9,362 research outputs found
Evolutionary Synthesis of Analog Electronic Circuits Using EDA Algorithms
DisertaÄnĂ prĂĄce je zamÄĹena na nĂĄvrh analogovĂ˝ch elektronickĂ˝ch obvodĹŻ pomocĂ algoritmĹŻ s pravÄpodobnostnĂmi modely (algoritmy EDA). PrezentovanĂŠ metody jsou na zĂĄkladÄ poĹžadovanĂ˝ch charakteristik cĂlovĂ˝ch obvodĹŻ schopny navrhnout jak parametry pouĹžitĂ˝ch komponent tak takĂŠ jejich topologii zapojenĂ. TĹi rĹŻznĂŠ metody vyuĹžitĂ EDA algoritmĹŻ jsou navrĹženy a otestovĂĄny na pĹĂkladech skuteÄnĂ˝ch problĂŠmĹŻ z oblasti analogovĂ˝ch elektronickĂ˝ch obvodĹŻ. PrvnĂ metoda je urÄena pro nĂĄvrh pasivnĂch analogovĂ˝ch obvodĹŻ a vyuĹžĂvĂĄ algoritmus UMDA pro nĂĄvrh jak topologie zapojenĂ tak takĂŠ hodnot parametrĹŻ pouĹžitĂ˝ch komponent. Metoda je pouĹžita pro nĂĄvrh admitanÄnĂ sĂtÄ s poĹžadovanou vstupnĂ impedancĂ pro ĂşÄely chaotickĂŠho oscilĂĄtoru. DruhĂĄ metoda je takĂŠ urÄena pro nĂĄvrh pasivnĂch analogovĂ˝ch obvodĹŻ a vyuĹžĂvĂĄ hybridnĂ pĹĂstup - UMDA pro nĂĄvrh topologie a metodu lokĂĄlnĂ optimalizace pro nĂĄvrh parametrĹŻ komponent. TĹetĂ metoda umoĹžĹuje nĂĄvrh analogovĂ˝ch obvodĹŻ obsahujĂcĂch takĂŠ tranzistory. Metoda vyuĹžĂvĂĄ hybridnĂ pĹĂstup - EDA algoritmus pro syntĂŠzu topologie a metoda lokĂĄlnĂ optimalizace pro urÄenĂ parametrĹŻ pouĹžitĂ˝ch komponent. Informace o topologii je v jednotlivĂ˝ch jedincĂch populace vyjĂĄdĹena pomocĂ grafĹŻ a hypergrafĹŻ.Dissertation thesis is focused on design of analog electronic circuits using Estimation of Distribution Algorithms (EDA). Based on the desired characteristics of the target circuits the proposed methods are able to design the parameters of the used components and theirs topology of connection as well. Three different methods employing EDA algorithms are proposed and verified on examples of real problems from the area of analog circuits design. The first method is capable to design passive analog circuits. The method employs UMDA algorithm which is used for determination of the parameters of the used components and synthesis of the topology of their connection as well. The method is verified on the problem of design of admittance network with desired input impedance function which is used as a part of chaotic oscillator circuit. The second method is also capable to design passive analog circuits. The method employs hybrid approach - UMDA for synthesis of the topology and local optimization method for determination of the parameters of the components. The third method is capable to design analog circuits which include also ac- tive components such as transistors. Hybrid approach is used. The topology is synthesized using EDA algorithm and the parameters are determined using a local optimization method. In the individuals of the population information about the topology is represented using graphs and hypergraphs.
Open-ended evolution to discover analogue circuits for beyond conventional applications
This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s10710-012-9163-8. Copyright @ Springer 2012.Analogue circuits synthesised by means of open-ended evolutionary algorithms often have unconventional designs. However, these circuits are typically highly compact, and the general nature of the evolutionary search methodology allows such designs to be used in many applications. Previous work on the evolutionary design of analogue circuits has focused on circuits that lie well within analogue application domain. In contrast, our paper considers the evolution of analogue circuits that are usually synthesised in digital logic. We have developed four computational circuits, two voltage distributor circuits and a time interval metre circuit. The approach, despite its simplicity, succeeds over the design tasks owing to the employment of substructure reuse and incremental evolution. Our findings expand the range of applications that are considered suitable for evolutionary electronics
Evolutionary Synthesis of Fractional Capacitor Using Simulated Annealing Method
Synthesis of fractional capacitor using classical analog circuit synthesis method was described in [6]. The work presented in this paper is focused on synthesis of the same problem by means of evolutionary method simulated annealing. Based on given desired characteristic function as input impedance or transfer function, the proposed method is able to synthesize topology and values of the components of the desired analog circuit. Comparison of the results given in [6] and results obtained by the proposed method will be given and discussed
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
Global design of analog cells using statistical optimization techniques
We present a methodology for automated sizing of analog cells using statistical optimization in a simulation based approach. This methodology enables us to design complex analog cells from scratch within reasonable CPU time. Three different specification types are covered: strong constraints on the electrical performance of the cells, weak constraints on this performance, and design objectives. A mathematical cost function is proposed and a bunch of heuristics is given to increase accuracy and reduce CPU time to minimize the cost function. A technique is also presented to yield designs with reduced variability in the performance parameters, under random variations of the transistor technological parameters. Several CMOS analog cells with complexity levels up to 48 transistors are designed for illustration. Measurements from fabricated prototypes demonstrate the suitability of the proposed methodology
Symbolic analysis tools-the state of the art
This paper reviews the main last generation symbolic analyzers, comparing them in terms of functionality, pointing out also their shortcomings. The state of the art in this field is also studied, pointing out directions for future research
Tensor Computation: A New Framework for High-Dimensional Problems in EDA
Many critical EDA problems suffer from the curse of dimensionality, i.e. the
very fast-scaling computational burden produced by large number of parameters
and/or unknown variables. This phenomenon may be caused by multiple spatial or
temporal factors (e.g. 3-D field solvers discretizations and multi-rate circuit
simulation), nonlinearity of devices and circuits, large number of design or
optimization parameters (e.g. full-chip routing/placement and circuit sizing),
or extensive process variations (e.g. variability/reliability analysis and
design for manufacturability). The computational challenges generated by such
high dimensional problems are generally hard to handle efficiently with
traditional EDA core algorithms that are based on matrix and vector
computation. This paper presents "tensor computation" as an alternative general
framework for the development of efficient EDA algorithms and tools. A tensor
is a high-dimensional generalization of a matrix and a vector, and is a natural
choice for both storing and solving efficiently high-dimensional EDA problems.
This paper gives a basic tutorial on tensors, demonstrates some recent examples
of EDA applications (e.g., nonlinear circuit modeling and high-dimensional
uncertainty quantification), and suggests further open EDA problems where the
use of tensor computation could be of advantage.Comment: 14 figures. Accepted by IEEE Trans. CAD of Integrated Circuits and
System
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