Multichannel seismic deconvolution

Deals with Bayesian estimation of 2D stratified structures from echosounding signals. This problem is of interest in seismic exploration, but also for nondestructive testing or medical imaging. The proposed approach consists of a multichannel Bayesian deconvolution method of the 2D reflectivity based upon a theoretically sound prior stochastic model. The Markov-Bernoulli random field representation introduced by Idier et al. (1993) is used to model the geometric properties of the reflectivity, and emphasis is placed on representation of the amplitudes and on deconvolution algorithms. It is shown that the algorithmic structure and computational complexity of the proposed multichannel methods are similar to those of single-channel B-G deconvolution procedures, but that explicit modeling of the stratified structure results in significantly better performances. Simulation results and examples of real-data processing illustrate the performances and the practicality of the multichannel approach     

Approximate distributed Bellman-Ford algorithms

Routing algorithms based on the distributed Bellman-Ford algorithm (DBF) suffer from exponential message complexity in some scenarios. We propose two modifications to the algorithm which result in a polynomial message complexity without adversely affecting the response time of the algorithm. However, the new algorithms may not compute the shortest path. Instead, the paths computed can be worse than the shortest path by at most a constant factor (<3). We call these algorithms approximate DBF algorithms. The modifications proposed to the original algorithm are very simple and easy to implement. The message complexity of the first algorithm, called the multiplicative approximate DBF, is O(nmlog(nΔ)) where Δ is the maximum length over all edges of an n-nodes m-edges network. The message complexity of the second algorithm, called the additive approximate DBF, is O(δ/Δ nm) where δ is the minimum length over all edges in the network     

Optimal and self-tuning deconvolution in time domain

This paper is concerned with both the optimal (minimum mean square error variance) and self-tuning deconvolution problems for discrete-time systems. When the signal model, measurement model, and noise statistics are known, a novel approach for the design of the optimal deconvolution filter, predictor, and smoother is proposed based on projection theory and innovation analysis in the time domain. The estimators are given in terms of an autoregressive moving average (ARMA) innovation model and one unilateral linear polynomial equation, where the ARMA innovation model is obtained by performing one spectral factorization. A self-tuning scheme can be incorporated when the noise statistics, the input model, and/or colored noise model are unknown. The self-tuning estimator is designed by identifying two ARMA innovation models     

Quantized incremental algorithms for distributed optimization

Wireless sensor networks are capable of collecting an enormous amount of data. Often, the ultimate objective is to estimate a parameter or function from these data, and such estimators are typically the solution of an optimization problem (e.g., maximum likelihood, minimum mean-squared error, or maximum a posteriori). This paper investigates a general class of distributed optimization algorithms for "in-network" data processing, aimed at reducing the amount of energy and bandwidth used for communication. Our intuition tells us that processing the data in-network should, in general, require less energy than transmitting all of the data to a fusion center. In this paper, we address the questions: When, in fact, does in-network processing use less energy, and how much energy is saved? The proposed distributed algorithms are based on incremental optimization methods. A parameter estimate is circulated through the network, and along the way each node makes a small gradient descent-like adjustment to the estimate based only on its local data. Applying results from the theory of incremental subgradient optimization, we find that the distributed algorithms converge to an approximate solution for a broad class of problems. We extend these results to the case where the optimization variable is quantized before being transmitted to the next node and find that quantization does not affect the rate of convergence. Bounds on the number of incremental steps required for a certain level of accuracy provide insight into the tradeoff between estimation performance and communication overhead. Our main conclusion is that as the number of sensors in the network grows, in-network processing will always use less energy than a centralized algorithm, while maintaining a desired level of accuracy.     

Discrete constrained iterative deconvolution algorithms with optimized rate of convergence

In this paper, some new constrained discrete deconvolution algorithms based on an iterative equation are presented. The constraints are—the signal extent (signal support)—the positivity—the level bounds. The algorithms minimize either the error energy or a positive functional. The connections with previous works are studied. An experimental comparison of the algorithms convergence speed is studied with a synthetic sequence to be recovered. The restoration error and both the deconvoluted signal and its spectrum show clearly the performances of the algorithms and their ability to achieve a spectral extrapolation. The deconvolution from noisy data is investigated.     

