Готові списки джерел за темами / Raw waveform

Добірка наукової літератури з теми "Raw waveform"

Автор: Grafiati

Опубліковано: 6 вересня 2023

Оновлено: 7 вересня 2023

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Статті в журналах з теми "Raw waveform"

Stelling, N., and K. Richter. "VOXEL BASED REPRESENTATION OF FULL-WAVEFORM AIRBORNE LASER SCANNER DATA FOR FORESTRY APPLICATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 755–62. http://dx.doi.org/10.5194/isprs-archives-xli-b8-755-2016.

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The advantages of using airborne full-waveform laser scanner data in forest applications, e.g. for the description of the vertical vegetation structure or accurate biomass estimation, have been emphasized in many publications. To exploit the full potential offered by airborne full-waveform laser scanning data, the development of voxel based methods for data analysis is essential. In contrast to existing approaches based on the extraction of discrete 3D points by a Gaussian decomposition, it is very promising to derive the voxel attributes from the digitised waveform directly. For this purpose, the waveform data have to be transferred into a 3D voxel representation. This requires a series of radiometric and geometric transformations of the raw full-waveform laser scanner data. Thus, the paper deals with the geometric aspects and describes a processing chain from the raw waveform data to an attenuationcorrected volumetric forest stand reconstruction. <br><br> The integration of attenuation-corrected waveform data into the voxel space is realised with an efficient parametric voxel traversal method operating on an octree data structure. The voxel attributes are derived from the amplitudes of the attenuation-corrected waveforms. Additionally, a new 3D filtering approach is presented to eliminate non-object voxel. Applying these methods to real full-waveform laser scanning data, a voxel based representation of a spruce was generated combining three flight strips from different viewing directions.

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Xu, D. C., B. D. Xu, E. J. Bao, Y. Y. Wu, A. Q. Zhang, Y. Y. Wang, G. L. Zhang, et al. "Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments." Journal of Instrumentation 17, no. 06 (June 1, 2022): P06040. http://dx.doi.org/10.1088/1748-0221/17/06/p06040.

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Abstract Photomultiplier tube (PMT) voltage waveforms are the raw data of many neutrino and dark matter experiments. Waveform analysis is the cornerstone of data processing. We evaluate the performance of all the waveform analysis algorithms known to us and find fast stochastic matching pursuit the best in accuracy. Significant time (up to × 2) and energy (up to × 1.07) resolution boosts are attainable with fast stochastic matching pursuit, approaching theoretical limits. Other methods also outperform the traditional threshold crossing approach in time resolution.

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Li, Shaobo, Yong Yao, Jie Hu, Guokai Liu, Xuemei Yao, and Jianjun Hu. "An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition." Applied Sciences 8, no. 7 (July 15, 2018): 1152. http://dx.doi.org/10.3390/app8071152.

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Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms.

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Younis, Raneen, and Andreas Reinhardt. "A Study on Fundamental Waveform Shapes in Microscopic Electrical Load Signatures." Energies 13, no. 12 (June 12, 2020): 3039. http://dx.doi.org/10.3390/en13123039.

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The number of globally deployed smart meters is rising, and so are the sampling rates at which they can meter electrical consumption data. As a consequence thereof, the technological foundation is established to track the power intake of buildings at sampling rates up to several k Hz . Processing raw signal waveforms at such rates, however, imposes a high resource demand on the metering devices and data processing algorithms alike. In fact, the ensuing resource demand often exceeds the capabilities of the embedded systems present in current-generation smart meters. Consequently, the majority of today’s energy data processing algorithms are confined to the use of RMS values of the data instead, reported once per second or even less frequently. This entirely eliminates the spectral characteristics of the signal waveform (i.e., waveform trajectories of electrical voltage, current, or power) from the data, despite the wealth of information they have been shown to contain about the operational states of the operative appliances. In order to overcome this limitation, we pursue a novel approach to handle the ensuing volume of load signature data and simultaneously facilitate their analysis. Our proposed method is based on approximating the current intake of electrical appliances by means of parametric models, the determination of whose parameters only requires little computational power. Through the identification of model parameters from raw measurements, smart meters not only need to transmit less data, but the identification of individual loads in aggregate load signature data is facilitated at the same time. We conduct an analysis of the fundamental waveform shapes prevalent in the electrical power consumption data of more than 50 electrical appliances, and assess the induced approximation errors when replacing raw current consumption data by parametric models. Our results show that the current consumption of many household appliances can be accurately modeled by a small number of parameterizable waveforms.

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Li, Zheming, and Wei He. "A Continuous Blood Pressure Estimation Method Using Photoplethysmography by GRNN-Based Model." Sensors 21, no. 21 (October 29, 2021): 7207. http://dx.doi.org/10.3390/s21217207.

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Compared with diastolic blood pressure (DBP) and systolic blood pressure (SBP), the blood pressure (BP) waveform contains richer physiological information that can be used for disease diagnosis. However, most models based on photoplethysmogram (PPG) signals can only estimate SBP and DBP and are susceptible to noise signals. We focus on estimating the BP waveform rather than discrete BP values. We propose a model based on a generalized regression neural network to estimate the BP waveform, SBP and DBP. This model takes the raw PPG signal as input and BP waveform as output. The SBP and DBP are extracted from the estimated BP waveform. In addition, the model contains encoders and decoders, and their role is to be responsible for the conversion between the time domain and frequency domain of the waveform. The prediction results of our model show that the mean absolute error is 3.96 ± 5.36 mmHg for SBP and 2.39 ± 3.28 mmHg for DBP, the root mean square error is 5.54 for SBP and 3.45 for DBP. These results fulfill the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed model can effectively estimate the BP waveform only using the raw PPG signal.

