Essays about: "Convolution Neural Network"
Showing result 1 - 5 of 63 essays containing the words Convolution Neural Network.
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1. Implementations and evaluation of machine learning algorithms on a microcontroller unit for myoelectric prosthesis control
University essay from Lunds universitet/Avdelningen för Biomedicinsk teknikAbstract : Using a microcontroller unit to implement different machine learning algorithms for myoelectric prosthesis control is currently feasible. Still there are hardware and timing constraints that need to be accounted for. READ MORE
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2. Convolution-compacted visiontransformers forprediction of localwall heat flux atmultiple Prandtlnumbers in turbulentchannel flow
University essay from KTH/Skolan för teknikvetenskap (SCI)Abstract : Predicting wall heat flux accurately in wall-bounded turbulent flows is critical for a variety of engineering applications, including thermal management systems and energy-efficient designs. Traditional methods, which rely on expensive numerical simulations, are hampered by increasing complexity and extremly high computation cost. READ MORE
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3. Deep Learning-Driven EEG Classification in Human-Robot Collaboration
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Human-robot collaboration (HRC) occurs when people and robots work together in a shared environment. Current robots often use rigid programs unsuitable for HRC. Multimodal robot programming offers an easier way to control robots using inputs like voice and gestures. READ MORE
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4. Robust Multi-Modal Fusion for 3D Object Detection : Using multiple sensors of different types to robustly detect, classify, and position objects in three dimensions.
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The computer vision task of 3D object detection is fundamentally necessary for autonomous driving perception systems. These vehicles typically feature a multitude of sensors, such as cameras, radars, and light detection and ranging sensors. READ MORE
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5. Deep convolution neural network for attention decoding in multi-channel EEG with conditional variational autoencoder for data augmentation
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : Objectives: This project aims to develop a deep learning-based attention decoding system that can distinguish between noise and speech in noise and also identify the direction of attended speech from the brain data recorded with electroencephalography (EEG) instruments. Two deep convolutional neural network (DCNN) models will be designed: (1) one DCNN model capable of classifying incoming segments of sound as speech or speech in background noise, and (2) one DCNN model identifying the direction (left vs. READ MORE