Greedy Detection

In this paper, we introduce a new greedy algorithm developed for communication systems characterized with multiple simultaneous data transmission. Specifically, we consider code-division multiple-access (CDMA) systems and systems employing space-time coding (STC). The optimum detection in such systems has an exponential complexity and cannot be used in practical systems. We show that performance close to the optimum performance yet with significantly lower complexity can be achieved by the proposed algorithm. Also, we show that its performance is significantly better than that of most of the existing sub-optimum schemes for a wide range of operating conditions.     

Effect of some operating variables on the flotation of aluminium particles

The effects of impeller speed, air flow rate, cell concentration, agitator size and cell volume on the recovery of aluminium particles have been studied in a KHD Humboldt Wedge AG machine, using alkyl benzene sodium sulphonate paste as collector and pine oil as frother. The recovery yield increased with increasing impeller speed, air flow rate, cell concentration and cell volume, up to a certain range, whereafter it decreased. A decrease in the recovery yield was observed on increasing the agitator size. A mathematical model has been developed to correlate percentage recovery with the above variables.     

Using hybrid automata for set-membership state estimation with uncertain nonlinear continuous-time systems

This paper deals with set-membership state estimation for continuous-time systems from discrete-time measurements, in the unknown but bounded error framework. The classical predictor–corrector approach to state estimation uses interval Taylor methods for solving the prediction phase, which are known to have poor performance in presence of large model or input uncertainty. In this paper, we show how to derive more efficient predictors by using a nonlinear hybridization method which builds hybrid automata to characterize the boundaries of reachable sets. The derived continuous–discrete set-membership predictor–corrector estimator is then tested with simulated data from a bioreactor. Our method is compared to classical continuous-time interval observers and is shown to have promising performance.     

Experimental investigation on frequency pulsation effects on a single pass plate heat exchanger performance

This paper investigates combined heat transfer improvement methods. These methods include introducing pulsating flow, adding nanofluids, and manipulating the flow's characteristics in a corrugated plate heat exchanger. Tests are carried out with multi-walled carbon nanotube (MWCNT), graphene nanoplate (GNP), and a mixture of GNP and MWCNT meeting the requirement of 0.01% nanofluids volume fraction and exposed to pulsation. Results demonstrated that the use of pulsating frequencies from 0 to 30 Hz of GNP-water, MIX nanofluids–water, and MWCNT–water nanofluids with a constant concentration of 0.01 wt% leads to a significant improvement in heat transfer. Using pure water at frequency f = 0 Hz as a benchmark, the Nusselt number of the mixture nanofluid increases by 15.2%, 27.5%, 40.4%, and 52.8% with the increase of frequency pulsation from 0 to 30 Hz with a slight effect on the pressure-drop at this low used constant nanofluid concentration = 0.01%. The highest Nusselt number value for GNP-water nanofluid improved by an amount of 58.3% at the highest frequency compared with pure water at f = 0 Hz.     

Feature extraction in palmprint recognition using spiral of moment skewness and kurtosis algorithm

Pattern Analysis and Applications - Because of their high recognition rates, coding-based approaches that use multispectral palmprint images have become one of the most popular palmprint...     

Exploring bioaugmentation strategies for azo dye CI Reactive Violet 5 decolourization using bacterial mixture: dye response surface methodology

The use of response surface methodology based on statistical design of experiments is becoming increasingly widespread in several sciences such as analytical and environmental chemistry. In the present study, the decolourization and the degradation efficiency of CI Reactive Violet 5 (CIRV5) was studied using a novel bacterial consortium. CIRV5 (1000 p.p.m.) biodegradation was investigated under shaking condition in mineral salt medium solution (MSM) at a 7.5 pH and a 25°C temperature. The degradation pathways were also predicted, using UV‐visible spectroscopy analysis. Under optimal conditions, the bacterial consortium was able to decolourize the dye completely (>99%) within 8 h. The colour and chemical oxygen demand (COD) removal were 99.29 and 94.93%, respectively. Polymerase chain reaction (PCR) technique was utilized to detect the adhesin genes ‘icaA’ and ‘icaD.’ Our results showed that Staphylococcus aureus had a high decolourization capacity. Phytotoxicity study using Triticum turgidum ssp durum showed the no toxicity of the produced products.     

