A medium access control (MAC) protocol design is proposed in this paper for next-generation industrial Internet of Things (IIoT) networks. Considering a nonfully connected network with multiple access points (APs), we aim to connect a massive number of IIoT devices densely populating the network and minimize the delay in channel access without packet collisions. To achieve this objective, we propose a device location-based medium access control design, which integrates scheduled access and carrier sensing. In our design, devices are assigned to time slots based on their locations, and the assignments are coordinated among APs to eliminate collisions while maximizing channel utilization. To analyze the performance of the proposed design, we derive the average delay each device experiences with the proposed scheduling scheme and verify our analysis via simulations of an IIoT network with 19 APs and over 17000 devices. The results show the effectiveness of the proposed design in supporting massive connections while at the same time achieving low delay.

Hyperspectral imaging, crucial for detecting and classifying earth’s surface elements, often requires complex optical equipment susceptible to environmental perturbations. A learning-based approach utilizing smart spectral sensors that considerably reduces hardware complexity while maintaining good resolution in spectral regions of interest is proposed in this paper. This method capitalizes on the spectral redundancy inherent in spectral sensors that have poor spectral resolution albeit the wavelength at which their responsivity curve peaks is continuously tunable. Central to our approach is the optimized superposition of photocurrents, corresponding to a family of responsivities that are tuned judiciously to appropriate wavelengths, along with their pairwise correlations, to render improved resolution spectrometry and enable continuously tunable sensing in spectral domain. The weights for this superposition are learnable parameters derived through a learning process utilizing a comprehensive library of spectra corresponding to various materials and filters. Through learning from this spectra library, the algorithm finds an optimal set of weights that can reconstruct any realistic spectrum as similar as possible. By employing a stochastic gradient-descent method to minimize mean-square error, the weights are generated to enable high-resolution estimation of the spectrum of the object being sensed. The method was applied to quantum-dot-in-a-well electrically tunable infrared photodetectors to estimate various spectra from the ECOSTRESS Spectral Library after being trained with 1,855,000 sample spectra. The method demonstrated a 17-fold improvement in target spectral estimation accuracy compared to prior methods. Additionally, when integrated with an Artificial Neural Network, the method achieved 96.38% accuracy in material classification. This innovation promises significant advances in high-resolution spectrometry and accurate material classification in various practical applications.

It has been previously demonstrated that it is possible to perform remote vibrometry using synthetic aperture radar (SAR) in conjunction with the discrete fractional Fourier transform (DFrFT). Specifically, the DFrFT estimates the chirp parameters (related to the instantaneous acceleration of a vibrating object) of a slow-time signal associated with the SAR image. However, ground clutter surrounding a vibrating object introduces uncertainties in the estimate of the chirp parameter retrieved via the DFrFT method. To overcome this shortcoming, various techniques based on subspace decomposition of the SAR slow-time signal have been developed. Nonetheless, the effectiveness of these techniques is limited to values of signal-to-clutter ratio ≥5 dB. In this paper, a new vibrometry technique based on displaced-phase-center antenna (DPCA) SAR is proposed. The main characteristic of a DPCA-SAR is that the clutter signal can be canceled, ideally, while retaining information on the instantaneous position and velocity of a target. In this paper, a novel method based on the extended Kalman filter (EKF) is introduced for performing vibrometry using the slow-time signal of a DPCA-SAR. The DPCA-SAR signal model for a vibrating target, the mathematical characterization of the EKF technique, and vibration estimation results for various types of vibration dynamics are presented.

Various large-scale failures and attacks (e.g., natural disasters, high-altitude electromagnetic pulses (HEMPs), weapons of mass destruction (WMDs), etc.) can instigate initial failures of critical network components (e.g., router, generator) in a cyber-physical infrastructure. In addition, the intra-dependency among various components inside a communication network and the inter-dependency with supporting power supply nodes can contribute to the propagation of the initial failures. Under the probabilistic initial failures of the networks' physical infrastructures, in this paper, we develop a stochastic model to track the functionality dynamics in a communication network considering dependency among communication nodes as well as with power nodes. A closed-form analytical formula is derived to find the steady state probabilities of all communication nodes in the communication network which are influenced by power node functionalities. Using this model, we show that both intra-dependencies among communication nodes and interdependencies with power nodes play a key role to the failure dynamics of communication network, which is also validated by numerical simulation on a real network topology.

