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Weighted Fuzzy C Means: A Novel Tumor Segmentation Approach in MR Brain Images 2

Weighted Fuzzy C Means: A Novel Tumor Segmentation Approach ...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Poshitha, M. Ramaraj, Kottaimalai Dilipkumar, S. Meena, Durga R. Ambika, B. Thilagaraj, M. Department of Computer Science and Engineering MVJ College of Engineering Bengaluru India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Electrical and Electronics Engineering Sri Krishna College of Technology Tamilnadu Coimbatore India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil India Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Tamilnadu Madurai India Department of Industrial Internet of Things MVJ College of Engineering Bengaluru India

ISBN: (纸本)9798350340235

The most prevalent primary brain tumor, glioma, is caused by glial cell carcinogenesis in the central nervous system. For numerous applications in the area of health care evaluation, brain tumor localization and separation from magnetic resonance images (MRI) are challenging yet crucial tasks. Several recently developed methods utilized four modalities: T1, T1c, T2, and FLAIR. This is because each brain imaging modality provides distinct and important information concerning every region of the tumor. The process of diagnosis, therapy selection, and risk variables detection depends on trustworthy and precise tumor segmentation and survival of patients forecasting. In this article, a state-of-the-art fuzzy-based system is introduced that uses multimodal MRI images to categorize brain tumors and estimate glioma survival. To address the drawbacks of the FCM, the suggested approach combined the weight function with the conventional Fuzzy C-means (FCM). Extensive tests are carried out on the different BRATS challenge datasets, demonstrating that the suggested approach achieves competitive outcomes. Evaluation on the BraTS dataset confirms the effectiveness of the developed Weighted FCM (WFCM), and the segmented results are compared with the ground truth images. A small number of performance metrics were also used for assessing the qualitative as well as quantitative outcomes. The resulting dissected images help medical professionals diagnose, medicate, or plan for medical intervention for the affected individuals earlier. © 2023 IEEE.

关键词： Brain tumor Brain Tumor Segmentation (BraTS) dataset Glioma Magnetic Resonance Imaging (MRI) Segmentation Weighted Fuzzy C-means (WFCM)

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Research on NLOS environmental ranging method based on gradient boosting tree 8

Research on NLOS environmental ranging method based on gradi...

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8th international conference on Electrical, Mechanical and computer engineering, ICEMCE 2024

作者： Li, Wenyu Cao, Shun Liu, Xianda Wang, Minghao Sheng, Shuan Shenyang University of Chemical Technology School of Information Engineering Liaoning Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control System Liaoning Shenyang China Shenyang National Research Institute of Intelligent Robots Co. Ltd. Liaoning China

ISBN: (纸本)9798331506230

Indoor distance measurement plays a critical role in positioning services. The complexity of indoor environments, coupled with a limited and small sample size, results in suboptimal distance accuracy. This paper proposes a method for non-line-of-sight distance measurement based on a gradient boosting tree model, effectively addressing the issue of sample size dependency. The random hyperparameter settings of the gradient boosting tree model lead to prolonged fitting times. Additionally, the model utilizes a fixed learning rate, which limits the analysis of features in small samples. To tackle these concerns, we develop a grid optimization algorithm that identifies the best hyperparameter combinations through a hyperparameter grid search. We also implement a weighted value approach and a cosine annealing algorithm to precisely update the learning rate according to training conditions, thus maximizing the learning of small sample features. Our proposed model significantly enhances distance accuracy in non-line-of-sight environments under constraints of training sample size and variety. Comparative experiments demonstrate that our model improves distance accuracy by 63% relative to other machine learning models, with a mean square error of only 0.168m. © 2024 IEEE.

关键词： Distance measurement

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Audio Assist: Enabling Object Detection Through Speech for the Visually Impaired 2

Audio Assist: Enabling Object Detection Through Speech for t...

