Isolating contour information from arbitrary images

Aspects of natural vision (physiological and perceptual) serve as a basis for attempting the development of a general processing scheme for contour extraction. Contour information is assumed to be central to visual recognition skills. While the scheme must be regarded as highly preliminary, initial results do compare favorably with the visual perception of structure. The scheme pays special attention to the construction of a smallest scale circular difference-of-Gaussian (DOG) convolution, calibration of multiscale edge detection thresholds with the visual perception of grayscale boundaries, and contour/texture discrimination methods derived from fundamental assumptions of connectivity and the characteristics of printed text. Contour information is required to fall between a minimum connectivity limit and maximum regional spatial density limit at each scale. Results support the idea that contour information, in images possessing good image quality, is (centered at about 10 cyc/deg and 30 cyc/deg). Further, lower spatial frequency channels appear to play a major role only in contour extraction from images with serious global image defects

A distributed self-reconfiguration algorithm for cylindrical lattice-based modular robots

International audienceModular self-reconfigurable robots are composed of independent connected modules which can self-rearrange their connectivity using processing, communication and motion capabilities, in order to change the overall robot structure. In this paper, we consider rolling cylindrical modules arranged in a two-dimensional vertical hexagonal lattice. We propose a parallel, asynchronous and fully decentralized distributed algorithm to self-reconfigure robots from an initial configuration to a goal one. We evaluate our algorithm on the millimeter-scale cylindrical robots, developed in the Claytronics project, through simulation of large ensembles composed of up to ten thousand modules. We show the effectiveness of our algorithm and study its performance in terms of communications, movements and execution time. Our observations indicate that the number of communications, the number of movements and the execution time of our algorithm is highly predictable. Furthermore, we observe execution times that are linear in the size of the goal shape

Object detection and recognition with event driven cameras

This thesis presents study, analysis and implementation of algorithms to perform object detection and recognition using an event-based cam era. This sensor represents a novel paradigm which opens a wide range of possibilities for future developments of computer vision. In partic ular it allows to produce a fast, compressed, illumination invariant output, which can be exploited for robotic tasks, where fast dynamics and signi\ufb01cant illumination changes are frequent. The experiments are carried out on the neuromorphic version of the iCub humanoid platform. The robot is equipped with a novel dual camera setup mounted directly in the robot\u2019s eyes, used to generate data with a moving camera. The motion causes the presence of background clut ter in the event stream. In such scenario the detection problem has been addressed with an at tention mechanism, speci\ufb01cally designed to respond to the presence of objects, while discarding clutter. The proposed implementation takes advantage of the nature of the data to simplify the original proto object saliency model which inspired this work. Successively, the recognition task was \ufb01rst tackled with a feasibility study to demonstrate that the event stream carries su\ufb03cient informa tion to classify objects and then with the implementation of a spiking neural network. The feasibility study provides the proof-of-concept that events are informative enough in the context of object classi\ufb01 cation, whereas the spiking implementation improves the results by employing an architecture speci\ufb01cally designed to process event data. The spiking network was trained with a three-factor local learning rule which overcomes weight transport, update locking and non-locality problem. The presented results prove that both detection and classi\ufb01cation can be carried-out in the target application using the event data

CMOS-3D smart imager architectures for feature detection

This paper reports a multi-layered smart image sensor architecture for feature extraction based on detection of interest points. The architecture is conceived for 3-D integrated circuit technologies consisting of two layers (tiers) plus memory. The top tier includes sensing and processing circuitry aimed to perform Gaussian filtering and generate Gaussian pyramids in fully concurrent way. The circuitry in this tier operates in mixed-signal domain. It embeds in-pixel correlated double sampling, a switched-capacitor network for Gaussian pyramid generation, analog memories and a comparator for in-pixel analog-to-digital conversion. This tier can be further split into two for improved resolution; one containing the sensors and another containing a capacitor per sensor plus the mixed-signal processing circuitry. Regarding the bottom tier, it embeds digital circuitry entitled for the calculation of Harris, Hessian, and difference-of-Gaussian detectors. The overall system can hence be configured by the user to detect interest points by using the algorithm out of these three better suited to practical applications. The paper describes the different kind of algorithms featured and the circuitry employed at top and bottom tiers. The Gaussian pyramid is implemented with a switched-capacitor network in less than 50 μs, outperforming more conventional solutions.Xunta de Galicia 10PXIB206037PRMinisterio de Ciencia e Innovación TEC2009-12686, IPT-2011-1625-430000Office of Naval Research N00014111031

A modern teaching environment for process automation

Emergence of the new technological trends such as Open Platform Communications Unified Architecture (OPC UA), Industrial Ethernet, cloud computing and the 5th wireless network (5G) enabled the implementation of Cyber-physical System (CPS) with flexible, configurable, scalable and interoperable business models. This provides new opportunities for the process automation systems. On the other hand, the constant urge of industries for cost and material efficient processes demands a new automation paradigm with the latest tools and technologies which should be taken into account while teaching future automation engineers. In this thesis, the modern teaching environment for process automation is designed, implemented and described. This work explains the connections, configurations and the test of three mini plants including the Multiple Heat Exchanger, the Three-tank system and the Mixing Tank. In addition, OPC UA communication between the server and its clients has been tested. The plants are a part of the state of the art of the architecture that provides the access of ABB 800xA to the cloud services via OPC UA over the 5G test wireless network. This new paradigm changes the old automation hierarchy and enables the cross layered communication in the old architecture. This modern teaching environment prepares the students for the future automation challenges with the latest tools and merges data analytics, cloud computing and wireless network studies with process automation. It also provides the unique chance of testing the future trends together in this unique process automation setup

Power System Simulation by Parallel Computation

The concept of parallel processing is applied to power system simulation. The Component Connection Model (CCM) and appropriate numerical methods, such as the Relaxation Algorithm, are established as a conceptual basis for the parallel simulation of small power networks and individual power system components. A commercially available multiprocessing system is introduced for the power system simulator, and the system is adapted to facilitate high-speed parallel simulations. Two separate strategies for controlling the parallel simulation, synchronous and asynchronous relaxation, are introduced, and their performances are evaluated for the parallel simulation of an induction motor drive system. The performances of the parallel methods are also compared to a similar simulation run on a single processor, and the results show that considerable simulation speed-up can be obtained when parallel processing is employed