Compact TSV modeling for low power application
[No abstract available]
Behavioral modeling of the static transfer function of ADCs using INL measurements
In this paper, we present a modeling approach for analog-to-digital converters (ADCs) based on modeling the static transfer function using integral nonlinearity (INL) measurements. The methodology relies on applying a Fast Fourier Transform (FFT) test to the output of a real ADC circuit and extracting the significant harmonics. These are used in a behavioral functional model to approximate the INL using a polynomial function. The resulting model is independent of the ADC type or structure, and is suitable for bottom-up system verification. We compare the performance of the new model with other
FPAA-based realization of filters with fractional laplace operators of different orders
A simple and direct procedure for implementing fractional-order filters with transfer functions that contain Laplace operators of different fractional orders is presented in this work. Based on a general fractional-order transfer function that describes fractional-order low-pass, high-pass, band-pass, band-stop and all-pass filters, the introduced concept deals with the consideration of this function as a whole, with its approximation being performed using a curve-fitting-based technique. Compared to the conventional procedure, where each fractional-order Laplace operator of the transfer
Equivalent lumped element models for various n-port through silicon vias networks
This paper proposes an equivalent lumped element model for various multi-TSV arrangements and introduces closed form expressions for the capacitive, resistive, and inductive coupling between those arrangements. The closed form expressions are in terms of physical dimensions and material properties and are driven based on the dimensional analysis method. The model's compactness and compatibility with SPICE simulators allows the electrical modeling of various TSV arrangements without the need for computationally expensive field-solvers and the fast investigation of a TSV impact on a 3-D circuit
On numerical approximations of fractional-order spiking neuron models
Fractional-order spiking neuron models can enrich model flexibility and dynamics due to the extra degrees of freedom. This paper aims to study the effects of applying four different numerical methods to two fractional-order spiking neuron models: the Fractional-order Leaky integrate-and-fire (FO-LIF) model and the Fractional-order Hodgkin–Huxley (FO-HH) model. Furthermore, some adjustments to these models are proposed, and the effect of reducing the memory size is investigated. We first propose a new realistic formulation for Fo-LIF model to better describe its functionality that is aligned
Modified fractional-order model for biomass degradation in an up-flow anaerobic sludge blanket reactor at Zenein Wastewater Treatment Plant
This paper presents a modified fractional-order model (FOM) for microorganism stimulation in an up-flow anaerobic sludge blanket (UASB) reactor treating low-strength wastewater. This study aimed to examine the famine period of methanogens due to biomass accumulation in the UASB reactor over long time periods at a constant organic loading rate (OLR). This modified model can investigate the substrate biodegradation in a UASB reactor while considering substrate diffusion into biological granules during the feast and famine periods of methanogens. The Grünwald-Letnikov numerical technique was used
An application of ELECTRE III to contractor selection
Contractor selection is carried out in order to choose a competent and capable contractor to do the work. To help in this selection, baselines are established to ensure that the contractors have the required skills, resources, and abilities to execute the project. Contractor selection is a multiple criteria decision making wherein several criteria are required to be evaluated simultaneously. This paper proposes a decisionmaking model for contractor selection utilizing ELECTRE III modeling. The steps of ELECTRE III model include; estimation of concordance indices, estimation of discordance
Chaos-Based RNG using Semiconductor Lasers with Parameters Variation Tolerance
Random numbers play an essential role in guaranteeing secrecy in most cryptographic systems. A chaotic optical signal is exploited to achieve high-speed random numbers. It could be generated by using one or more semiconductor lasers with external optical feedback. However, this system faces two major issues, high peak to average power ratio (PAPR) and parameter variations. These issues highly affected the randomness of the generated bitstreams. In this paper, we use a non-linear compression technique to compand the generated signal before it is quantized to avoid the effects of the PAPR. Also
Parameter Estimation of Two Spiking Neuron Models With Meta-Heuristic Optimization Algorithms
The automatic fitting of spiking neuron models to experimental data is a challenging problem. The integrate and fire model and Hodgkin–Huxley (HH) models represent the two complexity extremes of spiking neural models. Between these two extremes lies two and three differential-equation-based models. In this work, we investigate the problem of parameter estimation of two simple neuron models with a sharp reset in order to fit the spike timing of electro-physiological recordings based on two problem formulations. Five optimization algorithms are investigated; three of them have not been used to