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2nd international conference on automation, Computing and Renewable Systems, ICACRS 2023

作者： Shanmugapriya, S. Priya, Gondipalli Hari Sai, Dola Lokesh Jethin, Gogineni Kumar, Chintamani Vinod Department of Computer Science and Engineering Kalasalingam Academy Of Research And Education Tamilnadu Virudhunagar India

ISBN: (纸本)9798350340235

Object detection based on deep learning is an essential application in deep learning technology, which is distinguished by its superior feature learning and feature representation capabilities when compared to traditional object identification approaches. Generally, the normal people can identify where the objects are located easily but it’s not that easy for visually impaired people to identify the objects and its location. Also, it is very challenging to create a computer model to identify the objects. Actually, the object detected will be appear on the screen but it’s not useful for visually impaired that’s why we are using Audi Assist which helps us toconvert text to speech. And we are enabling the audio assist technology to gives the audio output after identifying the object. And it describes where the object is it located. This study utilizes a largedataset of objects like mobiles,water bottle, cups and many more. This model is not only trained to detect objects also detects persons. This technology has enormous promise for increasing the freedom and safety of visually impaired people by allowing them to explore and interact with their surroundings more confidently and efficiently. It is a big step towards inclusion and accessibility in our quickly expanding technology ecosystem. The objectsdetected are translated into meaningful audio cues, providing immediate to user. © 2023 IEEE.

关键词： Architecture Audio Assist computer Vision Deep Learning Object Classification Segmentation YOLO (You Only Look Once)

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Multi-branch Distribution Network Fault Localization Method Based on the Combination of Single and Double-ended Traveling Wave 5

Multi-branch Distribution Network Fault Localization Method ...

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5th international conference on Advances in Electrical engineering and computer Applications, AEECA 2024

作者： Tan, Weibin Xu, Guang Tian, Weiwei Zhang, Xiangxing Qi, Ang Huang, Xinyue Zhuhai XJ Electric Co. Ltd. Zhuhai China Shandong University of Science and Technology College of Electrical Engineering and Automation Qingdao China

ISBN: (纸本)9798350355253

To address the issue of low wavefront calibration accuracy in traveling wave fault location methods for multi-branch distribution networks, a technique for finding faults based on the combination of single and double-ended traveling wave methods is proposed. First, an analysis is conducted on the feeder lines of the distribution network's traveling wave transmitting properties. Based on the arrival time and polarity of the wavefront, traveling wave filtering conditions are constructed to obtain effective information on subsequent fault wavefronts. The location of the fault spot on the branch line is inferred from the effective information of multiple subsequent wavefronts in single-ended measurement data. The fault spot on the primary line is inferred based on the time difference of the first traveling wavefront between double-ended measurements. Thus, under the premise of configuring measurement terminals for branch lines, fault location in a distribution network with several branches is achieved. To address the difficulty in identifying subsequent wavefront, a fault location method according to the blend of single and double-ended traveling wave methods is proposed. Analysis is done on the traveling wave transmission properties in the distribution network's branch lines. Based on the arrival time and polarity of the wavefront, traveling wave filtering conditions are constructed to identify segments of traveling waves containing effective information, thus inferring the location of the fault spot on the branch line. Finally, the suggested approach is tested and a distribution network simulation system with several branches is constructed using MATLAB/SIMULINK simulation software. The suggested approach has high fault location accuracy and can calibrate the wavefront with effectiveness, according to simulation results. © 2024 IEEE.

关键词： MATLAB

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FortBoot: Fortifying Rooted-in-Device-Specific Security Through Secure Booting 32

FortBoot: Fortifying Rooted-in-Device-Specific Security Thro...

