检索结果-南通市图书馆

Photonic properties of novel Yb3＋ doped germanium-lead oxyfluoride glass-ceramics for laser cooling applications

Frontiers of Optoelectronics 2018年第2期11卷 189-198页

作者： Lauro J. Q. MAIA Jyothis THOMAS Yannick LEDEMI Kummara v. KRISHNAIAH denis SELETSKIY Younes MESSADDEQ Raman KASHYAP Instituto de Fisica Universidade Federal de Goias Av. Esperanga 1533 Campus Samambaia Goiania GO 74690-900 Brazil Department of Electrical Engineering Ecole Polytechnique de Montrral P.O. Box 6079 Station Centre-ville Montreal QC H3C 3A7 Canada Department of Engineering Physics Ecole Polytechnique de Montrral P.O. Box 6079 Station Centre-ville Montreal QC H3C 3A7 Canada Centre d'Optique Photonique et Laser 2375 Rue de la Terrasse Universite Laval Qurbec QC G1V 0A6 Canada

Abstract In recent years, our research group has developed and studied new rare-earth doped materials for the promising technology of solid-state laser cooling, which is based on anti-stokes fluorescence. To the best of our knowledge, our group is the only one in Canada leading the research into the properties of nanoparticles, glasses and glass-ceramics for optical refrigeration appli- cations. In the present work, optical properties of 50GeO2- 30PbF2-18PbO-2YbF3 glass-ceramics for laser cooling are presented and discussed as a function of crystallization temperature. Spectroscopic results show that samples have near infrared photoluminescence emission due to the 2F5/2 - 2F7/2 Yb3＋ transition, centered at -1016 nm with an excitation wavelength of 920 nm or 1011 nm, and the highest photoluminescence emission efficiency occurs for heat-treatment for 5 h at 350℃. The internal photolumi- nescence quantum yield varies between 99% and 80%, depending on the temperature of heat-treatment, being the most efficient under 1011 nm excitation. The 2F5/2 lifetime increases from 1.472 to 1.970 ms for heat treatments at 330℃ to 350℃, respectively, due to energy trapping and the low phonon energy of the nanocrystals. The sample temperature dependence was measured with a fiber Bragg grating sensor, as a function of input pump laser wavelength and processing temperature. These measure- ments show that the heating process approaches near zero for an excitation wavelength between 1020 and 1030 nm, which is an indication that phonons are removed effectivelly from the glass-ceramic materials, and they can be used for optical laser cooling applications. On theother hand, the temperature increase as a function of input laser power into samples remains constant between 920 and 980 nm wavelength excitation, a temperature variation of 36 K/W （temperature of 58℃/W） was attained under excitation at 950 nm, showing a possible use for biomedical applications to be explored.

关键词： optical refrigeration oxyfluoride glass-cera-mics Yb3+ doping quantum yield infrared emission lifetime

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Zr4+/F~– co-doped TiO2(anatase) as high performance anode material for lithium-ion battery

引用

Progress in Natural Science:Materials International 2018年第5期28卷 542-547页

作者： denis P.Opra Sergey v.Gnedenkov Sergey L.Sinebryukhov Elena I.voit Alexander A.Sokolov Alexander Yu.Ustinov veniamin v.Zheleznov Institute of Chemistry Far Eastern Branch of Russian Academy of Sciences Far Eastern Federal University

Zr4+and F~– co-doped TiO2with the formula of Ti0.97Zr0.03O1.98F0.02was facilely synthesized by a sol-gel template *** crystal structure,morphology,composition,surface area,and conductivity were characterized by Raman spectroscopy,energy-dispersive X-ray analysis,scanning electron microscopy,Brunauer-Emmett-Teller measurements,X-ray photoelectron spectroscopy,and electrochemical impedance *** results demonstrate that Zr4+and F~–homogeneously incorporated into TiO2,forming solid solution with an anatase ***0.97Zr0.03O1.98F0.02shows outstanding electrochemical properties as Li-ion battery anode in comparison with *** particular,upon 35-fold cycling at 1C-rate Zr4+/F~–co-doped TiO2delivers a reversible capacity of 163 mAh g–1,whereas Zr4+-doped TiO2gives only 34 mA h g–***,Zr4+/F~–co-doped TiO2retains a capacity of 138 mA h g–1during cycling even at 10 *** enhance performance originates from improved conductivity of Zr4+/F~–co-doped TiO2material through generation of Ti3+(serving as electron donors)into the crystal lattice and,possibly,due to F-doping blocked the anode surface from attack of HF formed as electrolyte decomposition product.