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Pashaei, Mohammad, Michael J. Starek, Craig L. Glennie, and Jacob Berryhill. "Terrestrial Lidar Data Classification Based on Raw Waveform Samples Versus Online Waveform Attributes." IEEE Transactions on Geoscience and Remote Sensing 60 (2022): 1–19. http://dx.doi.org/10.1109/tgrs.2021.3132356.

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Moriya, Hirokazu. "Phase-only correlation of time-varying spectral representations of microseismic data for identification of similar seismic events." GEOPHYSICS 76, no. 6 (November 2011): WC37—WC45. http://dx.doi.org/10.1190/geo2011-0021.1.

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Identification of similar seismic events is important for precise estimation of source locations and for evaluation of subsurface structure. Phase-only correlation is well known as a real-time image-matching method for fingerprint identification. I applied the phase-only correlation in a geophysical context to identify similar waveforms among microseismic events. The waveforms were first transformed into time-varying spectral representations to express frequency content in the time-frequency domain. The phase-only correlation function is calculated between two time-varying spectral representations and similarity is evaluated using the peak value of the phase-only correlation function. This method was applied to arbitrarily selected waveforms from aftershocks of an earthquake in Japan to assess its ability to identify similar waveforms perturbed by white noise. The detection of similarity of the proposed algorithm was compared to the similarity as detected by a 2D crosscorrelation function of the time-varying spectral representation and a 1D crosscorrelation of the raw waveform. This showed that the phase-only correlation function exhibits a sharp peak that quantifies similarity and dissimilarity over a wide range of signal-to-noise ratio (S/N) and remained unaffected by the length of the time window used to estimate time-varying spectral representations. Phase-only correlation may also have applications in other geophysical analyses and interpretations that are based on waveform and seismic image data.

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Cole, P. T., and M. Carlos. "Use of Advanced A.E. Analysis for Source Discrimination Using Captured Waveforms." Advanced Materials Research 13-14 (February 2006): 401–6. http://dx.doi.org/10.4028/www.scientific.net/amr.13-14.401.

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Conventional methods of acquiring and using acoustic emission (AE) discard the raw signal waveform after extracting signal features from it. The main reason for this is the number of bytes required to save hundreds of thousands of AE waveforms, using a modern high speed multichannel system the hard-drive may be quickly filled. One side effect of this “feature extraction” approach is that information is thrown away with the wave-form. The advent of systems capable of acquiring AE waveforms on all channels has opened up the opportunity to use this extra data to get more information about the source and the transmission path. This paper describes the use of acoustic emission waveforms to aid source discrimination, and presents data acquired during pressure testing of a slug-catcher.

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Zhang, Yu, Bei Wang, Jin Jing, Jian Zhang, Junzhong Zou, and Masatoshi Nakamura. "A Comparison Study on Multidomain EEG Features for Sleep Stage Classification." Computational Intelligence and Neuroscience 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/4574079.

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Feature extraction from physiological signals of EEG (electroencephalogram) is an essential part for sleep staging. In this study, multidomain feature extraction was investigated based on time domain analysis, nonlinear analysis, and frequency domain analysis. Unlike the traditional feature calculation in time domain, a sequence merging method was developed as a preprocessing procedure. The objective is to eliminate the clutter waveform and highlight the characteristic waveform for further analysis. The numbers of the characteristic activities were extracted as the features from time domain. The contributions of features from different domains to the sleep stages were compared. The effectiveness was further analyzed by automatic sleep stage classification and compared with the visual inspection. The overnight clinical sleep EEG recordings of 3 patients after the treatment of Continuous Positive Airway Pressure (CPAP) were tested. The obtained results showed that the developed method can highlight the characteristic activity which is useful for both automatic sleep staging and visual inspection. Furthermore, it can be a training tool for better understanding the appearance of characteristic waveforms from raw sleep EEG which is mixed and complex in time domain.

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Дисертації з теми "Raw waveform"

Zeghidour, Neil. "Learning representations of speech from the raw waveform." Thesis, Paris Sciences et Lettres (ComUE), 2019. http://www.theses.fr/2019PSLEE004/document.

