Flexible methods for blind separation of complex signals

One of the main matter in Blind Source Separation (BSS) performed with a neural network approach is the choice of the nonlinear activation function (AF). In fact if the shape of the activation function is chosen as the cumulative density function (c.d.f.) of the original source the problem is solved. For this scope in this thesis a flexible approach is introduced and the shape of the activation functions is changed during the learning process using the so-called “spline functions”. The problem is complicated in the case of separation of complex sources where there is the problem of the dichotomy between analyticity and boundedness of the complex activation functions. The problem is solved introducing the “splitting function” model as activation function. The “splitting function” is a couple of “spline function” which wind off the real and the imaginary part of the complex activation function, each of one depending from the real and imaginary variable. A more realistic model is the “generalized splitting function”, which is formed by a couple of two bi-dimensional functions (surfaces), one for the real and one for the imaginary part of the complex function, each depending by both the real and imaginary part of the complex variable. Unfortunately the linear environment is unrealistic in many practical applications. In this way there is the need of extending BSS problem in the nonlinear environment: in this case both the activation function than the nonlinear distorting function are realized by the “splitting function” made of “spline function”. The complex and instantaneous separation in linear and nonlinear environment allow us to perform a complex-valued extension of the well-known INFOMAX algorithm in several practical situations, such as convolutive mixtures, fMRI signal analysis and bandpass signal transmission. In addition advanced characteristics on the proposed approach are introduced and deeply described. First of all it is shows as splines are universal nonlinear functions for BSS problem: they are able to perform separation in anyway. Then it is analyzed as the “splitting solution” allows the algorithm to obtain a phase recovery: usually there is a phase ambiguity. Finally a Cramér-Rao lower bound for ICA is discussed. Several experimental results, tested by different objective indexes, show the effectiveness of the proposed approaches

Flexible methods for blind separation of complex signals

One of the main matter in Blind Source Separation (BSS) performed with a neural network approach is the choice of the nonlinear activation function (AF). In fact if the shape of the activation function is chosen as the cumulative density function (c.d.f.) of the original source the problem is solved. For this scope in this thesis a flexible approach is introduced and the shape of the activation functions is changed during the learning process using the so-called “spline functions”. The problem is complicated in the case of separation of complex sources where there is the problem of the dichotomy between analyticity and boundedness of the complex activation functions. The problem is solved introducing the “splitting function” model as activation function. The “splitting function” is a couple of “spline function” which wind off the real and the imaginary part of the complex activation function, each of one depending from the real and imaginary variable. A more realistic model is the “generalized splitting function”, which is formed by a couple of two bi-dimensional functions (surfaces), one for the real and one for the imaginary part of the complex function, each depending by both the real and imaginary part of the complex variable. Unfortunately the linear environment is unrealistic in many practical applications. In this way there is the need of extending BSS problem in the nonlinear environment: in this case both the activation function than the nonlinear distorting function are realized by the “splitting function” made of “spline function”. The complex and instantaneous separation in linear and nonlinear environment allow us to perform a complex-valued extension of the well-known INFOMAX algorithm in several practical situations, such as convolutive mixtures, fMRI signal analysis and bandpass signal transmission. In addition advanced characteristics on the proposed approach are introduced and deeply described. First of all it is shows as splines are universal nonlinear functions for BSS problem: they are able to perform separation in anyway. Then it is analyzed as the “splitting solution” allows the algorithm to obtain a phase recovery: usually there is a phase ambiguity. Finally a Cramér-Rao lower bound for ICA is discussed. Several experimental results, tested by different objective indexes, show the effectiveness of the proposed approaches

Algorithms for Blind Equalization Based on Relative Gradient and Toeplitz Constraints

Blind Equalization (BE) refers to the problem of recovering the source symbol sequence from a signal received through a channel in the presence of additive noise and channel distortion, when the channel response is unknown and a training sequence is not accessible. To achieve BE, statistical or constellation properties of the source symbols are exploited. In BE algorithms, two main concerns are convergence speed and computational complexity. In this dissertation, we explore the application of relative gradient for equalizer adaptation with a structure constraint on the equalizer matrix, for fast convergence without excessive computational complexity. We model blind equalization with symbol-rate sampling as a blind source separation (BSS) problem and study two single-carrier transmission schemes, specifically block transmission with guard intervals and continuous transmission. Under either scheme, blind equalization can be achieved using independent component analysis (ICA) algorithms with a Toeplitz or circulant constraint on the structure of the separating matrix. We also develop relative gradient versions of the widely used Bussgang-type algorithms. Processing the equalizer outputs in sliding blocks, we are able to use the relative gradient for adaptation of the Toeplitz constrained equalizer matrix. The use of relative gradient makes the Bussgang condition appear explicitly in the matrix adaptation and speeds up convergence. For the ICA-based and Bussgang-type algorithms with relative gradient and matrix structure constraints, we simplify the matrix adaptations to obtain equivalent equalizer vector adaptations for reduced computational cost. Efficient implementations with fast Fourier transform, and approximation schemes for the cross-correlation terms used in the adaptation, are shown to further reduce computational cost. We also consider the use of a relative gradient algorithm for channel shortening in orthogonal frequency division multiplexing (OFDM) systems. The redundancy of the cyclic prefix symbols is used to shorten a channel with a long impulse response. We show interesting preliminary results for a shortening algorithm based on relative gradient

