When the signal-to-noise ratio (SNR) falls below a certain level, the error of the time-of-flight (ToF) maximum likelihood estimator abruptly increases due to the well-known threshold effect. Nevertheless, operating near and below the threshold SNR value might be necessary for many remote sensing applications due to power-related constraints. These constraints may include a limit on the maximum power of a single source pulse or a limit on the total power used by multiple signals transmitted during a single measurement. For narrow-band signals, the threshold effect emerges mostly due to outliers induced by local maxima of the autocorrelation function of a source signal. Following the previously explored path of biosonar-inspired echo processing, this paper introduces a new method for ToF estimation in the presence of outliers. The proposed method employs a bank of phase-shifted unmatched filters for generating multiple biased but only partially correlated estimators (multiple experts). Using machine learning techniques, the information from the multiple experts is combined together for improving the near-the-threshold ToF estimation from a single echo.

The mean squared error of the classical maximum likelihood time-of-flight (ToF) estimator increases dramatically when the signal-to-noise ratio falls below a certain threshold. For narrow-band signals, this well-known threshold effect occurs largely due to the biased outliers which are induced by the local maxima of the source signal autocorrelation function. In our previous work (Part I), we have described a machine learning biosonar-inspired method for reducing the effect of the outlier bias on the accuracy of a single-echo estimate. In this paper, we extend this approach by introducing a method for combining multiple echo signals into a robust ToF estimator which is resilient to outliers. The individual bias-corrected estimates are combined together using the optimal weighted averaging (OWA) scheme which takes into account the uncertainties associated with inlier and outlier measurements. As in the single-echo case, a weak classifier and a bank of phase-shifted unmatched filters are used for estimating the probabilities of appearance of a specific outlier class in a single-echo estimate. Combined with the previously introduced single-echo bias-reduction method, the OWA scheme results in significant improvement in the ToF estimation accuracy.

In this work we present a model that uses a Dirichlet process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient of the spatial constraint, which is locally dependent on each site, modulates the time-variant transition probability matrix. In our model, we use the DP in combination with variational Bayesian inference to estimate the local coefficients and the time-dependent structure of the hidden states. In addition, the formulation of the NHMM within the DP framework does not require the specification of the number of states. Our results demonstrate that the proposed model can uncover the hidden states when the observed data is generated by a NHMM model and the number of hidden states is unknown.

In this work we introduce a probabilistic model for classifying segmented images. The proposed classifier is very general and it can deal both with images that were segmented with deterministic algorithms, such as the k-means algorithm, and with probabilistic clustering approaches, such as the Hidden Markov Random Field (HMRF) algorithm. Similarly, our model can be used on either binary images or on images that contain multiple clustering labels as well as on images with any cluster boundaries (sharp, fuzzy or irregular). We tested our classifier on real fMRI images and showed that it outperforms the region-based Maximum Likelihood k-means classifier. Furthermore, we showed that higher classification rates are obtained when the images are segmented using a probabilistic HMRF algorithm compared to deterministic k-means method.

BACKGROUND: Significance analysis at single gene level may suffer from the limited number of samples and experimental noise that can severely limit the power of the chosen statistical test. This problem is typically approached by applying post hoc corrections to control the false discovery rate, without taking into account prior biological knowledge. Pathway or gene ontology analysis can provide an alternative way to relax the significance threshold applied to single genes and may lead to a better biological interpretation. RESULTS: Here we propose a new analysis method based on the study of networks of pathways. These networks are reconstructed considering both the significance of single pathways (network nodes) and the intersection between them (links). We apply this method for the reconstruction of networks of pathways to two gene expression datasets: the first one obtained from a c-Myc rat fibroblast cell line expressing a conditional Myc-estrogen receptor oncoprotein; the second one obtained from the comparison of Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia derived from bone marrow samples. CONCLUSION: Our method extends statistical models that have been recently adopted for the significance analysis of functional groups of genes to infer links between these groups. We show that groups of genes at the interface between different pathways can be considered as relevant even if the pathways they belong to are not significant by themselves.

In this work we introduce a probabilistic model that utilizes spatial contextual information to aid recognition when dealing with ambiguous segmentations of handwritten patterns. The recognition problem is formulated as an optimization problem in a Bayesian framework by explicitly conditioning on the spatial configuration of the letters. As a consequence, and in contrast to HMMs, the proposed model can handle duration modeling without an increase in computational complexity. We test the model on a real-world handwriting dataset and discuss several factors that affect the recognition performance.

In this work, we present a context-based model for tracking object features. More specifically, the context is defined as a collection of features within the local region that surrounds the feature that is being tracked. The model does not rely on any knowledge about the object, and therefore the collection of contextual features in one frame is just a hypothesis that is then reconfirmed or rejected in subsequent frames. We applied the model to tracking horizontal and vertical edges extracted from real-time video streams that contain moving vehicles. Our results show that the performance of the system improves as the number of the contextual features increases.

