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2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)最新文献

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FRCNN based Deep Learning for Identification and Classification of Alopecia Areata 基于FRCNN的斑秃深度学习识别与分类
C. Saraswathi, B. Pushpa
Most patients suffer from scalp hair issues like dermatitis, baldness, and so on as an outcome of unhealthy lifestyles, hormonal imbalance, and so on. The most common type of alopecia is alopecia areata (AA), which is typically detected and diagnosed using medical image processing models. However, they are unreliable for striking or intersecting hairs and are directly influenced by design variables. Deep Learning (DL) in imaging data was thus used to detect and diagnose AA. Similarly, various DL models recognized and diagnosed various scalp hair conditions. Likewise, various scalp hair conditions were recognized and diagnosed by the various DL models. Hence, this paper proposes a Faster Residual Convolutional Neural Network (FRCNN) model to recognize AA and scalp conditions together for many individuals with different kinds of baldness. The main goal of the FRCNN is to use a Region-Of-Interest (ROI) projection layer to enhance the recognition accuracy of AA and scalp hair symptoms. In this FRCNN model, the given scalp and AA images are initially fed to the ROI projection layer and deep convolutional layer to extract feature maps, which are aggregated by the ROI pooling to get the final feature vector representation. Then, the obtained feature vector is passed to the Fully Connected (FC) layer accompanied by the softmax classifier to recognize the various conditions of AA. Finally, the test results show that the FRCNN on hair and scalp image databases achieves an accuracy of 84.3% compared to the other models.
由于不健康的生活方式、荷尔蒙失衡等原因,大多数患者都患有皮炎、秃顶等头皮毛发问题。最常见的脱发类型是斑秃(AA),通常使用医学图像处理模型进行检测和诊断。然而,它们对于撞击或交叉毛发是不可靠的,并且直接受到设计变量的影响。因此,利用成像数据中的深度学习(DL)来检测和诊断AA。同样,各种DL模型可以识别和诊断各种头皮头发状况。同样,各种DL模型也可以识别和诊断各种头皮毛发状况。为此,本文提出了一种快速残差卷积神经网络(FRCNN)模型来同时识别不同类型秃顶个体的AA和头皮状况。FRCNN的主要目标是利用感兴趣区域(ROI)投影层来提高AA和头皮毛发症状的识别精度。在该FRCNN模型中,首先将给定的头皮和AA图像馈送到ROI投影层和深度卷积层提取特征映射,然后通过ROI池对特征映射进行聚合,得到最终的特征向量表示。然后,将得到的特征向量与softmax分类器一起传递到Fully Connected (FC)层,以识别AA的各种情况。最后,测试结果表明,与其他模型相比,FRCNN在头发和头皮图像数据库上的准确率达到了84.3%。
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引用次数: 0
Implementation of a Dual-Chamber Pacemaker for Low-Power Applications 用于低功耗应用的双腔起搏器的实现
Pavankumar Bikki, Yenduri Dhiraj, R.V.S Nivas Kumar
In this paper, a Dual Chamber Cardiac Pacemaker is implemented for various heartbeat ranges with the least amount of delay. For diseases like arrhythmia that are life-threatening, pacemakers are required. Maintaining the appropriate heart rate requires a minimum delay between sensing and pacing. The heart of the pacemaker, the timing control unit, the logic unit, and the sensing amplifier make up the pulse generator. The timing control unit and the logic unit make the decision to pace the heart based on the output of a sensing amplifier, thus achieving the demand pacing need. In addition, the VVI, DDD, and rate-responsive approaches of the pacemaker were designed using the VHDL structural approach, considering the pacemaker's timing cycles. The demand pacemaker functions in line with the heart rate of the arrhythmia-afflicted patient, and its range may vary between patients. For the proposed work, a beats-per-minute (bpm) range of 30 to 70 has been chosen. The outcome demonstrates that the proposed work is superior regarding latency, computational complexity, and cost.
