Delay-dependent anti-windup synthesis for stability of constrained state delay systems using pole-constraints

This paper proposes the design of anti-windup compensator gain for stability of actuator input constrained state delay systems using constrained pole-position of the closed-loop. Based on Delay-Dependent Lyapunov-Krasovskii functionals and local sector conditions, a new LMI characterization is derived that ensures closed-loop asymptotic stability of constrained state delay systems while accounting upper bound fixed state delay and largest lower bound of the system’s pole-position in the formulation of anti-windup gain. Besides, at saturation, the method significantly nullifies the inherent slow dynamics present in the system. It is shown in the comparative numerical examples that the LMI formulation draws stability with improved time-domain performance.     

Studying re-opened bugs in open source software

Bug fixing accounts for a large amount of the software maintenance resources. Generally, bugs are reported, fixed, verified and closed. However, in some cases bugs have to be re-opened. Re-opened bugs increase maintenance costs, degrade the overall user-perceived quality of the software and lead to unnecessary rework by busy practitioners. In this paper, we study and predict re-opened bugs through a case study on three large open source projects—namely Eclipse, Apache and OpenOffice. We structure our study along four dimensions: (1) the work habits dimension (e.g., the weekday on which the bug was initially closed), (2) the bug report dimension (e.g., the component in which the bug was found) (3) the bug fix dimension (e.g., the amount of time it took to perform the initial fix) and (4) the team dimension (e.g., the experience of the bug fixer). We build decision trees using the aforementioned factors that aim to predict re-opened bugs. We perform top node analysis to determine which factors are the most important indicators of whether or not a bug will be re-opened. Our study shows that the comment text and last status of the bug when it is initially closed are the most important factors related to whether or not a bug will be re-opened. Using a combination of these dimensions, we can build explainable prediction models that can achieve a precision between 52.1–78.6 % and a recall in the range of 70.5–94.1 % when predicting whether a bug will be re-opened. We find that the factors that best indicate which bugs might be re-opened vary based on the project. The comment text is the most important factor for the Eclipse and OpenOffice projects, while the last status is the most important one for Apache. These factors should be closely examined in order to reduce maintenance cost due to re-opened bugs.     

Private personalized social recommendations in an IPTV system

In our connected world, recommender systems have become widely known for their ability to provide expert and personalize referrals to end-users in different domains. The rapid growth of social networks and new kinds of systems so called “social recommender systems” are rising, where recommender systems can be utilized to find a suitable content according to end-users' personal preferences. However, preserving end-users' privacy in social recommender systems is a very challenging problem that might prevent end-users from releasing their own data, which detains the accuracy of extracted referrals. In order to gain accurate referrals, social recommender systems should have the ability to preserve the privacy of end-users registered in this system. In this paper, we present a middleware that runs on end-users' Set-top boxes to conceal their profile data when released for generating referrals, such that computation of recommendation proceeds over the concealed data. The proposed middleware is equipped with two concealment protocols to give users a complete control on the privacy level of their profiles. We present an IPTV network scenario and perform a number of different experiments to test the efficiency and accuracy of our protocols. As supported by the experiments, our protocols maintain the recommendations accuracy with acceptable privacy level.     

A greedy gradient-simulated annealing selection hyper-heuristic

Educational timetabling problem is a challenging real world problem which has been of interest to many researchers and practitioners. There are many variants of this problem which mainly require scheduling of events and resources under various constraints. In this study, a curriculum based course timetabling problem at Yeditepe University is described and an iterative selection hyper-heuristic is presented as a solution method. A selection hyper-heuristic as a high level methodology operates on the space formed by a fixed set of low level heuristics which operate directly on the space of solutions. The move acceptance and heuristic selection methods are the main components of a selection hyper-heuristic. The proposed hyper-heuristic in this study combines a simulated annealing move acceptance method with a learning heuristic selection method and manages a set of low level constraint oriented heuristics. A key goal in hyper-heuristic research is to build low cost methods which are general and can be reused on unseen problem instances as well as other problem domains desirably with no additional human expert intervention. Hence, the proposed method is additionally applied to a high school timetabling problem, as well as six other problem domains from a hyper-heuristic benchmark to test its level of generality. The empirical results show that our easy-to-implement hyper-heuristic is effective in solving the Yeditepe course timetabling problem. Moreover, being sufficiently general, it delivers a reasonable performance across different problem domains.     

