Tunable Acoustic Lens for Acoustic Holograms

The object of the present document is to explain briefy the internship realized at imec (Leuven, Belgium), giving further details about the technical aspects of the realized project as well as the obtained results and the acquired knowledge. In the following sections the work done will also be related to the skills acquired during the Master's, and a final valoration from both the useful educative aspects and the possible lacks of knowledge to achieve the goals of the stage.2018/201

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

Machine learning in marine ecology: an overview of techniques and applications

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

Tunable Acoustic Lens for Acoustic Holograms

The object of the present document is to explain briefy the internship realized at imec (Leuven, Belgium), giving further details about the technical aspects of the realized project as well as the obtained results and the acquired knowledge. In the following sections the work done will also be related to the skills acquired during the Master's, and a final valoration from both the useful educative aspects and the possible lacks of knowledge to achieve the goals of the stage.2018/201

An ocean sound map along the track of a journey around the globe - Underwater soundscapes of the Great Coral Reef

The past years have seen increasing levels of noise pollution in the oceans, which has been demonstrated to have a negative impact in marine ecosy stems. This project consists in creating a World Sound Map where all the noise generated by the shipping industry (increased due to offshore industrial development ) is simulated and shown in an easy - to - understand wa y, so the public can access this informat ion. This Sound Map is part of the international project led by the LAB Listen to the Deep Ocean Environment (LIDO) , and it consists of an int eractive Web Application where the sound pressure distribution of the noise made by the ships can be checked in th e vicinity of the journey of a ship which is doing the Globe - Tour as a part of the LIDO project . This application can be used for many thi ngs related to underwater sound: eliminating shipping noise from ocean recordings, evaluating the impact of the presence of ships in different ecosystems or comparing the locations of the ships and marine fauna in order to predict the possible harmful impact of shipping industry in marine fauna

A Robust Method to Automatically Detect Fin Whale Acoustic Presence in Large and Diverse Passive Acoustic Datasets

The growing availability of long-term and large-scale passive acoustic recordings open the possibility of monitoring the vocal activity of elusive oceanic species, such as fin whales (Balaenoptera physalus), in order to acquire knowledge on their distribution, behavior, population structure and abundance. Fin whales produce low-frequency and high-intensity pulses, both as single vocalizations and as song sequences (only males) which can be detected over large distances. Numerous distant fin whales producing these pulses generate a so-called chorus, by spectrally and temporally overlapping single vocalizations. Both fin whale pulses and fin whale chorus provide a distinct source of information on fin whales present at different distances to the recording location. The manual review of vast amounts of passive acoustic data for the presence of single vocalizations and chorus by human experts is, however, time-consuming, often suffers from low reproducibility and in its entirety, it is practically impossible. In this publication, we present and compare robust algorithms for the automatic detection of fin whale choruses and pulses which yield good performance results (i.e., false positive rates < 3% and true positive rates > 76%) when applied to real-world passive acoustic datasets characterized by vast amounts of data, with only a small proportion of the data containing the target sounds, and diverse soundscapes from the Southern Ocean

Integració d'un sistema de detecció d'intrusions en passos a nivell en el sistema BLOCKSAT

La gran accidentalidad en los pasos a nivel de las vías ferroviarias y, sobretodo, las graves consecuencias que pueden acarrear han impulsado a lo largo de los últimos años a investigar sobre métodos para proteger los pasos a nivel en la medida de lo posible. Aun así, aunque se protejan con medidas preventivas para que los usuarios no invadan la vía en el momento en que cruza un tren, sigue habiendo fatalidades debido a la malinterpretación de los avisos por parte del usuario, al mal funcionamiento del sistema de aviso o protección, a la temeridad de los usuarios, al intento de suicidio o al mal cálculo del tiempo disponible para cruzar, así como causadas por objetos caídos, olvidados o situados en la vía con un propósito terrorista, siendo susceptibles a provocar el descarrilamiento del tren, con las fatales consecuencias que esto comporta. Así pues, recientemente han salido al mercado varios sistemas capaces de detectar intrusiones en los pasos a nivel que permiten consultar la ocupación del paso en el momento en que el tren se acerca, permitiéndole al conductor tener margen de maniobra para evitar un accidente en caso de que esté ocupado. El innovador sistema de gestión de tráfico ferroviario de la empresa SENER Ingeniería y Sistemas S.A., BLOCKSAT, consta de un sistema embarcado en el tren que le permite comunicarse con el centro de control y automatizar todo el proceso de comunicaciones para el control y la gestión del tráfico. Siendo el mercado estadounidense el principal interesado en dicho sistema y debido a las elevadas fatalidades anuales que se producen en el país, se ha considerado necesario integrar en BLOCKSAT un sistema que permita la detección de intrusiones en los pasos a nivel y se comunique con el sistema embarcado en el tren con el fin de avisar al maquinista con la mayor antelación posible. Con el fin de determinar la mejor solución a integrar, en el presente estudio se hace una caracterización de las estadísticas de los pasos a nivel en Estados Unidos y de los escenarios posibles y una búsqueda en el mercado determinar las posibles soluciones de detección, con la consecuente elección de instalar en cada paso a nivel un detector RLS 3060SH junto con un microprocesador STM32 L0, conectados con el tren mediante una red XBee SX y con un módulo para enviar mensajes de seguridad GSM SIM900 Una vez determinados el tipo de solución y los equipos que la forman, se procede a diseñar las comunicaciones entre el paso a nivel y el tren, desde el protocolo a seguir hasta el sistema de control, con su correspondiente algoritmo de funcionamiento. Con el fin de verificar el algoritmo en todas las posibles situaciones, posteriormente se procede a simular el intercambio de mensajes entre el paso a nivel y el tren MATLAB, permitiendo comprobar el comportamiento del sistema delante de cualquier error

