File size models for shared content over the BitTorrent Peer-to-Peer network

Peer-to-Peer (P2P) traffic has increased rapidly over the past few years, with file sharing providing the main drive behind such traffic. In this work we perform a measurement study of the content shared over the popular BitTorrent P2P file sharing network. We mathematically model the file size distributions of shared files after categorizing them into Audio, Video, Archive and CD image classes. For each of these categories we look into the most popular shared file formats and investigate their file size statistics. This provides an important milestone to building a realistic simulation framework for P2P systems, and for future analytical modeling of P2P networks.     

A hybrid model for spelling error detection and correction for Urdu language

Neural Computing and Applications - Detecting and correcting misspelled words in a written text are of great importance in many natural language processing applications. Errors can be broadly...     

Data aggregation and routing in Wireless Sensor Networks: Optimal and heuristic algorithms

A fundamental challenge in the design of Wireless Sensor Networks (WSNs) is to maximize their lifetimes especially when they have a limited and non-replenishable energy supply. To extend the network lifetime, power management and energy-efficient communication techniques at all layers become necessary. In this paper, we present solutions for the data gathering and routing problem with in-network aggregation in WSNs. Our objective is to maximize the network lifetime by utilizing data aggregation and in-network processing techniques. We particularly focus on the joint problem of optimal data routing with data aggregation en route such that the above mentioned objective is achieved. We present Grid-based Routing and Aggregator Selection Scheme (GRASS), a scheme for WSNs that can achieve low energy dissipation and low latency without sacrificing quality. GRASS embodies optimal (exact) as well as heuristic approaches to find the minimum number of aggregation points while routing data to the Base-Station (BS) such that the network lifetime is maximized. Our results show that, when compared to other schemes, GRASS improves system lifetime with acceptable levels of latency in data aggregation and without sacrificing data quality.     

Central Aggregator Intrusion Detection System for Denial of Service Attacks

Vehicle-to-grid technology is an emerging field that allows unused power from Electric Vehicles (EVs) to be used by the smart grid through the central aggregator. Since the central aggregator is connected to the smart grid through a wireless network, it is prone to cyber-attacks that can be detected and mitigated using an intrusion detection system. However, existing intrusion detection systems cannot be used in the vehicle-to-grid network because of the special requirements and characteristics of the vehicle-to-grid network. In this paper, the effect of denial-of-service attacks of malicious electric vehicles on the central aggregator of the vehicle-to-grid network is investigated and an intrusion detection system for the vehicle-to-grid network is proposed. The proposed system, central aggregator–intrusion detection system (CA-IDS), works as a security gateway for EVs to analyze and monitor incoming traffic for possible DoS attacks. EVs are registered with a Central Aggregator (CAG) to exchange authenticated messages, and malicious EVs are added to a blacklist for violating a set of predefined policies to limit their interaction with the CAG. A denial of service (DoS) attack is simulated at CAG in a vehicle-to-grid (V2G) network manipulating various network parameters such as transmission overhead, receiving capacity of destination, average packet size, and channel availability. The proposed system is compared with existing intrusion detection systems using different parameters such as throughput, jitter, and accuracy. The analysis shows that the proposed system has a higher throughput, lower jitter, and higher accuracy as compared to the existing schemes.     

Model checking communicative agent-based systems

Model checking is a formal technique used to verify communication protocols against given properties. In this paper, we propose a new model checking algorithm aims at verifying systems designed as a set of autonomous interacting agents. These software agents are equipped with knowledge and beliefs and interact with each other according to protocols governed by a set of logical rules. We present a tableauased version of this algorithm and provide the soundness, completeness, termination and complexity results. A case study about an agent-based negotiation protocol and its implementation are also described.     

Multiclass classification of Parkinson’s disease using different classifiers and LLBFS feature selection algorithm

Since approximately 90% of the people with PD (Parkinson’s disease) suffer from speech disorders including disorders of laryngeal, respiratory and articulatory function, using voice analysis disease can be diagnosed remotely at an early stage with more reliability and in an economic way. All previous works are done to distinguish healthy people from people with Parkinson’s disease (PWP). In this paper, we propose to go further by multiclass classification with three classes of Parkinson stages and healthy control. So we have used 40 features dataset, all the features are analyzed and 9 features are selected to classify PWP subjects in four classes, based on unified Parkinson’s disease Rating Scale (UPDRS). Various classifiers are used and their comparison is done to find out which one gives the best results. Results show that the subspace discriminant reach more than 93% overall classification accuracy.     

Stability analysis of discrete-time Lur’e systems

A class of Lyapunov functions is proposed for discrete-time linear systems interconnected with a cone bounded nonlinearity. Using these functions, we propose sufficient conditions for the global stability analysis, in terms of linear matrix inequalities (LMI), only taking the bounded sector condition into account. Unlike frameworks based on the Lur’e-type function, the additional assumptions about the derivative or discrete variation of the nonlinearity are not necessary. Hence, a wider range of cone bounded nonlinearities can be covered. We also show that there is a link between global stability LMI conditions based on this new Lyapunov function and a transfer function of an auxiliary system being strictly positive real. In addition, the novel function is considered in the local stability analysis problem of discrete-time Lur’e systems subject to a saturating feedback. A convex optimization problem based on sufficient LMI conditions is formulated to maximize an estimate of the basin of attraction. Another specificity of this new Lyapunov function is the fact that the estimate is composed of disconnected sets. Numerical examples reveal the effectiveness of this new Lyapunov function in providing a less conservative estimate with respect to the quadratic function.     

Mitochondrial Dysfunction Plays Central Role in Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD) is a global pandemic that affects one-quarter of the world’s population. NAFLD includes a spectrum of progressive liver disease from steatosis to nonalcoholic steatohepatitis (NASH), fibrosis, and cirrhosis and can be complicated by hepatocellular carcinoma. It is strongly associated with metabolic syndromes, obesity, and type 2 diabetes, and it has been shown that metabolic dysregulation is central to its pathogenesis. Recently, it has been suggested that metabolic- (dysfunction) associated fatty liver disease (MAFLD) is a more appropriate term to describe the disease than NAFLD, which puts increased emphasis on the important role of metabolic dysfunction in its pathogenesis. There is strong evidence that mitochondrial dysfunction plays a significant role in the development and progression of NAFLD. Impaired mitochondrial fatty acid oxidation and, more recently, a reduction in mitochondrial quality, have been suggested to play a major role in NAFLD development and progression. In this review, we provide an overview of our current understanding of NAFLD and highlight how mitochondrial dysfunction contributes to its pathogenesis in both animal models and human subjects. Further we discuss evidence that the modification of mitochondrial function modulates NAFLD and that targeting mitochondria is a promising new avenue for drug development to treat NAFLD/NASH.     

Artificial neural network application of modeling failure rate for Boeing 737 tires

This paper presents an application of artificial neural network (ANN) technique for conducting the reliability analysis of Boeing 737 tires. For this purpose, an ANN model utilizing the feed‐forward back‐propagation algorithm as a learning rule is developed. The inputs to the neural network are the flight operational time and the number of landings as independent variables and the output is the failure rate of the tires. Two years of data are used for failure rate prediction model and validation. Model validation, which reflects the suitability of the model for future predictions, is performed by comparing the predictions of the model with that of Weibull regression model. The results show that the failure rate predicted by the ANN is closer in agreement with the actual data than the failure rate predicted by the Weibull model. The present work also identifies some of the common tire failures and presents representative results based on the established model for the most frequently occurring tire failure. Copyright © 2010 John Wiley & Sons, Ltd.