Hankel rank reduction (HRR) is a method that, by prearranging the data in a Hankel matrix and performing rank reduction via singular value decomposition, suppresses the noise of a time-history vector comprised of the superposition of a finite number of sinusoids. In this letter, the HRR method is studied for performing clutter suppression in synthetic aperture radar (SAR)-based vibrometry. Specifically, three different applications of the HRR method are presented. First, resembling the SAR slow-time signal model, the HRR method is utilized for separating a chirp signal immersed in a sinusoidal clutter. Second, using simulated airborne SAR data with 10 dB of signal-to-clutter ratio, the HRR method is applied to perform target isolation and to improve the results of an SAR-based vibration estimation algorithm. Finally, the vibrometry approach combined with the HRR method is validated using actual airborne SAR data.

Cloud computing is being widely accepted and utilized in the business world. From the perspective of businesses utilizing the cloud, it is critical to meet their customers' requirements by achieving service-level-objectives. Hence, the ability to accurately characterize and optimize cloud-service performance is of great importance. In this paper a stochastic multi-tenant framework is proposed to model the service of customer requests in a cloud infrastructure composed of heterogeneous virtual machines. Two cloud-service performance metrics are mathematically characterized, namely the percentile and the mean of the stochastic response time of a customer request, in closed form. Based upon the proposed multi-tenant framework, a workload allocation algorithm, termed maxmin-cloud algorithm, is then devised to optimize the performance of the cloud service. A rigorous optimality proof of the max-min-cloud algorithm is also given. Furthermore, the resource-provisioning problem in the cloud is also studied in light of the max-min-cloud algorithm. In particular, an efficient resource-provisioning strategy is proposed for serving dynamically arriving customer requests. These findings can be used by businesses to build a better understanding of how much virtual resource in the cloud they may need to meet customers' expectations subject to cost constraints.

Network virtualization is a key provision for improving the scalability and reliability of cloud computing services. In recent years, various mapping schemes have been developed to reserve VN resources over substrate networks. However, many cloud providers are very concerned about improving service reliability under catastrophic disaster conditions yielding multiple system failures. To address this challenge, this work presents a novel failure region-disjoint VN mapping scheme to improve VN mapping survivability. The problem is first formulated as a mixed integer linear programming problem and then two heuristic solutions are proposed to compute a pair of failure region-disjoint VN mappings. The solution also takes into account mapping costs and load balancing concerns to help improve resource efficiencies. The schemes are then analyzed in detail for a variety of networks and their overall performances compared to some existing survivable VN mapping schemes.

A novel theory was recently reported for the avalanche multiplication process in avalanche photodiodes (APDs) under dynamic reverse-biasing conditions. It has been shown theoretically that the bit-synchronized, periodic modulation of the electric field in the multiplication region can offer improvements in the gain-bandwidth product by reducing intersymbol interference in optical receivers. This paper reports a rigorous formulation of the sensitivity of optical receivers that employ dynamically biased APDs. To enable the sensitivity analysis, a recurrence theory is developed to calculate the joint probability distribution function (PDF) of the stochastic gain and avalanche buildup time in APDs that are operated under dynamic biasing. It is shown that in an ideal buildup-time limited scenario, a minimum receiver sensitivity of -20 dBm is predicted at an optimal gain of approximately 47 for a 60 Gb/s communication system, compared to a minimum of 0 dBm in the static-bias case. The receiver sensitivity analysis also reveals that, as the peak-to-peak voltage of the dynamic reverse bias increases, the device optimal gain increases while maintaining a short avalanche buildup time and reduced ISI. Of course, a point of diminishing return exists in practice when the tunneling current in the multiplication region becomes dominant.

Target vibrations introduce nonstationary phase modulation, which is termed the micro-Doppler effect, into returned synthetic aperture radar (SAR) signals. This causes artifacts, or ghost targets, which appear near vibrating targets in reconstructed SAR images. Recently, a vibration estimation method based on the discrete fractional Fourier transform (DFrFT) has been developed. This method is capable of estimating the instantaneous vibration accelerations and vibration frequencies. In this paper, a deghosting method for vibrating targets in SAR images is proposed. For single-component vibrations, this method first exploits the estimation results provided by the DFrFT-based vibration estimation method to reconstruct the instantaneous vibration displacements. A reference signal, whose phase is modulated by the estimated vibration displacements, is then synthesized to compensate for the vibration-induced phase modulation in returned SAR signals before forming the SAR image. The performance of the proposed method with respect to the signal-to-noise and signalto-clutter ratios is analyzed using simulations. Experimental results using the Lynx SAR system show a substantial reduction in ghosting caused by a 1.5-cm 0.8-Hz target vibration in a true SAR image.

Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector.

While the reliability of distributed-computing systems (DCSs) has been widely studied under the assumption that computing elements (CEs) fail independently, the impact of correlated failures of CEs on the reliability remains an open question. Here, the problem of modeling and assessing the impact of stochastic, correlated failures on the service reliability of applications running on DCSs is tackled. The service reliability is modeled using an integrated analytical and Monte-Carlo (MC) approach. The analytical component of the model comprises a generalization of a previously developed model for reliability of non-Markovian DCSs to a setting where specific patterns of simultaneous failures in CEs are allowed. The analytical model is complemented by a MC-based procedure to draw correlated-failure patterns using the recently reported concept of probabilistic shared risk groups (PSRGs). The reliability model is further utilized to develop and optimize a novel class of dynamic task reallocation (DTR) policies that maximize the reliability of DCSs in the presence of correlated failures. Theoretical predictions, MC simulations, and results from an emulation testbed show that the reliability can be improved when DTR policies correctly account for correlated failures. The impact of correlated failures of CEs on the reliability and the key dependence of DTR policies on the type of correlated failures are also investigated.

Resource management (RM) is a critical action in systems with limited resources such as sensor networks. Efficient RM depends heavily upon the availability of accurate information on the state of available resources. However, exchange of state information incurs an overhead on the system. In this paper, the sensing and computing resource management in sensor networks is considered. A lossless, distributed source-coding framework is presented to model the exchange of state information. The framework enables the characterization of the interplay between the performance and overhead of RM by leveraging the correlation among the state information of various nodes. Moreover, the proposed framework enables an improved estimate of the lower bound for the minimum control overhead necessary to accurately describe the state of nodes in the network. This improvement is achieved by exploiting the correlation among the state information of nodes as well as the available information on prior resource allocation actions as side information.

In synthetic aperture radar (SAR) images of objects exhibiting low-level vibrations are accompanied by localized artifacts, or ghost targets, caused by the micro-Doppler present in the returned SAR signals. Conventional Fourier-transform-based SAR processing techniques are not fit to remove ghosting effects prior to image formation due to the non-stationary nature of the returned signals from vibrating objects. Recently, a method based on the discrete fractional Fourier transform (DFrFT) has been developed for estimating the instantaneous vibration accelerations and vibrating frequencies from returned SAR signals. Here, a novel image de-ghosting algorithm for vibrating targets in SAR imagery is proposed by employing the DFrFT-based vibration-estimation algorithm. The proposed de-ghosting method is applied to SAR data collected by the Lynx SAR system. Experimental results show a substantial reduction in ghosting caused by a 1.5-cm amplitude, 0.8-Hz vibration present in a test target.

A vibration estimation method for synthetic aperture radar (SAR) is presented based on a novel application of the discrete fractional Fourier transform (DFRFT). Small vibrations of ground targets introduce phase modulation in the SAR returned signals. With standard preprocessing of the returned signals, followed by the application of the DFRFT, the time-varying accelerations, frequencies, and displacements associated with vibrating objects can be extracted by successively estimating the quasi-instantaneous chirp rate in the phase-modulated signal in each subaperture. The performance of the proposed method is investigated quantitatively, and the measurable vibration frequencies and displacements are determined. Simulation results show that the proposed method can successfully estimate a two-component vibration at practical signal-to-noise levels. Two airborne experiments were also conducted using the Lynx SAR system in conjunction with vibrating ground test targets. The experiments demonstrated the correct estimation of a 1-Hz vibration with an amplitude of 1.5 cm and a 5-Hz vibration with an amplitude of 1.5 mm.

