Felipe Gomez-Castañeda

Low-voltage linear transconductor and a memory current using the MIFGMOS Transistor

The aim of this article is to probe the advantages that the Multi-Input Floating Gate MOS (MIFGMOS) transistor has versus the conventional MOSFET transistor in order to design analogue circuits with low-voltage operation and good linearity. To show this, the design and implementation of both a voltage to current converter (VIC) cell and a memory current cell (MIC) using MIFGMOS transistors is presented. The development is based on mathematical and simulation analysis as well as experimental results. Both cells present good performance and linearity according to theoretical analysis with a supply voltage of 1.7 V and a power consumption of about 20 μW, despite the long channel technology. These characteristics could be very important in analogue and mixed signal applications requiring low supply voltage and low power consumption. The cells presented here can be part of a sample and hold circuit operating in current mode, but applications are not restricted. Additionally, a comparison between simulation and experimental results obtained when we tested five 3-input MIFGMOS transistors are included to show their properties and behavior.

FGPA development and implementation of a solar panel emulator

This work describes the development and FPGA implementation of a solar panel emulator. First we created the electric analog model of the solar panel using the Mentor Graphics framework, using the irradiance and temperature variables of a meteorological database as input signals and then obtaining the short circuit current and the open circuit voltage parameters to finally train an artificial neural network using Matlab, to perform the modeling of the response of the solar panel. Once the neural network was optimized, this was described in VHDL to simulate its response, to finally make its implementation in FPGA digital device and to be able to compare these results with those of a commercial solar panel.

Multilayer perceptron network with integrated training algorithm in FPGA

In this manuscript we present the implementation of an artificial neural network type Multilayer Perceptron (ANN-MP or NNMP) in Field-Programmable Gate Arrays (FPGA), including Back-Propagation training method based on descendent gradient. This network has 2 reconfigurable hidden layers, adjustable parameters (epochs and ratio learning) and batch learning. The proposed architecture aims to reduce the number of logical elements to be used, so serial processing is utilized. In order to test the performance of the trained network, a nonlinear function was approximated with satisfactory results.

Photovoltaic panel emulator in FPGA technology using ANFIS approach

In this manuscript we present the implementation in FPGA of ANFIS system (Adaptive Network-based Fuzzy Inference Systems) for a two-input architecture with three membership functions per input and nine fuzzy rules, used to set up a photovoltaic panel emulator. The starting point is the photovoltaic panel electric analog model simulated with ELDO, a tool of Mentor Graphics Suite, having as inputs irradiation and temperature from a meteorological data base so we can obtain the short-circuit current (Isc) and open circuit voltage (Voc) of the panel. With this information, ANFIS was trained within Matlab environment to approximate the photovoltaic panel response. The training was carried out for both, current and voltage, independently, and once achieved minimum error parameters, they were downloaded into the FPGA implemented architecture in order to assess its performance.

Programmable CMOS CNN Cell Based on Floating-gate Inverter Unit

At present, the Cellular Neural Network (CNN) is a potential parallel structure able to perform image processing tasks in real-time when is effectively implemented in CMOS technology. The CNN silicon integration success is due mainly to the local connectivity of processing cells. In this work, an alternative design based on floating-gate MOS inverters is presented, which uses unipolar signals for solving binary tasks. The approach brings a fast response in a reduced silicon area, as shown through electrical simulations. A prototype cell in CMOS technology (AMI, 1.2 micron) was fabricated and tested for eight image processing tasks.

CMOS analog neurofuzzy prototype based on ANFIS

The architecture called ANFIS (Adaptive Neuro-Fuzzy Inference System) proposed by J.R. Jang (1993) is divided in five layers. Layers 1 and 2 in ANFIS were built by using a double-differential amplifier and a winner takes all circuit; to implement layers 3, 4 and 5, CMOS translinear blocks are used. The complete ANFIS architecture is implemented on a circuit board, using two CMOS circuits (N-well and 2 /spl mu/m minimum dimensions). The total system has two inputs with three membership functions each one, which generate a fuzzy space with nine subspaces and one single output. The system is used for classification of electrical signals.

Floating-Gate MOSFET parallel analog network for assignment problems

Floating-Gate-MOS Transistor (FGMOSFET) has several applications on analog circuit design; in some cases FGMOSFET simplifies high-complexity circuits because of its inherent properties. On the other side, working with FGMOSFET on subthreshold regime has allowed non-linear function implementation such as exponential and natural logarithm functions. Moreover, working on subthreshold regime is one of the most useful techniques for low-power designs. Taking advantage of FGMOSFETs properties, this work shows an analog CMOS circuit that finds the best solution of the combinatorial task of assignments in terms of Lagrange multipliers. This is in contrast to the Hopfield paradigm, which is often VLSI-costly. The building block in this circuit uses FGMOSFET transistors, has low silicon complexity and exhibits good electrical performance according to PSpice simulations. Final simulations were developed for a 0.5µm CMOS technology and the output currents reach an optimal solution in all cases. It might include threshold voltage programming circuits for reducing mismatch effects.

DSP Implementation of Extended Infomax ICA Algorithm for Blind Source Separation

In this paper we use a digital signal processing unit (DSP) to implement an extended Infomax independent component analysis (ICA) algorithm for blind source separation (BSS). In this work we are going to reproduce the performance of the net implementation as expected in Matlab. A first hardware implementation has been designed and the experimental results are presented. In the developed experiments we used recorded signals and then they were processed in the DSP unit in real-time. The performance comparison are made with 2times2 net

Fuzzy technique for image enhancement using B-spline

This paper presents an adaptable fuzzy algorithm for image enhancement. The method is based on the modification of the membership functions for minimizing the measure of fuzziness of an input image. The modification of the membership functions is used to denote the characteristic relationship between a group of pixels and its position inside a histogram. This consists in the optimization criterion of the contrast-enhancement procedure in an image. The experimental results are also presented to validate the proposed framework.

Voltage - Current Converter for a Memory Current Cell using Floating Gate transistors

This paper presents the analysis, design and implementation of a Voltage - Current Converter (VIC), for a Memory Current Cell (MIC) in a sample and hold process using Floating Gate MOS transistors and fabricated in 1.2 mum CMOS technology. The mathematical analysis of each cell is focused in show the advantages the floating gate transistor has versus conventional MOS transistor. Also, the cells are design taking into account low supply voltage and consequently, low power dissipation. We demonstrate the analytical results are according to simulation results. Both cells present good performance and linearity with a supply voltage of 1.7 V and tens of microwatts power consumption, despite the long channel technology. These characteristics are very important in analog and mixed signal applications, like mobile communications systems.