LMS algorithms for time delay estimation (TDE) is highly dependent on the spectral distribution of the desired random source signals of interest, thus the performance of TDE might be degraded, dramatically. To solve this problem, in this letter, a DCT-transform domain constrained adaptive normalized-LMS filtering scheme, referred to as the adaptive constrained DCT-LMS algorithm, is devised for TDE. Computer simulation results verify that the proposed scheme can be used to achieve desired performance, for input random signals with different spectral distributions; it outperforms the unconstrained DCT-LMS and time-domain constrained adaptive LMS algorithms.

We extend a graph spectral method for extracting clusters from graphs representing pairwise similarity between data to hypergraph data with hyperedges denoting higher order similarity between data. Our method is robust to noisy outlier data and the number of clusters can be easily determined. The unsupervised method extracts clusters sequentially in the order of the majority of clusters. We derive from the unsupervised algorithm a semi-supervised one which can extract any cluster irrespective of its majority. The performance of those methods is exemplified with synthetic toy data and real image data.

This paper proposes a gonio-spectral imaging system for measuring light reflection on an object surface by using two robot arms, a multi-band lighting system, and a monochrome digital camera. It allows four degrees of freedom in incident and viewing angles necessary for full parametrization of a reflection model function. Spectral images captured for various incident and viewing angles are warped as if they were all captured from the same viewing direction. The intensity of reflected light is thus recorded in a normalized image form for any incident and viewing directions. The normalized images are used to estimate reflection model parameters at each surface point. To ensure point-wise reflection modeling, a calibration method is also proposed based on a geometrical model of the robot arms and camera. The proposed system can deal with objects with surface texture. Experiments are done on system calibration, reflection model, and spectral estimation. The results using colored objects show the feasibility of the proposed imaging system.

In this paper, we study how a multidimensional local binary pattern (LBP) texture feature data can be visually explored and analyzed. The goal is to determine how true paper properties can be characterized with local texture features from visible light images. We utilize isometric feature mapping (Isomap) for the LBP texture feature data and perform non-linear dimensionality reduction for the data. These 2D projections are then visualized with original images to study data properties. Visualization is utilized in the manner of selecting texture models for unlabeled data and analyzing feature performance when building a training set for a classifier. The approach is experimented on with simulated image data illustrating different paper properties and on-line transilluminated paper images taken from a running paper web in the paper mill. The simulated image set is used to acquire quantitative figures on the performance while the analysis of real-world data is an example of semi-supervised learning.

In this letter we present steady-state analyses of a gradient algorithm (GA) for second-order adaptive infinite impulse response (IIR) notch filters. A method for deriving more accurate estimation mean square error (MSE) expressions than the recently proposed method is presented. The method is based on the estimation error power spectral density (PSD). Moreover, an expression for the estimation bias for the adaptive IIR notch filter with constrained poles and zeros is shown to be obtained from the estimation MSE expression. Simulations are presented to confirm the validity of the analyses.

An HEMT CCD (charge-coupled-device) matched filter for spread-spectrum communication was developed. For higher data rates, it was fabricated using a two-phase CCD based on HEMT technology. It operates at 1.6 GHz, and its calculated data rate is 100 Mbps with a PN data length of 16 bits (PN data rate is 1.6 GHz). And it attains a charge transfer efficiency (CTE) of 0.975 at 2 GHz. The HEMT CCD matched filter dissipates 173 mW from a 10-Vp-p supply, and its chip size is 0.961.03 mm. It will thus be useful for optical communication and other high-data-rate applications utilizing spread-spectrum (SS) communication.

We propose a novel strategy to obtain a high spatio-temporal resolution video. To this end, we introduce a dual sensor camera that can capture two video sequences with the same field of view simultaneously. These sequences record high resolution with low frame rate and low resolution with high frame rate. This paper presents an algorithm to synthesize a high spatio-temporal resolution video from these two video sequences by using motion compensation and spectral fusion. We confirm that the proposed method improves the resolution and frame rate of the synthesized video.

The rapid hybrid acquisition of PN sequences is proposed for DS/CDMA systems. The system introduces the excision CFAR method into the background power estimation. A mathematical analysis is done for the single path and multipath environments. The detection performance of the proposed scheme is compared with that of other acquisition schemes. Results show that the proposed method has better detection performance if the excision coefficient is properly selected.

