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The objective of this paper is to compare four dimension reduction techniques used for extracting the underlying systematic risk factors driving returns on equities of the Mexican Market. The methodology used compares the results of estimation produced by Principal Component Analysis (PCA), Factor Analysis (FA), Independent Component Analysis (ICA), and Neural Networks Principal Component Analysis (NNPCA) under three different perspectives. The results showed that in general: PCA, FA, and ICA produced similar systematic risk factors and betas; NNPCA and ICA produced the greatest number of fully accepted models in the econometric contrast; and, the interpretation of systematic risk factors across the four techniques was not constant. Additional research testing alternative extraction techniques, econometric contrast, and interpretation methodologies are recommended, considering the limitations derived from the scope of this work. The originality and main contribution of this paper lie in the comparison of these four techniques in both the financial and Mexican contexts. The main conclusion is that depending on the purpose of the analysis, one technique will be more suitable than another.

This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis, and Neural Networks Principal Component Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage Pricing Theory. This research is carried out according to two different perspectives. First, an evaluation from a theoretical and matrix scope is done, making parallelism among their particular mixing and demixing processes, as well as the at-tributes of the factors extracted by each method. Secondly, an empirical study to measure the level of accuracy in the reconstruction of the original variables is accomplished. In general, the results of this research point to Neural Networks Principal Component Analysis as the best technique from both theoretical and empirical standpoints. JEL Classification: G12, G15, C45. Resumen Este artículo compara las técnicas de reducción de dimensionalidad o de extracción de características: Análisis de Componentes Principales, Análisis Factorial, Análisis de Componentes Independientes y Análisis de Componentes Principales basado en Redes Neuronales, las cuales son usadas para extraer los factores de riesgo sistemático subyacentes que generan los rendimientos de las acciones de la Bolsa Mexicana de Valores, bajo un enfoque estadístico de la Teoría de Valoración por Arbitraje. Llevamos a cabo nuestra investigación de acuerdo a dos diferentes perspectivas. Primero, las evaluamos desde una perspectiva teórica y matricial, haciendo un paralelismo entre los particulares procesos de mezcla y separación de cada método. En segundo lugar, efectuamos un estudio empírico con el fin de medir el nivel de precisión en la reconstrucción de las variables originales.

