美国深空网的同波束干涉测量技术

讨论了美国NASA深空网(DSN)对深空飞船所采用的同波束干涉(SBI)技术概念和测量原理,并介绍了这一技术的应用和发展情况。     

一种使用遗传算法在高层次综合中完成互连优化的方法

提出一种使用遗传算法在高层次综合中完成互连优化的方法 .相比同类的研究 ,该方法的主要优势在于提出一种新颖的编码方法 ,并设计了相应的遗传算子 ,避免了在计算过程中不可行解的产生 .实验数据证明了该算法的有效性     

扩展的Deep Web质量估计模型研究

Deep Web中蕴涵了海量的高质量信息.文中从Deep Web数据源的功能属性和非功能属性两个方面对数据源的质量进行度量,建立了一种基于综合模糊评价指标体系的扩展的数据源质量估计模型.实验结果表明该模型得到的数据源质量排序序列和人工排序序列的Kendall’s距离较扩展前有了很大提高,而且质量估计结果也能使数据源的选择得到较高精确度.     

一种Deep Web数据源质量评估模型

分析了影响Deep Web数据源质量评估的若干因素,提出了一种Deep Web数据源质量评估模型.该模型从浏览器、Web数据库、Web服务器和用户四个方面对数据源进行质量评估.通过在真实的Deep Web数据源上进行实验验证,说明该方法是有效和可行的.     

亚微米BiCMOS电路纵向NPN管的ESD保护研究

文章以0.6μmN外延BiCMOS工艺为基础,研究了纵向NPN管的ESD保护行为,并对不同版图结构的纵向NPN管进行了ESD行为研究。实验表明,由于基区的内在电阻不一样,在该工艺条件下,CEB、CEBE结构比CBE、CBEB结构SNAPBACK效应明显,机器模式下ESD保护能力强。此外,还研究了兼容低压Vz工艺,单级保护NMOS输出管的纵向NPN器件的ESD行为,流片显示采用EB结齐纳击穿的纵向NPN能有效单级保护CMOS的输出级。     

Deep eutectic solvent promoted hydrothiocyanation of alkynoates leading to Z-3-thiocyanatoacrylates

A mild and efficient protocol for the selective construction of Z-3-thiocyanatoacrylates is described. Various alkynoates reacted with KSCN and H₂O by using cheap and recyclable deep eutectic solvent as the catalyst and reaction media to produce the corresponding products in excellent yields with mild reaction conditions and wide substrate scope.     

A deep learning ensemble for function prediction of hypothetical proteins from pathogenic bacterial species

Protein function prediction is a crucial task in the post-genomics era due to their diverse irreplaceable roles in a biological system. Traditional methods involved cost-intensive and time-consuming molecular biology techniques but they proved to be ineffective after the outburst of sequencing data through the advent of cost-effective and advanced sequencing techniques. To manage the pace of annotation with that of data generation, there is a shift to computational approaches which are based on homology, sequence and structure-based features, protein-protein interaction networks, phylogenetic profiles, and physicochemical properties, etc. A combination of these features has proven to be promising for protein function prediction in terms of improving prediction accuracy. In the present work, we have employed a combination of features based on sequence, physicochemical property, subsequence and annotation features with a total of 9890 features extracted and/or calculated for 171,212 reviewed prokaryotic proteins of 9 bacterial phyla from UniProtKB, to train a supervised deep learning ensemble model with the aim to categorize a bacterial hypothetical/unreviewed protein’s function into 1739 GO terms as functional classes. The proposed system being fully dedicated to bacterial organisms is a novel attempt amongst various existing machine learning based protein function prediction systems based on mixed organisms. Experimental results demonstrate the success of the proposed deep learning ensemble model based on deep neural network method with F1 measure of 0.7912 on the prepared Test dataset 1 of reviewed proteins.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

FPGA-based implementation of classification techniques: A survey

Recently, a number of classification techniques have been introduced. However, processing large dataset in a reasonable time has become a major challenge. This made classification task more complex and expensive in calculation. Thus, the need for solutions to overcome these constraints such as field programmable gate arrays (FPGAs). In this paper, we give an overview of the various classification techniques. Then, we present the existing FPGA based implementation of these classification methods. After that, we investigate the confronted challenges and the optimizations strategies. Finally, we highlight the hardware accelerator architectures and tools for hardware design suggested to improve the FPGA implementation of classification methods.