检索结果-内蒙古大学图书馆

Optical studies of Rhodamine B doped polymethyl methacrylate (PMMA) films

Materials Today: Proceedings 2021年 47卷 592-596页

作者： Shruti Chaitanya G.S. Mukherjee M. Banerjee Amit Jain Acropolis Institute of Technology and Research Indore Bypass Road Manglia Square Indore 453771 India Nanoscience and Nanotechnology Lab. School of Physics Devi Ahilya University (DAVV) Khandwa Road Indore 452001 India Defence Research & Development Organization (DRDO) Metcalfe House DESIDOC Complex Delhi 110 054 India Department of Engineering Sciences and Humanities Indore Institute of Science and Technology Pithampur Road Rau Indore 453331 India

In this paper the influence of fluorescent dye doping on the optical properties of polymethylmethacrylate (PMMA), in particular on the optical band gap and refractive index of PMMA host matrix is reported. Solution casting method was used to prepare the films of pure PMMA and rhodamine-B doped PMMA films containing varying levels of doping and their optical properties were evaluated. UV–Visible spectra were used to evaluate the optical parameters; uv-absorption spectra reveal absorption bands between 262 and 284 nm for pure PMMA. However, dye doped PMMA film showed absorption bands in the range between 262 and 560 nm depending upon the degree of doping of dye into the film. Optical absorption measurements showed strong modifications in optical properties as a result of doping dye guest molecules into the host PMMA matrix system. It is observed that all optical parameters such as absorption band, absorption edge, energy band gap (direct and indirect) got affected by doping of the dye. Indirect optical band gap of pure PMMA was observed as 4.72 eV but it reduced to 4.13 eV on incorporation of rhodamine-B into PMMA matrix. Studies on the metallization criterion (M) implicate better scope of electronic mobility with the increase in the dopant concentration into the film.

关键词： Polymethylmethacrylate (PMMA) Rhodamine-B dye Optical properties Bandgap Refractive index Metallization criterion

来源：评论

学校读者我要写书评

暂无评论

Experimental Analysis for Position Estimation using Trilateration and RSSI in Industry 4.0 11

Experimental Analysis for Position Estimation using Trilater...

引用

11th International Conference on System Modeling and Advancement in research Trends, SMART 2022

作者： Chouhan, Robin Singh Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Constantin, Neagu Bogdan Raboaca, Maria Simona Verma, Chaman Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India School of Computer Science and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida India Gheorghe Asachi Technical University of Iasi Department of Power Engineering Iasi Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

With the introduction of industry 4.0, the role of robots especially autonomous bots has increased exponentially. The next thing in order to improve the navigation for these bots is the need for a proper indoor navigation system. The paper proposes experimental analysis for estimating the position using trilateration. The experiment includes three wifi nodes operating at 2.4GHz working as wifi beacons. The paper provides an efficient and cost-effective solution to estimate the approximate position of the node in terms of coordinates. The paper also includes a working mobile application to showcase the node's position in real-time in the form of a 2d map. © 2022 IEEE.

关键词： Indoor positioning systems

来源：评论

学校读者我要写书评

暂无评论

An Intelligent and Cost-Effective IoT-Based Irrigation System Using Machine Learning

An Intelligent and Cost-Effective IoT-Based Irrigation Syste...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Sreedevi Kadiyala Chandra Srinivas Potluri Ali Hussen Birke Aashish Mehra Sneha Rajput Katakam Padmavathi Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Hyderabad India Department of Information Technology Werabe Werabe University Ethiopia Department of Information Systems Werabe Werabe University Ethiopia School of Retail Management Symbiosis University of Applied Sciences Indore India Prestige Institute of Management and Research Gwalior India Department of Electronics and Communication Stanley College of Engineering and Technology for Women Hyderabad India

