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2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)最新文献

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Transfer Learning Inspired Fish Species Classification 基于迁移学习的鱼类分类
Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566067
A. Agarwal, R. Tiwari, Vikas Khullar, R. Kaushal
Machine learning techniques enable systems to learn Important representations from input Image data. Convolutional neural networks (CNNs) are a specific implementation of machine learning techniques and are able to create expressive representations from the input image. Hence CNNs are well suited for image processing operations such as classification, clustering, and object detection, etc. The creation of a new effectual deep CNN model involves an extensive training phase. This requires very large datasets, huge computation environments, and longer execution time. Several established deep CNNs are readily available. These networks are pre-trained on massive databases of images. VGG, ResNet, and InceptionResNetVZ are the leading pre-trained CNN models currently being used in numerous image-processing studies. Possibly we can transfer knowledge learned from such models in order to address challenges in different domains. This can be achieved by repurposing a deep CNN model as a feature generator to produce effective features for content based information retrieval applications. This research work proposes a technique for recognizing fish using deep convolutional neural networks such as ResNet-50, InceptionResNetVZ, and VGG16 that have been pre-trained using transfer learning.
机器学习技术使系统能够从输入图像数据中学习重要的表示。卷积神经网络(cnn)是机器学习技术的具体实现,能够从输入图像中创建富有表现力的表示。因此,cnn非常适合于图像处理操作,如分类、聚类和目标检测等。创建一个新的有效的深度CNN模型需要一个广泛的训练阶段。这需要非常大的数据集、巨大的计算环境和更长的执行时间。几个已建立的深度cnn很容易获得。这些网络是在大量的图像数据库上进行预训练的。VGG, ResNet和InceptionResNetVZ是目前在许多图像处理研究中使用的领先的预训练CNN模型。也许我们可以转移从这些模型中学到的知识,以应对不同领域的挑战。这可以通过将深度CNN模型重新用作特征生成器来实现,为基于内容的信息检索应用程序生成有效的特征。本研究工作提出了一种使用深度卷积神经网络(如ResNet-50、InceptionResNetVZ和VGG16)识别鱼类的技术,这些神经网络已经使用迁移学习进行了预训练。
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引用次数: 8
Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks 使用卷积神经网络对乳腺癌组织病理图像进行分类
Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566015
Akanksha Madduri, Sai Sushma Adusumalli, Honey Sri Katragadda, Mohith Krishna Reddy Dontireddy, Pallikonda Sarah Suhasini
Breast Cancer is one of the mostly encountered cancers among women which involve the age group of 60-80 years mostly. The traditional methodology involves use of mammogram scan followed by various other clinical tests for assuring cancer prevailing in the body manually, which involves mistakes and delay in detection. Many times, it is detected using the biopsy method where tissue removed from the breast is studied under a microscope. This entire process is done by the histopathologies, and if he is not well trained, it may lead to wrong diagnosis. In order to improve the diagnosis by proper detection, automatic analysis of histopathology images has helped the pathologists in efficient diagnosis. Recently the Convolutional neural networks (CNN) have become a preferred deep learning method for breast cancer classification. In this paper, we have proposed CNN architecture based on Local Binary Pattern (LBP) images as input and then compare their classification results by a standard CNN based on origin images as input. Here, classification approach is proposed for automatic classification into either moderate stage or mild stage of cancer. An image dataset of 100 images is used in this approach and 80% of the dataset is used for training and the rest 20% of the images used for testing. 100% classification accuracy is obtained with CNN architecture. The results are compared with various state-of-art machine learning models.
