Analog Electronic Circuits Research Articles

Analysis and circuit implementation of a new four-dimensional non-autonomous hyper-chaotic system

A new four-dimensional non-autonomous hyper-chaotic system is presented by adding input signal to a hyper-chaotic system based on Lorenz system. The resulting hyper-chaotic system may have any dynamic beharior among the periodic orbits, two-tori, chaos and hyper-chaos by controlling the frequency of input signal, while the periodic orbits are more prominent. The phenomenon in well demonstrated by numerical simulations and bifurcation analysis. Finally, an analog electronic circuit is designed to implement the new system，and the experimental results of the non-autonomous hyper-chaotic circuit well agreed with the simulation results.

Flatness-based pre-compensation of laser diodes

A physical nonlinear dynamical model of a laser diode is considered. We propose a feed-forward control scheme based on differential flatness for the design of input-current modulations to compensate diode distortions. The goal is to transform without distortion a radio-frequency current modulation into a light modulation leaving the laser-diode and entering an optic fiber. We prove that standard physical dynamical models based on electron and photons balance are flat systems when the current is considered as control input, the flat output being the photon number (proportional to the light power). We prove that input-current is an affine map of the flat output, its logarithm and their time-derivatives up to order two. When the flat output is an almost harmonic signal with slowly varying amplitude and phase, these derivatives admit precise analytic approximations. It is then possible to design simple analogue electronic circuits to approximate the nonlinear computations required by our flatness-based inversion scheme. Simulations with the parameters of a commercial diode illustrate the practical interest of this pre-compensation scheme and its robustness versus modelling and analogue implementation errors.

Analog Genetic Encoding for the Evolution of Circuits and Networks

This paper describes a new kind of genetic representation called analog genetic encoding (AGE). The representation is aimed at the evolutionary synthesis and reverse engineering of circuits and networks such as analog electronic circuits, neural networks, and genetic regulatory networks. AGE permits the simultaneous evolution of the topology and sizing of the networks. The establishment of the links between the devices that form the network is based on an implicit definition of the interaction between different parts of the genome. This reduces the amount of information that must be carried by the genome, relatively to a direct encoding of the links. The application of AGE is illustrated with examples of analog electronic circuit and neural network synthesis. The performance of the representation and the quality of the results obtained with AGE are compared with those produced by genetic programming.

A Modular Fault-Diagnostic System for Analog Electronic Circuits Using Neural Networks With Wavelet Transform as a Preprocessor

We have developed a modular analog circuit fault- diagnostic system based on neural networks using wavelet decomposition, principal component analysis, and data normalization as preprocessors. Our proposed system has the ability to identify faulty components or modules in an analog circuit by analyzing its impulse response. In this approach, the circuit is divided into modules, which, in turn, are divided into smaller submodules successively. At each level, where a module is divided into submodules, a neural network is trained to identify the submodule that inherits the fault of interest from the parent module. This procedure finds the faulty component or module of any desirable size in an analog circuit by consecutive divisions of modules as many times as necessary. Our proposed approach has three advantages over the traditional neural-network-based diagnostic systems, which directly look for faulty components in the entire circuit. First, the performance of the modular systems is reliable and robust independent of the circuit size and can successfully classify similar fault classes with a significant overlap in the feature space where the traditional approach completely fails. Second, the modular approach requires significantly smaller neural network architectures, leading to much more efficient training. Third, for large real circuit boards, our diagnostic system proceeds to systematically reduce the size of the faulty modules until it is feasible to replace it.

The use of digital signal processors (DSPs) in real-time processing of multi-parametric bioelectronic signals

In many cases of bioanalytical measurement, calculation of large amounts of data, analysis of complex signal waveforms or signal speed can overwhelm the performance of microcontrollers, analog electronic circuits or even PCs. One method to obtain results in real time is to apply a digital signal processor (DSP) for the analysis or processing of measurement data. In this paper we show how DSP-supported multiplying and accumulating (MAC) operations, such as time/frequency transformation, pattern recognition by correlation, convolution or filter algorithms, can optimize the processing of bioanalytical data. Discrete integral calculations are applied to the acquisition of impedance values as part of multi-parametric sensor chips, to pH monitoring using light-addressable potentiometric sensors (LAPS) and to the analysis of rapidly changing signal shapes, such as action potentials of cultured neuronal networks, as examples of DSP capability.

A hyperchaotic Lorenz attractor and its circuit implementation

In this paper, a new hyperchaotic system is constructed by introducing an additional state variable into the third-order Lorenz system. Some basic properties, including dissipativity, equlibria, stability and Hopf bifurcation, of this hyperchaotic system are analyzed in detail, and the bifurcation routes to hyperchaos from periodic, chaotic evolutions are observed. The existence of hyperchaos is verified with Lyapunov exponent spectrum. Moreover, an analog electronic circuit is designed, and various hyperchaotic attractors of this system are observed from the circuit experiments.

