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A quasi-discrete Hankel transform for nonlinear beam propagation

Chinese Physics B 2009年第9期18卷 3893-3899页

作者：游开明文双春陈列尊王友文胡勇华 School of Information Engineering Wuhan University of Technology Department of Physics and Electronic Information Science Hengyang Normal University Key Laboratory of Micro/Nano Optoelectronic Devices of Ministry of Education School of Computer and CommunicationHunan University

We propose and implement a quasi-discrete Hankel transform algorithm based on Dini series expansion （DQDHT） in this paper. By making use of the property that the zero-order Bessel function derivative J0^1（0）=0, the DQDHT can be used to calculate the values on the symmetry axis directly. In addition, except for the truncated treatment of the input function, no other approximation is made, thus the DQDHT satisfies the discrete Parsevat theorem for energy conservation, implying that it has a high numerical accuracy. Further, we have performed several numerical tests. The test results show that the DQDHT has a very high numerical accuracy and keeps energy conservation even after thousands of times of repeating the transform either in a spatial domain or in a frequency domain. Finally, as an example, we have applied the DQDHT to the nonlinear propagation of a Gaussian beam through a Kerr medium system with cylindrical symmetry. The calculated results are found to be in excellent agreement with those based on the conventional 2D-FFT algorithm, while the simulation based on the proposed DQDHT takes much less computing time.

关键词： Hankel transform Kerr medium nonlinear propagation

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Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

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computer Systems Science & engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.S.Sujith Kumar G.Shanthi L.Raja Gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer Science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,*** the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of *** study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless *** proposed IM-EECNL technique involves two major processes namely node localization and ***,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the ***,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the ***,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and *** performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

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Analysis of Inherent and Foreign Factors Affecting Performance of Wireless Communication Protocols

Analysis of Inherent and Foreign Factors Affecting Performan...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Tanksale, Rushikesh S. Shaikh, Ayesha S. Nemade, Himali P. Sakhare, Nitin N. Vishwakarma Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9781665484251

In this paper, we are contributing physical testing of parameters and how they affect the real-time performance of wireless protocols which are Wi-Fi, Bluetooth, and LTE. All the testing scenarios considered for performance analysis are from the real world and are considered as being used by end-users, not from much technical background. All of these protocols are used majorly in Internet of Things, or a new paradigm called Roof Computing. Introducing all of these 3 protocols has a different type of implementation like Wi-Fi being used as a common point to share internet over a certain area. LTE being and faster and mobile-based service to access the internet on the go. While Bluetooth replacing its use case from data transfer from one device to another device from the early 2000s to 2010 replacing IR data transfer, after the introduction of BLE its use case shifted from data transfer to Audio peripherals, Communication between gaming systems and controller which can be Mouse, Keyboard, and Controllers which makes it perfect for such scenarios as it consumes very much less energy and having near-zero latency. a) for a Wi-Fi-based system small changes in manufacturing can lead to longevity of product along with an increase in Quality of Service just like adding heat sinks to budget level routers or providing a PSU which have higher rated power delivery than required. b) For LTE-based devices which are being mostly used in mobile devices manufacturers do certain malpractices so that they can sell the upper model than the user intended like locking carrier aggregation on chipset even though they support it. c) Bluetooth devices being used more and more in past years because the 3.5 mm headphone jack is being removed by certain mobile smartphone manufacturers. Devices available at entry-level or budget range don't provide certain Quality of Service factors like zero latency for audio channels and not utilizing all of the resources to sell higher-end products. The paper

关键词： Quality of service

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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

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computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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Blood Cell Detection and Counting via Deep Learning

Blood Cell Detection and Counting via Deep Learning

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2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Narsale, Achal Nalwade, Sakshi Badgire, Medha Survase, Sandhyarani Aher, Chetan. N. Aissms Institute of Information Technology Department of Computer Engineering Pune India

