Pedro Julián

Orthonormal high-level canonical PWL functions with applications to model reduction

An inner product is defined for the linear vector space PWL/sub H/[S] of all the piecewise linear (PWL) continuous mappings defined over a rectangular compact set S, using a simplicial partition H. This permits us to assure that PWL/sub H/[S] is a Hilbert space. Then, the notion of orthogonality is introduced and orthonormal bases of PWL functions are formulated. A relevant consequence of the approach is that the problem of function approximation can be translated to the more studied field of approximation in Hilbert spaces of finite dimension. As will be shown, this powerful theoretical framework can be used to generate an optimal scheme for model reduction.

A comparative study of sound localization algorithms for energy aware sensor network nodes

Sound localization using energy-aware hardware for sensor networks nodes is a problem with many applications in surveillance and security. In this paper, we evaluate four algorithms for sound localization using signals recorded in a natural environment with an array of commercial off-the-shelf microelectromechanical systems microphones and a specially designed compact acoustic enclosure. We evaluate performance of the algorithms and their hardware complexity which relates directly to energy consumption.

Canonical piecewise-linear approximation of smooth functions

This paper deals with the approximation of smooth functions using canonical piecewise-linear functions. The developing of tools in the field of analysis and control of nonlinear systems based on this kind of functions, as well as its efficiency in the representation of electronic devices, motivates the development of useful methods to obtain accurate approximations. A recursive method is proposed to obtain simultaneously all the parameters required and its convergence is studied. In addition, an iterative method to introduce new partitions on the domain, when the error obtained is not satisfactory, is described. This method takes advantage of the partitions already found to reduce the total number of parameters that the algorithm has to handle.

Synthesis of nonlinear multiport resistors: a PWL approach

This paper presents a method for the approximate synthesis of nonlinear multiport resistors. According to some fundamental circuit theory results, the general problem of synthesizing a multiport resistor with given constitutive equations corresponds to that of the synthesis of nonlinear controlled sources. Following this idea, in this paper, we focus on the design of nonlinear controlled sources using a piecewise-linear (PWL) approach. The constitutive equations are first approximated by resorting to canonical expressions for continuous PWL functions, and then implemented using a set of elementary building blocks. The proposed method is applied to the synthesis of the nonlinear resistive part of an equivalent circuit of the Hodgkin-Huxley nerve membrane model.

Synthesis of multiport resistors with piecewise-linear characteristics: a mixed-signal architecture

Non-linear multiport resistors are the main ingredients in the synthesis of non-linear circuits. Recently, a particular PWL representation has been proposed as a generic design platform (IEEE Trans. Circuits Syst.-I 2002; 49:1138–1149). In this paper, we present a mixed-signal circuit architecture, based on standard modules, that allows the electronic integration of non-linear multiport resistors using the mentioned PWL structure. The proposed architecture is fully programmable so that the unit can implement any user-defined non-linearity. Moreover, it is modular: an increment in the number of input variables can be accommodated through the addition of an equal number of input modules. Copyright

Simplicial RTD-based cellular nonlinear networks

Recently, a novel structure called the simplicial cellular neural network (CNN) has been introduced , which permits one to implement any Boolean/Gray-level function of any number of variables. This paper is devoted to explore novel circuit architectures for the implementation of the simplicial CNN based on resonant tunneling diodes. The final objective is to implement a fully programmable CNN in a hardware platform based on nanoelectronic devices.

A scalable and programmable simplicial CNN digital pixel processor architecture

We propose a programmable architecture for a single instruction multiple data image processor that has its foundation on the mathematical framework of a simplicial cellular neural networks. We develop instruction primitives for basic image processing operations and show examples of processing binary and gray scale images. Fabricated in deep submicron CMOS technologies, the complexity of the digital circuits and wiring in each cell is commensurate with pixel level processing.

RTD-BASED CELLULAR NEURAL NETWORKS WITH MULTIPLE STEADY STATES

In this paper, we study the relationship between the standard cellular neural network (CNN) and the resonant tunneling diode (RTD)-based CNN. We investigate the functional and advanced capabilities of a new generation of CNNs that exploit the multiplicity of steady states. We also include in the analysis higher order CNNs. Furthermore, some methods for designing RTD-based CNNs with multiple steady states are presented.

VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network

We present a low-power VLSI wake-up detector for a sensor network that uses acoustic signals to localize ground-based vehicles. The detection criterion is the degree of low-frequency periodicity in the acoustic signal, and the periodicity is computed from the “bumpiness” of the autocorrelation of a one-bit version of the signal. We then describe a CMOS ASIC that implements the periodicity estimation algorithm. The ASIC is fully functional and its core consumes 835 nanowatts. It was integrated into an acoustic enclosure and deployed in field tests with synthesized sounds and ground-based vehicles.

A low-power CMOS integrated circuit for bearing estimation

We present the design and testing of a micropower integrated circuit for the estimation of the bearing angle of a sound source with respect to a pair of microphones. The algorithm is based on a modified binary cross-correlation approach suitable for low-power operation. The circuit has been tested and operates at 600 /spl mu/W at performance levels matching theoretical simulations.