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arXiv 2024年

作者： Ouyang, Chongjun Wang, Zhaolin Chen, Yan Mu, Xidong Zhu, Peiying The School of Electrical and Electronic Engineering College of Engineering and Architecture University College DublinD04 V1W8 Ireland The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Queen’s University Belfast BelfastBT3 9DT United Kingdom The Wireless Technology Lab HUAWEI Technologies Canada Company Ltd. OttawaONK2K 3J1 Canada

Multiple-antenna technologies are advancing toward the development of extremely large aperture arrays and the utilization of extremely high frequencies, driving the progress of next-generation multiple access (NGMA). This evolution is accompanied by the emergence of near-field communications (NFC), characterized by spherical-wave propagation, which introduces additional range dimensions to the channel and enhances system throughput. In this context, a tutorial-based primer on NFC is presented, emphasizing its applications in multiuser communications and multiple access (MA). The following areas are investigated: i) the commonly used near-field channel models are reviewed along with their simplifications under various near-field conditions. ii) building upon these models, the information-theoretic capacity limits of NFC-MA are analyzed, including the derivation of sum-rate capacity and capacity region, and their upper limits for both downlink and uplink scenarios. iii) A detailed investigation of near-field multiuser beamforming design is presented, offering low-complexity and effective NFC-MA design methodologies in both the spatial and wavenumber (angular) domains. Throughout these investigations, near-field MA is compared with its far-field counterpart to highlight its superiority and flexibility in terms of interference management, thereby laying the groundwork for achieving NGMA. Copyright © 2024, The Authors. All rights reserved.

关键词： Near field communication

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segmentation Of Document Images Using Different Methods Like K-Mean Clustering And Fuzzy Clustering

Segmentation Of Document Images Using Different Methods Like...

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International Conference on computer Communication and Informatics (ICCCI)

作者： shanta s. Chikkond Rajashekhar salagar shriharsha s. veni Computer Science And Engineering BLDEA's V.P.Dr. P.G.Halakatti College Of Engineering & Technology Vijayapura India

ISBN: (纸本)9781728158754;9781728192994

In image processing, clustering or data grouping is the main initial process. In today's scenario, company data base size has increased significantly, with large amount of text and picture in these databases. As a picture, various pixels are gathered. The two pixels for grouping are hard to consider. The idea of image segmentation therefore plays a very significant influence in clustering, because it saves time and is also efficient. This paper explores the implementation in the field of image segmentation of standard k-mean and fuzzy k-mean clustering algorithms. Correct protocols have been introduced in order to evaluate and compare the two variants of the k-means and the fuzzy k-means. Exploratory outcomes show that fluorescent K-means algorithms that are logically optimized demonstrate helpfulness in the field of image analysis, providing equivalent and significantly progressive segmented results.

关键词： Image segmentation Protocols Image analysis Clustering algorithms Fluorescence Informatics standards

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Optimizing Heart Disease Detection: A Novel Approach Combining LIGX Hybrid Models and Advanced Data Handling

Optimizing Heart Disease Detection: A Novel Approach Combini...

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International Conference on I-sMAC (IoT in social, Mobile, Analytics and Cloud) (I-sMAC)

作者： v T Ram Pavan Kumar M K Pratyusha Alluri Neethika v s Pavan Kumar s Kaliappan Krishna Kishore Thota Department of Computer Science Kakaraparti Bhavanarayana College Vijayawada India B V Raju Institute of Technology Narsapur Medak India Department of Information Technology S.R.K.R Engineering College Bhimavaram India Department of CSE-AIML MLR Institute of Technology Hyderabad India Division of Research and Development Lovely Professional University Jalandhar Punjab India Department of CSE (Honors) Koneru Lakshmaiah Education Foundation (Deemed To Be University) India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This study improves the accuracy of heart disease prediction through the inclusion of data preprocessing strategies together with a mixture of model systems. Data cleaning was performed on the dataset with great attention, then balanced the class using synthetic Minority Oversampling Technique (sMOTE) and also using the IQR method to remove outliers. Feature selection was done to make further improvements in performance, the AL2C (ANOvA-L2 Combination) model was used for this purpose. To overcome the limitations of both LightGbM (LGbM) and XGboost, a new model named LIGX which is a combination of both the models was designed. This model’s accuracy turned out to be quite impressive and reached of 97.78%, as compared to the earlier studies and showed better precision, recall, and relatively more ruggedness. The findings suggest that the accuracy is boosted by ensemble method, learning and advanced preprocessing techniques. The results of the area support practical use of these techniques in clinical practice which means potential for enhancement of the diagnosis and treatment of heart diseases. subsequent studies will strive to continue rectifying these techniques and focus on including real-time data feed in order to maintain and improve the forecasting accuracy.

