Improved voice activity detection algorithm using wavelet and support vector machine

This paper proposes an improved voice activity detection (VAD) algorithm using wavelet and support vector machine (SVM) for European Telecommunication Standards Institution (ETS1) adaptive multi-rate (AMR) narrow-band (NB) and wide-band (WB) speech codecs. First, based on the wavelet transform, the original IIR filter bank and pitch/tone detector are implemented, respectively, via the wavelet filter bank and the wavelet-based pitch/tone detection algorithm. The wavelet filter bank can divide input speech signal into several frequency bands so that the signal power level at each sub-band can be calculated. In addition, the background noise level can be estimated in each sub-band by using the wavelet de-noising method. The wavelet filter bank is also derived to detect correlated complex signals like music. Then the proposed algorithm can apply SVM to train an optimized non-linear VAD decision rule involving the sub-band power, noise level, pitch period, tone flag, and complex signals warning flag of input speech signals. By the use of the trained SVM, the proposed VAD algorithm can produce more accurate detection results. Various experimental results carried out from the Aurora speech database with different noise conditions show that the proposed algorithm gives considerable VAD performances superior to the AMR-NB VAD Options 1 and 2, and AMR-WB VAD. (C) 2009 Elsevier Ltd. All rights reserved.

Effects of environmental conditions, human activity, reproduction, antler cycle and grouping on fecal glucocorticoids of free-ranging Pampas deer stags (Ozotoceros bezoarticus bezoarticus)

In this study, a commercial enzyme immunoassay (EIA) was validated in detecting glucocorticoids in Pampas deer feces, in order to investigate the influence of several factors on the adrenocortical function. Fecal samples, behavioral data and information concerning male grouping and antlers status were collected at a monthly basis during a 1 year period from free-ranging stags living at Emas National Park, Brazil (18 degrees S/52 degrees W). The results revealed that concentrations of fecal glucocorticoids in winter were significantly higher than those corresponding to spring and summer. In addition, dry season data presented higher levels than during the wet season. Significant difference was found between fecal levels of breeding stags in summer and nonbreeding stags, whereas no difference was observed between breeding stags in winter and nonbreeding stags. on the other hand, males from areas with frequent human disturbance exhibited higher glucocorticoid concentrations and flight distances than individuals from areas of lower human activity. Males with antlers in velvet had elevated levels compared with animals in hard antler or antler casting. Also, we found that glucocorticoid levels were higher in groups with three or more males than in groups with only one male. The flight distances showed positive correlation with fecal glucocorticoid. These data indicate that fecal glucocorticoid provides a useful approach in the evaluation of physiological effects of environment, inter-individuals relationship and human-induced stressors on free-ranging Pampas deer stags. (c) 2005 Elsevier B.V. All rights reserved.

Correlation between koilocytes and human papillomavirus detection by PCR in oral and oropharynx squamous cell carcinoma biopsies

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Adaptative techniques for the human faces detection

This paper presents results from an efficient approach to an automatic detection and extraction of human faces from images with any color, texture or objects in background, that consist in find isosceles triangles formed by the eyes and mouth.

Low frequency of human papillomavirus detection in prostate tissue from individuals from Northern Brazil

The presence of human papillomavirus (HPV) was evaluated in 65 samples of prostate tumours and six samples of prostates with benign prostatic hyperplasia from individuals from Northern Brazil. We used a highly sensitive test, the Linear Array HPV Genotyping Test, to detect 37 high and low-risk HPV types. In this study, only 3% of tumour samples showed HPV infection. Our findings support the conclusion that, despite the high incidence of HPV infection in the geographic regions studied, HPV was not associated with a higher risk of prostate cancer. To our knowledge, this is the first study evaluating the frequency of HPV detection in prostatic tissue of individuals from Brazil.

Human Presence Detection: analisi comparativa e caso di studio in Home Manager

La possibilità di monitorare l’attività degli utenti in un sistema domotico, sia considerando le azioni effettuate direttamente sul sistema che le informazioni ricavabili da strumenti esterni come la loro posizione GPS, è un fattore importante per anticipare i bisogni e comprendere le preferenze degli utenti stessi, rendendo sempre più intelligenti ed autonomi i sistemi domotici. Mentre i sistemi attualmente disponibili non includono o non sfruttano appieno queste potenzialità, l'obiettivo di sistemi prototipali sviluppati per fini di ricerca, quali ad esempio Home Manager, è invece quello di utilizzare le informazioni ricavabili dai dispositivi e dal loro utilizzo per abilitare ragionamenti e politiche di ordine superiore. Gli obiettivi di questo lavoro sono: - Classificare ed elencare i diversi sensori disponibili al fine di presentare lo stato attuale della ricerca nel campo dello Human Sensing, ovvero del rilevamento di persone in un ambiente. - Giustificare la scelta della telecamera come sensore per il rilevamento di persone in un ambiente domestico, riportando metodi per l’analisi video in grado di interpretare i fotogrammi e rilevare eventuali figure in movimento al loro interno. - Presentare un’architettura generica per integrare dei sensori in un sistema di sorveglianza, implementando tale architettura ed alcuni algoritmi per l’analisi video all’interno di Home Manager con l’aiuto della libreria OpenCV .