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Bien que les réseaux de neurones soient à présent utilisés dans la quasi-totalité des composants d’un système de reconnaissance de la parole, du modèle acoustique au modèle de langue, l’entrée de ces systèmes reste une représentation analytique et fixée de la parole dans le domaine temps-fréquence, telle que les mel-filterbanks. Cela se distingue de la vision par ordinateur, un domaine où les réseaux de neurones prennent en entrée les pixels bruts. Les mel-filterbanks sont le produit d’une connaissance précieuse et documentée du système auditif humain, ainsi que du traitement du signal, et sont utilisées dans les systèmes de reconnaissance de la parole les plus en pointe, systèmes qui rivalisent désormais avec les humains dans certaines conditions. Cependant, les mel-filterbanks, comme toute représentation fixée, sont fondamentalement limitées par le fait qu’elles ne soient pas affinées par apprentissage pour la tâche considérée. Nous formulons l’hypothèse qu’apprendre ces représentations de bas niveau de la parole, conjontement avec le modèle, permettrait de faire avancer davantage l’état de l’art. Nous explorons tout d’abord des approches d’apprentissage faiblement supervisé et montrons que nous pouvons entraîner un unique réseau de neurones à séparer l’information phonétique de celle du locuteur à partir de descripteurs spectraux ou du signal brut et que ces représentations se transfèrent à travers les langues. De plus, apprendre à partir du signal brut produit des représentations du locuteur significativement meilleures que celles d’un modèle entraîné sur des mel-filterbanks. Ces résultats encourageants nous mènent par la suite à développer une alternative aux mel-filterbanks qui peut être entraînée à partir des données. Dans la seconde partie de cette thèse, nous proposons les Time-Domain filterbanks, une architecture neuronale légère prenant en entrée la forme d’onde, dont on peut initialiser les poids pour répliquer les mel-filterbanks et qui peut, par la suite, être entraînée par rétro-propagation avec le reste du réseau de neurones. Au cours d’expériences systématiques et approfondies, nous montrons que les Time-Domain filterbanks surclassent systématiquement les melfilterbanks, et peuvent être intégrées dans le premier système de reconnaissance de la parole purement convolutif et entraîné à partir du signal brut, qui constitue actuellement un nouvel état de l’art. Les descripteurs fixes étant également utilisés pour des tâches de classification non-linguistique, pour lesquelles elles sont d’autant moins optimales, nous entraînons un système de détection de dysarthrie à partir du signal brut, qui surclasse significativement un système équivalent entraîné sur des mel-filterbanks ou sur des descripteurs de bas niveau. Enfin, nous concluons cette thèse en expliquant en quoi nos contributions s’inscrivent dans une transition plus large vers des systèmes de compréhension du son qui pourront être appris de bout en bout
While deep neural networks are now used in almost every component of a speech recognition system, from acoustic to language modeling, the input to such systems are still fixed, handcrafted, spectral features such as mel-filterbanks. This contrasts with computer vision, in which a deep neural network is now trained on raw pixels. Mel-filterbanks contain valuable and documented prior knowledge from human auditory perception as well as signal processing, and are the input to state-of-the-art speech recognition systems that are now on par with human performance in certain conditions. However, mel-filterbanks, as any fixed representation, are inherently limited by the fact that they are not fine-tuned for the task at hand. We hypothesize that learning the low-level representation of speech with the rest of the model, rather than using fixed features, could push the state-of-the art even further. We first explore a weakly-supervised setting and show that a single neural network can learn to separate phonetic information and speaker identity from mel-filterbanks or the raw waveform, and that these representations are robust across languages. Moreover, learning from the raw waveform provides significantly better speaker embeddings than learning from mel-filterbanks. These encouraging results lead us to develop a learnable alternative to mel-filterbanks, that can be directly used in replacement of these features. In the second part of this thesis we introduce Time-Domain filterbanks, a lightweight neural network that takes the waveform as input, can be initialized as an approximation of mel-filterbanks, and then learned with the rest of the neural architecture. Across extensive and systematic experiments, we show that Time-Domain filterbanks consistently outperform melfilterbanks and can be integrated into a new state-of-the-art speech recognition system, trained directly from the raw audio signal. Fixed speech features being also used for non-linguistic classification tasks for which they are even less optimal, we perform dysarthria detection from the waveform with Time-Domain filterbanks and show that it significantly improves over mel-filterbanks or low-level descriptors. Finally, we discuss how our contributions fall within a broader shift towards fully learnable audio understanding systems

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Erkmen, Baris I., Andre Tkacenko, and Clayton M. Okino. "Preamble Design for Symbol Timing Estimation from SOQPSK-TG Waveforms." International Foundation for Telemetering, 2009. http://hdl.handle.net/10150/606119.

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ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada
Data-aided symbol synchronization for bursty communications utilizes a predetermined modulation sequence, i.e., a preamble, preceding the payload. For effective symbol synchronization, this preamble must be designed in accordance with the modulation format. In this paper, we analyze preambles for shaped offset quadrature phase-shift keying (SOQPSK) waveforms. We compare the performance of several preambles by deriving the Cram´er-Rao bound (CRB), and identify a desirable one for the Telemetry Group variant of SOQPSK. We also demonstrate, via simulation, that the maximum likelihood estimator with this preamble approaches the CRB at moderate signal-to-noise ratio.

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Vemula, Hari Charan. "Multiple Drone Detection and Acoustic Scene Classification with Deep Learning." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1547384408540764.

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Ristorcelli, Thomas. "Evaluation de l'apport des visées multi-angulaires en imagerie laser pour la reconstruction 3D des couverts végétaux." Thesis, Toulouse, ISAE, 2013. http://www.theses.fr/2013ESAE0049/document.