The application of blind channel identification techniques toprestack seismic deconvolution

One objective of seismic signal processing is to identify the layered subsurface structure by sending seismic wavelets into the ground. This is a blind deconvolution process since the seismic wavelets are usually not measurable and therefore, the subsurface face layers are identified only by the reflected seismic signals. Conventional methods often approach this problem by making assumptions about the subsurface structures and/or the seismic wavelets. In this paper an alternative technique is presented. It applies blind channel identification methods to prestack seismic deconvolution. A unique feature of this proposed method is that no such assumptions are needed. In addition, it fits into the structure of current seismic data acquisition techniques, thus no extra cost is involved. Simulations on both synthetic and field seismic data demonstrate that it is a promising new method for seismic signal processing     

Stack algorithm for recursive deconvolution of Bernoulli-Gaussianprocesses [seismic exploration]

The deconvolution of pulse trains modeled as Bernoulli-Gaussian processes is addressed. The detector presented implements a stack Viterbi algorithm with a parametric level of suboptimality. Simulation results are satisfactory, even in the difficult case of a poor spectral content of the wavelet. The recursive and parallel structure of the method allows fast data processing on modern architectures     

Optimal LC-VCO design through evolutionary algorithms

The need for implementing low cost, fully integrated RF wireless transceivers has motivated the widespread use CMOS technology. However, in the particular case for voltage-controlled oscillators (VCO) where ever more stringent specifications in terms of phase-noise must be attained, the design of the on-chip LC tank is a challenging task, where fully advantage of the actual technologies characteristics must be pushed to nearly its limits. To overcome phase-noise limitations arising from the low quality factor of integrated inductors, optimization design methodologies are usually used. In this paper a model-based optimization approach is proposed. In this work the characterization of the oscillator behaviour is guaranteed by a set of analytical models describing each circuit element performance. A set of working examples for UMC130 technology, aiming the minimization of both VCO phase noise and power consumption, is addressed. The results presented, illustrate the potential of a GA optimization procedure design methodology yielding accurate and timely efficient oscillator designs. The validity of the results is checked against HSPICE/RF simulations.     

Optimal deconvolution filter design under parameters perturbation in transmission channels

An optimal deconvolution filter design method is proposed in this paper for signal transmission systems with small perturbation of parameters. The perturbative parameters of the transmission channel and noise model are of probabilistic structures. A realizable filter is derived to minimize the mean square estimation error from the viewpoint of frequency domain. The calculus of variation technique and the spectral factorization method are used in the design procedure. The design method is suitable for the deconvolution of both minimum-phase and nonminimum-phase perturbative transmission systems. The minimum mean square error of the optimal deconvolution filter is also discussed. Finally, an example is given to illustrate the simulation results of the proposed optimal deconvolution filter.This work was supported by the National Science Council under Contract NSC 79-0404-E-007-17.     

On generating distributed intelligence systems architectures usinggenetic algorithms

A methodology for generating large scale distributed intelligence systems (DIS's) using genetic algorithms is presented. An organizational structure (chromosome) is characterized by generic interactions (genes) among the individual nodes comprising it. The objective function evaluates each structure in the generated population against a set of structural constraints and some user defined criteria. The structures satisfying these constraints are feasible solutions to the design problem     

Channel allocation algorithms for WLANs using distributed optimization

The performance of a wireless local area network (WLAN) depends on the channel assignments among interfering access points (APs). Due to the limited number of non-overlapping channels, severe interference scenarios may arise if no appropriated spectrum planning is employed. In our study we focus on WLANs scenarios where APs may belong to different administrative domains, which is actually a very common situation in dense urban deployments. In such cases the use of centralized algorithms is not feasible and the already proposed distributed methods does not guarantee optimal channel assignment. In this paper, therefore, we formalize the channel allocation as a distributed constraint optimization problem (DCOP) and propose a new distributed channel assignment algorithm named DCAA-O, which can find the optimal solution to the channel assignment problem for a group of APs. A suboptimal strategy denoted DCAA-S is also investigated, which aims at reducing the number of control messages to be exchanged between APs, while still achieving a suboptimal solution which is very close to the optimal one. The simulation results show that the proposed algorithms are able to outperform the best known techniques both in terms of solution quality and number of exchanged messages.     

Resampling algorithms and architectures for distributed particle filters

In this paper, we propose novel resampling algorithms with architectures for efficient distributed implementation of particle filters. The proposed algorithms improve the scalability of the filter architectures affected by the resampling process. Problems in the particle filter implementation due to resampling are described, and appropriate modifications of the resampling algorithms are proposed so that distributed implementations are developed and studied. Distributed resampling algorithms with proportional allocation (RPA) and nonproportional allocation (RNA) of particles are considered. The components of the filter architectures are the processing elements (PEs), a central unit (CU), and an interconnection network. One of the main advantages of the new resampling algorithms is that communication through the interconnection network is reduced and made deterministic, which results in simpler network structure and increased sampling frequency. Particle filter performances are estimated for the bearings-only tracking applications. In the architectural part of the analysis, the area and speed of the particle filter implementation are estimated for a different number of particles and a different level of parallelism with field programmable gate array (FPGA) implementation. In this paper, only sampling importance resampling (SIR) particle filters are considered, but the analysis can be extended to any particle filters with resampling.     

