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An important aspect of the real-time image processing applications using orthogonal moments is the speed of their computation. They can be computed directly or via geometric moments (GMs). One of the fast methods to generate GMs is the usage of the cascaded digital filter outputs. However, a concern of this design is that the outputs of the digital filters, which operate as accumulators, increase exponentially as the orders of moment increase. It is shown in previous works, for an N × N image, the digital filter outputs are sampled at N or later instances. In this paper, we propose a new formulation to solve this problem by using a set of lower digital filter output values as the order of moments increases. This is achieved by sampling the digital filter outputs at earlier instances, N, N - 1, N - 2,…,N - p, where p is the maximum moment order. This method enables the usage of the lower digital filter output values for higher-order moments. As the moment order approaches N, the number of additions is approximately 45% less for the proposed method when compared with the existing methods, resulting in a corresponding reduction in computation time.

Noise reduction and signal separation are important functions of acoustic signal processing. This study presents a detailed analysis for designing an acoustic signal processing procedure based on the time-reversal method. For some applications, setting transducers to retransmit at source locations is impracticable. Modeling a wave propagation path between two points using impulse response function is one way to overcome this limitation. This paper introduces alternative methods to calculate impulse response function, including an adaptive digital filter, deconvolution with singular value decomposition and Tikhonov regularization, and correlation. A discussion is also provided on the applicable frequency range and anti-noise ability of the impulse response functions obtained by all three techniques through simulation, and subsequently applies them to the designed time reversal process to enhance the signal-to-noise ratio (SNR) and restore source signals through experimentation. The conclusions of this study are given based on the level of accuracy using the SNR and correlation coefficient as indicators, and the computation time required by alternative methods is also an important factor to be discussed for real-time system design. Results prove that the proposed passive time reversal process is capable of enhancing the SNR and restoring the source signal. The alternative methods of calculating the impulse response function offer various advantages, and should be selected according to the application. If the time-cost is the first consideration and there is no dominant noise source, then correlation is the best choice for calculating impulse response function. If completeness of the reconstructed signal is the key point, the optimal deconvolution process is appropriate. If noise reduction is the highest priority in extracting a useful signal from noisy environments while ensuring acceptable restoration capability and computation time, an adaptive digital filter is suitable.

This paper studies the extent to which real estate prices impact common stock prices in Hong Kong. Real estate-related firms account for over 30 percent of Hong Kong's stock market capitalization. The real estate markets are therefore major determinants of changes in common stock prices. This study, using data during the 1974-1998 period, not only supports empirically that both unexpected changes in residential and office property prices are important determinants of the change in stock prices for Hong Kong, it also finds that the property and stock price series are cointegrated. Impulse response function based on an error-correction VAR model is used to examine the dynamic relationships between real estate and common stock prices.

This study uses a cointegration analysis and vector autoregressive models to investigate the transmission of stock price movements among Taiwan and its major trading partners, Hong Kong, Japan and the United States. The results of Johansen cointegration test indicate that four stock markets considered are cointegrated with one cointegrating vector, which violates the semi-strong form of the market efficiency hypothesis. The results from Granger-causality test based on error-correction models suggest the relative leading roles of the U.S. and Japanese markets in driving fluctuations in the other two markets. In order to capture the impacts of the economic shocks, two dummy variables are incorporated into the models taking into account the U.S. stock crash of October 1997 (D97) and the previous spreading Asian finance crises (Dac). The results indicate that D97 significantly affects the U.S. stock market, but shows no significant impact on the others. The Dac, however, shows significant impacts on both the Japanese and the U.S. markets. The robustness of the relative leading roles of the U.S. and Japanese markets are further supported by the variance decompositions and impulsive response functions indicators. The Taiwan and Hong Kong markets are somewhat affected more by regional countries such as Japan than by the U.S.

The relation between China's RMB exchange rate and US real economy has recently become a hotly debated issue among scholars, researchers, and policymakers. Using monthly data from Nov. 2001–Nov. 2010, this paper employs the cointegration test and VECM model to capture the relationships among the Chinese RMB exchange rate, US–China bilateral trade, and the US unemployment rate. Results indicate that the US unemployment rate is negatively correlated with the RMB exchange rate. Impulse response analysis discloses the transmission mechanism of RMB appreciation: an appreciation of the RMB leads to a drop in US–China bilateral trade, which associates with a slight rise in the US unemployment rate. A potential explanation for these unconventional results lie in the complementary industrial structures between the two countries. Some policy suggestions are made at the end.

