György Cserey

Macromodeling of the Memristor in SPICE

In this paper, we present a new simulation program with integrated circuit emphasis macromodel of the recently physically implemented memristor. This macromodel could be a powerful tool for electrical engineers to design and experiment new circuits with memristors. Our simulation results show similar behavior to the already published measurements of the physical implementation. Our approach provides a solution for the modeling of boundary conditions following exactly the published mathematical model of HP Labs. The functionality of our macromodel is demonstrated with computer simulations. The source code of our macromodel is provided.

Development of a low cost 3D optical compliant tactile force sensor

This paper presents a new and low cost 3D optical compliant tactile sensor that is capable of measuring a three-axial directional force component as well as an incipient slip. The tactile sensor is composed of a hollow compliant convex surface made of silicon rubber placed on a three photodiode and an infra LED based sensor array arranged in a reflective configuration. Any force applied onto the convex surface will cause a deformation that will change the reflected amount of light to the photodiodes causing their output to change according to the applied force vector. Due to the optical method of measurement, the proposed sensor offers a highly dynamic sensory range, low noise, and high speed operation (kHz range). Experimental results demonstrate accurate contact point, force magnitude and force incidence angle detection.

GPU powered CNN simulator (SIMCNN) with graphical flow based programmability

In this paper, we introduce an innovative CNN algorithm development environment that significantly assists algorithmic design. The introduced graphical user interface uses Matlab Simulink with UMF-like program description, where direct functionality accompanies better accessability. The new generation of graphical cards incorporate many general purpose graphics processing units, giving the power of parallel computing to a simple PC environment cheaply. Therefore, analysis of CNN dynamics become more feasible with a common hardware setup. Our measurements demonstrate the efficiency of the realized system. In the case of simpler algorithms, real-time execution is also possible.

Object Outline and Surface-Trace Detection Using Infrared Proximity Array

In this paper, a method of detecting object outlines and creating surface traces using a low-cost distance measurement array of infrared light-emitting diode (LED)-photodiode pairs is demonstrated. For each active LED, the light level on the corresponding photodiode may be measured, generating an image of distance values whose pixel resolution is limited to the spacing between LED-photodiode pairs. Moreover, using several photodiodes to detect the reflected light from each LED, lateral resolution is enhanced, and the angle of reflection can also be detected. Experimental results demonstrate successful application of such data to detecting object outlines and surfaces, and show promising possibilities in using the array for simultaneous localization and mapping (SLAM). The developed sensor array is best suited for environments where prior information of object distance is available (e.g., production lines), or where only relative distance information is needed. In addition, the speed and low cost of the array makes it a good supplementary device to more precise, yet at the same time slower and more expensive distance measurement devices.

ANYONE CAN BUILD CHUA'S CIRCUIT: HANDS-ON-EXPERIENCE WITH CHAOS THEORY FOR HIGH SCHOOL STUDENTS

Chaos is a physical and mathematical phenomenon discovered by E. Lorenz in 1963. The first simple electronic implementation had been invented by L. O. Chua in 1984. This electronic circuit, called Chuas circuit was designed for ease of implementation. In the current brief we will explain chaos by building Chuas chaotic circuit using our Chuas circuit kit with inexpensive components. For readers without access to an oscilloscope, this paper proposes the use of a laptop/Personal Computer to capture the voltage waveforms generated from the circuit and plot the waveforms on a computer screen using a virtual oscilloscope software provided by the authors. The kit is available, the software is downloadable.