乳腺癌是妇女中最常见的癌症之一,主要发生在60-80岁年龄组。传统的方法包括使用乳房x光扫描,然后进行各种其他临床测试,以手动确保癌症在体内流行,这涉及到错误和延迟检测。很多时候,它是用活组织检查方法检测的,从乳房取出的组织在显微镜下研究。这整个过程都是由组织病理学完成的,如果他没有受过良好的训练,可能会导致错误的诊断。为了提高诊断的准确性,组织病理图像的自动分析有助于病理学家进行有效的诊断。近年来,卷积神经网络(CNN)已成为乳腺癌分类的首选深度学习方法。在本文中,我们提出了基于局部二值模式(Local Binary Pattern, LBP)图像作为输入的CNN架构,然后与基于原始图像作为输入的标准CNN进行分类结果的比较。在这里,提出了分类方法,自动分为中度和轻度癌症。该方法使用100张图像的图像数据集,数据集的80%用于训练,其余20%用于测试。采用CNN架构,分类准确率达到100%。将结果与各种最先进的机器学习模型进行比较。
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引用次数: 1
Circularly Polarized Antenna for ISM (5.8 GHz), Satellite Communications and UWB Applications 用于ISM (5.8 GHz)、卫星通信和超宽带应用的圆极化天线
Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9565960
N. Sharma, Anubhav Kumar, A. De, R. K. Jain
A compact and dual-band circularly polarized antenna with resonant frequency of 5.8GHz(ISM) and 7.6 GHz is proposed for biomedical, satellite and specific UWB applications. The 10 dB impedance bandwidth (IBW) of the antenna varies from 5.55 GHz to 5.94 GHz and 6.78 GHz to 8.78 GHz. The tilted arc-shaped radiator is used to perturb the current which is responsible for lower frequency band as well as circular polarization with the axial ratio extending from 5.77 GHz to 5.93 GHz, which covers 41% of the lower frequency band. The antenna is analyzed for wearable applications on a three-layer skin phantom model and the SAR value obtained is 0.2 W/Kg with Source power of 10mW, which is below the maximum permissible limit of 1.6W/Kg.
提出了一种谐振频率为5.8GHz和7.6 GHz的紧凑双频圆极化天线,用于生物医学、卫星和特定的超宽带应用。天线10db阻抗带宽(IBW)范围为5.55 GHz ~ 5.94 GHz和6.78 GHz ~ 8.78 GHz。倾斜的弧形辐射体用于扰动低频段电流和圆极化,轴向比从5.77 GHz扩展到5.93 GHz,覆盖41%的低频段。对该天线在三层皮肤幻影模型上的可穿戴应用进行了分析,得到的SAR值为0.2 W/Kg,源功率为10mW,低于1.6W/Kg的最大允许限值。
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引用次数: 2
Forecasting the Budget Required for Software under Development 预测软件开发所需的预算
Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566089
Vikas Rattan, Poonam Panwar, R. Mittal, Jaiteg Singh, Varun Malik
Approximating the budget of software is continuously vital problem for the software analysts to agree on the development of a project. Diverse methods for estimating the budget of software are used in literature based on the preferred reliability level as per client’s demand. Based on the literature a close correlation is found between software budget and its reliability. Hence, the architectural design of software is studied here using Discrete Time Markovian Chain (DTMC) for approximating its reliability and its association with software budget. Primarily, a conventional cost model known as generalized software cost model is used for evaluation of the software budget for eleven datasets obtained from literature. The proposed methodology is afterward used for approximation of the software budget and its overall reliability. The proposed methodology is then used to solve single objective problems of minimizing cost of software keeping reliability as constraint and maximizing reliability by keeping cost as constraint. At final, the proposed approach is used to analyse the software cost and reliability trade-off to provide best multi-objective solution to software clients.
