Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems

Findings in recent years on the sensitivity of convolutional neural networks to additive noise, light conditions and to the wholeness of the training dataset, indicate that this technology still lacks the robustness needed for the autonomous robotic industry. In an attempt to bring computer vision algorithms closer to the capabilities of a human operator, the mechanisms of the human visual system was analyzed in this work. Recent studies show that the mechanisms behind the recognition process in the human brain include continuous generation of predictions based on prior knowledge of the world. These predictions enable rapid generation of contextual hypotheses that bias the outcome of the recognition process. This mechanism is especially advantageous in situations of uncertainty, when visual input is ambiguous. In addition, the human visual system continuously updates its knowledge about the world based on the gaps between its prediction and the visual feedback. Convolutional neural networks are feed forward in nature and lack such top-down contextual attenuation mechanisms. As a result, although they process massive amounts of visual information during their operation, the information is not transformed into knowledge that can be used to generate contextual predictions and improve their performance. In this work, an architecture was designed that aims to integrate the concepts behind the top-down prediction and learning processes of the human visual system with the state of the art bottom-up object recognition models, e.g., deep convolutional neural networks. The work focuses on two mechanisms of the human visual system: anticipation-driven perception and reinforcement-driven learning. Imitating these top-down mechanisms, together with the state of the art bottom-up feed-forward algorithms, resulted in an accurate, robust, and continuously improving target recognition model

Educational hands-on testbed using Lego robot for learning guidance, navigation, and control

The aim of this paper is to propose an educational hands-on testbed using inexpensive systems composed of a Lego Mindstorms NXT robot and a webcam and easy-to-deal-with tools especially for learning and testing guidance, navigation, and control as well as search and obstacle mapping, however the extendibility and applicability of the proposed approach is not limited to only the educational purpose. In order to provide navigation information of the Lego robot in an indoor environment, an vision navigation system is proposed based on a colour marker detection robust to brightness change and an Extended Kalman filter. Furthermore, a spiral-like search, a command-to-line-of-sight guidance, a motor control, and two-dimensional Splinegon approximation are applied to sensing and mapping of a complex-shaped obstacle. The experimental result shows that the proposed testbed can be viewed as an efficient tool for the education of image processing and estimation as well as guidance, navigation, and control with a minimum burden of time and cost. © 2011 IFAC

Techniques in the Design of Thermal Pulse Flowmeters

Abstract-Various digital signal processing methods, which could be applicable to the design of a microcomputer based thermal pulse flow-meter, were examined. Two excitation methods were investigated: a single thermal pulse and a pseudorandom binary sequence signal (PRBS). The signal recovered downstream was processed by two alter-native numerical algorithms to recover the “time of flight”, i.e., by peak detection of the signal itself and the peak of the differentiated signal. The recovered thermal pulse and the “time of flight ” were then used to test the validity of two models: a diffusion-advection model and a simple time delay model. The delay model was found compatible with the data especially when the peak of the output signal derivative was used as a marker for determining the “time of flight. ” The single pulse injection method was found, in general, superior to the PRBS cross-correlation technique except for the ability of the latter to provide early indication of flow-rate variations. I

Dichotomy between Clustering Performance and Minimum Distortion . . .

In many signal such speech, bio-signals, protein chains, etc. there is a dependency between consecutive vectors. As the dependency is limited in duration such data can be called as Piecewise-DependentData (PDD). In clustering it is frequently needed to minimize a given distance function. In this paper we will show that in PDD clustering there is a contradiction between the desire for high resolution (short segments and low distance) and high accuracy (long segments and high distortion), i.e. meaningful clustering