Conferences related to Spectral Analysis

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2021 IEEE Pulsed Power Conference (PPC)

The Pulsed Power Conference is held on a biannual basis and serves as the principal forum forthe exchange of information on pulsed power technology and engineering.


2020 IEEE International Conference on Image Processing (ICIP)

The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.


ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.


IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

All fields of satellite, airborne and ground remote sensing.


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Periodicals related to Spectral Analysis

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


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Most published Xplore authors for Spectral Analysis

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Xplore Articles related to Spectral Analysis

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System spectral analysis of model ultra-wideband signals

2012 6th International Conference on Ultrawideband and Ultrashort Impulse Signals, 2012

The theoretical basis and practical peculiarities of a new integrated signal analysis method called as the system spectral analysis are considered. Using the simple analytical models of ultra-wideband signals in time domain, the possibilities, advantages and disadvantages of the system spectral analysis are described. The necessity and the expediency of simultaneously application of different linear and non-linear integral transforms during ...


Discriminant spectral analysis for facial expression recognition

2008 15th IEEE International Conference on Image Processing, 2008

Spectral analysis is a recently proposed method for feature extraction. Studies show that the features extracted by spectral analysis can also be used to classification. In this paper, we propose a nonlinear feature extraction method called discriminant spectral analysis (DSA) algorithm for facial expression recognition. DSA takes both intra-locality and inter-locality structure of the data into account, and the features ...


Spectral Analysis of DNA Sequence: The Exon's Location Method

2007 15th International Conference on Digital Signal Processing, 2007

The system's analysis concepts based on signal processing methods are considered as very important tools in the genomic field's exploration. Spectral analysis of DNA sequences reveals genome's specific periodicities. Studying one organism's genome requires three steps. First, the DNA coding method: the DNA's string data are transformed into numerical signal. Second periodicities are detected by spectral analysis. Third, following the ...


Spectral analysis and unsupervised SVM classification for skin hyper-pigmentation classification

2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 2010

Data reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hyperspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented ...


Spectral Analysis of Polarimetric Weather Radar Data with Multiple Processes in a Resolution Volume

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007

A new approach for the clear air velocity estimation in weather radar is presented. A combination of nonparametric with parametric spectral analysis allows us to identify and extract multiple processes caused by different scatterer types within a single radar resolution volume. An example of clear air observed using an S-band dual polarization radar is presented. Heretofore, migrating birds and wind-blown ...


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Educational Resources on Spectral Analysis

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IEEE.tv Videos

Louis Scharf receives the IEEE Jack S. Kilby Signal Processing Medal - Honors Ceremony 2016
Micro-Apps 2013: Creating and Analyzing Multi-Emitter Environment Test Signals with COTS Equipment
An Analysis of Phase Noise Requirements for Ultra-Low-Power FSK Radios: RFIC Interactive Forum 2017
Spatial-Spectral Materials for High Performance Optical Processing - IEEE Rebooting Computing 2017
How Will Record-Setting Spectral Efficiency Impact Real 5G Systems? - Panel from NIWeek 5G Summit
IMS 2011 Microapps - A Practical Approach to Verifying RFICs with Fast Mismatch Analysis
IMS MicroApps: Multi-Rate Harmonic Balance Analysis
IMS 2011 Microapps - Yield Analysis During EM Simulation
IMS 2012 Microapps - Improve Microwave Circuit Design Flow Through Passive Model Yield and Sensitivity Analysis
Breaking Spectral and Performance Barriers for Diode Lasers - Plenary Speaker, Manijeh Razeghi - IPC 2018
Hands-On with the Snorkel Mask Camera
Maker Faire 2008: Tube Time Clocks
New Approach of Vehicle Electrification: Analysis of Performance and Implementation Issue
A Flexible Testbed for 5G Waveform Generation and Analysis: MicroApps 2015 - Keysight Technologies
Spectrum Analysis: RF Boot Camp
Surgical Robotics: Analysis and Control Architecture for Semiautonomous Robotic Surgery
IMS 2012 Microapps - Generation and Analysis Techniques for Cost-efficient SATCOM Measurements Richard Overdorf, Agilent
Similarity and Fuzzy Logic in Cluster Analysis
IMS 2011 Microapps - Remcom's XFdtd and Wireless InSite: Advanced Tools for Advanced Communication Systems Analysis
IMS 2011 Microapps - STAN Tool: A New Method for Linear and Nonlinear Stability Analysis of Microwave Circuits

