32 research outputs found
Information Theory Applications in Signal Processing
Ministerio de Economía, Industria y Competitividad (MINECO) TEC2017-82807-PMinisterio de Economía, Industria y Competitividad (MINECO) TEC2014-53103-
Prediction of Satellite Shadowing in Smart Cities with Application to IoT
The combination of satellite direct reception and terrestrial 5G infrastructure is essential
to guarantee coverage in satellite based-Internet of Things, mainly in smart cities where buildings
can cause high power losses. In this paper, we propose an accurate and fast graphical method for
predicting the satellite coverage in urban areas and SatCom on-the-move scenarios. The aim is to
provide information that could be useful in the IoT network planning process, e.g., in the decision of
how many terrestrial repeaters are really needed and where they should be placed. Experiments show
that the shadowed areas predicted by the method correspond almost perfectly with experimental
data measured from an Eutelsat satellite in the urban area of Barcelona.Ministerio de Industria, Turismo y Comercio de España TSI-020301-2009-3
Analog‐to‐Digital Conversion for Cognitive Radio: Subsampling, Interleaving, and Compressive Sensing
This chapter explores different analog-to-digital conversion techniques that are suitable to be implemented in cognitive radio receivers. This chapter details the fundamentals, advantages, and drawbacks of three promising techniques: subsampling, interleaving, and compressive sensing. Due to their major maturity, subsampling- and interleaving-based systems are described in further detail, whereas compressive sensing-based systems are described as a complement of the previous techniques for underutilized spectrum applications. The feasibility of these techniques as part of software-defined radio, multistandard, and spectrum sensing receivers is demonstrated by proposing different architectures with reduced complexity at circuit level, depending on the application requirements. Additionally, the chapter proposes different solutions to integrate the advantages of these techniques in a unique analog-to-digital conversion process
L1-norm unsupervised Fukunaga-Koontz transform
Article number 107942The Fukunaga-Koontz transform (FKT) is a powerful supervised feature extraction method used in twoclass recognition problems, particularly when the classes have equal mean vectors but different covariance matrices. The present work proves that it is also possible to perform the FKT in an unsupervised
manner, sparing the need for labeled data, by using a variant of L1-norm Principal Component Analysis (L1-PCA) that minimizes the L1-norm in the feature space. Rigorous proof is given in the case of
data drawn from a mixture of Gaussians. A working iterative algorithm based on gradient-descent in the
Stiefel manifold is put forward to perform L1-norm minimization with orthogonal constraints. A number of numerical experiments on synthetic and real data confirm the theoretical findings and the good
convergence characteristics of the proposed algorithm
Unsupervised Common Spatial Patterns
The common spatial pattern (CSP) method
is a dimensionality reduction technique widely used in
brain-computer interface (BCI) systems. In the two-class
CSP problem, training data are linearly projected onto direc tions maximizing or minimizing the variance ratio between
the two classes. The present contribution proves that kurto sis maximization performs CSP in an unsupervised manner,
i.e., with no need for labeled data, when the classes follow
Gaussian or elliptically symmetric distributions. Numerical
analyses on synthetic and real data validate these findings
in various experimental conditions, and demonstrate the
interest of the proposed unsupervised approach.Ministerio de Economía y Competitividad (España) TEC2017-82807-
EEG signal processing in mi-bci applications with improved covariance matrix estimators
Article number 8688582n brain–computer interfaces (BCIs), the
typical models of the EEG observations usually lead to
a poor estimation of the trial covariance matrices, given
the high non-stationarity of the EEG sources. We propose
the application of two techniques that significantly improve
the accuracy of these estimations and can be combined
with a wide range of motor imagery BCI (MI-BCI) methods.
The first one scales the observations in such a way that
implicitly normalizes the common temporal strength of the
source activities. When the scaling applies independently
to the trials of the observations, the procedure justifies
and improves the classical preprocessing for the EEG data.