在本文中,实现了一种双腔心脏起搏器,用于各种心跳范围,并具有最小的延迟。对于像心律失常这样危及生命的疾病,起搏器是必需的。保持适当的心率需要最小的感知和起搏之间的延迟。起搏器的心脏、定时控制单元、逻辑单元和传感放大器组成脉冲发生器。定时控制单元和逻辑单元根据传感放大器的输出决定心脏起搏,从而实现起搏需求。此外,考虑起搏器的定时周期,采用VHDL结构方法设计了起搏器的VVI、DDD和心率响应方法。需求起搏器的功能与心律失常患者的心率一致,其范围可能因患者而异。对于提议的作品,每分钟的节拍(bpm)范围为30到70。结果表明,所提出的工作在延迟、计算复杂性和成本方面是优越的。
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引用次数: 0
Vedic Divider: A Novel Design for Deconvolution Algorithm based on Vedic Math 吠陀除法:一种基于吠陀数学的反卷积算法设计
K. Arun, P. Kalyani, Shaik Fouziya Samreen, Shereen
Convolution and deconvolution are commonly employed in digital signal processing. Binary division is used in the field of digital image processing for image restoration, red-eye removal, and blur reduction via deconvolution operations. Long sequences must commonly undergo convolution and deconvolution comparable to DSP in many applications. The essential prerequisite for speed in any application is an increase in the speed of its fundamental building block. Both convolution and deconvolution have a central component known as a multiplier or divider. It is the most important component of the system, yet it is also the slowest and most time-consuming. Many approaches for increasing the multiplier and divider's speed have been explored, but the Vedic multiplier and divider are currently the focus of interest. Because it operates more swiftly and with less energy. In this work, the convolution and deconvolution modules are accelerated using Vedic multiplier and divider. Xilinx ISE 14.7 can be used to accomplish this division algorithm's operation. The suggested design is contrasted with current FPGA topologies, including the non-restoring division Algorithm and other Vedic Dividers (Paravartya Sutra, Nikhilam Sutra).
卷积和反卷积是数字信号处理中常用的两种方法。二值分割用于数字图像处理领域,通过反卷积操作实现图像恢复、红眼去除和模糊减少。在许多应用中,长序列通常必须经过与DSP相当的卷积和反卷积。在任何应用程序中,速度的基本先决条件是提高其基本构建块的速度。卷积和反卷积都有一个中心分量,称为乘法器或除法器。它是系统中最重要的组成部分,但也是最慢、最耗时的。人们已经探索了许多提高乘数法和分法器速度的方法,但吠陀乘数法和分法器是目前关注的焦点。因为它运行更快,耗能更少。在这项工作中,使用吠陀乘法器和除法器加速卷积和反卷积模块。Xilinx ISE 14.7可用于完成该除法算法的操作。建议的设计与当前的FPGA拓扑进行了对比,包括非恢复除法算法和其他吠陀除法(Paravartya Sutra, Nikhilam Sutra)。
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引用次数: 0
Improvement in Synchronous Motor Speed Control System Using MPC Technique 用MPC技术改进同步电机调速系统
S. Sunori, M. Manu, P. Juneja
Constant speed applications are very prevalent pertaining to synchronous motors. Power factor correction is another application of these motors in power houses. The reason behind calling these motors synchronous is that they run with the same speed as the applied 3 phase ac frequency. This research work is focused on controlling the speed of synchronous motors. For a considered model of synchronous motor, Ziegler-Nichols tuning method based PID and Model predictive control (MPC) systems are designed in MATLAB. The comparison of performance between these two developed control systems has been done on basis of setpoint and disturbance responses.
