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In this paper, we investigate the robust sampled-data control invariance of Boolean control networks (BCNs) via semi-tensor product of matrices. A necessary and sufficient condition is obtained to check whether or not a given set is robust sampled-data control invariant under a given sampled-data state feedback controller (SDSFC). At last, the study of model about lac operon in the Escherichia coli...
We propose a unified deep neural network (DNN) approach to achieve both high-quality enhanced speech and high-accuracy automatic speech recognition (ASR) simultaneously on the recent REverberant Voice Enhancement and Recognition Benchmark (RE-VERB) Challenge. These two goals are accomplished by two proposed techniques, namely DNN-based regression to enhance reverberant and noisy speech, followed by...
In this paper, we introduce a procedure for the null broadening algorithm design with respect to the perturbation of the interference location. This method is based on maximizing the array output signal-to-interference-plus-noise-ratio (SINR) subject to quadratic constraints. The design problem can be cast as a fractional quadratically constrained quadratic programming (QCQP) problem that can be solved...
In this paper, we propose a compact and robust video fingerprinting scheme by using sparse represented features (SRF). The SRF are extracted by a two dimensional matching pursuit decomposition (2D-MPD) method. The motivation of using sparse features is that the sparse coding method can significantly reduce the data dimensionality and effectively retain the structure of the images. To further reduce...
Unmanned Aerial Vehicles (UAVs) is a kind of aircraft performing certain intelligence without pilot. To fulfill complex and practical tasks, accurate state estimation is an essential subject of UAVs. However, UAV is easy to be lost and hardly to be localized again in the unknown environments due to low features and strong flexibility in the previous SLAM system. In this paper, a rectification strategy...
Large scale forest mapping and change detection plays a significant role in the study of global change, particularly in the research of carbon source and sink. This paper presents results from forest/non-forest classification using ENVISAT-ASAR data. Both pixel-based and object-based classification method were developed for ASAR HH/HV images acquired on a single date. For the object-based classification,...
We present collaborative peer-to-peer algorithms for the problem of approximating frequency counts for popular items distributed across the peers of a large-scale network. Our algorithms are attack-resistant in the sense that they function correctly even in the case where an adaptive and computationally unbounded adversary causes up to a 1/3 fraction of the peers in the network to suffer Byzantine...
The covariance region descriptor recently proposed in [1] has been proved robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties as well as their correlation are characterized. The similarity of two covariance descriptor is measured on Riemannian manifolds. Relying on the same...
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