In addition, when the scaling is instantaneous and inde pendent for each sample, the procedure particularizes to
Tyler’s method in statistics for obtaining a distribution free estimate of scattering. In this case, the proposal pro vides an original interpretation of this existing method
as a technique that pursuits an implicit instantaneous
power-normalization of the underlying source processes.
The second technique applies to the classifier and improves
its performance through a convenient regularization of
the features covariance matrix. Experimental tests reveal
that a combination of the proposed techniques with the
state-of-the-art algorithms for motor-imagery classification
provides a significant improvement in the classification
results.Ministerio de Economía y Competitividad ( España) TEC2017-82807-
Multiple evoked and induced alpha modulations in a visual attention task: Latency
Alpha event-related desynchronization (ERD) has been widely applied to understand the
psychophysiological role of this band in cognition. In particular, a considerable number of
publications have described spectral alterations in several pathologies using this time-frequency approach. However, ERD is not capable of specifically showing nonphase (induced)
activity related to the presentation of stimuli. Recent studies have described an evoked and
induced activity in the early phases (first 200 ms) of stimulus processing. However, scarce
studies have analyzed induced and evoked modulations in longer latencies (>200 ms)
and their potential roles in cognitive processing. The main goal of the present study was to
analyze diverse evoked and induced modulations in response to visual stimuli. Thus, 58-
channel electroencephalogram (EEG) was recorded in 21 healthy subjects during the performance of a visual attention task, and analyses were performed for both target and standard stimuli. The initial result showed that phase-locked and nonphase locked activities
coexist in the early processing of target and standard stimuli as has been reported by previous studies. However, more modulations were evident in longer latencies in both evoked
and induced activities. Correlation analyses suggest that similar maps were present for
evoked and induced activities at different timepoints. In the discussion section, diverse proposals will be stated to define the potential roles of these modulations in the information processing for this cognitive task. As a general conclusion, induced activity enables the
observation of cognitive mechanisms that are not visible by ERD or ERP modulations
Information Theoretic Approaches for Motor-Imagery BCI Systems: Review and Experimental Comparison
Brain computer interfaces (BCIs) have been attracting a great interest in recent years.
The common spatial patterns (CSP) technique is a well-established approach to the spatial filtering
of the electroencephalogram (EEG) data in BCI applications. Even though CSP was originally
proposed from a heuristic viewpoint, it can be also built on very strong foundations using information
theory. This paper reviews the relationship between CSP and several information-theoretic
approaches, including the Kullback–Leibler divergence, the Beta divergence and the Alpha-Beta
log-det (AB-LD)divergence. We also revise other approaches based on the idea of selecting those
features that are maximally informative about the class labels. The performance of all the methods
will be also compared via experiments.Gobierno Español MICINN TEC2014-53103-
Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
This paper presents a system with location functionalities for the inventory of traffic signs based on passive RFID technology. The proposed system simplifies the current video-based techniques, whose requirements regarding visibility are difficult to meet in some scenarios, such as dense urban areas. In addition, the system can be easily extended to consider any other street facilities, such as dumpsters or traffic lights. Furthermore, the system can perform the inventory process at night and at a vehicle’s usual speed, thus avoiding interfering with the normal traffic flow of the road. Moreover, the proposed system exploits the benefits of the passive RFID technologies over active RFID, which are typically employed on inventory and vehicular routing applications. Since the performance of passive RFID is not obvious for the required distance ranges on these in-motion scenarios, this paper, as its main contribution, addresses the problem in two different ways, on the one hand theoretically, presenting a radio wave propagation model at theoretical and simulation level for these scenarios; and on the other hand experimentally, comparing passive and active RFID alternatives regarding costs, power consumption, distance ranges, collision problems, and ease of reconfiguration. Finally, the performance of the proposed on-board system is experimentally validated, testing its capabilities for inventory purposesMinisterio de Economía y Competitividad TEC2016-80396-C2-2-