This work presents and implements a low-cost irrigation system for smart agriculture that is based on the Internet of Things (IoT). In order to continuously monitor environmental data in real time, the system is equipped with a network of sensors, including pressure, temperature, moisture, and water level sensors. In order to anticipate when irrigation pumps will switch on, machine learning techniques including Artificial Neural Networks (ANN), Decision Trees (DT), Naive Bayes (NB), and Support Vector Machines (SVM) are linked depending on established parameters. The study shows that the ANN model can identify complicated patterns in the agricultural environment with an accuracy of up to 98.33%. Farmers are able to make well-informed decisions quickly thanks to the cloud connectivity and intuitive interface of remote monitoring and control. Because predictive modeling minimizes pump activation delays, it lessens the chance of both over- and under-irrigation. The suggested strategy makes the most use of available water and provides opportunities for precision farming, which is a major step forward for sustainable agriculture. The study's findings demonstrate how well the system uses resources and open the door for the future creation of innovative, scalable agricultural technology.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Heterogeneous Oblique Double Random Forest

arXiv

引用

arXiv 2023年

作者： Ganaie, M.A. Tanveer, M. Beheshti, I. Ahmad, N. Suganthan, P.N. Department of Robotics University of Michigan Ann ArborMI United States Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Department of International Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

The decision tree ensembles use a single data feature at each node for splitting the data. However, splitting in this manner may fail to capture the geometric properties of the data. Thus, oblique decision trees generate the oblique hyperplane for splitting the data at each non-leaf node. Oblique decision trees capture the geometric properties of the data and hence, show better generalization. The performance of the oblique decision trees depends on the way oblique hyperplanes are generate and the data used for the generation of those hyperplanes. Recently, multiple classifiers have been used in a heterogeneous random forest (RaF) classifier, however, it fails to generate the trees of proper depth. Moreover, double RaF studies highlighted that larger trees can be generated via bootstrapping the data at each non-leaf node and splitting the original data instead of the bootstrapped data recently. The study of heterogeneous RaF lacks the generation of larger trees while as the double RaF based model fails to take over the geometric characteristics of the data. To address these shortcomings, we propose heterogeneous oblique double RaF. The proposed model employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data based on the optimal linear classifier. The optimal hyperplane corresponds to the models based on the optimized impurity criterion. The experimental analysis indicates that the performance of the introduced heterogeneous double random forest is comparatively better than the baseline models. To demonstrate the effectiveness of the proposed heterogeneous double random forest, we used it for the diagnosis of Schizophrenia disease. The proposed model predicted the disease more accurately compared to the baseline models. © 2023, CC BY-NC-ND.

关键词： Decision trees

来源：评论

学校读者我要写书评

暂无评论

A ZEBRA Optimization Algorithm Search for Improving Localization in Wireless Sensor Network 2

A ZEBRA Optimization Algorithm Search for Improving Localiza...

引用

2nd International Conference on Technological Advancements in Computational sciences, ICTACS 2022

作者： Rana, Ajay Khurana, Virender Shrivastava, Amit Gangodkar, Durgaprasad Arora, Deepika Kumar Dixit, Anil Amity University Uttar Pradesh Greater Noida India Vaish College Department of Computer Science Rohtak India Chameli Devi Group of Institutions Department of CSE Indore India Graphic Era Deemed to BeUniversity Graphic Era Hill University Department of CSE Uttarakhand Dehradun India Lloyd Institute of Engineering and Technology Department of CSE Uttar Ptadesh Greater Noida India Uttaranchal University Division of Research & Innovation Uttarakhand India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

Wireless sensor networks (WSNs) make use of an abundance of sensor nodes in order to gain a deeper understanding of the world around them. If the data were not gathered in an open and honest fashion, then no one would be interested in them. In military applications, for instance, the detection of opponent movement relies substantially on the placement of sensor nodes in wireless sensor networks (WSNs). Discovering the locations of all target nodes while utilizing anchor nodes is the major purpose of the localization challenge. This research suggests two adjustments that could be made to the zebra optimization algorithm (ZOA) in order to improve upon its deficiencies, one of which being its tendency to get trapped in the local optimal solution. In versions 1 and 2 of the ZOA, the exploration and exploitation components have been modified to make use of improved global and local search algorithms. In order to assess how effective, the proposed ZOA versions 1 and 2 are, a large number of simulations have been run, each with a different combination of target nodes and anchor nodes and a different number of each. In order to solve the problem of node localization, ZOA, along with a number of other attempted optimization strategies, are employed, and the outcomes obtained by each strategy are compared. Versions 1 and 2 of ZOA perform far better than its competitors in terms of the mean localization error, the number of nodes that are successfully localized, and the computation time. ZOA versions 1 and 2 are proposed, and the initial ZOA is evaluated in terms of how accurately it localizes nodes and the number of errors it generates when provided with a range of possible values for the target node and the anchor node. The simulations prove without a reasonable doubt that the suggested ZOA variation 2 performs better than both the existing ZOA and the original proposal in a variety of ways. The proposed ZOA variation 2 is superior to the proposed ZOA variation 1, ZOA, and o

关键词： Sensor nodes

来源：评论

学校读者我要写书评

暂无评论

Soil Analysis and Classification to Improve the Crop Yield in Agriculture Using Fast Graph - CRNN Approach

Soil Analysis and Classification to Improve the Crop Yield i...

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Mathur Nadarajan Kathiravan Shivangi Bande Sadhana Tiwari Ashok Kumar Koshariya Manju M D Suganthi Department of Biotechnology Dr N. G. P Arts and Science College Coimbatore Tamil Nadu India Institute of Engineering & Technology Indore Madhya Pradesh India Prestige Institute of Engineering Management Research and IET Davv Indore Madhya Pradesh India Department of Plant Pathology Collage of Agriculture and Research Station Indira Gandhi Krishi Vishwavidyalaya Fingeshwar-Gariyaband Chhattisgarh India School of Agriculture SR University Warangal Telangana (SG) Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Thandalam Chennai Tamilnadu India

Changes in land use over the past few decades have left their mark on Earth's surface. Key ecosystem services provided by soils are currently being altered due to global environmental changes. In light of these factors, monitoring equipment is required to maintain a healthy ecological status and advance soil conservation on a local, regional, and global scale. This system demonstrates the potential utility of remote sensing in collecting soil properties. Methodologies based on various remote sensing devices and classification strategies have been developed for assessing soil characteristics. Soil analysis by remote sensing is a relatively new area of study in the agricultural sciences. The majority of people in India rely on agriculture as a means of subsistence and economic survival. The low cost of yield prediction has led to a decline in agricultural output in recent years. Predicting yields is challenging because of the many variables at play, including as soil, rainfall, and fertilizers. The quality of the soil is crucial for agricultural output. MSC and SNV are used in the suggested method for preprocessing. LDA is used for feature selection. The proposed method employs FGCRNN to train the envisioned model. The proposed model is evaluated in comparison to the CNN and GRU models. Its performance is superior to that of the other two types.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Method for Train Accident Prevention and Detection Using ResNet and Long Short-Term Memory Network

An Intelligent Method for Train Accident Prevention and Dete...

引用

Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： M. Jeyaselvi V. Kavitha S. Kalaiarasi Savanam Chandra Sekhar Nitin Girdharwal Sindhu K Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai India Department of Mathematics Vardhaman College of Engineering Hyderabad India Department of Data Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Department of BBA Koneru Lakshmaiah Education Foundation KL University Vijayawada Telangana India Faculty of Management Prestige Institute of Management & Research (PIMR) Indore India Master of Computer Applications M.Kumarasamy College of Engineering Karur Tamilnadu India

At unattended railroad crossings, numerous technologies have been developed to monitor trains, identify objects, and automatically open and close gates. In order to prevent accidents at the crossing, the currently planned systems may effectively close and open the gates upon the arrival and departure of trains. However, there are frequently incidents at railroad crossings caused by the unexpected arrival of trains. Indulging in headphone use is a leading cause of accidents on railroad tracks. The literature review revealed that no viable approach for preventing such mishaps had been developed. The purpose of this proposed system is to provide a comprehensive analysis of the current systems dedicated to the prevention of accidents in real time. Preprocessing, feature selection, and model performance evaluation are all used in the suggested method. The DWT method is used for preprocessing in the suggested method. PCA and LDA are used for feature selection. The suggested method employs ResNet LS TM to assess the efficiency of the model. Two conventional approaches, LSTM and GRU, are contrasted with the proposed methodology. The proposed method outperforms the other two.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring the Dynamical Nature and Radio Halo Emission of Abell 384: A Combined Radio, X-ray and Optical Study

arXiv

引用

arXiv 2024年

作者： Chatterjee, Swarna Pillay, Denisha Datta, Abhirup Raja, Ramij Knowles, Kenda Rahaman, Majidul Sikhosana, S.P. Department of Astronomy Astrophysics and Space Engineering Indian Institute of Technology Indore M.P. Indore India Centre for Radio Astronomy Techniques and Technologies Department of Physics and Electronics Rhodes University Makhanda6139 South Africa Astrophysics Research Centre University of KwaZulu-Natal South Africa South African Radio Astronomy Observatory Liesbeek House Settlers Way Mowbray Cape Town7705 South Africa Institute of Astronomy National Tsing Hua University Hsinchu300044 Taiwan School of Mathematics Statistics and Computer Science University of KwaZulu-Natal South Africa

Multiwavelength studies of galaxy clusters are crucial for understanding the complex interconnection of the thermal and non-thermal constituents of these massive structures and uncovering the physical processes involved in their formation and evolution. Here, we report a multiwavelength assessment of the galaxy cluster A384, which was previously reported to host a radio halo with a 660 kpc size at MeerKAT 1.28 GHz. The halo is slightly offset from the cluster centre. Our uGMRT observation reveals that the halo extends up to 690 kpc at 407 MHz with a nonuniform spectral index α4071284MHzMHz distribution varying from flat (-0.5) to steep (-1.3) values. In addition, we use legacy GMRT 608 MHz, XMM-Newton X-ray, and the Dark Energy Survey optical observations to obtain an extensive understanding of the dynamical nature of the galaxy cluster. The X-ray surface brightness concentration parameter (CSB = 0.16) and centroid shift (w = 0.057) reveal an ongoing dynamical disturbance in the cluster. This is also supported by the elongated 2-D optical galaxy density distribution map of the cluster. The obtained centre shift between optical and X-ray peaks and the asymmetry parameter from optical analysis further supports the dynamical disturbance in the cluster. The radio and X-ray surface brightness follows a sub-linear correlation. Our observations suggest that the cluster is currently in a merging state where particle re-acceleration in the turbulent ICM resulted in the radio halo emission. Copyright © 2024, The Authors. All rights reserved.

关键词： Dark energy

来源：评论

学校读者我要写书评

暂无评论

On the Origin of Diffuse Radio Emission in Abell 85 - Insights from new GMRT Observations

arXiv

引用

arXiv 2022年

作者： Rahaman, Majidul Raja, Ramij Datta, Abhirup Burns, Jack O. Rapetti, David Department of Astronomy Astrophysics and Space Engineering Indian Institute Of Technology Indore Indore India Center for Astrophysics and Space Astronomy Department of Astrophysical & Planterary Sciences Department of Physics University of Colorado Boulder Boulder CO80309 United States NASA Ames Research Center Moffett FieldCA94035 United States Research Institute for Advanced Computer Science Universities Space Research Association ColumbiaMD21046 United States

Extended, steep, and ultra-steep spectrum radio emission in a galaxy cluster is usually associated with recent mergers. Simulations show that radio phoenixes are aged radio galaxy lobes whose emission reactivates when a low Mach shock compresses it. A85 hosts a textbook example of a radio phoenix at about 320 kpc southwest of the cluster center. We present a new high resolution 325 MHz GMRT radio map illustrating this radio phoenix's complex and filamentary structure. The full extent of the radio structure is revealed for the first time from these radio images of A85. Using archival Chandra X-ray observations, we applied an automated 2-D shock finder to the X-ray surface brightness and Adaptive Circular Binning (ACB) temperature maps which confirmed a bow shock at the location of the radio phoenix. We also compared the Mach number from the X-ray data with the radio-derived Mach number in the same region using multi-frequency radio observations and find that they are consistent within the 1σ error level. Copyright © 2022, The Authors. All rights reserved.

关键词： Shock waves

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Brain Age Estimation: A Systematic Review

arXiv

引用

arXiv 2022年

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Ser, Javier Del Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Assam Silchar788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：