@article{Martino2015bbb,

title = {An Adaptive Population Importance Sampler: Learning From Uncertainty},

author = {Luca Martino and Victor Elvira and David Luengo and Jukka Corander},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7117437},

doi = {10.1109/TSP.2015.2440215},

issn = {1053-587X},

year  = {2015},

date = {2015-08-01},

journal = {IEEE Transactions on Signal Processing},

volume = {63},

number = {16},

pages = {4422--4437},

publisher = {IEEE},

abstract = {Monte Carlo (MC) methods are well-known computational techniques, widely used in different fields such as signal processing, communications and machine learning. An important class of MC methods is composed of importance sampling (IS) and its adaptive extensions, such as population Monte Carlo (PMC) and adaptive multiple IS (AMIS). In this paper, we introduce a novel adaptive and iterated importance sampler using a population of proposal densities. The proposed algorithm, named adaptive population importance sampling (APIS), provides a global estimation of the variables of interest iteratively, making use of all the samples previously generated. APIS combines a sophisticated scheme to build the IS estimators (based on the deterministic mixture approach) with a simple temporal adaptation (based on epochs). In this way, APIS is able to keep all the advantages of both AMIS and PMC, while minimizing their drawbacks. Furthermore, APIS is easily parallelizable. The cloud of proposals is adapted in such a way that local features of the target density can be better taken into account compared to single global adaptation procedures. The result is a fast, simple, robust, and high-performance algorithm applicable to a wide range of problems. Numerical results show the advantages of the proposed sampling scheme in four synthetic examples and a localization problem in a wireless sensor network.},

keywords = {Adaptive importance sampling, adaptive multiple IS, adaptive population importance sampler, AMIS, APIS, Estimation, Importance sampling, IS estimators, iterative estimation, iterative methods, Journal, MC methods, Monte Carlo (MC) methods, Monte Carlo methods, population Monte Carlo, Proposals, Signal processing algorithms, simple temporal adaptation, Sociology, Standards, Wireless sensor network, Wireless Sensor Networks},

pubstate = {published},

tppubtype = {article}

}

@article{Landa-Torres2012,

title = {Evaluating the Internationalization Success of Companies Through a Hybrid Grouping Harmony Search\textemdashExtreme Learning Machine Approach},

author = {Itziar Landa-Torres and Emilio G Ortiz-Garcia and Sancho Salcedo-Sanz and Mar\'{i}a J Segovia-Vargas and Sergio Gil-Lopez and Marta Miranda and Jose M Leiva-Murillo and Javier Del Ser},

url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6200298},

issn = {1932-4553},

year  = {2012},

date = {2012-01-01},

journal = {IEEE Journal of Selected Topics in Signal Processing},

volume = {6},

number = {4},

pages = {388--398},

abstract = {The internationalization of a company is widely understood as the corporative strategy for growing through external markets. It usually embodies a hard process, which affects diverse activities of the value chain and impacts on the organizational structure of the company. There is not a general model for a successful international company, so the success of an internationalization procedure must be estimated based on different variables addressing the status, strategy and market characteristics of the company at hand. This paper presents a novel hybrid soft-computing approach for evaluating the internationalization success of a company based on existing past data. Specifically, we propose a hybrid algorithm composed by a grouping-based harmony search (HS) approach and an extreme learning machine (ELM) ensemble. The proposed hybrid scheme further incorporates a feature selection method, which is obtained by means of a given group in the HS encoding format, whereas the ELM ensemble renders the final accuracy metric of the model. Practical results for the proposed hybrid technique are obtained in a real application based on the exporting success of Spanish manufacturing companies, which are shown to be satisfactory in comparison with alternative state-of-the-art techniques.},

keywords = {Companies, Company internationalization, corporative strategy, diverse activity, Economics, Electronic mail, ensembles, exporting, exporting performance, external markets, extreme learning machine ensemble, extreme learning machines, feature selection method, grouping-based harmony search, hard process, harmony search (HS), hybrid algorithm, hybrid algorithms, hybrid grouping harmony search-extreme learning ma, hybrid soft computing, international company, international trade, internationalization procedure, internationalization success, learning (artificial intelligence), Machine learning, organizational structure, Signal processing algorithms, Spanish manufacturing company, Training, value chain},

pubstate = {published},

tppubtype = {article}

}

@article{Vazquez2011,

title = {A Per-Survivor Processing Receiver for MIMO Transmission Systems With One Unknown Channel Order Per Output},

author = {Manuel A Vazquez and Joaquin Miguez},

url = {http://www.tsc.uc3m.es/~jmiguez/papers/P31_2011_A Per-Survivor Processing Receiver for MIMO Transmission Systems With One Unknown Channel Order Per Output.pdf http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6032763},

issn = {0018-9545},

year  = {2011},

date = {2011-01-01},

journal = {IEEE Transactions on Vehicular Technology},

volume = {60},

number = {9},

pages = {4415--4426},

abstract = {The order of a communications channel is the length of its impulse response. Recently, several works have tackled the problem of estimating the order of a frequency-selective multiple-input-multiple-output (MIMO) channel. However, all of them consider a single order, despite the fact that a MIMO channel comprises several subchannels (specifically, as many as the number of inputs times the number of outputs), each one possibly with its own order. In this paper, we introduce an algorithm for maximum-likelihood sequence detection (MLSD) in frequency- and time-selective MIMO channels that incorporates full estimation of the MIMO channel impulse response (CIR) coefficients, including one channel order per output. Simulation results following the analytical derivation of the algorithm suggest that the proposed receiver can achieve significant improvements in performance when transmitting through a MIMO channel that effectively comprises subchannels of different lengths.},

keywords = {Channel estimation, communication channel, Complexity theory, dynamic programming, frequency-selective MIMO channel, frequency-selective multiple-input multiple-output, maximum likelihood detection, maximum likelihood sequence detection, maximum likelihood sequence estimation, MIMO, MIMO channel impulse response coefficient, MIMO communication, MIMO transmission system, multipath channels, mutiple-input\textendashmultiple-output (MIMO), per-survivor processing receiver, Receiving antennas, Signal processing algorithms, time-selective MIMO channel, Transmitting antennas, Viterbi algorithm},

pubstate = {published},

tppubtype = {article}

}