A monaural speech enhancement method combining deep neural network (DNN) with low rank analysis and speech present probability is proposed in this letter. Low rank and sparse analysis is first applied on the noisy speech spectrogram to get the approximate low rank representation of noise. Then a joint feature training strategy for DNN based speech enhancement is presented, which helps the DNN better predict the target speech. To reduce the residual noise in highly overlapping regions and high frequency domain, speech present probability (SPP) weighted post-processing is employed to further improve the quality of the speech enhanced by trained DNN model. Compared with the supervised non-negative matrix factorization (NMF) and the conventional DNN method, the proposed method obtains improved speech enhancement performance under stationary and non-stationary conditions.

In this letter, a novel perceptually motivated single channel speech enhancement approach based on Deep Neural Network (DNN) is presented. Taking into account the good masking properties of the human auditory system, a new DNN architecture is proposed to reduce the perceptual effect of the residual noise. This new DNN architecture is directly trained to learn a gain function which is used to estimate the power spectrum of clean speech and shape the spectrum of the residual noise at the same time. Experimental results demonstrate that the proposed perceptually motivated speech enhancement approach could achieve better objective speech quality when tested with TIMIT sentences corrupted by various types of noise, no matter whether the noise conditions are included in the training set or not.

In this letter, we propose a novel speech separation method based on perceptual weighted deep recurrent neural network (DRNN) which incorporate the masking properties of the human auditory system. In supervised training stage, we firstly utilize the clean label speech of two different speakers to calculate two perceptual weighting matrices. Then, the obtained different perceptual weighting matrices are utilized to adjust the mean squared error between the network outputs and the reference features of both the two clean speech so that the two different speech can mask each other. Experimental results on TSP speech corpus demonstrate that the proposed speech separation approach can achieve significant improvements over the state-of-the-art methods when tested with different mixing cases.

In this letter, we explore joint optimization of perceptual gain function and deep neural networks (DNNs) for a single-channel speech enhancement task. A DNN architecture is proposed which incorporates the masking properties of the human auditory system to make the residual noise inaudible. This new DNN architecture directly trains a perceptual gain function which is used to estimate the magnitude spectrum of clean speech from noisy speech features. Experimental results demonstrate that the proposed speech enhancement approach can achieve significant improvements over the baselines when tested with TIMIT sentences corrupted by various types of noise, no matter whether the noise conditions are included in the training set or not.

This paper presents an inter-satellite link (ISL) reassignment method to optimize the snapshot routing performance for polar-orbit LEO satellite networks. When the snapshot routing tables are switching simultaneously in all satellites, we propose to reassign the inter-plane ISLs with regularity to improve the quality of the next snapshot, such as snapshot duration, on-board transceiver utilization ratio and end to end delay. Evaluations indicate that our method can attain equal-length snapshots regardless of the latitude of the polar area border, and so is superior to the natural partition method. Meanwhile, compared with the equal partition method which is used in the Iridium system, our method can prolong 82.87% snapshot duration, increase 8.68% on-board transceiver utilization ratio and reduce 5.30% average end to end delay of the whole network. Therefore, we believe that the ISL reassignment method can be efficiently applied in all practical polar-orbit LEO satellite networks.

In this paper, we analyze the rollback traffic in polar-orbit satellite networks that use the snapshot routing algorithm. The concept of diamond rollback links and polar rollback links are presented for the first time, and the numbers of diamond and polar rollback links in polar-orbit satellite networks are concisely formulated. Simulations are performed based on the Iridium and Teledesic system in NS2, and the results finally confirm our analysis. With this work, we can not only simplify the rollback loops avoidance scheme, but also provide guidance for future satellite network routing optimization and topology design.