IEEE-USA E-Books

  • System spectral analysis of model ultra-wideband signals

    The theoretical basis and practical peculiarities of a new integrated signal analysis method called as the system spectral analysis are considered. Using the simple analytical models of ultra-wideband signals in time domain, the possibilities, advantages and disadvantages of the system spectral analysis are described. The necessity and the expediency of simultaneously application of different linear and non-linear integral transforms during the system spectral analysis performance are explained. The system spectral analysis usage for the ultra-wideband signal time-frequency structure investigation is shown to be effective and useful.

  • Discriminant spectral analysis for facial expression recognition

    Spectral analysis is a recently proposed method for feature extraction. Studies show that the features extracted by spectral analysis can also be used to classification. In this paper, we propose a nonlinear feature extraction method called discriminant spectral analysis (DSA) algorithm for facial expression recognition. DSA takes both intra-locality and inter-locality structure of the data into account, and the features extracted by DSA have more discriminant power than traditional methods. Moreover, DSA is a nonlinear method which can effectively discover the intrinsic nonlinear manifold structure hidden in the data. Experimental results on Cohn-Kanade and JAFFE facial databases show the effectiveness of DSA algorithm.

  • Spectral Analysis of DNA Sequence: The Exon's Location Method

    The system's analysis concepts based on signal processing methods are considered as very important tools in the genomic field's exploration. Spectral analysis of DNA sequences reveals genome's specific periodicities. Studying one organism's genome requires three steps. First, the DNA coding method: the DNA's string data are transformed into numerical signal. Second periodicities are detected by spectral analysis. Third, following the evolution of this periodicity along the genome allows the exon's, or protein coding region's, location. In this paper, we expose the exon's location method: including the coding and spectral analysis steps. After, we propose a two dimensional graphical representation for the spectrum. The experimental result including a variety of coding indicator's methods reveals the importance of each base's combination, from one base to four group bases, in the exon's enhancement. The contributions in percent of each of this group's indicator are finally estimated.

  • Spectral analysis and unsupervised SVM classification for skin hyper-pigmentation classification

    Data reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hyperspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented by projection pursuit. Once the data is reduced, an SVM is used to differentiate the pathological from the healthy areas. As SVM is a supervised classification method, we propose a spatial criterion for spectral analysis in order to perform automatic learning.

  • Spectral Analysis of Polarimetric Weather Radar Data with Multiple Processes in a Resolution Volume

    A new approach for the clear air velocity estimation in weather radar is presented. A combination of nonparametric with parametric spectral analysis allows us to identify and extract multiple processes caused by different scatterer types within a single radar resolution volume. An example of clear air observed using an S-band dual polarization radar is presented. Heretofore, migrating birds and wind-blown insects that are mixed within each resolution volume caused such data to be unusable for meteorological interpretation. In this paper, we construct power spectral densities of polarimetric variables. We use the polarimetric spectral densities to differentiate the scatterer types within the observed radar resolution volume. We demonstrate how our combination of non-parametric and parametric spectral analysis can be used to retrieve the true wind velocity in situations with severe contamination by biological scatterers.

  • Rolling bearing fault diagnosis based on multipoint optimal minimum entropy deconvolution adjusted technique and direct spectral analysis

    This paper introduces a new idea of bearing fault diagnosis, which is to use the Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) Technique to filter the vibration signal, getting the output signal and directly to do spectral analysis, then to observe the fault characteristics for fault diagnosis. MOMEDA technique is an improved MED algorithm proposed by McDonald in 2016, which can enhance the repetition of pulses in the signal. In this paper, MOMEDA is applied to the fault signal processing of rolling bearing. For bearing fault signal with constant speed, we firstly use MOMEDA technology to filter the signal, then do the frequency spectrum analysis. In addition, for variable speed bearing fault signal, this paper uses MOMEDA technology combined with resampling order analysis technology to analyze variable speed signal. The simulation signals and the bearing experimental data of Case Western Reserve University were processed, from the results, the direct spectral analysis after MOMEDA processing can obtain the fault characteristic without traditional envelope analysis, for fault diagnosis of bearing is simple and effective.