ISBN: (纸本)9781665461092

A vital component of clinical medical diagnosis is blood cell count. CNN has devised an effective way of automatically counting blood cells using deep learning-based detection method. Inadequate bounding box alignment and overlapping item recognition are challenges for the CNN detection approach. We suggest a brand-new deep-learning technique called CNN to get over these restrictions. Channel, spatial attention mechanism is incorporated into the feature extraction network resulting in CNN. For residual fusion, CNN can assist the network in increasing detection accuracy by replacing the original feature vector and employing the filtered and weighted feature vector. The experimental results show that the typical CNN network may improve blood cell count detection performance without adding too many extra parameters, where the accuracy of identifying cells (RBCs, WBCs, and platelets) has been done. © 2022 IEEE.

关键词： Deep learning

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HVQ-VAE: Variational auto-encoder with hyperbolic vector quantization

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computer Vision and Image Understanding 2025年 258卷

作者： Shangyu Chen Pengfei Fang Mehrtash Harandi Trung Le Jianfei Cai Dinh Phung Faculty of Information Technology Department of Data Science and AI Monash University Exhibition Walk Clayton 3168 VIC Australia School of Computer Science and Engineering Southeast University Nanjing 210096 PR China Faculty of Engineering Department of Electrical and Computer Systems Engineering Monash University Exhibition Walk Clayton 3168 VIC Australia

Vector quantized-variational autoencoder (VQ-VAE) and its variants have made significant progress in creating discrete latent space via learning a codebook. Previous works on VQ-VAE have focused on discrete latent spaces in Euclidean or in spherical spaces. This paper studies the geometric prior of hyperbolic spaces as a way to improve the learning capacity of VQ-VAE. That being said, working with the VQ-VAE in the hyperbolic space is not without difficulties, and the benefits of using hyperbolic space as the geometric prior for the latent space have never been studied in VQ-VAE. We bridge this gap by developing the VQ-VAE with hyperbolic vector quantization. To this end, we propose the hyperbolic VQ-VAE (HVQ-VAE), which learns the latent embedding of data and the codebook in the hyperbolic space. Specifically, we endow the discrete latent space in the Poincaré ball, such that the clustering algorithm can be formulated and optimized in the Poincaré ball. Thorough experiments against various baselines are conducted to evaluate the superiority of the proposed HVQ-VAE empirically. We show that HVQ-VAE enjoys better image reconstruction, effective codebook usage, and fast convergence than baselines. We also present evidence that HVQ-VAE outperforms VQ-VAE in low-dimensional latent space.

关键词： Unsupervised learning

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A route optimization scheme for mobile IP with IP header extension

A route optimization scheme for mobile IP with IP header ext...

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IWCMC 2006 - 2006 International Wireless Communications and Mobile Computing Conference

作者： Wang, Nen-Chung Wu, Yi-Jung Department of Computer Science and Information Engineering Chaoyang University of Technology Taiwan

ISBN: (纸本)1595933069

Due to coming of the mobile-commerce, a variety of portable electronic equipment has been produced, including PDA, notebook, and cellular phone, etc. Mobile IP has become an important protocol for providing Internet connectivity to roaming mobile hosts. However, several problems have been found in Mobile IP and need to be solved. One of them is the triangle routing problem, in which all datagrams for a mobile host have to be routed first to the mobile host's home agent and then forwarded by the home agent to the mobile host. These conventional paths are often significantly longer than optimal path. IETF (Internet engineering Task Force) has proposed route optimization extensions in Mobile IP to solve this problem. However the scheme has difficulties and is almost infeasible, because the scheme needs to implement a binding cache for all mobile hosts and correspondent nodes to solve the triangle routing problem. In this paper, we propose a novel and simple route optimization scheme with IP header extension to solve the triangle routing problem in Mobile IP, and to provide a feasible and efficient Mobile IP service. Finally, we will conduct some simulation to prove that the proposed routing scheme outperforms the standard Mobile IP. Copyright 2006 ACM.

