Analyse des performances du codage multiniveau pour modulations à grand nombre d’états

In this paper, we analyze the performance of multilevel coding for high level QAM constellations. After the presentation of the encoder principle, we focus our study on a sub-optimal decoder which can be implemented very simply. We analyze the performance of the presented technique in terms of asymptotic gain and bit error rate characteristics. Finally, a pratical application in digital microwave radio links is considered, and one- and two-level coded 128-QAM are studied. At a bit error rate of 10^-8, it is shown that a 4 dB coding gain can be reached with moderate decoder complexity. These coding schemes are therefore very attractive for high-capacity microwave links applications.     

Infrared to ultraviolet measurements of two-photon absorption and n2 in wide bandgap solids

The bound electronic nonlinear refractive index, n₂, and two-photon absorption (2PA) coefficient, β, are measured in a variety of inorganic dielectric solids at the four harmonics of the Nd:YAG laser using Z scan. The specific materials studied are: barium fluoride (BaF₂), calcite (CaCO₃), potassium bromide (KBr), lithium fluoride (LiF), magnesium fluoride (MgF₂ ), sapphire (Al₂O₃), a tellurite glass (75%TeO₂+20%ZnO+5%Na₂O) and fused silica (SiO₂). We also report n₂ and β in three second-order, χ⁽²⁾, nonlinear crystals: potassium titanyl phosphate (KTiOPO₄ or KTP), lithium niobate (LiNbO₃), and β-barium berate (β-BaB₂O ₄ or BBO). Nonlinear absorption or refraction can alter the wavelength conversion efficiency in these materials. The results of this study are compared to a simple two-parabolic band model originally developed to describe zincblende semiconductors. This model gives the bandgap energy (E_g) scaling and spectrum of the change in absorption. The dispersion of nl as obtained from a Kramers-Kronig transformation of this absorption change scales as E_g^-1 . The agreement of this theory to data for semiconductors was excellent. However, as could be expected, the agreement for these wide bandgap materials is not as good, although general trends such as increasing nonlinearity with decreasing bandgap energy can be seen     

Mining network-level properties of Twitter altmetrics data

Social networking sites play a significant role in altmetrics. While 90% of all altmetric mentions come from Twitter, the known microscopic and macroscopic properties of Twitter altmetrics data are limited. In this study, we present a large-scale analysis of Twitter altmetrics data using social network analysis techniques on the ‘mention’ network of Twitter users. Exploiting the network-level properties of over 1.4 million tweets, corresponding to 77,757 scholarly articles, this study focuses on the following aspects of Twitter altmetrics data: (a) the influence of organizational accounts; (b) the formation of disciplinary communities; (c) the cross-disciplinary interaction among Twitter users; (d) the network motifs of influential Twitter users; and (e) testing the small-world property. The results show that Twitter-based social media communities have unique characteristics, which may affect social media usage counts either directly or indirectly. Therefore, instead of treating altmetrics data as a black box, the underlying social media networks, which may either inflate or deflate social media usage counts, need further scrutiny.

     

High resolution time delay estimation using sliding discrete Fourier transform

In this paper, we propose a novel time delay estimation approach based on sliding the discrete Fourier transform (DFT) analysis window, sample by sample, over the received short continuous wave (CW) pulse signal with the DFT evaluated successively. This approach uses the maximum magnitude of the spectrum and its corresponding phase offset to estimate the time delay (pulse echo mode) of the signal. We use the corresponding time as the first estimate, which is improved on the basis of the related phase. Examples are given of synthetic signals and simulated delays scenario, with and without added white noise. An underwater application, based on distance and speed of sound measurements using this approach in a water tank is demonstrated. The proposed method is shown to significantly outperform standard correlator-based approaches. Furthermore, the algorithm is simple to use and can be easily implemented, being based on phase detection using the sliding DFT.     

Reinforcement learning and optimal adaptive control: An overview and implementation examples

This paper provides an overview of the reinforcement learning and optimal adaptive control literature and its application to robotics. Reinforcement learning is bridging the gap between traditional optimal control, adaptive control and bio-inspired learning techniques borrowed from animals. This work is highlighting some of the key techniques presented by well known researchers from the combined areas of reinforcement learning and optimal control theory. At the end, an example of an implementation of a novel model-free Q-learning based discrete optimal adaptive controller for a humanoid robot arm is presented. The controller uses a novel adaptive dynamic programming (ADP) reinforcement learning (RL) approach to develop an optimal policy on-line. The RL joint space tracking controller was implemented for two links (shoulder flexion and elbow flexion joints) of the arm of the humanoid Bristol-Elumotion-Robotic-Torso II (BERT II) torso. The constrained case (joint limits) of the RL scheme was tested for a single link (elbow flexion) of the BERT II arm by modifying the cost function to deal with the extra nonlinearity due to the joint constraints.     

A new sliding function for discrete predictive sliding mode control of time delay systems

The control of time delay systems is still an open area for research. This paper proposes an enhanced model predictive discrete-time sliding mode control with a new sliding function for a linear system with state delay. Firstly, a new sliding function including a present value and a past value of the state, called dynamic surface, is designed by means of linear matrix inequalities （LMIs）. Then, using this dynamic function and the rolling optimization method in the predictive control strategy, a discrete predictive sliding mode controller is synthesized. This new strategy is proposed to eliminate the undesirable effect of the delay term in the closed loop system. Also, the designed control strategy is more robust, and has a chattering reduction property and a faster convergence of the system s state. Finally, a numerical example is given to illustrate the effectiveness of the proposed control.     

