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A signal control intersection increases not only vehicle delay, but also vehicle emissions and fuel consumption in that area. Because more and more fuel and air pollution problems have arisen recently, an intersection signal control optimization method which aims at reducing vehicle emissions, fuel consumption and vehicle delay is greatly needed. This paper proposes a signal control multi-object optimization method to reduce vehicle emissions, fuel consumption and vehicle delay simultaneously at an intersection. The optimization method combines Paramics microscopic traffic simulation software, Comprehensive Modal Emissions Model (CMEM), and genetic algorithm. An intersection in Haizhu District, Guangzhou, is used for a case study. The result of the case study shows the optimal timing scheme obtained from this method is better than the Webster timing scheme.

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Calcium imaging technique provides irreplaceable advantages in monitoring large population of neuronal activities simultaneously. However, due to the generally low signal to noise ratio (SNR) of the calcium signal and variability in dye properties, it is still challenging to faithfully infer neuronal spikes from these calcium signals, especially from in vivo experiments. In this study, we tackled the problem of both spike-rate and spike-event predictions using a data-driven approach, based on a public pool of dataset with simultaneously recorded calcium and electrophysiological signals using different dyes and recorded from different brain regions. We proposed the ENS2 (effective and efficient neural networks for spike inference from calcium signals) system using raw calcium inputs and it consistently outperforms state-of-the-arts algorithms in both spike-rate and spike-event predictions with reduced computational load. We have also demonstrated that factors such as sampling rates, smoothing window sizes and parametric evaluation metrics could readily bias the interpretation of inference performance. We concluded that optimizing our system for spike-event prediction could produce a more versatile inference system for real neuroscience studies.

A flexible visible light communication system based on probabilistic constellation shaping and orthogonal superposition modulation is proposed and demonstrated. Compared with non-PCS solution, it has a maximum 49% capacity improvement and achieves 3.34Gbps CoMP net-data-rate.

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