In more and more application areas, we are witnessing the emergence of complex workflows that combine computing, analytics and learning. They often require a hybrid execution infrastructure with IoT devices interconnected to cloud/HPC systems (aka Computing Continuum). Such workflows are subject to complex constraints and requirements in terms of performance, resource usage, energy consumption and financial costs. This makes it challenging to optimize their configuration and deployment. We propose a methodology to support the optimization of real-life applications on the Edge-to-Cloud Continuum. We implement it as an extension of E2Clab, a previously proposed framework supporting the complete experimental cycle across the Edge-to-Cloud Continuum. Our approach relies on a rigorous analysis of possible configurations in a controlled testbed environment to understand their behaviour and related performance tradeoffs. We illustrate our methodology by optimizing Pl@ntNet, a world-wide plant identification application. Our methodology can be generalized to other applications in the Edge-to-Cloud Continuum.

[email protected] is a world-scale participatory platform and information system dedicated to the monitoring of plant biodiversity through image-based plant identification. Nowadays, the mobile front-end of [email protected] has been downloaded by more than 4 millions users in about 170 countries and an active community of contributors produce and revise new observations everyday (it is often referred to as the "shazam of plants"). This paper presents a business proposal allowing enterprises or organizations to set up their own private collaborative workflow within [email protected] information system. The main added value is to allow them working on their own business object (e.g. plant disease diagnostic, deficiency measurements, railway lines maintenance, fishery surveillance, etc.) and with their own community of contributors and end-users (employees, sales representatives, clients, observers network, etc.). This business idea answers to a growing demand in agriculture and environmental economics. Actors in these domains begin to know that machine learning techniques are mature enough but the lack of training data and of efficient tools to collect them is a major breakthrough. A collaborative platform like [email protected] is the ideal tool to bridge this gap. It initiates a powerful positive feedback loop boosting the production of training data while improving the work of the employees.

Abstract In this article, we describe the latest developments of the minimally invasive hepatic surgery simulator prototype developed at INRIA. The goal of this simulator is to provide a realistic training test bed to perform laparoscopic procedures. Therefore, its main functionality is to simulate the action of virtual laparoscopic surgical instruments for deforming and cutting tridimensional anatomical models. Throughout this paper, we present the general features of this simulator including the implementation of several biomechanical models and the integration of two force‐feedback devices in the simulation platform. More precisely, we describe three new important developments that improve the overall realism of our simulator. First, we have developed biomechanical models, based on linear elasticity and finite element theory, that include the notion of anisotropic deformation. Indeed, we have generalized the linear elastic behaviour of anatomical models to ‘transversally isotropic’ materials, i.e. materials having a different behaviour in a given direction. We have also added to the volumetric model an external elastic membrane representing the ‘liver capsule’, a rather stiff skin surrounding the liver, which creates a kind of ‘surface anisotropy’. Second, we have developed new contact models between surgical instruments and soft tissue models. For instance, after detecting a contact with an instrument, we define specific boundary constraints on deformable models to represent various forms of interactions with a surgical tool, such as sliding, gripping, cutting or burning. In addition, we compute the reaction forces that should be felt by the user manipulating the force‐feedback devices. The last improvement is related to the problem of haptic rendering. Currently, we are able to achieve a simulation frequency of 25 Hz (visual real time) with anatomical models of complex geometry and behaviour. But to achieve a good haptic feedback requires a frequency update of applied forces typically above 300 Hz (haptic real time). Thus, we propose a force extrapolation algorithm in order to reach haptic real time. Copyright © 2002 John Wiley & Sons, Ltd.

We describe the latest developments of the minimally invasive hepatic surgery simulator prototype developed at INRIA. The goal of this simulator is to provide a realistic training test-bed for performing laparoscopic procedures. Therefore, its main functionality is to simulate the deformation and cutting of tri-dimensional anatomical models with the help of two virtual laparoscopic surgical instruments. Throughout the paper, we present the general features of the simulator including the implementation of different bio-mechanical models based on linear elasticity and finite element theory and the integration of two force-feedback devices in the simulation platform. More precisely, we describe two important developments that improve the overall realism of the simulator. First, we can create bio-mechanical models that include the notion of anisotropic deformation. Indeed, we have generalized the linear elastic behavior of anatomical models to "transversally isotropic" materials, i.e. materials having one privileged direction of deformation. The second improvement is related to the problem of haptic rendering. Currently, we are able to achieve a simulation frequency of 25 Hz (visual real-time) with anatomical models of complex geometry and behavior. But to achieve a good haptic feedback requires a frequency update of applied forces typically above 300 Hz (haptic real-time). Thus, we propose a force extrapolation algorithm in order to reach haptic real-time.

We present a simple method for performing real-time collision detection in a virtual surgery environment. The method relies on the graphics hardware for testing the interpenetration between a virtual deformable organ and a rigid tool controlled by the user. The method enables to take into account the motion of the tool between two consecutive time steps. For our specific application, the new method runs about a hundred times faster than the well known oriented-bonding-boxes tree method.