Essays about: "space computer"
Showing result 16 - 20 of 348 essays containing the words space computer.
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16. Introducing GA-SSNN: A Method for Optimizing Stochastic Spiking Neural Networks : Scaling the Edge User Allocation Constraint Satisfaction Problem with Enhanced Energy and Time Efficiency
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : As progress within Von Neumann-based computer architecture is being limited by the physical limits of transistor size, neuromorphic comuting has emerged as a promising area of research. Neuromorphic hardware tends to be substantially more power efficient by imitating the aspects of computations in networks of neurons in the brain. READ MORE
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17. Determining Important Features for Melanoma Classification Through Feature Selection
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Skin cancer is a common disease and malignant melanoma is the most dangerous form of it. Although dangerous, the survival rate of melanoma patients is high if the diagnosis is made at an early stage. Computer aided diagnostics has been shown to have potential in accurately diagnosing the disease utilizing machine learning. READ MORE
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18. Dynamic Shadows of Symmetric Shapes in a 2D Environment A case study in the field of Computer Graphics
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : With modern computer graphics it is easy to think that the computer has some inherent understanding of the 3D world around it. How else could a game let you experience a dynamic world so close to reality in real time. READ MORE
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19. Trainable Region of Interest Prediction: Hard Attention Framework for Hardware-Efficient Event-Based Computer Vision Neural Networks on Neuromorphic Processors
University essay from Lunds universitet/Institutionen för elektro- och informationsteknikAbstract : Neuromorphic processors are a promising new type of hardware for optimizing neural network computation using biologically-inspired principles. They can effectively leverage information sparsity such as in images from event-based cameras, and are well-adapted to processing event-based data in an energy-efficient fashion. READ MORE
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20. Real-time uncertainty estimation for deep learning
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Modern deep neural networks do not produce well calibrated estimates of their own uncertainty, unless specific uncertainty estimation techniques are applied. Common uncertainty estimation techniques such as Deep Ensembles and Monte Carlo Dropout necessitate multiple forward pass evaluations for each input sample, making them too slow for real-time use. READ MORE
