Instructors' perspectives on the pedagogy of architectural graduation projects: A qualitative study

In architectural education, graduation projects represent the transition from academia to the profession, and they are the courses that encourage architecture students to demonstrate the knowledge acquired and the skills developed throughout the educational program. This study aims to explore the instructors’ perspectives on improving the pedagogy of architectural graduation projects. To achieve this aim, this study has adopted a qualitative method, applied analytical and deductive methodologies, and conducted a survey among 44 academic architects from architectural schools worldwide. This paper is structured into three sections. The first section provides an overview of the topic and its current related issues. The second section introduces the survey, its methods, tools, and procedures, and the participants’ profiles. The third section presents the qualitative survey findings and their discussion. The conclusions of this study confirm the results of some previous studies. Some conclusions contradict common practices related to graduation projects, but the original approaches have been provided. The importance of this study inherits the relevance of adopting these courses; that is, for educators and employers to assess the extent to which graduates have acquired the competence necessary to practice architecture.     

Bases théoriques pour l’égalisation adaptative en mode autodidacte

Stability and convergence of adaptive algorithms are studied under a theoretical viewpoint, for the problem of discrete data transmission and their recovery by means of adaptive filtering (equalization). The relationships between adaptive equalization using a known training data sequence (training mode) and self-adaptive equalization using self-recovered data (self-learning mode) are pointed out. A comprehensive overview clarifies the satisfactory decaying behaviour of the transient error that is valid for both modes. Then stability of the self-learning mode is proved, under reasonable regularity conditions for the received signal at the channel output. Finally the self-learning equalizer, initiated with a short preamble in the training mode, is theoretically analyzed and its convergence towards optimality is proved, in agreement with practice.     

Convergence analysis of the sign algorithm without the independenceand gaussian assumptions

The paper is concerned with rigorous convergence analysis of the sign algorithm (SA) in the context of adaptive plant identification. Asymptotic time-averaged convergence for the mean absolute weight misalignment is proved for all values of the algorithm step size and initial weight vector. The paper has three main contributions with respect to available convergence results of the SA. The first is the deletion of the Gaussian assumption, which is important when covering the case of discrete valued data. No assumption about the distribution of the regressor sequence is used, except for the usual assumption of positive definite covariance matrix. The assumptions used about the noise allow nonexistence, unboundedness, and vanishing of the noise probability density function for arguments strictly different from zero. The second contribution is the deletion of the assumption of independent successive regressors. This deletion is important since, in applications, two successive regressors usually share all their components except two. Hence, they are strongly dependent, even for white plant input. The case of colored noise is also analyzed. Finally, the third contribution is the extension of the above results to the nonstationary case. The used assumptions allow nonstationarity of the plant input, plant noise, and plant parameters     

Transient and tracking performance bounds of the sign-signalgorithm

The paper provides a rigorous tracking analysis of the sign-sign algorithm when used in the identification of a time-varying plant with a white Gaussian input. The plant parameters vary according to a random walk model. The assumptions allow nonstationarity of the plant input, plant noise, and increments of the plant parameters. Upper bounds are derived for the long-term averages of the mean of the weight misalignment norm, mean absolute error, mean square weight misalignment, and mean square error. These bounds hold for all values of the algorithm step size, all initial filter weight settings, and all degrees of nonstationarity of the plant input, plant noise, and plant parameter increments. Lower bounds of the mean square weight misalignment and mean square error are also derived. The step sizes that minimize the above bounds are derived. A transient analysis of the algorithm is done in the case of a time-invariant plant. A tight lower bound of the convergence time is derived. The above analytical results are supported by computer simulations     

Convergence of the sign algorithm for adaptive filtering withcorrelated data

Convergence of a decreasing gain sign algorithm (SA) for adaptive filtering is analyzed. The presence of the hard limiter in the algorithm makes a rigorous analysis difficult. Therefore, there are few results available. Such results normally include restrictive assumptions such as the assumptions that successive observation vectors are independent and the new error signal of the adaptive filter has a time invariant probability density function. The former assumption is not valid in the context of adaptive filtering since two successive observation vectors share most of their components, while the latter assumption is a restriction on the adaptive weights whose evolution is a priori unknown. In lieu of using these assumptions, an almost-sure convergence of the SA is proved under the assumption that the sequence of observation vectors is M-dependent. This assumption allows strong correlation between successive observations     

