On the impact of physical layer awareness on scheduling and resource allocation in broadband multicellular IEEE 802.16 systems [Radio Resource Management and Protocol Engineering for IEEE 802.16]

Multicellular networks based on the IEEE 802.16 standard appear to be very promising candidates to provide end users with broadband wireless access. However, they also pose interesting challenges in terms of radio resource management, where several design choices are not specified in the standard, intentionally left open to implementors. For this reason, we focus in this article on scheduling and resource allocation, and investigate how they could operate in a cross-layer fashion. In particular, we describe the principles of joint scheduling and resource allocation for IEEE 802.16 operating in AMC mode, and discuss the critical role played by physical layer considerations, especially intercell interference estimation and channel state awareness, in the obtained performance. This leads to identifying key open issues and possible general solutions     

A Laguerre polynomial-based bound on the symbol error probability for adaptive antennas with optimum combining

We derive a simple closed-form upper bound on the symbol error probability for coherent detection of M-ary phase-shift keying using antenna arrays with optimum combining, in the presence of multiple uncorrelated equal-power cochannel interferers and thermal noise in a Rayleigh fading environment. The new bound, based on Laguerre polynomials, is valid for an arbitrary number of antenna elements as well as arbitrary number of interferers, and it is proven to be asymptotically tight. Comparisons with Monte Carlo simulation are also provided, showing that our bound is useful in many cases of interest.     

Determinization and minimization of finite acyclic automata by incremental techniques

The determinization of a nondeterministic finite automaton (FA) is the process of generating a deterministic FA (DFA) equivalent to (sharing the same regular language of) . The minimization of is the process of generating the minimal DFA equivalent to . Classical algorithms for determinization and minimization are available in the literature for several decades. However, they operate monolithically, assuming that the FA to be either determinized or minimized is given once and for all. By contrast, we consider determinization and minimization in a dynamic context, where augments over time: after each augmentation, determinization and minimization of into is required. Using classical monolithic algorithms to solve this problem is bound to poor performance. An algorithm for incremental determinization and minimization of acyclic finite automata, called IDMA, is proposed. Despite being conceived within the narrow domain of model‐based diagnosis and monitoring of active systems, the algorithm is general‐purpose in nature. Experimental evidence indicates that IDMA is far more efficient than classical algorithms in solving incremental determinization and minimization problems. Copyright © 2015 John Wiley & Sons, Ltd.     

EDEN: An Intelligent Software Environment for Diagnosis of Discrete-Event Systems

A software environment, called EDEN, that prototypes a recent approach to model-based diagnosis of discrete-event systems, is presented. The environment integrates a specification language, called SMILE, a model base, and a diagnostic engine. SMILE enables the user to create libraries of models and systems, which are permanently stored in the model base, wherein both final and intermediate results of the diagnostic sessions are hosted as well. Given the observation of a physical system gathered during its reaction to an external event, the diagnostic engine performs the a posteriori reconstruction of all the possible evolutions of the system over time and, then, draws candidate diagnoses out of them. The diagnostic method is described using a simplified example within the domain of power transmission networks. Strong points of the method include compositional modeling, support for model update, ability to focus on any sub-system, amenability to parallel execution, management of multiple faults, and broad notions of system and observation.     

Comparison of arc stability and variability of geometry of welds obtained by MIG/MAG and FCAW processes

Despite their similarity, the MIG/MAG and Tubular Electrode processes display particular features with regard to arc stability and variability in the characteristics of the beads. Thus, selection of one of these processes for a specific application will have to consider how these particular features affect the quality of the welds. To support this choice, the present study aimed to investigate how changing the average current affects the stability and regularity of metal transfer in welds carried out with constant voltage and pulsed current and compare the variability in the geometry and dilution of these welds. To achieve these aims, automatic welding was carried out, with steel ABNT 1020 as base metal and wire AWS ER70S-6 and AWS E71T-1 as filler metals. Besides the welds with variation in the average current, the tests involved determination of the parameters for occurrence of stable short circuiting, drop and pulsed transfer. The results showed that the voltage that gives the highest stability in short circuiting transfer is independent of the welding speed and increases with the wire speed, and also showed that welds made with hollow wire displayed greater variability compared to solid wire.     

