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IEEE 11th International Conference on Signal Processing (ICSP)

作者： Ning, Taikang Ning, James Atanasov, Nikolay Hsieh, Kai-Sheng Trinity Coll Dept Engn Hartford CT 06106 USA Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Univ Penn Dept Elect Engn Philadelphia PA USA Vet Gen Hosp Dept Pediat Kaohsiung Taiwan

ISBN: (纸本)9781467321976;9781467321969

This paper extends our previous studies and presents a fast, automatic cardiac auscultation scoring system that effectively identifies the first and second heart sounds (S-1 and S-2) and extracts clinical features of heart murmurs to assist clinical diagnosis. Using the indices derived from AR modeling, the underlying scoring system is capable of detecting and identifying S-1 and S-2, dissecting the systole and the diastole for further analysis and extracting heart murmurs features found within, such as timing, duration, loudness (intensity), pitch and shape of murmurs. To achieve a broader spectrum of application, only the relative duration difference between systole and diastole was used as the a priori information to identify S-1 and S-2. This algorithm is particularly suited for an embedded system implementation with ease of calculations while maintaining accuracy and effectiveness. The suggested has been successfully evaluated with multiple cardiac cycles, where each systole and diastole was accurately identified and isolated. The performance of the approach has met good success using clinical data from patients with a variety of systolic murmur episodes.

关键词： heart sounds heart murmurs AR modeling data segmentation

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Liver Tumor Detection Using Deep Learning Techniques 7

Liver Tumor Detection Using Deep Learning Techniques

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Deokate, Sarika T. Pede, Shailaja Dhotre, Kalyani Sawant, Aarti Dusunge, Chaitrali Kirve, Mrudula Pimpri Chinchwad College of Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798350304268

segmentation and categorization of Liver CT scan images is very crucial phase, many algorithms are used to detect the tumor from CT scan images.. We designed a model for liver tumor detection using U-net architecture. U-net is a convolutional neural network (CNN) which is generally used for image segmentation method, such as medical image analysis. The U-Net model is very much efficient for a multiple image segmentation assignments, and has been employed in a various types of applications, including medical image segmentation and biological image segmentation. It uses many convolutional layers to extract the features automatically. We have used 3DIRCADB dataset and we have got the accuracy around 98.61 % and dice coefficient is 75.97%. The accuracy is showing the efficiency of U-net architecture, but it requires big size dataset and high processing head. So we have used Thepades sorted block truncation coding (TSBTC) algorithm on the 3dircadb dataset and extracted the features of the dataset till 8ary. Then we have used different classifier families like function(Logistic), Bayes(BayesNet), Lazy(IB1, Kstar), Tree(RandomForest). We got highest accuracy of97.82% by using BayesNet classifier with TSBTC 'Septanary' version. Still work is going on to improve our results using the ensemble based classifier with TSBTC. © 2023 IEEE.

关键词： CNN(Convolutional Neural Network) data augmentation data segmentation Hepatocellular Carcinoma Thepades sorted block truncation coding U-net

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Artificial Intelligence-Based Customer Behavioral Analysis: Techniques, Applications, and Business Insights

Artificial Intelligence-Based Customer Behavioral Analysis: ...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Aneja, Aseem Chitkara University Institute of Engineering and Technology Centre for Interdisciplinary Research in Business and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319200