接近软件预算一直是软件分析人员就项目开发达成一致的关键问题。文献中根据客户需求的首选可靠性水平,使用了多种估算软件预算的方法。在文献分析的基础上,发现软件预算与其可靠性之间存在密切的相关关系。因此,本文使用离散时间马尔可夫链(DTMC)来近似其可靠性及其与软件预算的关联,研究软件的体系结构设计。首先,使用一种称为广义软件成本模型的传统成本模型来评估从文献中获得的11个数据集的软件预算。提出的方法随后用于估算软件预算及其总体可靠性。然后,将该方法用于解决以保持可靠性为约束的软件成本最小化和以保持成本为约束的可靠性最大化的单目标问题。最后,利用该方法对软件成本和可靠性权衡进行分析,为软件客户提供最佳的多目标解决方案。
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引用次数: 1
Design of Collision Free Automated Angular Parking 无碰撞自动转角泊车设计
Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9565950
Soumyo Das, A. Jose, Sai Ashish Kanna, Moby S. Philip, Anirudh Kumar
In this paper, the vehicle motion control for an automated angular reverse parking has been formulated and designed to track the planned path during auto angular park assist mode and aided in parking the vehicle diagonally in the parking slot without any side or rear collision. The diagonal reverse parking is one of the complicated functionalities and the quintessential features of the angular park assist system described in this paper helps the drivers for a successful maneuver during reverse parking. The architecture of the designed angular parking system which includes components such as situation assessment, mode manager, path planning, and controllers are formulated in enterprise architect. The trajectory for angular parking is designed for single maneuvering based on geometry and dynamics of vehicle maneuver which includes combination of circular, straight path during forward and reverse maneuver mode. The composite reverse motion control, including longitudinal support with braking to lateral controller, has been designed to follow the designed diagonal path precisely. The look rear concept based lateral motion control has been proposed in this paper to perform a successful parking while tracking planned trajectory. The objective of this research is to aid system to navigate host vehicle in a parking zone and help the system to follow planned trajectory precisely by minimizing the point-to-point positional error. The performance of proposed angular parking-controlled system is validated with kinematic vehicle model of Carmaker in loop while evaluating against pre-defined key performance indices of an angular parking.
本文设计了自动角度倒车的车辆运动控制,以实现汽车角度倒车辅助模式下车辆在规划路径上的跟踪,并辅助车辆对角线停在车位内,避免发生侧碰撞和后碰撞。对角线倒车是复杂的功能之一,本文描述的角度停车辅助系统的典型特征有助于驾驶员在倒车过程中成功机动。在企业架构中对设计的角度停车系统的体系结构进行了阐述,其中包括态势评估、模式管理器、路径规划和控制器等组件。根据车辆机动的几何和动力学原理,设计了单机动角泊车的轨迹,包括正、倒转机动模式下的圆、直线路径组合。设计了包括纵向支撑和制动到横向控制器的复合反向运动控制,以精确地遵循设计的对角路径。本文提出了基于后视概念的横向运动控制,以实现在跟踪规划轨迹的同时成功泊车。本研究的目的是通过最小化点对点定位误差,帮助系统在停车区域内导航主车辆,并帮助系统精确地遵循规划的轨迹。利用汽车制造商在环的运动学车辆模型对所提出的角度停车控制系统的性能进行了验证,并对预先定义的角度停车关键性能指标进行了评估。
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引用次数: 0
Analytical Study of Frequency Modulated Thermography for Defect Estimation in Carbon Fibre Reinforced Polymer 调频热成像技术在碳纤维增强聚合物缺陷估计中的分析研究
Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9565986
Anju Rani, V. Arora, K. Sekhar, R. Mulaveesala
Frequency modulated thermography (FMT) is an efficient thermographic technique for quantitative analysis of defects in any material. The paper presents analytical solution of heat transfer in a finite thickness sample with flat bottom hole defects located at different lateral dimensions. The carbon fibre reinforced polymer (CFRP) sample is subjected to frequency modulated thermal excitation and temperature variations are evaluated for defect detection analysis. The computed analytical solutions for different defect depths have been shown to agree with corresponding simulation results for CFRP sample. The present work highlights defect detection capability of FMT technique using matched filter approach.
