Beyond Market Baskets: Generalizing Association Rules to Dependence Rules

One of the more well-studied problems in data mining is the search for association rules in market basket data. Association rules are intended to identify patterns of the type: A customer purchasing item A often also purchases item B. Motivated partly by the goal of generalizing beyond market basket data and partly by the goal of ironing out some problems in the definition of association rules, we develop the notion of dependence rules that identify statistical dependence in both the presence and absence of items in itemsets. We propose measuring significance of dependence via the chi-squared test for independence from classical statistics. This leads to a measure that is upward-closed in the itemset lattice, enabling us to reduce the mining problem to the search for a border between dependent and independent itemsets in the lattice. We develop pruning strategies based on the closure property and thereby devise an efficient algorithm for discovering dependence rules. We demonstrate our algorithm's effectiveness by testing it on census data, text data (wherein we seek term dependence), and synthetic data.     

Classical Modal Display Logic in the Calculus of Structures and Minimal Cut-free Deep Inference Calculi for S5

We begin by showing how to faithfully encode the Classical ModalDisplay Logic (CMDL) of Wansing into the Calculus of Structures(CoS) of Guglielmi. Since every CMDL calculus enjoys cut-elimination,we obtain a cut-elimination theorem for all corresponding CoScalculi. We then show how our result leads to a minimal cut-freeCoS calculus for modal logic S5. No other existing CoS calculifor S5 enjoy both these properties simultaneously.     

A Discrete Water Cycle Algorithm for Cellular Network Cost Management

Wireless Personal Communications - Water Cycle Algorithm (WCA) is a nature-inspired metaheuristic algorithm. There is a lack of literature surrounding the use of WCA in modern cellular mobile...     

Tunable Assembly of sp3 Cross‐Linked 3D Graphene Monoliths: A First‐Principles Prediction

One of the biggest challenges in graphene applications is how one can fabricate 3D architectures comprising graphene sheets in which the resulting architectures have inherited graphene's excellent intrinsic properties but have overcome its shortcomings. Two series of 3D graphene monoliths (GMs) using zigzag or armchair graphene nanoribbons as building blocks and sp³ carbon chains as junction nodes are constructued, and calculations based on first principles are performed in order to predict their mechanical and electronic properties. The perfect match between sp² nanoribbons and sp³ linkers results in favorable energy and mechanical/dynamic stability. Owing to their tailored motifs, wine‐rack‐like pores, and rigid sp³ linkers, these GMs possess high surface areas, appreciable mechanical strength, and tunable band gaps. Negative linear compressibilities in a wide range are found for the zigzag GMs. By solving the problems of zero gap and dimensionality of graphene sheets simultaneously, these GMs offer a viable strategy towards many applications, e.g., microelectronic devices, energy storage, molecular sieves, sensitive pressure detectors, and telecommunication line systems.     

Performance comparison of quadrature amplitude modulation (QAM)-based single- and double-stage digital interpolators

In this article, the performance of quadrature amplitude modulation (QAM)-based single- and double-stage digital interpolators have been compared. The basic interpolator for up-sampling can be a combination of an expander unit with an interpolation lowpass filter in cascade. Complicated implementations can be done by connecting multiple expander and low-pass filter pairs in cascade. This article presents the efficient and effective implementation of digital interpolation systems for up-sampling of single- and double-stage digital interpolators. Comparison is done in terms of spectrum of generated signal, envelope power, modulated signal trajectory, input and output constellation and noise performance. In this article, the proposed interpolation filters have been simulated in Agilent's Advanced Design System (ADS).     

Design of Probability Density Function Targeting Energy Efficient Network for Coalition Based WSNs

Energy consumption is one of the important issues in wireless sensor network that rely on non chargeable batteries for power. Also, the sensor network has to maintain a desired sensing coverage area along with periodically sending of the sensed data to the base station. Therefore, coverage and the lifetime are the two important issues that need to be addressed. Effective deployment of wireless sensors is a major concern as the coverage and lifetime of any wireless sensor network depends on it. In this paper, we propose the design of a Probability Density Function (PDF) targeting the desired coverage, and energy efficient node deployment scheme. The suitability of the proposed PDF based node distribution to model the network architecture considered in this work has been analyzed. The PDF divides the deployment area into concentric coronas and provides a probability of occurrence of a node within any corona. Further, the performance of the proposed PDF is evaluated in terms of the coverage, the number of transmissions of packets and the lifetime of the network. The scheme is compared with the existing node deployment schemes based on various distributions. The percentage gain of the proposed PDF based node deployment is 32\(\%\) more than that when compared with the existing schemes. Thus, the simulation results obtained confirm the schemes superiority over the other existing schemes.     

An Adaptive Prediction Strategy with Clustering in Wireless Sensor Network

In Wireless Sensor Network, sensed data reflects two types of correlations of physical attributes: spatial and temporal. In this paper, a scheme named, Adaptive Prediction Strategy with ClusTering (APSCT) is proposed. In APSCT, a data-driven clustering and grey prediction model is used to exploit both the correlations. APSCT minimizes the transmission of messages in the network. However, the use of prediction includes additional computation overhead. There is a trade-off between prediction accuracy and energy consumption in computation and communication in wireless networks. This paper also gives an approach to calculate the upper and lower bound of the prediction interval which is used to evaluate different confidence levels and provides an energy-efficient sensor environment. Simulation is carried out on real-world data collected by Intel Berkeley Lab and results are compared with existing approaches.

     

Using Stochastic Differential Equation for Verification of Noise in Analog/RF Circuits

Today’s analog/RF design and verification face significant challenges due to circuit complexity, process variations and short market windows. In particular, the influence of technology parameters on circuits, and the issues related to noise modeling and verification still remain a priority for many applications. Noise could be due to unwanted interaction between the circuit elements or it could be inherited from the circuit elements. In addition, manufacturing disparity influence the characteristic behavior of the manufactured circuits. In this paper, we propose a methodology for modeling and verification of analog/RF designs in the presence of noise and process variations. Our approach is based on modeling the designs using stochastic differential equations (SDE) that will allow us to incorporate the statistical nature of noise. We also integrate the device variation due to 0.18μ m fabrication process in an SDE based simulation framework for monitoring properties of interest in order to quickly detect errors. Our approach is illustrated on nonlinear Tunnel-Diode and a Colpitts oscillator circuits.     

Enhanced DNA Sequencing Performance Through Edge‐Hydrogenation of Graphene Electrodes

The use of graphene electrodes with hydrogenated edges for solid‐state nanopore‐based DNA sequencing is proposed, and molecular dynamics simulations in conjunction with electronic transport calculations are performed to explore the potential merits of this idea. The results of the investigation show that, compared to the unhydrogenated system, edge‐hydrogenated graphene electrodes facilitate the temporary formation of H‐bonds with suitable atomic sites in the translocating DNA molecule. As a consequence, the average conductivity is drastically raised by about 3 orders of magnitude while exhibiting significantly reduced statistical variance. Furthermore, the effect of the distance between opposing electrodes is investigated and two regimes identified: for narrow electrode separation, the mere hindrance due to the presence of protruding hydrogen atoms in the nanopore is deemed more important, while for wider electrode separation, the formation of H‐bonds becomes the dominant effect. Based on these findings, it is concluded that hydrogenation of graphene electrode edges represents a promising approach to reduce the translocation speed of DNA through the nanopore and substantially improve the accuracy of the measurement process for whole‐genome sequencing.