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Ces travaux de recherche s’inscrivent dans la problématique scientifique de reconstruction du relief sous un couvert végétal à partir d’observations aéroportées. Le scanner laser aéroporté est une technique d’imagerie très prometteuse, notamment pour l’observation des zones forestières. Sa déclinaison "onde complète" consiste à émettre une impulsion laser et à enregistrer temporellement l’intégralité des échos de retour réfléchis par la scène. La forme des échos de retour fournit des informations sur l’épaisseur optique du couvert végétal. De nombreux systèmes commerciaux sont en exploitation, en particulier en topographie ou en bathymétrie. Mais ces systèmes ne sont pas dédiés à l’observation de la végétation. L’objectif de cette thèse est l’étude de l’intérêt de ces systèmes pour la construction de modèle numérique de terrain (MNT) sous couvert végétal. Elle est basée sur le développement d’outils de simulation du signal temporel incident au capteur lidar et de traitement des données. Dans un premier temps, le modèle physique de lidar onde complète, DELiS (n-Dimensional Estimation of Lidar Signals) a été développé. Il permet de simuler l’observation de scènes de végétation réalistes, tout en incluant la prise en compte de l’environnement extérieur (atmosphère, soleil) ainsi que des caractéristiques de la source et de la chaîne de détection (bruits de mesure). DELiS a été validé par confrontation à des résultats analytiques. Ensuite, DELIS a permis de comprendre et d’évaluer l’importance des diffusions multiples dans le couvert en fonction du champ de vue du lidar mais aussi de justifier l’utilisation d’acquisitions aéroportées petit champ pour simuler le signal d’un lidar spatial plus grand champ. Dans une deuxième étape, ses capacités de simulation ont été utilisées afin d’étudier l’intérêt du lidar onde complète pour la reconstruction d’un MNT sous couvert végétal. Dans ce but, nous avons développé et implémenté numériquement une méthode originale de traitement et de classification des données lidar onde complète permettant de séparer les échos lidar provenant du sol de ceux provenant de la végétation. Après classification des échos, nous avons reconstruit la géométrie du sol et des objets occultés par la végétation. Enfin, nous avons étudié comment combiner des données aéroportées acquises sous différents points de vue afin d’améliorer les reconstructions. Nos travaux montrent que le scanner laser aéroporté onde complète pourrait permettre d’obtenir en milieux forestier des reconstructions de la géométrie du terrain à des résolutions sub-métriques et avec une précision de l’ordre de 10 à 20 centimètres. La combinaison de visées multi-angulaire permet, par l’apport d’une quantité importante d’information supplémentaire, d’améliorer encore la reconstruction du MNT. Nous montrons cependant que les visées inclinées sont plus sensibles à la présence des troncs et branchages des arbres, éléments qui sont susceptibles d’introduire une erreur importante dans les processus de classification et de reconstruction. Pour cette raison, nous recommandons l’utilisation de la visée nadir pour la reconstruction mono-vue des modèles numériques de terrain, et nous proposons une méthode permettant de choisir de façon optimale les visées inclinées à ajouter pour l’observation détaillée d’une portion plus restreinte de la scène
This research work regards the scientific challenge of reconstructing the ground and the object presents under a vegetation cover from airborne observations. Airborne laser scanning is a promising technology. Full-waveform devices are able to record the complete temporal return signal following the emission of a short laser pulse towards the ground. This offers a great potential for remote sensing of forested areas, since the laser pulse will travel through the vegetation. Many commercial systems are already operated for topography or bathymetry. Scientists have been using these systems for vegetation observation, even if they are not dedicated to this purpose. The objective of this thesis is to study the relevance of full-waveform lidars for the geometric reconstruction of digital terrain models (DTM) under vegetation. We also aim at developing simulation and data processing toolsthat will help design and optimize future sensors dedicated to vegetation observation. Our first task was the development of a new physical simulator for full-waveform lidar measurement. The DELiS model (n-Dimensional Estimation of Lidar Signals) is able tosimulate the observation of complex and realistic vegetation scenes while accounting for atmosphere and sun perturbations, and simulating the multiple scattering of the laser pulse in the canopy. We have also implemented a sensor model for simulation of the measurement, amplification and digitization noises. This operational simulation tool is a key asset for future physical studies as well as for designing and optimizing future sensors and data processing methods. After validating the DELiS model by confrontation with analytical results, we have used it for studying the interest of full-waveform lidar for digital terrain models reconstruction under vegetation. For this purpose, we have developed a full-waveform lidar data processing method for decomposition of the signals and classification of the lidar echoes into two classes : ’ground’ and ’vegetation’. We were then able to reconstruct ground geometry.Finally, we have led a study on the combination of multi-angular acquisitions for improvement of the reconstructions.Our work shows that airborne full-waveform lidar observations may allow ground reconstruction with sub-metric resolutions and a precision of 10 to 20 centimeters in forested areas. Combining multiple viewing angles provides additional data, and helps improving the precision of the reconstructions. Yet, we show that non-nadir viewing is much more sensitive to trunks and branches. These elements may be the cause of an additional error in the classification and reconstruction processes. For this reason, we recommend using nadir viewing for single-view ground reconstruction, and propose a method for optimally selecting non-nadir views for the detailed observation of restricted areas of interest

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Menni, Tarek. "Borne de Cramér-Rao déterministe pour l'analyse des performances asymptotiques en estimation d'un radar actif." Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2012. http://tel.archives-ouvertes.fr/tel-00846933.