关键词： Heart Accuracy Predictive models Feature extraction Data models Real-time systems Cleaning Ensemble learning Reliability Diseases

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Deep Learning Models for Real-Time Object Recognition: Transforming Autonomous vehicle safety and Navigation Capabilities

Deep Learning Models for Real-Time Object Recognition: Trans...

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sustainable Communication Networks and Application (ICsCNA), International Conference on

作者： C. sharanya K.v. Karthikeya Mariya Princy Antony saviour P.s. Gomathi v. samuthira Pandi s.b. Prakalya Department of ECE Sathyabama Institute of Science and Technology Chennai Tamil Nadu India AT&T Hyderabad Telangana India Department of ECE St.Joseph University Chennai Department of ECE V.S.B. Engineering College Karur Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

Real-time object recognition has been transformed by deep learning models, which have contributed to revolutionary increases in the capabilities of autonomous cars in terms of both safety and navigation. Object identification and classification systems are able to process enormous volumes of sensor data with an unparalleled level of speed and accuracy. This is made possible by the utilization of convolutional neural networks (CNNs) and advanced neural architectures. The potential of deep learning to recognize objects, pedestrians, road signs, and barriers in dynamic situations is highlighted in this research, which investigates the vital role that deep learning plays in autonomous vehicle systems. several important difficulties, such as the optimization of the model, the efficiency of the computation, and the robustness in a variety of lighting and environments, are addressed. When it comes to enhancing vehicle decision-making, collision avoidance, and overall navigation performance, we also examine the impact that these models have on these areas. We demonstrate, by means of our research, that real-time deep learning-based object recognition is essential to the advancement of the dependability and safety of autonomous cars, hence paving the way for transportation systems that are both more secure and energy efficient.

关键词： Deep learning Navigation Computational modeling Decision making Real-time systems safety Object recognition Automobiles vehicle dynamics Autonomous vehicles

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Prediction of stock Price Using XG boost Integrated bi-Channel LsTM

Prediction of Stock Price Using XG Boost Integrated Bi-Chann...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Amey Adinath Choudhari Rajeswaran Ayyadurai Jayant Maruti Hudar v.K. Arthi Tasriqul Islam G Jayashree Hareesh JSPM's Rajashi Shahu College of Engineering Pune India IL Health & Beauty Natural Oils Co Inc California USA DBA BHS University Switzerland DBA in Data Science and AI PGDBM B.E. Electronics. Diploma in Computer Technology Navi Mumbai India Department of Management Studies Sri Ramakrishna Engineering College Coimbatore India Harvard University Cambridge Massachusetts USA Fahad Bin Sultan University Tabuk Saudi Arabia

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Investors' biggest problem is determining the appropriate stock price by studying financial information, which is a difficult process due to enormous and related inefficiencies. so, among the biggest challenges for investors is identifying a stock. Currently, it's very challenging to carry out research in the forecasts for the finance industry, including the stock market, currency value, and stock price. Due to its critical importance in financial consumers' decision-making, the prediction and examination of the stock price is yet another important research subject. A novel Xgboost Integrated bichannel Long/short-Term Memory (X-bLsTM) technique is implemented in this article to provide a sophisticated and perfect framework for the share market's price prediction The share value dataset is the first thing we collect to assess the way the suggested X-bLsTM strategy performs. Due to the existence of noisy data, min-max normalization is performed to preprocess the raw dataset. The efficiency of the proposed approach is assessed using several measures, and comparative between the proposed and current methods is also provided. The studies' findings show that the recommended approach outperforms alternative techniques for share value prediction.

关键词： Training Industries Electric potential Current measurement Decision making Noise measurement stock markets Forecasting Long short term memory Currencies

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Cup-to-Disc Ratio based Efficient Glaucoma Prediction on Fundus Images Using Tripartite Tier Convolutional Neural Network

Research Square

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Research square 2021年

作者： Prabaharan, G. Pitchai, R. Ch, Madhu babu Kalaiyarasi, M. Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Department of Electronics and Communication Engineering VSB Engineering College Tamilnadu Karur India

Glaucoma is a condition that causes permanent damage to the optic nerves, resulting in partial or total vision loss. In this paper, a deep learning model using Tripartite Tier Convolutional Neural Network (TTCNN) structure is proposed to detect the glaucomatous images from the normal images. The proposed system includes different steps such as preprocessing, attribute extraction, and glaucoma evaluation. Preprocessing discusses how to convert RGb fundus images to grayscale and how to improve fundus feature contrast. Then, the optic cup (OC) and optic disc (OD) boundaries are fragmented during the attribute extraction using TTCNN. Finally, the Cup-to-Disc Ratio (CDR) has been determined to diagnose glaucoma in the image. This system has been verified on two different publicly available datasets DRIvE and RIM-ONE, yielding an average sensitivity, specificity, accuracy, and precision in glaucoma diagnosis of 84.50%, 98.01%, 99%, and 84% respectively. The obtained results show that the proposed recognition system is suitable for detecting glaucoma with higher precision. © 2021, CC bY.

