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Nonconvex quadratic constraints can be linearized to obtain relaxations in a well-understood manner. We propose to tighten the relaxation by using second-order cone constraints, resulting in a convex quadratic relaxation. Our quadratic approximation to the bilinear term is compared to the linear McCormick bounds. The second-order cone constraints are based on linear combinations of pairs of variables. With good bounds on these linear combinations, the resulting constraints strengthen the McCormick bounds. Computational results are given, which indicate that the convex quadratic relaxation can dramatically improve the solution times for some problems. [ABSTRACT FROM AUTHOR]

In many modern advanced Wireless Sensor Network (WSN) applications, the system is expected to deliver intensive traffic loads in harsh RF environment. In this article, an MAC protocol has been proposed which will utilize the adaptive spreading code length technique to increase network performance for these applications. In this architecture, the system can automatically determine the time varying channel quality and set the optimum spreading code length to maximize the throughput while minimizing the energy usage. Due to this adaptive feature, the system is able to deliver reliable wireless service even in the harsh RF environment. The design of such a protocol is also backwards compatible to enable its employment in both traditional and advanced WSN scenarios. Finally, the proposed protocol has been implemented in a COTS WSN platform to obtain the experimental result, which demonstrates the ability of being implemented on typical resource constraints WSN devices. The experimental results have shown the efficiency advantages of the proposed MAC protocol delivering 139% higher throughput as well as having better energy performance than the standard IEEE 802.15.4 system. [ABSTRACT FROM AUTHOR]

A new trend in the field of Aeronautical Engine Health Monitoring is the implementation of wireless sensor networks (WSNs) for data acquisition and condition monitoring to partially replace heavy and complex wiring harnesses, which limit the versatility of the monitoring process as well as creating practical deployment issues. Augmenting wired with wireless technologies will fuel opportunities for reduced cabling, faster sensor and network deployment, increased data acquisition flexibility, and reduced cable maintenance costs. However, embedding wireless technology into an aero engine (even in the ground testing application considered here) presents some very significant challenges, for example, a harsh environment with a complex RF transmission channel, high sensor density, and high data rate. In this paper we discuss the results of the Wireless Data Acquisition in Gas Turbine Engine Testing (WIDAGATE) project, which aimed to design and simulate such a network to estimate network performance and derisk the wireless techniques before the deployment. [ABSTRACT FROM AUTHOR]

This paper introduces the basic operational concepts of the RoFnet -- reconfigurable radio over fiber network, which is a project supported by the Portuguese Foundation for Science and Technology. This project proposes an innovative radio over fiber optical access network architecture, which combines a low cost base station (BS) design, incorporating reflective semiconductor optical amplifiers, with fiber dispersion mitigation provided by optical single sideband modulation techniques. Optical wavelength division multiplexing (WDM) techniques are used to simplify the access network architecture allowing for different BSs to be fed by a common fiber. Different wavelength channels can be allocated to different BSs depending on user requirements. Additionally, in order to improve radio coverage within a cell, it is considered a sectorized antenna interface. The combination of subcarrier multiplexing with WDM, further simplifies the network architecture, by using a specific wavelength channel to feed an individual BS and different subcarriers to drive the individual antenna sectors within the BS. [ABSTRACT FROM AUTHOR]

This paper demonstrates the feasibility of an architecture that consolidates a number of deployed Passive Optical Network (PON) infrastructures into a long-reach, high-split ratio system which further increases equipment sharing between users. The demonstrated system allows the use of uncooled lasers with possible wavelength drift across a CWDM band (20 nm) with optical amplification and narrow optical filtering with no performance degradation. A complete study of potential implementations was performed with experimental, results showing that a target performance of 10-10 could be achieved over 120 km of standard fiber with transmitter wavelengths from 1542 to 1558 nm and DWDM backhaul wavelengths from 1520 to 1535 nm. This gives the potential to support up to 2560 users. [ABSTRACT FROM AUTHOR]

