scholarly journals Surgical data processing for smart intraoperative assistance systems

2017 ◽  
Vol 2 (3) ◽  
pp. 145-152 ◽  
Author(s):  
Ralf Stauder ◽  
Daniel Ostler ◽  
Thomas Vogel ◽  
Dirk Wilhelm ◽  
Sebastian Koller ◽  
...  
Keyword(s):  
Neural Networks ◽  
Dynamic Time Warping ◽  
Data Science ◽  
Markov Models ◽  
Context Aware ◽  
Time Warping ◽  
Surgical Assistance ◽  
Surgical Data ◽  
Assistance Systems ◽  
Dynamic Time

AbstractDifferent components of the newly defined field of surgical data science have been under research at our groups for more than a decade now. In this paper, we describe our sensor-driven approaches to workflow recognition without the need for explicit models, and our current aim is to apply this knowledge to enable context-aware surgical assistance systems, such as a unified surgical display and robotic assistance systems. The methods we evaluated over time include dynamic time warping, hidden Markov models, random forests, and recently deep neural networks, specifically convolutional neural networks.

Literacy by Way of Automatic Speech Recognition

2011 ◽  
pp. 2074-2118
Author(s):  
Russell Gluck ◽  
John Fulcher
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Warping ◽  
Oral Storytelling ◽  
Pattern Recognition Techniques ◽  
Dynamic Time ◽  
Over Time

The chapter commences with an overview of automatic speech recognition (ASR), which covers not only the de facto standard approach of hidden Markov models (HMMs), but also the tried-and-proven techniques of dynamic time warping and artificial neural networks (ANNs). The coverage then switches to Gluck’s (2004) draw-talk-write (DTW) process, developed over the past two decades to assist non-text literate people become gradually literate over time through telling and/or drawing their own stories. DTW has proved especially effective with “illiterate” people from strong oral, storytelling traditions. The chapter concludes by relating attempts to date in automating the DTW process using ANN-based pattern recognition techniques on an Apple Macintosh G4™ platform.

scholarly journals Cartographing dynamic stall with machine learning

2020 ◽  
Vol 5 (2) ◽  
pp. 819-838
Author(s):  
Matthew Lennie ◽  
Johannes Steenbuck ◽  
Bernd R. Noack ◽  
Christian Oliver Paschereit
Keyword(s):  
Machine Learning ◽  
Dynamic Time Warping ◽  
Data Science ◽  
Leading Edge ◽  
Separated Flows ◽  
Dynamic Stall ◽  
Energy Yield ◽  
Time Warping ◽  
Coherent Vortex ◽  
Dynamic Time

Abstract. Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge. Eventually the shear layer rolls up, and then a coherent vortex forms and then sheds downstream with its low-pressure core causing a lift overshoot and moment drop. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analyze cycle-to-cycle variations. Modern data science and machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures the fact that secondary and tertiary vorticity vary strongly, and in static stall with surging flow the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.

scholarly journals Reconocimiento del habla mediante el uso de la correlación cruzada y una perceptrón multicapa

Nova Scientia ◽  
2014 ◽  
Vol 6 (12) ◽  
pp. 108
Author(s):  
Carlos A. De Luna-Ortega ◽  
Miguel Mora-González ◽  
Julio C. Martínez-Romo ◽  
Francisco J. Luna-Rosas ◽  
Jesús Muñoz-Maciel
Keyword(s):  
Hidden Markov Models ◽  
Dynamic Time Warping ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Warping ◽  
Dynamic Time

En el presente artículo se da a conocer una alternativa algorítimica a los sistemas actuales de  reconocimiento automático del habla (ASR), mediante una propuesta en la forma de realizar la caracterización de las palabras basada en una aproximación que usa la extracción de coeficientes de la codificación de predicción lineal (LPC) y la correlación cruzada. La implementación consiste en extraer las características fonéticas mediante los coeficientes LPC, después se forman vectores de patrones de la pronunciación conformados por el promedio de los coeficientes LPC de  las muestras de las palabras obteniendo un vector característico de cada pronunciación mediante la autocorrelación de las secuencias de coeficientes LPC; estos vectores se utilizan para entrenar  un clasificador de tipo perceptrón multicapa (MLP). Se realizaron pruebas de desempeño previo entrenamiento con los diferentes patrones de las palabras a reconocer. Se utilizó la fonética de los dígitos del cero al nueve como vocabulario objetivo, debido a su amplia aplicación, y para estimar el desempeño de este método se utilizaron dos corpus de pronunciaciones: el corpus UPA, que contempla en su base de datos la pronuncación de la región occidente de México, y el corpus Tlatoa, que hace lo propio para la región centro de México. Las señales en ambos corpus fueron adquiridas en el lenguaje español,  y a una frecuencia de muestreo de 8kHz. Los porcentajes de reconocimiento obtenidos fueron del 96.7 y 93.3% para las modalidades de mono-locutor para el corpus UPA y múltiple-locutor para el corpus Tlatoa, respectivamente. Asimismo, se realizó una comparación contra dos métodos clásicos del reconocimiento de voz y del habla, Dynamic Time Warping  (DTW) y Hidden Markov Models (HMM).

HUMAN-LIKE DYNAMIC PROGRAMMING NEURAL NETWORKS FOR DYNAMIC TIME WARPING SPEECH RECOGNITION

1995 ◽  
Vol 06 (01) ◽  
pp. 79-89 ◽  
Author(s):  
CHINCHUAN CHIU ◽  
MICHAEL A. SHANBLATT
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Speech Recognition ◽  
Dynamic Time Warping ◽  
Hardware Implementation ◽  
Time Warping ◽  
Network Method ◽  
The Neural Networks ◽  
Dynamic Time ◽  
Better Than

This paper presents a human-like dynamic programming neural network method for speech recognition using dynamic time warping. The networks are configured, much like human’s, such that the minimum states of the network’s energy function represent the near-best correlation between test and reference patterns. The dynamics and properties of the neural networks are analytically explained. Simulations for classifying speaker-dependent isolated words, consisting of 0 to 9 and A to Z, show that the method is better than conventional methods. The hardware implementation of this method is also presented.

Other Measures for Face Recognition

2015 ◽  
pp. 81-92
Keyword(s):  
Face Recognition ◽  
Hidden Markov Models ◽  
Dynamic Time Warping ◽  
Edit Distance ◽  
Markov Models ◽  
Hidden Markov ◽  
Time Warping ◽  
Shape Comparison ◽  
3D Data ◽  
Dynamic Time

Other measures are employed to compute similarity between faces. Although some of them are very popular, such as edit distance or turning function distance, they may be more frequently used for object, vectors or shape comparison and less for faces. This paragraph collects all these measures and the works in which they are used for face recognition. Among them, Dynamic Time Warping (DTW), Hidden Markov Models (HMM), and Fréchet distance have been applied to 3D data.

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