Matey Yordanov

Mikhail Bychkov, Matey Yordanov, Andrei Kuchma

This paper introduces AURA, a novel hybrid spatiotemporal-chromatic framework designed for robust, real-time detection and classification of industrial smoke emissions. The framework addresses critical limitations of current monitoring systems, which often lack the specificity to distinguish smoke types and struggle with environmental variability. AURA leverages both the dynamic movement patterns and the distinct color characteristics of industrial smoke to provide enhanced accuracy and reduced false positives. This framework aims to significantly improve environmental compliance, operational safety, and public health outcomes by enabling precise, automated monitoring of industrial emissions.

Boris Kriuk, Matey Yordanov

This paper introduces GeloVec, a new CNN-based attention smoothing framework for semantic segmentation that addresses critical limitations in conventional approaches. While existing attention-backed segmentation methods suffer from boundary instability and contextual discontinuities during feature mapping, our framework implements a higher-dimensional geometric smoothing method to establish a robust manifold relationships between visually coherent regions. GeloVec combines modified Chebyshev distance metrics with multispatial transformations to enhance segmentation accuracy through stabilized feature extraction. The core innovation lies in the adaptive sampling weights system that calculates geometric distances in n-dimensional feature space, achieving superior edge preservation while maintaining intra-class homogeneity. The multispatial transformation matrix incorporates tensorial projections with orthogonal basis vectors, creating more discriminative feature representations without sacrificing computational efficiency. Experimental validation across multiple benchmark datasets demonstrates significant improvements in segmentation performance, with mean Intersection over Union (mIoU) gains of 2.1%, 2.7%, and 2.4% on Caltech Birds-200, LSDSC, and FSSD datasets respectively compared to state-of-the-art methods. GeloVec's mathematical foundation in Riemannian geometry provides theoretical guarantees on segmentation stability. Importantly, our framework maintains computational efficiency through parallelized implementation of geodesic transformations and exhibits strong generalization capabilities across disciplines due to the absence of information loss during transformations.