Spectral coded phase bipolar OCDMA technological implementation thanks to low index modulation filters

Telecommunication Systems - Optical code division multiple access (OCDMA) systems are attracting an increasing interest in optical fiber communication. This is due to the various advantages that...     

Direct profiling of lipid distribution in brain tissue using MALDI-TOFMS

Recent developments in mass spectrometry have permitted direct analysis of biomolecules in tissue. However, most studies have focused on proteins with emphasis on biomarker discovery. In the present work, matrix-assisted laser desorption/ionization mass spectrometry was used for the direct analysis of lipids in rat cerebellum. The lipid bilayer role as a storage depot for small organic molecules such as therapeutic drugs and pollutants such as DDT, as well as the ability to compare lipid profiles in healthy and diseased animal models, are a few of the many reasons why the direct probing of tissue to determine the qualitative and possibly quantitative lipid content could be a very useful tool. Molecular ions corresponding to cholesterol, phosphatidylcholines, sphingomyelins, and phosphatidylethanolamines were recorded in positive ion mode, while ones corresponding to phosphatidylinositols, sulfatides, and gangliosides were recorded in negative ion mode. Overall, representatives from all major categories of brain lipids including cholesterol, 15 phospholipid species (9 phosphatidylcholines, 1 sphingomyelin, 3 phosphatidylethanolamines, 2 phosphatidylinositols), 10 sulfatides (5 hydroxylated species and 5 nonhydroxylated species), and 7 species of gangliosides were detected.     

A review on (Sr,Ca)TiO3-based dielectric materials: crystallography,recent progress and outlook in energy-storage aspects

Journal of Materials Science - In pursuit of developing high-performance lead-free energy storage capacitors, strontium titanate (SrTiO3) and calcium titanate (CaTiO3) are widely recognised as...     

Prefix-projection global constraint and top-<Emphasis Type="Italic">k</Emphasis> approach for sequential pattern mining

Sequential pattern mining (SPM) is an important data mining problem with broad applications. SPM is a hard problem due to the huge number of intermediate subsequences to be considered. State of the art approaches for SPM (e.g., PrefixSpan Pei et al. 2001) are largely based on the pattern-growth approach, where for each frequent prefix subsequence, only its related suffix subsequences need to be considered, and the database is recursively projected into smaller ones. Many authors have promoted the use of constraints to focus on the most promising patterns according to the interests of the end user. The top-k SPM problem is also used to cope with the difficulty of thresholding and to control the number of solutions. State of the art methods developed for SPM and top-k SPM, though efficient, are locked into a rather rigid search strategy, and suffer from the lack of declarativity and flexibility. Indeed, adding new constraints usually amounts to changing the data-structures used in the core of the algorithm, and combining these new constraints often require new developments. Recent works (e.g. Kemmar et al. 2014; Négrevergne and Guns 2015) have investigated the use of Constraint Programming (CP) for SPM. However, despite their nice declarative aspects, all these modelings have scaling problems, due to the huge size of their constraint networks. To address this issue, we propose the Prefix-Projection global constraint, which encapsulates both the subsequence relation as well as the frequency constraint. Its filtering algorithm relies on the principle of projected databases which allows to keep in the variables domain, only values leading to a frequent pattern in the database. Prefix-Projection filtering algorithm enforces domain consistency on the variable succeeding the current frequent prefix in polynomial time. This global constraint also allows for a straightforward implementation of additional constraints such as size, item membership, regular expressions and any combination of them. Experimental results show that our approach clearly outperforms existing CP approaches and competes well with the state-of-the-art methods on large datasets for mining frequent sequential patterns, sequential patterns under various constraints, and top-k sequential patterns. Unlike existing CP methods, our approach achieves a better scalability.