Relevant bibliographies by topics / Module spectrogram

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Module spectrogram'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Module spectrogram"

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Sýkorová, Libuše, Oldřich Šuba, and Jana Knedlova. "Laser Micro-Machining and Temperature Field Simulation." Key Engineering Materials 581 (October 2013): 322–25. http://dx.doi.org/10.4028/www.scientific.net/kem.581.322.

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The parametric thermal field analyses were realized by the finite element method. The analysis was run in the software solver COSMOS/DesignSTAR thermal. The thermal module makes possible to realize cases of the thermal dependences on the material properties. Material data can be entered as a function of the temperature. The thermal and physical characteristic of the polymeric materials are changed very expressively. The output of the analysis was temperature distribution described by various colors and assigned values in the spectrogram.
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Hu, Kang, Shaoming Jin, Hong Ding, and Jin Cao. "The Bridge between Screening and Assessment: Establishment and Application of Online Screening Platform for Food Risk Substances." Journal of Food Quality 2021 (September 23, 2021): 1–9. http://dx.doi.org/10.1155/2021/2275471.

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In order to improve the risk identification ability of the technical support system of food safety supervision, an online screening platform for food risk substances (hereafter referred to as “platform”) was established. The platform aims at the qualitative analysis of unknown compounds and consists of three parts: a standard spectrum library, screening model, and online comparison module. The standard library contains the standard spectra of 527 food risk substances by high-performance liquid chromatography/high-resolution mass spectrometry. The screening comparison algorithm, the core of the screening model, is obtained through the improvement of the existing spectral library search algorithm. The inspector uploads the original spectrum file through the online comparison module; the online comparison module calls the corresponding script to convert the original spectrum file into a standard spectrum file and then uses the screening and comparison algorithm to achieve online real-time comparison. The comparison results are used to determine whether the sample to be tested contains the food risk substances contained in the standard library, so as to realize the preliminary screening of potential food risk substances. The platform supports the spectrogram data format of mainstream instrument manufacturers. The standard spectrogram database can be coconstructed and shared by cooperative laboratories to effectively enrich the types of food risk substances. Through laboratory comparison, data calibration, and model optimization, the screening accuracy of the platform can reach more than 97%. The platform adopts the Internet online screening method, which greatly facilitates the risk investigation and control of national food safety inspection and testing institutions. At the same time, the construction of the screening platform for food risk substances based on high-performance liquid chromatography/high-resolution mass spectrometry, the Internet, big data, and other technologies will provide a new technical means for food safety risk management and control. Hence, it can build a bridge between the screening of risk substances and illegally added substances, as well as risk assessment, risk management, and control.
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Jiang, Weiheng, Xiaogang Wu, Yimou Wang, Bolin Chen, Wenjiang Feng, and Yi Jin. "Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems." Sensors 21, no. 1 (January 1, 2021): 231. http://dx.doi.org/10.3390/s21010231.

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Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.
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Seo, Minji, and Myungho Kim. "Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition." Sensors 20, no. 19 (September 28, 2020): 5559. http://dx.doi.org/10.3390/s20195559.

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Speech emotion recognition (SER) classifies emotions using low-level features or a spectrogram of an utterance. When SER methods are trained and tested using different datasets, they have shown performance reduction. Cross-corpus SER research identifies speech emotion using different corpora and languages. Recent cross-corpus SER research has been conducted to improve generalization. To improve the cross-corpus SER performance, we pretrained the log-mel spectrograms of the source dataset using our designed visual attention convolutional neural network (VACNN), which has a 2D CNN base model with channel- and spatial-wise visual attention modules. To train the target dataset, we extracted the feature vector using a bag of visual words (BOVW) to assist the fine-tuned model. Because visual words represent local features in the image, the BOVW helps VACNN to learn global and local features in the log-mel spectrogram by constructing a frequency histogram of visual words. The proposed method shows an overall accuracy of 83.33%, 86.92%, and 75.00% in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Berlin Database of Emotional Speech (EmoDB), and Surrey Audio-Visual Expressed Emotion (SAVEE), respectively. Experimental results on RAVDESS, EmoDB, SAVEE demonstrate improvements of 7.73%, 15.12%, and 2.34% compared to existing state-of-the-art cross-corpus SER approaches.
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Lee. "Music and Voice Separation Using Log-Spectral Amplitude Estimator Based on Kernel Spectrogram Models Backfitting." Journal of the Acoustical Society of Korea 34, no. 3 (2015): 227. http://dx.doi.org/10.7776/ask.2015.34.3.227.

