Adaptive signal processing algorithms for noncircular complex data

The complex domain provides a natural processing framework for a large class of signals encountered in communications, radar, biomedical engineering and renewable energy. Statistical signal processing in C has traditionally been viewed as a straightforward extension of the corresponding algorithms in the real domain R, however, recent developments in augmented complex statistics show that, in general, this leads to under-modelling. This direct treatment of complex-valued signals has led to advances in so called widely linear modelling and the introduction of a generalised framework for the differentiability of both analytic and non-analytic complex and quaternion functions. In this thesis, supervised and blind complex adaptive algorithms capable of processing the generality of complex and quaternion signals (both circular and noncircular) in both noise-free and noisy environments are developed; their usefulness in real-world applications is demonstrated through case studies. The focus of this thesis is on the use of augmented statistics and widely linear modelling. The standard complex least mean square (CLMS) algorithm is extended to perform optimally for the generality of complex-valued signals, and is shown to outperform the CLMS algorithm. Next, extraction of latent complex-valued signals from large mixtures is addressed. This is achieved by developing several classes of complex blind source extraction algorithms based on fundamental signal properties such as smoothness, predictability and degree of Gaussianity, with the analysis of the existence and uniqueness of the solutions also provided. These algorithms are shown to facilitate real-time applications, such as those in brain computer interfacing (BCI). Due to their modified cost functions and the widely linear mixing model, this class of algorithms perform well in both noise-free and noisy environments. Next, based on a widely linear quaternion model, the FastICA algorithm is extended to the quaternion domain to provide separation of the generality of quaternion signals. The enhanced performances of the widely linear algorithms are illustrated in renewable energy and biomedical applications, in particular, for the prediction of wind profiles and extraction of artifacts from EEG recordings

New insights into foreground analysis of the WMAP five-year data using FASTICA

In this paper, we present a foreground analysis of the WMAP 5-year data using the FASTICA algorithm, improving on the treatment of the WMAP 3-year data in Bottino et al 2008. We revisit the nature of the free-free spectrum with the emphasis on attempting to confirm or otherwise the spectral feature claimed in Dobbler et al 2008b and explained in terms of spinning dust emission in the warm ionised medium. With the application of different Galactic cuts, the index is always flatter than the canonical value of 2.14 except for the Kp0 mask which is steeper. Irrespective of this, we can not confirm the presence of any feature in the free-free spectrum. We experiment with a more extensive approach to the cleaning of the data, introduced in connection with the iterative application of FASTICA. We confirm the presence of a residual foreground whose spatial distribution is concentrated along the Galactic plane, with pronounced emission near the Galactic center. This is consistent with the WMAP haze detected in Finkbeiner 2004. Finally, we attempted to perform the same analysis on full-sky maps. The code returns good results even for those regions where the cross-talk among the components is high. However, slightly better results in terms of the possibility of reconstructing a full-sky CMB map, are achieved with a simultaneous analysis of both the five WMAP maps and foreground templates. Nonetheless, some residuals are still present and detected in terms of an excess in the CMB power spectrum, on small angular scales. Therefore, a minimal mask for the brightest regions of the plane is necessary, and has been defined.Comment: Accepted for publication in MNRAS, 25 pages, 17 figures, 4 tables. Version with full resolution figures available at: http://www.mpa-garching.mpg.de/~bottino/downloads/bottino_etal.pd

Blind anti-collision methods for RFID system: a comparative analysis

Radio Frequency Identification (RFID) is one of the critical technologies of the Internet of Things (IoT). With the rapid development of IoT and the extensive use of RFID in our life, the step of RFID development should be faster. However, the tags in an RFID system are more and more utilized, both of them communicate in the same channel. The signal the reader received is mixed, and the reader cannot get the correct message the tags send directly. This phenomenon is often called a collision, which is the main obstacle to the development of the RFID system. Traditionally, the algorithm to solve the collision problem is called the anti-collision algorithm, the widely used anti-collision algorithm is based on Time Division Multiple Access (TDMA) like ALOHA-based and Binary search-based anti-collision algorithm. The principle of the TDMA-based anti-collision algorithm is to narrow the response of tags to one in each query time. These avoidance anti-collision algorithms performance poor when the number of tags is huge, thus, some researchers proposed the Blind Source Separation (BSS)-based anti-collision algorithm. The blind anti-collision algorithms perform better than the TDMA-based algorithms; it is meaningful to do some more research about this filed. This paper uses several BSS algorithms like FastICA, PowerICA, ICA_p, and SNR_MAX to separate the mixed signals in the RFID system and compare the performance of them. Simulation results and analysis demonstrate that the ICA_p algorithm has the best comprehensive performance among the mentioned algorithms. The FastICA algorithm is very unstable, and has a lower separation success rate, and the SNR_MAX algorithm has the worst performance among the algorithms applied in the RFID system. Some advice for future work will be put up in the end

On the conditions for valid objective functions in blind separation of independent and dependent sources

