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Deep-ASL enhancement technique in arterial spin labeling MRI - A novel approach for the error reduction of partial volume correction technique with linear regression algorithm

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JOURNAL OF COMPUTATIONAL SCIENCE 2022年第0期58卷 101546-101546页

作者： Shyna, A. Amma, Ushadevi C. John, Ansamma Kesavadas, C. Thomas, Bejoy APJ Abdul Kalam Technol Univ ThangalKunju Musaliar Coll Engn Dept Comp Sci & Engn Thiruvananthapuram Kerala India Amrita Vishwa Vidyapeetham Dept Elect & Commun Engn Amritapuri Campus Clappana Kerala India Sree Chitra Tirunal Inst Med Sci & Technol Dept Imaging Sci & Intervent Radiol Trivandrum Kerala India

Arterial Spin Labelling MRI is a noninvasive quantitative imaging technique for measuring Cerebral Blood Flow (CBF) that plays a vital role in diagnosing different neurological disorders. Limited signal-to-noise ratio and significant partial volume effect due to the low resolution of ASL images make an accurate CBF estimation difficult. This work proposes a deep learning based ASL enhancement algorithm (Deep-ASL ENHANCE), based on the principle of single image super resolution and multi-loss joint strategy with two reconstruction modules and one weighted fusion module that employ residual dense block as the basic building block. Lack of huge amount of low quality and high quality images for training this deep learning network, is addressed by generating simulated ASL images from structural images of ADNI2. The experiment is conducted and results are evaluated on a simulated dataset in terms of different metrics such as RMSE, PSNR and SSIM. The model is also validated using clinical ASL images with the help of two independent radiologists and the results are compared using Visual Quality Score (VQS). The deep learning model trained by using simulated ASL images shows more promising results on clinical ASL data. The effectiveness of using Deep-ASL ENHANCE as a preprocessing step to the partial volume correction technique with linear regression algorithm (LR) has been investigated using RMSE score and it is found that CBF quantification accuracy is improved compared to the standalone LR algorithm.

关键词： Arterial spin labeling Cerebral blood flow Partial volume effect linear regression algorithm Deeplearning Residual dense block

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Research on linear regression algorithm Based on S7-1500 PLC Marine Power Station

International Core Journal of Engineering

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International Core Journal of Engineering 2020年第5期6卷 294-300页

作者： Rongchao Wang Bohan Zhu Jiacheng Ni

In order to solve the problem that the best time of closing the ship power station and the car is difficult to catch, the problem of the car failure is caused. The influence of the parallel conditions and signal acquisition of the power station and the main switch action time on the closing command is analyzed. The linear regression algorithm is used to predict the optimal closing time, calculate the closing advance, and capture the optimal closing time. Based on the influence of main switch operation time and load disturbance at the moment of closing, the linear regression control system of ship power station based on S7-1500 PLC is designed. The experimental results show that the linear regression method captures the best time of closing and the impact current is small. High reliability, improved car success rate, and good promotion and application value.

关键词： Ship power station S7-1500 PLC The difference frequency voltage linear regression algorithm

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Segmentation of the ECG Signal by Means of a linear regression algorithm

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SENSORS 2019年第4期19卷 775-775页

作者： Aspuru, Javier Ochoa-Brust, Alberto Felix, Ramon A. Mata-Lopez, Walter Mena, Luis J. Ostos, Rodolfo Martinez-Pelaez, Rafael Univ Colima Fac Mech & Elect Engn Ave Univ 333 Colima 28000 Mexico Polytech Univ Sinaloa Acad Unit Comp Master Program Appl Sci Mazatlan 82199 Mexico Univ La Salle Bajio Fac Informat Technol Ave Univ 602 Guanajuato 37150 Mexico

The monitoring and processing of electrocardiogram (ECG) beats have been actively studied in recent years: new lines of research have even been developed to analyze ECG signals using mobile devices. Considering these trends, we proposed a simple and low computing cost algorithm to process and analyze an ECG signal. Our approach is based on the use of linear regression to segment the signal, with the goal of detecting the R point of the ECG wave and later, to separate the signal in periods for detecting P, Q, S, and T peaks. After pre-processing of ECG signal to reduce the noise, the algorithm was able to efficiently detect fiducial points, information that is transcendental for diagnosis of heart conditions using machine learning classifiers. When tested on 260 ECG records, the detection approach performed with a Sensitivity of 97.5% for Q-point and 100% for the rest of ECG peaks. Finally, we validated the robustness of our algorithm by developing an ECG sensor to register and transmit the acquired signals to a mobile device in real time.

