On the Design of a Novel Joint Network-Channel Coding Scheme for the Multiple Access Relay Channel

This paper proposes a novel joint non-binary network-channel code for the Time-Division Decode-and-Forward Multiple Access Relay Channel (TD-DF-MARC), where the relay linearly combines -- over a non-binary finite field -- the coded sequences from the source nodes. A method based on an EXIT chart analysis is derived for selecting the best coefficients of the linear combination. Moreover, it is shown that for different setups of the system, different coefficients should be chosen in order to improve the performance. This conclusion contrasts with previous works where a random selection was considered. Monte Carlo simulations show that the proposed scheme outperforms, in terms of its gap to the outage probabilities, the previously published joint network-channel coding approaches. Besides, this gain is achieved by using very short-length codewords, which makes the scheme particularly attractive for low-latency applications.Comment: 28 pages, 9 figures; Submitted to IEEE Journal on Selected Areas in Communications - Special Issue on Theories and Methods for Advanced Wireless Relays, 201

Traces in complex hyperbolic geometry.

We discuss the relationship between the geometry of complex hyperbolic manifolds and orbifolds and the traces of elements of the corresponding subgroup of SU(2, 1). We begin by showing how geometrical information about individual isometries is encoded by their trace. We then consider traces for groups Γ of isometries in two specific cases. First, we consider the case where Γ is a free group on two generators, which we view as the fundamental group of a three holed sphere. We indicate how to use this analysis to give complex hyperbolic Fenchel-Nielsen coordinates. Secondly, we consider the case where Γ is a triangle group generated by complex reflections in three complex lines. We keep in mind similar results from the more familiar setting of Fuchsian and Kleinian groups and we explain those examples from our point of view

The performance of multiple imputations for different number of imputations

Multiple imputation method is a widely used method in missing data analysis. The method consists of a three-stage process including imputation, analyzing and pooling. The number of imputations to be selected in the imputation step in the first stage is important. Hence, this study aimed to examine the performance of multiple imputation method at different numbers of imputations. Monotone missing data pattern was created in the study by deleting approximately 24% of the observations from the continuous result variable with complete data. At the first stage of the multiple imputation method, monotone regression imputation at different numbers of imputations (m=3, 5, 10 and 50) was performed. In the second stage, parameter estimations and their standard errors were obtained by applying general linear model to each of the complete data sets obtained. In the final stage, the obtained results were pooled and the effect of the numbers of imputations on parameter estimations and their standard errors were evaluated on the basis of these results. In conclusion, efficiency of parameter estimations at the number of imputation m=50 was determined as about 99%. Hence, at the determined missing observation rate, increase was determined in efficiency and performance of the multiple imputation method as the number of imputations increased

A novel Fireworks Algorithm with wind inertia dynamics and its application to traffic forecasting

Fireworks Algorithm (FWA) is a recently contributed heuristic optimization method that has shown a promising performance in applications stemming from different domains. Improvements to the original algorithm have been designed and tested in the related literature. Nonetheless, in most of such previous works FWA has been tested with standard test functions, hence its performance when applied to real application cases has been scarcely assessed. In this manuscript a mechanism for accelerating the convergence of this meta-heuristic is proposed based on observed wind inertia dynamics (WID) among fireworks in practice. The resulting enhanced algorithm will be described algorithmically and evaluated in terms of convergence speed by means of test functions. As an additional novel contribution of this work FWA and FWA-WID are used in a practical application where such heuristics are used as wrappers for optimizing the parameters of a road traffic short-term predictive model. The exhaustive performance analysis of the FWA and FWA-ID in this practical setup has revealed that the relatively high computational complexity of this solver with respect to other heuristics makes it critical to speed up their convergence (specially in cases with a costly fitness evaluation as the one tackled in this work), observation that buttresses the utility of the proposed modifications to the naive FWA solver

Flow networks: A characterization of geophysical fluid transport

We represent transport between different regions of a fluid domain by flow networks, constructed from the discrete representation of the Perron-Frobenius or transfer operator associated to the fluid advection dynamics. The procedure is useful to analyze fluid dynamics in geophysical contexts, as illustrated by the construction of a flow network associated to the surface circulation in the Mediterranean sea. We use network-theory tools to analyze the flow network and gain insights into transport processes. In particular we quantitatively relate dispersion and mixing characteristics, classically quantified by Lyapunov exponents, to the degree of the network nodes. A family of network entropies is defined from the network adjacency matrix, and related to the statistics of stretching in the fluid, in particular to the Lyapunov exponent field. Finally we use a network community detection algorithm, Infomap, to partition the Mediterranean network into coherent regions, i.e. areas internally well mixed, but with little fluid interchange between them.Comment: 16 pages, 15 figures. v2: published versio