Presently, the high and increasing number of worldwide permanent Global Navigation Satellite System (GNSS) receivers providing the raw measurements in real-time (RT) and openly from a large fleet of GNSS transmitters, has opened the door to an accurate real-time determination of the distribution of the ionospheric electron content, either at global scale. The motivations are diverse for such a scientific and technological challenging target, for instance: (1) to facilitate the fast carrier phase ambiguity resolution of roving users and corresponding fast decimeter-error level navigation at large distances (hundreds of kilometers) from supporting permanent GNSS receivers (see Hernández-Pajares et al. 2000, Juan et al. 2012, Olivares-Pulido et al. 2021); and (2) the possibility of generating in real-time realistic global maps of ionospheric storm state and Vertical Total Electron Content (VTEC) gradients like it can be done with a latency of 1 day (Liu et al. 2021a, 2022 respectively) based on UQRG GIMs, one of the best behaving ones in the International GNSS Service (IGS), see Roma-Dollase et al. (2018). This might be feasible thanks the recent advances in individual and collaborative real-time global VTEC mapping (see Yang et al. 2021 and Liu et al. 2021b, respectively) and in global VTEC forecasting (Monte-Moreno et al. 2022). These potential new RT products might be useful as ionospheric storm semaphores and quantifiers for example for contributing to the integrity of single-frequency GNSS based navigation in civil aviation. One possible evolution in this field might include the consistent 4D combination of the already existing RT GNSS measurements with the potential RT additional geodetic measurements sensitive to the ionospheric delay, like Doppler measurements (DORIS), and like dual-frequency vertical delay measurements over the oceans provided by altimeters, such as JASON-3 among others (Hernández-Pajares et al. 2021). This would significantly extend the coverage and accuracy at global scale, improving as well the vertical resolution. References Hernández‐Pajares, M., Juan, J. M., Sanz, J., & Colombo, O. L. (2000). Application of ionospheric tomography to real‐time GPS carrier‐phase ambiguities resolution, at scales of 400–1000 km and with high geomagnetic activity. Geophysical Research Letters, 27(13), 2009-2012. Juan, J. M., Hernández-Pajares, M., Sanz, J., Ramos-Bosch, P., Aragon-Angel, A., Orus, R., ... & Tossaint, M. (2012). Enhanced precise point positioning for GNSS users. IEEE transactions on geoscience and remote sensing, 50(10), 4213-4222. Liu, Q., Hernández‐Pajares, M., Lyu, H., Nishioka, M., Yang, H., Monte‐Moreno, E., ... & Orús‐Pérez, R. (2021a). Ionospheric storm scale index based on high time resolution UPC‐IonSAT global ionospheric maps (IsUG). Space Weather, 19(11), e2021SW002853. Liu, Q., Hernández-Pajares, M., Yang, H., Monte-Moreno, E., Roma-Dollase, D., García-Rigo, A., ... & Ghoddousi-Fard, R. (2021b). The cooperative IGS RT-GIMs: A reliable estimation of the global ionospheric electron content distribution in real time. Earth System Science Data, 13(9), 4567-4582. Liu, Q., Hernández‐Pajares, M., Yang, H., Monte‐Moreno, E., García‐Rigo, A., Lyu, H., ... & Orús‐Pérez, R. (2022). A New Way of Estimating the Spatial and Temporal Components of the Vertical Total Electron Content Gradient Based on UPC‐IonSAT Global Ionosphere Maps. Space Weather, 20(2), e2021SW002926. Monte-Moreno, E., Yang, H., & Hernández-Pajares, M. (2022). Forecast of the Global TEC by Nearest neighbour technique. Remote Sensing, 14(6), 1361. Olivares-Pulido, G., Hernández-Pajares, M., Lyu, H., Gu, S., García-Rigo, A., Graffigna, V., ... & Orús-Pérez, R. (2021). Ionospheric tomographic common clock model of undifferenced uncombined GNSS measurements. Journal of Geodesy, 95(11), 1-13. Roma-Dollase, D., Hernández-Pajares, M., Krankowski, A., Kotulak, K., Ghoddousi-Fard, R., Yuan, Y., ... & Gómez-Cama, J. M. (2018). Consistency of seven different GNSS global ionospheric mapping techniques during one solar cycle. Journal of Geodesy, 92(6), 691-706. Yang, H., Monte-Moreno, E., Hernández-Pajares, M., & Roma-Dollase, D. (2021). Real-time interpolation of global ionospheric maps by means of sparse representation. Journal of Geodesy, 95(6), 1-20.

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this article, we show the capability of a global navigation satellite system (GNSS) precise orbit determination (POD) low Earth orbit (LEO) data to detect anomalous ionospheric disturbances in the spectral range of the signals associated with earthquakes and tsunamis, applied to two of these events in Papua New Guinea (PNG) and the Solomon Islands during 2016. This is achieved thanks to the new PIES approach (POD-GNSS LEO Detrended Ionospheric Electron Content Significant Deviations). The significance of such ionospheric signals above the swarm LEOs is confirmed with different types of independent data: in situ electron density measurements provided by the Langmuir Probe (LP) onboard swarm LEOs, DORIS, and ground-based GNSS colocated measurements, as it is described in this article. In this way, we conclude the possible detection of the tsunami-related ionospheric gravity wave in PNG 2016 event, consistent with the most-recent theory, which shows that a tsunami (which is localized in space and time) excites a spectrum of gravity waves, some of which have faster horizontal phase speeds than the tsunami. We believe that this work shows as well the feasibility of a future potential monitoring system of ionospheric disturbances, to be made possible by hundreds of CubeSats with POD GNSS receivers among other appropriate sensors, and supported for real-time or near real-time confirmation and characterization by thousands of worldwide existing ground GNSS receivers. This work has been partially supported by the COSTO ESA-funded project (ESA ITT AO/1-9514/18/NL/IA). Also it was partially supported by the 2017 SGR-0851 grant of the Generalitat de Catalunya and by the EU project 101007599 - PITHIA-NRF. The data for this paper are available and they can be requested from H. Yang (h.yang@upc.edu) and M. Hernandez-Pajares (manuel.hernandez@upc.edu).