Investigating elective courses in architectural education

One of the approaches used by educational institutions to ensure that their programs reflect advances and changes in the architectural profession is the inclusion of elective courses. This study aims to establish a basis for integrating elective courses into architectural curricula by investigating the component of elective courses in 30 highly-ranked undergraduate architectural programs around the world. The need for this study arose as a result of the limited literature and lack of scientific foundation with which to support the process of merging elective courses into architectural curricula. This study has raised many questions in terms of direction, amount, subject, and timing of elective courses in architectural education. This study adopts an analytical deductive methodology supported by quantitative research. It is structured into four sections: topic overview, survey and its procedures, findings of the survey, anddiscussion. The discussion includes a proposal for integrating elective courses into architectural education. This study draws conclusions to its research questions, which broadens its impact on the quality of architectural programs and benefits for those concerned with architectural accreditation.     

Analysis of a sign algorithm with delayed data

An adaptive filter whose weights are adapted using a sign algorithm with a delayed error signal is analyzed. For stationary environments it is proved that the excess average absolute estimation error is bounded for all values of the error signal delay and the algorithm step size. For the nonstationary case when the optimal filter weights are time varying, the optimum step size which minimizes the excess average absolute error is derived. It is shown that the optimum step size does not depend on the additive noise power. The analytical results are supported by computer simulations     

Comparison of RLS, LMS, and sign algorithms for tracking randomlytime-varying channels

The performance of adaptive FIR filters governed by the recursive least-squares (RLS) algorithm, the least mean square (LMS) algorithm, and the sign algorithm (SA), are compared when the optimal filtering vector is randomly time-varying. The comparison is done in terms of the steady-state excess mean-square estimation error ξ and the steady-state mean-square weight deviation, η. It is shown that ξ does not depend on the spread of eigenvalues of the input covariance matrix, R, in the cases of the LMS algorithm and the SA, while it does in the case of the RLS algorithm. In the three algorithms, η is found to be increasing with the eigenvalue spread. The value of the adaptation parameter that minimizes ξ is different from the one that minimizes η. It is shown that the minimum values of ξ and η attained by the RLS algorithm are equal to the ones attained by the LMS algorithm in any one of the three following cases: (1) if R has equal eigenvalues, (2) if the fluctuations of the individual elements of the optimal vector are mutually uncorrelated and have the same mean-square value, or (3) if R is diagonal and the fluctuations of the individual elements of the optimal vector have the same mean-square value. Conditions that make the values of ξ and η of the LMS algorithm smaller (or greater) than the ones of the RLS algorithm are derived. For Gaussian input data, the minimum values of ξ and η attained by the SA are found to exceed the ones attained by the LMS algorithm by 1 dB independently of R and the mutual correlation between the elements of the optimal vector     

Second-order convergence analysis of stochastic adaptive linear filtering

The convergence of an adaptive filtering vector is studied, when it is governed by the mean-square-error gradient algorithm with constant step size. We consider the mean-square deviation between the optimal filter and the actual one during the steady state. This quantity is known to be essentially proportional to the step size of the algorithm. However, previous analyses were either heuristic, or based upon the assumption that successive observations were independent, which is far from being realistic. Actually, in most applications, two successive observation vectors share a large number of components and thus they are strongly correlated. In this work, we deal with the case of correlated observations and prove that the mean-square deviation is actually of the same order (or less) than the step size of the algorithm. This result is proved without any boundedness or barrier assumption for the algorithm, as it has been done previously in the literature to ensure the nondivergence. Our assumptions are reduced to the finite strong-memory assumption and the finite-moments assumption for the observation. They are satisfied in a very wide class of practical applications.     

Behavior of the least mean square algorithm with a periodically time‐varying input power

The paper analyzes the transient and steady‐state performances of a least mean square algorithm in the rarely‐studied situation of a time‐varying input power. A scenario of periodic pulsed variation of the input power is considered. The analysis is carried out in the context of tracking a Markov plant with a white Gaussian input. It is shown that the mean square deviation (MSD) converges to a periodic sequence having the same period as that of the variation of the input power. Expressions are derived for the convergence time and the steady‐state peak MSD. Surprisingly, it is found that neither the transient performance nor the steady‐state performance degrades with rapid variation of the input power. On the other hand, slow input power variation causes degradation in both the transient and steady‐state performances for given amplitude of variation of the input power. In the case of a time‐invariant plant, neither rapid nor slow variation of the input power causes degradation in the steady‐state performance. On the other hand, there is degradation in the transient performance for slow variation of the input power. Copyright © 2012 John Wiley & Sons, Ltd.