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L'émergence des formes d'onde numériques en radar et l'engouement de la communauté scientifique pour leur versatilité éprouvée en télécom, soulèvent naturellement chez les ingénieurs radaristes la question de l'amélioration effective des performances opérationnelles par ces nouvelles formes d'onde, notamment en matière de haute-résolution. Les travaux publiés sur le sujet sont prometteurs, à ceci près qu'ils sont le plus souvent basés sur des modèles théoriques un peu éloignés de la réalité opérationnelle ou sur des scénarios simplistes relativement à la capacité haute résolution envisagée (par exemple le faible nombre de sources pris en compte). En effet la prise en compte d'un modèle d'observation réaliste (large bande, à fréquence d'échantillonnage élevée) et de scénario à grand nombre de contributeurs conduit à des estimateurs dont la complexité d'implémentation n'est pas compatible des puissances de calcul actuelles. Une approche alternative, et compatible des puissances de calcul actuelles, pour la qualification des performances haute résolution est l'utilisation des bornes inférieures d'estimation, principalement la borne de Cramèr-Rao déterministe. L'examen de la littérature courante (notamment les monographies de référence) sur la borne de Cramèr-Rao déterministe a fait apparaître des lacunes relatives à sa formulation dans le contexte radar qui nous intéresse, à savoir MIMO large bande, multisources, multiparamètres à observations multiples. En effet dans la littérature courante, les observations multiples sont définies comme des réalisations multiples indépendantes d'un même modèle d'observation, alors qu'en radar il s'agit en général de la combinaison de modèles d'observation différents (variation de la forme d'onde). Ce constat a motivé l'essentiel de ce travail, à savoir l'établissement d'une expression analytique générale de la borne de Cramèr-Rao déterministe MIMO large bande, multisources, multiparamètres à modèles d'observations multiples pour la qualification (asymptotique) des performances en estimation d'un radar actif. Ce travail fournit un outil de comparaison des performances haute-résolution des différentes formes d'onde, dont les nouvelles formes d'onde numériques. De façon générale, l'expression analytique générale de la borne de Cramèr-Rao obtenue fournit la base théorique pour le développement des futurs radars à haute résolution.

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Fernández, Vicente Juan. "Reconfigurable Reflective Arrayed Waveguide Grating on Silicon Nitride." Doctoral thesis, Universitat Politècnica de València, 2021. http://hdl.handle.net/10251/165783.

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[ES] La presente tesis se ha centrado en el modelado, diseño y demonstración experimental por primera vez del dispositivo Reconfigurable Reflective Arrayed Waveguide Grating (R-RAWG). Para la consecución de este dispositivo que tiene posibilidades de uso en la espectrometría, una plataforma de nitruro de silicio llamada CNM-VLC se ha usado, ya que este material permite operar en un gran ancho de banda. Esta plataforma posee ciertas limitaciones y los elementos necesarios para el funcionamiento de este dispositivo tenían un performance bajo. Por ello, se ha desarrollado y validado una metodología que ha permitido obtener mejores divisores. Además, se ha diseñado un inverted taper que ha mejorado considerablemente el acoplo de luz al chip. Esto ha sido gracias a un exhaustivo análisis de opciones existentes en la literatura que también ha permitido escoger la mejor opción para realizar un espejo reconfigurable en la plataforma sin cambiar ni añadir ningún proceso de fabricación. Se han demostrado espejos reconfigurables gracias a utilizar divisores ópticos realimentados y también se ha desarrollado códigos que predicen el comportamiento del dispositivo experimentalmente. Con todo el trabajo realizado, se ha diseñado un R-RAWG para que pudiera operar en un gran ancho de banda y que los actuadores de fase no tuvieran peligro de estropearse. También se ha desarrollado un código para el modelado del R-RAWG que permite imitar la fabricación de estos dispositivos y que, gracias a esto, se ha desarrollado un método o algoritmo llamado DPASTOR, que usa algoritmos usados en machine learning, para optimizar la respuesta con tan sólo la potencia óptica de salida. Finalmente, se ha diseñado una PCB para poder conectar eléctricamente el chip fotónico y se ha desarrollado un método de medida que ha permitido tener una respuesta estable consiguiendo demostrar multitud de respuestas de filtros ópticos con el mismo dispositivo.
[CAT] La present tesi s'ha centrat en el modelatge, disseny i demonstració experimental per primera vegada del dispositiu Reconfigurable Reflective Arrayed Waveguide Grating (R-RAWG). Per a la consecució d'aquest dispositiu que té possibilitats d'ús en l'espectrometria, una plataforma de nitrur de silici anomenada CNM-VLC s'ha usat ja que aquest material permet operar en una gran amplada de banda. Aquesta plataforma posseeix certes limitacions i els elements necessaris per al funcionament d'aquest dispositiu tenien un performance baix. Per això, s'ha desenvolupat i validat una metodologia que ha permés obtindre millors divisors i també, gràcies als processos de fabricació, s'ha dissenyat un acoplador que ha millorat considerablement l'acoble de llum al xip. Això ha sigut gràcies a un exhaustiu analisis d'opcions existents en la literatura que també ha permés triar la millor opció per a realitzar un espill reconfigurable en la plataforma sense canviar ni afegir cap procés de fabricació. S'han demonstrat espills reconfigurables gràcies a utilitzar divisors realimentats i també s'ha desenvolupat codis que prediuen el comportament del dispostiu experimentalment. Amb tot el treball realitzat, s'ha dissenyat un R-RAWG fent ús de determinades consideracions perquè poguera operar en una gran amplada de banda i que els actuadors de fase no tingueren perill de desbaratar-se. També s'ha desenvolupat un codi per al modelatge del R-RAWG que permet imitar la fabricació d'aquests dispositius i que, gràcies a això, s'ha desenvolupat un mètode o algorisme anomenat DPASTOR, que usa algorismes usats en machine learning, per a optimitzar la resposta amb tan sols la potència òptica d'eixida. Finalment, s'ha dissenyat una PCB per a poder connectar elèctricament el xip fotònic i s'ha desenvolupat un mètode de mesura que ha permés tindre una resposta estable aconseguint demostrar multitud de respostes de filtres òptics amb el mateix dispositiu.
[EN] This thesis is focused on the modelling, design and experimental demonstration for the first time of Reconfigurable Reflective Arrayed Waveguide Grating (R-RAWG) device. In order to build this device, that can be employed in spectrometry, a silicon nitride platform termed CNM-VLC has been chosen since this material allows to operate in broad range of wavelengths. This platform has the necessary elements, but some limitations because the operation of this device had a low performance. Therefore, a methodology has been developed and validated, which has allowed to obtain better splitters. Also an inverted taper has been designed, which has considerably improved the coupling of light to the chip. This has been possible thanks to an exhaustive analysis of existing options in the literature, that has allowed choosing the best option to make a reconfigurable mirror on the platform without changing or adding new manufacturing steps. Reconfigurable mirrors have been demonstrated by using feedback splitters. Furthermore, codes have been developed to predict the behaviour of the actual device. With all the work done, a R-RAWG has been designed by using certain considerations so that it can operate over a broad wavelength range and the phase actuators are not in danger of being damaged. A code has also been developed for the modelling of the R-RAWG, which allows manufacturing imperfections to be considered, thanks to this, a method or algorithm called DPASTOR has been developed. DPASTOR resembles machine learning to optimise the response by just using the optical output power. Finally, a PCB and an assembly with the chip interconnected to it have been made and designed. Moreover, a measurement method has been developed, which has made it possible to have a stable response and to demonstrate a multitude of optical filter responses with the same device.
Fernández Vicente, J. (2021). Reconfigurable Reflective Arrayed Waveguide Grating on Silicon Nitride [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/165783
TESIS

