The ionospheric clutter in High Frequency Surface Wave Radar (HFSWR) is the reflection of electromagnetic waves from the ionosphere back to the receiver, which should be suppressed as much as possible for the primary purpose of target detection in HFSWR. However, ionospheric clutter contains vast quantities of ionospheric state information. By studying ionospheric clutter, some of the relevant ionospheric parameters can be inferred, especially during the period of typhoons, when the ionospheric state changes drastically affected by typhoon-excited gravity waves, and utilizing the time-frequency characteristics of ionospheric clutter at typhoon time, information such as the trend of electron concentration changes in the ionosphere and the direction of the typhoon can be obtained. The results of the processing of the radar data showed the effectiveness of this method.

A digital image is often deteriorated by impulse noise that may occur during processes such as transmission. An impulse noise converts the pixel data in the image into black (0) or white (255) values at a random frequency and is also called salt-and-pepper noise. In this paper, we identify the details of pixels that have been damaged by impulse noise by analyzing the frequency of the noisy image using non-harmonic analysis (NHA). From experimental results, we can confirm that this method shows superior performance compared to the recent PSNR denoising method. In addition, we show that the proposed method is particularly superior in eliminating impulse noise in images with high noise rates.

Detecting traffic anomalies is an indispensable component of overall security architecture. As Internet and traffic data with more sophisticated attacks grow exponentially, preserving security with signature-based traffic analyzers or analyzers that do not support massive traffic are not sufficient. In this paper, we propose a novel method based on combined sketch technique and S-transform analysis for detecting anomalies in massive traffic streams. The method does not require any prior knowledge such as attack patterns and models representing normal traffic behavior. To detect anomalies, we summarize the entropy of traffic data over time and maintain the summarized data in sketches. The entropy fluctuation of the traffic data aggregated to the same bucket is observed by S-transform to detect spectral changes referred to as anomalies in this work. We evaluated the performance of the method with real-world backbone traffic collected at the United States and Japan transit link in terms of both accuracy and false positive rates. We also explored the method parameters' influence on detection performance. Furthermore, we compared the performance of our method to S-transform-based and Wavelet-based methods. The results demonstrated that our method was capable of detecting anomalies and overcame both methods. We also found that our method was not sensitive to its parameter settings.

This paper describes an ultrasonic system that estimates the cell quantity of an artificial culture bone, which is effective for appropriate treat with a composite of this material and Bone Marrow Stromal Cells. For this system, we examine two approaches for analyzing the ultrasound waves transmitted through the cultured bone, including stem cells to estimate cell quantity: multiple regression and fuzzy inference. We employ two characteristics from the obtained wave for applying each method. These features are the amplitude and the frequency; the amplitude is measured from the obtained wave, and the frequency is calculated by the cross-spectrum method. The results confirmed that the fuzzy inference method yields the accurate estimates of cell quantity in artificial culture bone. Using this ultrasonic estimation system, the orthopaedic surgeons can choose the composites that contain favorable number of cells before the implantation.

There is no clear criterion yet for evaluating wipers based on performances of wiping raindrops and visibility in forward view. In the visibility evaluation in rainy driving, it is important to examine spatial frequency and contrast of objects in forward view. Spatial frequency and contrast of image which were affected by raindrops are calculated based on them of background board which were printed stripe patterns. Variations with time of power of analysed frequency and decreased contrast are synchronized with motion of the wiper for the all experimental cases. Moreover, we executed questionnaire, and evaluated the view of the background board. These results show that the proposed methods have been validated in evaluation with wiping performance.

There is a trend towards flexible radios which are able to cope with a range of wireless communication standards. For the integrated processing of widely different signals -- including single-carrier, multi-carrier, and spread-spectrum signals -- monolithic baseband receivers need universal formats for the signal representation and channel description. We consider a reconfigurable receiver architecture building on concepts from time-frequency (TF) signal analysis. The core elements are TF signal representations in form of a Gabor expansion along with a compatible parameterization of time-variant channels. While applicable to arbitrary signal types, the TF channel parameterization offers similar advantages as the frequency domain channel description employed by orthogonal frequency-division multiplexing receivers. The freedom in the choice of the underlying analysis window function and the scalability in time and frequency facilitate the handling of diverse signal types as well as the adaptation to radio channels with different delay and Doppler spreads. Optimized window shapes limit the inherent model error, as demonstrated using the example of direct-sequence spread-spectrum signaling.

