Lean et Agile pour accélerer votre idées business

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Institute of Information Systems (HES-SO)

L'Agile Tour Lausanne et celui de Sierre se sont réunis en 2013 pour proposer un événement unique en Suisse Romande, une journée de coaching ayant pour but de donner un coup d'accélérateur à votre idée/projet en mixant des outils d’entreprenariat et d’agilité! Une journée pour expérimenter comment la combinaison du Business Model Canvas et d’outils d’une méthodologie agile peut vous aider à réaliser une idée, à démarrer un projet ou un nouveau Business.

Formation

Lean & Agile pour accélérer votre idée business
1. Start with your idea…
3. Develop into Product Canvas

2. Build your Business Model Canvas

5. Iterate to refine
HES-SO Valais-Wallis
Page 1

4. Personas, Scenarios,
Product Backlog

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Creating an optimal travel plan is not an easy task, particularly for people with mobility disabilities, for whom even simple trips, such as eating out in a restaurant, can be extremely difficult. Many of their travel plans need to be made days or even months in advance, including the route and time of day to travel. These plans must take into account ways in which to navigate the area, as well as the most suitable means of transportation. In response to these challenges, this study was designed to develop a solution that used linked data technologies in the domains of tourism services and e-governance to build a smart city application for wheelchair accessibility. This smart phone application provides useful travel information to enable those with mobility disabilities to travel more easily.

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Institute of Information Systems (HES-SO)

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Institute of Information Systems (HES-SO)

We propose a novel imaging biomarker of lung cancer relapse from 3-D texture analysis of CT images. Three-dimensional morphological nodular tissue properties are described in terms of 3-D Riesz-wavelets. The responses of the latter are aggregated within nodular regions by means of feature covariances, which leverage rich intra- and inter-variations of the feature space dimensions. The obtained Riesz-covariance descriptors lie on a manifold governed by Riemannian geometry requiring specific geodesic metrics to locally approximate scalar products. The latter are used to construct a kernel for support vector machines (SVM). The effectiveness of the presented models is evaluated on a dataset of 92 patients with non-small cell lung carcinoma (NSCLC) and cancer recurrence information. Disease recurrence within a timeframe of 12 months could be predicted with an accuracy above 80, and highlighted the importance of covariance-based texture aggregation. At the end of the talk, computer tools will be presented to easily extract 3D radiomics quantitative features from PET-CT images.

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At the Knime Berlin summit 2016, Prof. Dr. Dominique Genoud presented a novel way to implement a KNIME workflow that perform machine learning and signal processing on an Android platform. The use case was to detect soft falls (not from a standing position) using an Android watch. This application has a big impact on how we can detect automatically when elderly people fall from their bed of their chair. This work was originally based on the Master Thesis in Business Administration realized by Vincent Cuendet in 2015 at the HES-SO with the help of the FST (Fédération Suisse pour les Téléthèses), an organization that helps disabled and elderly people to keep their autonomy.

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Institute of Information Systems (HES-SO)

Presented by Adrien Depeursinge, PhD, at MICCAI 2015 Tutorial on Biomedical Texture Analysis (BTA), Munich, Oct 5 2015. Texture-based imaging biomarkers complement focal, invasive biopsy based biomarkers by providing information on tissue structure over broad regions, non-invasively, and repeatedly across multiple time points. Texture has been used to predict patient survival, tissue function, disease subtypes and genomics (imagenomics and radiogenomics). Nevertheless, several challenges remain, such as: the lack of an appropriate framework for multi-scale, multi-spectral analysis in 2D and 3D; localization uncertainty of texture operators; validation; and, translation to routine clinical applications.

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Institute of Information Systems (HES-SO)

Mocodis is a web application facilitating the transfer of skills between senior and junior associates. It can be used in companies, institutions to capitalize on the experience of older employees, or can be used to train employees top down. Mocodis automatically generates dynamic micro-courses combining text, audio and video resources, and uses an algorithm to analyze user satisfaction to produce better courses at the next request.

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Institute of Information Systems (HES-SO)

This paper aims at reporting on the findings of two quantitative studies and one qualitative study conducted among HES-SO undergraduate and graduate students. We have outlined the characteristics of the “digital natives” generation of students attending our courses and have submitted a sample of these students to an experiment using the Google Glass, in order to assess whether the use of this new device could meet the students’ expectations for accessing enriched learning resources. This paper also presents some thoughts for consideration regarding future research to be lead in the field of innovative technologies and learning processes

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Institute of Information Systems (HES-SO)

This work presents a data-intensive solution to predict Photovoltaïque energy (PV) production. PV and other renewable sources have widely spread in recent years. Although those sources provide an environmentally-friendly solution, their integration is a real challenge in terms of power management as it depends on meteorological conditions. The ability to predict those variable sources considering meteorological uncertainty plays a key role in the management of the energy supply needs and reserves. This paper presents an easy-to-use methodology to predict PV production using time series analyses and sampling algorithms. The aim is to provide a forecasting model to set the day-ahead grid electricity need. This information is useful for power dispatching plans and grid charge control. The main novelties of our approach is to provide an easy implemented and flexible solution that combines classification algorithms to predict the PV plant efficiency considering weather conditions and nonlinear regression to predict weather forecasted errors in order to improve prediction results. The results are based on the data collected in the Techno-pôle’s microgrid in Sierre (Switzerland) described further in the paper. The best experimental results have been obtained using hourly historical weather measures (radiation and temperature) and PV production as training inputs and weather forecasted parameters as prediction inputs. Considering a 10 month dataset and despite the presence of 17 missing days, we achieve a Percentage Mean Absolute Deviation (PMAD) of 20% in August and 21% in September. Better results can be obtained with a larger dataset but as more historical data were not available, other months have not been tested.

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Institute of Information Systems (HES-SO)

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Institute of Information Systems (HES-SO)

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Institute of Information Systems (HES-SO)

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