Soft purification of N-doped and undoped multi-wall carbon nanotubes

A soft method for purifying multi-wall carbon nanotubes (N-doped and undoped) is presented. The technique includes a hydrothermal/ultrasonic treatment of the material in conjunction with other subsequent treatments, including the extraction of polyaromatic compounds, dissolution of metal particles, bundle exfoliation, and uniform dispersion. This method avoids harsh oxidation protocols that burn (via thermal treatments) or functionalize (by introducing chemical groups) the nanotubes. We show a careful analysis of each purification step and demonstrate that the technique is extremely efficient when characterizing the materials using scanning electron microscopy (SEM), energy dispersive x-ray analysis (EDAX), scanning tuneling electron microscopy (STEM), x-ray powder diffraction (XRD), diffuse reflectance Fourier transform infrared (DRFTIR) spectroscopy and thermogravimetric analysis (TGA).     

Average block error probability of multicell CDMA packet networks with fast power control under multipath fading

We derive the average block error probability of multicell code-division multiple access (CDMA) packet networks with fast power control under multipath fading. Unlike many previous works, we consider that users are connected to a base station on a minimum attenuation basis. An analytical approach for the calculation of the intracell and intercell statistics is presented. By making use of these expressions, the mean block error probability is analyzed as a function of the number of resolvable paths and the number of base stations to which mobiles can connect.     

Interference statistics of cellular DS/CDMA systems with base station diversity under multipath fading

A theoretical characterization of intracell and intercell interference statistics in cellular direct-sequence code-division multiple-access systems in a multipath environment is presented considering both fast and slow power control. Unlike many of the previous papers, mobiles are assumed to connect to a base station according to a minimum attenuation criterion, also known as base station diversity. Interference statistics are used to estimate system capacity and results have been validated by Monte Carlo simulations. Our results confirm that much greater capacity can be achieved when multipath fading is compensated by power control, while the relative benefits of perfect compensation of multipath fading decreases as the number of resolvable paths at the receiver increases.     

Automatic Identification of Defects on Eggshell Through a Multispectral Vision System

The objective of this research was to develop an off-line artificial vision system to automatically detect defective eggshells, i.e., dirty or cracked eggshells, by employing multispectral images with the final purpose of adapting the system to an on-line grading machine. In particular, this work was focused on studying the feasibility of identifying organic stains on brown eggshells (dirty eggshell) caused by blood, feathers, feces, etc., from natural stains caused by deposits of pigments on the outer layer of clean eggshells. During the analysis, a total of 384 eggs were evaluated (clean, 148; dirty, 236). Dirty samples were evaluated visually in order to classify them according to the kind of defect (blood, feathers, and white, clear or dark feces), and clean eggshells were classified on the basis of the color of the natural stains (clear or dark). For each sample, digital images were acquired by employing a charged coupled device camera endowed with 15 monochromatic filters (440?C940?nm). A Matlab? function was developed in order to automate the process and analyze images with the aim to classify samples as clean or dirty. The program was constituted by three major steps: first, the research of an opportune combination of monochromatic images in order to isolate the eggshell from the background; second, the detection of the dirt stains; third, the classification of the image samples into the dirty or clean group on the basis of the geometric characteristics of the stains (area in pixel). The proposed classification algorithm was able to correctly classify nearly 98% of the samples with a very low processing time (0.05?s). The robustness of the proposed classification was observed applying an external validation to a second set of samples (n?=?178), obtaining a similar percentage of correctly classified samples (97%).     

Novel Strategies for Nanoparticle-Based Radiosensitization in Glioblastoma

Radiotherapy (RT) is one of the cornerstones in the current treatment paradigm for glioblastoma (GBM). However, little has changed in the management of GBM since the establishment of the current protocol in 2005, and the prognosis remains grim. Radioresistance is one of the hallmarks for treatment failure, and different therapeutic strategies are aimed at overcoming it. Among these strategies, nanomedicine has advantages over conventional tumor therapeutics, including improvements in drug delivery and enhanced antitumor properties. Radiosensitizing strategies using nanoparticles (NP) are actively under study and hold promise to improve the treatment response. We aim to describe the basis of nanomedicine for GBM treatment, current evidence in radiosensitization efforts using nanoparticles, and novel strategies, such as preoperative radiation, that could be synergized with nanoradiosensitizers.     

