The modified critical ratio: towards sequencing with a continuous decision domain

This research introduces a new sequencing rule, known as the modified critical ratio rule that is an improvement of the classic critical ratio rule. Traditionally, the selection of the best sequencing rule depends on the structure of system parameters and the performance criteria of interest. The modified critical ratio has two advantages. First, it converts the decision domain from a discrete one to a continuous one. A change in system parameters (e.g. utilization) will result in a proportionate change in a rule parameter, rather than changing the rule itself. The second advantage is that the modified critical ratio yields a better performance when compared with other sequencing rules.     

A hybrid finite element/finite difference algorithm for compressible/incompressible viscoelastic liquids

A unified formulation is proposed for modelling compressible/incompressible viscoelastic liquids. The pure hyperbolic nature of the model overcomes some of the drawbacks of available models. The most important of these drawbacks is the mixed nature of the resulting systems of equations, with the subsequent consequence of having no general numerical algorithm for the solution. A new non‐dimensionalisation procedure is adopted. A hybrid least‐squares finite element/finite difference scheme coupled with a Newton‐Raphson's algorithm is used to solve the resulting system of equations. The method is used to predict the velocity and stress fields for different Weissenberg numbers for two benchmark problems.     

Localized pulses exhibiting a missilelike slow decay

We investigate the quasi-missile behavior of known localized wave solutions, such as the modified power spectrum and splash pulses. We demonstrate that source-free localized waves can exhibit slow decay rates analogous to Wu's missile solutions, which are characterized by an amplitude decay rate slower than 1/R over an unlimited range. When excited from a finite aperture, the missilelike decay is not exhibited by all localized waves showing such behavior in the source-free situation. On the other hand, localized wave missiles generated from a finite aperture have peaks that exhibit quasi-missile decay. In an extended intermediate range between the near- and the far-field regions, these pulses decay at a rate slower than 1/R before switching to the usual 1/R decay.     

Design of a low-power ZigBee receiver front-end for wireless sensors

This paper describes the design of a low cost, low-power ZigBee receiver for wireless sensor networks. The receiver consists of a low-noise amplifier, a pair of down-conversion mixers, and a pair of variable-gain low-pass filters. The LNA has a single-ended input, eliminating the need for an off-chip balun, a differential output, allowing it to drive a double-balanced mixer, and it uses noise cancellation to improve its noise performance. The mixers are double-balanced passive mixers to improve the receiver linearity and decrease its power consumption and flicker noise. Finally, the filter is a third-order Butterworth G_m-C filter with a variable input transconductor to provide gain programmability for the receiver. The design is made using 130 nm CMOS technology with 1.2 V supply. Simulation results show that the receiver can achieve a sensitivity level of −97 dBm while consuming only 6 mA.     

Open-loop output characteristics of a current feedback operational amplifier

The output characteristics of the basic current feedback operational amplifier (CFOA) in the linear region, on open-loop, would not appear to have been treated in the literature. This is possibly because it is not straightforward to determine, the problem being that the output saturates without the closed-loop connection. This paper considers a theoretical discussion that explains the results of simulation.     

Maximizing the Sustainability of Integrated Housing Recovery Efforts

The large-scale and catastrophic impacts of Hurricanes Katrina and Rita in 2005 challenged the efficacy of traditional postdisaster temporary housing methods. To address these challenges, the U.S. Congress appropriated $400 million to the Department of Homeland Security to support alternative housing pilot programs, which encourage innovative housing solutions that will facilitate sustainable and permanent affordable housing in addition to serving as temporary housing. Facilitating and maximizing the sustainability of postdisaster alternative housing is an important objective that has significant social, economic, and environmental impacts. This paper presents the development of a novel optimization model that is capable of (1) evaluating the sustainability of integrated housing recovery efforts under the alternative housing pilot program and (2) identifying the housing projects that maximize sustainability. An application example is analyzed to demonstrate the use of the developed model and its unique capabilities in maximizing the sustainability of integrated housing recovery efforts after natural disasters.     

HDLIDP: A Hybrid Deep Learning Intrusion Detection and Prevention Framework

Distributed denial-of-service (DDoS) attacks are designed to interrupt network services such as email servers and webpages in traditional computer networks. Furthermore, the enormous number of connected devices makes it difficult to operate such a network effectively. Software defined networks (SDN) are networks that are managed through a centralized control system, according to researchers. This controller is the brain of any SDN, composing the forwarding table of all data plane network switches. Despite the advantages of SDN controllers, DDoS attacks are easier to perpetrate than on traditional networks. Because the controller is a single point of failure, if it fails, the entire network will fail. This paper offers a Hybrid Deep Learning Intrusion Detection and Prevention (HDLIDP) framework, which blends signature-based and deep learning neural networks to detect and prevent intrusions. This framework improves detection accuracy while addressing all of the aforementioned problems. To validate the framework, experiments are done on both traditional and SDN datasets; the findings demonstrate a significant improvement in classification accuracy.     

Understanding the advisor–advisee relationship via scholarly data analysis

Advisor–advisee relationship is important in academic networks due to its universality and necessity. Despite the increasing desire to analyze the career of newcomers, however, the outcomes of different collaboration patterns between advisors and advisees remain unknown. The purpose of this paper is to find out the correlation between advisors’ academic characteristics and advisees’ academic performance in Computer Science. Employing both quantitative and qualitative analysis, we find that with the increase of advisors’ academic age, advisees’ performance experiences an initial growth, follows a sustaining stage, and finally ends up with a declining trend. We also discover the phenomenon that accomplished advisors can bring up skilled advisees. We explore the conclusion from two aspects: (1) Advisees mentored by advisors with high academic level have better academic performance than the rest; (2) Advisors with high academic level can raise their advisees’ h-index ranking. This work provides new insights on promoting our understanding of the relationship between advisors’ academic characteristics and advisees’ performance, as well as on advisor choosing.     

High dimensional nearest neighbor searching

As databases increasingly integrate different types of information such as time-series, multimedia and scientific data, it becomes necessary to support efficient retrieval of multi-dimensional data. Both the dimensionality and the amount of data that needs to be processed are increasing rapidly. As a result of the scale and high dimensional nature, the traditional techniques have proven inadequate. In this paper, we propose search techniques that are effective especially for large high dimensional data sets. We first propose VA⁺${\mathrm{VA}}^{+}$-file technique which is based on scalar quantization of the data. VA⁺${\mathrm{VA}}^{+}$-file is especially useful for searching exact nearest neighbors (NN) in non-uniform high dimensional data sets. We then discuss how to improve the search and make it progressive by allowing some approximations in the query result. We develop a general framework for approximate NN queries, discuss various approaches for progressive processing of similarity queries, and develop a metric for evaluation of such techniques. Finally, a new technique based on clustering is proposed, which merges the benefits of various approaches for progressive similarity searching. Extensive experimental evaluation is performed on several real-life data sets. The evaluation establishes the superiority of the proposed techniques over the existing techniques for high dimensional similarity searching. The techniques proposed in this paper are effective for real-life data sets, which are typically non-uniform, and they are scalable with respect to both dimensionality and size of the data set.