Ionospheric irregularities impair GNSS signals and, in turn, affect the performance of GNSS positioning. Such effects are especially evident for the high latitudes, which are currently gaining the attention of research and industry branches. These activities should be supported with reliable positioning and navigation services. Such needs motivate us to assess, for the first time, the impact of ionospheric irregularities on GNSS positioning performance in Greenland. We fill the gap and evaluate the performance of positioning methods that were not investigated comprehensively until now but meet the demands of a wide range of users. In this regard, we address the needs of mass-market users that most frequently employ single-frequency receivers and expect a meter to submeter-level accuracy in an absolute mode; and the users who require the highest precision solution based on geodetic-grade dual-frequency receivers. We take advantage of the datasets collected at the GNET permanent network in Greenland during three ionospheric storms, namely the St. Patrick storm of March 17, 2015, June 22, 2015, and August 25–­26, 2018. We discover a significant impact of the ionospheric disturbances on the ambiguity resolution performance and the accuracy of the float solution in RTK positioning. Next, assessing the single-frequency ionospheric-free PPP, we demonstrate that the model is generally unaffected by the ionospheric disturbances. Hence, the model is predestined for the application by the users of single-frequency receivers in the areas of frequent ionospheric disturbances. Finally, based on the observation analyses, we revealed that phase signals on the L2 frequency band are more prone to the cycle slips induced by ionospheric irregularities than those transmitted on the first one.

The objective of the work is to compare geomagnetic storm impact on the ionosphere parameters measured from ground-based GNSS permanent stations and Swarm satellites. The analyses compare the changes of vertical total electron content (VTEC) measured along the entire ionosphere cross-section to the variations of electron density (ED) on the orbit, at ~500 km altitude. The objective is how sensitive are the measures of electric field variations available on the Earth, with respect to those obtained from the satellite orbit. The study applies Swarm in-situ ED measured by Langmuir Probes (LP), topside TEC from onboard Swarm GNSS receivers and vertical TEC determined from ground-based GNSS stations available in the area of the Northern polar cap. Ground and satellite data were processed in different ways. Ground-based VTEC is available at number of stations providing heterogeneous but useful horizontal coverage. Therefore ROTI values were calculated from VTEC as gradient values capable to indicate the disturbances. These ROTI values were interpolated spatially to obtain maps. Swarm passes over the polar cap starting from 45° lasts for several minutes each, and repeat in this region approximately every 1.5 h. Such along-track collected small data samples are useless in horizontal correlation analysis. Therefore Swarm data disturbances, in this case, are extracted with the use of Fourier transform-based filtering and are also analyzed in the spectrograms based on short-term Fourier transform (STFT). The case study has used three geomagnetic storms, namely: the St. Patrick storm of March 17, 2015, the storm on June 22, 2015, and the storm on August 25-26, 2018. The results reveal differences in storm impact on VTEC measured by GNSS on the Earth, with respect to the storm influence on topside TEC and in-situ ED disturbances measured onboard the Swarm. The overall summary statistics provide some preliminary conclusions on different times of the reaction to the storm. Additionally, some interesting differences between FT filtering and a very popular moving average are shown, with respect to Swarm data. The research was done in the frame of the FORSWAR project (Forecasting Space Weather in the Arctic Region) funded by ESA.

