The relative motion between the transmitter and the receiver modifies the nonstationarity properties of the transmitted signal. In particular, the almost-cyclostationarity property exhibited by ...almost all modulated signals adopted in communications, radar, sonar, and telemetry can be transformed into more general kinds of nonstationarity. A proper statistical characterization of the received signal allows for the design of signal processing algorithms for detection, estimation, and classification that significantly outperform algorithms based on classical descriptions of signals.Generalizations of Cyclostationary Signal Processingaddresses these issues and includes the following key features:Presents the underlying theoretical framework, accompanied by details of their practical application, for the mathematical models of generalized almost-cyclostationary processes and spectrally correlated processes; two classes of signals finding growing importance in areas such as mobile communications, radar and sonar.Explains second- and higher-order characterization of nonstationary stochastic processes in time and frequency domains.Discusses continuous- and discrete-time estimators of statistical functions of generalized almost-cyclostationary processes and spectrally correlated processes.Provides analysis of mean-square consistency and asymptotic Normality of statistical function estimators.Offers extensive analysis of Doppler channels owing to the relative motion between transmitter and receiver and/or surrounding scatterers.Performs signal analysis using both the classical stochastic-process approach and the functional approach, where statistical functions are built starting from a single function of time.
This lively and accessible book describes the theory and applications of Hilbert spaces and also presents the history of the subject to reveal the ideas behind theorems and the human struggle that ...led to them. The authors begin by establishing the concept of 'countably infinite', which is central to the proper understanding of separable Hilbert spaces. Fundamental ideas such as convergence, completeness and dense sets are first demonstrated through simple familiar examples and then formalised. Having addressed fundamental topics in Hilbert spaces, the authors then go on to cover the theory of bounded, compact and integral operators at an advanced but accessible level. Finally, the theory is put into action, considering signal processing on the unit sphere, as well as reproducing kernel Hilbert spaces. The text is interspersed with historical comments about central figures in the development of the theory, which helps bring the subject to life.
The cell cycle-dependent nucleocytoplasmic transport of proteins is predominantly regulated by CDK kinase activities; however, it is currently difficult to predict the proteins thus regulated, ...largely because of the low prediction efficiency of the motifs involved. Here, we report the successful prediction of CDK1-regulated nucleocytoplasmic shuttling proteins using a prediction system for nuclear localization signals (NLSs). By systematic amino acid replacement analyses in budding yeast, we created activity-based profiles for different classes of importin-α-dependent NLSs that represent the functional contributions of different amino acids at each position within an NLS class. We then developed a computer program for prediction of the classical importin-α/β pathway-specific NLSs (cNLS Mapper, available at http//nls-mapper.iab.keio.ac.jp/) that calculates NLS activities by using these profiles and an additivity-based motif scoring algorithm. This calculation method achieved significantly higher prediction accuracy in terms of both sensitivity and specificity than did current methods. The search for NLSs that overlap the consensus CDK1 phosphorylation site by using cNLS Mapper identified all previously reported and 5 previously uncharacterized yeast proteins (Yen1, Psy4, Pds1, Msa1, and Dna2) displaying CDK1- and cell cycle-regulated nuclear transport. CDK1 activated or repressed their nuclear import activity, depending on the position of CDK1-phosphorylation sites within NLSs. The application of this strategy to other functional linear motifs should be useful in systematic studies of protein-protein networks.
With special relation to smart grids, this book provides clear and comprehensive explanation of how Digital Signal Processing (DSP) and Computational Intelligence (CI) techniques can be applied to ...solve problems in the power system. Its unique coverage bridges the gap between DSP, electrical power and energy engineering systems, showing many different techniques applied to typical and expected system conditions with practical power system examples. Surveying all recent advances on DSP for power systems, this book enables engineers and researchers to understand the current state of the art and to develop new tools. It presents: an overview on the power system and electric signals, with description of the basic concepts of DSP commonly found in power system problems the application of several signal processing tools to problems, looking at power signal estimation and decomposition, pattern recognition techniques, detection of the power system signal variations description of DSP in relation to measurements, power quality, monitoring, protection and control, and wide area monitoring a companion website with real signal data, several Matlab codes with examples, DSP scripts and samples of signals for further processing, understanding and analysis Practicing power systems engineers and utility engineers will find this book invaluable, as will researchers of electrical power and energy systems, postgraduate electrical engineering students, and staff at utility companies.
