Evaluation of simulated driving in comparison to laboratory-based tests to assess the pharmacodynamics of alprazolam and alcohol.

RATIONALE: Assessment of the effects of medicines on the risks of car driving must be derived from laboratory tests, simulated driving or real on-road driving tests. Relevance of tests is determined by their sensitivity and predictive ability for the probability of accidents or damage. This cannot be determined directly, but methods should be able to at least detect the effects of a positive control in dosage known to be clearly associated with increased risk. OBJECTIVES: A driving simulator was evaluated in comparison with a battery of validated tests of CNS performance, the NeuroCart®. Alcohol in a concentration exactly at the legal limit (0.5 g L-1) and well above (1.0 g L-1) as well as alprazolam (1 mg) was used as positive control. METHODS: This was a randomised, cross-over study using a double dummy blinded design in 24 healthy study subjects (12 M, 12 F) aged 20-43 years. Alcohol was infused intravenously using a validated clamping protocol to obtain concentrations of 0.5 g L-1 and on another occasion 1.0 g L-1. Alprazolam was given orally. Driving tests and lab tests were done at regular time intervals during a study day. RESULTS: Alprazolam and alcohol significantly affected the main parameters of driving in the simulator and affected scores of safe driving and alprazolam increased the odds ratio of a virtual crash. Several laboratory measurements of psychomotor performance were affected by the reference substances as expected and correlated significantly with the driving performance. CONCLUSIONS: The driving simulator can detect effects of reference substances at levels that are known to negatively affect driving.

Binary Codification Design for Compressive Imaging by Uniform Sensing.

Recently, an important set of high dimensional signals (HDS) applications has successfully implemented compressive sensing (CS) sensors in which their efficiency depends on physical elements that perform a binary codification over the HDS. The structure of the binary codification is crucial as it determines the HDS sensing matrices. For a correct reconstruction, this class of matrices drastically differs from the dense or i.i.d. assumptions usually made in CS. Therefore, current CS matrix design algorithms are impractical. This paper proposes a novel strategy to design structured, sparse, and binary HDS measurement matrices based on promoting linear independence between rows by minimizing the number of its zero singular values. The design constraints lead to keep uniform both, the number of non-zero elements per row and also the number of non-zero elements per column. An algorithm based on an optimal selection of non-zero entries positions is developed to implement this strategy. Simulations show that the proposed optimization improves the quality of the reconstructed HDS in up to 8 dB of PSNR compared with non-optimized matrices.