Present self-tuning regulator architectures based on recursive least-square estimation are computationally expensive and require large amount of resources and time in generating the first control signal due to computational bottlenecks imposed by the calculations involved in estimation stage, different stages of matrix multiplications and the number of intermediate variables at each iteration and precludes its use in applications that have fast required response times and those which run on embedded computing platforms with low-power or low-cost requirements with constraints on resource usage. A salient feature of this study is that a new modular parallel pipelined stochastic approximation-based self-tuning regulator architecture which reduces the time required to generate the first control signal, reduces resource usage and reduces the number of intermediate variables is proposed. Fast matrix multiplication, pipelining and high-speed arithmetic function implementations were used for improving the performance. Results of implementation demonstrate that the proposed architecture has an improvement in control signal generation time by 38% and reduction in resource usage by 41% in terms of multipliers and 44.4% in terms of adders compared with the best existing related work, opening up new possibilities for the application of online embedded self-tuning regulators.

Traffic congestion on roads wastes travel times and increases fuel consumption as well as gas emissions which are dangerous to human health. This has led to growing concern about environmental protection and energy conservation and a number of studies to increase fuel economy and reduce gas emissions. To increase travel times so as to reduce fuel consumption and gas emissions, traffic signals at intersections must be well implemented. It is therefore necessary to employ the current technology of wireless sensor networks to enhance the optimisation of the signalised intersections so as to address such a concern. In this study, a vehicular traffic control model was developed to optimise a signalised intersection, using wireless vehicle detectors. Real-time traffic volume gathered were analysed to obtain the peak hour traffic volume causing congestion. The intersection was modelled and simulated in Synchro7 as an actuated signalised model using results from the analysed data. The model for morning peak and evening peak periods gave optimal cycle lengths which result in the reduction of gas emissions, fuel consumption and delay at the intersection.