Neural Structures Using the Eigenstates of a Quantum Harmonic Oscillator

The main result of the paper is the use of orthogonal Hermite polynomials as the basis functions of feedforward neural networks. The proposed neural networks have some interesting properties: (i) the basis functions are invariant under the Fourier transform, subject only to a change of scale, (ii) the basis functions are the eigenstates of the quantum harmonic oscillator, and stem from the solution of Schrödinger's diffusion equation. The proposed feed-forward neural networks demonstrate the particle-wave nature of information and can be used in nonparametric estimation. Possible applications of the proposed neural networks include function approximation, image processing and system modelling.     

Impacts of a marine fish farm in Argolikos Gulf (Greece) on the water column and the sediment

The aim of this study was the qualitative and quantitative assessment of impacts on the physicochemical characteristics of the water column and the sediment caused by an intensive marine fish farm. The study area was the Plateia Island which is located in an open seawater area in the outer part of Argolikos Gulf (Northeastern Peloponnese, Greece). The particular plant has been in operation since 1988, producing about 350-400 tons per year of Gilthead seabream (Sparus auratus) and European seabass (Dicentrarchus labrax). The samples were taken on a three-season period started in August 2001 and ended in May 2002. During this study, the main physical and geochemical parameters on the water column and the sediment were investigated. Specifically, the parameters that were measured included: ammonium-nitrogen, phosphate and suspended solids in the water column and organic matter, organic carbon, and phosphorus (total and inorganic) in the sediment samples. The conclusions drawn from this work clearly verify that the most important impacts, mainly identified as increased nutrients and organics concentrations both in the water column and the sediment, were realized at the stations closest to the farm. These concentrations gradually decreased with increasing distance from the cages. The peak values in most of the parameters measured were obtained during summer or spring. However, it has to be mentioned that although the measured concentrations clearly depict the influence of the fish farm especially on the sediment, their values were, in general, considerably lower than those reported in relative studies in Greek fish farms.     

Hand dexterity assessment based on mouse pointer trajectory measurements in children with learning disabilities

Universal Access in the Information Society - We present an investigation of the systematic differences in upper limb motor skills between children with learning disabilities (LDs) and children...     

Colloidal Quantum Dot Infrared Lasers Featuring Sub-Single-Exciton Threshold and Very High Gain

The use of colloidal quantum dots (CQDs) as a gain medium in infrared laser devices has been underpinned by the need for high pumping intensities, very short gain lifetimes, and low gain coefficients. Here, PbS/PbSSe core/alloyed-shell CQDs are employed as an infrared gain medium that results in highly suppressed Auger recombination with a lifetime of 485 ps, lowering the amplified spontaneous emission (ASE) threshold down to 300 µJ cm⁻², and showing a record high net modal gain coefficient of 2180 cm⁻¹. By doping these engineered core/shell CQDs up to nearly filling the first excited state, a significant reduction of optical gain threshold is demonstrated, measured by transient absorption, to an average-exciton population-per-dot 〈N^th〉_g of 0.45 due to bleaching of the ground state absorption. This in turn have led to a fivefold reduction in ASE threshold at 〈N^th〉_ASE = 0.70 excitons-per-dot, associated with a gain lifetime of 280 ps. Finally, these heterostructured QDs are used to achieve near-infrared lasing at 1670 nm at a pump fluences corresponding to sub-single-exciton-per-dot threshold (〈N^th〉_Las = 0.87). This work brings infrared CQD lasing thresholds on par to their visible counterparts, and paves the way toward solution-processed infrared laser diodes.     

Extended Kalman and Particle Filtering for sensor fusion in motion control of mobile robots

Motion control of mobile robots and efficient trajectory tracking is usually based on prior estimation of the robots’ state vector. To this end Gaussian and nonparametric filters (state estimators from position measurements) have been developed. In this paper the Extended Kalman Filter which assumes Gaussian measurement noise is compared to the Particle Filter which does not make any assumption on the measurement noise distribution. As a case study the estimation of the state vector of a mobile robot is used, when measurements are available from both odometric and sonar sensors. It is shown that in this kind of sensor fusion problem the Particle Filter has better performance than the Extended Kalman Filter, at the cost of more demanding computations.     

Stochastic Processes and Neuronal Modelling: Quantum Harmonic Oscillator Dynamics in Neural Structures

This paper studies neural structures with weights that follow the model of the quantum harmonic oscillator (Q.H.O.). The proposed neural networks have stochastic weights which are calculated from the solution of Schrödinger’s equation under the assumption of a parabolic (harmonic) potential. These weights correspond to diffusing particles, which interact to each other as the theory of Brownian motion (Wiener process) predicts. The learning of the stochastic weights (convergence of the diffusing particles to an equilibrium) is analyzed. In the case of associative memories the proposed neural model results in an exponential increase of patterns storage capacity (number of attractors). Finally, it is shown that conventional neural networks and learning algorithms based on error gradient can be conceived as a subset of the proposed quantum neural structures. Thus, the complementarity between classical and quantum physics is also validated in the field of neural computation.     

Development and evaluation of alternative processes for sterilization and deodorization of cork barks and natural cork stoppers

Cork taint, one of the most known off-flavours in wine, is usually attributed to the presence of the aromatic compound 2,4,6-trichloroanisole (2,4,6-TCA) in cork stoppers made from the bark of the cork oak, Quercus suber. There are many major chemical/biochemical pathways through which 2,4,6-TCA can be formed during cork production. The estimated incidence of cork-tainted wine bottles ranges from 2–to 7%, costing global wine industry approximately US$ 10 billion annually. During this study, a laboratory-scale system was designed and constructed, to efficiently treat cork bark and stopper samples using ozone and/or other sterilizing gases, such as steam, via sequential application of pulsed vacuum–pressure cycles. The developed physicochemical processes were studied and evaluated in order to achieve both sterilization conditions and 2,4,6-TCA removal without affecting the mechanical and bottling properties of cork. According to the results, the application of ozone alone seems to be a promising treatment method for cork barks. However, the pulsed treatment ensures both sufficient removal (99%) of both bacteria and moulds from cork stoppers when combined with ozone or steam and satisfactory deodorization of cork stoppers achieving high percentages (90%) of 2,4,6-TCA removal when combined with steam. The operating cost of each alternative process (plain or pulsed, with or without ozone and/or steam) was estimated and compared, in laboratory scale, for the selection of the most efficient process, taking into account technicoeconomical aspects.     

Modeling of fermentative hydrogen production from sweet sorghum extract based on modified ADM1

The Anaerobic digestion model 1 (ADM1) framework can be used to predict fermentative hydrogen production, since the latter is directly related to the acidogenic stage of the anaerobic digestion process. In this study, the ADM1 model framework was used to simulate and predict the process of fermentative hydrogen production from the extractable sugars of sweet sorghum biomass. Kinetic parameters for sugars’ consumption and yield coefficients of acetic, propionic and butyric acid production were estimated using the experimental data obtained from the steady states of a CSTR. Batch experiments were used for kinetic parameter validation. Since the ADM1 does not account for metabolic products such as lactic acid and ethanol that are crucial during the fermentative hydrogen production process, the structure of the model was modified to include lactate and ethanol among the metabolites and to improve the predictions. The modified ADM1 simulated satisfactorily batch experiments although further modifications could be made in order to further improve the predictions for the hydrogenogenic process.