恒速应用是非常普遍的有关同步电机。功率因数校正是这些电机在发电厂中的另一个应用。称这些电动机为同步电动机的原因是它们以与应用的3相交流频率相同的速度运行。本文的研究重点是同步电机的转速控制。针对给定的同步电机模型,在MATLAB中设计了基于齐格勒-尼科尔斯整定方法的PID和模型预测控制(MPC)系统。基于设定值和扰动响应对两种控制系统的性能进行了比较。
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引用次数: 0
Designing a reliable and cost-effective Internet of Medical Things (IoMT) topology to minimize the maintenance and deployment cost 设计可靠且具有成本效益的医疗物联网(IoMT)拓扑,以最大限度地降低维护和部署成本
C. Raghavendra Rao, Grandhi Prasuna, Hari Kishan Chapala, N. Jeebaratnam, Durgaprasad Navulla, Ashish Verma
The Internet of Things (IoT) can now be used to automate healthcare facilities and make patient data available for use at any time and from any location via the Internet. Healthcare-related data is now shared and accessed via the host-based Internet paradigm. Latency, mobility, and security issues are all exacerbated by its location-dependent nature. For the present host-based Internet paradigm, which is already in place, NDN has been promoted as the next Internet paradigm. The new species, unfortunately, lacks a stable healthcare system. A lightweight certificate less (CLC) signature is used to build an NDN-IoMT framework in this paper. We employ the Hyper elliptic Curve Cryptosystem (HCC) since it is cheaper than the Elliptic Curve Cryptosystem, which provides higher security with a smaller key (ECC). In addition, we use AVISPA to verify the proposed scheme's safety. In order to determine the most cost-effective solution, we look at existing certificate less signature methods. Results reveal that our proposed method utilizes very little network resources. Finally, we put the architecture into action on NDN-IoMT.
物联网(IoT)现在可用于实现医疗保健设施的自动化,并通过互联网随时随地提供患者数据。现在,通过基于主机的Internet范例共享和访问与医疗保健相关的数据。延迟、移动性和安全性问题都因其位置依赖性而加剧。对于目前已经存在的基于主机的互联网范式,NDN已被推广为下一个互联网范式。不幸的是,这种新物种缺乏稳定的医疗体系。本文采用轻量级证书签名(CLC)来构建NDN-IoMT框架。我们采用超椭圆曲线密码系统(HCC),因为它比椭圆曲线密码系统便宜,可以用更小的密钥(ECC)提供更高的安全性。此外,我们使用AVISPA验证了所提出方案的安全性。为了确定最经济有效的解决方案,我们研究了现有的无证书签名方法。结果表明,该方法占用的网络资源非常少。最后,我们将该体系结构应用于NDN-IoMT。
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引用次数: 0
Static Voltage Stabilizer Using Modified SPWM Technique Controlled AC Chopper 基于改进SPWM技术控制交流斩波器的静态稳压器
Sagar Sanjay Rochwani, Siddharthsingh K. Chauhan, P. N. Tekwani
Voltage stabilizer for ac supply has become necessity for domestic as well as industrial applications. It retains devices / instruments from supply voltage impurities. Among all the available voltage stabilizer technologies, IGBT based static voltage stabilizer is the most advanced and fast responsive technique to get required voltage regulation. This paper presents design and operation of efficient and reliable method to use buck-boost transformer in series with ac chopper to achieve an effective voltage control using modified sinusoidal pulse width modulation. Simulation analysis of the presented stabilizer depicts satisfactory behavior.