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32nd IFIP/IEEE international conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Mohammad, Sajeed Farahmandi, Farimah Electrical and Computer Engineering Univeristy of Florida GainesvilleFL32608 United States

ISBN: (纸本)9798331539672

With the complex System-on-Chip (SoC) architectures ever-increasingly used in security-critical applications, realizing a secure boot procedure is of at-most importance, in which the integrity and authenticity of the firmware (FW) on hardware (HW), as well as the trustworthiness of the software (SW), will be verified. Considering the complexity of the existing solutions and their vulnerability to emerging attacks, in this paper, we propose FortBoot, which is a comprehensive framework to strengthen secure boot using an integration of (1) dynamic key generation for unique randomness against brute-force and guessing attacks;(2) Mutual FW-HW binding for restricting unauthorized and malicious FW load/execution;(3) anti-rollback measures for preventing the use of (maliciously) flawed/downgraded/altered FW to be loaded/executed on unauthorized HW;and (4) dynamic code attestation for providing real-time code integrity confirmation. To show its effectiveness, FortBoot is implemented on OpenTitan SoC, whose comprehensive security assessment verifies its resistance against a wide range of advanced attacks. © 2024 IEEE.

关键词： System-on-chip

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Ensuring the Long-term Security of Medical Records: Utilizing Blockchain and Sophisticated Encryption Methods 2

Ensuring the Long-term Security of Medical Records: Utilizin...

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2nd IEEE international conference on IoT, Communication and automation Technology, ICICAT 2024

作者： Divya, K. Nagaraju, V. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of ECE Chennai India

ISBN: (纸本)9798350368109

In the context of digital health documents, security is paramount in the digital world, and there is a need to come up invented solutions that will help to enhance the protection of the patient's records while ensuring that they can be easily accessed without compromising the functionality and ease of use. This problem can be ingeniously addressed through the integration of blockchain with HE and PRE, and the following steps will illustrate the mechanism of this solution. With the help of the proxy re-encryption (PRE) concept, an authorized third party is allowed to transform censor text encrypted with one key into cipher text encrypted with a different key without revealing the plain text message, making it a secure way of transferring data and managing its access. Homomorphic encryption (HE) is privacy-preserving, as calculations can be performed on encrypted materials without decrypting them. The described method applies homomorphic encryption to the digital health records so that safety can be maintained when calculations are performed on the encrypted data within a blockchain network. Proxy re-encryption is applied to address the issues of access control, where users can be allowed to access specific parts of data in a way that they wish. Thus, with the help of re-encryption techniques, an organization may grant access to some data subsets and, at the same time, ensure that only access is granted to some selected parties and none more than that. This technique fulfills the need for privacy, access control, and safe computation through the integration of homomorphic encryption, proxy re-encryption, and blockchain. It offers a full solution for safeguarding health records in a digital format. It provides great privacy to the patients' records and allows sharing data securely in a fixed and unalterable blockchain network for analysis. However, more research is necessary to probe the applicability of the approach on typical HISs. © 2024 IEEE.

关键词： Electronic health record

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YOLOv5 improved algorithm and its application in real-time inventory of corrugated paper

YOLOv5 improved algorithm and its application in real-time i...

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2024 international conference on Image Processing and Artificial Intelligence, ICIPAl 2024

作者： Wu, Qian Kang, Qinhao Yang, Yuxiang Li, Lin Han, Yu College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China College of Automation Chongqing University Chongqing400044 China

ISBN: (纸本)9781510681514

To detect corrugated paper quickly and accurately, a YOLOv5-based algorithm has been created. In order to improve the fusion of lower layer feature information, the operation of GSConv layer is introduced in the feature enhancement network algorithm. for better utilizing feature semantic information, the CARAFE upsampling operator is used instead of the original upsampling. MobileNetv3 was used to replace its backbone network to reduce computing costs and achieve lightweighting. Using the corrugated paper dataset constructed by the authors as an example, the improved algorithm was found to have a parameter size of 1.3 M based on the results., computational complexity of 1.8 GFLOPS, and weight size of 2.9 MB. Compared to the original YOLOv5n algorithm, these values decreased by 27.8%, 56.1%, and 21.6%. Its key technical indicators mAP@0.5 and FPS can be as high as 88.0% and 68.5 respectively. These results show that the improved algorithm satisfies the requirements for on-site deployment on mobile devices. The research shows that applying improved algorithms to conduct real-time and accurate inventory of corrugated packaging is a feasible approach. © 2024 SPIE.