关键词： Convolutional neural networks

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A miniaturized UWb monopole antenna for sub-6 GHz 5G wireless applications

A miniaturized UWB monopole antenna for sub-6 GHz 5G wireles...

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silchar subsection Conference (sILCON), IEEE

作者： Aparna Panja Arnab De Koyndrik bhattacharjee somnath Maity Ankan bhattacharya bappadittya Roy Partha Pratim sarkar Anup Kumar bhattacharjee Deparment of ECE National Institute of Technology Durgapur West Bengal India Dept. of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research(Deemed to be University) Vadlamudi Guntur A.P India Department of Civil Engineering Dr. B C Roy Engineering College Durgapur West Bengal India Deparment of ECE Government Women’s Polytechnic Jamshedpur Jharkhand India Dept. of Electronics and Communications Engineering Hooghly Engineering & Technology College Hooghly West Bengal India School of Electronics Engneering VIT-AP University Amaravati India Deparment of Engineering and Technological Studeies University of Kalyani Kalyani India

This article describes a miniaturized, ultra-wideband monopole antenna with dimensions of 23.00 x 20.00 x 1.60 mm 3 (0.23λ 0 x 0.23λ 0 x 0.016λ 0 ) designed for sub-6 GHz 5G wireless applications. The suggested layout is compatible with sub-6 GHz 5G, including satellite X-band (7.5-8.5 GHz), ITU n77 (3.3-4.2 GHz), and n79 (4.4-5 GHz) bands, 502.11a WLAN (5.15 - 5.85 GHz), and WiMAX bands (3.5/5.5 GHz). The suggested structure uses a fusion shape of rectangular, triangular, and trapezoidal slits on the ground plane and isosceles triangle, octagonal slot on the metal patch to improve the radiation characteristics and impedance bandwidth. The addition of rectangular slits which are incorporated into the bottom plane just below the microstrip line and trapezoidal-shaped slots shifts the lower cut-off frequency towards the left, while the octagon-shaped slot and the isosceles triangular slit on the radiating patch shift the higher cut-off frequency towards the right. The suggested structure operates over the range from 3.10 to 18.87GHz, having an equivalent fractional bandwidth of 143.56% and a peak gain of around 5.37 dbi. The antenna has a radiation efficiency of 80.00% and displays a decent s 11 ≤-10 db throughout the bandwidth. Results from measurement and simulation are good in agreement.

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Enhanced Particle swarm Optimization based Node Localization scheme in Wireless sensor Networks

Enhanced Particle Swarm Optimization based Node Localization...

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Augmented Intelligence and sustainable systems (ICAIss), International Conference on

作者： shashank singh R. Poonkuzhali G. Nithya R. Anil Kumar J. Kartigeyan s. Ramya Department of Computer Science and Engineering Integral University Lucknow Uttar Pradesh India Department of Information Technology K.S.Rangasamy College of Technology Tiruchengode Tamilnadu India Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women Maisammaguda Secunderabad Telangana India Electronics and Communication Engineering Aditya College of Engineering and Technology Surampalem Andhra Pradesh India Department of Electrical and Electronics Engineering J.B.Institute of Engineering and Technology Hyderabad Telangana India Master of Computer Applications M.Kumarasamy College of Engineering Thalavapalayam Karur Tamilnadu India

ISBN: (纸本)9781665489638

Wireless sensor networks (WsNs) are widely examined recently because of their various applications in processes which has to scatter over a vast region. sensor's locations in WsNs generally must be known. WsN comprises great quantity of sensors thereby installing global position system devices in all of them does not accept. some of the anchor nodes with well-known locations are generally utilized. Forecasting sensor node locations from radio strength signal index are regarded as a hard optimization issue. This article introduces an Enhanced Particle swarm Optimization based Node Localization scheme (EPsONLs) in WsN. The presented EPsONLs technique mainly aims to determine the location of the unknown nodes in the network. To do so, the presented EPsONLs model applies the PsO algorithm to identify the node position. In addition, the EPsONLs model determines the node location with the goal of minimizing localization error and time. An extensive-ranging experimental examination is performed and the outcomes are investigated under distinct prospects. The simulation outcome demonstrated the betterment of the EPsONLs model over recent approaches.

