Computer vision can be used for environment-adaptive control of robotic leg prostheses and exoskeletons. However, small-scale and private training datasets have impeded the development and dissemination of image classification algorithms (e.g., convolutional neural networks) to recognize the walking environment. To address these limitations, we developed ExoNet, a large-scale hierarchical dataset of wearable camera images (i.e., egocentric perception) of real-world walking environments.

Time Scale Modification (TSM) is a well-researched field; however, no effective objective measure of quality exists.  This paper details the creation, subjective evaluation, and analysis of a dataset for use in the development of an objective measure of quality for TSM. Comprised of two parts, the training component contains 88 source files processed using six TSM methods at 10 time scales, while the testing component contains 20 source files processed using three additional methods at four time scales.

Since there is no image-based personality dataset, we used the ChaLearn dataset for creating a new dataset that met the characteristics we required for this work, i.e., selfie images where only one person appears and his face is visible, labeled with the person's apparent personality in the photo.

These datasets are used to detect Intrusions in Controller Area Network (CAN) bus. Intrusions are detected using various Machine Learning and Deep Learning algorithms.

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