1,122 research outputs found
Unified End-to-End Speech Recognition and Endpointing for Fast and Efficient Speech Systems
Automatic speech recognition (ASR) systems typically rely on an external
endpointer (EP) model to identify speech boundaries. In this work, we propose a
method to jointly train the ASR and EP tasks in a single end-to-end (E2E)
multitask model, improving EP quality by optionally leveraging information from
the ASR audio encoder. We introduce a "switch" connection, which trains the EP
to consume either the audio frames directly or low-level latent representations
from the ASR model. This results in a single E2E model that can be used during
inference to perform frame filtering at low cost, and also make high quality
end-of-query (EOQ) predictions based on ongoing ASR computation. We present
results on a voice search test set showing that, compared to separate
single-task models, this approach reduces median endpoint latency by 120 ms
(30.8% reduction), and 90th percentile latency by 170 ms (23.0% reduction),
without regressing word error rate. For continuous recognition, WER improves by
10.6% (relative).Comment: To be published in Spoken Language Technology Workshop (SLT) 202
Characterization of damage evolution on metallic components using ultrasonic non-destructive methods
When fatigue is considered, it is expected that structures and machinery eventually fail. Still, when this damage is unexpected, besides of the negative economic impact that it produces, life of people could be potentially at risk. Thus, nowadays it is imperative that the infrastructure managers, ought to program regular inspection and maintenance for their assets; in addition, designers and materials manufacturers, can access to appropriate diagnostic tools in order to build superior and more reliable materials. In this regard, and for a number of applications, non-destructive evaluation techniques have proven to be an efficient and helpful alternative to traditional destructive assays of materials. Particularly, for the design area of materials, in recent times researchers have exploited the Acoustic Emission (AE) phenomenon as an additional assessing tool with which characterize the mechanical properties of specimens. Nevertheless, several challenges arise when treat said phenomenon, since its intensity, duration and arrival behavior is essentially stochastic for traditional signal processing means, leading to inaccuracies for the outcome assessment.
In this dissertation, efforts are focused on assisting in the characterization of the mechanical properties of advanced high strength steels during under uniaxial tensile tests. Particularly of interest, is being able to detect the nucleation and growth of a crack throughout said test. Therefore, the resulting AE waves generated by the specimen during the test are assessed with the aim of characterize their evolution.
For this, on the introduction, a brief review about non-destructive methods emphasizing the AE phenomenon is introduced. Next is presented, an exhaustive analysis with regard to the challenge and deficiencies of detecting and segmenting each AE event over a continuous data-stream with the traditional threshold detection method, and additionally, with current state of the art methods. Following, a novel AE event detection method is proposed, with the aim of overcome the aforementioned limitations. Evidence showed that the proposed method (which is based on the short-time features of the waveform of the AE signal), excels the detection capabilities of current state of the art methods, when onset and endtime precision, as well as when quality of detection and computational speed are also considered. Finally, a methodology aimed to analyze the frequency spectrum evolution of the AE phenomenon during the tensile test, is proposed. Results indicate that it is feasible to correlate nucleation and growth of a crack with the frequency content evolution of AE events.Cuando se considera la fatiga de los materiales, se espera que eventualmente las estructuras y las maquinarias fallen. Sin embargo, cuando este daño es inesperado, además del impacto económico que este produce, la vida de las personas podría estar potencialmente en riesgo. Por lo que hoy en día, es imperativo que los administradores de las infraestructuras deban programar evaluaciones y mantenimientos de manera regular para sus activos. De igual manera, los diseñadores y fabricantes de materiales deberían de poseer herramientas de diagnóstico apropiadas con el propósito de obtener mejores y más confiables materiales. En este sentido, y para un amplio número de aplicaciones, las técnicas de evaluación no destructivas han demostrado ser una útil y eficiente alternativa a los ensayos destructivos tradicionales de materiales. De manera particular, en el área de diseño de materiales, recientemente los investigadores han aprovechado el fenómeno de Emisión Acústica (EA) como una herramienta complementaria de evaluación, con la cual poder caracterizar las propiedades mecánicas de los especímenes. No obstante, una multitud de desafíos emergen al tratar dicho fenómeno, ya que el