Wireless Network Optimization for Massive V2I Data Collection using Multiobjective Harmony Search Heuristics

This paper proposes to improve the efficiency of the deploy- ment of wireless network infrastructure for massive data collection from vehicles over regional areas. The increase in the devices that are carried by vehicles makes it especially interesting being able to gain access to that data. From a decisional point of view, this collection strategy re- quires defining a wireless Vehicular-to-Infrastructure (V2I) network that jointly optimizes the level of service and overall CAPEX/OPEX costs of its deployment. Unfortunately, it can be intuitively noted that both optimization objectives are connecting with one another: adding more equipment will certainly increase the level of service (i.e. coverage) of the network, but costs of the deployment will rise accordingly. A deci- sion making tool blending together both objectives and inferring there- from a set of Pareto-optimal deployments would be of utmost utility for stakeholders in their process of provisioning budgetary resources for the deployment. This work will explore the extent to which a multi-objective Harmony Search algorithm can be used to compute the aforementioned Pareto-optimal set of deployment by operating on two different optimiza- tion variables: the geographical position on which wireless receivers are to be deployed and their type, which determines not only their coverage range but also their bandwidth and cost. In particular we will utilize a non-dominated sorting strategy criterion to select the harmonies (solu- tion vectors) evolved by Harmony Search heuristics

Joint Feature Selection and Parameter Tuning for Short-term Traffic Flow Forecasting based on Heuristically Optimized Multi-layer Neural Networks

Short-term traffic flow forecasting is a vibrant research topic that has been growing in interest since the late 70’s. In the last decade this vibrant field has shifted its focus towards machine learning methods. These techniques often require fine-grained parameter tuning to obtain satisfactory performance scores, a process that usually relies on man- ual trial-and-error adjustment. This paper explores the use of Harmony Search optimization for tuning the parameters of neural network jointly with the selection of the input features from the dataset at hand. Re- sults are discussed and compared to other tuning methods, from which it is concluded that neural predictors optimized via the proposed heuris- tic wrapper outperform those tuned by means of na ̈ıve parametrized algorithms, thus allowing for longer-term predictions. These promising results unfold potential applications of this technique in multi-location neighbor-aware traffic prediction

Building the Evryscope: Hardware Design and Performance

The Evryscope is a telescope array designed to open a new parameter space in optical astronomy, detecting short timescale events across extremely large sky areas simultaneously. The system consists of a 780 MPix 22-camera array with an 8150 sq. deg. field of view, 13" per pixel sampling, and the ability to detect objects down to Mg=16 in each 2 minute dark-sky exposure. The Evryscope, covering 18,400 sq.deg. with hours of high-cadence exposure time each night, is designed to find the rare events that require all-sky monitoring, including transiting exoplanets around exotic stars like white dwarfs and hot subdwarfs, stellar activity of all types within our galaxy, nearby supernovae, and other transient events such as gamma ray bursts and gravitational-wave electromagnetic counterparts. The system averages 5000 images per night with ~300,000 sources per image, and to date has taken over 3.0M images, totaling 250TB of raw data. The resulting light curve database has light curves for 9.3M targets, averaging 32,600 epochs per target through 2018. This paper summarizes the hardware and performance of the Evryscope, including the lessons learned during telescope design, electronics design, a procedure for the precision polar alignment of mounts for Evryscope-like systems, robotic control and operations, and safety and performance-optimization systems. We measure the on-sky performance of the Evryscope, discuss its data-analysis pipelines, and present some example variable star and eclipsing binary discoveries from the telescope. We also discuss new discoveries of very rare objects including 2 hot subdwarf eclipsing binaries with late M-dwarf secondaries (HW Vir systems), 2 white dwarf / hot subdwarf short-period binaries, and 4 hot subdwarf reflection binaries. We conclude with the status of our transit surveys, M-dwarf flare survey, and transient detection.Comment: 24 pages, 24 figures, accepted PAS