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Huang, Tzu-Yun, and 黃姿云. "A Dual Complementary Acoustic Embedding Network: Mining Discriminative Characteristics from Raw-waveform for Speech Emotion Recognition." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/y8zcm7.

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Agrawal, Purvi. "Neural Representation Learning for Speech and Audio Signals." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4824.

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Representation learning is the branch of machine learning consisting of techniques that are capable of automatically discovering meaningful representations from raw data for efficient information extraction. In recent years, following the trends in other streams of machine learning, representation learning using neural networks has attracted significant interest. For example, deep representation learning in the text domain using word embeddings has shown interesting semantic properties that make them widely useful for many natural language processing applications. In the speech processing field, representation learning has been a challenging task. This thesis is focused on developing neural methods for representation learning of speech and audio signals, with the goal of improving downstream applications that rely on these representations. For representation learning, we pursue two broad directions - supervised and unsupervised. In the case of speech/audio signals, we identify two stages of representation learning that are explored. The first stage is the learning of a time-frequency representation (the equivalent of spectrogram) from the raw audio waveform. The second stage is the learning of modulation representations (filtering the time-frequency representations along the temporal domain, called rate filtering and spectral domain, called scale filtering). In the first part of the thesis, we propose representation learning methods for speech data in an unsupervised manner. Using the modulation representation learning as the goal, we explore various neural architecture for unsupervised learning. These include restricted Boltzmann machines (RBM), variational autoencoders (VAE) and generative adversarial networks (GAN). For learning modulation representations that are distinct and irredundant, we propose different learning frameworks like external residual approach, skip connection based approach, and a modified cost function based approach. The methods developed for rate and scale representation learning are benchmarked using an automatic speech recognition (ASR) task on noisy and reverberant conditions. We also illustrate that the unsupervised representation learning can be extended to the first stage of learning time-frequency representations from raw waveforms. The second part of the thesis deals with supervised representation learning. Here, we propose a two-stage representation learning approach from raw waveform consisting of acoustic filterbank learning (time-frequency representation learning) from raw waveform followed by a modulation representation learning. This two-stage learning is directly optimized for the task at hand. The key novelty in the proposed framework consists of a relevance weighting mechanism that acts as a feature selection module. This is inspired by gating networks and provides a mechanism to weight the relevance of the acoustic and modulation representations for the task involved. The relevance weighting network can also utilize feedback from the previous predictions of the model for tasks like ASR. The proposed relevance weighting scheme is shown to provide significant performance improvements for ASR task and UrbanSound audio classification task. A detailed analysis yields insights into the interesting properties of the relevance weights that are captured by the model at the acoustic and modulation stages for speech and audio signals. In particular, the relevance weights are shown to succinctly capture phoneme characteristics in speech recognition tasks and the audio characteristics in the urban sound classification task. In summary, the thesis makes strides in the direction of unsupervised and supervised neural representation learning of speech and audio signals. While conventional methods of speech/audio processing involve deriving time-frequency spectrogram representations as the first step in most classification tasks, the work reported in the thesis argues that data driven representations from the raw signal with minimal assumptions can yield task specific flexibility and interpretability while also providing superior performance.

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Hsieh, Yu-Shih, and 謝雨蒔. "The Analysis and Comparison of the Action Potential Waveforms in Ventral Posterolateral Nucleus of the Rat Thalamus." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/43504880861748979191.

