Obstructive sleep apnea hypopnea syndrome (OSAHS) is a serious sleep disorder. As the typical symptom of OSAHS, snoring has been proved effective in OSAHS diagnosis and potential to replace the current laborious and expensive polysomnography. However, the lack of analysis on the characteristics of pathological snoring sounds limits the diagnosing performance. In this paper, we propose novel sound features for the classification of OSA, hypopnea and normal snores. The proposed features are based on percussive enhancing and positional encoding as the snores exhibit different percussive properties and temporal traits due to the disease generation mechanisms. To enhance the classification performance, we propose a multi-task learning framework to aid the main classification task by simultaneous learning of two related simple tasks. Experiments on real-recorded snoring sounds show that the proposed methods can greatly improve the classification AUC and ACC and the proposed system performs better than those in other literatures.

Coarse-Grained Reconfigurable Architectures (CGRA) is a promising solution for accelerating computation intensive tasks due to its good trade-off in energy efficiency and flexibility. One of the challenging research topic is how to effectively deploy loops onto CGRAs within acceptable compilation time. Modulo scheduling (MS) has shown to be efficient on deploying loops onto CGRAs. Existing CGRA MS algorithms still suffer from the challenge of mapping loop with higher performance under acceptable compilation time, especially mapping large and irregular loops onto CGRAs with limited computational and routing resources. This is mainly due to the under utilization of the available buffer resources on CGRA, unawareness of critical mapping constraints and time consuming method of solving temporal and spatial mapping. This article focus on improving the performance and compilation robustness of the modulo scheduling mapping algorithm for CGRAs. We decomposes the CGRA MS problem into the temporal and spatial mapping problem and reorganize the processes inside these two problems. For the temporal mapping problem, we provide a comprehensive and systematic mapping flow that includes a powerful buffer allocation algorithm, and efficient interconnection & computational constraints solving algorithms. For the spatial mapping problem, we develop a fast and stable spatial mapping algorithm with backtracking and reordering mechanism. Our MS mapping algorithm is able to map loops onto CGRA with higher performance and faster compilation time. Experiment results show that given the same compilation time budget, our mapping algorithm generates higher compilation success rate. Among the successfully compiled loops, our approach can improve 5.4 to 14.2 percent performance and takes x24 to x1099 less compilation time in average comparing with state-of-the-art CGRA mapping algorithms.

The SL0 algorithm for compressive sensing (CS) reconstruction uses smoothed ℓ0norm and introduces a sequence of smoothed functions to approximate the ℓ0norm. Therefore, the NP-hard problem of minimization of the ℓ0norm can be transferred to a convex optimization problem for smoothed functions. Considering the defects of SL0 algorithm in the iterative process and in order to choose an appropriate the ℓ0norm, we use Composite Inverse Proportion Model to approximate the ℓ0norm, introduce the thought of the OSL0 algorithm, and combine with the steepest descent method and the gradient projection principle to get the reconstruction signal, a new algorithm called Modified Smoothed ℓ0algorithm(MSL0) is proposed. Experimental results show that, under the same test conditions, the MSL0 algorithm is superior to SL0 and other same type algorithms both in the reconstruction quality and the performance.