The sparse unmixing of hyperspectral data has attracted much attention in recent years because it does not need to estimate the number of endmembers nor consider the lack of pure pixels in a given hyperspectral scene. However, the high mutual coherence of spectral libraries strongly affects the practicality of sparse unmixing. The collaborative sparse unmixing via variable splitting and augmented Lagrangian (CLSUnSAL) algorithm is a classic sparse unmixing algorithm that performs better than other sparse unmixing methods. In this paper, we propose a CLSUnSAL-based hyperspectral unmixing method based on dictionary pruning and reweighted sparse regression. First, the algorithm identifies a subset of the original library elements using a dictionary pruning strategy. Second, we present a weighted sparse regression algorithm based on CLSUnSAL to further enhance the sparsity of endmember spectra in a given library. Third, we apply the weighted sparse regression algorithm on the pruned spectral library. The effectiveness of the proposed algorithm is demonstrated on both simulated and real hyperspectral datasets. For simulated data cubes (DC1, DC2 and DC3), the number of the pruned spectral library elements is reduced by at least 94% and the runtime of the proposed algorithm is less than 10% of that of CLSUnSAL. For simulated DC4 and DC5, the runtime of the proposed algorithm is less than 15% of that of CLSUnSAL. For the real hyperspectral datasets, the pruned spectral library successfully reduces the original dictionary size by 76% and the runtime of the proposed algorithm is 11.21% of that of CLSUnSAL. These experimental results show that our proposed algorithm not only substantially improves the accuracy of unmixing solutions but is also much faster than some other state-of-the-art sparse unmixing algorithms.

Comparing with that of terrestrial networks, the location management in satellite networks is mainly restricted by three factors, i.e., the limited on-board processing (OBP), insufficient link resources and long propagation delay. Under these restrictions, the limited OBP can be smoothened by terrestrial gateway-based location management, the constraint from link resources demands the bandwidth-efficient management scheme and long propagation delay potentially lowers the management efficiency. Currently, the reduction of the management cost has always been the main direction in existing work which is based on the centralized management architecture. This centralized management has many defects, such as the non-optimal routing, scalability problem and single point of failure. To address these problems, this paper explores gateway-based distributed location management schemes for Low Earth Orbit (LEO) satellite networks. Three management schemes based on terrestrial gateways are proposed and analyzed: loose location management, precise location management, and the grouping location management. The analyses specifically analyze the cost of location queries and show their significant influence on the total cost which includes the location management and query. Starting from the above analysis, we speculate and prove the existence of the optimum scheme in grouping location management, which has the lowest total cost for the query frequency within given range. Simulation results validate the theoretical analysis on the cost and show the feature of latency in location queries, which provide a valuable insight into the design of the distributed location management scheme in satellite networks.

Border Gateway Protocol (BGP), with its advantages in routing isolation support and mature application, is a promising candidate to integrate satellite systems into the terrestrial IP network. However, with more and more ground stations accessing satellites by BGP, a significant amount of routing overhead can be produced on limited satellite links, especially for geostationary satellite networks with thousands of accessing terminals in extremely large areas. To solve this challenge, multicast transport of BGP was proposed, which takes advantage of the inherent broadcast property of wireless channels. However, its performance can be seriously degraded when interfered with the environment. In this paper, NCSR (Network Coding for Satellite network BGP Routing transport) [1] is explored in depth. Unlike existing counterparts, NCSR pays more attention to the lossy space links and can achieve reliability with more bandwidth savings. A greedy based coding algorithm is proposed to realize the network coding operation. To demonstrate the efficiency of NCSR, we conduct theoretical analyses and extensive simulations in typical scenarios of satellite systems. Simulation results show that NCSR can greatly reduce the bandwidth usage while achieving comparable latency. Discussions on practical considerations when applying network coding method for reliability assurance are also presented in detail.

In this paper, we propose a salient region detection method with multi-feature fusion and edge constraint. First, an image feature extraction and fusion network based on dense connection structure and multi-channel convolution channel is designed. Then, a multi-scale atrous convolution block is applied to enlarge reception field. Finally, to increase accuracy, a combined loss function including classified loss and edge loss is built for multi-task training. Experimental results verify the effectiveness of the proposed method.