This paper presents a parameter-free integer-programming-based algorithm for the global resolution of a linear program with linear complementarity constraints {LPCCs). The cornerstone of the algorithm is a mini max integer program formulation that characterizes and provides certificates for the three outcomes—infeasibility, unboundedness, or solvability—of an LPCC. An extreme point/ray generation scheme in the spirit of Benders decomposition is developed, from which valid inequalities in the form of satisfiability constraints are obtained. The feasibility problem of these inequalities and the carefully guided linear-programming relaxations of the LPCC are the workhorses of the algorithm, which also employs a specialized procedure for the sparsification of the satifiability cuts. We establish the finite termination of the algorithm and report computational results using the algorithm for solving randomly generated LPCCs of reasonable sizes. The results establish that the algorithm can handle infeasible, unbounded, and solvable LPCCs effectively. [ABSTRACT FROM AUTHOR]

This paper presents a general method for exact distance computation between convex objects represented as intersections of implicit surfaces. Exact distance computation algorithms are particularly important for applications involving objects that make intermittent contact such as in dynamic simulations and in haptic interactions. They can also be used in the narrow phase of hierarchical collision detection. In contrast to geometric approaches developed for polyhedral objects, we formulate the distance computation problem as a convex optimization problem. We use an interior point method to solve the optimization problem and demonstrate that, for general convex objects represented as implicit surfaces, interior point approaches are globally convergent, and fast in practice. Further, they provide polynomial-time guarantees for implicit surface objects when the implicit surfaces have self-concordant barrier functions. We use a primal-dual interior point algorithm that solves the Karush-Kuhn-Tucker (KKT) conditions obtained from the convex programming formulation. For the case of polyhedra and quadrics, we establish a theoretical time complexity of O(n1.5), where n is the number of constraints. We present implementation results for example implicit surface objects, including polyhedra, quadrics, and generalizations of quadrics such as superquadrics and hyperquadrics, as well as intersections of these surfaces. We demonstrate that in practice, the algorithm takes time linear in the number of constraints, and that distance computation rates of about 1 kHz can be achieved. We also extend the approach to proximity queries between deforming convex objects. Finally, we show that continuous collision detection for linearly translating objects can be performed by solving two related convex optimization problems. For polyhedra and quadrics, we establish that the computational complexity of this problem is also O(n1.5). [ABSTRACT FROM AUTHOR]

It is not straightforward to find a new feasible solution when several conic constraints are added to a conic optimization problem. Examples of conic constraints include semidefinite constraints and second order cone constraints. In this paper, a method to slightly modify the constraints is proposed. Because of this modification, a simple procedure to generate strictly feasible points in both the primal and dual spaces can be defined. A second benefit of the modification is an improvement in the complexity analysis of conic cutting surface algorithms. Complexity results for conic cutting surface algorithms proved to date have depended on a condition number of the added constraints. The proposed modification of the constraints leads to a stronger result, with the convergence of the resulting algorithm not dependent on the condition number. [ABSTRACT FROM AUTHOR]

The article discusses the study conducted regarding the importance of long-reach optical access technologies. Several technologies have been developed to make long-reach access real. The search for a cost-effective optical access solution has brought various possible solutions. The most suitable systems in deploying the long-reach optical access lies on the long-reach gigabit passive optical network (GPON) and wavelength converting-passive optical networks (WC-PON) because they allow operators to distribute standard passive optical networks (PON) and upgrade the long-reach in a simple manner.

Abstract: Given a complete graph with edge weight on each edge, we consider the problem of partitioning the vertices of graph into subcliques that have at least vertices, so as to minimize the total weight of the edges that have both endpoints in the same subclique. In this paper, we consider using the branch-and-price method to solve the problem. We demonstrate the necessity of cutting planes for this problem and suggest effective ways of adding cutting planes in the branch-and-price framework. The NP hard pricing problem is solved as an integer programming problem. We present computational results on large randomly generated problems. [Copyright &y& Elsevier]