Average service time, quality-of-service (QoS), and service reliability associated with heterogeneous parallel and distributed computing systems (DCSs) are analytically characterized in a realistic setting for which tangible, stochastic communication delays are present with nonexponential distributions. The departure from the traditionally assumed exponential distributions for event times, such as task-execution times, communication arrival times and load-transfer delays, gives rise to a non-Markovian dynamical problem for which a novel age dependent, renewal-based distributed queuing model is developed. Numerical examples offered by the model shed light on the operational and system settings for which the Markovian setting, resulting from employing an exponential-distribution assumption on the event times, yields inaccurate predictions. A key benefit of the model is that it offers a rigorous framework for devising optimal dynamic task reallocation (DTR) policies systematically in heterogeneous DCSs by optimally selecting the fraction of the excess loads that need to be exchanged among the servers, thereby controlling the degree of cooperative processing in a DCSs. Key results on performance prediction and optimization of DCSs are validated using Monte-Carlo (MC) simulation as well as experiments on a distributed computing testbed. The scalability, in the number of servers, of the age-dependent model is studied and a linearly scalable analytical approximation is derived.

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.

Synthetic-aperture radar (SAR) can be used to remotely estimate ground target vibrations by exploiting the Doppler in the returned signals. Recent studies suggest that time-frequency signal-processing tools can retrieve the vibration signature from the returned SAR signals. A vibration estimation method based on the fractional Fourier transform (FRFT) was reported earlier and it was tested on simulated SAR data. In this paper, a first-time demonstration of the FRFT-based vibration estimation method is reported using real SAR data collected by the Lynx (Ku-band) SAR system. The vibrating target is an aluminum triangular trihedral with lateral length of 15 inches. The FRFT-based algorithm is shown to successfully retrieve a 3 mm peak-to-peak amplitude, 5 Hz vibration of the target from real SAR data.

This paper presents a general framework for optimal task reallocation in heterogeneous distributed-computing systems and offers a rigorous analytical model for the stochastic execution time of a workload. The model takes into account the heterogeneity and stochastic nature of the tasks' service and transfer times, servers' failure times, as well as an arbitrary task-reallocation policy. The stochastic service, transfer and failure times are assumed to have general, age-dependent (non-exponential) distributions, resulting in a tandem distributed queuing system with non-Markovian dynamics. Auxiliary age variables are introduced in the analysis to capture the memory associated with the non-Markovian stochastic times, thereby enabling a regenerative age-dependent analytical characterization of the statistics of the execution time of a workload. The model is utilized to devise task reallocation policies that optimize three metrics: the average execution time of a workload, the quality-of-service in executing a workload by a prescribed deadline and the reliability in executing a workload. Implications of the non-exponential event times on these metrics are also studied. Key results are verified experimentally on a distributed-computing testbed.

Image segmentation is one of the most important and difficult tasks in digital image processing. It represents a key stage of automated image analysis and interpretation. Segmentation algorithms for gray-scale images utilize basic properties of intensity values such as discontinuity and similarity. However, it is possible to enhance edge-detection capability by means of using spectral information provided by multispectral (MS) or hyperspectral (HS) imagery. In this paper we consider image segmentation algorithms for multispectral images with particular emphasis on detection of multi-color or multispectral edges. More specifically, we report on an algorithm for joint spatio-spectral (JSS) edge detection. By joint we mean simultaneous utilization of spatial and spectral characteristics of a given MS or HS image. The JSSbased edge-detection approach, termed Spectral Ratio Contrast (SRC) edge-detection algorithm, utilizes the novel concept of matching edge signatures. The edge signature represents a combination of spectral ratios calculated using bands that enhance the spectral contrast between the two materials. In conjunction with a spatial mask, the edge signature give rise to a multispectral operator that can be viewed as a threedimensional extension of the mask. In the extended mask, the third (spectral) dimension of each hyper-pixel can be chosen independently. The SRC is verified using MS and HS imagery from a quantum-dot in a well infrared (IR) focal plane array, and the Airborne Hyperspectral Imager.

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.

Exact analysis as well as asymptotic analysis, based on large-deviation theory (LDT), are developed to compute the bit-error rate (BER) for ultrafast avalanche-photodiode (APD) based optical receivers assuming on-off keying and direct detection. The effects of intersymbol interference (ISI), resulting from the APD's stochastic avalanche buildup time, as well as the APD's dead space are both included in the analysis. ISI becomes a limiting factor as the transmission rate approaches the detector's bandwidth, in which case the bit duration becomes comparable to APD's avalanche buildup time. Further, the effect of dead space becomes significant in high-speed APDs that employ thin avalanche multiplication regions. While the exact BER analysis at the generality considered here has not been reported heretofore, the asymptotic analysis is a major generalization of that developed by Letaief and Sadowsky [IEEE Trans. Inform. Theory, vol. 38, 1992], in which the LDT was used to estimate the BER assuming APDs with an instantaneous response (negligible avalanche buildup time) and no dead space. These results are compared with those obtained using the common Gaussian approximation approach showing the inadequacy of the Guassian approximation when ISI noise has strong presence.