The determination of the ionospheric perturbation degree is essential to describe the ionosphere state for space weather monitoring. A new method for estimating the spatial and temporal components of the Vertical Total Electron Content (VTEC) gradient is introduced. The new method is based on VTEC estimated at each grid point of Global Ionosphere Map (GIM) by the UPC-IonSAT research group of the Universitat Politècnica de Catalunya. Depending on the requirement, the VTEC spatial gradients can be derived at selected regions or grid points of the GIM on a global scale. According to the comparison with previous studies over the Europe region during quiet ionosphere state and two severe geomagnetic storms, the new method has proven to be reliable and has a great potential for the monitoring of ionospheric perturbation degree on a global scale. In addition, the associated warning of disturbed ionosphere might be available in the context of the on-going development of real-time GIMs., Q. Liu is grateful for the financial support of the China Scholarship Council. This research has been done under the partial support of the ESA and European Commission (EC) funded eMONITOR/MoNEWIC (H2020-ESA-037) project and the EC funded PITHIA-NRF (H2020-INFRAIA-2018-2020 101007599) project.

We present the optical flow algorithm for forecasting the rate of total electron content index (OFROTI). It consists of a method for predicting maps of rapid fluctuations of ionospheric electron content in terms of global navigation satellite system (GNSS) dual-frequency phase measurements of the rate of change of total electron content index (ROTI). The forecast is made in space and time, at horizons up to more than 6 h. These forecast maps will consist of the ROTI spatial distribution in the northern hemisphere above 45° latitude. The prediction method models the ROTI spatial distribution as a pseudoconservative flux, i.e., exploiting the inertia of the flux of ROTI to determine the future position. This idea is implemented as a modification of the optical flow image processing technique. The algorithm has been modified to deal with the nonconservation of the ROTI quantity in time. We show that the method can predict both, the local value of ROTI and also the regions with ROTI above a given level, better than the prediction using the current map as forecast, i.e., predicting by a current map from horizons of 15 min up to 6 h. The method was tested on 11 representative active and calm days during 2015 and 2018 from the multi-GNSS (GPS, GLONASS, Galileo, and Beidou) multifrequency measurements of more than 250 multi-GNSS receivers above 45°N latitude, including the high rate (1 Hz) measurements of Greenland geodetic network (GNET) network among the International GNSS Service network. This work is funded by ESA ITT “Forecasting Space Weather Impacts on Navigation Systems in the Arctic (Green-land Area)” Expro+, Activity No. 1000026374. The GNET GNSS observations from Greenland was kindly provided by The Danish Agency for Data Supply and Efficiency, in the Danish Ministry of Energy, Utilities and Climate, Copenhagen, Denmark

The Real-Time Working Group (RTWG) of the International GNSS Service (IGS) is dedicated to providing high-quality data, high-accuracy products for Global Navigation Satellite System (GNSS) positioning, navigation, timing, and Earth observations. As one part of real-time products, the IGS combined Real-Time Global Ionosphere Map (RT-GIM) has been generated by the real-time weighting of the RT-GIMs from IGS real-time ionosphere centers including the Chinese Academy of Sciences (CAS), Centre National d’Etudes Spatiales (CNES), Universitat Politècnica de Catalunya (UPC), and Wuhan University (WHU). The performance of global Vertical Total Electron Content (VTEC) representation in all of the RT-GIMs has been assessed by VTEC from Jason3-altimeter during one month over oceans and dSTEC-GPS technique with 2-day observations over continental regions. According to the Jason3-VTEC and dSTEC-GPS assessment, the real-time weighting technique is sensitive to the accuracy of RT-GIMs. Compared with the performance of post-processed rapid Global Ionosphere Maps (GIMs) and IGS combined final GIM (igsg) during the testing period, the accuracy of UPC RT-GIM (after the transition of interpolation technique) and IGS combined RT-GIM (IRTG) is equivalent to the rapid GIMs and reaches around 2.7 and 3.0 TECU (TEC Unit, 1016 el/m2) over oceans and continental regions, respectively. The accuracy of CAS RT-GIM and CNES RT-GIM is slightly worse than the rapid GIMs, while WHU RT-GIM requires a further upgrade to obtain similar performance. In addition, the strong response to the recent geomagnetic storms has been found in the Global Electron Content (GEC) of IGS RT-GIMs (especially UPC RT-GIM and IGS combined RT-GIM). The IGS RT-GIMs turn out to be reliable sources of real-time global VTEC information and have great potential for real-time applications including range error correction for transionospheric radio signals (such as GNSS positioning, search and rescue, air traffic, radar altimetry, and radioastronomy), the monitoring of space weather (such as geomagnetic and ionospheric storms, ionospheric disturbance) and detection of natural hazards on a global scale (such as hurricanes/typhoons, ionospheric anomalies associated with earthquakes). All the IGS combined RT-GIMs generated and analyzed during the testing period are available at http://doi.org/10.5281/zenodo.4651445 (Liu et al., 2021b).

