A nonlinear receptive field model of the visual system

A nonlinear model for the visual system is proposed in relation to the dependence of receptive field size on luminance. The model is constructed based on the fact that the exponent of Stevens' brightness function varies depending on the target size. The model consists of two stages; the mechanism of spatial summation and the mechanism of 0.5-power in cascade. The mechanism of 0.5-power is responsible for an exponent 0.5 of the brightness function for the point target. The extent of the spatial summation changes depending on the luminance so that the model yields a brightness function with exponent 0.33 for the infinite target of uniform luminance distribution. The model predicts well the signal transfer function of vision, the charactreristics of the suprathreshold spatial summation and the mean luminance dependence of the spatial frequency characteristics for threshold contrast.     

A nonlinear electrical-thermal model of the skin

Presents a model for the skin which accounts for both the nonlinearities and the asymmetries in its voltage-current characteristic. This model consists of an electrical submodel and a heat transfer submodel. The electrical submodel uses nonlinear devices in which some parameters depend on skin temperature. The heat transfer submodel models the heat exchange between the skin, the surrounding tissues, and the ambient medium and calculates the temperature of the skin to update the necessary parameters of the electrical submodel. The model is based on experiments designed to determine: (1) the dry skin voltage-current characteristic; (2) the changes in the skin breakdown voltage with location; (3) the moist skin voltage-current characteristic; (4) the changes in the voltage-current characteristic of the skin with duration after the onset of stimulation; and (5) the effect of skin temperature on its voltage-current characteristic. During these experiments the authors used 84-mm² square Ag-AgCl electrodes to apply sinusoidal voltage of 0.2 and 20 Hz. The simulations were performed using the Advanced Continuous Simulation Language (ACSL), capable of solving differential and integral equations with variable coefficients. The model predicted the skin behavior satisfactorily for a large range of amplitudes and frequencies. The authors found that the breakdown occurred when the energy delivered to the skin exceeded a threshold. Above this threshold the voltage-current characteristic of the skin became nonlinear and asymmetric and, in a real situation, the subject would experience an uncomfortable sensation which could rapidly develop into pain     

A new oscillometry-based method for estimating the brachial arterial compliance under loaded conditions

We propose a new method for assessing the compliance of a compressed brachial artery using an oscillometry-based approach that is mathematically based on artery and air-cuff models. The cuff dynamics during the inflation period were characterized by simultaneously recording the cuff volume and internal pressure with a pressure transducer and an airflow meter, respectively, which yielded the envelope of the oscillation amplitudes (OAs) in the air cuff. This allowed the change in the arterial volume during each heartbeat at different cuff pressures to be calculated, yielding a changed volume-pressure curve. The oscillometry-derived loaded compliance of the brachial artery ( C _osci) can be determined as the dynamic changed volume divided by the pulse pressure. Furthermore, we developed a direct scheme to calibrate the calculated dynamic changed volume. In addition, the proposed C _osci was validated by comparing it with the compliance of the brachial artery (C _echo ) estimated echocardiographically from the brachial arterial blood flow in 32 patients whose lower limbs exhibited numbness or lack of strength. The results showed that C _osci and C _echo were significantly correlated between the cuff pressures levels and the mean arterial pressure, systolic pressure, and diastolic pressure ( r = 0.616, 0.571, and 0.666, respectively; p < 0.0001). This suggests that a useful measure of the loaded compliance can be derived from the pattern of the OA waveform in addition to oscillometry-based blood pressure measurements.     

输出不可量测非线性系统的神经模型参考自适应控制

该文针对被控对象输出不可量测的非线性系统,引入一个便于在线辨识的扩展神经网络模型,提出一种基于前馈-反馈结构的神经网络模型参考自适应控制方法。给出了具有全局收敛性的网络训练算法,并分析了控制系统的稳定性。仿真结果表明该控制方法是有效的,而且对网络初始权值的选取及被控对象特性参数的扰动都具有良好的鲁棒性。     

A unified yield model incorporating both defect and parametriceffects

A new approach to modeling yield is presented, which inherently includes both the effects of the conventional defect contributors and the parametric yield loss contributors often treated separately in existing yield models. These parametric yield losses are particularly important during the startup yield-improvement phase of new technology introduction, in many performance-sensitive products such as analog devices and high-speed digital devices, and in analyses of bin-split yields. By assuming a distribution in the size of defects, from point defects up to defects as large as or larger than a wafer, the parametric yield contributors can be viewed as simply rather large, design-dependent defects, which will render IC's unacceptable if any portion of the large defect overlaps the defect-sensitive area of a chip. In this way, the conventional Poisson model, or various extensions of the well-known Murphy model, can be augmented in a straightforward and general way to include parametric yield loss. It is shown that parametric yield losses introduce an additional die size dependence for yield that can help to account for the observed dependence of yield on die area. The model is compared to other models and to experimental yield data to illustrate both its utility in separating yield contributors and its close agreement with experimental yield data     