This paper extends a recently developed canonical-correlation feature-selection (CCFS) approach to a collective spatio-spectral feature-selection and classification framework for hyperspectral imagers. The work utilizes the concept of spectrally enhanced spatial features by integration of pixels' spatial and spectral information. In order to determine the most informative features, the proposed methodology employs a sequential spatio-spectral feature-selection approach that consists of two distinct stages: a spatially independent spectral feature extraction, based on the CCFS, followed by a spatially enhanced classification. The performance of the new methodology is tested on target detection and classification applications using remotely sensed imagery collected by the Air-borne Hyperspectral Imager (AHI). Sensitivity of the spatio-spectral feature-selection approach with respect to the initial set of sensor bands and with respect to the number and types of spatial features utilized during the classification stage is also studied.

The main focus of this paper is a rigorous development and validation of a novel canonical correlation feature- selection (CCFS) algorithm that is particularly well suited for spectral sensors with overlapping and noisy bands. The proposed approach combines a generalized canonical correlation analysis framework and a minimum mean-square-error criterion for the selection of feature subspaces. The latter induces ranking of the best linear combinations of the noisy overlapping bands and, in doing so, guarantees a minimal generalized distance between the centers of classes and their respective reconstructions in the space spanned by sensor bands. To demonstrate the efficacy and the scope of the proposed approach, two different applications are considered. The first one is separability and classification analysis of rock species using laboratory spectral data and a quantum-dot infrared photodetector (QDIP) sensor. The second application deals with supervised classification and spectral unmixing, and abundance estimation of hyperspectral imagery obtained from the Airborne Hyperspectral Imager sensor. Since QDIP bands exhibit significant spectral overlap, the first study validates the new algorithm in this important application context. The results demonstrate that proper postprocessing can facilitate the emergence of QDIP-based sensors as a promising technology for midwave- and longwave-infrared remote sensing and spectral imaging. In particular, the proposed CCFS algorithm makes it possible to exploit the unique advantage offered by QDIPs with a dot-in-a-well configuration, comprising their bias-dependent spectral response, which is attributable to the quantum Stark effect. The main objective of the second study is to assert that the scope of the new CCFS approach also extends to more traditional spectral sensors.

A regeneration-based approach is undertaken to derive an expression for the expected value of total service time for an arbitrary customer distribution when scheduling is performed in a distributed queuing system. The service times and the transfer delays for customers in the shared communication medium are both considered random. The theory is applied to a distributed wireless-sensor network and the interplay between the total service time and the energy consumption of each sensor is investigated.

A regeneration-theory approach is undertaken to analytically characterize the average overall completion time in a distributed system. The approach considers the heterogeneity in the processing rates of the nodes as well as the randomness in the delays imposed by the communication medium. The optimal one-shot load balancing policy is developed and subsequently extended to develop an autonomous and distributed load-balancing policy that can dynamically reallocate incoming external loads at each node. This adaptive and dynamic load balancing policy is implemented and evaluated in a two-node distributed system. The performance of the proposed dynamic load-balancing policy is compared to that of static policies as well as existing dynamic load-balancing policies by considering the average completion time per task and the system processing rate in the presence of random arrivals of the external loads.

In many distributed computing systems that are prone to either induced or spontaneous node failures, the number of available computing resources is dynamically changing in a random fashion. A load-balancing (LB) policy for such systems should therefore be robust, in terms of workload re-allocation and effectiveness in task completion, with respect to the random absence and re-emergence of nodes as well as random delays in the transfer of workloads among nodes. In this paper two LB policies for such computing environments are presented: The first policy takes an initial LB action to preemptively counteract the consequences of random failure and recovery of nodes. The second policy compensates for the occurrence of node failure dynamically by transferring loads only at the actual failure instants. A probabilistic model, based on the concept of regenerative processes, is presented to assess the overall performance of the system under these policies. Optimal performance of both policies is evaluated using analytical, experimental and simulation-based results. The interplay between node-failure/recovery rates and the mean load-transfer delay are highlighted

The proliferation of computational resources and data networks has enabled the conception and solution of information management problems of an unprecedented scale. These technologies expose vulnerabilities that may be exploited through happenstance or design. The engineering problems encountered in the design of networked data systems and software are familiar in the controls community: resource allocation and management, distributed decision-making, time-delay systems, discrete-event and hybrid systems analysis and design, detection and identification, stability, robustness and reliability, and large-scale systems.