Signal Processing for Music Analysis Muller, M.; Ellis, D. P. W.; Klapuri, A. ...
IEEE journal of selected topics in signal processing,
2011-Oct., 2011-10-00, 20111001, 2011-10, Letnik:
5, Številka:
6
Journal Article
Recenzirano
Odprti dostop
Music signal processing may appear to be the junior relation of the large and mature field of speech signal processing, not least because many techniques and representations originally developed for ...speech have been applied to music, often with good results. However, music signals possess specific acoustic and structural characteristics that distinguish them from spoken language or other nonmusical signals. This paper provides an overview of some signal analysis techniques that specifically address musical dimensions such as melody, harmony, rhythm, and timbre. We will examine how particular characteristics of music signals impact and determine these techniques, and we highlight a number of novel music analysis and retrieval tasks that such processing makes possible. Our goal is to demonstrate that, to be successful, music audio signal processing techniques must be informed by a deep and thorough insight into the nature of music itself.
Microcombs are powerful tools as sources of multiple wavelength channels for photonic radio frequency (RF) signal processing. They offer a compact device footprint, large numbers of wavelengths, and ...wide Nyquist bands. Here, we review recent progress on microcomb-based photonic RF signal processors, including true time delays, reconfigurable filters, Hilbert transformers, differentiators, and channelizers. The strong potential of optical micro-combs for RF photonics applications in terms of functions and integrability is also discussed.
The increasing complexity of power systems and the increase in power quality (PQ) data have made it necessary to develop different and simple signal-processing tools. In this study, an ...approximate-derivative (AD) signal-processing tool based on a simple mathematical processing approach was developed for the segmentation of PQ disturbances. Although the developed method was highly effective for the segmentation of noise-free signals, the method was unable to properly handle the segmentation of noisy signals. Thus, to mitigate this situation, a denoising method based on the Sqtwolog threshold was applied to the noisy signals. After denoising, the proposed AD method effectively performed the segmentation. Subsequently, AD and a single-level discrete wavelet transform (DWT) with Daubechies 4 mother wavelets were compared through simulations, which showed that successful results can be obtained using the proposed method. Furthermore, all simulations showed that the application of AD and single-level DWT to PQ signals under different conditions resulted in similar patterns with different amplitudes. Therefore, this study provides a different approach for analysing signal using single-level DWT.
The unnatural activities of brain due to seizure events are analysed by electroencephalogram (EEG) signals which are captured from the brain. In this work, a methodology is proposed to classify the ...seizure EEG signals. In the proposed method, a novel sparse spectrum based empirical wavelet transform (SS-EWT) is applied to decompose the EEG signal into coefficients. From the obtained SS-EWT coefficients, the cross-information potential and normalised energy are extracted as features. Then these features are ranked using the RELIEFF method to obtain significant features. After ranking, these features are fed into the k-nearest neighbour (k-NN) classifier to classify EEG signals corresponding to different brain activities. In this work, the first classification problem is the classification of the seizure (S), seizure-free (F), and normal (Z) EEG signals in which obtained classification accuracy (Acc) is $96.67\%$96.67%. The second classification problem is the classification of S and Z EEG signals in which $100\%$100% Acc is achieved by the proposed method.
In this paper, we consider parameter estimation of high-order polynomial-phase signals (PPSs). We propose an approach that combines the cubic phase function (CPF) and the high-order ambiguity ...function (HAF), and is referred to as the hybrid CPF-HAF method. In the proposed method, the phase differentiation is first applied on the observed PPS to produce a cubic phase signal, whose parameters are, in turn, estimated by the CPF. The performance analysis, carried out in the paper, considers up to the tenth-order PPSs, and is supported by numerical examples revealing that the proposed approach outperforms the HAF in terms of the accuracy and signal-to-noise-ratio threshold. Extensions to multicomponent and multidimensional PPSs are also considered, all supported by numerical examples. Specifically, when multicomponent PPSs are considered, the product version of the CPF-HAF outperforms the product HAF (PHAF) that fails to estimate parameters of components whose PPS order exceeds three.
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