Using the theory of optimal receivers, the range accuracy of echolocating systems can be expressed as a function of pulse bandwidth and SNR through the well-known Woodward equation. That equation, however, was developed in the limit of very high SNRs and assumes that the correct peak of the cross-correlation function is known a priori. We show that for increasing levels of noise, the accuracy of the cross-correlation receiver undergoes a sharp transition from the Woodward equation for a coherent receiver to a modified Woodward equation for a semicoherent receiver. Since this transition appears at different SNRs for pulses with different center frequencies and bandwidths, it is possible to choose the optimal pulse for any given SNR. We show that the adaptive method we propose outperforms the classical cross-correlation receiver for low SNRs. The same ideas can be applied to the case of a fixed broadband signal, by performing the cross correlation at the receiver end separately in a set of frequency bands with the appropriate center frequencies and bandwidths.

A uni(cid:12)ed, biophysically motivated Calcium-Dependent Learning model has been shown to account for various rate-based and spike time-dependent paradigms for inducing synaptic plasticity. Here, we investigate the properties of this model for a multi-synapse neuron that receives inputs with di(cid:11)erent spike-train statistics. In addition, we present a physiological form of metaplasticity, an activity-driven regulation mechanism, that is essential for the ro- bustness of the model. A neuron thus implemented develops stable and selective receptive (cid:12)elds, given various input statistics

In this work, we introduce an Interactive Parts (IP) model as an alternative to Hidden Markov Models (HMMs). We tested both models on a database of on-line cursive script. We show that im(cid:173) plementations of HMMs and the IP model, in which all letters are assumed to have the same average width, give comparable results. However , in contrast to HMMs, the IP model can handle duration modeling without an increase in computational complexity.

Most simple and complex cells in the cat striate cortex are both orientation and direction selective. In this article we use single-cell learning rules to develop both orientation and direction selectivity in a natural scene environment. We show that a simple principal component analysis rule is inadequate for developing direction selectivity, but that the BCM rule as well as similar higher-order rules can. We also demonstrate that the convergence of lagged and nonlagged cells depends on the velocity of motion in the environment, and that strobe rearing disrupts this convergence, resulting in a loss of direction selectivity.

We study several statistically and biologically motivated learning rules using the same visual environment: one made up of natural scenes and the same single-cell neuronal architecture. This allows us to concentrate on the feature extraction and neuronal coding properties of these rules. Included in these rules are kurtosis and skewness maximization, the quadratic form of the Bienenstock-Cooper-Munro (BCM) learning rule, and single-cell independent component analysis. Using a structure removal method, we demonstrate that receptive fields developed using these rules depend on a small portion of the distribution. We find that the quadratic form of the BCM rule behaves in a manner similar to a kurtosis maximization rule when the distribution contains kurtotic directions, although the BCM modification equations are computationally simpler.

We model a two-eye visual environment composed of natural images and study its effect on single cell synaptic modification. In particular, we study the effect of binocular cortical misalignment on receptive field formation after eye opening. We show that binocular misalignment affects principal component analysis (PCA) and Bienenstock, Cooper, and Munro (BCM) learning in different ways. For the BCM learning rule this misalignment is sufficient to produce varying degrees of ocular dominance, whereas for PCA learning binocular neurons emerge in every case.

In this paper we present a new version of the standard multilayer perceptron (MLP) algorithm for the state-of-the-art in neural net(cid:173) work VLSI implementations: the Intel Ni1000. This new version of the MLP uses a fundamental property of high dimensional spaces which allows the 12-norm to be accurately approximated by the It -norm. This approach enables the standard MLP to utilize the parallel architecture of the Ni1000 to achieve on the order of 40000, 256-dimensional classifications per second. 1 The Intel NilOOO VLSI Chip The Nestor/Intel radial basis function neural chip (Ni1000) contains the equivalent of 1024 256-dimensional artificial digital neurons and can perform at least 40000 classifications per second [Sullivan, 1993]. To attain this great speed, the Ni1000 was designed to calculate "city block" distances (Le. the II-norm) and thus to avoid the large number of multiplication units that would be required to calculate Euclidean dot products in parallel. Each neuron calculates the city block distance between its stored weights and the current input: neuron activity = L IWi - :eil (1) where w, is the neuron's stored weight for the ith input and :ei is the ith input. Thus the Nil000 is ideally suited to perform both the RCE [Reillyet al., 1982] and 748 Michael P. Perrone. Leon N. Cooper PRCE [Scofield et al., 1987] algorithms or any of the other commonly used radial basis function (RBF) algorithms. However, dot products are central in the calcula(cid:173) tions performed by most neural network algorithms (e.g. MLP, Cascade Correlation, etc.). Furthermore, for high dimensional data, the dot product becomes the compu(cid:173) tation bottleneck (i.e. most ofthe network's time is spent calculating dot products). If the dot product can not be performed in parallel there will be little advantage using the NilOOO for such algorithms. In this paper, we address this problem by showing that we can extend the NilOOO to many of the standard neural network algorithms by representing the Euclidean dot product as a function of Euclidean norms and by then using a city block norm approximation to the Euclidean norm. Section 2, introduces the approximate dot productj Section 3 describes the City Block MLP which uses the approximate dot productj and Section 4 presents ex(cid:173) periments which demonstrate that the City Block MLP performs well on the NIST OCR data and on human face recognition data. 2 Approximate Dot Product Consider the following approximation [Perrone, 1993]: 1