Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis

In this paper, a novel multi-view human movement recognition method is presented. A novel representation of multi-view human movement videos is proposed that is based on learning basic multi-view human movement primitives, called multi-view dynemes. The movement video is represented in a new feature space (called dyneme space) using these multi-view dynemes, thus producing a time invariant multi-view movement representation. Fuzzy distances from the multi-view dynemes are used to represent the human body postures in the dyneme space. Three variants of Linear Discriminant Analysis (LDA) are evaluated to achieve a discriminant movement representation in a low dimensionality space. The view identification problem is solved either by using a circular block shift procedure followed by the evaluation of the minimum Euclidean distance from any dyneme, or by exploiting the circular shift invariance property of the Discrete Fourier Transform (DFT). The discriminant movement representation combined with camera viewpoint identification and a nearest centroid classification step leads to a high human movement classification accuracy.     

Effectiveness of iron reducing bacteria for the removal of iron from bauxite ores

Current study investigates the possibility of obtaining the removal of iron from bauxite ores, by exploiting the metabolism of iron reducing microorganisms. The experiments were carried out using the microorganism Desulfuromonas palmitatis. The effectiveness of the treatment was evaluated on six different bauxite samples, containing the alumina oxide in the form of diaspore or boehmite. The Fe₂O₃ content of the samples varied between 16.3% and 22.3% and occurred mainly in the form of goethite or hematite, with one sample containing also chamosite. For comparison reasons the same treatment was also applied on three samples of synthetic Fe(III)-oxides, namely ferrihydrite, goethite and hematite. The removal of iron from the six bauxite samples was found to vary between 7% and 29% and the highest extraction corresponded to the sample containing the Fe(II) rich compound, chamosite. The bioavailability of Fe in the three synthetic oxides was highly dependant on their crystallinity. The amorphous ferrihydrite was almost completely dissolved, up to 95%, whereas dissolution of crystalline goethite and hematite did not exceed 9% and 1.2% respectively.     

Bandwidth limits of luminescent solar concentrators as detectors in free-space optical communication systems

Luminescent solar concentrators(LSCs)have recently emerged as a promising receiver technology in free-space optical communications due to their inherent ability to collect light from a wide field-of-view and concentrate it into small areas,thus leading to high optical gains.Several high-speed communication systems integrating LSCs in their detector blocks have already been demonstrated,with the majority of efforts so far being devoted to maximising the received optical power and the system's field-of-view.However,LSCs may pose a severe bottleneck on the bandwidth of such communication channels due to the comparably slow timescale of the fluorescence events involved,a situation further aggravated by the inherent reabsorption in these systems,and yet,an in-depth study into such dynamic effects remains absent in the field.To fill this gap,we have developed a comprehensive analytical solution that delineates the fundamental bandwidth limits of LSCs as optical detectors in arbitrary free-space optical links,and establishes their equivalence with simple RC low-pass electrical circuits.Furthermore,we demonstrate a time-domain Monte Carlo simulation platform,an indispensable tool in the multiparameter optimisation of LSC-based receiver systems.Our work offers vital insight into LSC system dynamic behaviour and paves the way to evaluate the technology for a wide range of applications,including visible light communications,high-speed video recording,and real-time biological imaging,to name a few.     

Mucoadhesive Electrospun Nanofiber-Based Hybrid System with Controlled and Unidirectional Release of Desmopressin

The sublingual mucosa is an attractive route for drug delivery, although challenged by a continuous flow of saliva that leads to a loss of drug by swallowing. It is of great benefit that drugs absorbed across the sublingual mucosa avoid exposure to the harsh environment of the gastro-intestinal lumen; this is especially beneficial for drugs of low physicochemical stability such as therapeutic peptides. In this study, a two-layered hybrid drug delivery system was developed for the sublingual delivery of the therapeutic peptide desmopressin. It consisted of peptide-loaded mucoadhesive electrospun chitosan/polyethylene oxide-based nanofibers (mean diameter of 183 ± 20 nm) and a saliva-repelling backing film to promote unidirectional release towards the mucosa. Desmopressin was released from the nanofiber-based hybrid system (approximately 80% of the loaded peptide was released within 45 min) in a unidirectional manner in vitro. Importantly, the nanofiber–film hybrid system protected the peptide from wash-out, as demonstrated in an ex vivo flow retention model with porcine sublingual mucosal tissue. Approximately 90% of the loaded desmopressin was retained at the surface of the ex vivo porcine sublingual mucosa after 15 min of exposure to flow rates representing salivary flow.     

Application of biological processes for the removal of arsenic from groundwaters

Bacteria are widespread, abundant, geochemically reactive components of aquatic environments. In particular, iron-oxidizing bacteria, are involved in the oxidation and subsequent precipitation of ferrous ions. Due to this property, they have been applied in drinking water treatment processes, in order to accelerate the removal of ferrous iron from groundwaters. Iron also exerts a strong influence on arsenic concentrations in groundwater sources, while iron oxides are efficient adsorbents in arsenic removal processes. In the present study, the removal of arsenic (III and V), during biological iron oxidation has been investigated. The results showed that both inorganic forms of arsenic could be efficiently treated, for the concentration range of interest in drinking water (50-200microg/L). In addition, the oxidation of trivalent arsenic was found to be catalyzed by bacteria, leading to enhanced overall arsenic removal, because arsenic in the form of arsenites cannot be efficiently sorbed onto iron oxides. This method comprises a cost competitive technology, which can find application in treatment of groundwaters with elevated concentrations of iron and arsenic.     

Carbon‐Nanotube–PDMS Composite Coatings on Optical Fibers for All‐Optical Ultrasound Imaging

Polymer–carbon nanotube composite coatings have properties that are desirable for a wide range of applications. However, fabrication of these coatings onto submillimeter structures with the efficient use of nanotubes has been challenging. Polydimethylsiloxane (PDMS)–carbon nanotube composite coatings are of particular interest for optical ultrasound transmission, which shows promise for biomedical imaging and therapeutic applications. In this study, methods for fabricating composite coatings comprising PDMS and multiwalled carbon nanotubes (MWCNTs) with submicrometer thickness are developed and used to coat the distal ends of optical fibers. These methods include creating a MWCNT organogel using two solvents, dip coating of this organogel, and subsequent overcoating with PDMS. These coated fibers are used as all‐optical ultrasound transmitters that achieve high ultrasound pressures (up to 21.5 MPa peak‐to‐peak) and broad frequency bandwidths (up to 39.8 MHz). Their clinical potential is demonstrated with all‐optical pulse‐echo ultrasound imaging of an aorta. The fabrication methods in this paper allow for the creation of thin, uniform carbon nanotube composites on miniature or temperature‐sensitive surfaces, to enable a wide range of advanced sensing capabilities.     

Facial expression recognition in image sequences using geometric deformation features and Support Vector Machines.

In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection.     

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.