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Inferring Intrinsic Beliefs Of Digital Images Using A Deep Autoencoder, Seok H. Lee
Inferring Intrinsic Beliefs Of Digital Images Using A Deep Autoencoder, Seok H. Lee
Computer Science and Computer Engineering Undergraduate Honors Theses
Training a system of artificial neural networks on digital images is a big challenge. Often times digital images contain a large amount of information and values for artificial neural networks to understand. In this work, the inference model is proposed in order to absolve this problem. The inference model is composed of a parameterized autoencoder that endures the loss of information caused by the rescaling of images and transition model that predicts the effect of an action on the observation. To test the inference model, the images of a moving robotic arm were given as the data set. The inference …
Bluetooth Low Energy Platform With Simblee, Calvin W. Freeman
Bluetooth Low Energy Platform With Simblee, Calvin W. Freeman
Computer Science and Computer Engineering Undergraduate Honors Theses
Bluetooth Low Energy provides a platform for many developers to implement low power communication for a wide range of applications. The Internet of Things (IoT) is an emerging concept that is gaining traction in the world of embedded systems. Bluetooth Low Energy is one of the latest enablers of this movement and the Simblee module improves BLE accessibility. This thesis provides an evaluation of the average power consumption the Simblee uses in different use cases. By leveraging one of the Simblee’s most notable features, the Ultra Low Power mode, very low power consumption can be achieved, potentially increasing battery life …