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.

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.

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.

In this paper, we give a denotational semantics of imperative programming languages as a CafeOBJ behavioral specification. Since CafeOBJ is an executable algebraic specification language, not only execution of programs but also semi-automatic verification of programs properties can be done. We first describe an imperative programming language with integer and Boolean types, called IPL. Next we discuss about how to extend IPL, that is, IPL with user-defined types. We give a notion of equivalent programs, which is defined by using the notion of the behavioral equivalence of behavioral specifications. We show a sufficient condition for the equivalence relation of programs, which reduces the task to prove programs to be equivalent.

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.

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.

The k-edge-connectivity augmentation problem for a specified set of vertices of a graph with degree constraints, kECA-SV-DC, is defined as follows: "Given an undirected multigraph G = (V,E), a specified set of vertices S ⊆V and a function g: V → Z+ ∪{∞}, find a smallest set E' of edges such that (V,E ∪ E') has at least k edge-disjoint paths between any pair of vertices in S and such that, for any v ∈ V, E' includes at most g(v) edges incident to v, where Z+ is the set of nonnegative integers." This paper first shows polynomial time solvability of kECA-SV-DC and then gives a linear time algorithm for 2ECA-SV-DC.

This paper presents an approach to specification-based testing of concurrent programs with representative test sequences generated from Statecharts. Representative test sequences are a subset of all possible interleavings of concurrent events that define the behaviors of a concurrent program. Because a program's correctness may be determined by checking whether a program implemented all behaviors in its specification or not, the program can be regarded as being correct if it can supply an alternative execution that has the same effects as the program's behavior with each representative test sequence. Based on this observation, we employ each representative test sequence as a seed to generate an automaton that accepts its equivalent sequences to reveal the same behavior. In order to effectively test a concurrent program, the automaton such generated accepts all sequences equivalent to the representative test sequence and it is used to control test execution. This paper describes an automated process of generating automata from a Statecharts specification and shows how the proposed approach works on Statecharts specifications through some examples.

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.

IP multicast technology is highly advantageous for various applications and future needs in the Internet. Yet, it is generally recognized that the IP multicast routing protocol is fairly complex and non-scalable and requires additional maintenance and operational cost to network administrators. Although there has been much research related to IP multicast and most router vendors already support basic IP multicast routing protocols, there is still a big gap between what is reported as the state-of-the-art in the literature from what is implemented in practice. In this paper, we clarify the complexities of traditional multicast communication and describe possible solutions using the one-to-many multicast communication model called Source-Specific Multicast (SSM). We explain this communication model and the corresponding routing architecture and examine the statistics obtained for the number of multicast routing entries in our backbone router, which is connected to the international backbone. We also introduce our international collaboration activities that are contributing to the deployment and promotion of IP multicast services in the Internet.

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.

Recently, system level design languages (SLDL), which can describe both hardware and software aspects of the design, are receiving attention. Mixed-signal extensions of SLDL enable current discrete-oriented SLDLs to describe and simulate not only digital systems but also digital-analog mixed-signal systems. The synchronization between discrete and continuous behaviors is widely regarded as a critical part in the extensions. In this paper, we present an event-driven synchronization mechanism for both timed and untimed system level designs through which discrete and continuous behaviors are synchronized via AD events and DA events. We also demonstrate how the synchronization mechanism can be incorporated into the kernel of SLDL, such as SpecC. In the extended kernel, a new simulation cycle, the AMS cycle, is introduced. Three case studies show that the extended SpecC-based system level design environment using our synchronization mechanism works well with timed/untimed mixed-signal system level description.

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.

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.

This paper introduces a modified particle swarm optimizer (PSO) called the Multi-Species Particle Swarm Optimizer (MSPSO) for locating all the global minima of multi-modal functions. MSPSO extend the original PSO by dividing the particle swarm spatially into a multiple cluster called a species in a multi-dimensional search space. Each species explores a different area of the search space and tries to find out the global or local optima of that area. We test our MSPSO for several multi-modal functions with multiple global optima. Our MSPSO can successfully locate all the global optima of all the test functions, and in particular, can locate all 18 global optima of the two-dimensional Shubert function. We also examined how the performance of MSPSO depends on various algorithm parameters.

The objective of this paper is to present a decision support system which uses a computer-based procedure to detect tumor blocks or lesions in digitized medical images. The authors developed a simple method with a low computation effort to detect tumors on T2-weighted Magnetic Resonance Imaging (MRI) brain images, focusing on the connection between the spatial pixel value and tumor properties from four different perspectives: 1) cases having minuscule differences between two images using a fixed block-based method, 2) tumor shape and size using the edge and binary images, 3) tumor properties based on texture values using spatial pixel intensity distribution controlled by a global discriminate value, and 4) the occurrence of content-specific tumor pixel for threshold images. Measurements of the following medical datasets were performed: 1) different time interval images, and 2) different brain disease images on single and multiple slice images. Experimental results have revealed that our proposed technique incurred an overall error smaller than those in other proposed methods. In particular, the proposed method allowed decrements of false alarm and missed alarm errors, which demonstrate the effectiveness of our proposed technique. In this paper, we also present a prototype system, known as PCB, to evaluate the performance of the proposed methods by actual experiments, comparing the detection accuracy and system performance.

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.

The 2-vertex-connectivity augmentation problem for a specified set of vertices of a graph with degree constraints, 2VCA-SV-DC, is defined as follows: "Given an undirected graph G = (V,E), a specified set of vertices S ⊆V with |S|3 and a function g:V→Z+∪{∞}, find a smallest set E' of edges such that (V,E ∪E') has at least two internally-disjoint paths between any pair of vertices in S and such that vertex-degree increase of each v ∈V by the addition of E' to G is at most g(v), where Z+ is the set of nonnegative integers." This paper shows a linear time algorithm for 2VCA-SV-DC.