The UQRG data are openly accessible (https://cddis.nasa.gov/archive/gnss/products/ionex) from Crustal Dynamics Data Information System (Noll, 2010). The Kp index is available (ftp://ftp.gfz-potsdam.de/pub/home/obs/Kp_ap_Ap_SN_F107) from GeoForschungsZentrum (Matzka et al., 2021) and Dst index is accessible (http://wdc.kugi.kyoto-u.ac.jp/dstdir/) from World Data Center for Geomagnetism, Kyoto (World Data Center for Geomagnetism et al., 2015)., The ionospheric storms have adverse effects on the radio communications, satellite communications and also the Global Navigation Satellite Systems (GNSS) application. A new Ionospheric storm Scale from Universitat Politècnica de Catalunya (UPC) Global Ionosphere Map (GIM), IsUG, is introduced for characterizing the ionospheric state on a global scale. The IsUG is based on the Vertical Total Electron Content (VTEC) derived from the continuously computed UPC Quarter-of-an-hour time resolution Rapid GIM (UQRG), taking as reference the ones during the period 1997 to 2014. It is similar to the I-scale index previously introduced, although it was over Japan and based on raw GNSS data. The dependence of the VTEC on season, local time and geographical location at each grid point of UQRG is removed by normalizing (i.e., by substracting the mean and dividing by the corresponding standard deviation) the percentage deviation of hourly median VTEC. After validating IsUG versus I-scale, the IsUG distribution is presented and analyzed at global scale during a severe geomagnetic storm from 7 to 10 November 2004 as an example of the potentialities of the new index. The results suggest that the IsUG global map has a great potential for the scientific study of ionospheric storms from a global perspective and also for space weather warning considering the accuracy of the recently developed real-time GIMs., The first author is grateful for the financial support of the China Scholarship Council (CSC). This research has been done under the partial support of the European Space Agency and European Commission funded eMONITOR/MoNEWIC (H2020-ESA-037) project and the European Commission funded PITHIA-NRF (H2020-INFRAIA-2018-2020 101007599) project.

In this paper, we propose a method for the generation of real-time global ionospheric map (RT-GIM) of vertical total electron content (VTEC) from GNSS measurements. The need for interpolation arises from the fact that the ionospheric pierce point (IPP) measurements from satellites to stations are not distributed uniformly over the ionosphere, leaving unfilled gaps at oceans or poles. The method we propose is based on using a high-quality historical database of post-processed GIMs that comprises more than two solar cycles, calculates the GIM by weighted superposition on a subset of the database with the compatible solar condition. The linear combination of GIMs in the database was obtained by minimizing a ℓ distance between VTEC measurements at the IPPs and the VTECs from the database, adding a ℓ penalization on the weights to assure a sparse solution. The process uses a Sun-fixed geomagnetic reference frame. This method uses the atomic decomposition/least absolute shrinkage and selection operator (LASSO), which will be denoted as atomic decomposition interpolator of GIMs (ADIGIM). As the computation is done in milliseconds, the interpolation is performed in real time. In this work, two products were developed, denoted as UADG and UARG, the UADG in real time and UARG with a latency of 24 h to benefit from the availability of a greater number of stations. The altimeter JASON3 VTEC measurements were used as reference. The quality of interpolated RT-GIMs from day 258 of 2019 to 155 of the year 2020 is compared with other RT/non-RT GIM products such as those from International GNSS Service (IGS), Centre National d’Etudes Spatiales (CNES), Chinese Academy of Sciences (CAS), Polytechnic University of Catalonia (UPC) and others. The RT ADIGIM performance proved to be better, nearly as good as the rapid or final GIMs computed retrospectively with delays of hours to days. Besides, the non-RT ADIGIM quality is as good or better than most GIM products. The oceanic regions have been included in the assessment which showed that ADIGIM interpolation gives the best estimation (referred to JASON3). The developed method, UADG, will constitute the next-generation UPC RT-GIM, and also UARG will improve the current product UQRG (the current UPC rapid GIM product computed retrospectively) due to its complementary information., This work has been partially supported by the Project PID2019-107579RB-I00 (MICINN). Also was partially supported by the 2017 SGR-0851 Grant of the Generalitat de Catalunya and by the EU Project 101007599 - PITHIA-NRF.