An ocean sound map along the track of a journey around the globe - Underwater soundscapes of the Great Coral Reef

The past years have seen increasing levels of noise pollution in the oceans, which has been demonstrated to have a negative impact in marine ecosy stems. This project consists in creating a World Sound Map where all the noise generated by the shipping industry (increased due to offshore industrial development ) is simulated and shown in an easy - to - understand wa y, so the public can access this informat ion. This Sound Map is part of the international project led by the LAB Listen to the Deep Ocean Environment (LIDO) , and it consists of an int eractive Web Application where the sound pressure distribution of the noise made by the ships can be checked in th e vicinity of the journey of a ship which is doing the Globe - Tour as a part of the LIDO project . This application can be used for many thi ngs related to underwater sound: eliminating shipping noise from ocean recordings, evaluating the impact of the presence of ships in different ecosystems or comparing the locations of the ships and marine fauna in order to predict the possible harmful impact of shipping industry in marine fauna

A robust method to automatically detect fin whale acoustic presence in large and diverse passive acoustic datasets

The growing availability of long-term and large-scale passive acoustic recordings open the possibility of monitoring the vocal activity of elusive oceanic species, such as fin whales (Balaenoptera physalus), in order to acquire knowledge on their distribution, behavior, population structure and abundance. Fin whales produce low-frequency and high-intensity pulses, both as single vocalizations and as song sequences (only males) which can be detected over large distances. Numerous distant fin whales producing these pulses generate a so-called chorus, by spectrally and temporally overlapping single vocalizations. Both fin whale pulses and fin whale chorus provide a distinct source of information on fin whales present at different distances to the recording location. The manual review of vast amounts of passive acoustic data for the presence of single vocalizations and chorus by human experts is, however, time-consuming, often suffers from low reproducibility and in its entirety, it is practically impossible. In this publication, we present and compare robust algorithms for the automatic detection of fin whale choruses and pulses which yield good performance results (i.e., false positive rates 76%) when applied to real-world passive acoustic datasets characterized by vast amounts of data, with only a small proportion of the data containing the target sounds, and diverse soundscapes from the Southern Ocean

Integració d'un sistema de detecció d'intrusions en passos a nivell en el sistema BLOCKSAT

La gran accidentalidad en los pasos a nivel de las vías ferroviarias y, sobretodo, las graves consecuencias que pueden acarrear han impulsado a lo largo de los últimos años a investigar sobre métodos para proteger los pasos a nivel en la medida de lo posible. Aun así, aunque se protejan con medidas preventivas para que los usuarios no invadan la vía en el momento en que cruza un tren, sigue habiendo fatalidades debido a la malinterpretación de los avisos por parte del usuario, al mal funcionamiento del sistema de aviso o protección, a la temeridad de los usuarios, al intento de suicidio o al mal cálculo del tiempo disponible para cruzar, así como causadas por objetos caídos, olvidados o situados en la vía con un propósito terrorista, siendo susceptibles a provocar el descarrilamiento del tren, con las fatales consecuencias que esto comporta. Así pues, recientemente han salido al mercado varios sistemas capaces de detectar intrusiones en los pasos a nivel que permiten consultar la ocupación del paso en el momento en que el tren se acerca, permitiéndole al conductor tener margen de maniobra para evitar un accidente en caso de que esté ocupado. El innovador sistema de gestión de tráfico ferroviario de la empresa SENER Ingeniería y Sistemas S.A., BLOCKSAT, consta de un sistema embarcado en el tren que le permite comunicarse con el centro de control y automatizar todo el proceso de comunicaciones para el control y la gestión del tráfico. Siendo el mercado estadounidense el principal interesado en dicho sistema y debido a las elevadas fatalidades anuales que se producen en el país, se ha considerado necesario integrar en BLOCKSAT un sistema que permita la detección de intrusiones en los pasos a nivel y se comunique con el sistema embarcado en el tren con el fin de avisar al maquinista con la mayor antelación posible. Con el fin de determinar la mejor solución a integrar, en el presente estudio se hace una caracterización de las estadísticas de los pasos a nivel en Estados Unidos y de los escenarios posibles y una búsqueda en el mercado determinar las posibles soluciones de detección, con la consecuente elección de instalar en cada paso a nivel un detector RLS 3060SH junto con un microprocesador STM32 L0, conectados con el tren mediante una red XBee SX y con un módulo para enviar mensajes de seguridad GSM SIM900 Una vez determinados el tipo de solución y los equipos que la forman, se procede a diseñar las comunicaciones entre el paso a nivel y el tren, desde el protocolo a seguir hasta el sistema de control, con su correspondiente algoritmo de funcionamiento. Con el fin de verificar el algoritmo en todas las posibles situaciones, posteriormente se procede a simular el intercambio de mensajes entre el paso a nivel y el tren MATLAB, permitiendo comprobar el comportamiento del sistema delante de cualquier error