We study the high-frequency asymptotic analysis methods for the scattered fields by a cylindrically curved conducting surface excited by the incident wave on the curved surface from the convex side. We first derive the novel hybrid ray-mode solution for the scattered fields near the concave surface by solving a canonical problem formulated under the assumption that the cylindrically curved conducting surface possesses only one edge. Then by applying the ray tracing technique and the idea of Keller's GTD (Geometrical Theory of Diffraction), the solutions derived for the canonical problem are extended to account for the problem of the radiation from and the scattering by the other edge of the cylindrically curved surface. We confirm the validity of the novel asymptotic representations proposed in the present study by comparing both with the numerical results obtained from the method of moment and the experimental results performed in the anechoic chamber.

A method to detect open node defects that cannot be detected by the conventional IDDQ test method has previously been proposed employing a sinusoidal wave superposed on the DC supply voltage. The present paper proposes a strategy to improve the detectability of the test method by means of frequency analysis of the supply current. In this strategy, defects are detected by determining whether secondary harmonics of the sinusoidal wave exist in the supply current. The effectiveness of the method is confirmed by experiments on two CMOS NAND gate packages (SSIs).

In this paper, analysis and synthesis methods of emotional voice for man-machine natural interface is developed. First, the emotional voice (neutral, anger, sadness, joy, dislike) is analyzed using time-frequency representation of speech and similarity analysis. Then, based on the result of emotional analysis, a voice with neutral emotion is transformed to synthesize the particular emotional voice using time-frequency modifications. In the simulations, five types of emotion are analyzed using 50 samples of speech signals. The high average discrimination rate is achieved in the similarity analysis. Further, the synthesized emotional voice is subjectively evaluated. It is confirmed that the emotional voice is naturally generated by the proposed time-frequency based approach.

In this paper, we propose a method of linear time-varying filtering of discrete time signals. The objective of this method is to derive a component, of an input signal, whose alias-free generalized discrete time-frequency distribution [Jeong & Williams 1992] concentrates on a specific region of a time-frequency plane. The method is essentially realized by computing an orthogonal projection of an input onto a subspace that is spanned by orthonormal signals, whose distributions concentrate on the region. We show that such orthonormal signals can be derived as eigenvectors of a matrix whose components are explicitly expressed by using the kernel of the distribution and the regions. This result shows that we can design such a filter prior to processing of the input if the specific region is given as a priori. This result is a generalization of [Hlawatsch & Kozek 1994], that is originally derived for the continuous Wigner distributions, to the discrete distributions.

In this paper, we propose a novel higher order time-frequency distribution (GDH) for a discrete time signal. This distribution is defined over the original discrete time-frequency grids through a delicate discretization of an equivalent expression of a higher order distribution, for a continuous time signal, in [4]. We also present a constructive design method, for the kernel of the GDH, by which the distribution satisfies (i) the alias free condition as well as (ii) the marginal conditions. Numerical examples show that the proposed distributions reasonably suppress the artifacts which are observed severely in the Wigner distribution and its simple higher order generalization.

Scattering responses from a dielectric sphere are analyzed in the time-frequency domain by using two types of wavelet transform in order to reveal the scattering mechanisms. In the resulting time-frequency displays, various scattering processes including reflection, refraction, and diffraction can be clearly resolved and identified. The delay time of each scattering process agrees well with that obtained by the ray theory. Furthermore, the natural frequencies that are not easy to extract by the conventional Fourier analysis can be extracted.

Recently many researches concerning heart sound analysis are being processed with development of digital signal processing and electronic components. But there are few researches about recognition of heart sound, especially full cardiac cycled heart sound. In this paper, three new recognition methods about full cardiac cycled heart sound were proposed. The first method recognizes the characteristics of heart sound by integrating important peaks and analyzing statistical variables in time domain. The second method builds a database by principal components analysis on training heart sound set in time domain. This database is used to recognize new input of heart sound. The third method builds the same sort of the database not in time domain but in time-frequency domain. We classify the heart sounds into seven classes such as normal (NO) class, pre-systolic murmur (PS) class, early systolic murmur (ES) class, late systolic murmur (LS) class, early diastolic murmur (ED) class, late diastolic murmur (LD) class and continuous murmur (CM) class. As a result, we could verify that the third method is better efficient to recognize the characteristics of heart sound than the others and also than any precedent research. The recognition rates of the third method are 100% for NO, 80% for PS and ES, 67% for LS, 93 for ED, 80% for LD and 30% for CM.

