No-Reference Video Quality Assessment using novel hybrid features and two-stage hybrid regression for score level fusion

This paper presents a novel No-Reference Video Quality Assessment (NR-VQA) model that utilizes proposed 3D steerable wavelet transform-based Natural Video Statistics (NVS) features as well as human perceptual features. Additionally, we proposed a novel two-stage regression scheme that significantly improves the overall performance of quality estimation. In the first stage, transform-based NVS and human perceptual features are separately passed through the proposed hybrid regression scheme: Support Vector Regression (SVR) followed by Polynomial curve fitting. The two visual quality scores predicted from the first stage are then used as features for the similar second stage. This predicts the final quality scores of distorted videos by achieving score level fusion. Extensive experiments were conducted using five authentic and four synthetic distortion databases. Experimental results demonstrate that the proposed method outperforms other published state-of-the-art benchmark methods on synthetic distortion databases and is among the top performers on authentic distortion databases. The source code is available at https://github.com/anishVNIT/two-stage-vqa.     

Time-series clustering – A decade review

Clustering is a solution for classifying enormous data when there is not any early knowledge about classes. With emerging new concepts like cloud computing and big data and their vast applications in recent years, research works have been increased on unsupervised solutions like clustering algorithms to extract knowledge from this avalanche of data. Clustering time-series data has been used in diverse scientific areas to discover patterns which empower data analysts to extract valuable information from complex and massive datasets. In case of huge datasets, using supervised classification solutions is almost impossible, while clustering can solve this problem using un-supervised approaches. In this research work, the focus is on time-series data, which is one of the popular data types in clustering problems and is broadly used from gene expression data in biology to stock market analysis in finance. This review will expose four main components of time-series clustering and is aimed to represent an updated investigation on the trend of improvements in efficiency, quality and complexity of clustering time-series approaches during the last decade and enlighten new paths for future works.     

关于提升电视专题节目制作质量的对策研究

在广播电视行业不断发展条件下,电视专题节目制作也受到受众高度重视。这就应提高电视专题节目制作质量,在满足广大受众日常观看要求同时,使得我国广播电视行业向着现代化方向发展。针对电视专题节目制作进行研究,首先阐述电视专题节目制作特点,之后阐述电视专题节目制作质量问题和相关提升对策。在解决电视专题节目制作问题同时,提升电视专题节目制作质量,继而彰显电视专题节目制作优势。     

PX型高效旋风分离器的研究开发

根据对PV型旋风分离器气固流动特性的深入研究,分析了几处影响细粉分离的薄弱环节,并针对性地提出了进气口倾斜、排气管切口及排尘口加防返混锥等结构改进措施.在φ300mm实验室模型试验台上,通过大量对比试验,开发出了一种比现有PV型性能更优良的新一代高效旋风分离器-PX型旋风分离器.实验室对比试验结果表明,相同条件下,PX型比PV型效率提高0.2～1.1个百分点,压力降降低10%～15%.     

闪光灯泵浦Nd3+:GGG激光特性研究

用闪光灯泵浦掺钕钆镓石榴石(Nd^3 ：GGG)激光晶体，研究了脉冲宽度0．5ms、1ms、1．5ms和2ms的放电泵浦下激光输出，实验获得了1．92J的最大激光能量．     

An Exploratory Study of E-Business Success Factors

E-business success factors are Important for traditional enterprises to implement e-business. This topic is attracting more and more researchers to study. This paper makes an exploratory study on the factors influencing e-business success. Firstly, based on the literature review, 52 factors are suggested. Secondly, two rounds of survey with Delphi method are conducted. Qualitative and quantitative analysis are used to identify 57 factors. This is the foundation of empirical study.     

基于WVD的防空雷达直升机信号特征分析

基于直升机目标旋转部件的调制模型，本文针对防空雷达提出了分析直升机信号特征的一种新方法——WVD法，并对直升机目标旋转部件调制回波信号在不同噪声情况下加以仿真分析，结果表明：用WVD分析雷达回波中直升机信号特征是一种非常有效的方法。     

晋北吕梁群石榴石的特征及指示意义

石榴石是山西岚县袁家村一带-娄烦县出露的吕梁群地层中主要的特征变质矿物，其次为黑云母、十字石等，对这些特征变质矿物进行电子探针的成分分析及其相关计算，并利用不同采样点的矿物对估算该点变质温度和压力，最后得到石榴石的成分变化与变质程度之间的关系，进而阐明了石榴石中成分变化对变质温度与压力的指示意义。     

An Effective Software Pipelining Algorithm for Clustered Embedded VLIW Processors

This paper proposes a software pipelining framework, CALiBeR (ClusterAware Load Balancing Retiming Algorithm), suitable for compilers targetingclustered embedded VLIW processors. CALiBeR can be used by embedded systemdesigners to explore different code optimization alternatives, that is, high-qualitycustomized retiming solutions for desired throughput and program memory sizerequirements, while minimizing register pressure. An extensive set of experimentalresults is presented, demonstrating that our algorithm compares favorablywith one of the best state-of-the-art algorithms, achieving up to 50% improvementin performance and up to 47% improvement in register requirements. In orderto empirically assess the effectiveness of clustering for high ILP applications,additional experiments are presented contrasting the performance achievedby software pipelined kernels executing on clustered and on centralized machines.     

FindOut: Finding Outliers in Very Large Datasets

Finding the rare instances or the outliers is important in many KDD (knowledge discovery and data-mining) applications, such as detecting credit card fraud or finding irregularities in gene expressions. Signal-processing techniques have been introduced to transform images for enhancement, filtering, restoration, analysis, and reconstruction. In this paper, we present a new method in which we apply signal-processing techniques to solve important problems in data mining. In particular, we introduce a novel deviation (or outlier) detection approach, termed FindOut, based on wavelet transform. The main idea in FindOut is to remove the clusters from the original data and then identify the outliers. Although previous research showed that such techniques may not be effective because of the nature of the clustering, FindOut can successfully identify outliers from large datasets. Experimental results on very large datasets are presented which show the efficiency and effectiveness of the proposed approach. Received 7 September 2000 / Revised 2 February 2001 / Accepted in revised form 31 May 2001 Correspondence and offprint requests to: A. Zhang, Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA. Email: azhang@cse.buffalo.eduau