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Creating The Masthead
Creating The Masthead
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Creating The Mastheas
Creating The Mastheas
ShaniceLamara
Practise Aspects
Practise Aspects
ShaniceLamara
We present VIRTUS, a system for running blood flow simulations in the cloud. Thanks to our mesher one can directly operate on CT/MRI images, generate the mesh, define the boundary conditions and finally solve and visualize the results. In addition, thanks to GPU time-dependent flow simulations can be solved up to x3 faster than on multi-core CPU.
Blood Flow Simulations in the Cloud
Blood Flow Simulations in the Cloud
University of Zurich
Artficial Intelligence (AI) studies and designs intelligent agents, e.g. systems that perceive their environment and take actions that maximize the chances of success. In microscopy a success is often understood when the automated image analysis effciently detects phenotypes, as in biological screens, or retrieves a diagnostically relevant statistics from images, as in the medical diagnosis. During the talk I will present two applications aimed at supporting high-content screening and diagnosis of cervical cancer where subdomains of AI, e.g. Evolutionary Algorithm, Neural Networks and Machine Learning techniques were applied.
Artificial Intelligence in High Content Screening and Cervical Cancer Diagnosis
Artificial Intelligence in High Content Screening and Cervical Cancer Diagnosis
University of Zurich
PhD describes methods for segmentation of cells in phase contrast microscopy. The PhD was realized partly at Max Planck Institute of Cell Biology and Dresden in Buchholz Lab.
Multimodal Image Processing in Cytology
Multimodal Image Processing in Cytology
University of Zurich
Creating The Bottom Page
Creating The Bottom Page
ShaniceLamara
Practice Stages
Practice Stages
ShaniceLamara
Recommandé
Creating The Masthead
Creating The Masthead
ShaniceLamara
Creating The Mastheas
Creating The Mastheas
ShaniceLamara
Practise Aspects
Practise Aspects
ShaniceLamara
We present VIRTUS, a system for running blood flow simulations in the cloud. Thanks to our mesher one can directly operate on CT/MRI images, generate the mesh, define the boundary conditions and finally solve and visualize the results. In addition, thanks to GPU time-dependent flow simulations can be solved up to x3 faster than on multi-core CPU.
Blood Flow Simulations in the Cloud
Blood Flow Simulations in the Cloud
University of Zurich
Artficial Intelligence (AI) studies and designs intelligent agents, e.g. systems that perceive their environment and take actions that maximize the chances of success. In microscopy a success is often understood when the automated image analysis effciently detects phenotypes, as in biological screens, or retrieves a diagnostically relevant statistics from images, as in the medical diagnosis. During the talk I will present two applications aimed at supporting high-content screening and diagnosis of cervical cancer where subdomains of AI, e.g. Evolutionary Algorithm, Neural Networks and Machine Learning techniques were applied.
Artificial Intelligence in High Content Screening and Cervical Cancer Diagnosis
Artificial Intelligence in High Content Screening and Cervical Cancer Diagnosis
University of Zurich
PhD describes methods for segmentation of cells in phase contrast microscopy. The PhD was realized partly at Max Planck Institute of Cell Biology and Dresden in Buchholz Lab.
Multimodal Image Processing in Cytology
Multimodal Image Processing in Cytology
University of Zurich
Creating The Bottom Page
Creating The Bottom Page
ShaniceLamara
Practice Stages
Practice Stages
ShaniceLamara
Creating The Bottom Page
Creating The Bottom Page
ShaniceLamara
EA-driven based optimization as a potential tool for geometry optimization and reaction path following in computational chemistry.
Evolutionary-driven Optimization in Computational Chemistry
Evolutionary-driven Optimization in Computational Chemistry
University of Zurich
Project Analysis
Project Analysis
ShaniceLamara
Analysing Cover Images
Analysing Cover Images
ShaniceLamara
SpeedIT FLOW is a RANS single-phase fluid flow solver that runs fully on GPU. It supports OpenFOAM format and is about 3x faster than OpenFOAM running on 2xCPU.
SpeedIT FLOW
SpeedIT FLOW
University of Zurich
Completing The Magazine
Completing The Magazine
ShaniceLamara
Evolution-based Reaction Path Following
Evolution-based Reaction Path Following
University of Zurich
The SpeedIT provides a partial acceleration of sparse linear solvers. Acceleration is achieved with a single reasonably priced NVIDIA Graphics Processing Unit (GPU) that supporst CUDA and proprietary advanced optimisation techniques. Check also SpeedIT FLOW, our RANS single phase flow solver that runs fully on GPU: vratis.com/blog
SpeedIT : GPU-based acceleration of sparse linear algebra
SpeedIT : GPU-based acceleration of sparse linear algebra
University of Zurich
The project aims at development of efficient segmentation method for the CBIR system. Mean-shift segmentation generates a list of potential objects which are meaningful and then these objects are clustered according to a predefined similarity measure. The method was tested on benchmark data and F-Score of .30 was achieved.
Content-based Image Retrieval
Content-based Image Retrieval
University of Zurich
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En vedette
Creating The Bottom Page
Creating The Bottom Page
ShaniceLamara
EA-driven based optimization as a potential tool for geometry optimization and reaction path following in computational chemistry.
Evolutionary-driven Optimization in Computational Chemistry
Evolutionary-driven Optimization in Computational Chemistry
University of Zurich
Project Analysis
Project Analysis
ShaniceLamara
Analysing Cover Images
Analysing Cover Images
ShaniceLamara
SpeedIT FLOW is a RANS single-phase fluid flow solver that runs fully on GPU. It supports OpenFOAM format and is about 3x faster than OpenFOAM running on 2xCPU.
SpeedIT FLOW
SpeedIT FLOW
University of Zurich
Completing The Magazine
Completing The Magazine
ShaniceLamara
Evolution-based Reaction Path Following
Evolution-based Reaction Path Following
University of Zurich
The SpeedIT provides a partial acceleration of sparse linear solvers. Acceleration is achieved with a single reasonably priced NVIDIA Graphics Processing Unit (GPU) that supporst CUDA and proprietary advanced optimisation techniques. Check also SpeedIT FLOW, our RANS single phase flow solver that runs fully on GPU: vratis.com/blog
SpeedIT : GPU-based acceleration of sparse linear algebra
SpeedIT : GPU-based acceleration of sparse linear algebra
University of Zurich
The project aims at development of efficient segmentation method for the CBIR system. Mean-shift segmentation generates a list of potential objects which are meaningful and then these objects are clustered according to a predefined similarity measure. The method was tested on benchmark data and F-Score of .30 was achieved.
Content-based Image Retrieval
Content-based Image Retrieval
University of Zurich
En vedette
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Creating The Bottom Page
Creating The Bottom Page
Evolutionary-driven Optimization in Computational Chemistry
Evolutionary-driven Optimization in Computational Chemistry
Project Analysis
Project Analysis
Analysing Cover Images
Analysing Cover Images
SpeedIT FLOW
SpeedIT FLOW
Completing The Magazine
Completing The Magazine
Evolution-based Reaction Path Following
Evolution-based Reaction Path Following
SpeedIT : GPU-based acceleration of sparse linear algebra
SpeedIT : GPU-based acceleration of sparse linear algebra
Content-based Image Retrieval
Content-based Image Retrieval
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