Optimization model for handoff-aware channel assignment problem for multi-radio wireless mesh networks

Optimal channel assignment (CA) in multi-radio wireless mesh networks is an NP-hard problem for which solutions usually leave several links interfering. Most of these solutions usually consider the overall throughput as the main optimization objective. However, other objectives have to be considered in order to provide better quality wireless connections to non stationary users. In this paper, we propose a multi-objective optimization model that, besides maximizing throughput, improves fairness and handoff experience of mesh clients. In this model, we use the Jain’s index to maximize users’ fairness and we allow same-channel assignments to links involved in the same high handoff traffic, thus reducing handoff-triggered re-routing characterized by its high latency. Then, we propose a centralized variable neighborhood search and a Tabu search heuristics to efficiently solve our model as an offline CA process. Moreover, in order to adapt to traffic dynamics caused especially by user handoffs, we propose an online CA scheme that carefully re-assigns channels to interfaces with the purpose of continuously minimizing the re-routing overhead/latency during user handoffs. We further improve this online scheme using load balancing. Simulation results show the good performance of our proposed approach in terms of delay, loss rate, overall throughput and fairness. Particularly, performance results of our online handoff-aware CA show the effectiveness of handoffs not involving path re-routing in decreasing the delay, especially when considering load balancing.     

Exploring the Potential of SME Alliances in the Construction Sector

The paper explores the adoption of information and communication technology-enabled alliance modes of operation by small- and medium-sized enterprises (SMEs) in the construction sector. It argues that SME alliance modes of operations promote business process innovation and allow SMEs to compete in new ways, get better reward for their work, and gain greater financial strength, which in turn, will give them the financial capability to move forward and develop their products and services. Findings from the multiple case study approach used to conduct the research are discussed, including: (1) the concept of an SME alliance and its key features; (2) business relationships management in an SME alliance; (3) SME alliance viability and sustainability; (4) the role of information and communication technologies in an alliance; and (5) technical innovation management in an alliance regime of peers. Recommendations for future research in the form of open questions to the research community are given.     

Past, present and future of information and knowledge sharing in the construction industry: Towards semantic service-based e-construction?

The paper reviews product data technology initiatives in the construction sector and provides a synthesis of related ICT industry needs. A comparison between (a) the data centric characteristics of Product Data Technology (PDT) and (b) ontology with a focus on semantics, is given, highlighting the pros and cons of each approach. The paper advocates the migration from data-centric application integration to ontology-based business process support, and proposes inter-enterprise collaboration architectures and frameworks based on semantic services, underpinned by ontology-based knowledge structures. The paper discusses the main reasons behind the low industry take up of product data technology, and proposes a preliminary roadmap for the wide industry diffusion of the proposed approach. In this respect, the paper stresses the value of adopting alliance-based modes of operation.     

Efficient metaheuristics for pick and place robotic systems optimization

This paper deals with a pick and place robotic system design problem. The objective is to present an efficient method which is able to optimize the performances of the robotic system. By defining the suitable combination of scheduling rules, our method allows each robot to perform the assigned pick and place operations in real time in order to maximize the throughput rate. For that, we have developed different resolution methods which define the scheduling rule for each robot in order to seize the products from the first side of the system and to place them on the second side. We suggest three metaheuristics which are the ant colony optimization, the particle swarm optimization and the genetic algorithm. Then, we try to select the best algorithm which is able to get the best solutions with the lowest execution times. This is the main advantage of our methods compared to exact methods. This fact represents a great interest taking in consideration that our methods must respect a strong industrial constraint regarding the functioning of a real industrial robotic system. This constraint states that the answer time to manage the seizing strategies of the robots must be less than 1 second. Numerical results show that the different algorithms perform optimally for the tested instances in a reasonable computational time.     

Nonlocality in pure and mixed n-qubit <Emphasis Type="Italic">X</Emphasis> states

Nonlocality for general multiqubit X states is studied in detail. Pure and mixed states are analyzed as far as their maximum amount of nonlocality is concerned, and analytic results are obtained for important families of these states. The particular form of nonzero diagonal and antidiagonal matrix elements makes the corresponding study easy enough to obtain exact results. We also provide a numerical recipe to randomly generate an important family of X states endowed with a given degree of mixture.     

Melting metal powder particles in an inductively coupled r.f. plasma torch

A numerical model is developed for the prediction of melting metal powder particles in an inductively coupled r.f. plasma torch. The model is developed for dilute spray conditions where the gas phase flow is not affected by the loading condition. The governing equation for the gas phase flow contains the source terms from the electromagnetic field. The theoretical calculations have shown that particle thermal history and its velocity are greatly affected by the plasma operating conditions (i.e., carrier gas flow rate, injector location, and power level,etc.). Without the proper control of particle trajectories, particles may bounce around the fireball and exit the torch as unmelted or resolidified solid particles. With the insertion of an injector or injecting particles with a high carrier gas flow rate, the predictions show that even relatively small size particles can be directed into the fireball and maintained in the molten state before they impact on the substrate. Consequently, more uniform and dense deposits can be achieved.     

Preoperative staging of rectal carcinoma by magnetic resonance imaging with a pelvic phased-array coil

BACKGROUND: The use of surface coils for magnetic resonance imaging (MRI) allows enhanced image definition and so potentially more accurate staging of colorectal cancer. Endorectal coil imaging is invasive, operator dependent and impossible in a high proportion of patients due to rectal stricture. The phased-array pelvic coil, however, is non-invasive and applicable to all rectal tumours. METHODS: A pelvic phased-array coil was used for preoperative MRI staging of 38 primary rectal carcinomas. Results were expressed according to the Dukes and tumour nodes metastasis (TNM) classifications. After resection of the tumour, the stage predicted on MRI was compared with the pathological classification. RESULTS: The overall accuracy of preoperative staging with the pelvic phased-array coil was 55 per cent for both Dukes class and T stage. Assessment of nodal involvement gave an overall accuracy for MRI of 76 per cent with a sensitivity of 57 per cent and specificity of 88 per cent. CONCLUSION: Use of a pelvic phased-array coil did not improve the staging accuracy of MRI to a clinically useful level.     

On-line maintenance job scheduling and assignment to resources in distributed systems by heuristic-based optimization

A heuristic-based optimization algorithm is proposed in this paper for on-line scheduling and assignment of preventive maintenance jobs to processors, to minimize under availability constraints, on a given time-window, the total cost of the maintenance operations of a distributed system. This algorithm minimizes the cost of discharge of preventive maintenance tasks or jobs, while assigning the tasks along with balancing the processors load. It is shown that the problem is NP-hard. To solve it, the concept of job emergency is introduced and the priority rule for total flow time (PRTF) criterion is used in an adapted heuristic job-scheduling model. In addition, the algorithm considers the constraints of precedence among consecutive standby jobs and their emergency. It is depicted the specific properties of the proposed heuristic allowing jobs scheduling in the right order. Computational results illustrate the efficiency of the approach implemented on different system configurations.     

Investigation and Analysis of Data Rate for Free Space Optical Communications System Under Dust Conditions

Wireless Personal Communications - In this study, the performance of free-space optics (FSO) communication link under dust conditions was analysed. The data were modulated by the non-return-to-zero...