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Full-Text Articles in Computer Engineering
Intrinsic Functions For Securing Cmos Computation: Variability, Modeling And Noise Sensitivity, Xiaolin Xu
Intrinsic Functions For Securing Cmos Computation: Variability, Modeling And Noise Sensitivity, Xiaolin Xu
Doctoral Dissertations
A basic premise behind modern secure computation is the demand for lightweight cryptographic primitives, like identifier or key generator. From a circuit perspective, the development of cryptographic modules has also been driven by the aggressive scalability of complementary metal-oxide-semiconductor (CMOS) technology. While advancing into nano-meter regime, one significant characteristic of today's CMOS design is the random nature of process variability, which limits the nominal circuit design. With the continuous scaling of CMOS technology, instead of mitigating the physical variability, leveraging such properties becomes a promising way. One of the famous products adhering to this double-edged sword philosophy is the Physically …
Multi-Classifier Fusion Strategy For Activity And Intent Recognition Of Torso Movements, Abhijit Kadrolkar
Multi-Classifier Fusion Strategy For Activity And Intent Recognition Of Torso Movements, Abhijit Kadrolkar
Doctoral Dissertations
As assistive, wearable robotic devices are being developed to physically assist their users, it has become crucial to develop safe, reliable methods to coordinate the device with the intentions and motions of the wearer. This dissertation investigates the recognition of user intent during flexion and extension of the human torso in the sagittal plane to be used for control of an assistive exoskeleton for the human torso. A multi-sensor intent recognition approach is developed that combines information from surface electromyogram (sEMG) signals from the user’s muscles and inertial sensors mounted on the user’s body. Intent recognition is implemented by following …
Intrinsically Motivated Exploration In Hierarchical Reinforcement Learning, Christopher M. Vigorito
Intrinsically Motivated Exploration In Hierarchical Reinforcement Learning, Christopher M. Vigorito
Doctoral Dissertations
The acquisition of hierarchies of reusable skills is one of the distinguishing characteristics of human intelligence, and the learning of such hierarchies is an important open problem in computational reinforcement learning (RL). In humans, these skills are learned during a substantial developmental period in which individuals are intrinsically motivated to explore their environment and learn about the effects of their actions. The skills learned during this period of exploration are then reused to great effect later in life to solve many unfamiliar problems very quickly. This thesis presents novel methods for achieving such developmental acquisition of skill hierarchies in artificial …