Performance of MB-OFDM UWB and WiMAX IEEE 802.16e converged radio-over-fiber in PON

Experimental results about the performance of converged radio-over- fiber transmission including multiband- OFDM UWB and WiMAX 802.16e wireless over a passive optical network are reported in this paper. The experimental study indicates that UWB and WiMAX converged transmission is feasible over the proposed distribution set-up employing a single wavelength. However, the results indicate that there is an EVM penalty of 3.2 dB for a UWB 10 km SSMF transmission in presence of WiMAX wireless

UWB wireless coexistence by fibre-based photonic ADC interference monitoring

The interference monitoring of UWB wireless picocell clusters through an in-house fibre installation is proposed and evaluated in a proof-of-concept experiment. UWB clusters enable range extension of UWB technology providing Gbit/s communications in home or office buildings. Coexistence of a large number of UWB clusters with other wireless services is guaranteed by a photonic analog-to-digital converter employing the in-house optical fiber installation

Magnetic nanoparticles for biosensing and immunoprecipitation

Our world is rapidly changing and its future is on our hands. Great effort is being done against overexploitation of natural resources, uncontrolled hunting and pollution. A great concerning fact is due to pollution which is causing a continuous greenhouse effect and new cancer cases every single day. Nowadays, it is possible to improve the detection of lethal elements in the environment, to fight against cancer in a smarter manner, with less pain and with more efficiency but, more important, to use the same low-cost, fast and environmentally friendly tool for these purposes and more. This reality is thanks to previous works and findings regarding the Magnetic Nanoparticles (MNPs), which are employable in a wide variety of applications such as magnetic recording media, resonance imaging, heavy metals ions removal and biomedicine (specifically in the hyperthermic treatment of malignant cells, site-specific drug delivery and separation of proteins and cell population). MNPs have special properties such as superparamagnetic, high field irreversibility, high saturation field, extra anisotropy contributions or shifted loops after field cooling, biocompatibility, long durability, low toxicity and cost. In this context, this project intends 1) to develop through a novel synthesis method, a biosensor capable to detect mercury in water by irreversible inhibition of the enzyme Horseradish Peroxidase attached onto the surface of different coated MNPs being able to approximate its detections to those limits stablished by the Environmental Protecting Agency of the United States of America; and 2) to use these high valuable nanoparticles as an immunoprecipitation vehicle through the attachment of a polyclonal antibody onto the surface of functionalized MNPs, selective against a suppressor protein. MNPs of about 10 nm were obtained within one minute via co-precipitation method enhanced by high power ultrasound. Experimental design has been used in order to optimize the preparation process from hours to just one minute. The composition, structure, size and morphology analyses of these MNPs have been carried out through X-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric analysis and scanning electron microscopy showing the correct achievement of the MNPs. Moreover, different coating agents have been tested in order to functionalize MNPs surface with the aim of attaching later biomolecules, such as enzymes and antibodies

Integrated performance analysis of UWB wireless optical transmission in FTTH networks

The optical transmission of full standard ECMA_368 OFDM_UWB signals 400 Mbit/s per single user over 50 km SSMF, and the impact of optical transmission in the radio performance experimentally analyzed in this paper

Ultra-wideband radio signals distribution in FTTH networks

The use of an ultra-wideband (UWB) radio technique is proposed as a viable solution for the distribution of high-definition audio/video content in fiber-to-the-home (FTTH) networks. The approach suitability is demonstrated by the transmission of standards-based UWB signals at 1.25 Gb/s along different FTTH fiber links with 25 km up to 60 km of standard single-mode fiber length in a laboratory experiment. Experimental results suggest that orthogonal frequency-division-multiplexed UWB signals exhibit better transmission performance in FFTH networks than impulse radio UWB signals

Optical distribution of OFDM and impulse-radio UWB in FTTH networks

Proposal, experimental demonstration and performance comparison of impulse-radio UWB and OFDM UWB distribution in FTTH networks for high-definition audio/video broadcasting is presented. OFDM-UWB exhibits better performance compared with its impulse-radio counterpart with better spectral efficiency

