A design procedure of discrete-time cellular neural networks (DTCNN) to be used as associative memories for robot vision is presented The choice of cellular neural networks is motivated by their ...architecture, suitable for storing images, and their locally connected structure which is effective for the hardware implementation of the designed memories. In particular, taking into account the constraints dictated by the discrete-time cellular neural networks structure in this paper a design procedure of DTCNNs, which also enables memories to recognize correctly the event of superimposition of tools, is developed. To this purpose, a cellular associative memory which behaves as an optimal linear associative memory (OLAM) is synthesized. The performances of the designed network are investigated and its behaviour as an optimal linear associative memory is confirmed by means of an example of recognition of superimposed tools handled by a robot in an assembly line.
Production of neutrinos is abundant at LHC. Flavour composition and energy reach of the neutrino flux from proton-proton collisions depend on the pseudorapidity \(\eta\). At large \(\eta\), energies ...can exceed the TeV, with a sizeable contribution of the \(\tau\) flavour. A dedicated detector could intercept this intense neutrino flux in the forward direction, and measure the interaction cross section on nucleons in the unexplored energy range from a few hundred GeV to a few TeV. The high energies of neutrinos result in a larger \(\nu\)N interaction cross section, and the detector size can be relatively small. Machine backgrounds vary rapidly while moving along and away from the beam line. Four locations were considered as hosts for a neutrino detector: the CMS quadruplet region (~25 m from CMS Interaction Point (IP)), UJ53 and UJ57 (90 and 120 m from CMS IP), RR53 and RR57 (240 m from CMS IP), TI18 (480 m from ATLAS IP). The potential sites are studied on the basis of (a) expectations for neutrino interaction rates, flavour composition and energy spectrum, (b) predicted backgrounds and in-situ measurements, performed with a nuclear emulsion detector and radiation monitors. TI18 emerges as the most favourable location. A small detector in TI18 could measure, for the first time, the high-energy \(\nu\)N cross section, and separately for \(\tau\) neutrinos, with good precision, already with 300 fb\(^{-1}\) in the LHC Run3.
Heteroassociative memories via cellular neural networks Brucoli, Michele; Carnimeo, Leonarda; Grassi, Giuseppe
International journal of circuit theory and applications,
May/June 1998, Letnik:
26, Številka:
3
Journal Article
In this paper a global design method for associative memories using discrete‐time cellular neural networks (DTCNNs) is presented. The proposed synthesis technique enables to realize associative ...memories with several advantageous features. First of all, grey‐level as well as bipolar images can be stored. Moreover, the proposed approach generates networks with learning and forgetting capabilities. Finally, it is possible to design networks with any kind of predetermined interconnection structure. In particular, neighbourhoods without line crossings can be chosen, greatly simplifying the VLSI implementation of the designed DTCNNs.
In the first part of this work a model of a multilevel threshold network is presented and a stability analysis is carried out using basic notions deriving from non‐linear dynamical system theory. The synthesis procedure is then developed by means of a pseudoinversion technique, assuring learning and forgetting capabilities of the designed DTCNN. The use of a neighbourhood without line crossings is also discussed. Simulation results are reported to show the capability of the proposed approach.
A design methodology of cellular neural networks (CNN) for heteroassociative and autoassociative memories is presented. A new synthesis procedure of continuous-time CNN for heteroassociative memories ...is developed, which assures global stability and robustness to the designed networks. A proper representation of discrete-time CNN characterized by multilevel output junctions is introduced to store memory vectors with b-bit length components. The suggested approach provides considerably simple network architectures suitable for VLSI implementation.
In this paper an efficient method is illustrated to obtain a secure communication system by means of the synchronization of hyperchaotic circuits. The approach consists in driving the receiver with a ...proper number of transmitted signals, each of them constituted by a chaotic masking waveform added to an information-bearing signal, and by keeping the transmitting system equivalent to the receiving one by means of a feedback technique. The suggested method assures a recovery of the information signals without degradation, and enhances the security of the communication scheme, since the utilization of several driving signals makes more difficult for an undesirable listener to synchronize with the transmitter. Simulation results are reported to confirm the capability of the proposed approach.
In this work a design of a space-varying cellular neural network (CNN) in order to behave as an associative memory is presented. To this purpose, a new class of space-varying cellular neural networks ...with a nonsymmetric interconnection structure is considered. A stability analysis is firstly carried out. Then, a learning algorithm, based on the relaxation method, is used to compute the feedback parameters of the considered network. Simulation tests are reported to confirm the validity of the suggested approach.
A parallel method for the transient stability simulation of power systems is presented. The trapezoidal rule is used to discretize the set of algebraic-differential equations which describes the ...transient stability problem. A parallel Block-Newton relaxation technique is used to solve the overall set of algebraic equations concurrently on all the time steps. The parallelism in space of the problem is also exploited. Furthermore, the parallel-in-time formulation is used to change the time steps between iterations by a nested iteration multigrid technique, in order to enhance the convergence of the algorithm. The method has the same reliability and model-handling characteristics of typical dishonest Newton-like procedures. Test results on realistic power systems are presented to show the capability and usefulness of the suggested technique
In this paper an approach to achieve a complete practical synchronization of hyperchaotic circuits with parameter mismatch via an impulsive scalar signal is presented. The chosen scalar signal is ...constituted by a sequence of samples of selected state variables alternatively transmitted for identical time frames. The approach is effectively applied to hyperchaotic circuits with parameter mismatch constituted by two bidirectionally coupled Chua's oscillators.
The use of the micro-grid paradigm with extensive inverter interfaced generation raises the problem of severely restricted fault levels when operating in a power island. This paper presents a review ...of the conventional distribution network protection practices and then discusses their limitations when applied to inverter dominated micro-grids. The use of voltage measurement based fault detection is considered and is followed by consideration of how to apply this technique in conjunction with an adaptive form of protection. A potential solution for small micro-grids is presented in the form of voltage controlled overcurrent devices to enable the use of lower current threshold settings. Key issues for the design of network protection within micro-grids are summarised.
