We consider a wired network of analog sensors in one dimension. Each sensor measures some physical quantity and "communicates" with its adjacent neighbors. The base station can access the network by communicating with the sensors at the boundaries. We show that by making measurements at the boundary (i.e. from the first sensor) we can determine the values of the unknown field values along the entire network stably. The method is based on building an analogy with classical one dimensional inverse scattering (Helmholtz equation) for which the Chen-Rokhlin algorithm provides a highly surprising stable method for recovering the scattering parameters. Simulation results validating the scheme for thousands of sensors are presented.

In mixed-signal designs, substrate noise originating from the digital part can seriously influence the functionality of the analog part. As such, accurately modeling the properties of the substrate as a noise-propagator is becoming ever more important. A model can be obtained through the Finite Element Method (FEM) or the Boundary Element Method (BEM). The FEM performs a full 3D discretization of the substrate, which makes this method very accurate and flexible but also slow. The BEM only discretizes the contact areas on the boundary of the substrate, which makes it less flexible, but significantly faster. A combination between BEM and FEM can be efficient when we need flexibility and speed at the same time. This paper briefly describes the BEM and the FEM and their combination, but mainly concentrates on the theoretical validation of the combined method and the experimental verification through implementation in the SPACE layout to circuit extractor and comparison with commercial BEM and FEM tools.

No abstract available.

The authors present a methodology to design fixed-size systolic arrays. It allows a systematic and hierarchical mapping of full-size arrays to fixed-size arrays. Two processor-clustering techniques are described. They can be used to achieve the following design objectives: (1) transforming inefficient arrays into efficient arrays, (2) reducing the size of an array, (3) reducing the dimension of an array, and (4) balancing local memory and external communication of processors. A technique is described to cluster processors in such a way that the number of I/O pins of the resulting processor is independent of the number of processors that are clustered. The approach presented unifies and generalizes array reduction techniques.>

A key aspect of a CAD framework is the management of meta data, i.e. high level information about the structure and status of the design.In this paper we present the architecture of a modular frame-based IC design system and elucidate the software structure adopted for the management of meta data in its CAD framework.The meta data management software module is efficient, operates in a distributed hardware environment, controls concurrent access, and offers a high level query interface for meta data browsing.

A new applicative model is presented for the description and analysis of both synchronous and asynchronous VLSI networks at the top levels of abstraction.The model is based on two powerful paradigms, the Applicative State Transition concept of Backus [l] for the descriptive aspects and the theory of single token time(d) Petri Nets [2, 3, 41 for the communicative aspects

The partial lossless inverse scattering (PLIS) problem, which plays a fundamental role in the solution of a alpha -stationary estimation problems, is considered. Some open problems connected with this theory are treated. Necessary and sufficient conditions for a Hermitian matrix to be positive (say, a covariance matrix) are derived in terms of its alpha -stationary wave representation. A generalization of the recursive construction of PLIS solutions is proposed. It is shown that each invariant subspace of the backward shift operator has a corresponding solution of the PLIS problem, provided the operator that generalized the classical Pick matrix is positive definite. It is also shown that, under fairly general conditions, all solutions of the H/sup infinity / type are essentially obtained in this way, and a characterization of the residual wave quantities generated by the scatterer is given. Stochastic models and forward and backward order-recursive innovation filters are derived.>

An efficient method is presented to model the parasitic capacitance of VLSI interconnections. It is valid for conductors in a stratified medium which is considered to be a good approximation for the Si-SiO/sub 2/ system of which ICs are made. The model approximates the charge density on the conductors as a continuous function on a web of edges. Each base function in the approximation has the form of a 'spider' of edges. The model has very low complexity as compared to previously presented models and achieves a high degree of precision within the range of validity of the stratified medium.>

Abstract The computer‐aided design of dedicated pipelined processors for numerical applications and signal processing requires design tools that support system refinement, partitioning strategies and the transformation of behavioural descriptions into structure. This in turn poses problems of design data management which are considered here. We show that an object‐oriented data management system is a step towards solving the problems. The method proposed here is based on a systematic specification of data structures and data access methods, using abstract data type specifications. As a result, the data management is completely transparent to the application programs. The data‐management system is written in Enhanced C (EC), a set‐orientated extension of the language C.