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碩士
國立臺灣大學
動物學研究研究所
94
The ventral posterolateral thalamic nucleus ( VPL ) plays an important role in conveying the somatosensory signals. It contains different sorts of neurons which may generate diverse action potential waveforms, their reactions to innocuous or noxious stimuli also diverge. This study investigates the classification of action potential waveforms in VPL, the relationship between different recording microelectrodes or functional groups and waveforms were further compared. The result shows that VPL waveforms could be divided into four main groups by the features such as the peak-to-peak amplitude, the peak-to-peak duration, etc. The materials of recording microelectrodes affect the kinds of waveforms being collected. However, the different functional groups of VPL neurons do not correspond to particular kinds of waveforms.

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Bouse, Scott. "Investigation of Transfer Function Analysis as a Means to Predict Strain on Rat Tibiae from Ankle Torque Waveforms." 2009. http://hdl.handle.net/1969.1/ETD-TAMU-2009-12-7226.

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Electrical Muscle Stimulation (EMS) is used as a countermeasure in animal disuse studies that seek to determine which forms of exercise are most effective in mitigating the effects of disuse atrophy on bone and muscle. Although EMS has been used for many years in our lab and others, few researchers have been able to quantify the levels of strain on rat tibiae during EMS and far fewer have investigated the causal relationship between torque produced at the ankle and strain on the tibia. This thesis sought to investigate the relationship between ankle torque and tibial strain by using a combination of techniques, namely: (1) the addition of rosette strain gages, (2) improved synchronization between ankle torque and tibial strain recordings, and (3) spectral analysis between torque and strain waveforms. In previous work, few methods existed to align torque and strain recordings temporally, as those data were recorded on separate computers and synchronizing events were not captured. Attempting to create a torque-strain crossplot with unsynchronized data does not always yield valid results, so a method of reliably synchronizing those data is required. This thesis developed methods to capture simultaneous (synchronizing) events in both torque and strain recordings and then used those captured events to synchronize data between two computers. Following that synchronization, stiffness calculations were run on torque-strain crossplots to determine linear-model relationships between torque and strain for each method of synchronization. The results from those regressions were then used to determine if one or more synchronization techniques are superior to others, in terms of repeatability or precision. The results of these analyses have shown that using portions of the curves can dramatically increase computing speed while providing high levels of repeatability in synchronization measures. After synchronization techniques had been investigated, 3-element rosette data were used to calculate the principal strains on the surface of the tibiae, and the percentage of principal strains that are accounted for in the axial direction. Since the strain environment changes along the axis of the bone, the principal strain data were plotted versus the distance from proximal epiphysis to rosette gage, and statistical analysis was presented. After rosette data were analyzed, the torque and strain data pairs were fed into a signal processing suite for the purpose of transfer function calculation. Using the synchronization methods outlined above, two means of synchronization were compared in the transfer function program. Results for these analyses demonstrated that transfer functions are slightly dependent on synchronization methods, but that calculated gains do not differ between synchronization techniques. The specific shapes of the transfer functions highlight the relative attenuation/amplification of frequencies in torque and strain signals. Specifically, a range of frequencies, commonly called a band, between 24 and 32Hz is attenuated by the soft tissues and mechanical linkages in the lower leg of rats. This finding gives researchers looking to increase or decrease modeling stimulus to bone a new piece of information about the relative efficiency of EMS exercise. For example, EMS performed at 24-25Hz might produce less strain in the tibia than EMS at 22-23Hz, despite the 22-23Hz stimulation producing marginally less torque.

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Книги з теми "Raw waveform"

Electronics via waveform analysis. New York: Springer-Verlag, 1993.

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McAlpine, Kenneth B. The Ultimate Soundtracker? Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190496098.003.0006.

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In the early days of home computing, writing music was as much a technical as a creative process. This chapter explores how the launch of a software music package, Ultimate Soundtracker, for Commodore’s Amiga created a new, symbolic way to compose and edit music. It was sample-based and structured music using a grid-style interface that could be navigated using the computer keyboard, and its music files distributed both, making it easy to share—and copy—others’ musical ideas. This ‘open-source’ approach allowed nonprogrammers and nonmusicians to experiment with music making and for the sound to promulgate. This was also the period from which the term ‘chiptune’ emerged; the Amiga’s sample-based chipset allowed it to create other sounds beside raw electronic waveforms, and chiptune was used to highlight tracks written in the 8-bit sound chip style.

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Частини книг з теми "Raw waveform"

Li, Wencheng, Zhenhua Tan, Zhenche Xia, Danke Wu, and Jingyu Ning. "PF-Net: Personalized Filter for Speaker Recognition from Raw Waveform." In Mobile Multimedia Communications, 362–74. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23902-1_28.

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Sarma, Mousmita, Kandarpa Kumar Sarma, and Nagendra Kumar Goel. "Children’s Age and Gender Recognition from Raw Speech Waveform Using DNN." In Lecture Notes in Networks and Systems, 1–9. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2774-6_1.

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Maset, Eleonora, Roberto Carniel, and Fabio Crosilla. "Unsupervised Classification of Raw Full-Waveform Airborne Lidar Data by Self Organizing Maps." In Image Analysis and Processing — ICIAP 2015, 62–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23231-7_6.

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Madariaga, Raúl. "Waveform Synthesis by Ray Theoretical Methods." In Digital Seismology and Fine Modeling of the Lithosphere, 49–78. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4899-6759-6_4.