Existing TDMA-based MAC protocols for wireless sensor networks are not specifically built to consider communication channels that are prone to fading. We describe the impact of periodically changing environment on small-scale fading effects in industrial indoor wireless networks. Using a site-specific ray tracer, we show that the position of nodes and the periodic movements of objects with constant velocities in the environment have significant impact on signal fading. Finding that fading is approximately periodic, we propose a TDMA-based MAC protocol for wireless sensor networks built for industrial applications that uses link state dependent scheduling. In our approach, nodes gather samples of the channel quality and generate prediction sets from the sample sets in independent slots. Using the prediction sets, nodes only wake up to transmit/receive during scheduled slots that are predicted to be clear and sleep during scheduled slots that may potentially cause a transmitted signal to fade. We simulate our proposed protocol and compare its performance with the well published Z-MAC protocol. We found that our protocol significantly improves packet throughput and energy consumption as compared to Z-MAC. We also found that in conditions which are not perfect under our assumptions, the performance of our protocol degrades gracefully.

From the viewpoint of statistical inverse problems, identification of transfer functions in feedback models is applied for neurodynamics of somatosensory cortices, and brain communication among active regions can be expressed in terms of transfer functions. However, brain activities have been investigated mainly by averaged waveforms in the conventional magnetoencephalography analysis, and thus brain communication among active regions has not yet been identified. It is shown that brain communication among two more than three brain regions is determined, when fluctuations related to concatenate averaged waveforms can be obtained by using a suitable blind source separation method. In blind identification of feedback model, some transfer functions or their impulse responses between output variables of current dipoles corresponding to active regions are identified from reconstructed time series data of fluctuations by the method of inverse problem. Neurodynamics of somatosensory cortices in 5 Hz median nerve stimuli can be shown by cerebral communication among active regions of somatosensory cortices in terms of impulse responses of feedback model.

The Viscous Grain Shearing (VGS) theory predicts the existence of a compressional wave and a shear wave in an unconsolidated marine sediment. Although it is known that, subject to certain constraints, the shear wave satisfies causality, the causal nature of the compressional wave is less well understood. In this paper, the VGS compressional-wave speed and attenuation are examined in three frequency regimes, where it is shown that they follow approximately frequency power laws. It is then proved that the VGS propagation factor, which is a combination of the phase speed and attenuation, is a causal transform: its inverse Fourier transform is zero for all times prior to the onset of the source. The derivation of this result, which is a necessary condition if the VGS compressional wave equation is to satisfy causality, includes the development of a technique for evaluating a class of previously unknown integrals. This integration procedure relies on a limiting argument combined with certain Fourier transforms, the latter taking the form of “improper” integrals, which, it is shown, can be expressed explicitly based on the properties of generalized functions. An expression for the impulse response of the VGS compressional wave is also developed and shown to satisfy causality, although the transition from zero to the peak level is abrupt, quite unlike the perfectly smooth behavior exhibited by the impulse response of the VGS shear wave, which is maximally flat everywhere in the medium at the instant the source is activated.

In acoustics, knowledge of the impulse response of a system is often important. While impulse responses may be measured, they may also be predicted using numerical methods. This work considers the generation of impulse responses from frequency-domain finite element solutions. It is shown that these impulse responses, obtained by inverse Fourier transformation, are noncausal. Through error analysis, it is demonstrated that the noncausality can be reduced by increasing the duration of a source signal used to excite a simulated system. It is found that increasing the source signal duration increases the dispersion-related phase error present in the simulated impulse responses. The findings of this study are used to simulate an impulse response for a system with a nonuniform, frequency-dependent, complex impedance boundary condition.

In this paper, the sound field due to a line source moving above a homogeneous semi-infinite fluid medium emitting an arbitrary time signal is deduced. The source moves with a constant velocity parallel to the flat interface at a fixed height. The expression for the velocity potential is obtained from the impulse response of the stationary line source by including the motion of the source in the source density function. The resulting sound field is expressed in terms of convolution integrals. The correctness of the formulas is verified by comparing them with the results known for the case of a continuous harmonic line source. Two examples, the sound field due to a delta pulse and a Gaussian pulse, are computed and presented. Finally, the relation between the solutions obtained here and the ones obtained when the source moves above an absorbing impedance surface is shown.