Adriana Dapena

A blind signal separation method for multiuser communications

A new approach based on the constant modulus (CM) criterion is proposed to separate instantaneous linear mixtures of signals using a linear memoryless multiple input multiple output (MIMO) system. Even though a nonconvex cost function is minimized, analyses show that minima correspond to parameter settings where perfect separation is achieved.

A hybrid DCT-SVD image-coding algorithm

We propose an image-coding algorithm which combines the discrete cosine transform (DCT) and the singular value decomposition (SVD). The DCT is used to transform those blocks in the source image that exhibit a high correlation, while blocks with greater high-frequency content are transformed using the SVD. A simple criterion is used to decide which transform should be used on each block. Simulation results show that the new hybrid algorithm provides good distortion, bit rate, and image quality-especially in images which are less spatially correlated.

A novel frequency domain approach for separating convolutive mixtures of temporally-white signals☆

Abstract In this paper we present a new technique for separating convolutive mixtures of statistically independent non-Gaussian temporally-white signals. The technique is termed blind because the separation is performed without resorting to an a priori knowledge of the sources or the mixing system. The blind source separation (BSS) problem is solved by interpreting a time domain convolution as several instantaneous mixtures in the frequency domain. The sources at each frequency bin are recovered using an extension of the adaptive algorithm proposed by Dapena and Castedo in Signal Process. 75 (1999) 11–27. By extending the stability analysis presented there, we prove that the only attractors of the algorithm correspond to the points where perfect separation is achieved. We also propose novel strategies to remove the permutation and the amplitude indeterminacies that appear when the sources are recovered in a different order or with different amplitude in some frequency bins. The permutation problem is solved by clustering the outputs according to their second- or fourth-order cumulants. Afterwards, the amplitude indeterminacy is corrected taking into account the values predicted by the stability analysis.

Blind channel identification in Alamouti coded systems: a comparative study of eigendecomposition methods in indoor transmissions at 2.4 GHz†

This paper focuses on blind channel estimation in Alamouti coded systems with one receiving antenna working in indoor scenarios where the flat fading assumption is reasonable. A comparative study of several channel estimation techniques in both simulated and realistic scenarios is presented. The tested methods exploit the orthogonality property of the Alamouti coded channel matrix, and are based on the eigendecomposition of a square matrix made up of second-order statistics (SOS) or higher order statistics (HOS) of the observed signals. An experimental evaluation is carried out on a testbed developed at the University of A Coruna (UDC) and operating at 2.4 GHz. The results show the superior performance of the SOS-based blind channel estimation technique in both line of sight (LOS) and non-LOS (NLOS) channels.

Home Automation System Based on Intelligent Transducer Enablers

This paper presents a novel home automation system named HASITE (Home Automation System based on Intelligent Transducer Enablers), which has been specifically designed to identify and configure transducers easily and quickly. These features are especially useful in situations where many transducers are deployed, since their setup becomes a cumbersome task that consumes a significant amount of time and human resources. HASITE simplifies the deployment of a home automation system by using wireless networks and both self-configuration and self-registration protocols. Thanks to the application of these three elements, HASITE is able to add new transducers by just powering them up. According to the tests performed in different realistic scenarios, a transducer is ready to be used in less than 13 s. Moreover, all HASITE functionalities can be accessed through an API, which also allows for the integration of third-party systems. As an example, an Android application based on the API is presented. Remote users can use it to interact with transducers by just using a regular smartphone or a tablet.

Blind channel estimation based on maximizing the eigenvalue spread of cumulant matrices in (2 × 1) Alamouti's coding schemes

The popular Alamouti orthogonal space time code attains full transmit diversity in multiple antenna systems. This paper addresses the problem of blind channel identification in (2 × 1) Alamouti coded systems. Under the assumption of independent symbol substreams, the channel can be estimated from the eigendecomposition of matrices composed of second- or higher-order statistics (cumulants) of the received signal. The so-called joint approximate diagonalization of eigenmatrices (JADE) method for blind source separation via independent component analysis is optimal in that it tries to simultaneously diagonalize a full set of fourth-order cumulant matrices. To reduce computational complexity, we perform the eigenvalue decomposition of a single cumulant matrix, which is judiciously chosen by maximizing its expected eigenvalue spread. Simulation results show that the resulting technique outperforms existing blind Alamouti channel estimation methods and achieves a performance close to JADEs at a fraction of the computational cost. Copyright

Blind Source Separation in the Frequency Domain: A Novel Solution to the Amplitude and the Permutation Indeterminacies

This paper deals with the separation of convolutive mixtures of statistically independent signals (sources) in the frequency domain. The convolutive mixture is decomposed in several problems of separating instantaneous mixtures which are independently solved. In addition, we propose a method to remove the indeterminacies which occur when all the individual separating systems do not extract the sources in the same order and with the same amplitude.We will show that both the permutation and the amplitude indeterminacies can be solved using second-order statistics when the sources are temporally-white.

Testbed-Assisted Learning for Digital Communications Courses

We introduce testbed‐assisted learning as an effective means for teaching digital communications. Laboratory teaching activities of digital communications courses benefit very much from utilizing a hardware testbed, since it greatly facilitates the understanding of very important effects introduced by real‐world transceivers. We overcome the main drawback of communications hardware, that is, the cumbersome low‐level programming interfaces provided by hardware manufacturers, by introducing a distributed multilayer software architecture. This architecture provides different abstraction levels to access hardware testbeds, releasing students from the low‐level interaction with the hardware. Also, the distributed nature of this architecture results in a high flexibility of operation. This way, students can focus on learning communications topics without devoting any time to low‐level programming, that is usually out of the scope of digital communications courses. Thanks to testbed‐assisted learning, they are able to perform illustrative experiments to understand digital communications concepts (e.g., source coding, modulation, space‐time coding, etc.) and to test algorithms without developing a new program from scratch, speeding up both the implementation and the debugging tasks. However, those students interested in hardware implementations can use the software architecture to access and interact with lower programming levels until they are as close as possible to the hardware.

Inversion of the sliding Fourier transform using only two frequency bins and its application to source separation

In this paper we show that the Fourier transform can be inverted using only two frequency bins when it is computed over sliding windows with one-point delay and the window length is less than the number of frequencies. This two conditions allow to recover the time-domain signal by multiplying the frequency-domain signal times a 2 × 2 invertible matrix. We also show how this result can be used to separate convolutive mixtures of signals with a reduced computational cost.

Vehicle Classification Using the Discrete Fourier Transform with Traffic Inductive Sensors

Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.