Desorption‐ and Decomposition‐Based Techniques for the Regeneration of Activated Carbon

Activated carbon (AC) is an adsorbent used in most adsorption processes related to micro removal of pollutants from the water phase, but the application is limited due to high costs and environmental issues related to the disposal after saturation. Different regeneration techniques, reducing costs and minimizing environmental impact, are reported for the reuse of saturated AC. These techniques can be performed via two separate strands: regeneration based only on desorption of adsorbed compounds in AC, or based on the decomposition of pollutants adsorbed on AC. Literature on the regeneration of saturated ACs is reviewed and promising techniques are highlighted. One of the most challenging bottlenecks preventing the commercial application of regeneration technologies is the difficulty of scaling‐up.     

Supervised Multivariate Analysis of Hyper-spectral NIR Images to Evaluate the Starch Index of Apples

Fruit maturity indexes are crucial in harvest time determination and commercial context. The harvest time of apples, matching the desired commercial characteristics, is assessed through starch–iodine test in practice. Fruit halves are dipped into iodine solution and patterns are visually evaluated by experts comparing them to reference charts. Aim of the work was to study the relationships of near infrared (NIR) spectral images (1,000–1,700 nm), starch/starch-free patterns visually assessed and RGB color images. Spectral images of 88 Golden Delicious Klon B apples were sampled at seven different maturity stages. Partial least-squares discriminant analysis (PLSDA) technique was used on hyper-spectral NIR images to classify single pixels using its NIR reflectance spectrum. The response variable (i.e. the classification for each pixel) was identified through the matching of single pixel obtained with the color images, segmented in two classes (starch and starch-free), and the NIR hyper-spectral matrix. Mean hyper-spectral classification obtained through PLSDA modeling on individual apple correctly classified 80.81% of the total pixels, while the unique model, i.e. a single model including all the fruits, resulted in 66.33%. In the latter case, the relationship with the RGB classification showed high values (Pearson correlation coefficient r = 0.95). The present work shows the feasibility of NIR imaging spectroscopy as a tool for apple fruit maturity determination, avoiding expert’s subjective interpretation by traditional starch index assignments.     

Scaling-up of the electrodeposition process of nano-composite coating for corrosion and wear protection

The codeposition of hard nanoparticles into metal matrix electrodeposits usually leads to the increase of the coating hardness and abrasion resistance and causes a change to the microstructure of the deposits leading to more compact, nanostructured coatings with an increased corrosion resistance. Very often the laboratory scale results are not easily transferable to an industrial scale due to the introduction of new process variables such as the geometry and the dimensions of the component to coat.The aim of the present work was the study, in laboratory scale, of nano-composite nickel matrix coatings containing SiC nanoparticles and the transfer of this technology in industrial scale. The deposits have been produced using a Watts type bath containing 20 g/l of nanoparticles, under galvanostatic conditions using a current density of 2 A/dm². The deposits have been studied regarding their microstructure, abrasion and corrosion resistance. Based on the satisfactory results of the laboratory tests, the second part of this work contains the scaling-up and the industrialization of the process and the electrodeposition of the composite coating on ship propeller models and profiles as well as on train axles. The prototype parts were tested under actual working conditions.     

Diagnosis of Active Systems by Automata-Based Reasoning Techniques

This paper presents a method for the diagnosis of active systems, these being a class of distributed asynchronous discrete-event systems, such as digital networks, communication networks, and power transmission protection systems. Formally, an active system is viewed as a network of communicating automata, where each automaton describes the behavior of a system component. The diagnostic method encompasses four steps, namely system modeling, reconstruction planning, behavior reconstruction, and diagnosis generation. System modeling formally defines the structure and behavior of system components, as well as the topology of the active system. Based on optimization criteria, reconstruction planning breaks down the problem of system behavior reconstruction into a hierarchical decomposition. Behavior reconstruction yields an intensional representation of all the dynamic behaviors that are consistent with the available system observation. Eventually, diagnosis generation extracts diagnostic information from the reconstructed behaviors. The diagnostic method is applied to a case study in the power transmission network domain. Unlike other proposals, our approach both deals with asynchronous events and does not require any global diagnoser to be built off-line. The method, which is substantiated by an ongoing implementation, is scalable, incremental, and amenable to parallelism, so that real size problems can be handled.