调频热成像(FMT)是一种有效的热成像技术,用于定量分析任何材料的缺陷。本文给出了具有不同横向尺寸的平底孔缺陷的有限厚度试样的传热解析解。对碳纤维增强聚合物(CFRP)试样进行了调频热激励,并对温度变化进行了评估,用于缺陷检测分析。不同缺陷深度下的解析解与CFRP试样的模拟结果吻合较好。本文着重介绍了利用匹配滤波方法的FMT技术的缺陷检测能力。
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引用次数: 1
Sentiment and Emotion Analysis for Effective Human-Machine Interaction during Covid-19 Pandemic Covid-19大流行期间有效人机交互的情绪和情感分析
Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566147
G. Prasad, Akriti Dikshit, S. Lalitha
With the onset of Covid-19, interactions between humans and machines have increased at a rapid rate. Helping the machine identify the emotion and sentiment of the user plays a key role in making these interactions feel more natural. To do so, existing models for Speech Emotion Recognition (SER) and Sentiment Analysis (SA) focus on the detection of either only emotion or sentiment on acted databases. Unlike these existing works, this work presents a simple model with a comparatively small speech feature vector, to detect both emotion and sentiment from the spontaneous database, Multimodal Emotion Lines Dataset (MELD). This contains voice samples similar to those in a real-time environment. Speech features such as Mel Frequency Cepstral Coefficients (MFCC), Entropy, Teager Energy Operator have been extracted from the voice samples and are classified using Logit Boost, Logistic and Multiclass classifier. The performance of the model is improved by using feature selection techniques such as Backward elimination and Gaussian distribution coefficients. The proposed model is simple, and the results are comparable to existing work on the MELD database.
随着新冠肺炎疫情的爆发,人与机器之间的互动迅速增加。帮助机器识别用户的情感和情绪在使这些交互感觉更自然方面起着关键作用。为了做到这一点,现有的语音情感识别(SER)和情感分析(SA)模型只关注在行为数据库中检测情感或情感。与这些现有的工作不同,这项工作提出了一个简单的模型,具有相对较小的语音特征向量,从自发数据库多模态情感线数据集(MELD)中检测情感和情绪。这包含了类似于实时环境中的语音样本。从语音样本中提取了Mel频率倒谱系数(MFCC)、熵、Teager能量算子等语音特征,并使用Logit Boost、Logistic和Multiclass分类器进行分类。利用反向消去和高斯分布系数等特征选择技术提高了模型的性能。所提出的模型简单,结果与MELD数据库上的现有工作相当。
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引用次数: 2
Potato Blight: Deep Learning Model for Binary and Multi-Classification 马铃薯枯萎病:二元和多重分类的深度学习模型
Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566079
V. Kukreja, Anupam Baliyan, Vikas Salonki, R. Kaushal
Detection of plant crop diseases has become an active field of research day by day due to increasing the demand for such systems and techniques as crop diseases are now become a common part of agriculture. Focusing on this demand and need, we have developed a Convolutional neural network (CNN)-based Deep learning (DL) multi-classification model which classifies the total of 900 real-time collected images of potato crop plants having healthy and potato blight (PB) disease images based on their PB disease severity level, along with this binary classification has also been done to simply classify the healthy and disease crop leaf. A total of four disease severity levels have been taken into account which resulted in a binary classification accuracy of 90.77% and 94.77% of best multi-classification accuracy. This work will be a great contribution in the field of potato disease recognition and detection using DL approaches.
由于作物病害已成为农业的一个共同组成部分,对这些系统和技术的需求日益增加,植物作物病害的检测日益成为一个活跃的研究领域。针对这一需求,我们开发了一种基于卷积神经网络(CNN)的深度学习(DL)多分类模型,该模型根据实时采集的900张马铃薯作物健康和马铃薯枯萎病(PB)图像的PB疾病严重程度进行分类,同时还进行了这种二分类,对健康和病害作物叶片进行简单分类。共考虑了4种疾病严重程度,二元分类准确率为90.77%,最佳多重分类准确率为94.77%。该工作将为马铃薯病害的深度学习识别和检测领域做出重要贡献。
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引用次数: 12
Design of Gradient Boosting Ensemble Classifier with Variation of Learning Rate for Automated Cardiac Data Classification 基于学习率变化的梯度增强集成分类器的心脏数据自动分类设计
Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566084
Saumendra Kumar Mohapatra, Rashmita Khilar, Abhishek Das, M. Mohanty
Cardiac data classification is an emerging research area in recent days. Machine learning-based automatic classification model is one of the essential aspects for the diagnosis of cardiac disease. The performance of a model can be improved by combining multiple models to solve a single problem. In this work, the authors have adopted a modified gradient boosting ensemble learning-based classifier for classifying the cardiac data collected from the UCI machine learning repository. The data set contains the samples of 303 patients with 13 attributes related to cardiac symptoms. The classification is done by using two types of gradient boosting ensemble classifier. In the first step, the classification is performed with a fixed learning rate of 0.01 for every tree. Further to improve the performance the learning rate is changed for each tree. From the result, it is observed that the accuracy is increasing with variation in learning rate. 91% accuracy is observed while the learning rate of 0.81 is considered. The performance is compared with the earlier works and is observed that the proposed model is providing a better result.
