A Tutorial on GrÖbner Bases With Applications in Signals and Systems

This paper is a tutorial on GrÖbner bases and a survey on the applications of GrÖbner bases in the broad field of signals and systems. A reasonably detailed review is given of several fundamental theoretical issues that occur in the use of GrÖbner bases in multidimensional signals and systems applications. These topics include the primeness of multivariate polynomial matrices, multivariate unimodular polynomial matrix completion, and prime factorization of multivariate polynomial matrices. A brief review is also presented on the wide-ranging applications of GrÖbner bases in multidimensional as well as one-dimensional circuits, networks, control, coding, signals, and systems and other related areas like robotics and applied mechanics. The impact and scope of GrÖbner bases in signals and systems are highlighted with respect to what has already been accomplished as a stepping stone to expanding future research.      

Space–Time Power Schedule for Distributed MIMO Links Without Instantaneous Channel State Information at the Transmitting Nodes

A space-time optimal power schedule for multiple distributed multiple-input multiple-output (MIMO) links without the knowledge of the instantaneous channel state information (CSI) at the transmitting nodes is proposed. A readily computable expression for the ergodic sum capacity of the MIMO links is derived. Based on this expression, which is a non-convex function of power allocation vectors, a projected gradient algorithm is developed to optimize the power allocation. For a symmetric set of MIMO links with independent identically distributed channels, it is observed that the space-time optimal power schedule reduces to a uniform isotropic power schedule when nominal interference is low, or to an orthogonal isotropic power schedule when nominal interference is high. Furthermore, the transition region between the latter two schedules is seen to be very sharp in terms of nominal interference-to-noise ratio (INR). For MIMO links with correlated channels, the corresponding space-time optimal power schedule is developed based on the knowledge of the channel correlation matrices. It is shown that the channel correlation has a great impact on the ergodic capacity and the optimality of different power scheduling approaches.     

Testing communication and computer networks: an overview

Many activities in today's organizations are becoming ever more dependent on communications and computer networks, and network managers are faced with the enormous challenge of increasing the availability and efficiency of their infrastructures, which grow both in size and complexity. This makes it crucial to plan network operation systematically, to define and implement appropriate procedures for regular monitoring and performance assessment, and to set up proper tools for maintenance and troubleshooting purposes. Furthermore, proactive network testing must be pursued; it is vital to gather some ideas of normal network operation documented in order to have a comparison term when problems occur. The article has dealt with the measurement instruments and procedures currently adopted for network testing and advances in the field for the I&M research community.     

Power Distribution Fault Cause Identification With Imbalanced Data Using the Data Mining-Based Fuzzy Classification E-Algorithm

Power distribution systems have been significantly affected by many outage-causing events. Good fault cause identification can help expedite the restoration procedure and improve the system reliability. However, the data imbalance issue in many real-world data sets often degrades the fault cause identification performance. In this paper, the E-algorithm, which is extended from the fuzzy classification algorithm by Ishibuchi to alleviate the effect of imbalanced data constitution, is applied to Duke Energy outage data for distribution fault cause identification. Three major outage causes (tree, animal, and lightning) are used as prototypes. The performance of E-algorithm on real-world imbalanced data is compared with artificial neural network. The results show that the E-algorithm can greatly improve the performance when the data are imbalanced     

The LilyPad Arduino: Toward Wearable Engineering for Everyone

Electronic textiles, or e-textiles, are an increasingly important part of wearable computing, helping to make pervasive devices truly wearable. These soft, fabric-based computers can function as lovely embodiments of Mark Weiser's vision of ubiquitous computing: providing useful functionality while disappearing discreetly into the fabric of our clothing. E-textiles also give new, expressive materials to fashion designers, textile designers, and artists, and garments stemming from these disciplines usually employ technology in visible and dramatic style. Integrating computer science, electrical engineering, textile design, and fashion design, e-textiles cross unusual boundaries, appeal to a broad spectrum of people, and provide novel opportunities for creative experimentation both in engineering and design. Moreover, e-textiles are cutting- edge technologies that capture people's imagination in unusual ways. (What other emerging pervasive technology has Vogue magazine featured?) Our work aims to capitalize on these unique features by providing a toolkit that empowers novices to design, engineer, and build their own e-textiles.     

Integrating trust into the CyberCraft Initiative via the Trust Vectors model [Application Notes]

This research supports the hypothesis that the Trust Vector model can be modified to fit the CyberCraft Initiative, and that there are limits to the utility of historical data. This research proposed some modifications and expansions to the Trust Model Vector, and identified areas for future research.     

Toward Human Level Machine Intelligence - Is It Achievable? The Need for a Paradigm Shift

Officially, AI was born in 1956. Since then, very impressive progress has been made in many areas - but not in the realm of human level machine intelligence. During much of its early history, AI "was rife "with exaggerated expectations. A headline in an article published in the late forties of last century was headlined, "Electric brain capable of translating foreign languages is being built". Today, more than half a century later, we do have translation software, but nothing that can approach the quality of human translation. Clearly, achievement of human level machine intelligence is a challenge that is hard to meet. A prerequisite to achievement of human level machine intelligence is mechanization of these capabilities and, in particular, mechanization of natural language understanding. To make significant progress toward achievement of human level machine intelligence, a paradigm shift is needed. More specifically, what is needed is an addition to the armamentarium of AI of two methodologies: (a) a nontraditional methodology of computing with words (CW) or more generally, NL-Computation; and (b) a countertraditional methodology "which involves a progression from computing with numbers to computing with words. The centerpiece of these methodologies is the concept of precisiation of meaning. Addition of these methodologies to AI would be an important step toward the achievement of human level machine intelligence and its applications in decision-making, pattern recognition, analysis of evidence, diagnosis, and assessment of causality. Such applications have a position of centrality in our infocentric society.     

Evaluating software reuse alternatives: a model and its application to an industrial case study

We propose a model that enables software developers to systematically evaluate and compare all possible alternative reuse scenarios. The model supports the clear identification of the basic operations involved and associates a cost component with each basic operation in a focused and precise way. The model is a practical tool that assists developers to weigh and evaluate different reuse scenarios, based on accumulated organizational data, and then to decide which option to select in a given situation. The model is currently being used at six different companies for cost-benefit analysis of alternative reuse scenarios; we give a case study that illustrates how it has been used in practice.     

Second-order stresses and strains in heterogeneous steels: Self-consistent modeling and X-ray diffraction analysis

Theoretical and experimetal methods have been developed to characterize the effect of mechanical loading on the mesoscopic and macroscopic mechanical state of polycrystalline materials. Ferritic and austenitic single-phase materials were first analyzed, then phase interaction was studied in a multiductile phase material (austeno-ferritic duplex steel) and a natural reinforced composite (pearlitic steel). The theoretical method is based on the self-consistent approach in which elastic and plastic characteristics of the phases have been applied through the micromechanical behavior of single-crystal-using slip systems and microscopic hardening. The effects of a crystallographic texture and phase interaction during loading and after unloading were studied. The elastic and plastic anisotropy of the grains having the same crystallographic orientation were assessed by diffraction strain analysis. The simulation was compared with the experiments performed using the X-ray diffraction technique. In the considered duplex and pearlitic steels, it was observed that the ferrite stress state is much lower than the austenite and cementite ones. The results of diffraction strain distribution have showed the pertinence of the models and give valuable information, for example, for the yield stress and the hardening parameters of each phase in a two-phase material.