Series combination of a switched dc-dc converter and a linear regulator for high frequency RF envelope amplifier

Classical linear amplifiers such as A, AB and B offer very good linearity suitable for RF power amplifiers. However, its inherent low efficiency limits its use especially in base-stations that manage tens or hundreds of Watts. The use of linearization techniques such as Envelope Elimination and Restoration (EER) allow an increase of efficiency keeping good linearity. This technique requires a very fast dc-dc power converter to provide variable voltage supply to the power amplifier. In this paper, several alternatives are analyzed to implement the envelope amplifier based on a cascade association of a switched dc-dc converter and a linear regulator. A simplified version of this approach is also suitable to operate with Envelope Tracking technique.

PSO and neural networks: Optimal combination to solve non linear complex problems

Swarm colonies reproduce social habits. Working together in a group to reach a predefined goal is a social behaviour occurring in nature. Linear optimization problems have been approached by different techniques based on natural models. In particular, Particles Swarm optimization is a meta-heuristic search technique that has proven to be effective when dealing with complex optimization problems. This paper presents and develops a new method based on different penalties strategies to solve complex problems. It focuses on the training process of the neural networks, the constraints and the election of the parameters to ensure successful results and to avoid the most common obstacles when searching optimal solutions.

The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

Brain structural and functional recovery following initiation of combination antiretroviral therapy

NeuroAIDS persists in the era of combination antiretroviral therapies. We describe here the recovery of brain structure and function following 6 months of therapy in a treatment-naive patient presenting with HIV-associated dementia. The patient’s neuropsychological test performance improved and his total brain volume increased by more than 5 %. Neuronal functional connectivity measured by magnetoencephalography changed from a pattern identical to that observed in other HIV-infected individuals to one that was indistinguishable from that of uninfected control subjects. These data suggest that at least some of the effects of HIV on the brain can be fully reversed with treatment.

Phytostabilization of metals in mine soils using Brassica juncea in combination with organic amendments

Background and aims The high metal bioavailability and the poor conditions of mine soils yield a low plant biomass, limiting the application of phytoremediation techniques. A greenhouse experiment was performed to evaluate the effects of organic amendments on metal stabilization and the potential of Brassica juncea L. for phytostabilization in mine soils. Methods Plants were grown in pots filled with soils collected from two mine sites located in Central Spain mixed with 0, 30 and 60 tha?1 of pine bark compost and horse- and sheep-manure compost. Plant biomass and metal concentrations in roots and shoots were measured. Metal bioavailability was assessed using a rhizosphere-based method (rhizo), which consists of a mixture of low-molecular-weight organic acids to simulate root exudates. Results Manure reduced metal concentrations in shoots (10?50 % reduction of Cu and 40?80 % of Zn in comparison with non-amended soils), bioconcentration factor (10?50 % of Cu and 40?80 % of Zn) and metal bioavailability in soil (40?50 % of Cu and 10?30 % of Zn) due to the high pH and the contribution of organic matter. Manure improved soil fertility and was also able to increase plant biomass (5?20 times in shoots and 3?30 times in roots), which resulted in a greater amount of metals removed from soil and accumulated in roots (increase of 2?7 times of Cu and Zn). Plants grown in pine bark treatments and in non-amended soils showed a limited biomass and high metal concentrations in shoots. Conclusions The addition of manure could be effective for the stabilization of metals and for enhancing the phytostabilization ability of B. juncea in mine soils. In this study, this species resulted to be a potential candidate for phytostabilization in combination with manure, differing from previous results, in which B. juncea had been recognized as a phytoextraction plant.

Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction

An innovative background modeling technique that is able to accurately segment foreground regions in RGB-D imagery (RGB plus depth) has been presented in this paper. The technique is based on a Bayesian framework that efficiently fuses different sources of information to segment the foreground. In particular, the final segmentation is obtained by considering a prediction of the foreground regions, carried out by a novel Bayesian Network with a depth-based dynamic model, and, by considering two independent depth and color-based mixture of Gaussians background models. The efficient Bayesian combination of all these data reduces the noise and uncertainties introduced by the color and depth features and the corresponding models. As a result, more compact segmentations, and refined foreground object silhouettes are obtained. Experimental results with different databases suggest that the proposed technique outperforms existing state-of-the-art algorithms.

Scalable UWB photonic generator based on the combination of doublet pulses

We propose and experimentally demonstrate a scalable and reconfigurable optical scheme to generate high order UWB pulses. Firstly, various ultra wideband doublets are created through a process of phase-tointensity conversion by means of a phase modulation and a dispersive media. In a second stage, doublets are combined in an optical processing unit that allows the reconfiguration of UWB high order pulses. Experimental results both in time and frequency domains are presented showing good performance related to the fractional bandwidth and spectral efficiency parameters.

A study of feature combination for vehicle detection based on image processing

Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.

