检索结果-内蒙古大学图书馆

4th International Conference on Sustainable Technologies for Industry 4.0, STI 2022

作者： Kamrujjaman, Md. Hossain, Md. Anwar Hoang Hai, Nguyen Ahmed, Feroz Bangladesh University of Business and Technology Department of Electrical and Electronic Engineering Bangladesh Hanoi University of Science and Technology School of Electronics and Telecommunication Viet Nam Independent University of Bangladesh Department of Electrical and Electronic Engineering Bangladesh

ISBN: (纸本)9781665490450

In this work, a photonic crystal fiber (PCF) based surface plasmon resonance (SPR) sensor has been designed for water pollution detection. Using the finite element method (FEM), the performance of a dual-side polished PCF has been investigated and reported. The plasmonic material for this design is a thin silver (Ag) layer deposited outside the PCF framework. In addition, a thin layer of Titanium Oxide (TiO2) was added to protect the silver layer from oxidation. Utilizing the wavelength interrogation method in the sensing range of RI 1.31-1.35, the proposed sensor shows a maximum sensitivity of 3000 nm/RIU at a silver layer thickness of 40 nm. A sensitivity of 2250 nm/RIU was obtained, particularly in the specific value of RI-1.33. Furthermore, a maximum sensitivity of 475 RIU-1 and a maximum sensor resolution of 5.83×10-6 RIU were obtained utilizing the amplitude interrogation method. Since the sensor has a sensing range of 1.31 to 1.35 with high sensitivity, it would be a great option for biosensing, organic chemical sensing, and other analytes detection in that range. © 2022 IEEE.

关键词： Finite element method

来源：评论

学校读者我要写书评

暂无评论

Star shape symmetric engineered meta surface to sense food additives (benzoic acid/copper sulfate) at terahertz spectrum

引用

Optik 2024年 312卷

作者： Chowdhury, Nazimul Mowla Soliman, Md Mohiuddin Islam, Mohammad Tariqul Alam, Touhidul Soliman, Mohamed S. Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Bangladesh Center for Advanced Electronic and Communication Engineering Department of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment 43600 UKM Bangi Selangor Malaysia 43600 UKM Bangi Selangor Malaysia Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia

This research proposed a technique for detecting the sensitivity of benzoic acid (C6H5COOH) and copper sulfate (CuSO4), which is excessively used in foods. The novelty of this work is that it detects the sensitivity of two food additives with the same metasurface. Although it offers the benefits of miniaturization, minimal sample consumption, and cost-efficiency, it is an easier, faster, and nondestructive detection method than other traditional approaches. The proposed design is based on a polyimide substrate with an aluminum resonator that forms a star shape on the substrate. The transmission frequency of the metasurface was determined at 0.91 THz with a transmission coefficient of 36.6 dB. The metasurface sensor is insensitive to polarization and has a large angle of incident. The transmission spectrum shows that the amplitude of the transmission peaks usually changes with variations in the refractive index of the analytes. The proposed sensor effectively detects benzoic acid (C6H5COOH) with a sensitivity of 0.175 THz/RIU and, the sensitivity of the Copper Sulfate (CuSO₄) is 0.225 THz/RIU with the Q factor values of 12 and 10.55 respectively. These sensitivity results and transmission intensities of different analyte thicknesses show that this proposed THz metasurface sensor can be used to detect additives in foods. © 2024 Elsevier GmbH

关键词： Benzoic acid

来源：评论

学校读者我要写书评

暂无评论

Design and Analysis of an Optical Fiber Network for Vehicles Sensing System

Design and Analysis of an Optical Fiber Network for Vehicles...

