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The construction of sensing matrix is a fundamental issue in compressed sensing (CS). This paper introduces a new deterministic construction, referred to as Toeplitzed structurally chaotic matrix (TSCM), which possesses the advantages of both random and structural sensing matrices. We derive the matrix by first multiplying an orthonormal matrix with a chaotic-based Toeplitz matrix, and then subsampling the resultant matrix to obtain the structural one. Theoretically, we show that the entries of the TSCM are asymptotically normally distributed with that of arbitrary sparsifying matrices, yielding low mutual coherence that guarantees faithful recovery. Moreover, the proposed scheme is implementation friendly and hardware efficient, since its entries have almost no randomness and are easy to generate. Extensive numerical results via Matlab suggest that the TSCM outperforms the state-of-the-art matrix schemes and demonstrate its promising potentials.

In skeleton‐based action recognition, the graph convolutional network (GCN) has achieved great success. Modelling skeleton data in a suitable spatial‐temporal way and designing the adjacency matrix are crucial aspects for GCN‐based methods to capture joint relationships. In this study, we propose the spatial‐temporal slowfast graph convolutional network (STSF‐GCN) and design the adjacency matrices for the skeleton data graphs in STSF‐GCN. STSF‐GCN contains two pathways: (1) the fast pathway is in a high frame rate, and joints of adjacent frames are unified to build ‘small’ spatial‐temporal graphs. A new spatial‐temporal adjacency matrix is proposed for these ‘small’ spatial‐temporal graphs. Ablation studies verify the effectiveness of the proposed adjacency matrix. (2) The slow pathway is in a low frame rate, and joints from all frames are unified to build one ‘big’ spatial‐temporal graph. The adjacency matrix for the ‘big’ spatial‐temporal graph is obtained by computing self‐attention coefficients of each joint. Finally, outputs from two pathways are fused to predict the action category. STSF‐GCN can efficiently capture both long‐range and short‐range spatial‐temporal joint relationships. On three datasets for skeleton‐based action recognition, STSF‐GCN can achieve state‐of‐the‐art performance with much less computational cost.

In recent years, neural network-based voice conversion methods have been rapidly developed, and many different models and neural networks have been applied in parallel voice conversion. However, the over-smoothing of parametric methods [e.g., bidirectional long short-term memory (BLSTM)] and the waveform collapse of neural vocoders (e.g., WaveNet) still have negative impacts on the quality of the converted voices. To overcome this problem, we propose a BLSTM and WaveNet-based voice conversion method cooperated with waveform collapse suppression by post-processing. This method firstly uses BLSTM to convert the acoustic features between parallel speakers, and then synthesizes pre-converted voice with WaveNet. Subsequently, several alternative iterations of BLSTM post-processing is performed, and the final converted voice is generated by WaveNet. The proposed method can directly generate converted audio waveforms and avoid the waveform-collapsed speech caused by a single WaveNet generation effectively. The experimental results indicate that acoustic features trained by using the BLSTM network could achieve better results than conventional baselines. From our experiments on VCC2018, the usage of WaveNet could alleviate the problem of over-smoothing, which contributes to improving the similarity and naturalness of the final results of voice conversion.