Image-Based Screening for the Identification of Bright Greenish Yellow Fluorescence on Pistachio Nuts and Cashews

The development of screening methodologies for a rapid identification of crops contaminated with aflatoxin is of great interest to agro-food industry. The objective of this work was to develop an image algorithm able to identify bright greenish yellow fluorescence (BGYF) on pistachio nuts and cashews. Previous researchers established that the presence of BGYF indicates that there is a high probability of aflatoxin contamination. Since BGYF is not a definitive indicator of aflatoxin contamination, samples emitting fluorescence should be removed and tested for aflatoxins by chemical means. This study, conducted in a static way, is an important step towards the development of a new more accurate and automatic aflatoxin screening method based on a vision system. In this work, a total of 352 samples of pistachio nuts and cashews were evaluated, half of which came from lots contaminated with aflatoxin. Two images in the 410–600 nm optical range were acquired for each sample. Imaging algorithms were developed to identify samples with fluorescent stains caused by BGYF. According to the image analysis results, nut samples were classified into two groups: fluorescent stains (FS) and non-fluorescent stains. Both BGYF and non-fluorescent samples were analyzed for aflatoxin. The laboratory analysis results showed a high correlation with the camera classification: pistachios and cashews placed in the FS group by the vision system contained 92 % and 82 % of the total number of nuts contaminated with aflatoxin, respectively. Moreover, a discriminant analysis of reflectance data was carried out in order to select the optimal optical range to detect BGYF, both in pistachio nuts, i.e., 480 and 520 nm, and in cashews, i.e., 440 and 600 nm.     

Effects of multipath fading on BER statistics in cellular CDMAnetworks with fast power control

An analytical approach for the calculation of the bit error rate statistics in a cellular code-division multiple access system with imperfect fast power control is presented. Usually, imperfections in power control after despreading are modeled as a lognormal random variable. It has been shown that, in a fading channel, the standard deviation of this variable is a function of whether or not the mobile is communicating with the base station where the power is measured. We show that when this fact is not taken into account the results obtained are too optimistic     

Numerical simulation of the aluminum–zinc–steel galvanic system for new designs of automotive chassis

The requirement for lighter vehicles in the automotive industry promotes designs based on the combination of different metallic alloys. Such an approach, however, leads to galvanic-corrosion risks, which compromise the durability of vehicles. One proposal to minimize such risks is to separate some of the chassis components by a Zn washer. The present work uses the finite element method to evaluate such an innovative design. The capacity of the washer to protect its aluminum alloy and carbon steel neighbors is assessed. As a worst-case scenario, the bare metals are in contact with NaCl solution. Two electrolyte layer thicknesses are assumed: in the micrometer and in the millimeter range. Each case requires different mathematical models. For the thin film case, the zinc washer is able to protect its neighbors from corrosion. However, it sustains large corrosion rates, and thus its protection is effective only during short periods. Furthermore, as the Zn surface degrades and thus recesses, the “protective field” is blocked by the neighboring metal-walls. The thicker the electrolyte layer, the weaker the Zn protective capability and, at some point, the corrosion of the aluminum alloy is unavoidable.     

A hybrid deep learning neural approach for emotion recognition from facial expressions for socially assistive robots

We have recently seen significant advancements in the development of robotic machines that are designed to assist people with their daily lives. Socially assistive robots are now able to perform a number of tasks autonomously and without human supervision. However, if these robots are to be accepted by human users, there is a need to focus on the form of human–robot interaction that is seen as acceptable by such users. In this paper, we extend our previous work, originally presented in Ruiz-Garcia et al. (in: Engineering applications of neural networks: 17th international conference, EANN 2016, Aberdeen, UK, September 2–5, 2016, proceedings, pp 79–93, 2016. https://doi.org/10.1007/978-3-319-44188-7_6), to provide emotion recognition from human facial expressions for application on a real-time robot. We expand on previous work by presenting a new hybrid deep learning emotion recognition model and preliminary results using this model on real-time emotion recognition performed by our humanoid robot. The hybrid emotion recognition model combines a Deep Convolutional Neural Network (CNN) for self-learnt feature extraction and a Support Vector Machine (SVM) for emotion classification. Compared to more complex approaches that use more layers in the convolutional model, this hybrid deep learning model produces state-of-the-art classification rate of \(96.26\%\), when tested on the Karolinska Directed Emotional Faces dataset (Lundqvist et al. in The Karolinska Directed Emotional Faces—KDEF, 1998), and offers similar performance on unseen data when tested on the Extended Cohn–Kanade dataset (Lucey et al. in: Proceedings of the third international workshop on CVPR for human communicative behaviour analysis (CVPR4HB 2010), San Francisco, USA, pp 94–101, 2010). This architecture also takes advantage of batch normalisation (Ioffe and Szegedy in Batch normalization: accelerating deep network training by reducing internal covariate shift. http://arxiv.org/abs/1502.03167, 2015) for fast learning from a smaller number of training samples. A comparison between Gabor filters and CNN for feature extraction, and between SVM and multilayer perceptron for classification is also provided.