交流电源稳压器已成为家庭和工业应用的必需品。它使设备/仪器免受电源电压杂质的影响。在现有的稳压器技术中,基于IGBT的静态稳压器是最先进、响应速度最快的稳压器技术。本文介绍了一种高效可靠的方法,利用交流斩波器串联降压变压器,利用修正正弦脉宽调制实现有效的电压控制。该稳定器的仿真分析结果令人满意。
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引用次数: 0
Possibilities of Edge Repetition using Double Mean Labeling 使用双均值标记的边缘重复的可能性
Nandhini M, M. V, V. Balaji
In this research article we improvised for absolute detection of edge Labeling by finding out the ordered pairs for an edge label. There are some theorems proves the distinctiveness of the edge labels in previous articles. Suppose that, if all the adjacent nodes from the parent node receives equal value for the edges, such that, the parent node gets changes according to the adjacency of the parent node to achieve non-distinct edge values. Here, the label of the adjacent vertices is static and parent node splits into multifarious singleton vertex, which depends on the dimension n. Thus, the following theorem and proofs emerge from the idea of non-distinct edge labeling of the star graphs. Here we Constructed the Successor Vertex Graphs of Edge Recursion and the lemma is stated of how the vertex function looks for set of all ordered pairs of vertex label to achieve singleton edge labels using double mean labeling.
在本文中,我们通过找出边缘标记的有序对来实现边缘标记的绝对检测。在前面的文章中有一些定理证明了边标记的独特性。假设,如果与父节点相邻的所有节点接收到的边值相等,则父节点根据父节点的邻接性进行更改,以实现边值的不区分。在这里,相邻顶点的标记是静态的,父节点根据维数n分裂为多个单点顶点。因此,从星图的非明显边标记思想中产生了以下定理和证明。本文构造了边递归的后继顶点图,并给出了顶点函数如何利用双均值标记寻找顶点标记的所有有序对的集合来实现单边标记的引理。
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引用次数: 0
Artificial Neural Networks Model for Predicting the Strength of FRP-Contained Concrete frp混凝土强度预测的人工神经网络模型
Merrisha John
Numerous studies have demonstrated that FRP (Fibre-reinforced Polymer) can significantly increase the strength of concrete columns. Numerous mathematical equations and manual methods are available for predicting the strength of concrete columns composed of FRP, all of which are time-consuming tasks. This present study develops a novel computerized method for determining the axial strain and axial strength of FRP (Fibre-reinforced Polymer)-confined concrete columns utilizing real-time experimental data and artificial neural networks (ANNs). In order to increase prediction accuracy, an ANN model is trained and evaluated using experimental data collected in real-time. Additionally, advanced pre-processing techniques are applied in this study to minimize noise and enhance the prediction accuracy of the suggested ANN model. To demonstrate the efficacy of this proposed strategy, this model is trained and verified using the data set. The experimental outcomes from training and validation have been compared to recent methods. It is evident from the comparison results that the proposed method has reduced MAE, RSME and regression values.
大量研究表明,FRP(纤维增强聚合物)可以显著提高混凝土柱的强度。预测FRP混凝土柱强度的数学方程和人工方法有很多,但都是费时的工作。本研究开发了一种新的计算机方法来确定FRP(纤维增强聚合物)约束混凝土柱的轴向应变和轴向强度,利用实时实验数据和人工神经网络(ann)。为了提高预测精度,利用实时收集的实验数据对人工神经网络模型进行训练和评估。此外,本研究采用了先进的预处理技术,以减少噪声,提高所建议的人工神经网络模型的预测精度。为了证明该策略的有效性,使用数据集对该模型进行了训练和验证。训练和验证的实验结果与最近的方法进行了比较。对比结果表明,该方法降低了MAE、RSME和回归值。
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引用次数: 0
Machine Learning Enabled Traffic Sign Detection System 机器学习交通标志检测系统
K. Rajaram, M. N. V. Kumar, C. Nageswari, S. Rajan, C. M. Rubesh
The Traffic Sign Detection system is a component of an advanced driver assist system that notifies and prompts the driver regarding traffic signals and boards in front. An well organized concurrent signal detection and warning structure are presented to assist better with the existing Intelligent Transport System (ITS) and to improve the safety systems for the identification of regulatory indicators. On-board cameras record real-time video and are associated with a computing device for further processing. The process includes image framing which is blurred and distorted with Gaussian noise because of the movement of the vehicle and ambient disturbances. Hence the input image is enhanced using the median filter and nonlinear Lucy-Richardson for deconvolution. This algorithm is best suited for implementation due to its efficiency in providing an optimal and effective graded output of the processed image. Colour segmentation is performed using Y CbCr colour spacing following shape filtering algorithms using template matching. Then, using processed colour-corrected samples, the required sign is extracted as colour and shape from processed photos, allowing the sign to be distinguished from its foreground and background. The role of the classification module is to find the category of noticed traffic indications captured utilizing Multilayer Perceptron neural systems. Compared to other available systems, the proposed system outshines in every aspect treated to obtain the optimum output. The proposed method is one of the major applications of machine learning which uses Lucy-Richardson and the colour segmenting process. The developed system is implemented efficiently and results close to proximity are obtained.
