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Flexible organic artificial synapse with ultrashort-term plasticity for tunable time-frequency signal processing

Chinese Chemical Letters 2023年第12期34卷 236-240页

作者： Yao Ni Lu Liu Jiulong Feng Lu Yang Wentao Xu Institute of Photoelectronic Thin Film Devices and Technology Key Laboratory of Photoelectronic Thin Film Devices and Technology of TianjinCollege of Electronic Information and Optical EngineeringEngineering Research Center of Thin Film Photoelectronic Technology of Ministry of EducationSmart Sensing Interdisciplinary Science CenterNankai UniversityTianjin 300350China Shenzhen Research Institute of Nankai University Shenzhen 518000China

A flexible organic artificial synapse(OAS)for tunable time-frequency signal processing was fabricated using a tri-blend film that had been fabricated using a one-step solution *** combined with a chitosan film,this OAS can achieve an ultrashort-term retention time of only 49 ms for instant electricalcomputing applications;this is the shortest retention time yet achieved by a two-terminal artificial *** array of these flexible OASs can withstand a high bending strain of 5%for 10^(4) cycles;this deformation endurance is a new *** OAS was also sensitive to the number and frequency of electrical inputs;a tunable cut-off frequency enables dynamic filtering for use in image detail *** work provides a new resource for development of future neuromorphic computing devices。

关键词： Flexible organic artificial synapse Tri-blend film time-frequency signal processing Ultrashort-term plasticity Dynamic filtering

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time-frequency signal processing: Today and future

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DIGITAL signal processing 2021年 119卷 103216-103216页

作者： Akan, Aydin Cura, Ozlem Karabiber Izmir Univ Econ Dept Elect & Elect Engn Izmir Turkey Izmir Katip Celebi Univ Dept Biomed Engn Izmir Turkey

Most real-life signals exhibit non-stationary characteristics. processing of such signals separately in the time-domain or in the frequency-domain does not provide sufficient information as their spectral properties change over time. Traditional methods such as the Fourier transform (FT) perform a transformation from time-domain to frequency-domain allowing a suitable spectral analysis but looses the spatial/temporal information of the signal components. Hence, it is not easy to observe a direct relationship between the time and frequency characteristics of the signal. This makes it difficult to extract useful information by using only time- or frequency-domain analysis for further processing purposes. To overcome this problem, joint time-frequency (TF) methods are developed and applied to the analysis and representation of non-stationary signals. In addition to revealing a time-dependent energy distribution information, TF methods have successfully been utilized in the estimation of some parameters related to the analyzed signals. In this paper, we briefly summarize the existing methods and present several state-of-the-art applications of TF methods in the classification of biomedical signals. We also point out some future perspectives for the processing of non-stationary signals in the joint TF domain. (C) 2021 Elsevier Inc. All rights reserved.

关键词： time-frequency analysis (TFA) time-frequency distributions (TFD) Non-stationary signals time-frequency signal processing Machine learning Deep learning

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time-frequency analysis of non-stationary neutron noise in a small modular nuclear reactor

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ANNALS OF NUCLEAR ENERGY 2020年 136卷 107009-000页

作者： Tayefi, Shima Pazirandeh, Ali Saadi, Mohsen Kheradmand Islamic Azad Univ Sci & Res Branch Dept Nucl Engn North Ashrafi Esfahani Blvd Tehran Iran

In this work, for the first time, the time-frequency analysis technique is proposed to analyze a non-stationary neutron noise. The work investigates on an appropriate way to solve the neutron noise equation in the time-frequency domain in an SMR reactor. To actualize this procedure, a computer program has been developed. This program is based on the use of three main modules. The first is a static calculation by a finite difference method and a power iteration algorithm for providing a database of the required neutronic parameters. The second is the neutron noise calculations in the time-frequency domain and the extraction of the noise characteristics by using the Wigner-Ville distribution. The third is the categorization of the neutron noise sources based on their extracted characteristics such as the source type, the source location, the number of sources, the time of the occurrence, the instance frequency and the noise amplitude. In the first module, the static calculations are benchmarked by the referenced values. Then, in order to validate the static noise calculation, the obtained result is compared to the result of the noise calculation in zero frequency. The results of adjoint and direct methods are compared with each other to confirm the validity of the dynamic noise calculation. The obtained results show a good agreement with each other. In addition, the obtained results of the proposed method and Fourier method are compared with each other at the initial time. Moreover, various noise sources, including mono and multi-frequency, are solved in the time-frequency domain to evaluate the proposed method's capability. All results show that the proposed method has a good capability to solve neutron noise equation. The results obtained show that the time-frequency method gives comprehensive results in comparison with frequency methods. Finally, it is concluded that analyzing the noise sources in the time-frequency domain can be used as a remarkable approach to in

关键词： Neutron noise Noise analysis Wigner-Ville distribution time-frequency signal processing

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Feature Extraction in Fractional Fourier Domain for Classification of Passive Sonar signals