3D geometry reconstruction using Large Infrared Proximity Array for robotic applications

In this paper, we propose a novel Large Infrared Proximity Array (LIPA), which is capable of reproducing 3D images of the target object. An IPA uses infrared sensors and infrared emitters to accurately measure distance and thus, creates 3D monographic geometry of the sensed objects in real time. In the current setup infrared emitters (LEDs) and photo transistors are used, placed in one package. In many applications determining an obstacles height, orientation, or distance with a very high resolution is not needed (less than 1–2mm is enough) but the sensor often should have a big sensing area. Contrary to nowadays techniques we tried to form a rather large but sparse sensor array to cover as big part of the surrounding area as possible. In addition it has the detection range of 1cm to 15cm (it can be increased depending on the application requirements). 64 LED and photo transistor pairs were used and placed in an 8 × 8 matrix order creating a 9cm × 9cm sensor array. We also demonstrate how the resolution of these kind of systems can be increased. The concept of the proposed sensor array spans a wide range of potential applications, i.e. in robotics.

Immune response inspired spatial–temporal target detection algorithms with CNN-UM

In this paper we show that, similar to the nervous system and the genetic system, the immune system provides a prototype for a ‘computing mechanism.’ We are presenting an immune response inspired algorithmic framework for spatial–temporal target detection applications using CNN technology (IEEE Trans. Circuits Syst. II 1993; 40(3):163–173; Foundations and Applications. Cambridge University Press: Cambridge, 2002). Unlike most analogic CNN algorithms (IEEE Trans. Circuits Syst. 1988; 35(10):1257–1290; Foundations and Applications. Cambridge University Press: Cambridge, 2002) here we will detect various targets by using a plethora of templates. These algorithms can be implemented successfully only by using a computer upon which thousands of elementary, fully parallel spatial–temporal actions can be implemented in real time. In our tests the results show a statistically complete success rate, and we are presenting a special example of recognizing dynamic objects. Results from tests in a 3D virtual world with different terrain textures are also reported to demonstrate that the system can detect unknown patterns and dynamical changes in image sequences. Applications of the system include in explorer systems for terrain surveillance. Copyright

2D and 3D level-set algorithms on GPU

Locating object boundaries, modeling shapes is still an interesting and important task in many applications such as computer vision, object detection, image segmentation and tracking. In this paper we show the implementation of 2D and 3D algorithms based on the level sets using the advantages residing in todays common GPUs. One main goal of this paper is to contribute a development and give one new local-parallel implementation of a fast level set based algorithm via the locally organized processing elements and memory. This algorithm can model and detect any object with arbitrary complex shape and can be applied to situations where no or very few a priori information is available. Our accelerated implementation can handle more initial curves and surfaces which can fuse or merge according to the requirements. This might be a good base to achieve fast and robust detection, segmentation or tracking in medical or autonomous tasks.

GPU boosted CNN simulator library for graphical flow-based programmability

A graphical environment for CNN algorithm development is presented. The new generation of graphical cards with many general purpose processing units introduces the massively parallel computing into PC environment. Universal Machine on Flows- (UMF) like notation, highlighting image flows and operations, is a useful tool to describe image processing algorithms. This documentation step can be turned into modeling using our framework backed with MATLAB Simulink and the power of a video card. This latter relatively cheap extension enables a convenient and fast analysis of CNN dynamics and complex algorithms. Comparison with other PC solutions is also presented. For single template execution, our approach yields run times 40x faster than that of the widely used Candy simulator. In the case of simpler algorithms, real-time execution is also possible.

Fast, parallel implementation of particle filtering on the GPU architecture

In this paper, we introduce a modified cellular particle filter (CPF) which we mapped on a graphics processing unit (GPU) architecture. We developed this filter adaptation using a state-of-the art CPF technique. Mapping this filter realization on a highly parallel architecture entailed a shift in the logical representation of the particles. In this process, the original two-dimensional organization is reordered as a one-dimensional ring topology. We proposed a proof-of-concept measurement on two models with an NVIDIA Fermi architecture GPU. This design achieved a 411- μ s kernel time per state and a 77-ms global running time for all states for 16,384 particles with a 256 neighbourhood size on a sequence of 24 states for a bearing-only tracking model. For a commonly used benchmark model at the same configuration, we achieved a 266- μ s kernel time per state and a 124-ms global running time for all 100 states. Kernel time includes random number generation on the GPU with curand. These results attest to the effective and fast use of the particle filter in high-dimensional, real-time applications.