Antonio J. Caamaño

End-to-End Average BER in Multihop Wireless Networks over Fading Channels

This paper addresses the problem of finding an analytical expression for the end-to-end Average Bit Error Rate (ABER) in multihop Decode-and-Forward(DAF) routes within the context of wireless networks. We provide an analytical recursive expression for the most generic case of any number of hops and any single-hop ABER for every hop in the route. Then, we solve the recursive relationship in two scenarios to obtain simple expressions for the end-to-end ABER, namely: (a) The simplest case, where all the relay channels have identical statistical behaviour; (b) The most general case, where every relay channel has a different statistical behaviour. Along with the theoretical proofs, we test our results against simulations. We then use the previous results to obtain closed analytical expressions for the end-to-end ABER considering DAF relays over Nakagami-m fading channels and with various modulation schemes. We compare these results with the corresponding expressions for Amplify-and-Forward (AAF) and, after corroborating the theoretical results with simulations, we conclude that DAF strategy is more advantageous than the AAF over Nakagami-m fading channels as both the number of relays and m-index increase.

Nd3+ doped LaF3 nanoparticles as self-monitored photo-thermal agents

In this work, we demonstrate how LaF3 nanoparticles activated with large concentrations (up to 25%) of Nd3+ ions can simultaneously operate as biologically compatible efficient nanoheaters and fluorescent nanothermometers under single beam (808 nm) infrared laser excitation. Nd3+:LaF3 nanoparticles emerge as unique multifunctional agents that could constitute the first step towards the future development of advanced platforms capable of simultaneous deep tissue fluorescence bio-imaging and controlled photo-thermal therapies.

Electrostatic forces between sharp tips and metallic and dielectric samples

A detailed analysis of electrostatic interactions between a dc-biased tip and a metallic or insulating sample is presented. By using a simple method to calculate capacitances and forces, tip shape effects on the force versus tip-sample distance curves are dicussed in detail. For metallic samples the force law, except for a constant background, only depends on the tip radius of curvature. In contrast, for dielectric samples the forces depend on the overall geometry of the tip. Interestingly, we found that the contact (adhesion) force does not depend on the tip size and is bound by a simple expression which only depends on the applied bias and the sample dielectric constant.

Optimal Cross-Layer Resource Allocation in Cellular Networks Using Channel- and Queue-State Information

Algorithms that jointly allocate resources across different layers are envisioned to boost the performance of wireless systems. Recent results have revealed that two of the most important parameters that critically affect the resulting cross-layer designs are channel- and queue-state information (QSI). Motivated by these results, this paper relies on stochastic convex optimization to develop optimal algorithms that use instantaneous fading and queue length information to allocate resources at the transport (flow-control), link, and physical layers. Focus is placed on a cellular system, where an access point exchanges information with different users over flat-fading orthogonal channels. Both uplink and downlink setups are considered. The allocation strategies are obtained as the solution of a constrained utility maximization problem that involves average performance metrics. It turns out that the optimal allocation at a given instant depends on the instantaneous channel-state information (CSI) and Lagrange multipliers, which are associated with the quality-of-service (QoS) requirements and the operating conditions of the system. The multipliers are estimated online using stochastic approximation tools and are linked with the window-averaged length of the queues. Capitalizing on those links, queue stability and average queuing delay of the developed algorithms are characterized, and a simple mechanism is devised to effect delay priorities among users.

Advanced support vector machines for 802.11 indoor location

Due to the proliferation of ubiquitous computing services, locating a device in indoor scenarios has received special attention during recent years. A variety of algorithms are based on Wi-Fi measurements of the received signal strength and estimate the relation between this one and position using previous measurements at known locations. This problem naturally fits in well with learning algorithms such as neural networks, or support vector machines (SVM). However, existing machine learning techniques do not significantly outperform other simpler techniques, such as k-nn. This is mainly due to the fact that these solutions do not include significant a priori information. In this paper, we propose a technique to enhance these algorithms by including certain a priori information within the learning machine, using the spectral information of the training set, and a complex output to take advantage of the cross information in the two dimensions of the location. Specifically, we modify a SVM algorithm to obtain three advanced methods incorporating this information: one using an autocorrelation kernel, another using a complex output, and a third one combining both. These algorithms are compared to the k-nn and an SVM with Gaussian kernel, showing that including the a priori information improves the location performance.