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Donnelly, Patrick J., and Parker Carlson. "Transposition of Simple Waveforms from Raw Audio with Deep Learning." In Artificial Intelligence in Music, Sound, Art and Design, 341–56. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-29956-8_22.

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Oshio, Kotaro, Hidetaka Onodera, Masashi Uchida, Yuichiro Tanaka, and Takuo Hashimoto. "Assessment of Brain Compliance Using ICP Waveform Analysis in Water Intoxication Rat Model." In Brain Edema XV, 219–21. Vienna: Springer Vienna, 2013. http://dx.doi.org/10.1007/978-3-7091-1434-6_41.

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Summa, D. A., T. N. Claytor, M. H. Jones, M. J. Schwab, and S. C. Hoyt. "3-D Visualization of X-Ray and Neutron Computed Tomography (CT) and Full Waveform Ultrasonic (UT) Data." In Review of Progress in Quantitative Nondestructive Evaluation, 927–34. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-4791-4_119.

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Kristek, Frantisek, Marian Grman, and Karol Ondrias. "In Vivo Measurement of H2S, Polysulfides, and “SSNO− Mix”-Mediated Vasoactive Responses and Evaluation of Ten Hemodynamic Parameters from Rat Arterial Pulse Waveform." In Methods in Molecular Biology, 109–24. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9528-8_8.

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Rhode, Kawal, Gareth Ennew, Tryphon Lambrou, Alexander Seifalian, and David Hawkes. "In-Vitro Validation of a Novel Model-Based Approach to the Measurement of Arterial Blood Flow Waveforms from Dynamic Digital X-ray Images." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001, 291–300. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45468-3_35.

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Bansal, Dipali. "Emerging Technologies and ICT Solutions in Healthcare." In Handbook of Research on Healthcare Administration and Management, 268–86. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0920-2.ch016.

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The reaction of a human body under stress, on the onset of a disease or on being physically challenged is reflected by the fine changes in the human physiological parameters and hence is required to be repeatedly measured. The acquisition of data if done in real time enhances the sense of connectedness with the health care providers by sharing raw or interpreted physiological data. Emerging ICT tools in healthcare sector help in creating modular, software-defined test systems with improved throughput and flexibility for lesser overall costs. They also assist in designing advanced algorithms and developing prototype on off-the shelf hardware in a remarkable time frame. This chapter thus focuses on design and development of a system to acquire vital human physiological parameters like ECG, EMG and Carotid pulse waveform using latest technologically advanced ICT tools.

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Тези доповідей конференцій з теми "Raw waveform"

Pashaei, Mohammad, Michael J. Starek, and Jacob Berryhill. "Full-Waveform Terrestrial Lidar Data Classification Using Raw Digitized Waveform Signals." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883782.

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Muckenhirn, Hannah, Vinayak Abrol, Mathew Magimai-Doss, and Sébastien Marcel. "Understanding and Visualizing Raw Waveform-Based CNNs." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2341.

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Agrawal, Purvi, and Sriram Ganapathy. "Unsupervised Raw Waveform Representation Learning for ASR." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2652.

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Ravanelli, Mirco, and Yoshua Bengio. "Speaker Recognition from Raw Waveform with SincNet." In 2018 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2018. http://dx.doi.org/10.1109/slt.2018.8639585.

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Pashaei, Mohammad, Michael J. Starek, Philippe Tissot, and Jacob Berryhill. "Full-Waveform Terrestrial Lidar Data Classification Using Raw Samples of Digitized Waveform." In IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021. http://dx.doi.org/10.1109/igarss47720.2021.9553327.

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Sainath, Tara N., Ron J. Weiss, Kevin W. Wilson, Arun Narayanan, and Michiel Bacchiani. "Factored spatial and spectral multichannel raw waveform CLDNNs." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7472644.

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Kethireddy, Rashmi, Sudarsana Reddy Kadiri, and Suryakanth V. Gangashetty. "Learning Filterbanks from Raw Waveform for Accent Classification." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206778.

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Platen, P. von, Chao Zhang, and P. C. Woodland. "Multi-Span Acoustic Modelling Using Raw Waveform Signals." In Interspeech 2019. ISCA: ISCA, 2019. http://dx.doi.org/10.21437/interspeech.2019-2454.

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Juvela, Lauri, Vassilis Tsiaras, Bajibabu Bollepalli, Manu Airaksinen, Junichi Yamagishi, and Paavo Alku. "Speaker-independent Raw Waveform Model for Glottal Excitation." In Interspeech 2018. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/interspeech.2018-1635.

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Jung, Jee-weon, Youjin Kim, Hee-Soo Heo, Bong-Jin Lee, Youngki Kwon, and Joon Son Chung. "Pushing the limits of raw waveform speaker recognition." In Interspeech 2022. ISCA: ISCA, 2022. http://dx.doi.org/10.21437/interspeech.2022-126.

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Звіти організацій з теми "Raw waveform"

Nishimura, K., B. Dey, D. Aston, D. W. G. S. Leith, B. Ratcliff, D. Roberts, L. Ruckman, D. Shtol, G. S. Varner, and J. Va’vra. A Detailed Study of FDIRC Prototype with Waveform Digitizing Electronics in Cosmic Ray Telescope Using 3D Tracks. Office of Scientific and Technical Information (OSTI), July 2012. http://dx.doi.org/10.2172/1055456.

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