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JOURNAL OF signal processing SYSTEMS FOR signal IMAGE AND VIDEO TECHNOLOGY 2019年第5期91卷 511-520页

作者： Jahromi, Mehdi Shadlou Bagheri, Vahid Rostami, Habib Keshavarz, Ahmad Persian Gulf Univ Sch Engn Elect Engn Dept Bushehr 75168 Iran Persian Gulf Univ Sch Engn Comp Engn Dept Bushehr 75168 Iran

The acoustic signals radiated from the marine vessels contain information about their machinery characteristics that can be useful for the detection and classification purposes. To achieve reliable accuracy in classification task, informative discriminant features should be extracted from received signals. STFT (Short time Fourier Transform) is the most basic and popular signal processing method in the classification process of passive sonar signals, but it suffers from some fatal shortages. In this work, we present an improved spectrogram based on the windowed fractional-Fourier transform of the acoustic signal with the optimal FrFT order, which accounts for signals with multiple non-stationary components. The discriminating capability of two groups of features extracted from the processed signal, using PCA and LDA techniques, have been compared. The achieved results declare the significant improvement in the classification accuracy by the new proposed method using the LDA feature extraction technique and the proper order of FrFT. However, in order to eliminate the constraints of searching for the proper FrFT-order and increasing the reliability and stability of practical sonar classification systems, a parallel combination of the proposed method is introduced.

关键词： Passive sonar signal Classification time-frequency signal processing STFrFT-LDA

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Fast Spectrogram Inversion Using Multi-Head Convolutional Neural Networks

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IEEE signal processing LETTERS 2019年第1期26卷 94-98页

作者： Arik, Sercan O. Jun, Heewoo Diamos, Gregory Baidu Silicon Valley Artificial Intelligence Lab Sunnyvale CA 94089 USA

We propose the multi-head convolutional neural network (MCNN) for waveform synthesis from spectrograms. Nonlinear interpolation in MCNN is employed with transposed convolution layers in parallel heads. MCNN enables significantly better utilization of modern multi-core processors than commonly used iterative algorithms like Griffin-Lim, and yields very fast (more than 300 x real time) runtime. For training of MCNN, we use a large-scale speech recognition dataset and losses defined on waveforms that are related to perceptual audio quality. We demonstrate that MCNN constitutes a very promising approach for high-quality speech synthesis, without any iterative algorithms or autoregression in computations.

关键词： reconstruction deep learning convolutional neural networks short-time Fourier transform spectrogram time-frequency signal processing speech synthesis

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Algebraic representation for fractional Fourier transform on one-dimensional discrete signal models

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IET signal processing 2018年第2期12卷 143-148页

作者： Zhang, Zhi-Chao Sichuan Univ Coll Math Chengdu 610065 Sichuan Peoples R China

Algebraic signal processing provides a general framework for studying theoretical problems (sampling, transform domain analysis etc.) in the classical signal processing. In this study, the authors extend algebraic representation for the conventional Fourier transform (FT) to the fractional FT (FRFT) domain, from which the algebraic structures for the FRFT on infinite and finite one-dimensional signal models are obtained. They show that FRFTs on the infinite and finite discrete-time (DT) signal models, respectively, are none other than the DTFRFT and the closed-form discrete FRFT. They also derive FRFTs on the infinite and finite discrete-nearest neighbour signal models, and finally they discuss their applications in optical and time-frequency signal processing.

关键词： signal processing algebra Fourier transforms algebraic representation algebraic signal processing fractional Fourier transform one-dimensional discrete signal models FRFT finite discrete-time signal models infinite discrete-time signal models time-frequency signal processing

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Informed Single Channel Speech Separation With time-frequency Exemplar GMM-HMM Model

Informed Single Channel Speech Separation With Time-Frequenc...

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IEEE International Conference on Digital signal processing (DSP)

作者： Wang, Qi Woo, W. L. Dlay, S. S. Chin, C. S. Gao, Bin Newcastle Univ Sch Elect & Elect Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England Newcastle Univ Sch Marine & Sci Technol Newcastle Upon Tyne NE1 7RU Tyne & Wear England Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China

ISBN: (纸本)9781479980581

In recent studies, the problem of Single Channel Speech Separation (SCSS) have been efficiently tackled by introducing additional cues from the original target source in the form of Informed Source Separation (ISS). In this paper, a more realistic situation is considered where an additional user/listener generated Exemplar source is introduced to aid the separation process instead of using the original target source. The Exemplar source consists of patterns that need to be transformed, extracted, regulated and calibrated to generate an utterance dependent (UD) model that could statistically represent the target source. The proposed method uses general speaker independent (SI) features along with the generated UD features are modelled and combined in a joint probability model to achieve reliable separation. Unlike most model-based approaches, the proposed method does not require Speaker Dependent training on individual sources of the mixture, and is therefore much more efficient and less restrictive.