SBT Approach towards Analog Electronic Circuit Fault Diagnosis

An approach for the fault diagnosis of single and multiple faults in linear analog electronic circuits is proposed in this paper. The simulation-before-test (SBT) diagnosis approach proposed in this write up basically consists of obtaining the frequency response of fault free/faulty circuit. The peak frequency and the peak amplitude from the error response are observed and processed suitably to extract distinct signatures for faulty and nonfaulty conditions under maximum tolerance conditions for other network components. The artificial neural network classifiers are then used for the classification of fault. Networks of reasonable dimensions are shown to be capable of robust diagnosis of analog circuits including effects due to tolerances. This is a unique contribution of this paper. Fault computation time is drastically reduced from the traditional analysis techniques. This results in a direct dollar savings at test time. A comparison of the proposed work with the previous works which also employ preprocessing techniques, reveals that our algorithm performs significantly better in fault diagnosis of analog circuits due to our proposed preprocessing techniques.

Synchronization of electronic genetic networks

We describe a simple analog electronic circuit that mimics the behavior of a well-known synthetic gene oscillator, the repressilator, which represents a set of three genes repressing one another. Synchronization of a population of such units is thoroughly studied, with the aim to compare the role of global coupling with that of global forcing on the population. Our results show that coupling is much more efficient than forcing in leading the gene population to synchronized oscillations. Furthermore, a modification of the proposed analog circuit leads to a simple electronic version of a genetic toggle switch, which is a simple network of two mutual repressor genes, where control by external forcing is also analyzed.

Analogue electronic circuit diagnosis based on ANNs

Feed-forward artificial neural networks (ANNs) have been applied to the diagnosis of nonlinear dynamic analogue electronic circuits. Using the simulation-before-test (SBT) approach, a fault dictionary was first created containing responses observed at all inputs and outputs of the circuit. The ANN was considered as an approximation algorithm to capture mapping enclosed within the fault dictionary and, in addition, as an algorithm for searching the fault dictionary in the diagnostic phase. In the example given DC and small signal frequency domain measurements were taken as these data are usually given in device’s data-sheets. A reduced set of data per fault (DC output values, the nominal gain and the 3 dB cut-off frequency, measured at one output terminal) was recorded. Soft (parametric) and catastrophic (shorts and opens) defects were introduced and diagnosed simultaneously and successfully. Large representative set of faults was considered, i.e., all possible catastrophic transistor faults and qualified representatives of soft transistor faults were diagnosed in an integrated circuit. The generalization property of the ANNs was exploited to handle noisy measurement signals.

Analysis, circuit implementation and synchronization of a new three-dimensional chaotic system

Center manifold theory presented an approach that the order of a system can be reduced. This paper investigates the basic characteristic of a new chaotic system by the approach. A flow equation on the center manifold is obtained and the pitchfork bifurcation is analyzed for the new chaotic system. An analog electronic circuit is designed to implement the new system, so the chaotic attractor are verified. We explain the significant distinction of application due to different frequencies used in numerical simulation and circuit implementation. Finally, the synchronization of the new chaotic system is realized by using the single variable coupling feedback, and the circuit of synchronization is presented.

Novel analog readout electronics for microacoustic thickness shear-mode sensors

In this paper, a novel purely analog electronic readout circuit for sensors based on thickness shear-mode (TSM) resonators is introduced. This system can, e.g., be used for viscosity sensing of liquids, even if the liquid shows a comparatively high viscosity. In contrast to conventionally utilized oscillator circuits, the present circuit eliminates the disturbing influence of the static capacitance of the sensor by means of a dedicated synchronous detector circuit. The sensor is externally driven by a voltage-controlled oscillator (VCO), which is tuned to the resonator's fundamental frequency by means of a control circuit utilizing a frequency modulation of the VCO signal. When the control loop has settled, either the obtained resonance frequency or the damping of the sensor, which is indicated by means of a DC output voltage, can be used as the output signal providing a measure for the viscosity.

Chaos suppression in the parametrically driven Lorenz system

We predict theoretically and verify experimentally the suppression of chaos in the Lorenz system driven by a high-frequency periodic or stochastic parametric force. We derive the theoretical criteria for chaos suppression and verify that they are in a good agreement with the results of numerical simulations and the experimental data obtained for an analog electronic circuit.

Quench detection and data acquisition system for SST-1 superconducting magnets

Superconducting magnet system of steady state superconducting Tokamak (SST-1) shall be operating in a very noisy environment. Presences of high inductive voltages in the magnets during off-normal events like VDE, plasma disruption and PF magnet ramp ups, etc. has made quench detection and data acquisition a challenging task. A hybrid of analog electronic circuits and software-controlled data acquisition system has been developed and tested to safeguard the magnets. This paper will describe the electronic hardware circuits developed for signal conditioning, high voltage suppression, fail-proof quench detection and for noise elimination algorithms and their testing. The SST-1 superconducting magnets will have large number of sensors like voltage taps, Venturi flow meter, strain gages, hall probes, pressure sensors, temperature sensors and displacement transducers. A real time data acquisition system has been designed using VMEbus for monitoring and storing signals from all these sensors and initiating control action in case of off-normal events. The paper will also describe the configuration of the data acquisition system with emphasis on hardware used and the software developed for it.

