Search Results for author:
Found 10 papers, 5 papers with code
Enhanced Performance of Pre-Trained Networks by Matched Augmentation Distributions
Specifically, we demonstrate that running inference on the center crop of an image is not always the best as important discriminatory information may be cropped-off.
A wearable sensor vest for social humanoid robots with GPGPU, IoT, and modular software architecture
This article presents an affordable wearable sensor vest, and an open-source software architecture with the Internet of Things (IoT) for social humanoid robots.
On-Policy Robust Adaptive Discrete-Time Regulator for Passive Unidirectional System using Stochastic Hill-climbing Algorithm and Associated Search Element
In this paper, a novel adaptive regulator is proposed for first-order unidirectional passive systems.
EMG Signal Classification Using Reflection Coefficients and Extreme Value Machine
Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with high accuracy is available.
Self-Supervised Features Improve Open-World Learning
This paper identifies the flaws in existing open-world learning approaches and attempts to provide a complete picture in the form of \textbf{True Open-World Learning}.
A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels
Open-world learning is related to but also distinct from a multitude of other learning problems and this paper briefly analyzes the key differences between a wide range of problems including incremental learning, generalized novelty discovery, and generalized zero-shot learning.
Automatic Open-World Reliability Assessment
However, during operations, we cannot directly assess accuracy as there are no labels.
End-to-End Learning of Speech 2D Feature-Trajectory for Prosthetic Hands
To achieve a lower error in real-time, we can optimize a similar CNN for a more powerful embedded GPGPU such as NVIDIA AGX Xavier.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Convolutional Neural Networks for Speech Controlled Prosthetic Hands
In this paper, we show the development of deep convolutional neural networks (CNN) for speech control of prosthetic hands that run in real-time on a NVIDIA Jetson TX2 developer kit.
Deep learning approach to control of prosthetic hands with electromyography signals
In this paper, we propose a deep learning approach to control prosthetic hands with raw EMG signals.