The Interaction Between Forest Fires and Human Activity in Southern Switzerland

The impact of human activities on the fire regime in southern Switzerland was studied using (pre)historical charcoal and pollen data from lake sediments and statistical data from the 20th century. The cultural impact on forest fire was established by correlating charcoal-influx data with pollen percentages of anthropogenic indicators such as Plantago lanceolata, the Cerealia (sum of Avena t., Triticum t. and Hordeum t.) and Secale. During the 20th century, fire frequency was correlated with precipitation, dry and very dry periods and landscape management indicators. The effects of human activity on the fire regime are clearly recognisable since at least the Neolithic period. Using palaeoecological or statistical data, the variations in fire regime originating from anthropogenic actions may be differentiated from those due to climatic changes if they are sufficiently conspicuous.

Local Human Activity in two communities of Formosa, Argentina

author must provide abstract

Secure access control by means of human stress detection

This paper proposes a stress detection system based on fuzzy logic and the physiological signals heart rate and galvanic skin response. The main contribution of this method relies on the creation of a stress template, collecting the behaviour of previous signals under situations with a different level of stress in each individual. The creation of this template provides an accuracy of 99.5% in stress detection, improving the results obtained by current pattern recognition techniques like GMM, k-NN, SVM or Fisher Linear Discriminant. In addition, this system can be embedded in security systems to detect critical situations in accesses as cross-border control. Furthermore, its applications can be extended to other fields as vehicle driver state-of-mind management, medicine or sport training.

Localization and activity detection Based on the fusion of environment And inertial sensors = Localización y detección de actividad Basadas en integración de sensores y dispositivos inerciales

