Although the Kalman filter algorithms are well suited to be executed on most digital systems, they become slow when applied large-scale dynamic systems. Therefore, efficient execution of for time-critical and applications is essence. This work aims address this necessity by developing a novel framework improve performance generalized with unknown inputs (GKF-UI) using multithreaded-multicore pr...

Fault diagnosis of composite asynchronous sequential machines with parallel composition is addressed in this paper. An adversarial input can infiltrate one of two submachines comprising the composite asynchronous machine, causing an unauthorized state transition. The objective is to characterize the condition under which the controller can diagnose any fault occurrence. Two control configuratio...

Much of the human-machine dialogue research in the literature tacitly assumes a “synchronous” dialogue model; user talks, system acts, system replies. In particular, the user is not supposed to interrupt the system, neither when it talks nor acts. In this paper, we argue that the synchronous model is not appropriate for most interesting real-life applications, but there is a need for asynchrono...

State-of-the-art deep reinforcement learning models rely on asynchronous training using multiple learner agents and their collective updates to a central neural network. In this thesis, one of the most recent asynchronous policy gradientbased reinforcement learning methods, i.e. asynchronous advantage actor-critic (A3C), will be examined as well as improved using prior research from the machine...

The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage ActorCritic algorithm, makes them exceptionally suitable for CPU computations. However, given the fact that deep reinforcement learning often deals with interpreting visual information, a large part of the train and inference time is spent performing convolutions. In this work we...

Due to the non-stationarity of EEG signals, online training and adaptation is essential to EEG based brain-computer interface (BCI) systems. Asynchronous BCI offers more natural human-machine interaction, but it is a great challenge to train and adapt an asynchronous BCI online because the user’s control intention and timing are usually unknown. This paper proposes a novel motor imagery based a...

A Time Encoding Machine is a real-time asynchronous mechanism for encoding amplitude information into a time sequence. We investigate the operating characteristics of a machine consisting of a feedback loop containing an adder, a linear filter and a Schmmitt trigger. We show how to recover the amplitude information of a bandlimited signal from the time sequence loss-free.

In this paper, an asynchronous pipeline instruction simulator, ARAS is presented. With this sim-ulator, one can design selected instruction pipelines and check their performance. Performance measurements of the pipeline connguration are obtained by simulating the execution of benchmark programs on the machine architectures developed. Depending on the simulation results obtained by using ARAS, t...