Human Salusin-alpha ELISA Kit

Unconstrained videos, captured by inexperienced amateurs, suffer from wide-ranging quality aspects such as poor illumination, considerable camera motion, cluttered background, occlusion and substantial viewpoint variations. Event recognition in these videos is a challenging task in the field of computer vision, serving as potential solution to several practical applications. In this paper, we propose a novel approach for recognizing events in the unconstrained videos that relics on the temporal features of the representative frames (key-frames) in each video. This graph-based key-frame extraction technique exploits the inter-frame temporal variation for the entire video and reduces temporal redundancy amongst consecutive frames. The problem is thereby formulated as a constrained optimization problem, where the temporal distinctness as well as the distance of demarcation amongst the chosen frames is maximized simultaneously. For classification, we propose a model that fuses a deep residual network with the Recurrent Neural Network (RNN) model of Long Short-Term Memory (LSTM) to capture long-term temporal motion patterns. Experimental results reveal better outcome than most of the state-of-the-art methods based on test data from Columbia Consumer Video (CCV), Kodak's consumer video and UCF-101 benchmark datasets. The videos having high content diversity, pose as classification challenges to the research.

In this paper surge control in a compression system using a close-coupled valve (CCV) is proposed. The control design is based on Lyapunov control theory in combination with neural networks (NNs) and focuses on minimization of loss of energy in the compressor system. The approach allows for control design with guaranteed region of attraction when considering saturated controls. The CCV modifies the characteristics of the compressor and thus stabilizes the equilibrium beyond the original surge line at the expense of a pressure drop over the valve. Two control laws for the compression system are proposed. The first control law provides a global asymptotically stable equilibrium. The second control law focuses on minimization of the pressure drop over the valve, and local asymptotically stability is proven.