20 research outputs found

    Assessing sheep behavior through low-power microcontrollers in smart agriculture scenarios

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    Automatic animal monitoring can bring several advantages to the livestock sector. The emergence of low-cost and low-power miniaturized sensors, together with the ability of handling huge amounts of data, has led to a boost of new intelligent farming solutions. One example is the SheepIT solution that is being commercialized by iFarmtec. The main objectives of the solution are monitoring the sheep’s posture while grazing in vineyards, and conditioning their behaviour using appropriate stimuli, such that they only feed from the ground or from the lower branches of the vines. The quality of the monitoring procedure has a linear correlation with the animal condition capability of the solution, i.e., on the effectiveness of the applied stimuli. Thus, a Real-Time mechanism capable of identifying animal behaviour such as infraction, eating, walking or running movements and standing position is required. On a previous work we proposed a solution based on low-power microcontrollers enclosed in collars wearable by sheep. Machine Learning techniques have been rising as a useful tool for dealing with big amounts of data. From the wide range of techniques available, the use of Decision Trees is particularly relevant since it allows the retrieval of a set of conditions easily transformed in lightweight machine code. The goal of this paper is to evaluate an enhanced animal monitoring mechanism and compare it to existing ones. In order to achieve this goal, a real deployment scenario was availed to gather relevant data from sheep’s collar. After this step, we evaluated the impact of several feature transformations and pre-processing techniques on the model learned from the system. Due to the natural behaviour of sheep, which spend most of the time grazing, several pre-processing techniques were tested to deal with the unbalanced dataset, particularly resorting on features related with stateful history. Albeit presenting promising results, with accuracy over 96%, these features resulted in unfeasible implementations. Hence, the best feasible model was achieved with 10 features obtained from the sensors’ measurements plus an additional temporal feature. The global accuracy attained was above 91%. Howbeit, further research shall assess a way of dealing with this kind of unbalanced datasets and take advantage of the insights given by the results achieved when using the state’s history.publishe

    Lawrence, Spring 2021

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    https://lux.lawrence.edu/alumni_magazines/1116/thumbnail.jp

    A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

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    In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

    An Overview of Computational Approaches for Interpretation Analysis

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    It is said that beauty is in the eye of the beholder. But how exactly can we characterize such discrepancies in interpretation? For example, are there any specific features of an image that makes person A regard an image as beautiful while person B finds the same image displeasing? Such questions ultimately aim at explaining our individual ways of interpretation, an intention that has been of fundamental importance to the social sciences from the beginning. More recently, advances in computer science brought up two related questions: First, can computational tools be adopted for analyzing ways of interpretation? Second, what if the "beholder" is a computer model, i.e., how can we explain a computer model's point of view? Numerous efforts have been made regarding both of these points, while many existing approaches focus on particular aspects and are still rather separate. With this paper, in order to connect these approaches we introduce a theoretical framework for analyzing interpretation, which is applicable to interpretation of both human beings and computer models. We give an overview of relevant computational approaches from various fields, and discuss the most common and promising application areas. The focus of this paper lies on interpretation of text and image data, while many of the presented approaches are applicable to other types of data as well.Comment: Preprint submitted to Digital Signal Processin

    A Sandbox in Which to Learn and Develop Soar Agents

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    It is common for military personnel to leverage simulations (and simulators) as cost-effective tools to train and become proficient at various tasks (e.g., flying an aircraft and/or performing a mission, among others). These training simulations often need to represent humans within the simulated world in a realistic manner. Realistic implies creating simulated humans that exhibit behaviors that mimic real-world decision making and actions. Typically, to create the decision-making logic, techniques developed from the domain of artificial intelligence are used. Although there are several approaches to developing intelligent agents; we focus on leveraging and open source project called Soar, to define agent behavior. This research took an off-the-shelf open-source software product (called the AI sandbox) that facilitates the creation of 3D virtual worlds and interfaced it to the Soar package. Because the world created by the sandbox is rich in features, easily configurable using a simple scripting system, and visually engaging, it\u27s ideal as a learning platform to develop Soar agents more aligned with military simulations. In summary, this research develops a platform (or learning environment) to learn how to develop Soar-based agents

    Intelligent modelling of temperature propagation induced by therapeutic ultrasound

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    Dissertação de Mestrado, Engenharia Electrónica e Telecomunicações, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015Este tese pretende estudar a possibilidade de aplicar um uma abordagem inovativa para estimar a propagação de temperatura em tecidos durante termoterapias, num paradigma não invasivo. A referência do estado da arte é imposta pela uso de técnicas de ressonância magnética (MRI), onde são obtidas resoluções de temperatura com erros absolutos inferiores a 0:5 oC=cm3. Propõe-se estimar a evolução da temperatura através do uso de modelos preditivos, baseados em redes neuronais b-spline, evoluídas pelo algoritmo ASMOD. Inicialmente os dados utilizados foram caracterizados de forma a que o leitor possa avaliar se os dados em questão são representativos e adequados do fenómeno físico que se pretende modelar. Gradualmente a complexidade do ambiente visado na modelação foi aumentada, resultando em três diferentes tipologias de modelo: SPSI, MPSI e MPMI. Para cada uma das tipologias as variáveis de interesse foram indentificadas bem como as estruturas de rede mais adequadas para o tipologia em questão
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