关键词： Location awareness Wireless sensor networks sensor systems sensors Indexes Particle swarm optimization Forecasting

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Expression of Concern for: A scalable Platform to Collect, store, visualize and Analyze big Data in Real- Time

Expression of Concern for: A Scalable Platform to Collect, S...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Pravin Ramdas Kshirsagar Dhoma Harshavardhan Reddy Mallika Dhingra Dharmesh Dhabliya Ankur Gupta Department of Artificial Intelligence S B Jain College of Engineering and Technology Nagpur Maharashtra India Trainee Security Analyst JunoClinic Davman Technology Services Private Limited Mumbai Maharashtra India Department of Mathematics and Statistics Manipal University Jaipur Jaipur Rajasthan India Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India

ISBN: (数字)9798350336238

ISBN: (纸本)9798350336245

Examination and evaluation of sentiment The most popular area for dissecting and extracting knowledge from communication data from many sources, such as Facebook, Instagram, Twitter, Amazon, and so on, is known as mining. It plays a fundamental role in enabling the organizations to work productively on advancing the business system and gaining the most comprehensive understanding of the buyer's feedback on their product. It entails calculating a person's behavior in terms of his buying preferences and then placing yourself in his shoes about the commercial aspect of a relationship. You may think of this component as a situation, person, blog post, or tangible experience. Appraisals, checks, and Reflections created by customers may be divided into smaller, more notable pieces for usage by large businesses. The analysis of comparable purchasing behavior may be used to understand the needs of the customer and foresee future opportunities to assist them. E-business Associations may track the uses and passions associated with their Particulars via this internal assessment and adopt better marketing mechanism to give a tailored shopping experience for their customers, so improving their hierarchical advantage. by using the Programming interface key, Twitter data for this article was collected from Twitter. similarly, we really want to finish setting up the systems. Additionally, the system supported NLP approaches, thus we wish to implement the calculation as a crucial backslide. The exploratory problems exhibit perfection.

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Electronic structure and surface band bending of sn-doped β−Ga2O3 thin films studied by x-ray photoemission spectroscopy and ab initio calculations

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Physical Review b 2024年第11期110卷 115120页

作者： Jiaye Zhang Zhenni Yang siliang Kuang Ziqi Zhang shenglong Wei Joe Willis Tien-Lin Lee Piero Mazzolini Oliver bierwagen shanquan Chen Zuhuang Chen Duanyang Chen Hongji Qi David scanlon Kelvin H. L. Zhang State Key Laboratory of Physical Chemistry of Solid Surfaces College of Chemistry and Chemical Engineering Hangzhou Institute of Optics and Fine Mechanics Hangzhou 311421 People's Republic of China Department of Chemistry Harwell Science and Innovation Campus Didcot OX11 0DE United Kingdom Department of Mathematical Physical and Computer Sciences Leibniz-Institut im Forschungsverbund Berlin e.V. Hausvogteiplatz 5-7 10117 Berlin Germany School of Materials Science and Engineering Key Laboratory of Materials for High Power Laser and Research Center of Laser Crystal School of Chemistry The University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom

The bulk and surface electronic structures of sn-doped β−Ga2O3 thin films have been studied by soft and hard x-ray photoemission spectroscopy (soft PEs at 1486.6 ev and HAXPEs at 5920 ev). The experimental spectra are compared with density functional theory calculated density of states in the valence band and conduction band. Excellent agreement was found between experimental spectra and calculated density of states by taking into account the photoionization cross section of different orbitals involved in the valence and conduction bands. The electronic states derived from Ga 4s character are selectively enhanced by HAXPEs. This allows us to infer that the states at the conduction band and bottom of the valence band contain pronounced Ga 4s character. The occupation of the lower conduction band in degenerately sn-doped Ga2O3 is clearly observed by HAXPEs, which allows for direct measurement of burstein-Moss shift and band-gap renormalization as a function of sn doping. A comparison of the valence band spectra of sn-doped Ga2O3 films with si-doped samples suggests that sn doping has different effects on the electronic structure than si doping. An in-gap electronic state is observed for sn-doped Ga2O3, which is attributed to self-compensating sn2+ related defects. Furthermore, a larger band-gap renormalization is found in sn-doped samples, because the sn 5s dopant orbital mixes strongly with the host Ga 4s derived conduction band. Finally, a comparison of the valence band and core-level spectra excited with soft and hard x rays allows us to identify an upward band bending at the surface region of sn-doped Ga2O3 films.

关键词： Doping effects Electronic structure surface states Doped semiconductors Thin films Wide band gap systems Ab initio calculations Density functional calculations Laser ablation Photoemission spectroscopy

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