Artificial Intelligence (AI) has revolutionized the way businesses get their client behavior, empowering a more profound understanding of shopper inclinations and impelling the advancement of focused showcasing techniques. This paper centers on the application of the K-means clustering calculation inside AI for client behavioral investigation. The point is to investigate the procedures, applications, and commerce bits of knowledge determined from this strategy. The paper starts by presenting the elemental concepts of K-means clustering and its part in fragmenting expansive datasets into unmistakable clusters based on comparable characteristics. At that point digs into the different strategies utilized in pre-processing and includes extraction to optimize the algorithm's execution, guaranteeing exact and proficient examination of client behavior. Moreover, this consideration exhibits the wide-ranging applications of K-means in client behavioral investigation, emphasizing its noteworthiness in client division, personalized promoting, and item suggestion frameworks. By viably categorizing clients into particular bunches based on their inclinations and buy history, businesses can tailor their showcasing endeavors, move forward in client engagement, and boost general sales. Moreover, the paper highlights the urgent part of K-means in giving important trade experiences. By analyzing client clusters, businesses can distinguish rising patterns, anticipate future buyer behaviors, and refine their item offerings to meet advancing requests. Moreover, the integration of K-means with other AI procedures, such as normal dialect handling and assumption examination, empowers a comprehensive understanding of client input and opinion and encourages improving client fulfillment and brand dependability. This investigation contributes to the developing body of information on AI-based client behavioral investigation, emphasizing the significance of K-means in encouraging data-driven deci

关键词： Artificial Intelligence (AI) Customer Behavior Analysis data segmentation etc K-means Clustering

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Website Information Extraction Method and Algorithms Based on Big data Technology

Website Information Extraction Method and Algorithms Based o...

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4th International Workshop on Materials Engineering and Computer Sciences (IWMECS)

作者： Zhang Guoming Nanchang Inst Sci & Technol Nanchang 330108 Jiangxi Peoples R China

ISBN: (纸本)9781912407408

Based on the rich experience of teaching practice and theoretical analysis, it scientifically explained the cognitive semantic theory in vocabulary teaching methods, and apply them to the practice, in order to test the effectiveness of new vocabulary teaching method. This paper has conducted the empirical research combined vocabulary teaching empirical analysis with questionnaire survey. The results show that, English vocabulary teaching in higher vocational colleges under the guidance of the cognitive semantics theory helps to strengthen the students' vocabulary cognitive thinking, to construct complete and systematic vocabulary semantic network, greatly stimulate students' vocabulary learning interest and initiative, improve the students' vocabulary learning efficiency, to improve students' vocabulary level in a maximum degree.

关键词： data segmentation Path algorithm Big data technology Web site information extraction

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Vision-based segmentation of continuous mechanomyographic grasping sequences

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2008年第2期55卷 765-773页

作者： Alves, Natasha Chau, Tom Univ Toronto Inst Biomat & Biomed Engn Toronto ON M5S 3G9 Canada Bloorview Res Inst Toronto ON M4G 1R8 Canada

In detecting motor related activity from mechanomyographic (MMG) recordings, the acquisition of long, continuous streams of MMG signals is typically preferred over the painstaking collection of individual, isolated contractions. However, a major challenge with continuous collection is the subsequent separation of the MMG data stream into segments representing individual contractions. This paper proposes a method for segmenting continuously recorded MMG data streams using computer vision while providing a highly reduced set of key images for rapid human expert verification. Transverse plane video recordings of functional grasp sequences were synchronized with the acquisition of, MMG signals from the forearm. An automatic, vision-based algorithm exploiting skin color detection, motion estimation, and template matching provided segmentation cues for MMG signals arising from multiple grips. The automatic segmentation method tolerated extraneous hand movements, differentiated among multiple grips and estimated grip transition times. Our implementation segmented two grips with an average accuracy of 97.8 +/- 4%, and up to seven grips with an accuracy of 73 +/- 20%. The automatically extracted contraction initiation and termination times were within 173 +/- 133 ms of the times obtained via manual seginentation. It is suggested that the proposed method would be particularly conducive to the assembly of large collections of signals for training MMG-driven prostheses.