关键词： HMM GMM Factorial HMM Speech Separation Informed Source Separation time-frequency signal processing

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Wavelet basis selection for enhanced speech parametrization in speaker verification

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INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY 2014年第1期17卷 27-36页

作者： Ganchev, Todor Siafarikas, Mihalis Mporas, Iosif Stoyanova, Tsenka Tech Univ Varna Dept Elect Varna 9010 Bulgaria Univ Patras Dept Elect & Comp Engn Rion 26500 Greece Univ Patras Dept ECE Patras 26500 Greece Technol Educ Inst Patras Dept Mech Engn Patras 26334 Greece

We study the inherent properties of nine wavelet functions and subsequently evaluate their applicability as basis functions in a speech parametrization scheme that is advantageous for speaker verification. Particularly, the inherent properties of nine candidate basis functions are initially analysed and their advantages and disadvantages are discussed. Subsequently, all candidates are employed in a well-proven speech parametrization scheme, and the resulting speech features are computed. Finally, these speech features are evaluated in a common experimental set-up on the speaker verification task. The experimental results, obtained on two well-known speaker recognition databases, show that the Battle-Lemari wavelet function is the most advantageous one, among all other functions evaluated here, since it leads to the most beneficial speech descriptors. When compared to the baseline Mel-frequency cepstral coefficients (MFCC), a relative reduction of the equal error rate by 4.2 % was observed on the 2001 NIST speaker recognition evaluation database, and by 2.3 % on the Polycost speaker recognition database.

关键词： time-frequency signal processing Wavelets Speech analysis Parametric representation of speech Speaker recognition

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A nonparametric feature for neonatal EEG seizure detection based on a representation of pseudo-periodicity

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MEDICAL ENGINEERING & PHYSICS 2012年第4期34卷 437-446页

作者： Stevenson, N. J. O'Toole, J. M. Rankine, L. J. Boylan, G. B. Boashash, B. Natl Univ Ireland Univ Coll Cork Neonatal Brain Res Grp Cork Ireland Univ Deusto DeustoTech Bilbao Spain Qatar Univ Coll Engn Doha Qatar Future Assist Financial Serv Gold Coast Australia Univ Queensland Clin Res Ctr Brisbane Qld Australia

Automated methods of neonatal EEG seizure detection attempt to highlight the evolving, stereotypical, pseudo-periodic, nature of EEG seizure while rejecting the nonstationary, modulated, coloured stochastic background in the presence of various EEG artefacts. An important aspect of neonatal seizure detection is, therefore, the accurate representation and detection of pseudo-periodicity in the neonatal EEG. This paper describes a method of detecting pseudo-periodic components associated with neonatal EEG seizure based on a novel signal representation;the nonstationary frequency marginal (NFM). The NFM can be considered as an alternative time-frequency distribution (TFD) frequency marginal. This method integrates the TFD along data-dependent, time-frequency paths that are automatically extracted from the TFD using an edge linking procedure and has the advantage of reducing the dimension of a TFD. The reduction in dimension simplifies the process of estimating a decision statistic designed for the detection of the pseudo-periodicity associated with neonatal EEG seizure. The use of the NFM resulted in a significant detection improvement compared to existing stationary and nonstationary methods. The decision statistic estimated using the NFM was then combined with a measurement of EEG amplitude and nominal pre- and post-processing stages to form a seizure detection algorithm. This algorithm was tested on a neonatal EEG database of 18 neonates, 826 h in length with 1389 seizures, and achieved comparable performance to existing second generation algorithms (a median receiver operating characteristic area of 0.902;IQR 0.835-0.943 across 18 neonates). (C) 2011 IPEM. Published by Elsevier Ltd. All rights reserved.

关键词： Neonatal EEG Fourier transform time-frequency distributions Nonstationary Matched filter Neonate Seizure detection time-frequency signal processing

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Instantaneous frequency Tracking under Model Uncertainty via Dynamic Model Averaging and Particle Filtering

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2011年第6期10卷 1810-1819页

作者： Liu, Bin Shenzhen Kuang Chi Inst Adv Technol Shenzhen 518000 Guangdong Peoples R China

We consider a state-space modeling approach for online estimation of a signal's instantaneous frequency (IF). We take into account the general case wherein the IF may vary over time irregularly and, therefore, given several plausible models, there is uncertainty which is the best model to use. We address this dynamical model uncertainty problem using a strategy called dynamic model averaging (DMA), in which a set of state-space models is combined with a Markov chain model for the correct model. The model transition process is specified in terms of forgetting, leading to a highly parsimonious representation. We provide six candidate models, each representing a specific evolution law of the IF. A novel particle filtering algorithm is proposed to implement this DMA strategy. Simulation results show that our method can yield accurate IF estimates in challenging settings, where the IF structure embedded in the signal changes abruptly and irregularly in a low signal-noise-ratio environment.

关键词： Instantaneous frequency tracking model uncertainty particle filtering time-frequency signal processing

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