引用

electrical and Information Technology (IEIT), International Conference on

作者： Muhammad Athallah Adriansyah Yus Natali Catur Apriono Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Depok Indonesia Telecommunication Engineering Program Faculty of Engineering Telkom University Jakarta Indonesia

ISBN: (数字)9798331516888

ISBN: (纸本)9798331516895

The increasing number of accidents on toll roads, predominantly caused by human error, underscores the critical need for enhanced vehicular safety systems. This research introduces a novel optical fiber network design that utilizes polymer optical fiber (POF) as both a sensor and communication medium to significantly improve the reliability and response time of vehicle safety mechanisms. The system was carefully developed and simulated using OptiSystem software, incorporating key components such as a continuous-wave laser operating at 1310 nm, NRZ encoding, and a Mach-Zehnder modulator, all chosen for their ability to optimize data transmission and minimize signal degradation. Comprehensive simulations were conducted to evaluate the system's performance, focusing on parameters like bit error rate (BER), signal integrity, and response time. The results demonstrate that the proposed design achieves a Q factor of 5.2628, an output power of -14.049 dBm, and a rise time of 0.07 ns. Additionally, the system's power margin of 13.951 dB confirms its robustness and suitability for real-world implementation with the potential to be a viable solution for advancing vehicular safety technologies.

关键词： Q-factor Optical fibers Vehicle safety Optical fiber networks Encoding Time factors Polymers Optical signal processing Power generation Fiber lasers

来源：评论

学校读者我要写书评

暂无评论

Cancer Detection Using Au-Ta2o5 Coated Pcf Based Spr Ri Sensor

SSRN

引用

SSRN 2024年

作者： Saiduzzaman, A.B.M. Rafi Sarker, Hasan Priyanka, Das Arafat, M.D. Rahman Ahsan, M.D. Habib Khan Sakib, M.D. Ikbal Department of Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

With an emphasis on detecting the presence of cancer cells within a RI of 1.25 to 1.40, this paper introduces and quantitatively explores the usage of a straightforward plasmonic sensor based on a photonic crystal fiber (PCF) in the visible to near-infrared range for bio-analyte sensing. Using a finite element method (FEM) mode solver technique under the scattering boundary condition with a perfectly matched layer, the proposed sensor's sensing and guiding properties are studied. Gold (Au), which is chemically nonreactive, and tantalum pentoxide (Ta2O5) are deposited on particular parts of the PCF to ease the challenges and reduce the expense of depositing metal on the whole surface of the PCF. The simulation results reveal that the proposed sensor has the highest amplitude and wavelength sensitivity of 853.52 RIU-1 and 21000 nm/RIU respectively. Additionally, it allows for a maximum figure of merit and sensor resolution of 233.33 RIU-1 and 8×10-6 RIU respectively. However, the sensor's excellent detection capability suggests that it might be utilized to detect a wide variety of chemical substances and biological contaminants. © 2024, The Authors. All rights reserved.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Multimedia Classification via Tensor Linear Discriminant Analysis

引用

IEEE Transactions on Broadcasting 2024年第4期70卷 1139-1152页

作者： Chang, Shih-Yu Wu, Hsiao-Chun Yan, Kun Chih-Hao Huang, Scott Wu, Yiyan San Jose State University Department Of Applied Data Science San JoseCA95192 United States Louisiana State University School Of Electrical Engineering And Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center For Ai Applications Taoyuan32003 Taiwan Guilin University Of Electronic Technology Guangxi Key Laboratory Of Wireless Wideband Communication And Signal Processing Department Of Information And Telecommunication Guilin541004 China National Tsing Hua University Department Of Electrical Engineering Hsinchu300 Taiwan Western University Department Of Electrical And Computer Engineering LondonONN6A 3K7 Canada