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Iñiguez-Garín, E., D. Hiriart, J. Ramirez-Velez, J. M. Núñez-Alfonso, J. Herrera, and J. Castro-Chacón. "A new spectropolarimeter for the San Pedro Martir National Observatory." Proceedings of the International Astronomical Union 9, S302 (August 2013): 154–55. http://dx.doi.org/10.1017/s1743921314001951.

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AbstractWe present the design of a stellar spectropolarimeter to measure the magnetic field of point sources. The polarization module interfaces the Boller and Chivens (B&Ch) intermediate-low resolution (R ~ 500 − 4000) spectrograph to the 2.1-m telescope of the San Pedro Martir National Astronomical Observatory in Mexico. The module uses a Savart plate to split the beam into two orthogonal states of polarization and a quarter (half) waveplate to measure circular (linear) polarization. The module is mounted to the telescope and it feeds the spectrograph through a set of four fibers, two for the polarized star images and two for the spectrograph calibration lamp. The instrument will be capable of measuring polarization in spectral lines to determine the longitudinal and transversal fields in magnetic stars.
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Djermanov, I., S. Iordanova, and I. Koleva. "Spectrograph coupled with CCD module for high resolution spectroscopy measurements." Journal of Physics: Conference Series 63 (April 1, 2007): 012029. http://dx.doi.org/10.1088/1742-6596/63/1/012029.

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Ozaki, Shinobu. "Design study of an image slicer module for a multiobject spectrograph." Journal of Astronomical Telescopes, Instruments, and Systems 5, no. 03 (June 6, 2019): 1. http://dx.doi.org/10.1117/1.jatis.5.3.035001.

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KIM, KANG-MIN, BE-HO JANG, INWOO HAN, JEONG GYUN JANG, HYUN CHUL SUNG, MOO-YOUNG CHUN, SIEK HYUNG, TAE-SEOG YOON, and STEVEN S. VOGT. "DESIGN AND MANUFACTURING OF THE CASSEGRAIN INTERFACE MODULE OF THE BOAO ECHELLE SPECTROGRAPH." Journal of The Korean Astronomical Society 35, no. 4 (December 1, 2002): 221–27. http://dx.doi.org/10.5303/jkas.2002.35.4.221.

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Bazovkin, V. M., N. A. Valisheva, A. A. Guzev, V. M. Efimov, A. P. Kovchavtsev, G. L. Kuryshev, I. I. Lee, V. G. Polovinkin, and A. S. Stroganov. "Fast IR spectrograph (0.5–3.0 μm) based on a hybrid 1 × 384 InAs module." Optoelectronics, Instrumentation and Data Processing 43, no. 4 (August 2007): 332–36. http://dx.doi.org/10.3103/s875669900704005x.

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Dissertations / Theses on the topic "Module spectrogram"

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Kubánková, Anna. "Automatická klasifikace digitálních modulací." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-233424.

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This dissertation thesis deals with a new method for digital modulation recognition. The history and present state of the topic is summarized in the introduction. Present methods together with their characteristic properties are described. The recognition by means of artificial neural is presented in more detail. After setting the objective of the dissertation thesis, the digital modulations that were chosen for recognition are described theoretically. The modulations FSK, MSK, BPSK, QPSK, and QAM-16 are concerned. These modulations are mostly used in modern communication systems. The method designed is based on the analysis of module and phase spectrograms of the modulated signals. Their histograms are used for the examination of the spectrogram properties. They provide information on the count of carrier frequencies in the signal, which is used for the FSK and MSK recognition, and on the count of phase states on which the BPSK, QPSK, and QAM-16 are classified. The spectrograms in that the characteristic attributes of the modulations are visible are obtained with the segment length equal to the symbol length. It was found that it is possible to correctly recognize the modulation with the known symbol length at the signal-to-noise ratio at least 0 dB. That is why it is necessary to detect the symbol length prior to the spectrogram calculation. Four methods were designed for this purpose: autocorrelation function, cepstrum analysis, wavelet transform, and LPC coefficients. These methods were algorithmized and analyzed with signals disturbed by the white Gaussian noise, phase noise and with signals passed through a multipass fading channel. The method of detection by means of cepstrum analysis proved the most suitable and reliable. Finally the new method for digital modulation recognition was verified with signals passed through a channel with properties close to the real one.
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Millioz, Fabien. "Deux approches de segmentation temps-fréquence : détection par modèle statistique et extraction de contour par le champ de vecteurs de réallocation." Phd thesis, Grenoble INPG, 2009. http://tel.archives-ouvertes.fr/tel-00421599.

