An Architectural Framework for Accurate Characterization of Network Traffic

In networks carrying large volume of traffic, accurate traffic characterization is necessary for understanding the dynamics and patterns of network resource usage. Previous approaches to flow characterization are based on random sampling of the packets (e.g., Cisco's NetFlow) or inferring characteristics solely based on long lived flows (LLFs) or on lossy data structures (e.g., bloom filters, hash tables). However, none of these approaches takes into account the heavy-tailed nature of the Internet traffic and separates the estimation algorithm from the flow measurement architecture.In this paper, we propose an alternate approach to traffic characterization by closely linking the flow measurement architecture with the estimation algorithm. Our measurement framework stores complete information related to short lived flows (SLFs) while collecting partial information related to LLFs. For real-time separation of LLFs and SLFs, we propose a novel algorithm based on typical sequences from Information theory. The distribution (pdf) and sample space of the underlying traffic is estimated using the non-parametric Parzen window technique and likelihood function defined over the Coupon collector problem. We validate the accuracy and performance of our estimation technique using traffic traces from the internal LAN in our laboratory and from National Library for Applied Network Research (NLANR).     

Automated analysis of images in documents for intelligent document search

Authors use images to present a wide variety of important information in documents. For example, two-dimensional (2-D) plots display important data in scientific publications. Often, end-users seek to extract this data and convert it into a machine-processible form so that the data can be analyzed automatically or compared with other existing data. Existing document data extraction tools are semi-automatic and require users to provide metadata and interactively extract the data. In this paper, we describe a system that extracts data from documents fully automatically, completely eliminating the need for human intervention. The system uses a supervised learning-based algorithm to classify figures in digital documents into five classes: photographs, 2-D plots, 3-D plots, diagrams, and others. Then, an integrated algorithm is used to extract numerical data from data points and lines in the 2-D plot images along with the axes and their labels, the data symbols in the figure’s legend and their associated labels. We demonstrate that the proposed system and its component algorithms are effective via an empirical evaluation. Our data extraction system has the potential to be a vital component in high volume digital libraries.     

BLAST-SSAHA Hybridization for Credit Card Fraud Detection

A phenomenal growth in the number of credit card transactions, especially for online purchases, has recently led to a substantial rise in fraudulent activities. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. In real life, fraudulent transactions are interspersed with genuine transactions and simple pattern matching is not often sufficient to detect them accurately. Thus, there is a need for combining both anomaly detection as well as misuse detection techniques. In this paper, we propose to use two-stage sequence alignment in which a profile analyzer (PA) first determines the similarity of an incoming sequence of transactions on a given credit card with the genuine cardholder's past spending sequences. The unusual transactions traced by the profile analyzer are next passed on to a deviation analyzer (DA) for possible alignment with past fraudulent behavior. The final decision about the nature of a transaction is taken on the basis of the observations by these two analyzers. In order to achieve online response time for both PA and DA, we suggest a new approach for combining two sequence alignment algorithms BLAST and SSAHA.     

Extraction efficiency of saturated-gain high-power dye laser amplifiers: effect of nonlinear signal absorption

The effect of nonlinear signal absorption (NLSA) due to ground-state absorption and excited-state absorption in a transversely pumped high-power dye laser amplifier is theoretically examined with a one-dimensional steady-state model for a dye amplifier pumped by a copper vapor laser. A well-approximated analytical expression for the extraction efficiency is derived, from which the effect of NLSA in reducing the amplifier efficiency can be appreciated immediately and can also be interpreted in terms of certain characteristic lengths. The reduction in efficiency due to NLSA is found to be largely independent of the pump power, provided that the signal power is increased linearly with the pump power to continue to saturate the amplifier gain and suppress amplified spontaneous emission.     

Secrecy Trade-off at the Physical Layer over Mixed Fading Multicast Channels Employing Antenna Diversity

This paper deals with the secrecy performance analysis of a multicast network over mixed fading scenarios in which a cluster of passive eavesdroppers is trying to overhear the secret transmission. Our key contribution is to prevent this malicious attack of the illegitimate receivers. Rayleigh/ Rician mixed fading channels are considered to model alternately the multicast/ eavesdropper and eavesdropper/ multicast channels as such mixed fading scenarios are often encountered in cellular communication where only one link (either multicast or eavesdropper) undergo a line-of-sight propagation path. At first, we derive the probability density functions for the single-input-multiple-output multicast scenarios and then the secrecy analysis is carried out by obtaining closed-form expressions for the performance matrices such as the probability of non-zero secrecy multicast capacity, ergodic secrecy multicast capacity, and secure outage probability for multicasting. The derived expressions are beneficial to investigate how the antenna diversity can combat the detrimental impact of fading as well as the number of multicast users and eavesdroppers, and improve the secrecy level to the acceptable limit. Moreover, the best secure scenario in terms of the secrecy parameters is obtained when the multicast channels undergo Rician fading whereas the eavesdropper channels experience Rayleigh fading. Finally, the analytical expressions are justified via the Monte-Carlo simulations.

     

Outage Performance of Uplink (UL) NOMA Network

Wireless Personal Communications - This paper proposes and analyses the power allocation coefficient normalization for successive interference cancellation in power-domain non-orthogonal multiple...     

Supply chain risk mitigation competency: an individual-level knowledge-based perspective

Supply chain managers are responsible for making decisions regarding supply chain risk in order to mitigate the impact of supply chain disruptions. This study develops and tests a theoretical model that leverages the individual-level knowledge-based view perspective to understand the process through which risk mitigation orientation of the supply chain manager contributes to his/her absorptive capacity. A supply chain manager’s absorptive capacity, in turn, enhances his/her ability to effectively mitigate supply chain risk. Study findings demonstrate that supply chain managers with high-risk mitigation orientation have greater level of absorptive capacity which enhances their risk mitigation competency. This study represents the first development and testing of a model that examines individual-level knowledge management factors that affect supply chain risk mitigation competency. This research emphasises the importance of the individual supply chain manager in managing risk and illustrates how theoretical perspectives from the knowledge management, supply chain risk and organisational behaviour literature can be fruitfully adopted to explain behaviour in the field of supply chain risk management.     

Intelligent System Application to Monitor the Smart City Building Lighting

A smart city incorporates infrastructure methods that are environmentally responsible, such as smart communications, smart grids, smart energy, and smart buildings. The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality, with energy resources taking precedence. To achieve optimal energy management in the multidimensional system of a city tribe, it is necessary not only to identify and study the vast majority of energy elements, but also to define their implicit interdependencies. This is because optimal energy management is required to reach this objective. The lighting index is an essential consideration when evaluating the comfort indicators. In order to realize the concept of a smart city, the primary objective of this research is to create a system for managing and monitoring the lighting index. It is possible to identify two distinct phases within the intelligent system. Once data collection concludes, the monitoring system will be activated. In the second step, the operation of the control system is analyzed and its effect on the performance of the numerical model is determined. This evaluation is based on the proposed methodology. The optimized results were deemed satisfactory because they maintained the brightness index value (79%) while consuming less energy. The intelligent implementation system generated satisfactory outcomes, which were observed 1.75 times on average.