关键词： segmentation Digital Signal Processing ECG Sensor linear regression algorithm identification waves

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linear regression algorithm Against Device Diversity for Indoor WLAN Localization System

Linear Regression Algorithm Against Device Diversity for Ind...

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IEEE Global Telecommunications Conference (GLOBECOM)

作者： Zhang, Liye Ma, Lin Xu, Yubin Li, Cheng Harbin Inst Technol Commun Res Ctr Harbin 150001 Heilongjiang Peoples R China Minist Publ Secur Key Lab Police Wireless Digital Commun Harbin 150001 Heilongjiang Peoples R China Mem Univ Fac Engn Elect & Comp Engn St John NF Canada

ISBN: (纸本)9781509050192

In recent years, received signal strength (RSS) based indoor localization system using WLAN has attracted considerable attention. However, signal strength variations across diverse devices becomes a major problem in this system, especially in the crowdsourcing based localization system. In this paper, the linear regression algorithm is proposed to solve this problem automatically. First of all, the problem of device diversity and the adverse effects caused by this problem are analyzed. Then the intrinsic relationship between different RSS values collected by different devices is mined by the linear regression algorithm. The problem of device diversity will be handled by this algorithm. In crowdsourcing systems, when the major problem is eliminated, a unique radio-map can be created in the offline phase and the user's location can be estimated by a localization algorithm in the online phase. Experimental results show that the proposed method results in a higher reliability and localization accuracy.

关键词： Indoor localization system Received signal strength Crowdsourcing Device diversity linear regression algorithm

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Indoor Localization Method Based on AP and Local linear regression algorithm 17

Indoor Localization Method Based on AP and Local Linear Regr...

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IEEE 17th International Conference on Communication Technology (ICCT)

作者： Zhang, Yongjun Ye, Ling Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing Peoples R China

ISBN: (纸本)9781509039449

In this paper, an indoor localization method based on AP and local linear regression algorithm is presented. The method mainly makes use of Wi-Fi signal strength values collected from a small amount of deployed APs, and then the local linear regression algorithm is used to study and measure the degree of signal attenuation caused by the complex interior environment. So, the parameter of the algorithm is different for each AP. The circular area with radius of distance calculated by that algorithm with the signal strength value from deployed APs intersect, which forms a weighted overlapping area. By calculating the weight of each grid in this area, we can choose the one with the max probability as our predicted position. The experimental results show that the proposed method can control the positioning error in the range of 3 meters, which indicates that the method can provide a reasonable reference or even precise positioning result under low cost for indoor localization.

关键词： linear regression algorithm signal overlapping AP deploying indoor localization

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Indoor Localization Method Based on AP and Local linear regression algorithm

Indoor Localization Method Based on AP and Local Linear Regr...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Yongjun Zhang Ling Ye Beijing University of Posts and Telecommunications State Key Laboratory of Information Photonics and Optical Communications

关键词： linear regression algorithm signal overlapping AP deploying indoor localization

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Design of artistic graphic symbols based on intelligent guidance marking system

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NEURAL COMPUTING & APPLICATIONS 2023年第6期35卷 4255-4266页

作者： Guo, Yongqiang Anyang Normal Univ Sch Fine Arts Anyang 455000 Henan Peoples R China