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Les représentation temps-fréquence montrent l'évolution spectral d'un signal dans le temps. L'objectif de ces travaux de recherche est de proposer deux principes de segmentation du plan temps-fréquence, cherchant à déterminer quelles sont les zones temps-fréquence présentant un intérêt relatif au signal analysé.

La première méthode s'appuie sur une méthode statistique, modélisant le signal analysé par un signal d'intérêt à segmenter perturbé par un bruit blanc gaussien additif de variance inconnue. Le but est de déterminer le support temps-fréquence, ensemble des points sur lesquels l'énergie du signal à segmenter est répartie. Une détection de type Neyman-Pearson à probabilité de fausse alarme fixée permet de détecter les points temps-fréquence contenant du signal, à niveau de bruit connu. L'algorithme proposé est itératif, estimant le niveau de bruit à partir des points non segmentés, ce niveau de bruit servant à détecter de nouveaux points contenant du signal. Un critère basé sur le kurtosis spectral des points non segmentés permet de définir l'arrêt des itérations.

La seconde méthode est basée sur le principe de la réallocation du spectrogramme, en tant que source d'information sur le spectrogramme. La réallocation déplace l'énergie du spectrogramme sur le centre de gravité local de l'énergie. Aux frontière d'un motif temps-fréquence, l'énergie sera déplacée vers l'intérieur du motif. Ainsi, les vecteur
s de réallocation, décrivant le déplacement de l'énergie du pectrogramme par la réallocation, sont localement parallèles sur la frontière d'un motif. Nous définissons alors un « degré de parallélisme » pour chaque vecteur, égal au nombre de ses vecteurs voisins qui lui sont parallèles. Un algorithme de type suivi de crête, parcourant le plan temps-fréquence en suivant les maximums de ce degré de parallélisme, construit alors un contour entourant le motif temps-fréquence.
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Aragonda, Haricharan. "Demodulation of Narrowband Speech Spectrograms." Thesis, 2014. http://hdl.handle.net/2005/2777.

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Speech is a non-stationary signal and contains modulations in both spectral and temporal domains. Based on the type of modulations studied, most speech processing algorithms can be classified into short-time analysis algorithms, narrow-band analysis algorithms, or joint spectro-temporal analysis algorithms. While traditional methods of speech analysis study the modulation along either time (Short-time analysis algorithms) or frequency (Narrowband analysis) at a time. A new class of algorithms that work simultaneously along both temporal as well as spectral dimensions, called the spectro-temporal analysis algorithms, have become prominent over the past decade. Joint spectro-temporal analysis (also referred to as 2-D speech analysis) has shown promise in applications such as formant estimation, pitch estimation, speech recognition, etc. Over the past decade, 2-D speech analysis has been independently motivated from several directions. Broadly these motivations for 2-D speech models can be grouped into speech-production motivated, source-separation/machine- learning motivated and neurophysiology motivated. In this thesis, we develop 2-D speech model based on the speech production motivation. The overall organization of the thesis is as follows: We first develop the context of 2-D speech processing in Chapter one, we then proceed to develop a 2-D multicomponent AM-FM model for narrowband spectrogram patch of voiced speech and experiment with the perceptual significance of number of components needed to represent a spectrogram patch in Chapter two. In Chapter three we develop a demodulation algorithm called the inphase and the quadrature phase demodulation (IQ), compared to the state-of-the art sinusoidal demodulation, the AM obtained using this method is more robust to carrier estimation errors. The demodulation algorithm was verified on call voiced sentences taken from the TIMIT database. In chapter four we develop a demodulation algorithm based on Riesz transform, a natural extension of the Hilbert transform to higher dimensions, unlike the sinusoidal and the IQ demodulation techniques, Riesz-transform-based demodulation does not require explicit carrier estimation and is also robust to pitch discontinuous in patches. The algorithm was validated on all voiced sentences from the TIMIT database. Both IQ and Riesz-transform-based methods were found to give more accurate estimates of the 2-D AM (relates to vocal tract) and 2-D carrier (relates to source) compared with the sinusoidal modulation. In Chapter five we show application of the demodulated AM and carrier to pitch estimation and for creation of hybrid sounds. The hybrid sounds created were found to have better perceptual quality compared with their counterparts created using the linear prediction analysis. In Chapter six we summarize the work and present with possible directions of future research.
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Book chapters on the topic "Module spectrogram"

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Giacomucci, Scott. "Sociometric Social Work with Communities." In Social Work, Sociometry, and Psychodrama, 345–71. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6342-7_18.