关键词： Internet protocols

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A Hybrid Method of Domain Lexicon Construction for Opinion Targets Extraction Using Syntax and Semantics

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Journal of computer Science & technology 2016年第3期31卷 595-603页

作者： Chun Liao Chong Feng Sen Yang He-Yan Huang Department of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology Beijing 100081 China

Opinion targets extraction of Chinese microblogs plays an important role in opinion mining. There has been a significant progress in this area recently, especially the method based on conditional random field （CRF）. However, this method only takes lexicon-related features into consideration and does not excavate the implied syntactic and semantic knowledge. We propose a novel approach which incorporates domain lexicon with groups of syntactical and semantic features. The approach acquires domain lexicon through a novel way which explores syntactic and semantic information through Part- of-Speech, dependency structure, phrase structure, semantic role and semantic similarity based on word embedding. And then we combine the domain lexicon with opinion targets extracted from CRF with groups of features for opinion targets extraction. Experimental results on COAE2014 dataset show the outperformance of the approach compared with other well-known methods on the task of opinion targets extraction.

关键词： domain lexicon opinion targets extraction syntactic structure semantic role word embedding

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Semantic Segmentation Using Region Proposals and Weakly-Supervised Learning 12

Semantic Segmentation Using Region Proposals and Weakly-Supe...

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12th International Conference on computer and Knowledge engineering, ICCKE 2022

作者： Taghizadeh, Maryam Chalechale, Abdolah Razi University Department of Computer Engineering and Information Technology Kermanshah Iran

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

Region proposal plays an important role in computer vision and successfully improves performance. This paper presents an efficient method using the region proposal for semantic segmentation. The main aim is to generate annotated data for deep learning techniques effortlessly. For this purpose, a region proposal algorithm is used to convert an image into several regions. According to defined rules, regions are explored, and some precise regions are selected. A new algorithm is introduced to generate useful masks only by supervising annotated data in the form of the bounding box. After that, these masks are fed to a deep semantic segmentation network. The proposed method shows promising results for weakly supervised learning semantic segmentation on the VOC2012 dataset. Also, this method can be employed to generate massive annotated data automatically and used to train deep networks. © 2022 IEEE.

关键词： Semantic Segmentation

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Advancements in Depression Severity Prediction: A Multi-Modal Machine Learning Approach Integrating MCQs and Audio Data 2

Advancements in Depression Severity Prediction: A Multi-Moda...

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Pimpalkar, Shilpa P. Chavan, Anish Avinash Gawali, Sadhana Vijay Dalvi, Shreya Sanjay Department of Computer Engineering AISSMS Institute of Information Technology Pune India

ISBN: (纸本)9798350383867

This research presents a machine-learning framework for predicting depression severity by leveraging Multiple-Choice Questions (MCQs) and transcribed audio data. Two distinct data sources are employed, with regression techniques (linear, decision tree, random forest, support vector) applied to both MCQs and audio data. The severity predictions from individual regression models are amalgamated using a weighted average. Additionally, a combined machine learning model integrates classification algorithms (logistic regression, decision tree classification, random forest classification, support vector classification) for both MCQ and audio data. Trained separately, these models contribute to an integrated prediction of depression severity levels-minimal, mild, moderate, and severe. The MCQs utilized originate from the Beck Depression Inventory-2 (BDI-2) questionnaire. This comprehensive approach, encompassing subjective self-report and objective audio indicators, aims to enhance the accuracy of depression severity predictions, offering a valuable tool for early detection and intervention in mental health. The first key component of our approach involves utilizing the structured information derived from the Beck Depression Inventory-2 (BDI-2) questionnaire, which is represented by MCQs. This well-established instrument is widely used for assessing depression, providing a standardized set of questions that capture self-reported symptoms and emotional states. Leveraging the richness of MCQ responses, we employ regression techniques such as linear regression, decision tree regression, random forest regression, and support vector regression to predict the severity level of depression for each participant. © 2024 IEEE.

关键词： Prediction models

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