comportamiento de su intensidad, duración y aparición es esencialmente estocástico desde el punto de vista del procesado de señales tradicional, conllevando a resultados imprecisos de las evaluaciones. Esta disertación se enfoca en colaborar en la caracterización de las propiedades mecánicas de Aceros Avanzados de Alta Resistencia (AAAR), para ensayos de tracción de tensión uniaxiales, con énfasis particular en la detección de fatiga, esto es la nucleación y generación de grietas en dichos componentes metálicos. Para ello, las ondas mecánicas de EA que estos especímenes generan durante los ensayos, son estudiadas con el objetivo de caracterizar su evolución. En la introducción de este documento, se presenta una breve revisión acerca de los métodos existentes no destructivos con énfasis particular al fenómeno de EA. A continuación, se muestra un análisis exhaustivo respecto a los desafíos para la detección de eventos de EA y las y deficiencias del método tradicional de detección; de manera adicional se evalúa el desempeño de los métodos actuales de detección de EA pertenecientes al estado del arte. Después, con el objetivo de superar las limitaciones presentadas por el método tradicional, se propone un nuevo método de detección de actividad de EA; la evidencia demuestra que el método propuesto (basado en el análisis en tiempo corto de la forma de onda), supera las capacidades de detección de los métodos pertenecientes al estado del arte, cuando se evalúa la precisión de la detección de la llegada y conclusión de las ondas de EA; además de, cuando también se consideran la calidad de detección de eventos y la velocidad de cálculo. Finalmente, se propone una metodología con el propósito de evaluar la evolución de la energía del espectro frecuencial del fenómeno de EA durante un ensayo de tracción; los resultados demuestran que es posible correlacionar el contenido de dicha evolución frecuencial con respecto a la nucleación y crecimiento de grietas en AAAR's.Postprint (published version
Transcutaneous measurement of volume blood flow
Blood flow velocity measurements, using Doppler velocimeter, are described. The ability to measure blood velocity using ultrasound is derived from the Doppler effect; the change in frequency which occurs when sound is reflected or transmitted from a moving target. When ultrasound of the appropriate frequency is transmitted through a moving blood stream, the blood cells act as point scatterers of ultrasonic energy. If this scattered ultrasonic energy is detected, it is found to be shifted in frequency according to the velocity of the blood cells, nu, the frequency of the incident sound, f sub o, the speed of sound in the medium, c, and the angle between the sound beam and the velocity vector, o. The relation describing this effect is known as the Doppler equation. Delta f = 2 f sub o x nu x cos alpha/c. The theoretical and experimental methods are evaluated
Towards Automatic Creation of Annotations to Foster Development of Named Entity Recognizers
Named Entity Recognition (NER) is an essential step for many natural language processing tasks, including Information Extraction. Despite recent advances, particularly using deep learning techniques, the creation of accurate named entity recognizers continues a complex task, highly dependent on annotated data availability. To foster existence of NER systems for new domains it is crucial to obtain the required large volumes of annotated data with low or no manual labor. In this paper it is proposed a system to create the annotated data automatically, by resorting to a set of existing NERs and information sources (DBpedia). The approach was tested with documents of the Tourism domain. Distinct methods were applied for deciding the final named entities and respective tags. The results show that this approach can increase the confidence on annotations and/or augment the number of categories possible to annotate. This paper also presents examples of new NERs that can be rapidly created with the obtained annotated data. The annotated data, combined with the possibility to apply both the ensemble of NER systems and the new Gazetteer-based NERs to large corpora, create the necessary conditions to explore the recent neural deep learning state-of-art approaches to NER (ex: BERT) in domains with scarce or nonexistent data for training
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Quantitative analysis of the focused ultrasound-induced blood-brain barrier opening with applications in neurodegenerative disorders