Linear discriminant analysis (LDA) is a well-known feature-extraction technique for data analytic and pattern classification. As the dimensionality of multimedia data has increased in this big era, it is often to characterize data by tensors. Over the past two decades, researchers have thus explored to extend LDA to the general tensor space, especially in two common ways: LDA of tensors using tensor decomposition methods (by conversion of tensors to matrices) and LDA of tensors built upon the T-product. However, both of the aforementioned approaches have restrictions thereby. A critical problem about how to carry out LDA of arbitrary scatter tensors based on the Einstein product still remains unsolved by the existing methods. Therefore, we propose a novel tensor LDA (a.k.a. TLDA) approach, which can carry out the LDA of arbitrary-dimensional scatter-tensors without any need of tensor decomposition. Besides, for reducing the computation time, we also design a parallel paradigm to execute our proposed TLDA in this work. Numerical experiments conducted over real multimedia data demonstrate the efficacy of our proposed new TLDA in terms of classification accuracy. Moreover, the comparison of the classification accuracies, computational-complexities, and memory-complexities of our proposed novel TLDA scheme and other existing tensor-based LDA methods is made. By leveraging TLDA for high-dimensional feature extraction, segmentation, and user-item interaction data processing, future multimedia recommendation systems can facilitate more accurate, engaging, and satisfactory user experience over the Internet. © 1963-12012 IEEE.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Cancer Detection Using Au-Ta2o5 Coated Pcf Based Spr Ri Sensor

SSRN

引用

SSRN 2024年

作者： Sarker, Hasan Rafi, A.B.M. Saiduzzaman Das, Priyanka Rahman, Arafat Khan, Ahsan Habib Ikbal, Sakib Department of Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

With an emphasis on detecting the presence of cancer cells within a RI of 1.25 to 1.40, this paper introduces and quantitatively explores the usage of a straightforward plasmonic sensor based on a photonic crystal fiber (PCF) in the visible to near-infrared range for bio-analyte sensing. Using a finite element method (FEM) mode solver technique under the scattering boundary condition with a perfectly matched layer, the proposed sensor's sensing and guiding properties are studied. Gold (Au), which is chemically nonreactive, and tantalum pentoxide (Ta2O5) are deposited on particular parts of the PCF to ease the challenges and reduce the expense of depositing metal on the whole surface of the PCF. The simulation results reveal that the proposed sensor has the highest amplitude and wavelength sensitivity of 853.52 RIU^-1 and 21000 nm/RIU respectively. Additionally, it allows for a maximum figure of merit and sensor resolution of 233.33 RIU^-1 and 8×10^-6 RIU respectively. However, the sensor's excellent detection capability suggests that it might be utilized to detect a wide variety of chemical substances and biological contaminants. © 2024, The Authors. All rights reserved.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Advanced Rice Leaf Disease Detection Using AgriRover: Enhancing Agricultural Monitoring and Crop Health Management

Advanced Rice Leaf Disease Detection Using AgriRover: Enhanc...

引用

Power, electrical, and electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： Oishi Jyoti Abdus Salam Ziharul Islam Rifath Sazid Hasan Md. Abu Ismail Siddique Department of Electrical & Computer Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electronic & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

Agriculture is pivotal for the economy of Bangladesh, providing livelihoods for the majority of its population and contributing significantly to its GDP. Rice stands out as the most significant crop among various agricultural products, yielding the highest volume of production in this country. A variety of diseases affecting rice crops can lead to reduced production, thereby impacting both the economy and demand. For this, early detection of these diseases can mitigate their detrimental effects on healthy crops and facilitate prompt pesticide application. This research involves the creation of a machine learning algorithm trained for identifying and categorizing healthy and diseased rice crops. Subsequently, the system is integrated into a rover that makes the system capable of traversing rugged land to assess the crops. A convolutional neural network known as DenseNet, comprising 201 layers, is introduced and leveraged as a pre-trained model for the classification of six categories of rice diseases. To ensure practicality and dependability in real-world scenarios, a rover is specifically engineered, enabling the deployment of the model. The rover is equipped to capture images of rice leaves and autonomously determine whether they are diseased or healthy, hence improving the overall detection and classification system of crops. The suggested approach represents a significant advancement in the holistic management of agricultural monitoring and crop health.

关键词： Training Technological innovation Machine learning algorithms Transfer learning Crops Sensors Reliability Sustainable development Monitoring Diseases

来源：评论

学校读者我要写书评

暂无评论

Smart Waste Management Robot: Integrating IoT, Artificial Intelligence, SLAM, and CNN-Based Advanced Image Processing for Real-Time Trash Collection and Categorization

Smart Waste Management Robot: Integrating IoT, Artificial In...