This paper presents a new statistical model-based voice activity detection (VAD) algorithm in the wavelet domain to improve the performance in non-stationary environments. Due to the efficient time-frequency localization and the multi-resolution characteristics of the wavelet representations, the wavelet transforms are quite suitable for processing non-stationary signals such as speech. To utilize the fact that the wavelet packet is very efficient approximation of discrete Fourier transform and has built-in de-noising capability, we first apply wavelet packet decomposition to effectively localize the energy in frequency space, use spectral subtraction, and employ matched filtering to enhance the SNR. Since the conventional wavelet-based spectral subtraction eliminates the low-power speech signal in onset and offset regions and generates musical noise, we derive an improved multi-band spectral subtraction. On the other hand, noticing that fixed threshold cannot follow fluctuations of time varying noise power and the inability to adapt to a time-varying environment severely limits the VAD performance, we propose a statistical model-based VAD algorithm in wavelet domain with an adaptive threshold. We perform extensive computer simulations and compare with the conventional algorithms to demonstrate performance improvement of the proposed algorithm under various noise environments.

In this paper, we propose a combination-based novel technique of dithered subband coding with spectral subtraction for improving the perceptual quality of coded audio at low bit rates. It is well known that signal-correlated distortion is audible when the audio signal is quantized at bit rates lower than the lower bound of perceptual coding. We show that this problem can be overcome by applying the dithering quantization process in each subband. Consequently, the quantization noise is rendered into a signal-independent white noise; this noise is then estimated and removed by spectral subtraction at the decoder. Experimental results show an effective improvement by the proposed method over the conventional one in terms of better SNR and human listening test results. The proposed method can be combined with other existing or future coding methods such as perceptual coding to improve their performance at low bit rates.

Constraint-based software specifications enable run-time monitoring to detect probable risk events and ensure the desired system behavior. SpecTRM-RL is a well-developed constraint-based specification method for computer-controlled systems. However, it is desirable to express constraints in familiar visual models. To provide better visualization and popularity, we developed methods to represent all the SpecTRM-RL constraint types in UML. We have also extended SpecTRM's constraints by adding relational and global constraints, and then expressed them in OCL. Safety verification of these specifications is also proposed. We developed a systematic way to construct fault trees for safety analysis based on UML diagrams. Due to the generality of UML as well as the defensive manner of constraints and fault tree analysis, our approach can be adapted for both general applications and safety-critical applications.

A spread-spectrum clock generator (SSCG) using fractional-N phase-locked loop (PLL) with an extended range sigma-delta (ΣΔ) modulator is presented in this paper. The proposed ΣΔ modulator simply adds an extra output bit in the first stage modulator. It can enlarge the input range about three times as compared to the conventional modulator and solve the saturation problem when the input exceeds the boundary of the conventional modulator. A flexible digital modulation controller can generate center and down spread-spectrum modulation and each has spread ratios of 0.4%, 0.8%, 1.6% and 3.2%. The proposed SSCG has been fabricated in TSMC 0.35-µm double-poly quadruple-metal CMOS process with output frequency of 300 MHz. The active area is 0.630.62 mm2 and the power consumption is 17.5 mW.

An unsupervised adaptive signal processing method of principal components analysis (PCA) neural networks (NN) based on signal eigen-analysis is proposed to permit the eigenstructure analysis of lower signal to noise ratios (SNR) direct sequence spread spectrum (DS) signals. The objective of eigenstructure analysis is to estimate the pseudo noise (PN) of DS signals blindly. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is two periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. Lastly, the PN sequence can be estimated by the principal eigenvector of autocorrelation matrix. Since the duration of temporal window is two periods of PN sequence, the PN sequence can be reconstructed by the first principal eigenvector only. Additionally, the eigen-analysis method becomes inefficient when the estimated PN sequence is long. We can use an unsupervised adaptive method of PCA NN to realize the PN sequence estimation from lower SNR input DS-SS signals effectively.

Software inspection is a widely acknowledged effective quality improvement method in software development by detecting defects involved in software artifacts and removing them. In research on software inspection, constructing computer supported inspection systems is a major topic in the field. A lot of systems have been reported. However few inspection support systems for model diagrams, especially UML diagrams, have been emerged. We identified four key requirements an inspection support system for UML diagrams should have. They are as follows: 1) direct annotations are given to model diagrams, 2) version management is provided so that evolution of artifacts can be managed, 3) the whole inspection process should be supported, 4) horizontal and vertical readings are supported. This paper describes design and implementation of our inspection support system for UML diagrams to realize the four requirements.