The electron content distribution of the north and south polar ionosphere from 2001 to the beginning of 2019 is analyzed by using the UQRG global ionospheric map (GIM) of vertical total electron content (VTEC), computed every 15 min by UPC-IonSAT with a tomographic-kriging combined technique. We first show that the accuracy of UQRG GIM is slightly better than that of the GIMs of other analysis centers on the whole and also over both poles. Second, we show examples of polar VTEC features in UQRG GIM, previously reported by different authors and with higher-resolution techniques. Third, by means of an unsupervised clustering algorithm, learning vector quantization, we characterize the main features of the ionospheric electron content climatology, separately for the north and south polar regions., The UQRG GIMs, baseline of this work, are openly accessible from IGS server (ftp://cddis.gsfc.nasa.gov/gps/products/ionex/YEAR/DOY/uqrgDOY0.YYi.Z) and from UPC server (https://chapman.upc.es/tomion/rapid/YEAR/DOY_YYMMDD.15min/uqrgDOY0.YYi.Z) being YEAR and YY the four‐ and two‐digit year identifiers, MM is month number, DD is day of month, and DOY is the day of year, all of them with leading zeros when needed (see, for instance, ftp://cddis.gsfc.nasa.gov/gps/products/ionex/2002/003/uqrg0030.02i.Z and https://chapman.upc.es/tomion/rapid/2002/003_020103.15min/uqrg0030.02i.Z). Any missing file can be requested from the authors, in particular from Manuel Hernández‐Pajares (manuel.hernandez@upc.edu). This work has been supported by the POLarGIMs project funded by European Commission Joint Research Centre (JRC). We thank the National Space Science Data Center (NSSDC), Space Physics Data Facility (SPDF), and Principal Investigator K. W. Ogilvie at GSFC, A. J. Lazarus at MIT, and A. Aran at Universitat de Barcelona (UB), for the access of IMF data.

In this paper, the authors summarize a simple and efficient approach developed to mitigate the problem in precise Global Navigation Satellite Systems (GNSS) positioning originated by the most frequent ionospheric wave signatures: the medium-scale traveling ionospheric disturbances (MSTIDs). The direct GNSS Ionospheric Interferometry technique (hereinafter dGII), presented in this paper, is applied for correcting MSTID effects on precise Real Time Kinematic (RTK) and tropospheric determination. It consists of the evolution of the former climatic Differential Delay Mitigation Model for MSTIDs (DMTID), for real-time conditions, using ionospheric data from a single permanent receiver only. The performance is demonstrated with networks of GNSS receivers in Poland, treated as users under real-time conditions, during two representative days in winter and summer seasons (days 353 and 168 of year 2013). In range domain, dGII typically reduces the ionospheric delay error up to 10–90% of the value when the MSTID mitigation model is not applied. The main dGII impact on precise positioning is that we can obtain reliable RTK position faster. In particular, the ambiguity success rate parameter increases, from 74% to 83%, with respect to the original uncorrected observations. The average of time to first fix is shortened from 30 s to 13 s. The improvement in troposphere estimaton, due to any potential impact of the MSTID mitigation model, was most difficult to demonstrate.