Comparative analysis of distributed power control algorithms in CDMA

This paper presents comparative analysis of various algorithms of distributed power control used in Code Division Multiple Access (CDMA) systems. These algorithms include Distributed Balancing power control algorithm (DB), Modified Distributed Balancing power control algorithm (MDB), Fully Distributed Power Control algorithm (FDPC), Distributed Power Control algorithm (DPC), Distributed Constrained Power Control algorithm (DCPC), Unconstrained Second-Order Power Control algorithm (USOPC), Constrained Second-Order Power Control algorithm (CSOPC), and Fixed Step Distributed Power Control algorithm (FSDPC). These algorithms are compared based on the rate of convergence to the target signal-to-interference ratio, the rate of convergence of the power, and the rate of convergence of utilities. Simulation results show that the FDPC is the best choice according to these parameters.     

Logical process partitioning in distributed simulation using genetic algorithms

This paper presents a method to partition models in logical processes in the context of distributed simulation. The proposed method uses genetic algorithms to decide on the viability and the partitioning technique most indicated. The input parameters to the genetic algorithm are information about the model (number of elements, communication, arrival and service taxes), and the architecture where the simulation is executed. As result, we have the number of logical processes and their mapping on the distributed environment. Two models were used to evaluate the proposed method.     

Bearing estimation for a distributed source: modeling, inherentaccuracy limitations and algorithms

The problem of using sensor array measurements to estimate the bearing of a radiating source surrounded by local scatterers is considered. The concept of “partial coherence” is introduced to account for temporal as well as spatial correlation effects often encountered in a Rayleigh fading-type propagation channel formed between a source and sensor array elements. A simple parametric model for temporal channel correlation is presented, yielding an overall spatio-temporal channel model that is more realistic than formerly proposed models (which assume either full or zero temporal channel correlation). Thus, previously proposed “distributed source” models for bearing estimation problems are generalized to a parametric spatio-temporal model for what is called “partially coherently distributed (PCD) sources”. A study of the associated Cramer-Rao Bound (CRB) is undertaken for a simple but illustrative problem formulation. The inherent limitations in bearing estimation accuracy for this spatio-temporal problem are seen to lie between the cases of zero and full temporal correlation, becoming more severe as temporal channel correlation increases. In addition, the associated maximum likelihood estimators for source bearing are proposed, and their performance is compared with that predicted by the CRB     

Optimal time-frequency deconvolution filter design fornonstationary signal transmission through a fading channel: AF filterbank approach

The purpose of this paper is to develop a new approach-time-frequency deconvolution filter-to optimally reconstruct the nonstationary (or time-varying) signals that are transmitted through a multipath fading and noisy channel. A deconvolution filter based on an ambiguity function (AF) filter bank is proposed to solve this problem via a three-stage filter bank. First, the signal is transformed via an AF analysis filter bank so that the nonstationary (or time-varying) component is removed from each subband of the signal. Then, a Wiener filter bank is developed to remove the effect of channel fading and noise to obtain the optimal estimation of the ambiguity function of the transmitted signal in the time-frequency domain. Finally, the estimated ambiguity function of the transmitted signal in each subband is sent through an AF synthesis filter bank to reconstruct the transmitted signal. In this study, the channel noise may be time-varying or nonstationary. Therefore, the optimal separation problem of multicomponent nonstationary signals is also solved by neglecting the transmission channel     

Optimal and robust kernel algorithms for passive stochasticapproximation

The problem of estimating a root of an equation f(x )=0 is considered in the situation where the values of f( x) are measured with random errors at random points and the choice of these points cannot be controlled. Nonlinear modification of the recursive Hardle-Nixdorf method is studied. Almost sure and mean square convergence is proved, and the rate of convergence is estimated. The optimal choice of parameters and of a kernel is presented; it is shown that for the optimal procedure the lower bound for the accuracy of arbitrary methods of solving the problem is attained     

Optimal algorithms for synthesis of reliable application-specificheterogeneous multiprocessors

Fast and optimally-reliable application-specific multiprocessor-synthesis is critical in system-level design, especially in medical, automotive, space, and military applications. Previous work in multiprocessor-synthesis and task-allocation for performance and reliability requires exponential time, and therefore, is useful only for small examples. We present the first deterministic and provably-optimal algorithm (RELSYN-OPT) to synthesize real-time, reliable multiprocessors using a heterogeneous library of N processors and L link types. We prove that for a series-parallel graph with M subtasks and nested-depth d, the worst-case computational complexity of RELSYN-OPT Is O(M·(L+N)·N^d). For tree-structured task graphs, RELSYN-OMT runs in O(M·(L+N)), and is asymptotically optimum, RELSYN-OPT, because of its speed, applies to static and dynamic task allocation for an ultra-reliable distributed processing environment for which, until now, research has produced only suboptimal heuristic solutions