A new mathematical framework for modeling speckled imagery is introduced based on embedding the spatial correlation properties of speckled imagery, obtained from statistical optics, into a Markov-random-field (MRP) framework. The model is then used to perform speckle-noise reduction through the utilization of a global energy-minimization algorithm, which consists of simulated annealing in conjunction with the metropolis sampler algorithm. Comparative experimental results using both simulated as well as real synthetic-aperture-radar (SAR) imagery show that the proposed speckle-reduction technique outperforms existing speckle-noise filtering methods. This success is attributable to the ability of the proposed model to capture the physical spatial statistics of speckle within the confines of a MRF framework.

In synthetic-aperture-radar (SAR) imaging, large volumes of data are normally processed and transported over airborne or space-based platforms. The development of fast and robust algorithms for processing and analysis of this type of data is therefore of great importance. It has been demonstrated recently that a Markov-random-field (MRF) model, based on the statistical properties of coherent imaging, provides an ideal framework to describe the spatial correlation within SAR imagery in the presence of speckle noise, which is present in all SAR imagery. When combined with Gibbs-energy-minimization techniques, the MRF-framework has also led to the development of effective and efficient speckle-reducing image restoration algorithms. In this work, the convexity of the Gibbs energy function for SAR imagery is established thereby facilitating the development of a novel image segmentation algorithm for speckled SAR imagery. The segmentation algorithm is too based on minimizing the Gibbs energy function, which is attained without the need for computationally intensive global optimization techniques such as simulated annealing. A comparative experimental analysis, using real SAR imagery, of the proposed segmentation algorithm against a statistical-thresholding approach is undertaken showing the advantage of the proposed approach in the presence of the speckle noise. Notably, unlike the thresholding technique, the proposed algorithm can be applied to speckled imagery directly without the need for preprocessing the imagery for speckle-noise reduction.

A complete analytical solution to one-shot load balancing in a two-node distributed system is presented and verified experimentally. The model takes into account uncertainty in network delays and the variability in the processors speeds. The system consisting of two Transmeta processors connected through a wireless LAN, utilizing an 802.11b access point, is used as the testbed. The statistics of the network delays are estimated experimentally and used in the analytical model to show the effect of the balancing instant and gain on the overall completion times of various workloads. The analytical results are verified by comparing them to actual experiments and Monte-Carlo simulation. The effect of the communication delay on the choice of the gain and the balancing instant is clearly observed. Finally, the model is used to determine the optimal gain and balancing instant for different initial load distributions.

Abstract An important factor for high‐speed optical communication is the availability of ultrafast and low‐noise photodetectors. Among the semiconductor photodetectors that are commonly used in today's long‐haul and metro‐area fiber‐optic systems, avalanche photodiodes (APDs) are often preferred over p‐i‐n photodiodes due to their internal gain, which significantly improves the receiver sensitivity and alleviates the need for optical pre‐amplification. Unfortunately, the random nature of the very process of carrier impact ionization, which generates the gain, is inherently noisy and results in fluctuations not only in the gain but also in the time response. Recently, a theory characterizing the autocorrelation function of APDs has been developed by us which incorporates the dead‐space effect, an effect that is very significant in thin, high‐performance APDs. The research extends the time‐domain analysis of the dead‐space multiplication model to compute the autocorrelation function of the APD impulse response. However, the computation requires a large amount of memory space and is very time consuming. In this research, we describe our experiences in parallelizing the code in MPI and OpenMP using CAPTools. Several array partitioning schemes and scheduling policies are implemented and tested. Our results show that the code is scalable up to 64 processors on a SGI Origin 2000 machine and has small average errors. Copyright © 2004 John Wiley & Sons, Ltd.