Memoryless nonlinear system identification with unknown model order

The problem of identifying a general memoryless input/output system from measurements of inputs and the corresponding outputs is considered. The measured output is sought to be represented as the linear combination of known functions of the input with some additive noise. The choice of model order to be used to fit the data is the main issue addressed, and a cost function involving the prediction error and the model order is derived. The cost function under certain approximations is shown to be similar to one obtained by H. Akaike (1969, 1970). If there is a real system generating the data, it is shown that the expected value of this cost function is always minimized at the true value of the order as long as the noise variance satisfies certain conditions. Asymptotic results for some cases are derived. An efficient algorithm is proposed for identifying the model order. Some simulation results using the proposed algorithm are also presented     

A model selection method for nonlinear system identification based FMRI effective connectivity analysis

In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study.     

A nonlinear model for the GaAs ballistic diode

A simple formula is presented for the current-voltage relationship of the GaAs ballistic diode. Also, closed-form expressions are derived for the harmonics and intermodulation current contents resulting from exciting the diode by a multisinusoidal input voltage.     

A finite-difference transmission line matrix method incorporating anonlinear device model

A variable-mesh combination of the expanded-node transmission line matrix (TLM) and finite-difference-time-domain (FD-TD) methods for solving time-domain electromagnetic problems is described. It retains the physical process of wave propagation and the numerical stability of the former and it has the computational efficiency of the latter. This full-wave finite-difference transmission line matrix (FD-TLM) method utilizes transmission lines of differing impedances to implement a three-dimensional variable mesh, which makes practical the simulation of structures having fine details, such as digital integrated circuits (ICs). Circuit models for lumped resistors, capacitors, diodes, and MESFETs have been developed and included for use in simulating digital and microwave ICs. The validity of the variable mesh implementation is verified by comparing an FD-TLM simulation of a picosecond pulse generator structure with electrooptical measurements, and the validity of the device model implementation is verified by comparing an FD-TLM simulation of a MESFET logic inverter with a SPICE simulation     

Lightning return-stroke model incorporating current distortion

A lightning return-stroke model of transmission-line (TL) type, which incorporates both the attenuation and distortion of the waveform of the propagating current, is proposed. With the proposed model, the influence of the distortion of the propagating current on associated electromagnetic field waveforms is investigated and found to be small.     

A nonlinear model of the phasic dynamics of muscle activation

This paper presents a phasic excitation-activation (PEXA) model of the process of motoneuron excitation and the resultant activation and force development of a motor unit. The model input is an amount of depolarizing current (as when injected with an intracellular electrode) and the model output is muscle force. The model includes dynamics and nonlinearities similar to phenomena discovered experimentally by others: the firing rate response of motoneurons to steps of depolarizing current and the "catch-like enhancement" of force produced by overlapping motor neuron action potentials. The parameter values used in this model are derived from experimentally measured data and expressed in physical units, and model predictions extend to published data beyond those used in generating the model parameter values.     

A nonlinear model for context-dependent modulation of the binocular VOR

Studies on the behavior of the vestibulo-ocular reflex (VOR) reveal that the monocular reflex gain is adjusted according to target position relative to each eye. In this paper, we present a nonlinear approach in modeling the viewing-context dependency of the slow-phase angular VOR. We show that including appropriate nonlinearities in the responses of premotor neurons in the brainstem is sufficient to account for the online modulation of the VOR with target position. This approach allows very complex behaviors in response to sensory patterns without resorting to currently assumed cortical computations. A local premotor topology with nonlinear properties has repercussions in the study of all ocular reflexes, since it implies context dependent dynamics in all behavioral responses (pursuit, optokinetic, VOR, saccades, etc.) that share this network. Local nonlinearities in spinal circuits could similarly influence the context dependence of other motor systems (such as stretch reflex modulation during rhythmic walking).     

A thermodynamic noise model for nonlinear resistors

A Gaussian white noise model for passive nonlinear resistors, valid in the linear quadratic current-voltage (I-V) approximation at zero bias, is suggested. Although approximative, the model, which has originally been developed by R.L. Stratonovich, is thermodynamically well founded. The model is applied to the shot noise of exponential diodes and tunnel junctions as well as to the thermal noise of JFET's and MOSFET's. Within the range of the linear-quadratic approximation, the results are in full agreement with the well-known device specific standard low frequency thermal and shot noise models for these devices. The model can he considered as a linear-quadratic short circuit noise current generalization of Nyquist's formula     

A nonlinear model for fractal image coding

After a very promising start, progress in fractal image coding has been relatively slow recently. Most improvements have been concentrating on better adaptive coding algorithms and on search strategies to reduce the encoding time. Very little has been-done to challenge the linear model of the fractal transformations used so far in practical applications. In this paper, we explain why effective nonlinear transformations are not easy to find and propose a model based on conformal mappings in the geometric domain that are a natural extension of the affine model. Our compression results show improvements over the linear model and support the hope that a deeper understanding of the notion of self-similarity would further advance fractal image coding.     

