Hardware Implementation of Algorithms Research Papers (original) (raw)

In this contribution, we propose the use of Cellular Neural Networks as an application for the image segmentation of cinematographic image sequences. The proposed approach is based on a Cellular Neural network cost function that takes... more

In this contribution, we propose the use of Cellular Neural Networks as an application for the image segmentation of cinematographic image sequences. The proposed approach is based on a Cellular Neural network cost function that takes into account motion and colour. Cellular Neural Networks are of particular interest for hardware implementation due to the inherent parallelism and initial results using an FPGA simulator are also presented.

Fuzzy logic control is one of the most interesting fields where fuzzy theory can be effectively applied. Fuzzy logic techniques attempt to imitate human thought processes in technical environments. In doing so, the fuzzy logic approach... more

Fuzzy logic control is one of the most interesting fields where fuzzy theory can be effectively applied. Fuzzy logic techniques attempt to imitate human thought processes in technical environments. In doing so, the fuzzy logic approach allows the designer to handle ...

This paper presents a neural network implementation using reconfigurable devices (FPGA) and a signal processor (DSP) available in a flexible codesign platform. The network is described using C and VHDL languages, for the software and... more

This paper presents a neural network implementation using reconfigurable devices (FPGA) and a signal processor (DSP) available in a flexible codesign platform. The network is described using C and VHDL languages, for the software and hardware parts respectively. The software part is responsible for the learning phase and the hardware part is responsible for the propagation phase. Our objective for

This paper introduces a neuro-fuzzy controller (NFC) for the speed control of a PMSM. A four layer neural network (NN) is used to adjust input and output parameters of membership functions in a fuzzy logic controller (FLC). The back... more

This paper introduces a neuro-fuzzy controller (NFC) for the speed control of a PMSM. A four layer neural network (NN) is used to adjust input and output parameters of membership functions in a fuzzy logic controller (FLC). The back propagation learning algorithm is used for training this network. The performance of the proposed controller is verified by both simulations and

Reservoir Computing is a recent pattern recognition tech- nique that combines temporal processing capabilities with fast learning rates and excellent convergence properties. The system consists of two parts: a recurrently connected... more

Reservoir Computing is a recent pattern recognition tech- nique that combines temporal processing capabilities with fast learning rates and excellent convergence properties. The system consists of two parts: a recurrently connected network of simple nodes (e.g. neurons) called the reservoir, and a so-called readout function which can be any tra- ditional statistical technique and which computes the actual output. The choice of the node with which we build the reservoir is very broad, and in this case we use stochastich bitstream neurons. Classical sigmoidal neurons perform a weighted sum of their inputs, followed by a non-linearity. This uses a lot of additions and multiplications, which is not hardware efficient at all. Stochastic bitstream neurons circumvent this problem by communi- cating through stochastic bitstreams instead of analog values, which trans- forms additions and multiplications to simple bitwise operations, thus al- lowing an efficient hardware implementation. For t...

Will it or won't it? The 1999 IEEE 1149.4 Standard for a mixed-signal test bus is on the cusp of industrial acceptance, but it's not clear whether industry will pick it up. This study, by two leading European research... more

Will it or won't it? The 1999 IEEE 1149.4 Standard for a mixed-signal test bus is on the cusp of industrial acceptance, but it's not clear whether industry will pick it up. This study, by two leading European research institute, delves into the details of hardware implementation and, in so doing, contributes to the growing literature on this topic.

Scientists are, all the time, in a struggle with uncertainty which is always a threat to a trustworthy scientific knowledge. A very simple and natural idea, to defeat uncertainty, is that of enclosing uncertain measured values in real... more

Scientists are, all the time, in a struggle with uncertainty which is always a threat to a trustworthy scientific knowledge. A very simple and natural idea, to defeat uncertainty, is that of enclosing uncertain measured values in real closed intervals. On the basis of this idea, interval arithmetic is constructed. The idea of calculating with intervals is not completely new in mathematics: the concept has been known since Archimedes, who used guaranteed lower and upper bounds to compute his constant Pi. Interval arithmetic is now a broad field in which rigorous mathematics is associated with scientific computing. This connection makes it possible to solve uncertainty problems that cannot be efficiently solved by floating-point arithmetic. Today, application areas of interval methods include electrical engineering, control theory, remote sensing, experimental and computational physics, chaotic systems, celestial mechanics, signal processing, computer graphics, robotics, and computer-assisted proofs. The purpose of this book is to be a concise but informative introduction to the theories of interval arithmetic as well as to some of their computational and scientific applications.