This paper explores the computational capacity of a novel local computational model that expands the conventional analogical and logical dynamic neural models, based on the charge and discharge of a capacity or in the use of a D flip-flop. The local memory capacity is augmented to behave as an S states automaton and some control elements are added to the memory. The analogical or digital calcul...

This paper addresses the problem of extracting video objects from head−shoulder video sequences. A method based on accumulative frame difference is proposed. First, a preliminary motion analysis is performed to each block of the frame and the blocks with fast moving edges are detected. Then, for each block, we accumulate frame difference with a different amount of frames, based on its motion at...

While most of the existing artificial neural networks (ANN) models for building energy prediction are static in nature, this paper evaluates the performance of adaptive ANN models that are capable of adapting themselves to unexpected pattern changes in the incoming data, and therefore can be used for the realtime on-line building energy prediction. Two adaptive ANN models are proposed and teste...

We report a simple extension of the Z-scan technique where pulse trains characteristic of a Q-switched and mode-locked Nd:YAG laser are employed to investigate the dynamics of third-order optical nonlinearities. The method allows discriminating between fast and accumulative nonlinearities, and was applied to the investigation of liquid samples, namely chloroform, benzene, and a solution of Disp...

We report experimental results on a highly efficient optical limiter that uses a three-mirror, multipass ring configuration. The optical limiter's enhanced performance is based on the accumulative effect that is achieved when the laser beam passes through the sample several consecutive times. Its feasibility was demonstrated for media with different limiting action processes, namely, reverse sa...

For most reinforcement learning approaches, the learning is performed by maximizing an accumulative reward that is expectedly and manually defined for specific tasks. However, in real world, rewards are emergent phenomena from the complex interactions between agents and environments. In this paper, we propose an implicit generic reward model for reinforcement learning. Unlike those rewards that...

HfO2 based ferroelectric field-effect transistors (FeFETs) are promising for low-power and high-speed non-volatile memories. However, FeFETs can be susceptible to write disturbs due their accumulative switching behavior, which limits application in large scale memory arrays. Here we show that with a thin SiNx interfacial layer immune pulsed under both <inline-formula xmlns:mml="http://www.w3.or...