关键词： Signal detection

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Transformative Technology in Poultry Management: 3D CNNs for Broiler Chicken Weight Prediction 1

Transformative Technology in Poultry Management: 3D CNNs for...

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1st IEEE international conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Anuprabha, K. Kaliappan, Vishnu Kumar Baniekal Hiremath, Gangadhar Kpr Institute of Engineering and Technology Department of Computer Science Engineering Tamilnadu Coimbatore641407 India Wasp Autonomous Systems Pvt Ltd Karnataka Ballari India

ISBN: (纸本)9798350372748

In global food production, broiler chickens hold significant importance, which are traditionally managed through manual labour for both meat and egg production. However, the poultry farming industry has increasingly turned to automation to address challenges associated with manual labour and enhance efficiency. This research introduces a novel methodology for estimating chicken weight utilizing top-view videos as input and leveraging 3D Convolutional Neural Networks (CNNs). Accurate weight assessment is a critical aspect of poultry farming, influencing feeding strategies and resource allocation. Traditional manual weighing methods are laborious and time-intensive, necessitating the exploration of automated alternatives. The methodology involves dataset collection, preprocessing, and employing 3D CNNs for effective feature extraction. 3D CNN architecture, adept at capturing spatial and temporal features, consists of convolutional and pooling layers, followed by flatten and dense layers for weight prediction. The model achieves a remarkable accuracy of 95%, as evidenced in the experimental results. Experimental validation showcases the potential of this method as a non-invasive and efficient tool for estimating chicken weight. It contributes to the growing field of automated livestock management in agriculture. © 2024 IEEE.

关键词： Convolutional neural networks

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A Multi-stream Convolutional Model Using Attention Module for sEMG Gesture Recognition 24

A Multi-stream Convolutional Model Using Attention Module fo...

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4th international conference on Control and Intelligent Robotics, ICCIR 2024

作者： Yang, Yongyue Chen, Wenjie Zhi, Yali Liu, Yuanyuan Sun, Xiantao College of Electrical Engineering and Automation Anhui University Hefei Anhui China

ISBN: (纸本)9798400709937

Surface electromyographic (sEMG) signals, a pivotal technology in non-invasive Human-computer Interaction (HCI), exhibit promising applications in the realm of gesture action recognition. In recent years, advancements in deep learning technology have shown significant improvements in the recognition abilities of EMG signal-based gesture recognition systems. In this paper, we propose a new gesture recognition network based on sEMG signals, which embeds two different attention mechanism modules, Coordinate Attention and Criss-Cross Attention, in a multi-stream Gate Recurrent Unit (GRU) model. Thereby, the network can appropriately assign learning weights to cross-channel information and global information, thus improving the learning ability. We also fuse the joint acceleration signals with the surface EMG signals to further improve the performance of the model. The findings indicate that the method has an accuracy of 84.83% in the experiments based on the NinaPro DB2 dataset. Through the experiments above, this work achieves a certain degree of improvement and enhancement. © 2024 held by the owner/author(s).

关键词： Deep learning

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Out-of-Distribution Robustness Forecasting for Offshore Wind Power via Matching Based Transformer 22

Out-of-Distribution Robustness Forecasting for Offshore Wind...

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22nd IEEE international conference on Industrial Informatics, INDIN 2024

作者： Lu, Jianmou Chen, Yuanye Fang, Fang Xiao, Feng Liu, Zhijie School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798331527471

Due to extreme external environments and changes in component health status, offshore wind turbines can generate out-of-distribution (OOD) data compared to normal operation. Traditional wind power forecasting (WPF) models are typically trained using historical data. Nevertheless, these models fail to maintain robustness with respect to OOD data. This paper introduces the matching based Transformer (MatTF) to mine shared knowledge from data across different distributions, thereby addressing the OOD problem. First, an algorithm for the detection of distribution boundaries is designed to partition the training data into subsets. Then, adaptive factors are added to the self-attention module in traditional transformer to solve the distribution matching problem. Finally, the effectiveness of the model is verified through case study comparing with other WPF models. © 2024 IEEE.

关键词： Wind forecasting

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