关键词： data segmentation electromyogram gesture recognition mechanomyogram (MMG) multifunction prostheses

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A Novel Multireceiver SAS RD Processor

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Zhang, Xuebo Yang, Peixuan Wang, Yanmei Shen, Wenyan Yang, Jiachong Wang, Junfeng Ye, Kun Zhou, Mingzhang Sun, Haixin Whale Wave Technol Inc Kunming 650200 Peoples R China Acoust Signal & Elect Sci & Technol Corp Lanzhou 730050 Peoples R China Tianjin Univ Technol Dept Commun Engn Tianjin 300384 Peoples R China Xiamen Univ Sch Informat Key Lab Southeast Coast Marine Informat Intelligen Minist Nat Resources Xiamen 361005 Peoples R China Xiamen Univ Key Lab Underwater Acoust Commun & Marine Informat Minist Educ Xiamen 361005 Peoples R China Xiamen Univ Shenzhen Res Inst Shenzhen 518000 Peoples R China

Classic multireceiver synthetic aperture sonar (SAS) reconstruction algorithms relying upon Loffeld's bistatic formula (LBF) should first utilize the data segmentation approach to remove the multireceiver deformation (MD) term. Usually, the small sub-block is required by traditional LBF-based imaging algorithms to produce high-performance results. To address this issue, we describe a receiver-by-receiver-based imaging algorithm. The presented method first reformulates the original LBF to range dependent and independent terms. Then, the image reconstruction receiver-by-receiver is carried out. The LBF reformulation and the quadratic expansion of range-dependent term needed by subsequent range-Doppler (RD) algorithm would generate approximation error corrected by the data segmentation approach in the frequency domain. After performing the range cell migration correction (RCMC) receiver-by-receiver, the signal in the RD domain related to each receiver is coherently synthesized, and the azimuth offset is thereafter corrected. The focusing result would be produced when the azimuth inverse Fourier transform (IFT) is completed. On the basis of simulation and real data test experiments, conventional focusing approach with wide sub-block is seriously affected by ghost targets, which are successfully avoided by presented method with the same wide sub-block. It indicates that the presented method relaxes the requirement for the sub-block width.

关键词： data segmentation image formation Loffeld's bistatic formula (LBF) multireceiver synthetic aperture sonar (SAS) range-Doppler (RD) algorithm

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User-friendly nonlinear nonparametric estimation framework for vibro-acoustic industrial measurements with multiple inputs

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2020年 145卷 106926-106926页

作者： Csurcsia, Peter Zoltan Peeters, Bart Schoukens, Johan Siemens Ind Software NV Interleuvenlaan 68 B-3001 Leuven Belgium Vrije Univ Brussel Dept Engn Technol Pl Laan 2 B-1050 Elsene Belgium

This paper introduces a user-friendly estimation framework for industrial measurements of vibro-acoustic systems with multiple inputs. Many mechanical structures are inherently nonlinear and there is no unique solution for modeling nonlinear systems. This is especially true when multiple-input, multiple-output (MIMO) systems are considered. This paper addresses the questions related to the user-friendly semi-automatic processing of MIMO measurements with respect to the design of experiment and the analysis of the measured data. When the proposed framework is used, with a minimal user interaction, it is easily possible a) to decide, if the underlying system is linear or not, b) to decide if the linear framework is still accurate (safe) enough to be used, and c) to tell the inexperienced (non-expert) user how much can be gained using an advanced nonlinear framework. The proposed nonparametric industrial framework is illustrated on various real-life measurements. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： MIMO systems Nonparametric estimation Nonlinear system identification User-friendly methods Automatic processing data segmentation Transient elimination Direct field acoustic excitation Ground vibration testing

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An image encryption scheme based on chaotic system and compressed sensing for multiple application scenarios *

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INFORMATION SCIENCES 2023年第1期642卷

作者： Wang, Chao Song, Ling Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China

In this paper, we propose a novel image encryption scheme based on a chaotic system and compressed sensing for multiple application scenarios. It is a dedicated scheme for JPEG format that makes full use of the characteristics of DCT data. The proposed scheme generates a key from a plain image with random noise, which is then used to generate the parameters needed in the scheme through using a new chaotic system structure. This structure employs data segmentation for the DCT data of a plain image and processes sparse data and non-sparse data separately: it permutes non-sparse data, and applies permutation and compressed sensing to sparse data. By merging the processed data and performing efficient confusion, we obtain the final encrypted data. The reverse operation is image decryption, and partial decryption can be performed quickly since sparse data is not required. Such partial decryption can be used for multiple applications, such as copyright protection platforms and machine learning based on private data. Simulation experiments and a performance analysis show that the key generation method and efficient confusion of our scheme can improve the security of image encryption. Furthermore, the data segmentation and reconstruction algorithm based on a cyclic matrix can greatly improve decryption efficiency.