关键词： Li-ion batteries TiO2(anatase) Anode Co-doping Sol-gel template Process Electrochemical performance

来源：

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Deep Learning Based Intelligent and Sustainable Smart Healthcare Application in Cloud-Centric IoT

引用

Computers, Materials & Continua 2021年第2期66卷 1987-2003页

作者： K.v.Praveen P.M.Joe Prathap S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Irina v.Pustokhina denis A.Pustokhin Department of Information Technology St.Peters College of Engineering and TechnologyChennai600054India Department of Information Technology RMD Engineering CollegeChennai601206India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabad501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabad500075India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security,reliability,and *** healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service *** futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things(IoT)in cloud computing *** energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering *** current research article presents a new Oppositional Glowworm Swarm Optimization(OGSO)algorithmbased clustering with Deep Neural Network(DNN)called OGSO-DNN model for distributed healthcare *** OGSO algorithm was applied in this study to select the Cluster Heads(CHs)from the available IoT *** selected CHs transmit the data to cloud server,which then executes DNN-based classification process for healthcare *** extensive simulation analysis was carried out utilizing a student perspective healthcare data generated from UCI repository and IoT devices to forecast the severity level of the disease among *** proposed OGSO-DNN model outperformed previous methods by attaining the maximum average sensitivity of 96.956%,specificity of 95.076%,the accuracy of 95.764%and F-score value of 96.888%.

关键词： IoT devices healthcare deep learning energy efficiency glowworm swarm optimization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

High Security for De-Duplicated Big Data Using Optimal SIMON Cipher

引用

Computers, Materials & Continua 2021年第5期67卷 1863-1879页

作者： A.Muthumari J.Banumathi S.Rajasekaran P.vijayakarthik K.Shankar Irina v.Pustokhina denis A.Pustokhin Department of Computer Science and Engineering University College of EngineeringRamanathapuram623513India Department of Information Technology University College of EngineeringNagercoil629004India Department of EEE PSN College of Engineering(Anna University)Tirunelveli627152India Department of Information Science and Engineering Sir M.Visvesvaraya Institute of TechnologyBangalore56215India Department of Computer Applications Alagappa UniversityKaraikudi63003India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cloud computing offers internet location-based affordable,scalable,and independent *** computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events,for instance,pandemic *** handle massive information,clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data,with elevated velocity and modified *** deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the *** deduplication also saves network *** this paper,a new cloud-based big data security technique utilizing dual encryption is *** clustering model is utilized to analyze the Deduplication process hash *** kernel Fuzzy C means(MKFCM)was used which helps cluster the data stored in cloud,on the basis of confidence data encryption *** confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher(OSC)technique is *** security process involving dual encryption with the optimization model develops the productivity *** this paper,the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering *** results proved that the proposed technique achieved maximum accuracy and minimum encryption time.

关键词： Cloud security deduplication clustering optimization big data dual encryption

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification

引用

Computers, Materials & Continua 2021年第5期67卷 2393-2407页

作者： J.Banumathi A.Muthumari S.Dhanasekaran S.Rajasekaran Irina v.Pustokhina denis A.Pustokhin K.Shankar Department of Information Technology University College of EngineeringNagercoil629004India Department of Computer Science and Engineering University College of EngineeringRamanathapuram623513India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Electrical Engineering PSN College of EngineeringTirunelveli627152India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly *** classification of HSI becomes a critical process in the domain of hyperspectral data *** massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground ***,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high *** this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC ***,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier ***,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML *** addition,kernel extreme learning machine(KELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class *** extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University *** obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.

关键词： Hyperspectral imagery deep learning xception kernel extreme learning map parameter tuning

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Spatial distribution of vespa velutina individuals hunting at domestic honeybee hives： heterogeneity at a local scale

引用

Insect Science 2014年第6期21卷 765-774页

作者： Karine Monceau Olivier Bonnard Jerome Moreau denis Thiery INRA UMR1065 Sante et Agroecologie du Vignoble F-33883 Villenave d'Omon ISvv UMR1065 Sante et Agroecologie du Vignoble Bordeaux Sciences Agro Universite de Bordeaux F-33883 Villenave d'Omon Equipe Ecologie Evolutive UMR 6282 Biogeosciences 6 Bd Gabriel Universite de Bourgogne F-21000 Dijon France

Since its recent introduction into Europe, the yellow-legged hornet, vespa velutina, has become a major predator of the domestic honeybee, Apis mellifera, but little is known about its hunting behavior. We studied v. velutina hunting behavior by a capture- mark-recapture procedure in an experimental apiary. A total of 360 hornets were captured and tagged, and we determined：（i） the number of hornets visiting the apiary and the changes in time, （ii） the average number of individual visits per half-day and the time elapsed between consecutive recaptures, and （iii） the individual and global distribution of the hornets in the apiary. More than 50% of the marked hornets were recaptured at least once, this increased to 74% in considering the first marked individuals. We estimated 350 hornets visiting the patch daily with at least 1 visit per half-day. The number of marked hornets decreased over time while the number of unmarked ones increased, suggesting a turnover of individuals. The reduction of the delay between consecutive visits indicates that hornets became more efficient over time. Most of the hornets （88%） were recaptured in front of different hives but, overall, the global distribution was aggregative. Hornets were mainly recaptured in front of 1 hive which was neither the smallest nor the biggest colony, suggesting that the major cue used by hornets is not the amount of food. We hypothesize that the defensive behavior of the honeybee colony could explain our results which may be promising to further studies.