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AbstractThis chapter uncovers Moreno’s often overlooked contributions to community practice through his sociometric approaches. His early work with communities is presented including in refugee camps, prisons, a reform school, and his psychiatric hospital. Brief introductions to social work with communities, therapeutic communities, and the Sanctuary Model are included. Multiple action-based sociometry processes are described with their utility for use in diverse community settings and example prompts. Sociometry processes depicted include spectrograms, locograms, floor checks, step-in sociometry, hands-on-shoulder sociograms, and the circle of strength safety structure.
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Elziaat, Hadeer, Nashwa El-Bendary, and Ramadan Moawad. "Multi-Feature Fusion and Machine Learning." In Advances in Data Mining and Database Management, 95–118. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6659-6.ch006.

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Freezing of gait (FoG) is a common symptom of Parkinson's disease (PD) that causes intermittent absence of forward progression of patient's feet while walking. Accordingly, FoG momentary episodes are always accompanied with falls. This chapter presents a novel multi-feature fusion model for early detection of FoG episodes in patients with PD. In this chapter, two feature engineering schemes are investigated, namely time-domain hand-crafted feature engineering and convolutional neural network (CNN)-based spectrogram feature learning. Data of tri-axial accelerometer sensors for patients with PD is utilized to characterize the performance of the proposed model through several experiments with various machine learning (ML) algorithms. Obtained experimental results showed that the multi-feature fusion approach has outperformed typical single feature sets. Conclusively, the significance of this chapter is to highlight the impact of using feature fusion of multi-feature sets through investigating the performance of a FoG episodes early detection model.
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Bhattacharjee, Soumyendu, Subham Ghosh, and Biswarup Neogi. "Real-Time Cardio Monitoring and Characterization of Diseases Introducing Statistical and Spectrogram Analysis." In Coronary and Cardiothoracic Critical Care, 78–101. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8185-7.ch005.

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This chapter provides significant theoretical and systematic frameworks and the latest empirical research towards the development of an automated cardiovascular disorder diagnostic system with the classifier. This research proposes an accurate non-invasive model of the cardiovascular diseases diagnostic system. This model of diagnostic system supports superior mobility with continuous real-time monitoring facility. This monitoring system will uncover a new dimension towards cardiovascular research. A physician will able to monitor several heart patients remotely with this device. Also, they can prescribe proper medicine remotely to the patient in an emergency. The system also has a provision to alert the patient by predicting the specific type of cardiovascular disorder accurately. An automated cardiovascular disorder diagnostic system development focuses towards the prime objective. Another objective is the adoption of modern classification technique for detecting cardiovascular disorders with high accuracy.
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Bhattacharjee, Soumyendu, Subham Ghosh, and Biswarup Neogi. "Real-Time Cardio Monitoring and Characterization of Diseases Introducing Statistical and Spectrogram Analysis." In Advances in Medical Technologies and Clinical Practice, 182–205. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7796-6.ch009.

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This chapter provides significant theoretical and systematic frameworks and the latest empirical research towards the development of an automated cardiovascular disorder diagnostic system with the classifier. This research proposes an accurate non-invasive model of the cardiovascular diseases diagnostic system. This model of diagnostic system support superior mobility with continuous real-time monitoring facility. This monitoring system will uncover a new dimension towards cardiovascular research. A physician will able to monitor several heart patients remotely with this device. Also, they can prescribe proper medicine remotely to the patient in an emergency. The system also has a provision to alert the patient by predicting the specific type of cardiovascular disorder accurately. An automated cardiovascular disorder diagnostic system development focuses towards the prime objective. Another objective is the adoption of modern classification technique for detecting cardiovascular disorders with high accuracy.
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Conference papers on the topic "Module spectrogram"

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Liutkus, Antoine, Zafar Rafii, Bryan Pardo, Derry Fitzgerald, and Laurent Daudet. "Kernel spectrogram models for source separation." In 2014 4th Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA). IEEE, 2014. http://dx.doi.org/10.1109/hscma.2014.6843240.

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Ganapathy, Sriram. "Robust speech processing using ARMA spectrogram models." In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178928.