This paper summarizes the definition and validation of two complementary new strategies, to invert incomplete Global Navigation Satellite System Radio-Occultation (RO) ionospheric measurements, such as the ones to be provided by the future EUMETSAT Polar System Second Generation. It will provide RO measurements with impact parameter much below the Low Earth Orbiters' height (817 km): from 500 km down approximately. The first presented method to invert truncated RO data is denoted as Abel-VaryChap Hybrid modeling from topside Incomplete Global Navigation Satellite System RO data, based on simple First Principles, very precise, and well suited for postprocessing. And the second method is denoted as Simple Estimation of Electron density profiles from topside Incomplete RO data, is less precise, but yields very fast estimations, suitable for Near Real-Time determination. Both techniques will be described and assessed with a set of 546 representative COSMIC/FORMOSAT-3 ROs, with relative errors of 7% and 11% for Abel-VaryChap Hybrid modeling from topside Incomplete Global Navigation Satellite System RO data and Simple Estimation of Electron density profiles from topside Incomplete RO data, respectively, with 20 min and 15 s, respectively, of computational time per occultation in our Intel I7 PC.

Agents’ perceptions on the state of the economy can be affected during economic crises. Tendency surveys are the main source of agents’ expectations. The main objective of this study is to assess the impact of the 2008 financial crisis on agents’ expectations. With this aim, we evaluate the capacity of survey-based expectations to anticipate economic growth in the United States, Japan, Germany and the United Kingdom. We propose a symbolic regression (SR) via genetic programming approach to derive mathematical functional forms that link survey-based expectations to GDP growth. By combining the main SR-generated indicators, we generate estimates of the evolution of GDP. Finally, we analyse the effect of the crisis on the formation of expectations, and we find an improvement in the capacity of agents’ expectations to anticipate economic growth after the crisis in all countries except Germany.

Tendency surveys are the main source of agents' expectations. This study has a twofold aim. First, it proposes a new method to quantify survey-based expectations by means of symbolic regression (SR) via genetic programming. Second, it combines the main SR-generated indicators to estimate the evolution of GDP, obtaining the best results for the Czech Republic and Hungary. Finally, it assesses the impact of the 2008 financial crisis, finding that the capacity of agents' expectations to anticipate economic growth in most Central and Eastern European economies improved after the crisis.

The use of biometrics has been successfully applied to security applications for some time. However, the extension of other potential applications with the use of biometric information is a very recent development. This paper summarizes the field of biometrics and investigates the potential of utilizing biometrics beyond the presently limited field of security applications. There are some synergies that can be established within security-related applications. These can also be relevant in other fields such as health and ambient intelligence. This paper describes these synergies. Overall, this paper highlights some interesting and exciting research areas as well as possible synergies between different applications using biometric information.