Constructed wetland as a low cost and sustainable solution for wastewater treatment adapted to rural settlements: the Chorfech wastewater treatment pilot plant

The paper presents the detailed design and some preliminary results obtained from a study regarding a wastewater treatment pilot plant (WWTPP), serving as a multistage constructed wetland (CW) located at the rural settlement of 'Chorfech 24' (Tunisia). The WWTPP implemented at Chorfech 24 is mainly designed as a demonstration of sustainable water management solutions (low-cost wastewater treatment), in order to prove the efficiency of these solutions working under real Tunisian conditions and ultimately allow the further spreading of the demonstrated techniques. The pilot activity also aims to help gain experience with the implemented techniques and to improve them when necessary to be recommended for wide application in rural settlements in Tunisia and similar situations worldwide. The selected WWTPP at Chorfech 24 (rural settlement of 50 houses counting 350 inhabitants) consists of one Imhoff tank for pre-treatment, and three stages in series: as first stage a horizontal subsurface flow CW system, as second stage a subsurface vertical flow CW system, and a third horizontal flow CW. The sludge of the Imhoff tank is treated in a sludge composting bed. The performances of the different components as well as the whole treatment system were presented based on 3 months monitoring. The results shown in this paper are related to carbon, nitrogen and phosphorus removal as well as to reduction of micro-organisms. The mean overall removal rates of the Chorfech WWTPP during the monitored period have been, respectively, equal to 97% for total suspended solids and biochemical oxygen demand (BOD5), 95% for chemical oxygen demand, 71% for total nitrogen and 82% for P-PO4. The removal of E. coli by the whole system is 2.5 log units.     

Three-qubit Grover’s algorithm using superconducting quantum interference devices in cavity-QED

We present a scheme for the implementation of three-qubit Grover’s algorithm using four-level superconducting quantum interference devices (SQUIDs) coupled to a superconducting resonator. The scheme is based on resonant, off-resonant interaction of the cavity field with SQUIDs and application of classical microwave pulses. We show that adjustment of SQUID level spacings during the gate operations, adiabatic passage, and second-order detuning are not required that leads to faster implementation. We also show that the marked state can be searched with high fidelity even in the presence of unwanted off-resonant interactions, level decay, and cavity dissipation.     

Amplitudes distribution over two copies of small quantum subsystems

We propose a new formulation of quantum algorithm which allows to distribute amplitudes over two copies of small quantum subsystems. The new method gives a fixed number of copies and applied to the control of multi-qubit system. The analysis for the amount of error due to the distribution process has been presented for a system of 10 qubits with a small quantum subsystems to be copied. The present scheme provides a new way to distribute amplitudes over small quantum subsystems.     

Optisim: A System Simulation Methodology for Optically Interconnected HPC Systems

Although CAD tools have significantly assisted electronic system simulation, the system-level optoelectronics modeling field has lagged behind due to a lack of simulation methodologies and tools. Optisim, a system-level modeling and simulation methodology of optical interconnects for HPC systems, can provide computer architects, designers, and researchers with a highly optimized, efficient, and accurate discrete-event environment to test various HPC systems.     

Use of a simulation game in delivering blended lifelong learning in the construction industry – Opportunities and Challenges

Continuing professional development (CPD) and life-long learning are vital to both individual and organisational success. For higher education, the intensive resource requirements requisite in the development of e-learning content and the challenges in accommodating different learning styles, developing an e-learning program can be a resource intensive exercise. A blended learning program has been developed in Ireland in an attempt to address the CPD needs of Irish construction professionals. This initiative attempts to strike a balance between the considerable resources required in the development of an e-learning initiative while addressing staff concerns in integrating technology in the delivery of programs. This pilot explores the issues encountered in integrating a simulation game, called MERIT, on a module as part of this blended learning program. The key finding from this research indicates that simulation games can play a very effective role in the delivery of lifelong learning opportunities aimed at the construction industry. However, management of the integration of a simulation game into a program requires careful planning, establishing key milestone dates and encouraging online collaboration through assigning of marks for effort and use of voice over IP communications.     

Data aggregation and routing in Wireless Sensor Networks: Optimal and heuristic algorithms

A fundamental challenge in the design of Wireless Sensor Networks (WSNs) is to maximize their lifetimes especially when they have a limited and non-replenishable energy supply. To extend the network lifetime, power management and energy-efficient communication techniques at all layers become necessary. In this paper, we present solutions for the data gathering and routing problem with in-network aggregation in WSNs. Our objective is to maximize the network lifetime by utilizing data aggregation and in-network processing techniques. We particularly focus on the joint problem of optimal data routing with data aggregation en route such that the above mentioned objective is achieved. We present Grid-based Routing and Aggregator Selection Scheme (GRASS), a scheme for WSNs that can achieve low energy dissipation and low latency without sacrificing quality. GRASS embodies optimal (exact) as well as heuristic approaches to find the minimum number of aggregation points while routing data to the Base-Station (BS) such that the network lifetime is maximized. Our results show that, when compared to other schemes, GRASS improves system lifetime with acceptable levels of latency in data aggregation and without sacrificing data quality.