High-Level Technology Mapping for Memories

In this paper, we consider memory-mapping problems in High-Level Synthesis. We focus on the port mapping, bit-width mapping and word mapping, respectively. A 0-1 Integer Linear Programming (ILP) technique is used to solve the mapping problems, which synthesizes the source memory using one or more memory modules from a target memory library at a higher level. This method can not only perform bit-width mapping and word mapping, but it can also perform port mapping at the same time. Experimental results indicate that ILP approach is an effective method for memory reuse in high-level synthesis

マイクロケミカルペンで作製したナノワイヤーセンサーの開発

東京都立大

Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization

Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on S-transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel S-transform cepstrum coefficients), and chaotic features (12). Finally, radar chart and F-score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation

NP and NS1 proteins of H5N1 virus significantly upregulated IFITM1, IFITM2, and IFITM3 in A549 cells

Background: Avian influence virus H5N1 causes serious public health concern with significant morbidity and mortality from poultry to humans. Interferon-induced transmembrane (IFITM) proteins usually protect cells from many virus infections by viral entry and replication.Objectives: The purpose of this study was to investigate whether H5N1 viral proteins involved in regulation IFITM1, IFITM2, and IFITM3 following H5N1 infection.Methods: NS1, M1, NP, PB2, HA and NA genes of H5N1 virus were generated by PCR and cloned into pcDNA3.1/myc-His (+) A vector for genes over-expression experiments. Gene expression levels was performed using Real-time PCR.Results: Research displayed that NS1, M1, NP, and PB2 proteins of H5N1 virus increased IFITM1, IFITM2, and IFITM3 expression in A549 cells, only IFITM1 was upregulated by M1 in HEK293T cells. However, our study did not find that HA and NA of H5N1 virus affected IFITM genes family or interferon genes expression.Conclusion: Taken together, our data suggested that IFITM1, IFITM2, and IFITM3 might be directly upregulated via NS1, M1, NP, and PB2 proteins during H5N1 avian influenza virus infection. This study provided new insights into the influence of NS1 and NP proteins on regulation of IFITM1, IFITM2, and IFITM3 expression following H5N1 infection.Keywords: Influenza Virus, H5N1, NS1 protein, NP protein, IFITMs