Morphological and Thermodynamic Comparison of the Lesions Created by 4 Open‐Irrigated Catheters in 2 Experimental Models

New generation open‐irrigated catheters aim to improve irrigation efficiency. This may change lesion patterns, challenging operators. Indeed, safety issues have recently arisen. We aimed to experimentally assess 4 open‐irrigated catheters, comparing lesion size, safety, and heat transfer.

Fundamental Frequency and Regularity of Cardiac Electrograms With Fourier Organization Analysis

Dominant frequency analysis (DFA) and organization analysis (OA) of cardiac electrograms (EGMs) aims to establish clinical targets for cardiac arrhythmia ablation. However, these previous spectral descriptions of the EGM have often discarded relevant information in the spectrum, such as the harmonic structure or the spectral envelope. We propose a fully automated algorithm for estimating the spectral features in EGM recordings This approach, called Fourier OA (FOA), accounts jointly for the organization and periodicity in the EGM, in terms of the fundamental frequency instead of dominant frequency. In order to compare the performance of FOA and DFA-OA approaches, we analyzed simulated EGM, obtained in a computer model, as well as two databases of implantable defibrillator-stored EGM. FOA parameters improved the organization measurements with respect to OA, and averaged cycle length and regularity indexes were more accurate when related to the fundamental (instead of dominant) frequency, as estimated by the algorithm (p <; 0.05 comparing f0 estimated by DFA and by FOA). FOA yields a more detailed and robust spectral description of EGM compared to DFA and OA parameters.

Gold nanorods for optimized photothermal therapy: the influence of irradiating in the first and second biological windows

The light-to-heat conversion efficiency of gold nanorods (GNRs) with surface plasmon resonances in the first (700–950 nm) and second (1000–1350 nm) biological windows has been studied by Quantum Dot based Fluorescence Nanothermometry. It has been found that red-shifting the GNR longitudinal surface plasmon resonance wavelength (λSPR) from the first to the second biological window is accompanied by a remarkable (close to 40%) reduction in their heating efficiency. Based on numerical simulations, we have concluded that this lower heating efficiency is caused by a reduction in the absorption efficiency (ratio between absorption and extinction cross sections). Thermal stability and ex vivo experiments have corroborated that GNRs with λSPR at around 800 nm seem to be especially suitable for efficient photothermal therapies with minimum collateral effects.

Scalable Data-Coupled Clustering for Large Scale WSN

Self-organizing algorithms (SOAs) for wireless sensor networks (WSNs) usually seek to increase the lifetime, to minimize unnecessary transmissions or to maximize the transport capacity. The goal left out in the design of this type of algorithms is the capability of the WSN to ensure an accurate reconstruction of the sensed field while maintaining the self-organization. In this work, we formulate a general framework where the data from the resulting clusters ensures the well-posedness of the signal processing problem in the cluster. We develop the second-order data-coupled clustering (SODCC) algorithm and the distributed compressive-projections principal component analysis (D-CPPCA) algorithm, that use second-order statistics. The condition to form a cluster is that D-CPCCA does not fail to resolve the Principal Components in any given cluster. We show that SODCC is scalable and has similar or better message complexity than other well-known SOAs. We validate these results with extensive computer simulations using an actual LS-WSN. We also show that the performance of SODCC is, comparative to other state-of-the-art SOAs, better at any compression rate and needs no prior adjustment of any parameter. Finally, we show that SODCC compares well to other energy efficient clustering algorithms in terms of energy consumption while excelling in data reconstruction Average SNR.

Spectrally adapted Mercer kernels for support vector nonuniform interpolation

Interpolation of nonuniformly sampled signals in the presence of noise is a widely analyzed problem in signal processing applications. Interpolators based on Support Vector Machines (SVM) with Gaussian and sinc Mercer kernels have been previously proposed, obtaining good performance in terms of regularization and sparseness. In this paper, inspired in the classical spectral interpretation of the Wiener filter, we explore the impact of adapting the spectrum of the SVM kernel to that of the observed signal. We provide a theoretical foundation for this approach based on a continuous-time equivalent system for interpolation. We study several kernels with different degrees of spectral adaptation to band-pass signals, namely, modulated kernels and autocorrelation kernels. The proposed algorithms are evaluated with extensive simulations with synthetic signals and an application example with real data. Our approach is compared with SVM with Gaussian and sinc kernels and with other well known interpolators. The SVM with autocorrelation kernel provides the highest performance in terms of signal to error ratio in several scenarios. We conclude that the estimated (or actual if known) autocorrelation of the observed sequence can be straightforwardly used as a spectrally adapted kernel, outperforming the classic SVM with low pass kernels for nonuniform interpolation.