Abstracts of manuscripts submitted in 1991 for publication

This volume contains the abstracts of manuscripts submitted for publication during calendar year 1991 by the staff and students of the Woods Hole Oceanographic Institution. We identify the journal of those manuscripts which are in press or have been published. The volume is intended to be informative, but not a bibliography. The abstracts are listed by title in the Table of Contents and are grouped into one of our five departents, Marine Policy Center, Coastal Research Center, or the student category. An author index is presented in the back to facilitate locating specific papers

Validity of the overclaiming technique as a method to account for response bias in self-assessment questions : analysis on the basis of the PISA 2012 data

The presented work is devoted to study the validity of overclaiming technique (OCT) as a measure of response (positivity) bias. Three main aims of the analyses performed were: a) assess methods' utility to enhance predictive validity of self-report by accounting for response biases, b) investigate proposed mechanisms of overclaiming, c) expand nomological network of the method by presenting a wide set of both individual-level and cluster-level (school) correlates. The obtained results pointed that OCT can be used in order to account for response biases in self-report data. Important differences regarding use and interpretation of the different OCT scoring systems were found and commented. Two systems, one based of signal detection theory (SDT), other on item response theory model (IRT), were proposed as viable scorings of OCT. Choice between them is not trivial as it influences results' interpretation and model specification. Three possible mechanisms of overclaiming were tested: a) motivated response bias (self-favouring bias, socially desirable responding), b) memory bias (overgeneralised knowledge or faulty memory control) and c) response styles and careless responding. The results pointed that all three mechanisms are probable and that overclaiming is most probably a heterogenous phenomenon of multiple causes. However, the analyses pointed out that one of the memory bias hypotheses, the overgeneralised knowledge account, does not hold and that there is much more evidence for the competitive metacognitive account. It is to said that overclaiming is at least partially attributable to insufficient monitoring of one's knowledge. Evidence for a relation between careless responding and overclaiming was also obtained, indicating that at least some of the overclaimed responses can be attributed due to inattentive responding. Obtained results on the relations between response styles and overclaiming were complicated; they warrant further studies as the results here probably greatly depend on the technical details of analysis, e.g. response style definition and coding adopted. The analysed cluster-level covariates demonstrated that only very limited portion of OCT variance can be ascribed to the school-level of analysis. Gender, socio-economic status and locus of control proved to be significantly related to overclaiming among the individual-level correlates assessed. Boys yielded higher overclaiming bias than girls and students of external locus of control were more biased in their self-reports in comparison to students of internal locus of control. The work comprises also analysis of the PISA's OCT latent structure. The results evidenced bifactor structure of the scale, with the general factor interpreted as math ability while the two specific factors were given a tentative explanation concentrated around item difficulty (one specific factor emerged for easy items, one for hard items). These findings point to a multi-dimensional character of OCT and a large role played by domain ability in OCT responding. Moreover, latent class analysis (LCA) performed identified an "overclaiming" group among the participants which was characterised by high overclaiming and unwarrantedly high self-report profile regarding math-related abilities and social life. However, this group counted only around 9% of the total sample. Implications of these findings are commented in the work, along with theoretical integration and ideas for future studies with the use of OCT