交通标志检测系统是高级驾驶员辅助系统的一个组成部分,它通知并提示驾驶员前方的交通信号和车牌号。提出了一种组织良好的并发信号检测和预警结构,以更好地协助现有的智能交通系统(ITS),并改进识别监管指标的安全系统。机载摄像机记录实时视频,并与进一步处理的计算设备相关联。该过程包括图像分帧,由于车辆的运动和环境干扰,图像分帧会受到高斯噪声的模糊和扭曲。因此,使用中值滤波和非线性Lucy-Richardson进行反卷积增强输入图像。该算法最适合于实现,因为它在提供处理图像的最优和有效的分级输出效率。颜色分割使用Y CbCr颜色间距执行,然后使用模板匹配的形状滤波算法。然后,使用处理过的颜色校正样本,从处理过的照片中提取所需的标志的颜色和形状,从而使标志与前景和背景区分开来。分类模块的作用是利用多层感知器神经系统找到已注意的交通指示的类别。与其他可用系统相比,该系统在各方面都表现突出,以获得最佳输出。所提出的方法是机器学习的主要应用之一,它使用了Lucy-Richardson和颜色分割过程。所开发的系统得到了有效的实现,并获得了接近接近的结果。
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引用次数: 0
A Delay Compensation Approach for IoT-Enabled Networks with Different Control Strategies 基于不同控制策略的物联网网络延迟补偿方法
Padmaja Mishra, Rajesh Kumar Patjoshi, A. Yadav
The Internet of things (IoT) becomes a new era for the imminent industry to provide an intelligent environment to control systems in real-time. Considerably, for accomplishing the delay analysis in the IoT system, it is necessary to consider the appropriate control system for the precise identification of IoT terminals and the efficient regulation of their access to the network. Therefore, the study considers about different control strategies such as PID (proportional integral derivative) $2^{text{nd}}$ process of Ziegler's Nichols, and PID Pole placement technique for finding the critical delay values under an IoT network environment. The control system is designed by considering different network constraints. Based on the network constraints and the controllers, a significant model is designed for calculating the maximum delay concerning sensor and controller along with controller and things. The controllers are premeditated using the transfer function of the particular plant i.e thing. The planned method designed here is to come across the value of delay via different design techniques using a PID controller. Finally, simulation results confirm the effectiveness of the proposed controller under MATLAB/Simulink environment.
物联网(IoT)成为即将到来的工业的新时代,为实时控制系统提供智能环境。因此,为了完成物联网系统中的时延分析,需要考虑合适的控制系统,以精确识别物联网终端并有效调节其接入网络。因此,研究考虑了不同的控制策略,如Ziegler's Nichols的PID (proportional integral derivative) $2^{text{nd}}$过程,以及PID极点放置技术来寻找物联网网络环境下的临界延迟值。考虑了不同的网络约束条件,设计了控制系统。基于网络约束和控制器,设计了传感器和控制器以及控制器和物体的最大时延计算模型。控制器是预先设定的,使用特定工厂的传递函数。这里设计的计划方法是通过使用PID控制器的不同设计技术来处理延迟的值。最后,在MATLAB/Simulink环境下进行了仿真,验证了所提控制器的有效性。
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引用次数: 0
期刊
2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT)
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