The blood-brain barrier poses a formidable impediment to the treatment of adult-onset neurodegenerative disorders, by prevention of most drugs from gaining access to the brain parenchyma. Focused ultrasound (FUS), in conjunction with systemically administered microbubbles, has been shown to open the blood-brain barrier (BBB) locally, reversibly and non invasively both in rodents and in non-human-primates. Initially, we demonstrate a monotonic increase of the BBB opening volume with close to normal incidence angle, detectable by diffusion tensor imaging; the employed contrast-free magnetic resonance protocol that revealed the anisotropic nature of the diffusion gradient. Implementation of this optimized BBB opening technique in Parkinsonian mice, coupled with the administration of trophic growth factors, induced restorative effects in the dopaminergic neurons, the main cellular target of the pathological process in Parkinson’s disease. The immune response initiated by the FUS-induced BBB disruption has been proven pivotal in reducing proteinaceous aggregates from the brain through the activation of a gliosis cascade. Therefore, we investigated this immunomodulatory effect in Alzheimer’s disease. The neuropathological hallmarks of Alzheimer’s disease include aggregation of amyloid beta into plaques and accumulation of tau protein into neurofibrillary tangles. Tau pathology correlates well with impaired neuronal activity and dementia and was found to be attenuated after the application of ultrasound that correlated with increased microglia activity. Given the beneficial effect of this methodology on the Alzheimer’s pathologies when studied separately, we explored the application of FUS in brains subjected concurrently to amyloidosis and tau phosphorylation. Our findings indicate the reduction of tau protein and decrease in the amyloid load from brains treated with ultrasound, accompanied by spatial memory improvement. Overall, in this dissertation, we established an optimized targeting and detection protocol, pre-clinical implementation of which confirmed its ameliorative effects as a drug-delivery adjuvant or an immune response stimulant. These preclinical findings support the immense potential of such a methodology that significantly contributes to the treatment of different neurodegenerative disorders curbing their progression
Using wireless sensors and networks program for chemical particle propagation mapping and chemical source localization
Chemical source localization is a challenge for most of researchers. It has extensive applications, such as anti-terrorist military, Gas and oil industry, and environment engineering. This dissertation used wireless sensor and sensor networks to get chemical particle propagation mapping and chemical source localization. First, the chemical particle propagation mapping is built using interpolation and extrapolation methods. The interpolation method get the chemical particle path through the sensors, and the extrapolation method get the chemical particle beyond the sensors. Both of them compose of the mapping in the whole considered area. Second, the algorithm of sensor fusion is proposed. It smooths the chemical particle paths through integration of more sensors\u27 value and updating the parameters. The updated parameters are associated with including sensor fusion among chemical sensors and wind sensors at same positions and sensor fusion among sensors at different positions. This algorithm improves the accuracy and efficiency of chemical particle mapping. Last, the reasoning system is implemented aiming to detect the chemical source in the considered region where the chemical particle propagation mapping has been finished. This control scheme dynamically analyzes the data from the sensors and guide us to find the goal. In this dissertation, the novel algorithm of modelling chemical propagation is programmed using Matlab. Comparing the results from computational fluid dynamics (CFD) software COMSOL, this algorithm have the same level of accuracy. However, it saves more computational times and memories. The simulation and experiment of detecting chemical source in an indoor environment and outdoor environment are finished in this dissertation --Abstract, page iii
Blind Multiridge Detection and Reconstruction Using Ultrasonic Signals
Time-frequency signal analysis has been widely applied in the modern radar, acoustic, sonar and ultrasonic signal processing techniques. Recently, the nondestructive testing (NDT) techniques via the ultrasonic instrumentation have shown the striking capability of the quality control for the material fabrication industry. In this thesis, we first provide a general mathematical model for the ultrasonic signals collected by pulse-echo sensors and then design a totally blind, novel, signal processing NDT technique relying on neither a priori signal information nor any manual effort. The signature signal can be blindly extracted by using the automatic optimal frame size selection for further modeling and characterization of the ultrasonic signal using Gabor analysis. This modeled signature signal is used for multiridge detection and for reconstruction of the signal. The detected ridge information can be used to estimate the transmission and attenuation coefficients, shear modulus, and Young’s modulus associated with any arbitrary material sample for fabrication quality control. Thus, our algorithm can be applied for ultrasonic signal characterization and ridge detection in non-destructive testing for new material fabrication. Experimental results show that the ridge detection performance by our proposed method is superior to that of the existing techniques
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