Rethinking Separable Convolutional Encoders for End-to-End Semantic Image Segmentation

With the development of science and technology, the middle volume and neural network in the semantic image segmentation of the codec show good development prospects. Its advantage is that it can extract richer semantic features, but this will cause high costs. In order to solve this problem, this article mainly introduces the codec based on a separable convolutional neural network for semantic image segmentation. This article proposes a codec based on a separable convolutional neural network for semantic image segmentation research methods, including the traditional convolutional neural network hierarchy into a separable convolutional neural network, which can reduce the cost of image data segmentation and improve processing efficiency. Moreover, this article builds a separable convolutional neural network codec structure and designs a semantic segmentation process, so that the codec based on a separable convolutional neural network is used for semantic image segmentation research experiments. The experimental results show that the average improvement of the dataset by the improved codec is 0.01, which proves the effectiveness of the improved SegProNet. The smaller the number of training set samples, the more obvious the performance improvement.

Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis

For cross-modal subspace clustering, the key point is how to exploit the correlation information between cross-modal data. However, most hierarchical and structural correlation information among cross-modal data cannot be well exploited due to its high-dimensional non-linear property. To tackle this problem, in this paper, we propose an unsupervised framework named Cross-Modal Subspace Clustering via Deep Canonical Correlation Analysis (CMSC-DCCA), which incorporates the correlation constraint with a self-expressive layer to make full use of information among the inter-modal data and the intra-modal data. More specifically, the proposed model consists of three components: 1) deep canonical correlation analysis (Deep CCA) model; 2) self-expressive layer; 3) Deep CCA decoders. The Deep CCA model consists of convolutional encoders and correlation constraint. Convolutional encoders are used to obtain the latent representations of cross-modal data, while adding the correlation constraint for the latent representations can make full use of the information of the inter-modal data. Furthermore, self-expressive layer works on latent representations and constrain it perform self-expression properties, which makes the shared coefficient matrix could capture the hierarchical intra-modal correlations of each modality. Then Deep CCA decoders reconstruct data to ensure that the encoded features can preserve the structure of the original data. Experimental results on several real-world datasets demonstrate the proposed method outperforms the state-of-the-art methods.

A High Throughput Hardware Architecture for Parallel Recursive Systematic Convolutional Encoders

During the last years, recursive systematic convolutional (RSC) encoders have found application in modern telecommunication systems to reduce the bit error rate (BER). In view of the necessity of increasing the throughput of such applications, several approaches using hardware implementations of RSC encoders were explored. In this paper, we propose a hardware intellectual property (IP) for high throughput RSC encoders. The IP core exploits a methodology based on the ABCD matrices model which permits to increase the number of inputs bits processed in parallel. Through an analysis of the proposed network topology and by exploiting data relative to the implementation on Zynq 7000 xc7z010clg400-1 field programmable gate array (FPGA), an estimation of the dependency of the input data rate and of the source occupation on the parallelism degree is performed. Such analysis, together with the BER curves, provides a description of the principal merit parameters of a RSC encoder.