引用

International Conference on Computer and Information Technology (ICCIT)

作者： Md. Muhaiminul Islam Md. Rakib Hossain Farzana Akter Md. Faruk Hossain Sham Datto Department of Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Department of Electrical & Electronic Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

In response to the growing global problem of inefficient waste management, urban and rural areas face increasing waste collection, segregation, and disposal challenges. Current systems frequently lack automation, resulting in high operational costs and environmental harm from delayed waste disposal. To address these issues, this paper describes designing and implementing an AI-powered smart trash collection and segmentation robot. The robot uses Simultaneous Localization and Mapping (SLAM) for precise navigation, combining LiDAR and GPS modules to ensure real-time localization and path planning. Equipped with the proposed CNN model, the robot performs advanced image classification and segmentation, achieving a detection accuracy of 95% for trash and 97% for non-trash items. The system’s precision in detecting trash is 96%, with a recall of 97%, demonstrating its capability to operate effectively in real-world scenarios. This solution not only automates waste collection but also promotes sustainability by reducing human intervention and optimizing energy consumption. Experimental results demonstrate the system’s high accuracy in waste detection and successful navigation using SLAM, making it a promising solution for modern waste management challenges. With further improvements in handling more complex waste types and advancing its sustainability features, this system provides a promising outlook for the future of automated waste management.

关键词： Waste management Location awareness Image segmentation Simultaneous localization and mapping Accuracy Navigation Real-time systems Artificial intelligence Sustainable development Robots

来源：评论

学校读者我要写书评

暂无评论

Interference Management Using Power Allocation for Downlink Transmission of Femtocell-Macrocell Ultra Dense Networks 9

Interference Management Using Power Allocation for Downlink ...

引用

9th International Conference on Information Technology, Computer and electrical engineering, ICITACEE 2022

作者： Suryani, Sri Susanto, Misfa Sadnowo, Ageng Hasim, Sitronella Nurfitriani Hamdani, Fadil University of Lampung Bandar Department of Electrical Engineering Lampung Indonesia Institute Technology of Sumatera South Study Program of Telecommunication Engineering Lampung Indonesia

ISBN: (数字)9781665471503

ISBN: (纸本)9781665471480

Femtocell is a promising technology to increase network capacity and coverage, especially in indoor areas. It has low power, short range. The Home Base Station/HeNB (Femtocell Base Station) can be installed by users using connection of personal equipment such as laptops, notepads and cell phones, and it is activated by service provider. By deploying femtocells into existing macrocell/microcell areas, the radio resources are shared among femtocells and existing macrocells. However, the sharing of limited available bandwidth frequency licenses cause interference problems between femtocells and/or macrocells. The interference problem becomes more complex when the number of deployed femtocells is many and dense creating a phenomenon of Ultra Dense Network (UDN). To cope with this interference problem, this paper proposes a power allocation method for femtocells deployed in macrocells areas (UDN) at downlink transmissions. In this method the power transmitted at HeNB to each femtocell user is adjusted in such a way as to reduce or increase the transmitted power at HeNB. If the estimated SINR value at current time is less than 20 dB, the power on the HeNB is increased by such a way so SINR is expectedly to be 20 dB for next transmission time. However, if the obtained SINR at current transmission time is more than 20 dB, then the transmission power at the HeNB is reduced so that the SINR value becomes 20 dB for the next transmission time. Extensive simulation has been carried out and the performance parameters in terms of SINR, throughput, and BER have been measured and compared to the baseline system, i.e., the system without power allocation method. The simulation results show that the system that applies power allocation method which is used in the cell edge area is better than the baseline system both in the center and the edge of cells. © 2022 IEEE.

关键词： Femtocell

来源：评论

学校读者我要写书评

暂无评论

SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

引用

IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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：