Open-set signal recognition provides a new approach for verifying the robustness of models by introducing novel unknown signal classes into the model testing and breaking the conventional closed-set assumption, which has become very popular in real-world scenarios. In the present work, we propose an efficient open-set signal recognition algorithm, which contains three key sub-modules: the signal representation sub-module based on a vision transformer (ViT) structure, a set distance metric sub-module based on Wasserstein distance, and a class space compression sub-module based on reciprocal point separation and central loss. In this algorithm, the representing features of signals are established based on transformer-based neural networks, i.e., ViT, in order to extract global information about time series-related data. The employed reciprocal point is used in modeling the potential unknown space without using the corresponding samples, while the distance metric between different class spaces is mathematically modeled in terms of the Wasserstein distance instead of the classical Euclidean distance. Numerical experiments on different open-set signal recognition tasks show that the proposed algorithm can significantly improve the recognition efficiency in both known and unknown categories.

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Open-Set Signal Recognition Based on Transformer and Wasserstein Distance

Orthogonal learning metaheuristics for structural optimization

Haze and fog are big reasons for road accidents. The haze occurrence in the air lowers the images quality captured by visible camera sensors. Haze brings inconvenience to numerous computer vision applications as it diminishes the scene visibility. Haze removal techniques recuperate the color and scene contrast. These haze removal techniques are extensively utilized in numerous applications like outdoor surveillance, object detection, consumer electronics, etc. Haze removal is commonly performed under the physical degradation model, which requires a solution of an ill-posed inverse issue. Different dehazing algorithms was recently proposed to relieve this difficulty and has acknowledged a great deal of consideration. Dehazing is basically accomplished through four major steps: hazy images acquisition process, estimation process (atmospheric light, transmission map, scattering phenomenon, and visibility or haze level), enhancement process (improved visibility level, reduce haze or noise level), restoration process (restore enhanced image, image reconstruction). This four-step dehazing process makes it possible to provide a step-by-step approach to the complex solution of the ill-posed inverse problem. Our detailed survey and experimental analysis on different dehazing methods that will help readers understand the effectiveness of the individual step of the dehazing process and will facilitate development of advanced dehazing algorithms. The overall objective of this review paper is to explore the various methods for efficiently removing the haze and short comings of the earlier presented techniques used in the revolutionary era of image processing applications.

Bin Xiao

Papers

Recent advancement in haze removal approaches