关键词： Apis mellifera capture-mark-recapture learning predation vespidae yellow-legged hornet

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

引用

Computers, Materials & Continua 2021年第9期68卷 2877-2893页

作者： D.venugopal T.Jayasankar Mohamed Yacin Sikkandar Mohamed Ibrahim Waly Irina v.Pustokhina denis A.Pustokhin K.Shankar Department of ECE KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Data fusion is one of the challenging issues,the healthcare sector is facing in the recent *** diagnosis from digital imagery and treatment are deemed to be the right *** Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for *** generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of *** use manual planimetry,a time-consuming process for segmenting CT scan *** Learning(DL)is the most preferred method to increase the efficiency of diagnosing *** this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as *** proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and *** input image is first preprocessed using the Gaussian Filtering(GF)technique to remove ***,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the ***,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful ***,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of *** researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic *** findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its *** future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.

关键词： Intracerebral hemorrhage fusion model feature extraction deep features classification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Behavioral syndrome in a native and an invasive hymenoptera species

引用

Insect Science 2015年第4期22卷 541-548页

作者： Karine Monceau Jerome Moreau Juliette Poidatz Olivier Bonnard denis Thiery INRA UMR 1065 Sante et Agroecologie du Vignoble F-33883 Villenave d'Omon France ISvv UMR 1065 Sante et Agroecologie du vignoble Bordeaux Sciences Agro Universite de Bordeaux F-33883 Villenave d'Omon France Universite de Bourgogne UMR CNRS 6282 Biogeosciences 6 bd Gabriel 21000 Dijon France

Recent studies have focused on the role of behavior in biological invasions. Individuals may differ consistently in time for several behavioral traits （personality） which covary （behavioral syndrome） resulting in different behavioral types, some of them favoring invasion. Social hymenopterans have a strong potential to be invaders and their success depends primarily on the foundresses＇ ability to found viable colonies. They are expected to be active, explorative and bold for optimally establishing their nest. In Europe, 2 homet species coexist： the native vespa crabro and the invasive vespa velutina. These 2 species may compete for nesting sites and we suggest that the initial success of v. velutina has been favored by its behavior in outperforming v. crabro for the traits involved in nest initiation. Here, we （i） defined the personality of v. crabro and v：. velutina, （ii） tested for the existence of behavioral syndrome in these species, and （iii） compared their performances using an open-field test. Our results show that v. crabro foundresses behave consistently but not v. velutina; this lack of consistency being mainly due to reduced variance among individuals. This result questions the possibility of detecting consistent behavioral differences in species having recently undergone a strong bottleneck. Both species exhibit the same correlations between activity, boldness and exploration and v. velutina clearly outperforms 14. crabro for all traits. Our results suggest that activity, boldness, and exploration are implicated in both hornet nest initiation and invasion process which contributed to explain why social hymenopterans are so successful at colonization.

关键词： animal personality biological invasion invasion syndrome vespa crabro vespa velutina vespidae

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Metaheuristic Clustering Protocol for Healthcare DataCollection in MobileWireless Multimedia Sensor Networks

引用

Computers, Materials & Continua 2021年第3期66卷 3215-3231页

作者： G G.Kadiravan P.Sujatha T.Asvany R.Punithavathi Mohamed Elhoseny Irina v.Pustokhina denis A.Pustokhin K.Shankar Department of Computer Science Pondicherry UniversityPuducherry605014India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Faculty of Computers and Information Mansoura UniversityEgypt Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudiIndia

Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare *** in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia *** compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia ***,clustering techniques are deployed to achieve low amount of energy *** current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency *** DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node *** addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)*** supports multi-hop data transmission that assists in effectively balancing the available *** simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse *** simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.

关键词： Smart sensor environment healthcare data multimedia big data processing clustering mobility energy efficiency

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Privacy Preserving Blockchain Technique to Achieve Secure and Reliable Sharing of IoT Data

引用

Computers, Materials & Continua 2020年第10期65卷 87-107页

作者： Bao Le Nguyen E.Laxmi Lydia Mohamed Elhoseny Irina v.Pustokhina denis A.Pustokhin Mahmoud Mohamed Selim Gia Nhu Nguyen K.Shankar Faculty of Information Technology Duy Tan UniversityDa Nang550000Vietnam Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Faculty of Computers and Information Mansoura UniversityMansoura35516Egypt Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow115093Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Mathematics College of Science&Humanities in AlaflajPrince Sattam bin Abdulaziz UniversityAlaflaj16278Saudi Arabia Graduate School Duy Tan UniversityDa Nang550000Vietnam Department of Computer Applications Alagappa UniversityKaraikudi630003India

In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and *** studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in *** this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support vector Machine(ACOMKSvM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data *** program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSvM training algorithms in partial views of IoT data,collected from various data ***,ECC is used to create effective and accurate privacy that protects ACOMKSvM secure learning *** this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed *** security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSvM model for data *** examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI *** simulation outcome indicated that the ACOMKSvM model has outperformed all the compared methods under several aspects.

关键词： Blockchain optimization elliptical curve cryptosystem security ant colony optimization multi kernel support vector machine

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：