This special issue aims to cover some problems related to non-linear and non-conventional speech processing. The origin of this volume is in the ISCA Tutorial and Research Workshop on Non-Linear Speech Processing, NOLISP’09, held at the Universitat de Vic (Catalonia, Spain) on June 25–27, 2009. The series of NOLISP workshops started in 2003 has become a biannual event whose aim is to discuss alternative techniques for speech processing that, in a sense, do not fit into mainstream approaches. A selected choice of papers based on the presentations delivered at NOLISP’09 has given rise to this issue of Cognitive Computation. The papers hereinafter deal with the following topics: What would happen if we were able to overcome the limitations of audiovisual human systems? The answer could be found in the paper by V. Espinosa-Duro, M. Faundez-Zanuy, and J. Mekyska. A growing recurrent self-organizing model for phoneme recognition is presented by Ch. Jlassi, N. Arous, and N. Ellouze. An algorithm for voiced/unvoiced decision and pitch estimation from speech signals based on classification of peaks using the auto-correlation of the speech multi-scale product is presented by M. A. Ben Massoud, A. Bouzid, and N. Elloze. A feature reduction system based on the Discriminative Common Vector is proposed by C.M. Travieso, M. del Pozo, M.A. Ferrer, and J. B. Alonso. A new approach to the diagnosis of Ataxia SCA-2 by application of independent component analysis to a set of data obtained by electro-oculography is presented by R. V. Garcia, F. Rojas, C. G. Puntonet, B. San Roman, L. Velazquez, and R. Rodriguez. A formant tracking technique based on Fourier ridges detection aiming at improving the performance of these algorithms is presented by I. Jemaa, K. Ouni and Y. Laprie A new architecture for vocabulary independent keyword detection for cognitive virtual agents (SEMAINE system) is proposed by M. Wollmer, F. Eyber, A. Graves, B. Schuller, and G. Rigoll. Their word spotting model is composed of a dynamic-Bayesian network and a bidirectional long–short-term memory recurrent neural network. An insight into non-linear transformations for improving the performance of an entropy-based voice activity detector is made in the paper by two guest editors (J. S. and V.Z.). By no means has the range of topics in this Special Issue covered all directions in non-linear speech processing. We would rather consider it a sample presenting several areas in progress. In any case, we hope that they will be of interest for the readers of Cognitive Computation. Our thanks are due to the Editor-in-Chief, Dr. Amir Hussain, for permanent support in preparation of this issue. Of course, this issue would have been impossible without the contributions of all authors. On the other hand, anonymous referees have also done a great job by carefully J. Sole-Casals (&) V. Zaiats Department of Digital Technologies and Information, Escola Politecnica Superior, Universitat de Vic, c/. de la Laura, 13, 08500 Vic (Barcelona), Spain e-mail: jordi.sole@uvic.cat

Face recognition is probably the most natural way to perform a biometric authentication between human beings. However, the available technology for automatic systems still presents some drawbacks and is far away from human performance. In this paper we use the same DCT feature extraction approach presented in previous ICCST'03 and ICCST'05. However, we improve the experimental results using a Radial Basis Function (RBF) neural network in combination with the coding of the recognized class. We explain why the RBF, do not have the limitations of other classifiers such as the MLP. We also propose a method for dealing with the high number of classes associated to the task of face recognition which takes into account the limitations of the RBF as classifiers, and discuss the weakness of these methods when the number of training samples is limited. We have performed an exhaustive study about the neural network architecture and parameters, which has let us to establish relevant conclusions about the optimal configuration.

It is well known the relationship between source separation and blind deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal independence between samples can be used to retrieve the original signal, in the same manner as spatial independence is used for source separation. In this paper we propose the use of a Genetic Algorithm (GA) to blindly invert linear channels. The use of GA may be more appropriate in the case of small number of samples, where other gradient-like methods fails because of poor estimation of statistics. The experimental results show that the presented method is able to invert unknown filters with good numerical results, even if only 100 samples or less are available.

In this paper we present a method for blind deconvolution of linear channels based on source separation techniques, for real word signals. This technique applied to blind deconvolution problems is based in exploiting not the spatial independence between signals but the temporal independence between samples of the signal. Our objective is to minimize the mutual information between samples of the output in order to retrieve the original signal. In order to make use of use this idea the input signal must be a non-Gaussian i.i.d. signal. Because most real world signals do not have this i.i.d. nature, we will need to preprocess the original signal before the transmission into the channel. Likewise we should assure that the transmitted signal has non-Gaussian statistics in order to achieve the correct function of the algorithm. The strategy used for this preprocessing will be presented in this paper. If the receiver has the inverse of the preprocess, the original signal can be reconstructed without the convolutive distortion.

Publication in the conference proceedings of EUSIPCO, Toulouse, France, 2002

In this paper we propose an endpoint detection system based on the use of several features extracted from each speech frame, followed by a robust classifier (i.e Adaboost and Bagging of decision trees, and a multilayer perceptron) and a finite state automata (FSA). We present results for four different classifiers. The FSA module consisted of a 4-state decision logic that filtered false alarms and false positives. We compare the use of four different classifiers in this task. The look ahead of the method that we propose was of 7 frames, which are the number of frames that maximized the accuracy of the system. The system was tested with real signals recorded inside a car, with signal to noise ratio that ranged from 6 dB to 30dB. Finally we present experimental results demonstrating that the system yields robust endpoint detection.