IMoG -- a methodology for modeling future microelectronic innovations

[Context and motivation] The automotive industry is currently undergoing a fundamental transformation towards software defined vehicles. The automotive market of the future demands a higher level of automation, electrification of the power train, and individually configurable comfort functions. [Question/problem] These demands pose a challenge to the automotive development cycle, because they introduce complexity by larger and not yet well explored design spaces that are difficult to manage. [Principal ideas/results] To cope with these challenges, the main players along the value chain have an increased interest in collaborating and aligning their development efforts along joint roadmaps. Roadmap development can be viewed as a field of requirements engineering with the goal to capture product aspects on an appropriate level of abstraction to speed up investment decisions, reduce communication overhead and parallelize development activities, while complying with competition laws. [Contribution] In this paper, we present a refinement of the "Innovation Modeling Grid" (IMoG), which encompasses a methodology, a process and a proposed notation to support joint analysis of development roadmaps. IMoG is focused on the automotive domain, yet there are clear potentials for other applications.Comment: 15 pages, 7 figure

IMoG - a methodology for modeling future microelectronic innovations

[Context and motivation] The automotive industry is currently undergoing a fundamental transformation towards software defined vehicles. The automotive market of the future demands a higher level of automation, electrification of the power train, and individually configurable comfort functions. [Question/problem] These demands pose a challenge to the automotive development cycle, because they introduce complexity by larger and not yet well explored design spaces that are difficult to manage. [Principal ideas/results] To cope with these challenges, the main players along the value chain have an increased interest in collaborating and aligning their development efforts along joint roadmaps. Roadmap development can be viewed as a field of requirements engineering with the goal to capture product aspects on an appropriate level of abstraction to speed up investment decisions, reduce communication overhead and parallelize development activities, while complying with competition laws. [Contribution] In this paper, we present a refinement of the "Innovation Modeling Grid" (IMoG), which encompasses a methodology, a process and a proposed notation to support joint analysis of development roadmaps. IMoG is focused on the automotive domain, yet there are clear potentials for other applications

Using Guided Simulation to Assess Driver Assistance Systems

Abstract. The goal of our approach is the model-based prediction of the effects of driver assistance systems. To achieve this we integrate models of a driver and a car within a simulation environment and face the problem of analysing the emergent effects of the resulting complex system with discrete, numeric and probabilistic components. In particular, it is difficult to assess the probability of rare events, though we are specifically interested in critical situations which will be infrequent for any reasonable system. For that purpose, we use a quantitative logic which enables us to specify criticality and other properties of simulation runs. An online evaluation of the logic permits us to define a procedure which guides the simulation towards critical situations and allows to estimate the risk connected with the introduction of the assistance system

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset