Optical bistability in a fiber ring cavity with synchronous pulsed pump: anomalous and normal dispersion

Passive fiber cavities are basic nonlinear optical systems that are described by simple models - however they have a rich spectrum of complex behaviors (optical bistability, period-doubling bifurcations, chaos, modulational instability). From a practical point of view their study would have direct implications in the understanding of more complex cavity-based optical devices such as fiber lasers, Fabry-Perot lasers or APM lasers. Moreover, passive fiber cavities have potential applications in telecommunications as ultra-short pulse generators and as optical memories using their bistable behavior. This work is a contribution to the study of this bistable behavior with pulsed input, so that the device studied could be used for pulsed optical memory storage.Peer ReviewedPostprint (published version

Performance analysis of feedback-free collision resolution NDMA protocol

To support communications of a large number of deployed devices while guaranteeing limited signaling load, low energy consumption, and high reliability, future cellular systems require efficient random access protocols. However, how to address the collision resolution at the receiver is still the main bottleneck of these protocols. The network-assisted diversity multiple access (NDMA) protocol solves the issue and attains the highest potential throughput at the cost of keeping devices active to acquire feedback and repeating transmissions until successful decoding. In contrast, another potential approach is the feedback-free NDMA (FF-NDMA) protocol, in which devices do repeat packets in a pre-defined number of consecutive time slots without waiting for feedback associated with repetitions. Here, we investigate the FF-NDMA protocol from a cellular network perspective in order to elucidate under what circumstances this scheme is more energy efficient than NDMA. We characterize analytically the FF-NDMA protocol along with the multipacket reception model and a finite Markov chain. Analytic expressions for throughput, delay, capture probability, energy, and energy efficiency are derived. Then, clues for system design are established according to the different trade-offs studied. Simulation results show that FF-NDMA is more energy efficient than classical NDMA and HARQ-NDMA at low signal-to-noise ratio (SNR) and at medium SNR when the load increases.Peer ReviewedPostprint (published version

An evolutionary voting for k-nearest neighbours

This work presents an evolutionary approach to modify the voting system of the k-nearest neighbours (kNN) rule we called EvoNN. Our approach results in a real-valued vector which provides the optimal relative con-tribution of the k-nearest neighbours. We compare two possible versions of our algorithm. One of them (EvoNN1) introduces a constraint on the resulted real-valued vector where the greater value is assigned to the nearest neighbour. The second version (EvoNN2) does not include any particular constraint on the order of the weights. We compare both versions with classical kNN and 4 other weighted variants of the kNN on 48 datasets of the UCI repository. Results show that EvoNN1 outperforms EvoNN2 and statistically obtains better results than the rest of the compared methods

On the evolutionary optimization of k-NN by label-dependent feature weighting

Different approaches of feature weighting and k-value selection to improve the nearest neighbour technique can be found in the literature. In this work, we show an evolutionary approach called k-Label Dependent Evolutionary Distance Weighting (kLDEDW) which calculates a set of local weights depending on each class besides an optimal k value. Thus, we attempt to carry out two improvements simultaneously: we locally transform the feature space to improve the accuracy of the k-nearest-neighbour rule whilst we search for the best value for k from the training data. Rigorous statistical tests demonstrate that our approach improves the general k-nearest-neighbour rule and several approaches based on local weighting

An evolutionary-weighted majority voting and support vector machines applied to contextual classification of LiDAR and imagery data fusion

Data classification is a critical step to convert remotely sensed data into thematic information. Environmental researchers have recently maximized the synergy between passive sensors and LiDAR (Light Detection and Ranging) for land cover classification by means of machine learning. Although object-based paradigm is frequently used to classify high resolution imagery, it often requires a high level of expertise and time effort. Contextual classification may lead to similar results with a decrease in time and costs for non-expert users. This work shows a novel contextual classifier based on a Support Vector Machine (SVM) and an Evolutionary Majority Voting (SVM–EMV) to develop thematic maps from LiDAR and imagery data. Subsequently, the performance of SVM–EMV is compared to that achieved by a pixel-based SVM as well as to a contextual classified based on SVM and MRF. The classifiers were tested over three different areas of Spain with well differentiated environmental characteristics. Results show that SVM-EMV statistically outperforms the rest (SVM, SVM–MRF) for the three datasets obtaining a 77%, 91% and 92% of global accuracy for Trabada, Huelva and Alto Tajo, respectively.Xunta de Galicia CSO2010-15807Ministerio de Ciencia y Tecnología TIN2011-28956-C02Junta de Andalucia P11-TIC-752

On the evolutionary weighting of neighbours and features in the k-nearest neighbour rule

This paper presents an evolutionary method for modifying the behaviour of the k-Nearest-Neighbour clas sifier (kNN) called Simultaneous Weighting of Attributes and Neighbours (SWAN). Unlike other weighting methods, SWAN presents the ability of adjusting the contribution of the neighbours and the significance of the features of the data. The optimization process focuses on the search of two real-valued vectors. One of them represents the votes of neighbours, and the other one represents the weight of each feature. The synergy between the two sets of weights found in the optimization process helps to improve significantly, the classification accuracy. The results on 35 datasets from the UCI repository suggest that SWAN statistically outperforms the other weighted kNN method

Passive Fiber Ring Flip-Flop Memory Based on Polarization Dynamics

Passive fiber ring resonators synchronously pumped by cw trains of ultrashort pulses are shown to exhibit polarization symmetry breaking. Application of this feature to all-optical storage is considered. The proposed storage device operates in the flip-flop mode which makes possible all-optical writing and erasing operations on individual bits. Numerical simulations suggest that a storage capacity of the order of thousand bits could be attainable at bit rates of hundreds of Gb/s with a maximum information processing rate of several Gb/s.Peer Reviewe

A Preliminary Study of the Suitability of Deep Learning to Improve LiDAR-Derived Biomass Estimation

Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information about forest structure. Bio physical models have taken advantage of the use of LiDAR-derived infor mation to improve their accuracy. Multiple Linear Regression (MLR) is the most common method in the literature regarding biomass estima tion to define the relation between the set of field measurements and the statistics extracted from a LiDAR flight. Unfortunately, there exist open issues regarding the generalization of models from one area to another due to the lack of knowledge about noise distribution, relation ship between statistical features and risk of overfitting. Autoencoders (a type of deep neural network) has been applied to improve the results of machine learning techniques in recent times by undoing possible data corruption process and improving feature selection. This paper presents a preliminary comparison between the use of MLR with and without preprocessing by autoencoders on real LiDAR data from two areas in the province of Lugo (Galizia, Spain). The results show that autoen coders statistically increased the quality of MLR estimations by around 15–30%

Holographic collisions in confining theories

We study the gravitational dual of a high-energy collision in a confining gauge theory. We consider a linearized approach in which two point particles traveling in an AdS-soliton background suddenly collide to form an object at rest (presumably a black hole for large enough center-of-mass energies). The resulting radiation exhibits the features expected in a theory with a mass gap: late-time power law tails of the form t −3/2, the failure of Huygens" principle and distortion of the wave pattern as it propagates. The energy spectrum is exponentially suppressed for frequencies smaller than the gauge theory mass gap. Consequently, we observe no memory effect in the gravitational waveforms. At larger frequencies the spectrum has an upward-stairway structure, which corresponds to the excitation of the tower of massive states in the confining gauge theory. We discuss the importance of phenomenological cutoffs to regularize the divergent spectrum, and the aspects of the full non-linear collision that are expected to be captured by our approach

Intrathecal cell therapy with autologous stromal cells increases cerebral glucose metabolism and can offer a new approach to the treatment of Alzheimer's type dementia

After recent observations that intrathecal administration of autologous bone marrow mesenchymal stromal cells (MSCs) increases cerebral metabolism in patients with severe traumatic brain injury (TBI), we examined this type of cell therapy in Alzheimer's type dementia. Three patients with clinical diagnosis of Alzheimer's disease received every 3 months 100million autologous MSCs by intrathecal route, until a total dose of 300million. During cell therapy the patients showed arrest in neurological deterioration and two of them manifested clear improvement of previous symptoms. A global increase in cerebral glucose metabolism, measured using 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET), was observed after every administration of cell therapy. Our present findings suggest that intrathecal administrations of autologous MSCs can be a new strategy for the treatment of Alzheimer's dementiaWe thank the institutions supporting the development of our cell therapy program, in particular Mapfre and Rafael del Pino Foundation