Spiral microchannel with stair-like cross section for size-based particle separation

Particle separation has a variety of applications in biology, chemistry and industry. Among them, circulating tumor cells (CTCs) separation has drawn significant attention to itself due to its high impact on both cancer diagnosis and therapeutics. In recent years, there has been growing interest in using inertial microfluidics to separate micro/nano particles based on their sizes. This technique offers label-free, high-throughput and efficient separation and can be easily fabricated. However, further improvements are needed for potential clinical applications. In this study, a novel inertial separation technique using spiral microchannel having stair-like cross section is introduced. The design fundamental concepts, design criteria and efficacy are investigated thoroughly using a robust numerical model; moreover, it is experimentally tested on the fabricated spiral microchannel. Based on the results, in contrast to conventional spiral microchannels, in which the flow vortices are located latitudinal, the two vortices are uniquely placed longitudinally in the stair-like cross section. The numerical and experimental results indicate that there is a size-dependent volume flow rate threshold defined for each particle size determining which vortices become equilibrated and consequently facilitate their separation. According to the results, using stair-like cross section, the separation throughput and resolution, as the two important design criteria in CTCs’ separation techniques, are significantly improved compared to the conventional spiral microchannels.     

Characterization of uncertainties in the operation and economics of the proposed seawater desalination plant in the Gaza Strip

In the Gaza Strip, the available freshwater sources are severely polluted and overused. Desalination of seawater through reverse osmosis (RO) has become the most realistic option to meet a rapidly growing water demand. It is estimated that the Gaza Strip will need to develop a seawater desalination capacity of about 120,000 m³/d by the year 2008, and an additional 30,000 m³/d by the year 2016 in order to maintain a fresh water balance in the coastal aquifer and to fulfill the water demand for different uses in a sustainable manner. Cost and reliability of a large RO facility are still subject to much uncertainty. The cost of seawater desalination by RO systems varies with facility size and lifetime, financing conditions, intake type and pre-treatment requirements, power requirements, recovery rate, chemicals cost, spare parts cost, and membrane replacement cost. The permeate salinity is a function of feed water temperature, recovery rate, and permeate flux. The quantity of water produced depends mainly on plant size, recovery rate, and operating load factor. Many of these parameters are subject to a great deal of uncertainty. The objective of this work is to develop a probabilistic model for the simulation of seawater reverse osmosis processes using a Bayesian belief network (BBN) approach. This model represents a new application of probabilistic modeling tools to a large-scale complex system. The model is used to: (1) characterize the different uncertainties involved in the RO process; (2) optimize the RO process reliability and cost; and (3) study how uncertainty in unit capital cost, unit operation and maintenance (O&M) cost, and permeate quality is related to different input variables. The model utilizes information from journal articles, books, expert opinions, and technical reports related to the study area, and can be used to support operators and decision makers in the design of RO systems and formulation of operational policies. The structure of the model is not specific to the Gaza Strip and can be easily populated with data from any large-scale RO plant in any part of the world.     

Bifurcation and its implications for a novel autothermal circulating fluidized bed membrane reformer for the efficient pure hydrogen production

The present investigation is a prelude to the experimental exploration of Static Bifurcation Behavior (SBB) in a novel Autothermal Circulating Fluidized Bed Membrane Reformer (ACFBMR) for pure hydrogen production by steam reforming of heavy hydrocarbons. Important impacts of a wide range of design and operating parameters on the reformer performance are explored with two reformer configurations. One is with the catalyst regeneration before the gas-solid separation and another is with the catalyst regeneration after the gas-solid separation. For both configurations there are three steady states (multiplicity of the steady states, static bifurcation behavior). The system behavior in the bifurcation region is quite complex and defies the simple logic of non-autothermal processes. For the first configuration, on the branch of upper temperature steady state the carbon formation and deposition on the nickel catalyst is highest, while the net hydrogen yield is highest on the branch of lower temperature steady state. For the second configuration, the conversion of heptane is always 100%. In the multiplicity region, the order of net hydrogen yield from high to low is the middle, upper and lower temperature steady states, while the order of reformer exit carbon flow rate from high to low is the lower, middle and upper temperature steady states. Efficient production of pure hydrogen for fuel cells requires fundamental and practical understanding of their bifurcation behaviors.     

Complexity, bifurcation and chaos in natural and man-made lumped and distributed systems

Complexity is a very diversified and branched subject and, ironically, is itself quite complex. In this paper, although we present the different aspects and definitions of complexity, we concentrate on its chemical/biological engineering relevance, especially for reaction/diffusion and hydrodynamic processes. System theory is used as the common language to unify concepts, and emphasis is given to bifurcation, chaos as the basis of behavioral complexity and the configuration of processes as the basis for structural complexity. Natural processes are grouped under biocomplexity, while man-made processes are treated as complexity alone. We restrict our attention in this paper to systems that do not change their structure during the process, so that self-organizational criticality is explained, but not utilized. Computational complexity is intrinsically inherent in all the processes we consider, but it is not given much attention in this paper. Despite these severe limitations on the scope of our paper, the subject is still quite complex and branched, and this paper tries to bring it to the attention and interest of a wider spectrum of chemical/biological engineers in both academia and industry.