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.
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.
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.
The derivation of tight estimation lower bounds is a key tool to design and assess the performance of new estimators. In this contribution, first, the authors derive a new compact Cramér–Rao bound ...(CRB) for the conditional signal model, where the deterministic parameter's vector includes a real positive amplitude and the signal phase. Then, the resulting CRB is particularised to the delay, Doppler, phase, and amplitude estimation for band-limited narrowband signals, which are found in a plethora of applications, making such CRB a key tool of broad interest. This new CRB expression is particularly easy to evaluate because it only depends on the signal samples, then being straightforward to evaluate independently of the particular baseband signal considered. They exploit this CRB to properly characterise the achievable performance of satellite-based navigation systems and the so-called real-time kinematics (RTK) solution. To the best of the authors’ knowledge, this is the first time these techniques are theoretically characterised from the baseband delay/phase estimation processing to position computation, in terms of the CRB and maximum-likelihood estimation.
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.
Dealing with low probability of intercept (LPI) radar signals in very low signal-to-noise ratio (SNR) stages requires reconnaissance systems to employ an effectively practical method for detecting ...and fully characterising the received signals. Following the aim of LPI signal detection, in this study, a novel method based on concentrating the energy of the signal in one particular row of the time–frequency domain matrix is proposed. It is shown that the proposed method enables to detect and estimate the parameters of multiple low-power linear-frequency-modulated signals. A comparison has been made between the performance of the matched filter and the proposed method, and it is shown the proposed method has approximately the same performance and low computational complexity as matched filters. This method is also able to detect the poly-phase signals such as Frank-coded signals and also non-linear-frequency-modulated signals, as well. To show the effectiveness of the method, extensive simulations are carried out with different LPI radar waveforms corrupted with additive white Gaussian noise of SNR down to −25 dB.
PDF