Background foreground segmentation with RGB-D Kinect data: An efficient combination of classifiers

Low cost RGB-D cameras such as the Microsoft’s Kinect or the Asus’s Xtion Pro are completely changing the computer vision world, as they are being successfully used in several applications and research areas. Depth data are particularly attractive and suitable for applications based on moving objects detection through foreground/background segmentation approaches; the RGB-D applications proposed in literature employ, in general, state of the art foreground/background segmentation techniques based on the depth information without taking into account the color information. The novel approach that we propose is based on a combination of classifiers that allows improving background subtraction accuracy with respect to state of the art algorithms by jointly considering color and depth data. In particular, the combination of classifiers is based on a weighted average that allows to adaptively modifying the support of each classifier in the ensemble by considering foreground detections in the previous frames and the depth and color edges. In this way, it is possible to reduce false detections due to critical issues that can not be tackled by the individual classifiers such as: shadows and illumination changes, color and depth camouflage, moved background objects and noisy depth measurements. Moreover, we propose, for the best of the author’s knowledge, the first publicly available RGB-D benchmark dataset with hand-labeled ground truth of several challenging scenarios to test background/foreground segmentation algorithms.

An engineering modification of the blade element momentum combination applicable in vertical descent flight :fundamentals and validation

An engineering modification of blade element/momentum theory is applied to describe the vertical autorotation of helicopter rotors. A full non‐linear aerodynamic model is considered for the airfoils, taking into account the dependence of lift and drag coefficients on both the angle of attack and the Reynolds number. The proposed model, which has been validated in previous work, has allowed the identification of different autorotation modes, which depend on the descent velocity and the twist of the rotor blades. These modes present different radial distributions of driven and driving blade regions, as well as different radial upwash/downwash patterns. The number of blade sections with zero tangential force, the existence of a downwash region in the rotor disk, the stability of the autorotation state, and the overall rotor autorotation efficiency, are all analyzed in terms of the flight velocity and the characteristics of the rotor. It is shown that, in vertical autorotation, larger blade twist leads to smaller values of descent velocity for a given thrust generated by the rotor in the autorotational state.

A model-based approach for assessing in vivo combination therapy interactions

We present an approach for evaluating the efficacy of combination antitumor agent schedules that accounts for order and timing of drug administration. Our model-based approach compares in vivo tumor volume data over a time course and offers a quantitative definition for additivity of drug effects, relative to which synergism and antagonism are interpreted. We begin by fitting data from individual mice receiving at most one drug to a differential equation tumor growth/drug effect model and combine individual parameter estimates to obtain population statistics. Using two null hypotheses: (i) combination therapy is consistent with additivity or (ii) combination therapy is equivalent to treating with the more effective single agent alone, we compute predicted tumor growth trajectories and their distribution for combination treated animals. We illustrate this approach by comparing entire observed and expected tumor volume trajectories for a data set in which HER-2/neu-overexpressing MCF-7 human breast cancer xenografts are treated with a humanized, anti-HER-2 monoclonal antibody (rhuMAb HER-2), doxorubicin, or one of five proposed combination therapy schedules.

Concomitant combination therapy for HIV infection preferable over sequential therapy with 3TC and non-nucleoside reverse transcriptase inhibitors

Exposure to 3TC of HIV-1 mutant strains containing non-nucleoside reverse transcriptase inhibitor (NNRTI)-specific mutations in their reverse transcriptase (RT) easily selected for double-mutant viruses that had acquired the characteristic 184-Ile mutation in their RT in addition to the NNRTI-specific mutations. Conversely, exposure of 3TC-resistant 184-Val mutant HIV-1 strains to nine different NNRTIs resulted in the rapid emergence of NNRTI-resistant virus strains at a time that was not more delayed than when wild-type HIV-1(IIIB) was exposed to the same compounds. The RTs of these resistant virus strains had acquired the NNRTI-characteristic mutations in addition to the preexisting 184-Val mutation. Surprisingly, when the 184-Ile mutant HIV-1 was exposed to a variety of NNRTIs, the 188-His mutation invariably occurred concomitantly with the 184-Ile mutation in the HIV-1 RT. Breakthrough of this double-mutant virus was markedly accelerated as compared with the mutant virus selected from the wild-type or 184-Val mutant HIV-1 strain. The double (184-Ile + 188-His) mutant virus showed a much more profound resistance profile against the NNRTIs than the 188-His HIV-1 mutant. In contrast with the sequential chemotherapy, concomitant combination treatment of HIV-1-infected cells with 3TC and a variety of NNRTIs resulted in a dramatic delay of virus breakthrough and resistance development.

A peptide derived from the non-receptor-binding region of urokinase plasminogen activator inhibits glioblastoma growth and angiogenesis in vivo in combination with cisplatin

The urokinase plasminogen activator system is involved in angiogenesis and tumor growth of malignant gliomas, which are highly neovascularized and so may be amenable to antiangiogenic therapy. In this paper, we describe the activity of Å6, an octamer capped peptide derived from the non-receptor-binding region of urokinase plasminogen activator. Å6 inhibited human microvascular endothelial cell migration but had no effect on the proliferation of human microvascular endothelial cells or U87MG glioma cells in vitro. In contrast, Å6 or cisplatin (CDDP) alone suppressed subcutaneous tumor growth in vivo by 48% and 53%, respectively, and, more strikingly, the combination of Å6 plus CDDP inhibited tumor growth by 92%. Such combination treatment also greatly reduced the volume of intracranial tumor xenografts and increased survival of tumor-bearing animals when compared with CDDP or Å6 alone. Tumors from the combination treatment group had significantly reduced neovascularization, suggesting a mechanism involving Å6-mediated inhibition of endothelial cell motility, thereby eliciting vascular sensitivity to CDDP-mediated toxicity. These data suggest that the combination of an angiogenesis inhibitor that targets endothelial cells with a cytotoxic agent may be a useful therapeutic approach.