Cyber Deception Reactive: TCP Stealth Redirection to On-Demand Honeypots

Cybersecurity is developing rapidly, and new methods of defence against attackers are appearing, such as Cyber Deception (CYDEC). CYDEC consists of deceiving the enemy who performs actions without realising that he/she is being deceived. This article proposes designing, implementing, and evaluating a deception mechanism based on the stealthy redirection of TCP communications to an on-demand honey server with the same characteristics as the victim asset, i.e., it is a clone. Such a mechanism ensures that the defender fools the attacker, thanks to stealth redirection. In this situation, the attacker will focus on attacking the honey server while enabling the recollection of relevant information to generate threat intelligence. The experiments in different scenarios show how the proposed solution can effectively redirect an attacker to a copied asset on demand, thus protecting the real asset. Finally, the results obtained by evaluating the latency times ensure that the redirection is undetectable by humans and very difficult to detect by a machine

Using problem- based learning approach to experience values from a different perspective in the efl classroom

In this research project we are looking a way to teach values in the EFL classroom through of the implementation of some stages of the Problem Based Learning approach. At the same time, this study pretend to show a different perspective to educate human beings not only teaching knowledge but also teaching values that it is important in the integral education of human beings. In order to develop this purpose, we applied a series of activities that involved vocabulary about lack of respect using real situations in which our twenty participants took part of the problem and they tried to find possible solutions

Nonlinear Parametric and Neural Network Modelling for Medical Image Classification

System identification and artificial neural networks (ANN) are families of algorithms used in systems engineering and machine learning respectively that use structure detection and learning strategies to build models of complex systems by taking advantage of input-output type data. These models play an essential role in science and engineering because they fill the gap in those cases where we know the input-output behaviour of a system, but there is not a mathematical model to understand and predict its changes in future or even prevent threats. In this context, the nonlinear approximation of systems is nowadays very popular since it better describes complex instances. On the other hand, digital image processing is an area of systems engineering that is expanding the analysis dimension level in a variety of real-life problems while it is becoming more attractive and affordable over time. Medicine has made the most of it by supporting important human decision-making processes through computer-aided diagnosis (CAD) systems. This thesis presents three different frameworks for breast cancer detection, with approaches ranging from nonlinear system identification, nonlinear system identification coupled with simple neural networks, to multilayer neural networks. In particular, the nonlinear system identification approaches termed the Nonlinear AutoRegressive with eXogenous inputs (NARX) model and the MultiScales Radial Basis Function (MSRBF) neural networks appear for the first time in image processing. Along with the above contributions takes place the presentation of the Multilayer-Fuzzy Extreme Learning Machine (ML-FELM) neural network for faster training and more accurate image classification. A central research aim is to take advantage of nonlinear system identification and multilayer neural networks to enhance the feature extraction process, while the classification in CAD systems is bolstered. In the case of multilayer neural networks, the extraction is carried throughout stacked autoencoders, a bottleneck network architecture that promotes a data transformation between layers. In the case of nonlinear system identification, the goal is to add flexible models capable of capturing distinctive features from digital images that might be shortly recognised by simpler approaches. The purpose of detecting nonlinearities in digital images is complementary to that of linear models since the goal is to extract features in greater depth, in which both linear and nonlinear elements can be captured. This aim is relevant because, accordingly to previous work cited in the first chapter, not all spatial relationships existing in digital images can be explained appropriately with linear dependencies. Experimental results show that the methodologies based on system identification produced reliable images models with customised mathematical structure. The models came to include nonlinearities in different proportions, depending upon the case under examination. The information about nonlinearity and model structure was used as part of the whole image model. It was found that, in some instances, the models from different clinical classes in the breast cancer detection problem presented a particular structure. For example, NARX models of the malignant class showed higher non-linearity percentage and depended more on exogenous inputs compared to other classes. Regarding classification performance, comparisons of the three new CAD systems with existing methods had variable results. As for the NARX model, its performance was superior in three cases but was overcame in two. However, the comparison must be taken with caution since different databases were used. The MSRBF model was better in 5 out of 6 cases and had superior specificity in all instances, overcoming in 3.5% the closest model in this line. The ML-FELM model was the best in 6 out of 6 cases, although it was defeated in accuracy by 0.6% in one case and specificity in 0.22% in another one