Los sensores inerciales (acelerómetros y giróscopos) se han ido introduciendo poco a poco en dispositivos que usamos en nuestra vida diaria gracias a su minituarización. Hoy en día todos los smartphones contienen como mínimo un acelerómetro y un magnetómetro, siendo complementados en losmás modernos por giróscopos y barómetros. Esto, unido a la proliferación de los smartphones ha hecho viable el diseño de sistemas basados en las medidas de sensores que el usuario lleva colocados en alguna parte del cuerpo (que en un futuro estarán contenidos en tejidos inteligentes) o los integrados en su móvil. El papel de estos sensores se ha convertido en fundamental para el desarrollo de aplicaciones contextuales y de inteligencia ambiental. Algunos ejemplos son el control de los ejercicios de rehabilitación o la oferta de información referente al sitio turístico que se está visitando. El trabajo de esta tesis contribuye a explorar las posibilidades que ofrecen los sensores inerciales para el apoyo a la detección de actividad y la mejora de la precisión de servicios de localización para peatones. En lo referente al reconocimiento de la actividad que desarrolla un usuario, se ha explorado el uso de los sensores integrados en los dispositivos móviles de última generación (luz y proximidad, acelerómetro, giróscopo y magnetómetro). Las actividades objetivo son conocidas como ‘atómicas’ (andar a distintas velocidades, estar de pie, correr, estar sentado), esto es, actividades que constituyen unidades de actividades más complejas como pueden ser lavar los platos o ir al trabajo. De este modo, se usan algoritmos de clasificación sencillos que puedan ser integrados en un móvil como el Naïve Bayes, Tablas y Árboles de Decisión. Además, se pretende igualmente detectar la posición en la que el usuario lleva el móvil, no sólo con el objetivo de utilizar esa información para elegir un clasificador entrenado sólo con datos recogidos en la posición correspondiente (estrategia que mejora los resultados de estimación de la actividad), sino también para la generación de un evento que puede producir la ejecución de una acción. Finalmente, el trabajo incluye un análisis de las prestaciones de la clasificación variando el tipo de parámetros y el número de sensores usados y teniendo en cuenta no sólo la precisión de la clasificación sino también la carga computacional. Por otra parte, se ha propuesto un algoritmo basado en la cuenta de pasos utilizando informaiii ción proveniente de un acelerómetro colocado en el pie del usuario. El objetivo final es detectar la actividad que el usuario está haciendo junto con la estimación aproximada de la distancia recorrida. El algoritmo de cuenta pasos se basa en la detección de máximos y mínimos usando ventanas temporales y umbrales sin requerir información específica del usuario. El ámbito de seguimiento de peatones en interiores es interesante por la falta de un estándar de localización en este tipo de entornos. Se ha diseñado un filtro extendido de Kalman centralizado y ligeramente acoplado para fusionar la información medida por un acelerómetro colocado en el pie del usuario con medidas de posición. Se han aplicado también diferentes técnicas de corrección de errores como las de velocidad cero que se basan en la detección de los instantes en los que el pie está apoyado en el suelo. Los resultados han sido obtenidos en entornos interiores usando las posiciones estimadas por un sistema de triangulación basado en la medida de la potencia recibida (RSS) y GPS en exteriores. Finalmente, se han implementado algunas aplicaciones que prueban la utilidad del trabajo desarrollado. En primer lugar se ha considerado una aplicación de monitorización de actividad que proporciona al usuario información sobre el nivel de actividad que realiza durante un período de tiempo. El objetivo final es favorecer el cambio de comportamientos sedentarios, consiguiendo hábitos saludables. Se han desarrollado dos versiones de esta aplicación. En el primer caso se ha integrado el algoritmo de cuenta pasos en una plataforma OSGi móvil adquiriendo los datos de un acelerómetro Bluetooth colocado en el pie. En el segundo caso se ha creado la misma aplicación utilizando las implementaciones de los clasificadores en un dispositivo Android. Por otro lado, se ha planteado el diseño de una aplicación para la creación automática de un diario de viaje a partir de la detección de eventos importantes. Esta aplicación toma como entrada la información procedente de la estimación de actividad y de localización además de información almacenada en bases de datos abiertas (fotos, información sobre sitios) e información sobre sensores reales y virtuales (agenda, cámara, etc.) del móvil. Abstract Inertial sensors (accelerometers and gyroscopes) have been gradually embedded in the devices that people use in their daily lives thanks to their miniaturization. Nowadays all smartphones have at least one embedded magnetometer and accelerometer, containing the most upto- date ones gyroscopes and barometers. This issue, together with the fact that the penetration of smartphones is growing steadily, has made possible the design of systems that rely on the information gathered by wearable sensors (in the future contained in smart textiles) or inertial sensors embedded in a smartphone. The role of these sensors has become key to the development of context-aware and ambient intelligent applications. Some examples are the performance of rehabilitation exercises, the provision of information related to the place that the user is visiting or the interaction with objects by gesture recognition. The work of this thesis contributes to explore to which extent this kind of sensors can be useful to support activity recognition and pedestrian tracking, which have been proven to be essential for these applications. Regarding the recognition of the activity that a user performs, the use of sensors embedded in a smartphone (proximity and light sensors, gyroscopes, magnetometers and accelerometers) has been explored. The activities that are detected belong to the group of the ones known as ‘atomic’ activities (e.g. walking at different paces, running, standing), that is, activities or movements that are part of more complex activities such as doing the dishes or commuting. Simple, wellknown classifiers that can run embedded in a smartphone have been tested, such as Naïve Bayes, Decision Tables and Trees. In addition to this, another aim is to estimate the on-body position in which the user is carrying the mobile phone. The objective is not only to choose a classifier that has been trained with the corresponding data in order to enhance the classification but also to start actions. Finally, the performance of the different classifiers is analysed, taking into consideration different features and number of sensors. The computational and memory load of the classifiers is also measured. On the other hand, an algorithm based on step counting has been proposed. The acceleration information is provided by an accelerometer placed on the foot. The aim is to detect the activity that the user is performing together with the estimation of the distance covered. The step counting strategy is based on detecting minima and its corresponding maxima. Although the counting strategy is not innovative (it includes time windows and amplitude thresholds to prevent under or overestimation) no user-specific information is required. The field of pedestrian tracking is crucial due to the lack of a localization standard for this kind of environments. A loosely-coupled centralized Extended Kalman Filter has been proposed to perform the fusion of inertial and position measurements. Zero velocity updates have been applied whenever the foot is detected to be placed on the ground. The results have been obtained in indoor environments using a triangulation algorithm based on RSS measurements and GPS outdoors. Finally, some applications have been designed to test the usefulness of the work. The first one is called the ‘Activity Monitor’ whose aim is to prevent sedentary behaviours and to modify habits to achieve desired objectives of activity level. Two different versions of the application have been implemented. The first one uses the activity estimation based on the step counting algorithm, which has been integrated in an OSGi mobile framework acquiring the data from a Bluetooth accelerometer placed on the foot of the individual. The second one uses activity classifiers embedded in an Android smartphone. On the other hand, the design of a ‘Travel Logbook’ has been planned. The input of this application is the information provided by the activity and localization modules, external databases (e.g. pictures, points of interest, weather) and mobile embedded and virtual sensors (agenda, camera, etc.). The aim is to detect important events in the journey and gather the information necessary to store it as a journal page.

Efficiency of human activity on information spreading on Twitter

In this work we study Twitter data to understand influence dynamics in social networks. We define user efficiency on Twitter, as the ratio between the emergent spreading process and the activity employed by the user. We characterize this property by means of a quantitative analysis of the structural and dynamical patterns emergent from human interactions, and show it to be universal across several Twitter conversations.