Hierarchical Self-organizing Network Research Articles

Self-organized night video enhancement for surveillance systems

Night video enhancement techniques are widely used for identifying suspicious activities captured by night visual surveillance systems. However, artificial light sources present in the surroundings deteriorate the visual quality of the video captured during night. This non-uniform illumination reduces the object identification and tracking capability of a real-time visual security system. Thus, a uniform enhancement technique is insufficient for handling such uneven illumination. In this paper, we propose a novel night video enhancement scheme based on a hierarchical self-organizing network. This proposed scheme automatically groups and enhances the neighboring pixels of dark and light regions in each frame. In this scheme, two-level self- organizing neural networks were hierarchically arranged to group similar pixels present in the night video frame. We applied the no-reference-based performance evaluation metrics for measuring the objective quality of the video. The experimental results showed that our proposed approach considerably enhances the visual perception of the video captured at night under varied illumination conditions.

Mapping and hierarchical self-organizing neural networks for VLSI placement

We have developed mapping and hierarchical self-organizing neural networks for placement of very large scale integrated (VLST) circuits. In this paper, we introduce MHSO and MHSO2 as two versions of mapping and hierarchical self-organizing network (MHSO) algorithms. By using the MHSO, each module in the placement wins the competition with a probability density function that is defined according to different design styles, e.g., the gate arrays and standard cell circuits. The relation between a placement carrier and movable modules is met by the algorithm's ability to map an input space (somatosensory source) into an output space where the circuit modules are located, MHSO2 is designed for macro cell circuits. In this algorithm, the shape and dimension of each module is simultaneously considered together with the wire length by a hierarchical order. In comparison with other conventional placement approaches, the MHSO algorithms have shown their distinct advantages. The results for benchmark circuits so far obtained are quite comparable to simulated annealing (SA), but the computation time is about eight-ten times faster than with SA.