Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments

Deep learning has unleashed the great potential in many fields and now is the most significant facilitator for video analytics owing to its capability to providing more intelligent services in a complex scenario. Meanwhile, the emergence of fog computing has brought unprecedented opportunities to provision intelligence services in infrastructure-less environments like remote national parks and rural farms. However, most of the deep learning algorithms are computationally intensive and impossible to be executed in such environments due to the needed supports from the cloud. In this paper, we develop a video analytic framework, which is tailored particularly for the fog devices to realize video analytic service in a rapid manner. Also, the convolution neural networks are used as the core processing unit in the framework to facilitate the image analysing process

Propagating Characteristics of Pulsed Laser in Rain

To understand the performance of laser ranging system under the rain weather condition, we need to know the propagating characteristics of laser pulse in rain. In this paper, the absorption and attenuation coefficients were calculated based on the scattering theories in discrete stochastic media, and the propagating characteristics of laser pulse in rain were simulated and analyzed using Monte-Carlo method. Some simulation results were verified by experiments, and the simulation results are well matched with the experimental data, with the maximal deviation not less than 7.5%. The results indicated that the propagating laser beam would be attenuated and distorted due to the scattering and absorption of raindrops, and the energy attenuation and pulse shape distortion strongly depended on the laser pulse widths

Research on the compression algorithm of the infrared thermal image sequence based on differential evolution and double exponential decay model.

This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method