Avoiding Divergence in the Shalvi-Weinstein Algorithm

The most popular algorithms for blind equalization are the constant-modulus algorithm (CMA) and the Shalvi-Weinstein algorithm (SWA). It is well-known that SWA presents a higher convergence rate than CMA. at the expense of higher computational complexity. If the forgetting factor is not sufficiently close to one, if the initialization is distant from the optimal solution, or if the signal-to-noise ratio is low, SWA can converge to undesirable local minima or even diverge. In this paper, we show that divergence can be caused by an inconsistency in the nonlinear estimate of the transmitted signal. or (when the algorithm is implemented in finite precision) by the loss of positiveness of the estimate of the autocorrelation matrix, or by a combination of both. In order to avoid the first cause of divergence, we propose a dual-mode SWA. In the first mode of operation. the new algorithm works as SWA; in the second mode, it rejects inconsistent estimates of the transmitted signal. Assuming the persistence of excitation condition, we present a deterministic stability analysis of the new algorithm. To avoid the second cause of divergence, we propose a dual-mode lattice SWA, which is stable even in finite-precision arithmetic, and has a computational complexity that increases linearly with the number of adjustable equalizer coefficients. The good performance of the proposed algorithms is confirmed through numerical simulations

Proceedings of the Fifth International Mobile Satellite Conference 1997

Satellite-based mobile communications systems provide voice and data communications to users over a vast geographic area. The users may communicate via mobile or hand-held terminals, which may also provide access to terrestrial communications services. While previous International Mobile Satellite Conferences have concentrated on technical advances and the increasing worldwide commercial activities, this conference focuses on the next generation of mobile satellite services. The approximately 80 papers included here cover sessions in the following areas: networking and protocols; code division multiple access technologies; demand, economics and technology issues; current and planned systems; propagation; terminal technology; modulation and coding advances; spacecraft technology; advanced systems; and applications and experiments

Store brands in hard discounters : the psychological processes underpinning consumer evaluation

This thesis examines the evaluation of grocery store brands from a consumer perspective, with a particular focus on the value end of the market and Hard Discounters (HDs), with an investigation of the psychological processes leading to purchase preferences. Grocery store brands represent a rapidly evolving landscape and account for over half of all grocery sales in the UK and some other European markets. Recent share gains in the UK by HDs, Aldi and Lidl, have fuelled store brand growth, as they sell ranges which consist almost entirely of their own brands. HD products are popular and well-liked by consumers but are distinct from other store brands as they are ‘copycats’ of leading national brands and do not carry the name of the parent store. Although store brands are well-researched, only a handful of recent studies have included HDs, and little is known about how consumers appraise the own-brand products they sell. The purpose of this research is to establish the underpinning psychological processes of store brand evaluation, extending the investigation to HDs through a series of three interrelated studies. A review of marketing literature specifically looking at grocery store brands (GSBs). revealed the importance of extrinsic cues in GSB evaluation, including the image of the parent store, the price, and the packaging. Furthermore, characteristic psychological traits of some consumers lead to increased likelihood of store band purchasing, known as store brand proneness. Using the Persuasion Knowledge Model and self-construal as a theoretical basis, a conceptual framework was developed to examine store brand evaluation in three separate studies. The first study focussed on store image, investigating the effect of self-construal on implicit and explicit consumer perceptions, using an implicit association test (IAT). In the second study, price, and the similarity of GSB packaging to the national brand were interrogated. Again, the impact of self-construal on outcomes was reviewed on data collected from an online consumer panel. A further examination of packaging was undertaken in a final study which investigated the effect self-construal on how GSB packaging designs are visually assessed by consumers. Data were collected using remote eye tracking. Findings from the three studies in this thesis make a contribution to knowledge regarding the psychological processes underpinning consumer GSB perceptions. In particular, the influence of self-construal on store brand evaluation is demonstrated across each of the studies. The first study highlights how social bias impacts upon store image preferences stated by individuals, acknowledging the importance of implicit measures in 3 future studies. The key contribution from the second study outlines a decision-making process for store brand evaluation, detailing the relationship between heuristics, persuasion knowledge and self-construal. Findings demonstrate that when self-construal is a dominant influence in making store brand choices, preferences made based on persuasion knowledge are reversed. The final study offers a first known insight into the impact of self-construal on patterns of visual attention. The characteristic cognitive processes aligned to interdependent self-construal lead to an increase in the visual attention being paid by individuals. This adds a new dimension for investigation to the emerging field of eye-tracking studies and visual attention domain. For practitioners in the grocery retail industry, findings from this thesis enhance understanding of consumer store brand preferences and importantly the influence of self-construal. The opportunity to use primes for self-construal to amplify GSB interest presents itself. In addition to this, the use of implicit measures such as an IAT test or eye tracking to capture visual attention, enable deeper insights into shopper preferences to be collected which can be used for commercial advantage

The end of stigma? Understanding the dynamics of legitimisation in the context of TV series consumption

This research contributes to prior work on stigmatisation by looking at stigmatisation and legitimisation as social processes in the context of TV series consumption. Using in-depth interviews, we show that the dynamics of legitimisation are complex and accompanied by the reproduction of existing stigmas and creation of new stigmas