This paper is, in half part, written in review nature, and presents recent theoretical results on linear-filtering and -prediction problems of nonstationary Gaussian processes. First, the basic concepts, signal and noise, are mathematically characterized, and information sources are defined by linear stochastic differential equations. Then, it is shown that the solution to a conventional problem of filtering or prediction of a nonstationary time series is, in principle, reducible to a problem, of which solution is given by Kalman-Bucy's theory, if one can solve a problem of finding the canonical representation of a Gaussian process such that it has the same covariance functions as those of the time series under consideration. However, the problem mentioned above is left open. Further, the problem of time-frequency analysis is discussed, and physical realizability of the evolutionary, i.e., the online, spectral analyzer is shown. Methods for dealing with differential operators are presented and their basic properties are clarified. Finally, some of related open problems are proposed.

Scattering data from radar targets are analyzed in the time-frequency domain by using wavelet transform, and the scattering mechanisms are investigated. The wavelet transform used here is a powerful tool for the analysis of scattering data, because it can provide better insights into scattering mechanisms that are not immediately apparent in either the time or frequency domain. First, two types of wavelet transforms that are applied to the time domain data and to the frequency domain data are defined, and the multi-resolution characteristics of them are discussed. Next, the scattering data from a conducting cylinder, two parallel conducting cylinders, a parallel-plate waveguide cavity, and a rectangular cavity in the underground are analyzed by using these wavelet transforms to reveal the scattering mechanisms. In the resulting time-frequency displays, the scattering mechanisms including specular reflection, creeping wave, resonance, and dispersion are clearly observed and identified.

In general, a time-limited signal such as a single sinusoidal waveform framed by a frame period T can be utilized for conveying a multi-level symbol in data transmission. If such a signal is analyzed by the conventional DFT (Discrete Fourier Transform) analysis, the infinite number of frequency components with frequency spacing fD = T1 is needed. This limits the accuracy with which the original frequency of the unframed sinusoidal waverform can be identified. It is especially difficult to identify two similar framed sinusoids whose frequency spacing is narrower than fD. An analytical principle for time-limited signals is therefore proposed by introducing the concept of an Extended Frame into DFT. Waveform analysis more accurate than DFT is achieved by taking into account multiple correlations between extended frames made of an input frame signal and the element frequency components corresponding to the length of each extended frame. In this approach, it is possible to use arbitrary element frequency spacing less than fD. It also allows an element frequency to be selected as a real number times of fD, rather than as an integer times of fD that is used for DFT. With this analyzing mechanism, it is verified that an input frame signal with only the frequency components which coincide with any of the element frequencies can be exactly analyzed. The disturbance caused by the input white noise is examined. As a result, it is found that the superior noise suppression function is achieved by this method over a conventional matched filter. In addition, the error caused by using a finite number of element frequencies and the A/D conversion accuracy required for sampling an input signal are examined, and it is shown that these factors need not impede practical implementation. For this reason, this principle is useful for multi-ary transmission systems, noise tolerant receivers, or systems requiring precise filtering of time limited waveforms.

In order to evaluate quantitatively TMJ sound, TMJ sound in normal subject group, CMD patient group A with palpable sounds unknown to them, CMD patient group B with palpable sounds known to them, and CMD patient group C with audible sounds were detected by a contact microphone, and frequency analysis of the power spectra was performed. The power spectra of TMJ sound of normal subject group and patient group A showed patterns with frequency values below 100 Hz, whereas the power spectra of patient groups B and C showed distinctively different patterns with peaks of frequency component exceeding 100 Hz. As regards the cumulative frequency value, the patterns for each group clearly differed from those of other groups; in particular the 80% cumulative frequency value showed the greatest difference. From these results, it is assumed that the 80% cumulative frequency value can be used as an effective indicator for quantitative evaluation of TMJ sound.

Time-frequency representations (TFRs) have been developed as tools for analysis of non-stationary signals. Signal dependent TFRs are known to perform well for a much wider range of signals than any fixed (signal independent) TFR. This paper describes customised and sequential versions of the signal dependent TFR proposed in [1]. The method, which is based on the use of the Radon transform at distance zero in the ambiguity domain, is simple and effective in dealing with both simulated and real data. The use of the described method for time-scale analysis is also presented. In addition, the paper investigates a simple technique for detection of noisy chirp signals using the Radon transfrom in the ambiguity domain.