Second-order adaptive Volterra system identification based ondiscrete nonlinear Wiener model

The authors present the nonlinear LMS adaptive filtering algorithm based on the discrete nonlinear Wiener (1942) model for second-order Volterra system identification application. The main approach is to perform a complete orthogonalisation procedure on the truncated Volterra series. This allows the use of the LMS adaptive linear filtering algorithm for calculating all the coefficients with efficiency. This orthogonalisation method is based on the nonlinear discrete Wiener model. It contains three sections: a single-input multi-output linear with memory section, a multi-input, multi-output nonlinear no-memory section and a multi-input, single-output amplification and summary section. For a white Gaussian noise input signal, the autocorrelation matrix of the adaptive filter input vector can be diagonalised unlike when using the Volterra model. This dramatically reduces the eigenvalue spread and results in more rapid convergence. Also, the discrete nonlinear Wiener model adaptive system allows us to represent a complicated Volterra system with only few coefficient terms. In general, it can also identify the nonlinear system without over-parameterisation. A theoretical performance analysis of steady-state behaviour is presented. Computer simulations are also included to verify the theory     

A macroscopic model of nonlinear constitutive relations insuperconductors

A macroscopic model is proposed for nonlinear electromagnetic phenomena in superconductors. Nonlinear constitutive relations are derived by modifying the linear London's equations. The superelectron number density as a function of applied macroscopic current density, n _s(J), is derived from a distribution of electron velocities at a certain temperature T. At temperature T≠0 K, the function n_s(J) has a smooth variation near the macroscopic critical current density J_c. Agreement has been found between this n _s(J,T) model and the temperature dependence of n_s in the two-fluid model. The nonlinear conductivities σ_s(J) and σ_n(J) are obtained from the London's equation with the modified n_s(J) function. Nonlinear resistance R(I), kinetic inductance L_k(I) and surface impedance Z_s(I) in thin wire, slab, and strip geometries are calculated     

Separation of the nonlinear source-pull from the nonlinear system behavior

In this paper, we describe a measurement technique to identify a nonlinear system in the presence of nonlinear source-pull. Models identified with continuous-wave measurement data are not generalizable when nonlinear source-pull is present. This is demonstrated on measured data and compared with the performance of the proposed technique. The method is based on two-tone signals with very close frequencies that excite the system at the fundamental and harmonic frequencies. By varying the phase relation between the beat components, the system's nonlinear behavior is separated from the nonlinear source-pull. Note that such excitations can be generated using commercially available synthesizers with single-sideband in-phase-quadrature modulation options.     

A nonlinear spatially variant object-dependent system model forprediction of partial volume effects and scatter in PET

Accurate quantitation of small lesions with positron emission tomography (PET) requires correction for the partial volume effect. Traditional methods that use Gaussian models of the PET system were found to be insufficient. A new approach that models the non-Gaussian object-dependent scatter was developed. The model consists of eight simple functions with a total of 24 parameters. Images of line and disk sources in circular and elliptical cylinders, and an anthropomorphic chest phantom were used to determine the parameter values. Empirical rules to determine these parameter values for various objects based on those for a reference object, a 21.5-cm circular cylinder, were also proposed. For seven spheroids and a 3.4-cm cylinder, pixel values predicted by the model were compared with the measured values. The model-to-measurement-ratio was 1.03±0.07 near the center of the spheroids and 0.99±0.03 near the center of the 3.4-cm cylinder. In comparison, the standard single Gaussian model had corresponding ratios of 1.27±0.09 and 1.24±0.03, respectively, and the corresponding ratios for a double Gaussian model were 1.13±0.09 and 1.05±0.01. Scatter fraction (28.5%) for a line source in the 21.5-cm cylinder was correctly estimated by our model. Because of scatter. The authors found that errors in the measurement of activity in spheroids with diameters from 0.6 to 3.4 cm were more significant than previously appreciated     

A rain attenuation model for satellite link attenuation predictions incorporating the spatial inhomogeneity of rainrate

A model describing the spatial inhomogeneity of rainrate within a rain cell is derived and incorporated into a specific attenuation model to yield a more exact relationship than has been used heretofore relating rainrate and the attendant attenuation at frequencies above 10 GHz. The resulting unified attenuation model relates terrestrial and slant path attenuation, effective propagation distance and path diversity gain to parameters characterizing the earth-station or stations (e.g. location, height, latitude, elevation angle and baseline distance from another station), the frequency of operation, and the two possible raintypes admitted into the model (i.e. convective rain and residual or debris rain). The model is particularly suited for the theoretical investigation of path diversity gain since this concept owes its existence to spatial inhomogeneity of rainrate. This inhomogeneous rain attenuation model can be interfaced with an appropriate statistical rainrate prediction model, such as the Crane two-component model, to yield more precise attenuation predictions.