Amplitude banded RLS approach to time variant channel equalization

This paper proposes a non-linear adaptive algorithm, the amplitude banded RLS (ABRLS) algorithm, as an adaptation procedure for time variant channel equalizers. In the ABRLS algorithm, a coefficient matrix is updated based on the amplitude level of the received sequence. To enhance the capability of tracking for the ABRLS algorithm, a parallel adaptation scheme is utilized which involves the structures of decision feedback equalizer (DFE). Computer simulations demonstrate that the novel ABRLS based equalizer provides a significant improvement relative to the conventional RLS DFE on a rapidly time variant communication channel.

Effects of the Interaction Between Reaction Component of Personal Need for Structure and Role Perceptions on Employee Attitudes in Long-Term Care for Elderly People

This study examined the interaction of reaction component of personal need for structure (reaction to lack of structure, RLS) and role perceptions in predicting job satisfaction, job involvement, affective commitment, and occupational identity among employees working in long-term care for elderly people. High-RLS employees experienced more role conflict, had less job satisfaction, and experienced lower levels of occupational identity than did low-RLS employees. We found individual differences in how problems in roles affected employees' job attitudes. High-RLS employees experienced lower levels of job satisfaction, job involvement, and affective commitment, irrespective of role-conflict levels. Low-RLS employees experienced detrimental job attitudes only if role-conflict levels were high. Our results suggest that high-RLS people benefit less from low levels of experienced role conflicts.

Building Support Vector Machines in the Context of Regularised Least Squares

This paper formulates a linear kernel support vector machine (SVM) as a regularized least-squares (RLS) problem. By defining a set of indicator variables of the errors, the solution to the RLS problem is represented as an equation that relates the error vector to the indicator variables. Through partitioning the training set, the SVM weights and bias are expressed analytically using the support vectors. It is also shown how this approach naturally extends to Sums with nonlinear kernels whilst avoiding the need to make use of Lagrange multipliers and duality theory. A fast iterative solution algorithm based on Cholesky decomposition with permutation of the support vectors is suggested as a solution method. The properties of our SVM formulation are analyzed and compared with standard SVMs using a simple example that can be illustrated graphically. The correctness and behavior of our solution (merely derived in the primal context of RLS) is demonstrated using a set of public benchmarking problems for both linear and nonlinear SVMs.