We consider a watermark application to assist in the integrity maintenance and verification of the associated images. There is a great benefit in using WM in the context of authentication since it does not require any additional storage space for supplementary metadata, in contrast with cryptographic signatures, for instance. However there is a fundamental problem in the case of exact authentication: How to embed a signature into a cover message in such a way that it would be possible to restore the watermarked cover image into its original state without any error? There are different approaches to solve this problem. We use the watermarking method consisting of modulo addition of a mark and investigate it in detail. Our contribution lies in investigating different modified techniques of both watermark embedding and detection in order to provide the best reliability of watermark authentication. The simulation results for different types of embedders and detectors in combination with the pictures of watermarked images are given.

This paper investigates limitations of adjacent channel power ratio (ACPR) improvement in predistortion (pre-D) linearizer used with nonlinear RF power amplifiers (PAs) when the PA model is not perfectly acquired in pre-D design. The error between the physical PA and the nonlinear model is expanded by pre-D function and its power spectral density (PSD) works as limitations in ACPR improvement of the pre-D linearizer. An analytical estimation of ACPR limitations in RF PAs driven by digitally modulated input signal is derived using a formulation of autocorrelation function. The analysis technique is validated with the example of the memory polynomial PA model with the quasi-memoryless pre-D linearizer. The technique is also verified by comparing predicted ACPR limitation with measured limitation for a RF PA with 802.11g input signal.

The Hilbert transformation together with empirical mode decomposition (EMD) produces Hilbert spectrum (HS) which is a fine-resolution time-frequency representation of any nonlinear and non-stationary signal. The EMD decomposes the mixture signal into some oscillatory components each one is called intrinsic mode function (IMF). Some modification of the conventional EMD is proposed here. The instantaneous frequency of every real valued IMF component is computed with Hilbert transformation. The HS is constructed by arranging the instantaneous frequency spectra of IMF components. The HS of the mixture signal is decomposed into subspaces corresponding to the component sources. The decomposition is performed by applying independent component analysis (ICA) and Kulback-Leibler divergence based K-means clustering on the selected number of bases derived from HS of the mixture. The time domain source signals are assembled by applying some post processing on the subspaces. We have produced experimental results using the proposed separation technique.

This study shows the effectiveness of using gamma distribution in the speech power domain as a more general prior distribution for the model-based speech enhancement approaches. This model is a super-set of the conventional Gaussian model of the complex spectrum and provides more accurate prior modeling when the optimal parameters are estimated. We develop a method to adapt the modeled distribution parameters from each actual noisy speech in a frame-by-frame manner. Next, we derive and investigate the minimum mean square error (MMSE) and maximum a posterior probability (MAP) estimations in different domains of speech spectral magnitude, generalized power and its logarithm, using the proposed gamma modeling. Finally, a comparative evaluation of the MAP and MMSE filters is conducted. As the MMSE estimations tend to more complicated using more general prior distributions, the MAP estimations are given in closed-form extractions and therefore are suitable in the implementation. The adaptive estimation of the modeled distribution parameters provides more accurate prior modeling and this is the principal merit of the proposed method and the reason for the better performance. From the experiments, the MAP estimation is recommended due to its high efficiency and low complexity. Among the MAP based systems, the estimation in log-magnitude domain is shown to be the best for the speech recognition as the estimation in power domain is superior for the noise reduction.

In frequency-domain multiplexing (FDM) for TES signals, a magnetic field summation method utilizing a multi-input SQUID has the fundamental merit of small degradation of the signal-to-noise ratio. We formulated shifts of the operation point due to a common impedance and cross talk currents. These effects are evaluated for several FDM methods, and the requirements for the bandwidth and filters are summarized. The design parameters of multi-input SQUIDs and a flux locked loop driving circuits are also presented.

In this paper by using an exactly analytic approach the clock jitter in the feedback path of the continuous time Delta Sigma modulators (CT DSM) is modeled as an additive jitter noise, providing a time invariant model for a jittery CT DSM. Then for various DAC waveforms the power spectral density (psd) of the clock jitter at the output of DAC is derived and by using an approximation the in-band power of the clock jitter at the output of the modulator is extracted. The simplicity and generality of the proposed approach are the main advantages of this paper. The MATALB and HSPICE simulation results confirm the validity of the proposed formulas.