It is well known that independent sources can be blindly detected and separated, one by one, from linear mixtures by identifying local extrema of certain objective functions (contrasts), like negentropy, Non-Gaussianity measures, kurtosis, etc. It was also suggested in [1], and verified in practice in [2,4], that some of these measures remain useful for particular cases with dependent sources, but not much work has been done in this respect and a rigorous theoretical ground still lacks. In this paper, it is shown that, if a specific type of pairwise dependence among sources exists, called Linear Conditional Expectation (LCE) law, then a family of objective functions are valid for their separation. Interestingly, this particular type of dependence arises in modeling material abundances in the spectral unmixing problem of remote sensed images. In this work, a theoretical novel approach is used to analyze Shannon entropy (SE), Non-Gaussianity (NG) measure and absolute moments of arbitrarily order, i.e. Generic Absolute (GA) moments for the separation of sources allowing them to be dependent. We provide theoretical results that show the conditions under which sources are isolated by searching for a maximum or a minimum. Also, simple and efficient algorithms based on Parzen windows estimations of probability density functions (pdfs) and Newton-Raphson iterations are proposed for the separation of dependent or independent sources. A set of simulation results on synthetic data and an application to the blind spectral unmixing problem are provided in order to validate our theoretical results and compare these algorithms against FastICA and a very recently proposed algorithm for dependent sources, the Bounded Component Analysis algorithm (BCA). It is shown that, for dependent sources verifying the LCE law, the NG measure provides the best separation results.Fil: Caiafa, Cesar Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico la Plata. Instituto Argentino de Radioastronomia (i); Argentina. Universidad de Buenos Aires. Facultad de Ingeniería; Argentin

Rethinking LDA: moment matching for discrete ICA

We consider moment matching techniques for estimation in Latent Dirichlet Allocation (LDA). By drawing explicit links between LDA and discrete versions of independent component analysis (ICA), we first derive a new set of cumulant-based tensors, with an improved sample complexity. Moreover, we reuse standard ICA techniques such as joint diagonalization of tensors to improve over existing methods based on the tensor power method. In an extensive set of experiments on both synthetic and real datasets, we show that our new combination of tensors and orthogonal joint diagonalization techniques outperforms existing moment matching methods.Comment: 30 pages; added plate diagrams and clarifications, changed style, corrected typos, updated figures. in Proceedings of the 29-th Conference on Neural Information Processing Systems (NIPS), 201

Hybrid solutions to instantaneous MIMO blind separation and decoding: narrowband, QAM and square cases

Future wireless communication systems are desired to support high data rates and high quality transmission when considering the growing multimedia applications. Increasing the channel throughput leads to the multiple input and multiple output and blind equalization techniques in recent years. Thereby blind MIMO equalization has attracted a great interest.Both system performance and computational complexities play important roles in real time communications. Reducing the computational load and providing accurate performances are the main challenges in present systems. In this thesis, a hybrid method which can provide an affordable complexity with good performance for Blind Equalization in large constellation MIMO systems is proposed first. Saving computational cost happens both in the signal sep- aration part and in signal detection part. First, based on Quadrature amplitude modulation signal characteristics, an efficient and simple nonlinear function for the Independent Compo- nent Analysis is introduced. Second, using the idea of the sphere decoding, we choose the soft information of channels in a sphere, and overcome the so- called curse of dimensionality of the Expectation Maximization (EM) algorithm and enhance the final results simultaneously. Mathematically, we demonstrate in the digital communication cases, the EM algorithm shows Newton -like convergence.Despite the widespread use of forward -error coding (FEC), most multiple input multiple output (MIMO) blind channel estimation techniques ignore its presence, and instead make the sim- plifying assumption that the transmitted symbols are uncoded. However, FEC induces code structure in the transmitted sequence that can be exploited to improve blind MIMO channel estimates. In final part of this work, we exploit the iterative channel estimation and decoding performance for blind MIMO equalization. Experiments show the improvements achievable by exploiting the existence of coding structures and that it can access the performance of a BCJR equalizer with perfect channel information in a reasonable SNR range. All results are confirmed experimentally for the example of blind equalization in block fading MIMO systems

Multimodal methods for blind source separation of audio sources

The enhancement of the performance of frequency domain convolutive blind source separation (FDCBSS) techniques when applied to the problem of separating audio sources recorded in a room environment is the focus of this thesis. This challenging application is termed the cocktail party problem and the ultimate aim would be to build a machine which matches the ability of a human being to solve this task. Human beings exploit both their eyes and their ears in solving this task and hence they adopt a multimodal approach, i.e. they exploit both audio and video modalities. New multimodal methods for blind source separation of audio sources are therefore proposed in this work as a step towards realizing such a machine. The geometry of the room environment is initially exploited to improve the separation performance of a FDCBSS algorithm. The positions of the human speakers are monitored by video cameras and this information is incorporated within the FDCBSS algorithm in the form of constraints added to the underlying cross-power spectral density matrix-based cost function which measures separation performance. [Continues.