Automated Selection of Test Frequencies for Fault Diagnosis in Analog Electronic Circuits

This paper suggests three novel methods for selecting the frequencies of sinusoidal test signals to be used in fault diagnosis of analog electronic circuits. The first and second methods are based on a sensitivity analysis and show to be particularly effective in linear circuits where a priori information and designer experience can be exploited. Conversely, the third method selects the input frequencies to be used for diagnostic purposes without requiring any hypothesis about the circuit or testing design background. As such, the method is particularly appealing in complex -possibly nonlinear - circuits where the designer experience is of little value and an effective blind approach saves both designer and testing time. The suggested frequency selection methods are then contrasted to each other against performance and computational complexity.

High-<tex>$T_c$</tex>Electronic Planar Gradiometer Constructed From Magnetometers on a Chip

We have fabricated a high-T/sub c/ electronic planar gradiometer composed of dc SQUIDs on a 10 mm /spl times/ 10 mm SrTiO/sub 3/ bicrystal substrate. The SQUIDs were patterned in a single-layer YBa/sub 2/Cu/sub 3/O/sub 7-/spl delta// thin film which is prepared by pulsed laser deposition. The outputs of the magnetometers are added and subtracted by analog electronic circuits to form the gradiometer. These magnetometers could be used to construct two sets electronic planar first-order gradiometers or an electronic planar second-order gradiometer, which could measure the elements /spl part/B/sub z///spl part/x and /spl part/B/sub z///spl part/y or /spl part//sup 2/B/sub z///spl part/x/spl part/y, respectively. The noise spectra of each magnetometer and a first-order electronic planar gradiometer were measured.

Analogia eletrônica no ensino de física

Este artigo introduz um procedimento para o projeto de circuitos eletrônicos analógicos simples que emulam o comportamento dinâmico de sistemas físicos e matemáticos. Tais circuitos podem ser utilizados como uma aproximação experimental para desenvolvimento de atividades experimentais de ensino, sendo facilmente aplicáveis em qualquer laboratório. Como exemplo, as versões eletrônicas dos sistemas caóticos de Duffing e Rössler são projetadas e implementadas, resultando em protótipos simples, baratos, robustos, versáteis e funcionais.

Component value selection for active filters using parallel tabu search algorithm

The determined component values by means of conventional approach to the design of analogue electronic circuits, typically do not coincide with all manufactured standard values and the designer selects the nearest standard value, thus causing a design deviation. In general, a better set of standard values will exist in the discrete domain of standard component values. However, this set will be in a solution space of all possible component-value combinations that is a highly complex discrete search problem. Tabu search is a heuristic optimisation algorithm, which simulates the general rules of intelligent problem solving and has the ability of discovering the global minima in a multi-modal search space. In this work, a novel method based on a parallel tabu search algorithm is described for the component value selection of the analogue active filter.

Direction selective ergonomics and human factors ‐ a layout for analog VLSI

We propose herein a motion detection artificial vision model which uses analog electronic circuits. The proposed model is comprised of four layers. The first layer is a differentiation circuit of the large capacitor and resistance (CR) coefficient, and the second layer is a differentiation circuit of the small CR coefficient. Thus, the speed of the movement object is detected. The third layer is a difference circuit for detecting the movement direction, and the fourth layer is a multiple circuit for detecting pure motion output. The model was shown to be capable of detecting a movement object in the image. Moreover, the proposed model can be used to detect two or more objects, which is advantageous for detection in an environment in which several objects are moving in multiple directions simultaneously. From a technological viewpoint, the proposed model facilitates clarification of the mechanism of the biomedical vision system, which should enable design and simulation by an analog electric circuit for detecting the movement and speed of objects.

Nonlinear dynamics extraction for time-delay systems using modular neural networks synchronization and prediction

It is shown that the nonlinear dynamics of chaotic time-delay systems can be reconstructed using a new type of neural network with two modules: one for nonfeedback part with input data delayed by the embedding time, and a second one for the feedback part with input data delayed by the feedback time. The method is applied to both simulated and experimental data from an electronic analog circuit of the Mackey–Glass system. Better results are obtained for the modular than for feedforward neural networks for the same number of parameters. It is found that the complexity of the neural network model required to reconstruct nonlinear dynamics does not increase with the delay time. Synchronization between the data and the model with diffusive coupling is also achieved. We have also shown by iterating the model from the present point that the dynamics can be predicted with a forecast horizon larger than the feedback delay time.

Multi-layered analog electronic circuits for motion detection using biomedical vision and brain model

We propose herein a motion detection artificial vision model which uses analog electronic circuits. The proposed model is comprised of four layers. The first layer is a differentiation circuit of the large CR coefficient, and the second layer is a di