This paper presents an in-depth study and analysis of the design of the symbolization of art graphics through a linear regression algorithm. The improved method first selects a suitable range of learning rate and lets the learning rate transform in the range of values according to the characteristics of cosine function transformation, so that the learning rate gradually decreases at different rates in different periods of training to achieve the effect of a faster convergence of logistic regression loss function and better convergence value and improve the training efficiency and classification accuracy of logistic regression algorithm. At the same time, the modules of data reordering and regularization penalty method are added to make the logistic regression algorithm model have better generalization ability. Based on the interrelationship between guidance signage system, graphic symbols, and emotional expression, we focus on the emotional characteristics and presentation of graphic symbols and explore their emotional transmission in the guidance signage system, thus the impact on human cognitive behavior. We will summarize the significance of the emotional expression of graphic symbols in the guidance signage system. Graphic symbols as the soul of the guidance signage system, are also the most impactful visual elements, their rich expression, presenting a unique visual aesthetic, more in line with the aesthetic interests of modern people. It conveys information, plays a guiding function at the same time, establishes a sense of place, creates a specific emotional atmosphere to convey a certain emotional experience;the message is more deeply rooted.

关键词： linear regression algorithm Art graphics Symbolic design

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Non-Invasive Glucometer using Near Infrared Technology 2

Non-Invasive Glucometer using Near Infrared Technology

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Patel, Himali Darure, Anushka Nayak, Atharva Narvekar, Avantika Bhagat, Sudesh Vivekananda Educ Soc Inst Technol Dept Elect & Telecommun Mumbai Maharashtra India

ISBN: (纸本)9798350391558;9798350379990

In recent times, the increasing global incidence of diabetes has exacerbated the discomfort and infections arising from the invasive nature of traditional commercial glucose meters. The exploration of non-invasive blood glucose monitoring technology has become pivotal in international research, promising alleviation for a considerable population of diabetic patients. This study introduces an innovative non-invasive glucometer, incorporating a linear regression algorithm. Representing a transformative solution to the challenges posed by conventional blood-based glucose monitoring in diabetes management, the device, equipped with advanced sensor technology and leveraging a carefully curated dataset, achieves unparalleled accuracy while minimizing user discomfort. This advancement holds promise for significantly enhancing the experience and well-being of individuals managing diabetes.

关键词： linear regression algorithm non- invasive method max30100 sensor arduino firebase glucometer

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Noise Prediction: A Case Study of Gjilan City 4th

Noise Prediction: A Case Study of Gjilan City

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Bajrami, Buen Veljanovska, Kostandina Faculty of Information and Communication Technologies University St. Kliment Ohridski - Bitola 1 Maj bb. Bitola7000 Macedonia

ISBN: (纸本)9789819751990

Recently, we have started to hear more often about the smart city. This is a new term, but recently there is a growing trend, which uses computer sciences to create an integrated, intelligent system, which is easily used and managed by people. Such smart grid technologies can directly impact everything from operations to infrastructure maintenance and planning. A key part of this research will be noise monitoring in specific areas of the city depending on population, traffic density, number of cars, and wind speed. We will make real-time measurements in four areas per day and hourly. The data will be placed in a database, where its data will be processed through two algorithms on our platform designed in PHP. The two forecasting algorithms we used are linear regression and the ARIMA Forecasting Model. We have also included the correlation coefficient to see the report of the parameters. At the end, we have presented the factors that influence the increase in noise as well as the recommendations that we believe will help prevent noise in the city. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： ARIMA model algorithm Correlation coefficient linear regression algorithm Noise prediction

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Application Research Color Management in theTransfer Printing Based on the linear regression algorithm

Application Research Color Management in theTransfer Printin...

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2nd International Conference on Frontiers of Manufacturing and Design Science (ICFMD 2011)

作者： Guo, Linghua Zhang, Meiyun Guo, Xinhua Liu, Guodong Shaanxi Univ Sci & Technol Xian 710021 Peoples R China Jinan Univ Zhuhai 519070 Peoples R China

ISBN: (纸本)9783037852828

There are many factors that effect the quality of the heat transfer printing products. The CRT in prepress is the most important factor. In this paper, the color ladder segmentation is spaced at 5. The color of the CRT is tested with X-RITE color monitor optiomizer. According to the data and the curve, the function model of color characteristic features is set up. We made the common test plates which is to verify the error of the function model. As a result, The range of color error in the heat transferring printing is reduced. The color management profile is put forward, the quality of transferring printing is improved. A reference is provided for the color management in the heat transfer printing.

关键词： linear regression algorithm display heat transfer printing color management profile

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