The structural properties of parallel recursive algorithms point to the feasibility of a Hierarchical Flow-graph integration (HIFI) design method for VLSI array processor design. The Hierarchical approach allows the designer to focus attention at the appropriate level of detail. Flow-graphs are used because they offer a powerful and convenient tool for describing many signal processing algorithms. Integration is used here to mean top-down integration - from algorithm analysis to VLSI array - as opposed to merely an integration of electronic components. The HIFI method is proposed as a design and description tool aiming specially at VLSI arrays for signal processing algorithms. The major issues involved are: the recursive algorithm decomposition, abstract notations, flow-graph (structural) and functional (behavior) description, temporal and structural decomposition, bi-directional mapping between graphic and textual codes, simulation and verification tools, and mapping from virtual array to actual architecture.

The paper presents a revised functional description of Volder's Coordinate Rotation Digital Computer algorithm (CORDIC), as well as allied VLSI implementable processor architectures. Both pipelined and sequential structures are considered. In the general purpose or multi-function case, pipeline length (number of cycles), function evaluation time and accuracy are all independent of the various executable functions. High regularity and minimality of data-paths, simplicity of control circuits and enhancement of function evaluation speed are ensured, partly by mapping a unified set of micro-operations, and partly by invoking a natural encoding of the angle parameters. The approach benefits the execution speed in array configurations, since it will allow pipelining at the bit level, thereby providing fast VLSI implementations of certain algorithms exhibiting substantial structural pipelining or parallelism.

Methods to construct rational solutions of the lossless inverse scattering (LIS) problem for one-port passive digital systems are described. The first method is recursive and can be viewed as a generalization of the celebrated Schur algorithm. The second method is global and leads to a parametrization of what we call fundamental solutions from which all LIS solutions may be constructed. Quite a few classical problems in estimation theory and network theory may be viewed as special cases of the LIS problem. With each fundamental solution there is a solution of a corresponding estimation problem leading to a prediction and a modeling filter for a given stochastic process.

We consider the modelling of a covariance function δ(t) + k(t) where t is continuous time. For instance, it is of interest to have a method for computing a continuous model of the oral cavity. To achieve this one uses a sampling of the speech signal which is high compared to the Nyquist frequency and one hopes to be able to distill from it a time continuous reflection function. The problem here, however, is one of numerical stability. By increasing the sampling and using classical methods one will lose control on accuracy. In order to stabilize the procedure we have investigated how the celebrated Schur method [1],[2],[3] may be extended to the time-continuous case. It turns out that a novel equation arises which expresses the causality of the modeling filter and which, when used on our problem, achieves a high degree of numerical stability. Incidentally we show also how the method is related to classical inverse scattering problems for lossless transmission lines. In fig. 1-4 we show a number of covariance testfunctions, their corresponding continuous-time reflection functions and their errors. As a conclusion we may state that we arrive at an optimal behaviour of the algorithm, even in the presence of noise, as is illustrated in fig. 5. In conclusion we discuss the relation of our technique with methods presented in estimation theory [4],[5], in electromagnetism [6] and in embedding theory [7].

Given the power spectrum of a signal, we wish to approximate it using a recursive procedure. The resulting transfer function must be stable and we shall moreover request that it be stably invertible. We wish also to have a good measure of the approximation error. The approximating transfer function will be implemented by means of an orthogonal filter which contains a cascade of filtersections derived from the recursive approximation procedure.

An exact and approximate realization theory for estimation and model filters of second-order stationary stochastic sequences is presented. The properties ofJ-lossless matrices as a unifying framework are used. Necessary and sufficient conditions for the exact realization of an estimation filter and a model filter as a submatrix of aJ-lossless system are deduced. An extension of the so-called Schur algorithm yields an approximateJ-lossless realization based on partial past information about the process. The geometric properties of such partial realizations and their convergence are studied. Finally, connections with the Nevanlinna-Pick problem are made, and how the techniques presented constitute a generalization of many aspects of the Levinson-Szegö theory of partial realizations is shown. As a consequence generalized recursive formulas for reproducing kernels and Christoffel-Darboux formulas are obtained. In this paper the scalar case is considered. The matrix case will be considered in a separate publication.

In this paper a novel theory and algorithm for spectral factorization is presented. It is based on a criterion for minimal extraction of a so-called "elementary factor." Although not all positive para-hermitian matrices can be minimally factored into elementary factors, still the method can be adapted to fit the general case by increasing the degree in a well-controlled way and removing the nonminimal units of degree at the end. The method is, in this sense, strictly minimal. Moreover, the algorithm produces the spectral factor in ali cases where such a factorization does exist. Also, an independent proof of the famous spectral factorization result of Youla is obtained, so that the completeness of the method is ascertained. The procedure results in a workable and optimally minimal algorithm.