心脏数据分类是近年来一个新兴的研究领域。基于机器学习的自动分类模型是心脏疾病诊断的重要方面之一。通过组合多个模型来解决单个问题,可以提高模型的性能。在这项工作中,作者采用了一种改进的梯度增强集成学习分类器,用于对从UCI机器学习存储库收集的心脏数据进行分类。该数据集包含303例患者的样本,这些患者具有13个与心脏症状相关的属性。采用两种梯度增强集成分类器进行分类。在第一步中,以0.01的固定学习率对每棵树进行分类。为了进一步提高性能,每棵树的学习率都被改变。结果表明,随着学习速率的变化,准确率呈上升趋势。在考虑0.81的学习率时,观察到91%的准确率。将该模型的性能与先前的工作进行了比较,结果表明所提出的模型提供了更好的结果。
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引用次数: 2
Indian Sign Language Gesture Recognition in Real-Time using Convolutional Neural Networks 基于卷积神经网络的印度手语手势实时识别
Pub Date : 2021-08-26 DOI: 10.1109/SPIN52536.2021.9566005
Aniket Kumar, M. Madaan, Shubham Kumar, Aniket Saha, Suman Yadav
Communication is a basic requirement of an individual to exchange feelings, thoughts, and ideas, but the hearing and speech impaired community finds it difficult to interact with the vast majority of people. Sign language facilitates communication between the hearing and speech impaired person and the rest of society. The Rights of Persons with Disabilities (RPWD) Act, 2016, was also passed by the Indian government, which acknowledges Indian Sign Language (ISL) and mandates the use of sign language interpreters in all government-aided organizations and the public sector proceedings. Unfortunately, a large percentage of the Indian population is not familiar with the semantics of the gestures associated with ISL. To bridge this communication gap, this paper proposes a model to identify and classify Indian Sign Language gestures in real-time using Convolutional Neural Networks (CNN). The model has been developed using OpenCV and Keras implementation of CNNs and aims to classify 36 ISL gestures representing 0-9 numbers and A-Z alphabets by converting them to their text equivalents. The dataset created and used consists of 300 images for each gesture which were fed into the CNN model for training and testing purposes. The proposed model was successfully implemented and achieved 99.91% accuracy for the test images.
沟通是一个人交流感情、思想和想法的基本要求,但听力和语言障碍群体发现很难与绝大多数人互动。手语有助于听力和语言障碍者与社会其他人之间的交流。印度政府还通过了《2016年残疾人权利法案》,该法案承认印度手语(ISL),并要求在所有政府援助组织和公共部门诉讼中使用手语翻译。不幸的是,很大一部分印度人并不熟悉与ISL相关的手势的语义。为了弥补这种交流差距,本文提出了一个使用卷积神经网络(CNN)实时识别和分类印度手语手势的模型。该模型是使用OpenCV和Keras实现的cnn开发的,旨在通过将代表0-9数字和A-Z字母的36个ISL手势转换为对应的文本来对它们进行分类。创建和使用的数据集由每个手势的300张图像组成,这些图像被输入CNN模型用于训练和测试目的。该模型成功实现,对测试图像的准确率达到99.91%。
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引用次数: 1
期刊
2021 8th International Conference on Signal Processing and Integrated Networks (SPIN)
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