Video based speaker line velocity estimation and endoscopic 3D imaging


Video based speaker line velocity estimation and endoscopic 3D imaging – In this paper, we propose a new deep learning paradigm called Support Vector Machine (SVM) with deep CNNs. SVM is a deep training and learning paradigm that is capable of handling challenging scenarios. Our SVM architecture combines a deep network with an end-to-end CNN architecture. In this paper, we further focus on the application of deep learning for speaker line velocity estimation from a camera. We trained the SVM architecture on the frame-by-frame data acquired from four real world speakers on different days. We show that our SVM architecture successfully outputs the velocity estimation in a fast, accurate and accurate manner. We also compare the quality of the end-to-end training of the SVM architecture and the SVM end-to-end training on the MNIST dataset, demonstrating that the SVM architecture is able to perform better.

Fuzzy proteins are powerful and versatile molecular machines, and one of the key ingredients in protein synthesis is a set of proteins that represent a given protein structure. In this work, we present a method for fuzzy-protein synthesis that includes a set of fuzzy proteins as elements. This framework allows us to construct and understand fuzzy-protein clusters directly from fuzzy protein sequences. We present the algorithm which performs some experiments, including for the first time a complete description of a multi-dimensional fuzzy protein system, and demonstrate the effect the proposed method can have on the classification of protein sequences.

This paper proposes an efficient genetic algorithm for the identification of the molecular structure of a single protein. This algorithm has been tested on the problem of protein identification by means of molecular biology. This paper describes the proposed method, how the method is implemented, the procedure to test it and the experiments that it implements.

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Video based speaker line velocity estimation and endoscopic 3D imaging

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  • A Fast Convex Formulation for Unsupervised Model Selection on Graphs

    Proteomics: a theoretical platform for the analysis of animal protein sequence dataFuzzy proteins are powerful and versatile molecular machines, and one of the key ingredients in protein synthesis is a set of proteins that represent a given protein structure. In this work, we present a method for fuzzy-protein synthesis that includes a set of fuzzy proteins as elements. This framework allows us to construct and understand fuzzy-protein clusters directly from fuzzy protein sequences. We present the algorithm which performs some experiments, including for the first time a complete description of a multi-dimensional fuzzy protein system, and demonstrate the effect the proposed method can have on the classification of protein sequences.

    This paper proposes an efficient genetic algorithm for the identification of the molecular structure of a single protein. This algorithm has been tested on the problem of protein identification by means of molecular biology. This paper describes the proposed method, how the method is implemented, the procedure to test it and the experiments that it implements.


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