What is to our knowledge a new scene-based algorithm for nonuniformity correction in infrared focal-plane array sensors has been developed. The technique is based on the inverse covariance form of the Kalman filter (KF), which has been reported previously and used in estimating the gain and bias of each detector in the array from scene data. The gain and the bias of each detector in the focal-plane array are assumed constant within a given sequence of frames, corresponding to a certain time and operational conditions, but they are allowed to randomly drift from one sequence to another following a discrete-time Gauss-Markov process. The inverse covariance form filter estimates the gain and the bias of each detector in the focal-plane array and optimally updates them as they drift in time. The estimation is performed with considerably higher computational efficiency than the equivalent KF. The ability of the algorithm in compensating for fixed-pattern noise in infrared imagery and in reducing the computational complexity is demonstrated by use of both simulated and real data.

The physical infrastructure of communication networks is vulnerable to spatially correlated failures arising from various physical stresses such as natural disasters (earthquakes and hurricanes) as well as malicious coordinated attacks using weapons of mass destruction. Some disaster events such as earthquakes and terrorist attacks may occur in more than one location in a short period of time. Hence multiple sets of correlated link failures may occur if more events occurred before the previous set of failed links were repaired. Here, the statistical properties of induced-failure patterns depend upon the spatial interaction among stress centers (e.g., interaction among earthquake or attack locations). This paper presents a stochastic model, based on spatial point processes, for representing stress centers in geographical plane in order to facilitate the modeling of spatially inhomogeneous and correlated link failures in communication networks. This model is then used to further generate scenarios with inhibition or clustering between stress centers, which enables detailed assessment of vulnerabilities of the network to the level of inhomogeneity and spatial correlation in the stress-event centers. Detailed simulation results are presented to compare network reliability for various scenarios of link failures and to find geographically vulnerable areas of a network as well as worst-case scenarios of stress-events. Overall, this effort will provide some critical knowledge and simulation capability for other focus areas of research in network reliability and survivability.

Images rendered by uncooled microbolometer-based infrared (IR) cameras are severely degraded by the spatial non-uniformity (NU) noise. The NU noise imposes a fixed-pattern over the true images, and the intensity of the pattern changes with time due to the temperature instability of such cameras. In this paper, we present a novel model and a compensation algorithm for the spatial NU noise and its temperature-dependent variations. The model separates the NU noise into two components: a constant term, which corresponds to a set of NU parameters determining the spatial structure of the noise, and a dynamic term, which scales linearly with the fluctuations of the temperature surrounding the array of microbolometers. We use a black-body radiator and samples of the temperature surrounding the IR array to offline characterize both the constant and the temperature-dependent NU noise parameters. Next, the temperature-dependent variations are estimated online using both a spatially uniform Hammerstein-Wiener estimator and a pixelwise least mean squares (LMS) estimator. We compensate for the NU noise in IR images from two long-wave IR cameras. Results show an excellent NU correction performance and a root mean square error of less than 0.25 ${}^{\circ }$ C, when the array’s temperature varies by approximately 15 ${}^{\circ }$ C.

The spatial fixed-pattern noise (FPN) inherently generated in infrared (IR) imaging systems compromises severely the quality of the acquired imagery, even making such images inappropriate for some applications. The FPN refers to the inability of the photodetectors in the focal-plane array to render a uniform output image when a uniform-intensity scene is being imaged. We present a noise-cancellation-based algorithm that compensates for the additive component of the FPN. The proposed method relies on the assumption that a source of noise correlated to the additive FPN is available to the IR camera. An important feature of the algorithm is that all the calculations are reduced to a simple equation, which allows for the bias compensation of the raw imagery. The algorithm performance is tested using real IR image sequences and is compared to some classical methodologies.

Biomedical text classification algorithms, which currently support clinical decision-making processes, call for expensive training texts due to the low availability of labeled corpus and the cost of manual annotation by specialized professionals. The active learning (AL) approach to classification heavily lessens such cost by reducing the number of labeled documents required to achieve specified performance. This article introduces a query strategy and a stopping criterion that transform CREGEX, a regular-expressions-based text classification algorithm, in an AL biomedical text classifier. The query strategy samples the training dataset, trading off the greedy learning achieved by the regular expressions classification precision and the conservative learning induced by text sequence alignment classification. The sustained reduction in the variance of the query strategy scores is used as a stopping criterion. The AL classifier was compared with Support Vector Machine (SVM), Naïve Bayes (NB), and a classifier based on Bidirectional Encoder Representations from Transformers (BERT), using three datasets with biomedical information in Spanish on smoking habits, obesity, and obesity types. The learning curve results indicate that AL in CREGEX allowed to efficiently reduce the number of training examples for equal performance than the rest of the classifiers, obtaining areas under the learning curve greater than 85% in all cases. The stopping criterion applied to the AL process allowed to use, on average, approximately 32% to 50% of the total training examples with differences in performance concerning the maximum value of the learning curve not exceeding 2%. This performance demonstrates the effectiveness of using AL in a biomedical text classifier based on regular expressions, which is attributable to such expressions' ability to represent intricate sequential patterns in training texts considered most informative.

In distributed computing systems (DCSs) where server nodes can fail permanently with nonzero probability, the system performance can be assessed by means of the service reliability, defined as the probability of serving all the tasks queued in the DCS before all the nodes fail. This paper presents a rigorous probabilistic framework to analytically characterize the service reliability of a DCS in the presence of communication uncertainties and stochastic topological changes due to node deletions. The framework considers a system composed of heterogeneous nodes with stochastic service and failure times and a communication network imposing random tangible delays. The framework also permits arbitrarily specified, distributed load-balancing actions to be taken by the individual nodes in order to improve the service reliability. The presented analysis is based upon a novel use of the concept of stochastic regeneration, which is exploited to derive a system of difference-differential equations characterizing the service reliability. The theory is further utilized to optimize certain load-balancing policies for maximal service reliability; the optimization is carried out by means of an algorithm that scales linearly with the number of nodes in the system. The analytical model is validated using both Monte Carlo simulations and experimental data collected from a DCS testbed.