  • Facial expression recognition based on the daul-tree complex wavelet transform and supervised spectral analysis

    A novel feature extraction method is proposed in this paper named DTCW-SA which is based on the dual-tree complex wavelet transform and supervised spectral analysis for facial expression recognition. Although holding the property of multi-resolution entirely, compared with traditional wavelet and Gabor transform, the attractive characteristics of DT-CWT are better orientation selectivity, approximate shift-invariance and lower redundancy. Different with existing DT-CWT, we extend images to appropriate size by interpolation before transform instead of copying the values of last row or column when decomposition of each scale. What's more, the method of supervised spectral analysis can dig nonlinear information hidden in the data. Experiments on JAFFE database and CK database illustrate the efficiency of DTCW-SA, and the highest average rate of six expressions reaches 97.8% on CK database.

  • Use of higher order spectral analysis for the identification of sudden cardiac death

    Sudden cardiac death (SCD) is a major health concern. In time domain, detection of such condition involving monitoring the 24-h ambulatory ECG is a big issue. Here, the presented work describes higher order spectral analysis carried out on the normal portion of SCD-ECG and the analyzed parameters are compared with that of healthy person ECG. The developed algorithm detects the chances of myocardial infarction in prior, on the basis of higher order spectral analysis of an SCD-ECG. Specifically, quadratic phase coupling techniques are applied on QRS complex to extract information from the SCD-ECG signal providing the basis with which a signal suggesting predisposition of the patient to suffer a cardiac arrest can be differentiated from a normal ECG signal. The algorithm requires short segments of ECG to detect the possibility of SCD. The proposed algorithm is tested on MIT-BIH database signals and the results obtained established that it is possible to analyze and predict whether an individual is susceptible to cardiac arrest or not. Certain parameters like energy are evaluated and analyzed from the normal portion of QRS complex of SCD-ECG and are compared with that of the healthy person ECG. This primitive idea can extend the research aspect in view of analyzing the ECG signal to identify the predisposition to other cardiac diseases.

  • ℓ1-norm based nonparametric and semiparametric approaches for robust spectral analysis

    The problem of frequency estimation can be solved by parametric, non- parametric or semi-parametric methods. The representative nonparametric and semiparametric methods, namely, iterative adaptive approach (IAA) and sparse learning via iterative minimization (SLIM) have been recently proposed. Since both of them are not robust to impulsive noise, their extensions, ℓ1-IAA and ℓ1-SLIM are derived to provide accurate spectral estimation in the presence of the heavy-tailed noise in this paper. In our study, the nonlinear frequency estimation problem is mapped to a linear model whose parameters are updated according to the ℓ1-norm and iteratively reweighted least squares. Simulation results are included to demonstrate the outlier resistance performance of the proposed algorithms.

  • Classification and diagnosis of broken rotor bar faults in induction motor using spectral analysis and SVM

    In this paper, we propose to detect and localize the broken bar faults in multi-winding induction motor using Motor current signature (MCSA) combined to Support Vector Machine (SVM). The analysis of stator currents in the frequency domain is the most commonly used method, because induction machine faults often generates particular frequency components in the stator current spectrum. In order to obtain a more robust diagnosis, we propose to classify the feature vectors extracted from the magnitude of spectral analysis using multi-class SVM to discriminate the state of the motor. Finally, in order to validate our proposed approach, we simulated the multi-winding induction motor under Matlab software. Promising results were obtained, which confirms the validity of the proposed approach.



Standards related to Spectral Analysis

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Jobs related to Spectral Analysis

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