Generic and efficient framework for search trees on flash memory storage systems

Tree index structures are crucial components in data management systems. Existing tree index structure are designed with the implicit assumption that the underlying external memory storage is the conventional magnetic hard disk drives. This assumption is going to be invalid soon, as flash memory storage is increasingly adopted as the main storage media in mobile devices, digital cameras, embedded sensors, and notebooks. Though it is direct and simple to port existing tree index structures on the flash memory storage, that direct approach does not consider the unique characteristics of flash memory, i.e., slow write operations, and erase-before-update property, which would result in a sub optimal performance. In this paper, we introduce FAST (i.e., Flash-Aware Search Trees) as a generic framework for flash-aware tree index structures. FAST distinguishes itself from all previous attempts of flash memory indexing in two aspects: (1) FAST is a generic framework that can be applied to a wide class of data partitioning tree structures including R-tree and its variants, and (2) FAST achieves both efficiency and durability of read and write flash operations through memory flushing and crash recovery techniques. Extensive experimental results, based on an actual implementation of FAST inside the GiST index structure in PostgreSQL, show that FAST achieves better performance than its competitors.     

Efficiency of magnetic coupled boost DC‐DC converters mainly dedicated to renewable energy systems: influence of the coupling factor

This paper presents a specific analysis of an individual basic magnetically coupled direct current‐to‐direct current (DC–DC) converter specially designed for integration in a distributed architecture of renewable energy generators for smart grid applications. In such distributed architecture dedicated for renewable energy, parallel high‐voltage DC presents many advantages over the classical centralized one. We show that in such setup, high voltage can be advantageously produced using a specific magnetically coupled boost converter, and we point out the influence of the coupling factor, generally considered equal to one, on the overall performance of the converter and on the global energy efficiency of the installation. In this study, the generalized concepts of system energy parameters of DC–DC converters are introduced and applied to the transient analysis. Consequently, the operation of a magnetic coupled DC–DC converter with a recovery stage is modeled. The simulation results are compared with those of the behavioral study, deduced from the model pointing out the large influence of the coupling factor value on the global behavior and mainly on the value of the recovery voltage, in all the various parts of the switching cycle. The renewable energy generator operating parameters, such as current and voltage values, can then be predicted in a more useful way to compute new similar DC–DC converter systems. Copyright © 2014 John Wiley & Sons, Ltd.     

Specific cationic photoinitiators for near UV and visible LEDs: Iodonium versus ferrocenium structures

Two iodonium salts based on a coumarin chromophore are investigated for polymerization upon light emitting diode irradiations (LEDs). They work as one‐component photoinitiators. They initiate the cationic polymerization of epoxides (under air) and vinylethers (laminate) upon exposure to violet LEDs (385 and 405 nm). Excellent polymerization profiles are recorded. Their efficiency is quite similar to that of a ferrocenium salt. Interpenetrating polymer networks can also be obtained through a concomitant cationic/radical photopolymerization of an epoxy/acrylate blend monomer. The light absorption properties of these new salts as well as the involved photochemical mechanisms are investigated for the first time through electron spin resonance, laser flash photolysis, steady state photolysis experiments. Molecular orbital calculations are also used to shed some light on the initiation mechanisms. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42759.     

Direction of Arrival Estimation using EM-ESPRIT with Nonuniform Arrays

This paper deals with the problem of the Direction Of Arrival (DOA) estimation with nonuniform linear arrays. The proposed method is based on the Expectation Maximization method where ESPRIT is used in the maximization step. The key idea is to iteratively interpolate the data to a virtual uniform linear array in order to apply ESPRIT to estimate the DOA. The iterative approach allows one to improve the interpolation using the previously estimated DOA. One of this method’s novelties lies in its capacity of dealing with any nonuniform array geometry. This technique manifests significant performance and computational advantages over previous algorithms such as Spectral MUSIC, EM-IQML and the method based on manifold separation technique. EM-ESPRIT is shown to be more robust to additive noise. Furthermore, EM-ESPRIT fully exploits the advantages of using a nonuniform array over a uniform array: simulations show that for the same aperture and with a smaller number of sensors, the nonuniform array presents almost identical performance as the equivalent uniform array.     

Incremental Evaluation of Sliding-Window Queries over Data Streams

Two research efforts have been conducted to realize sliding-window queries in data stream management systems, namely, query revaluation and incremental evaluation. In the query reevaluation method, two consecutive windows are processed independently of each other. On the other hand, in the incremental evaluation method, the query answer for a window is obtained incrementally from the answer of the preceding window. In this paper, we focus on the incremental evaluation method. Two approaches have been adopted for the incremental evaluation of sliding-window queries, namely, the input-triggered approach and the negative tuples approach. In the input-triggered approach, only the newly inserted tuples flow in the query pipeline and tuple expiration is based on the timestamps of the newly inserted tuples. On the other hand, in the negative tuples approach, tuple expiration is separated from tuple insertion where a tuple flows in the pipeline for every inserted or expired tuple. The negative tuples approach avoids the unpredictable output delays that result from the input-triggered approach. However, negative tuples double the number of tuples through the query pipeline, thus reducing the pipeline bandwidth. Based on a detailed study of the incremental evaluation pipeline, we classify the incremental query operators into two classes according to whether an operator can avoid the processing of negative tuples or not. Based on this classification, we present several optimization techniques over the negative tuples approach that aim to reduce the overhead of processing negative tuples while avoiding the output delay of the query answer. A detailed experimental study, based on a prototype system implementation, shows the performance gains over the input-triggered approach of the negative tuples approach when accompanied with the proposed optimizations     

Nonlinear adaptive filtering using kernel‐based algorithms with dictionary adaptation

Nonlinear adaptive filtering has been extensively studied in the literature, using, for example, Volterra filters or neural networks. Recently, kernel methods have been offering an interesting alternative because they provide a simple extension of linear algorithms to the nonlinear case. The main drawback of online system identification with kernel methods is that the filter complexity increases with time, a limitation resulting from the representer theorem, which states that all past input vectors are required. To overcome this drawback, a particular subset of these input vectors (called dictionary) must be selected to ensure complexity control and good performance. Up to now, all authors considered that, after being introduced into the dictionary, elements stay unchanged even if, because of nonstationarity, they become useless to predict the system output. The objective of this paper is to present an adaptation scheme of dictionary elements, which are considered here as adjustable model parameters, by deriving a gradient‐based method under collinearity constraints. The main interest is to ensure a better tracking performance. To evaluate our approach, dictionary adaptation is introduced into three well‐known kernel‐based adaptive algorithms: kernel recursive least squares, kernel normalized least mean squares, and kernel affine projection. The performance is evaluated on nonlinear adaptive filtering of simulated and real data sets. As confirmed by experiments, our dictionary adaptation scheme allows either complexity reduction or a decrease of the instantaneous quadratic error, or both simultaneously. Copyright © 2015 John Wiley & Sons, Ltd.