SASEGAN-TCN: Speech enhancement algorithm based on self-attention generative adversarial network and temporal convolutional network.

Traditional unsupervised speech enhancement models often have problems such as non-aggregation of input feature information, which will introduce additional noise during training, thereby reducing the quality of the speech signal. In order to solve the above problems, this paper analyzed the impact of problems such as non-aggregation of input speech feature information on its performance. Moreover, this article introduced a temporal convolutional neural network and proposed a SASEGAN-TCN speech enhancement model, which captured local features information and aggregated global feature information to improve model effect and training stability. The simulation experiment results showed that the model can achieve 2.1636 and 92.78% in perceptual evaluation of speech quality (PESQ) score and short-time objective intelligibility (STOI) on the Valentini dataset, and can accordingly reach 1.8077 and 83.54% on the THCHS30 dataset. In addition, this article used the enhanced speech data for the acoustic model to verify the recognition accuracy. The speech recognition error rate was reduced by 17.4%, which was a significant improvement compared to the baseline model experimental results.

QoS-driven resource allocation in fog radio access network: A VR service perspective.

While immersive media services represented by virtual reality (VR) are booming, They are facing fundamental challenges, i.e., soaring multimedia applications, large operation costs and scarce spectrum resources. It is difficult to simultaneously address these service challenges in a conventional radio access network (RAN) system. These problems motivated us to explore a quality-of-service (QoS)-driven resource allocation framework from VR service perspective based on the fog radio access network (F-RAN) architecture. We elaborated details of deployment on the caching allocation, dynamic base station (BS) clustering, statistical beamforming and cost strategy under the QoS constraints in the F-RAN architecture. The key solutions aimed to break through the bottleneck of the network design and to deep integrate the network-computing resources from different perspectives of cloud, network, edge, terminal and use of collaboration and integration. Accordingly, we provided a tailored algorithm to solve the corresponding formulation problem. This is the first design of VR services based on caching and statistical beamforming under the F-RAN. A case study provided to demonstrate the advantage of our proposed framework compared with existing schemes. Finally, we concluded the article and discussed possible open research problems.

The complete mitochondrial genome of freshwater crab Sinopotamon xiushuiense (Decapoda: brachyura: Potamoidea).

We report for the first time the complete mitochondrial genome of Sinopotamon xiushuiense, which is found to be 18,460 bp in length, and contains 13 protein-coding genes (PCGs), 2 ribosomal RNA (rRNA) genes, 22 transfer RNA (tRNA), and 1 non-coding region known as the D-loop. In addition, the mitogenome has 18 intergenic regions ranging from 1 to 1221 bp in length. The complete mitogenome of S. xiushuiense is the longest among brachyurans reported in GenBank before November 2015, and its AT content is slightly lower than that in Geotholphusa dehaani. The mitogenome data provides a basis for further studies on population genetics and phylogenetics.

Complete mitochondrial genome of the Tamiops swinhoei (Rodentia: Sciuridae).

The complete mitochondrial genome of Tamiops swinhoei has been determined in this study. It is 16,513 bp in size and consists of 2 rRNA genes, 13 protein-coding genes, 22 tRNA genes and one non-coding region (D-loop). The overall base composition of the heavy strand of the T. swinhoei mitochondrial genome is A: 32.63%, T: 28.67%, C: 26.33% and G: 12.37%. The alignment of the Tamiops species control regions exhibited high genetic variability and rich A + T content (63.42%).