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A subband decomposition scheme for video signals, in which the original or difference frames are each decomposed into 16 equal-size frequency subbands, is considered. Westerink et al. [4] have shown that the distribution of the sample values in each subband can be modeled with a "generalized Gaussian" probability density function (pdf) where three parameters, mean, variance, and shape are required to uniquely determine the pdf. To estimate the shape parameter, a series of statistical goodness-of-fit tests such as Kolmogorov-Smirnov or chi-squared tests have been used in [4]. A simple alternative method to estimate the shape parameter for the gencralizcd Gaussian pdf is proposed that significantly reduces the number of computations by eliminating the need for any statistical goodness-of-fit test. ~NTRODr'('7'10NSubband decomposition is used in video and image processing a\ a compression tool. The signal is decomposed into frequency wbbands and each subband is encoded independently. In ii lossy encoding scheme. the sample values in each subband. or their prediction errors after passing through a DPCM loop. are quantized. To design an appropriate quantizer, the distribution of the subband samples needs to be known [ I ] . It has been assumed that the probability density function of the subband values and their prediction errors is Lnp/uc,iarz [2], 131. While this assumption seems more valid for the prediction errors of the sample values. the Laplacian distribution cannot adequately model the distribution of sample values.Figs. 1 and 2 show the histograms of two subbands generated from the Flower sequence. The histogram of subband 2 of the original frame 1 and the histogram of subband 2 of the difference between frame 1 and frame 2 along with the Laplacian pdf's with the same mean and variance are shown, respectively. The mismatch between the test Laplacian pdf's and the actual histograms has also been observed for other test sequences.Westerink rf ci/. 141 have shown that the distribution of' the sample values in each subband can be modeled with a generalized Gaussian pdf where three parameters. namely. mean. variance. and shape. are required to uniquely specify the analytic pdf. In this paper. we propose :I simple method that enables us to tind the best shape parameter for the generalired Gaussian distribution that best tits the data of each subband.In Section II. the class of generalized Gausian pdf's as the best candidate for this purpose is presented. To determine the best shape parameter for generalized Gaussian pdf, a simple method is developed in Section 111. Section IV contains the simulation results and concluding remarks are presented in Section V. G~NERALIZED GALWAN PDFThe class of genrrrr1i:rd Gcrus.sitrri probability distribution functions has been used in 141 to model the distribution of the subband values of images. It has been shown that this class of pdYs can

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With the increase in use of information technology in advanced demand side management and given the growth in power consumption in the computation and communications sectors, a new class of cyber-intrusion plans is emerging that aims to alter the load through the Internet and by means of automatic and distributed software intruding agents. These attacks work by compromising direct load control command signals, demand side management price signals, or cloud computation load distribution algorithms to affect the load at the most crucial locations in the grid in order to cause circuite overflow or other malfunctions and damage the power system equipments. To gain insights into these less-examined yet important intrusion strategies, in this paper, we identify a variety of practical loads that can be volnurable to Internet-based load altering attacks. In addition, we overview a collection of defence mechanisms that can help in blocking these attacks or minimizing the damage caused by them. Our simulation results based on the standard setting in the IEEE 24bus Reliability Test System show that our proposed cost-efficent load protection strategy can significantly reduce the cost of load protection while it guarantees that no Internet-based load altering attack may overload the power distribution system.

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Abstract-The continuously increasing complexity of communication networks and the increasing diversity and unpredictability of traffic demand has led to a consensus view that the automation of the management process is inevitable. Currently, network and service management techniques are mostly manual, requiring human intervention, and leading to slow response times, high costs, and customer dissatisfaction. In this paper we present AutoNet, a self-organizing management system for core networks where robustness to environmental changes, namely traffic shifts, topology changes, and community of interest is viewed as critical. A framework to design robust control strategies for autonomic networks is proposed. The requirements of the network are translated to graph-theoretic metrics and the management system attempts to automatically evolve to a stable and robust control point by optimizing these metrics. The management approach is inspired by ideas from evolutionary science where a metric, network criticality, measures the survival value or robustness of a particular network configuration. In our system, network criticality is a measure of the robustness of the network to environmental changes. The control system is designed to direct the evolution of the system state in the direction of increasing robustness. As an application of our framework, we propose a traffic engineering method in which different paths are ranked based on their robustness measure, and the best path is selected to route the flow. The choice of the path is in the direction of preserving the robustness of the network to the unforeseen changes in topology and traffic demands. Furthermore, we develop a method for capacity assignment to optimize the robustness of the network.

Fog computing is an emerging technology to address computing and networking bottlenecks in large scale deployment of IoT applications. It is a promising complementary computing paradigm to cloud computing where computational, networking, storage and acceleration elements are deployed at the edge and network layers in a multi-tier, distributed and possibly cooperative manner. These elements may be virtualized computing functions placed at edge devices or network elements on demand, realizing the ''computing everywhere'' concept. To put the current research in perspective, this paper provides an inclusive taxonomy for architectural, algorithmic and technologic aspects of fog computing. The computing paradigms and their architectural distinctions, including cloud, edge, mobile edge and fog computing are subsequently reviewed. Practical deployment of fog computing includes a number of different aspects such as system design, application design, software implementation, security, computing resource management and networking. A comprehensive survey of all these aspects from the architectural point of view is covered. Current reference architectures and major application-specific architectures describing their salient features and distinctions in the context of fog computing are explored. Base architectures for application, software, security, computing resource management and networking are presented and are evaluated using a proposed maturity model. INDEX TERMS Cloud Computing, edge computing, fog computing, Internet of Things (IoT), advanced internet architecture.