关键词： Image encryption DCT data segmentation Chaotic system Compressed sensing

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Enhanced curve-based segmentation method for point clouds of curved and irregular structures

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第4期35卷 045014-045014页

作者： Song, Limei Zhang, Zongyang Xu, Chongdi Yang, Yangang Zhu, Xinjun Tiangong Univ Tianjin Key Lab Intelligent Control Elect Equipmen Tianjin 300387 Peoples R China Tianjin Univ Technol & Educ Sch Mech Engn Tianjin 300222 Peoples R China

This paper proposes an improved method for model-based segmentation (MS) of curved and irregular mounded structures in 3D measurements. The proposed method divides the point cloud data into several levels according to the reasonable width calculated from the density of points. Then, it fits a curve model with 2D points for each level separately. The classification results of specific types are merged to obtain specific structural measurement data in 3D space. We use MS method, difference of normals based segmentation, region growing algorithm, constrained planar cuts, and locally convex connected patches as a control group. The results show that the proposed method achieves higher accuracy with a mean intersection merge ratio of more than 0.8238, at least 37.92% higher than other methods. The method proposed in this paper requires less time to process than other methods. Therefore, the proposed method effectively and efficiently segments the measurement data of curved and irregular mounded structures in 3D measurements. The method proposed in this paper has also been applied in the practical robotic grinding task. The root mean square error of the grinding amount is less than 2 mm, and good grinding results are achieved.

关键词： data segmentation model-based approach curved contour structures

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A Robust Regime Shift Change Detection Algorithm for Water-Flow Dynamics

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WATER 2023年第8期15卷 1571-1571页

作者： Dastour, Hatef Gupta, Anil Achari, Gopal Hassan, Quazi K. K. Univ Calgary Dept Geomat Engn 2500 Univ Drive NW Calgary AB T2N 1N4 Canada Alberta Environm & Pk Resource Stewardship Div 3535 Res Rd NWUniv Res Pk Calgary AB T2L 2K8 Canada Univ Calgary Dept Civil Engn 2500 Univ Drive NW Calgary AB T2N 1N4 Canada

Stream and river monitoring have an influential role in agriculture, the fishing industry, land surveillance, the oil and gas industry, etc. Recognizing sudden changes in the behavior of streamflow could also provide tremendous insight for decision-making and administration purposes. The primary purpose of this study is to offer a new robust Regime Shift Change Detection (RSCD) algorithm which can identify periods and regime changes without any assumptions regarding the length of these periods. A regime shift algorithm using two different refined method approaches is proposed in this article. The RSCD with Relative Difference (RSCD-RD) and RSCD with Growth Rate (RSCD-GR) are the two main specializations of this regime shift algorithm. We compared these two specializations on train and test datasets and commented on the advantages and each specialization. RSCD-GR and RSCD-RD were equally effective in detecting regime changes when thresholds were pinpointed for each station and season. However, RSCD-RD outperformed RSCD-GR when general thresholds were used for cold and warm months. A strength of RSCD-GR is the ability to investigate newly observed data separately, while RSCD-RD may require re-investigation of historical data in some cases. A regime change was detected in the monthly streamflow data of the Athabasca River at Athabasca (07BE001) in May 2007, while no such change was observed in the monthly streamflow data of the Athabasca River below Fort McMurray (07DA001). The discrepancy could be attributed to factors such as the clarity of the river water from Saskatchewan or the utilization of industrial water. Additional investigation might be required to determine the underlying causes.

关键词： time series analysis data segmentation machine learning SARIMA random forest regression

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