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Tecza, Matthias, Niranjan A. Thatte, Ian J. Lewis, James Lynn, Wing Lau, Stephanie Yang, Ian A. Tosh, and Martyn Wells. "Design study for the KMOS spectrograph module." In SPIE Astronomical Telescopes + Instrumentation. SPIE, 2004. http://dx.doi.org/10.1117/12.552235.

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Tecza, Matthias, Ian J. Lewis, James Lynn, Stephanie Yang, Niranjan A. Thatte, Ian A. J. Tosh, and Marc J. Ferlet. "Opto-mechanical design of the KMOS spectrograph module." In SPIE Astronomical Telescopes + Instrumentation, edited by Ian S. McLean and Masanori Iye. SPIE, 2006. http://dx.doi.org/10.1117/12.672009.

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Dugnol, B., C. Fernández, G. Galiano, and J. Velasco. "Evolution Nonlinear Diffusion‐Convection PDE Models for Spectrogram Enhancement." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS: International Conference on Numerical Analysis and Applied Mathematics 2008. American Institute of Physics, 2008. http://dx.doi.org/10.1063/1.2990882.

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Tsuzuki, Toshihiro, Shinobu Ozaki, Satoshi Miyazaki, and Jason Fucik. "Image slicer module for Wide Field Optical Spectrograph (WFOS)." In Ground-based and Airborne Instrumentation for Astronomy VII, edited by Hideki Takami, Christopher J. Evans, and Luc Simard. SPIE, 2018. http://dx.doi.org/10.1117/12.2309324.

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Yan, Ruqiang, and Robert X. Gao. "Multi-Scale Enveloping Spectrogram for Bearing Defect Detection." In World Tribology Congress III. ASMEDC, 2005. http://dx.doi.org/10.1115/wtc2005-63541.

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This paper presents a new signal processing technique for bearing defect detection, called Multi-Scale Enveloping Spectrogram (MUSENS). The technique decomposes vibration signals measured on rolling bearings into different scales by means of a continuous wavelet transform (CWT). The envelope signal in each scale is then calculated from the modulus of the wavelet coefficients. Subsequently, Fourier transform is performed repetitively on the envelope of the signal at each scale, resulting in an “envelop spectrum” of the original signal at the various scales. The final output is a three-dimensional scale-frequency map that indicates the intensity and location of the defect-related frequency lines.
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Li, Li, Hirokazu Kameoka, Tomoki Toda, and Shoji Makino. "Speech Enhancement Using Non-Negative Spectrogram Models with Mel-Generalized Cepstral Regularization." In Interspeech 2017. ISCA: ISCA, 2017. http://dx.doi.org/10.21437/interspeech.2017-1492.

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Jauregui, Juan, and Oscar De Santiago Duran. "Experimental Validation of Friction Models for Rotor-Rubbing." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91926.

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Abstract The work presented here is a continuation of a set of experiments that were designed for predicting friction forces during rotor rubbing. The experimental set up consisted of a rotor rubbing a fixed surface. The surface had two force sensors, one aligned with the tangential force, and the other aligned in the radial direction. This set up allowed us to measure the friction component and the normal force. The measurements were complemented with a couple of accelerometers mounted on the bearings, and the accelerations and the friction force were measured simultaneously. All the data were analyzed using the Continuous Wavelet Transform (CWT) and the cross-correlation function. The CWT produces a spectrogram that is useful for identifying the nonlinear behavior of the phenomenon. The cross-correlation is used to measure the similarities between the friction force and the acceleration measurements. At low friction levels, experimental results show a sub-synchronous vibration at half of the rotating speed. This pattern is always present regardless of the friction source, but it is impossible to reproduce this effect using current friction models. The experimental results were compared to numerical results, these results were computed with the Muszynska’s model that is based on the physics of the phenomenon, but their predictions differ significantly from the experimental results. One of the reasons for these discrepancies lay on the fact that rotor-rubbing models consider the friction as an external force instead of modifying the system parameters.
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Di Rico, Gianluca, Christophe Giordano, Simone Esposito, Nicoletta Sanna, Guido Agapito, Andrea Tozzi, Ernesto Oliva, Marco Xompero, and Marco Bonaglia. "ELT-HIRES the High Resolution Spectrograph for the ELT: the IFU-SCAO module." In Ground-based and Airborne Instrumentation for Astronomy VII, edited by Hideki Takami, Christopher J. Evans, and Luc Simard. SPIE, 2018. http://dx.doi.org/10.1117/12.2311289.

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