Reducing fuel consumption through modular vehicle architectures

By identifying energy waste streams in vehicles fuel consumption and introducing the concept of lean driving systems, a technological gap for reducing fuel consumption was identified. This paper proposes a solution to overcome this gap, through a modular vehicle architecture aligned with driving patterns. It does not address detailed technological solutions; instead it models the potential effects in fuel consumption through a modular concept of a vehicle and quantifies their dependence on vehicle design parameters (manifesting as the vehicle mass) and user behavior parameters (driving patterns manifesting as the use of a modular car in lighter and heavier mode, in urban and highway cycles). Modularity has been functionally applied in automotive industry as manufacture and assembly management strategies; here it is thought as a product development strategy for flexibility in use, driven by environmental concerns and enabled by social behaviors. The authors argue this concept is a step forward in combining technological solutions and social behavior, of which eco-driving is a vivid example, and potentially evolutionary to a lean, more sustainable, driving culture.

Real-time 3D interactive segmentation of echocardiographic data through user-based deformation of B-spline explicit active surfaces

Image segmentation is an ubiquitous task in medical image analysis, which is required to estimate morphological or functional properties of given anatomical targets. While automatic processing is highly desirable, image segmentation remains to date a supervised process in daily clinical practice. Indeed, challenging data often requires user interaction to capture the required level of anatomical detail. To optimize the analysis of 3D images, the user should be able to efficiently interact with the result of any segmentation algorithm to correct any possible disagreement. Building on a previously developed real-time 3D segmentation algorithm, we propose in the present work an extension towards an interactive application where user information can be used online to steer the segmentation result. This enables a synergistic collaboration between the operator and the underlying segmentation algorithm, thus contributing to higher segmentation accuracy, while keeping total analysis time competitive. To this end, we formalize the user interaction paradigm using a geometrical approach, where the user input is mapped to a non-cartesian space while this information is used to drive the boundary towards the position provided by the user. Additionally, we propose a shape regularization term which improves the interaction with the segmented surface, thereby making the interactive segmentation process less cumbersome. The resulting algorithm offers competitive performance both in terms of segmentation accuracy, as well as in terms of total analysis time. This contributes to a more efficient use of the existing segmentation tools in daily clinical practice. Furthermore, it compares favorably to state-of-the-art interactive segmentation software based on a 3D livewire-based algorithm.