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Blood simulation is an important part in the virtual surgery training system. However, the huge computational complexity and authenticity of blood simulation is of great challenge to the surgical training system. In this paper, a simulation method based on GPU-accelerated is used for blood simulation in surgical training system. The grid method is used to divide the target area, create space grid...
Virtual reality represents an emerging technology, which can be successfully used to develop training tools in many domains, such as military, space, education or healthcare. The current paper proposes a training strategy for a sensory substitution device in order to improve the ability of visually impaired people to be autonomous, thus to increase the quality of their lives. The core of the strategy...
This paper seeks to address the disconnect between different stages of the FPGA CAD flow that often adversely affects the quality of results of the implemented designs. In particular, a machine-learning framework is presented, consisting of a suite of classification and regression techniques, to model the underlying relationship between the characteristics of circuits and the best CAD algorithm (and...
The very fast evolution of the application field in microelectronics requires the adaption of higher education to engineer and master students in order to answer to the industrial, “Research and Development”, and economical needs. In parallel, the development of our digital society, which proposes more and more massive open on-line courses, maintains the students in a theoretical knowledge. Therefore...
This work proposes a robotic object recognition system that takes advantage of the contextual information latent in human-like environments in an online fashion. To fully leverage context, it is needed perceptual information from (at least) a portion of the scene containing the objects of interest, which could not be entirely covered by just an one-shot sensor observation. Information from a larger...
We propose a new computer-aided detection system that uses 3D convolutional neural networks (CNN) for detecting lung nodules in low dose computed tomography. The system leverages both a priori knowledge about lung nodules and confounding anatomical structures and data-driven machine-learned features and classifier. Specifically, we generate nodule candidates using a local geometric-model-based filter...
In order to promote the integration of information technology and professional curriculum, optimize the teaching process and effect of higher vocational curriculum, and improve the ability of teachers to use modern information technology. At the same time, the modern vocational education advocates “work and study, the integration of theory and practice, do in Teaching, learning by doing.” Numerical...
Stock price prediction is a challenging task owing to the complexity patterns behind time series. Autoregressive integrated moving average (ARIMA) model and back propagation neural network (BPNN) model are popular linear and nonlinear models for time series forecasting respectively. The integration of two models can effectively capture the linear and nonlinear patterns hidden in a time series and...
Prostate Cancer (PCa) is highly prevalent and is the second most common cause of cancer-related deaths in men. Multiparametric MRI (mpMRI) is robust in detecting PCa. We developed a weakly supervised computer-aided detection (CAD) system that uses biopsy points to learn to identify PCa on mpMRI. Our CAD system, which is based on a deep convolutional neural network architecture, yielded an area under...
Response to cardiac resynchronization therapy (CRT) may be improved if coronary venous anatomy is fused with information on regional left ventricular (LV) function to guide LV lead placement. We propose a method to register a 3D model of the LV and its corresponding coronary venous tree (reconstructed from biplane X-ray fluoroscopy) for CRT applications. A template coronary venous tree (CVT) centerline,...
In this paper, we present a novel deep learning model termed Deep Autoencoding-Classification Network (DACN) for HEp-2 cell classification. The DACN consists of an autoencoder and a normal classification convolutional neural network (CNN), while the two architectures shares the same encoding pipeline. The DACN model is jointly optimized for the classification error and the image reconstruction error...
The simulator based on interactive virtual reality can solve many drawbacks in traditional surgery training, and it is widely used to train apprentices. A real-time and realistic guidewire model is a challenging task for the simulator, which is used to simulate minimally invasive vascular surgery. In this paper, we propose a fast and stable physical model to simulate the behavior of the guidewire...
Automated segmentation of brain structures from MR images is an important practice in many neuroimage studies. In this paper, we explore the utilization of a multi-view ensemble approach that relies on neural networks (NN) to combine multiple decision maps in achieving accurate hippocampus segmentation. Constructed under a general convolutional NN structure, our Ensemble-Net networks explore different...
The use of Virtual Reality (VR) simulators has increased rapidly in the field of medical surgery for training purposes. In this paper, the design and development of a Virtual Surgical Environment (VSE) for training residents in an orthopaedic surgical process called Less Invasive Stabilization System (LISS) surgery is discussed; LISS plating surgery is a process used to address fractures of the femur...
There is a growing interest to utilize Computer Graphics (CG) renderings to generate large scale annotated data in order to train machine learning systems, such as Deep convolutional neural networks, for Computer Vision (CV). However, there has been a long debate on the usefulness of CG generated data for tuning CV systems (even from the 1980's). Especially, the impact of modeling errors and computational...
Incorporating 3D information has proven to be effective in many computer vision tasks and it is no exception in the context of facial analysis. However, limited application has been witnessed in face hallucination (FH), probably due to the difficulty of fitting 3D models onto low-resolution (LR) images. This paper presents a pure 3D approach to address this problem. By extending the LR image formation...
We introduce T-LESS, a new public dataset for estimating the 6D pose, i.e. translation and rotation, of texture-less rigid objects. The dataset features thirty industry-relevant objects with no significant texture and no discriminative color or reflectance properties. The objects exhibit symmetries and mutual similarities in shape and/or size. Compared to other datasets, a unique property is that...
This paper deals with virtual reality in context of Industry 4.0 projects. Due to sufficient computing power, virtual reality allows visualization of virtual objects in both professional and public spheres. With virtual reality, solving complex projects has become much easier, especially of those based on Industry 4.0 standards. Industry 4.0 projects require connection between all systems of modern...
The profits and benefits offered by Virtual Reality technology had drawn attention of professionals from several scientific fields, including the power systems', either for training or maintenance. For this purpose, 3D modeling is evidently pointed out as an imperative process for the conception of a Virtual Environment. Before the complexity of Hydroelectric Power Plants and Virtual Reality's contribution...
Here we describe the design and usability evaluation of a mixed reality prototype to simulate the role of a tank platoon leader, who is an individual who not only is a tank commander, but also directs a platoon of three other tanks with their own respective tank commanders. The domain of tank commander training has relied on physical simulators of the actual Abrams tank and encapsulates the whole...
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