We investigate solution of the maximum cut problem using a polyhedral cut and price approach. The dual of the well-known SDP relaxation of maxcut is formulated as a semi-infinite linear programming problem, which is solved within an interior point cutting plane algorithm in a dual setting; this constitutes the pricing (column generation) phase of the algorithm. Cutting planes based on the polyhedral theory of the maxcut problem are then added to the primal problem in order to improve the SDP relaxation; this is the cutting phase of the algorithm. We provide computational results, and compare these results with a standard SDP cutting plane scheme. [ABSTRACT FROM AUTHOR]

We investigate solution of the maximum cut problem using a polyhedral cut and price approach. The dual of the well-known SDP relaxation of maxcut is formulated as a semi-infinite linear programming problem, which is solved within an interior point cutting plane algorithm in a dual setting; this constitutes the pricing (column generation) phase of the algorithm. Cutting planes based on the polyhedral theory of the maxcut problem are then added to the primal problem in order to improve the SDP relaxation; this is the cutting phase of the algorithm. We provide computational results, and compare these results with a standard SDP cutting plane scheme. [ABSTRACT FROM AUTHOR]

The presence of complementarity constraints brings a combinatorial flavour to an optimization problem. A quadratic programming problem with complementarity constraints can be relaxed to give a semidefinite programming problem. The solution to this relaxation can be used to generate feasible solutions to the complementarity constraints. A quadratic programming problem is solved for each of these feasible solutions and the best resulting solution provides an estimate for the optimal solution to the quadratic program with complementarity constraints. Computational testing of such an approach is described for a problem arising in portfolio optimization. [ABSTRACT FROM AUTHOR]

A cutting plane method for linear programming is described. This method is an extension of Atkinson and Vaidya's algorithm, and uses the central trajectory. The logarithmic barrier function is used explicitly, motivated partly by the successful implementation of such algorithms. This makes it possible to maintain primal and dual iterates, thus allowing termination at will, instead of having to solve to completion. This algorithm has the same complexity (O(nL²) iterations) as Atkinson and Vaidya's algorithm, but improves upon it in that it is a ‘long-step’ version, while theirs is a ‘short-step’ one in some sense. For this reason, this algorithm is computationally much more promising as well. This algorithm can be of use in solving combinatorial optimization problems with large numbers of constraints, such as the Traveling Salesman Problem. [ABSTRACT FROM AUTHOR]

Cutting plane methods require the solution of a sequence of linear programs, where the solution to one provides a warm start to the next. A cutting plane algorithm for solving the linear ordering problem is described. This algorithm uses the primal-dual interior point method to solve the linear programming relaxations. A point which is a good warm start for a simplex-based cutting plane algorithm is generally not a good starting point for an interior point method. Techniques used to improve the warm start include attempting to identify cutting planes early and storing an old feasible point, which is used to help recenter when cutting planes are added. Computational results are described for some real-world problems; the algorithm appears to be competitive with a simplex-based cutting plane algorithm. [ABSTRACT FROM AUTHOR]

A new year always brings new challenges and opportunities, and 2020 is no different for the IEEE Transactions on Education. In keeping with the start of a new decade, some of the changes are quite major. [ABSTRACT FROM AUTHOR]

It is a tremendous honor for me to be selected as the next Editor-in-Chief (EIC) of such a prestigious journal as the IEEE Transactions on Education. I am most grateful to the journal's Steering Committee for having faith in me and giving me this opportunity to shape the continued success of this premiere publication. The opportunity to lead such a respected journal is a huge privilege but also a significant responsibility. Engineering education is reaching a turning point. The rapid cycle of change in the industries our students enter has increased calls from those industry partners for a different type of graduate. This call is being heard by the engineering education community, but progress is slow; support is growing, but both faculty and senior management alike are looking to make evidence-based decisions on what changes work. This landscape presents an unparalleled opportunity for a publication like the IEEE Transactions on Education—a respected publication from an internationally-recognized body, whose focus is on the scholarship of application, discovery and of integration. As more and more engineering educators take on the challenge of designing their curriculum for the engineers needed in the latter half of the 21st century, they need to find a home where they are supported to produce high-quality papers. These papers should not only present evidence-based educational practice, but also provide inspiration for other researchers to implement their ideas and join the education research community. [ABSTRACT FROM AUTHOR]