A theory is presented addressing the fundamental limits of image estimation in a setup that uses two photon-correlated beams. These beams have the property that their photon arrivals, as a point process, are ideally synchronized in time and space. The true image represents the spatial distribution of the optical transmittance (or reflectance) of an object. In this setup, one beam is used to probe the image while the other is used as a reference providing additional information on the actual number of photons impinging on the object. This additional information is exploited to reduce the effect of quantum noise associated with the uncertainty in the number of photons per pixel. A stochastic model for the joint statistics of the two observation matrices is developed and used to obtain a local maximum-likelihood estimator of the image. The model captures the nonideal nature of the correlation between the photons of the beams by means of a simple random translation model. The mean-square error of the estimator is evaluated and compared to the corresponding conventional techniques. Conditions for the performance advantage of the proposed estimator are examined in terms of key system parameters. The theoretical predictions are demonstrated by means of simulation.

A computationally efficient method for image registration is investigated that can achieve an improved performance over the traditional two-dimensional (2-D) cross-correlation-based techniques in the presence of both fixed-pattern and temporal noise. The method relies on transforming each image in the sequence of frames into two vector projections formed by accumulating pixel values along the rows and columns of the image. The vector projections corresponding to successive frames are in turn used to estimate the individual horizontal and vertical components of the shift by means of a one-dimensional (1-D) cross-correlation-based estimator. While gradient-based shift estimation techniques are computationally efficient, they often exhibit degraded performance under noisy conditions in comparison to cross-correlators due to the fact that the gradient operation amplifies noise. The projection-based estimator, on the other hand, significantly reduces the computational complexity associated with the 2-D operations involved in traditional correlation-based shift estimators while improving the performance in the presence of temporal and spatial noise. To show the noise rejection capability of the projection-based shift estimator relative to the 2-D cross correlator, a figure-of-merit is developed and computed reflecting the signal-to-noise ratio (SNR) associated with each estimator. The two methods are also compared by means of computer simulation and tests using real image sequences.

The sufficient statistic for performing the likelihood ratio test for pairwise interaction point processes is well-known; however, the evaluation of its performance is a very difficult problem. It is shown that the distribution of the sufficient statistic can be approximated by the distribution of a Poisson-driven shot-noise random variable, which can be readily computed.

The performance of the likelihood ratio test is considered for a many-point interaction point process featuring a reduced number of isolated points. Limit theorems are proved that establish the Poissonian asymptotic distribution of the log-likelihood function for point processes with the isolated-point-penalization joint probability density function. The asymptotic distribution is used to approximate the detection probability associated with the likelihood ratio test. The approximation is compared to empirical results generated using Markov-chain Monte Carlo simulation. The reported results provide an efficient alternative method to simulation in assessing the performance of hypothesis testing for the point-process model considered.

A method that does not use numerical integration is presented for approximating the cumulative distribution of integer-valued random variables from their characteristic functions. Bounds on the approximation error are also given. The method is then used to compute photomultiplier counting distributions.

The performance of neural networks for which weights and signals are modeled by shot-noise processes is considered. Examples of such networks are optical neural networks and biological systems. We develop a theory that facilitates the computation of the average probability of error in binary-input/binary-output multistage and recurrent networks. We express the probability of error in terms of two key parameters: the computing-noise parameter and the weight-recording-noise parameter. The former is the average number of particles per clock cycle per signal and it represents noise due to the particle nature of the signal. The latter represents noise in the weight-recording process and is the average number of particles per weight. For a fixed computing-noise parameter, the probability of error decreases with the increase in the recording-noise parameter and saturates at a level limited by the computing-noise parameter. A similar behavior is observed when the role of the two parameters is interchanged. As both parameters increase, the probability of error decreases to zero exponentially fast at a rate that is determined using large deviations. We show that the performance can be optimized by a selective choice of the nonlinearity threshold levels. For recurrent networks, as the number of iterations increases, the probability of error increases initially and then saturates at a level determined by the stationary distribution of a Markov chain.

Bit-error rates are computed for an on-off keying optical communication system using avalanche photodiodes (APDs). We use a model for the APD that includes dead space and the finite response time. Dead space is the minimum distance that a newly generated carrier must travel in order to acquire sufficient energy to become capable of causing an impact ionization in the multiplication region of the APD. The detector's finite impulse response and its randomness are important for high data-rate systems. Using an exact analysis, we show that the presence of dead space enhances the performance at relatively low data rates. Using a Gaussian approximation technique with the exact mean and variance, we demonstrate that dead space degrades the performance at-high data rates since it is responsible for longer tails in the impulse response function of the APD, which in turn increases the effect of intersymbol interference.>