The use of chaotic sequences as spectral spreading sequences in direct-sequence spread-spectrum (DS/SS) communication systems is proposed. The error probabilities of such systems are investigated and shown to be, for all practical purposes, identical to the conventional DS/SS systems which use binary signature sequences. Among the advantages of the use of chaotic sequences in DS/SS are the availability of a great number of them, the ease of their generation, as well as their inherent improvement in the security of transmission.>

A statistical model, based on experimental data, is presented to characterize the multipath propagation medium in typical manufacturing environments. The statistical behavior of the medium is described by a time-varying impulse response which includes the distribution of the parameters of the channel such as the gain, the interarrival time, and the number of paths. The resolvable components in a number of experimentally measured pulse responses of indoor factory radio channels were used to estimate the channel statistics. The interarrival times of the signals were modeled by the Weibull distribution. A model for the distribution of signals was presented using the modified beta distribution. The path gain coefficients were represented using the Rayleigh, Rician, or log-normal distributions. The model for the factory channel is flexible enough to fit the data for other indoor sites by changing the parameters of the factory channel appropriately relative to the size, topography, and other structural features of the site.>

The authors detail the results of narrowband propagation measurements performed at five factories. The extensive empirical data indicate that path loss is dependent upon local surroundings and is log-normally distributed, temporal fading is Rician, and small-scale signal fluctuations due to receiver motion are primarily Rayleigh, although Rician and log-normal distributions fit some of the data. Shadowing effects of common factory equipment likely to obstruct indoor radio paths are also examined.>

A beacon method for locating autonomous vehicles in a flexible manufacturing environment is presented, and typical positioning errors of such a method are computed. Data obtained from an experimental beacon navigation system support the analytical results which indicate excellent positioning accuracy is possible over a large workspace, and that unlike dead reckoning systems, navigation errors are dependent solely upon the vehicle's position in the workspace and not the distance traveled.>

This paper presents a graph-theoretic approach to determining an optimal task (or routing) assignment of p autonomous vehicles (AVs) among m workstations in a flexible manufacturing system (FMS) to minimize the assignment completion time, as well as to achieve load balancing among the AVs. The task assignment problem is equivalent to an optimal routing assignment of destinating the m workstations to the p autonomous vehicles. A cost function is defined in terms of job execution time and traveling time performed by the AVs. Optimization of the cost function is based on the minimax of the job execution time and the minimization of max-min of the traveling time. The optimal task assignment problem is solved by a state-space search method - the A* algorithm. If potential collisions exist on the optimal routing assignment, then dynamic collision detection must be carried out during the state-space search to guarantee an optimal collision-free routing assignment. This collision avoidance can be easily taken care of by using an ordered collision matrix to adjust the arrival time of every AV arriving at the center of the "collision zone" if a potential collision is detected. Again the A* search strategy can be utilized to obtain an optimal collision-free routing assignment with load balancing.

This paper deals with the analysis of DPLL's operating in the presence of non-Gaussian noise. The loop model developed here allows for multiple input samples each loop sampling period, complex processing of the input sample set, and various noise distributions. In particular, six different robust estimators are used as processors of the input: these are the trimmed mean estimator, a one-step HuberM-type estimator, a newM-type estimator, and three versions of a zero-memory nonlinear (ZNL) estimator: hole punch, limiter, and sign detector. The tracking performance of a second-order DPLL is simulated and analyzed for five different types of noise: Gaussian, contaminated Gaussian, Laplace, Middleton ClassA, and generalized exponential. Comparisons are made for signal-to-noise ratios ranging from 0 to - 10 dB. It is found that the ZNL estimators provide performance superior to that of the other robust estimators, especially for noise densities that are heavy tailed.

A graph-theoretic approach for determining an optimal task (or routing) assignment of p autonomous vehicles (AV's) among m workstations in a flexible manufacturing system which both minimizes the assignment completion time and balances the load among the AV's is presented. This task assignment problem is equivalent to an optimal routing assignmenl of destinating the m workstations to the p autonomous vehicles. A cost function is defined in terms of the job execution time and the traveling time performed by the AV's. Optimization of the objective function is based on the minimax of the job execution time and the minimization of max-min of the traveling time. This optimal task assignment problem is known to be NP-complete. Thus the problem is solved by a state-space search method-the A algorithm. The A algorithm is a classical minimum-cost graph search method. It is guaranteed to find an optimal solution if the evaluation function which utilizes the heuristic information about the problem for speeding up the search is properly defined. If potential collisions exist on the optimal routing assignment, then dynamic collision detection must be carried out during the state-space search to guarantee an optimal collision-free routing assignment. This collision avoidance can be taken care of by using an ordered collision matrix to adjust the arrival time of every AV arriving at the center of the "collision zone" if a potential collision is detected. Again, the A search strategy can be utilized to obtain an optimal collision-free routing assignment, and the optimal assignment obtained also achieves load balancing of the p AV's.

This paper deals with the analysis of digital phase-locked loop (DPLL) models that take into account the discrete nature of the analog-to-digital converter (ADC) and the numerical controlled oscillator (NCO). The models are general enough to allow for different kinds of noise distributions and nonuniform qnantization. Weak assumptions on the nature of the loop parameters reduce the models to finite Markov chain problems. It is shown that the resulting probability transition matrices are "lumpable," which translates into convenient Computational savings. In this manner, an exact statistical analysis is possible not only for first-order but also for second-order loops. Numerical results give evidence that for appropriate ADC and NCO characteristics, few quantization levels are needed to match the performance of models that ignore qnantization. However, when the values of these characteristics are not proper, a substantial change in performance can occur. This change in the loop behavior cannot be analyzed or predicted by those models that neglect the quantization effects. It is also found that the use of a nonlinear ADC improves the loop behavior when the noise density distribution is heavy-tailed, e.g., atmospheric noise.

Exhaustive feature selection algorithms are optimal because all possible combinations of features are tested against a predetermined criterion. Suboptimal algorithms that trade performance for speed by considering only a subset of all feature combinations are generally preferred. An implementation of the exhaustive search feature selection (ESFS) method is described for the Bayes Gaussian statistics. The algorithm significantly reduces the computational and time requirements normally associated with optimal algorithms. The performance of this algorithm is compared to that of two suboptimal algorithms-forward sequential features selection and stepwise linear discriminant analysis. Results show that this implementation provides a moderate improvement in classification accuracy and is well suited for evaluating the performance of suboptimal algorithms.

Transformation of seismic signals into their analytic signal representation permits the unique separation of envelope, instantaneous phase, instantaneous frequency and apparent polarity. These parameters are useful in extracting the physical properties of a seismic signal and help in geophysical and geological interpretation. Deconvolution methods can improve the quality of the analytic seismic signal representation. From simulation studies it is found that time and space adaptive deconvolution significantly improves the quality of the analytic signal representation.

The problem of reconstruction of a continuous densely sampled uniform grid scalar surface from track-type geophysical surveys is discussed. Examples of track-type signal sources include earth gamma ray radiation and magnetic fields measured at low altitude (typically 500 ft.) along tracks spaced from a fraction to several miles apart. The signal model investigated assumes the geophysical surface consists of a narrow band isotropic stochastic signal process. The sampling process is characterized by a high sampling rate along track and a low sampling rate across track. A reconstruction filter approach is described which attempts to provide isotropic reconstruction to minimize the error in representation of scene features.

The performance of random reference correlators is examined in terms of sample rate and signal-to-noise ratio. A method to improve the performance is suggested that requires three-level quantization. Comparisons are made using weakly correlated jointly Gaussian processes as inputs to the correlator and independent samples in each channel.