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Notfallmedizin up2date 2024年第3期19卷 317-331页

作者： Dominik Knott Lutz Siegl Katharina Siegl Klinik X Anästhesiologie Intensivmedizin Schmerztherapie und Notfallmedizin Bundeswehrkrankenhaus Berlin Berlin Deutschland

Sonography is an established noninvasive diagnostic tool in the clinical context of an emergency department. Its use in the prehospital setting is still rare despite its importance to use someone’s resources purposeful and its importance in emergency medicine guidelines. In this article we show the advantages and disadvantages of prehospital point-of-care ultrasound (pPOCUS). We reflect organizational hurdles implementing pPOCUS as well as describing the technical preconditions for an easy and meaningful use. Furthermore, we explain teaching issues for pPOCUS and with a standard operating procedure (SOP) we show how pPOCUS could be implemented in the prehospital setting using some cardinal symptoms as examples.

关键词： Notfallmedizin Point of Care Ultraschall Notfallsonografie fast algorithmus Polytrauma Reanimation emergency medicine point of care ultrasound emergency sonography fast algorithm polytrauma resuscitation

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Efficient allotment of resources in heterogeneous communication

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WIRELESS NETWORKS 2021年第6期27卷 3761-3783页

作者： Naidu, Kalpana Parchuri, Akshara NIT Warangal Dept ECE Hanamkonda India

Heterogeneous networks assist in enhancing the data rates. In this context, heterogeneous networks comprising eNodeB, Femtocell and Wi-Fi Access Point (AP) operate conjointly to render higher data rates to the User Equipment (UE). Moreover, as Wi-Fi AP avails of mostly 2.4 GHz ISM band and does not use more of 5 GHz frequency band, Femtocell exploits this unused 5 GHz spectrum for its own data transmission. To be specific, Femtocell adopts both the licensed band and un-licensed bands to improve the capacity of UE. But, while transmitting data to UE, interferences created from one network (either eNodeB or Femtocell or Wi-Fi AP) to the users of other network are to be controlled. At the same time, capacity reaching to UE from multiple networks is optimized by allotting optimum powers and vital number of channels from multiple networks. Consecutively, all the aforementioned process is labelled as 'HEN-BULB' (HEterogeneous Networking in Both Unlicensed and Licensed Bands). Essentially, while resolving HEN-BULB, the distinctiveness of the proposed algorithm lies in evaluating the required number of channels in advance. Thereafter, optimal power allocation for these channels is accomplished by making use of the estimated lagrange multipliers. As a result, proposed algorithm unravels the HEN-BULB with lesser number of floating point operations in contrast to the generally used Iterative algorithms.

关键词： HetNet 5G LTE fast algorithm Iterative water filling Resource allocation

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Machine Learning-Based fast Intra Mode Decision for HEVC Screen Content Coding via Decision Trees

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第5期30卷 1481-1496页

作者： Kuang, Wei Chan, Yui-Lam Tsang, Sik-Ho Siu, Wan-Chi Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

The screen content coding (SCC) extension of high efficiency video coding (HEVC) improves coding gain for screen content videos by introducing two new coding modes, namely, intra block copy (IBC) and palette (PLT) modes. However, the coding gain is achieved at the increased cost of computational complexity. In this paper, we propose a decision tree-based framework for fast intra mode decision by investigating various features in the training sets. To avoid the exhaustive mode searching process, a sequential arrangement of decision trees is proposed to check each mode separately by inserting a classifier before checking a mode. As compared with the previous approaches where both IBC and PLT modes are checked for screen content blocks (SCBs), the proposed coding framework is more flexible which facilitates either the IBC or PLT mode to be checked for SCBs such that computational complexity is further reduced. To enhance the accuracy of decision trees, dynamic features are introduced, which reveal the unique intermediate coding information of a coding unit (CU). Then, if all the modes are decided to be skipped for a CU at the last depth level, at least one possible mode is assigned by a CU-type decision tree. Furthermore, a decision tree constraint technique is developed to reduce the rate-distortion performance loss. Compared with the HEVC-SCC reference software SCM-8.3, the proposed algorithm reduces computational complexity by 47.62% on average with a negligible Bjontegaard delta bitrate (BDBR) increase of 1.42% under all-intra (AI) configurations, which outperforms all the state-of-the-art algorithms in the literature.

关键词： Encoding Copper Decision trees Partitioning algorithms High efficiency video coding Videos Computational complexity Screen content coding (SCC) high efficiency video coding (HEVC) fast algorithm machine learning decision tree

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Severity Recognition of Aloe vera Diseases Using AI in Tensor Flow Domain

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Computers, Materials & Continua 2021年第2期66卷 2199-2216页

作者： Nazeer Muhammad Rubab Nargis Bibi Oh-Young Song Muhammad Attique Khan Sajid Ali Khan Department of IT&CS Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripur22620Pakistan COMSATS University Islamabad Wah CampusWah Cantt47040Pakistan Fatima Jinnah Women University Rawalpindi44000Pakistan Software Department Sejong UniversitySeoulSouth Korea Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Software Engineering Foundation University IslamabadIslamabadPakistan

Agriculture plays an important role in the economy of all ***,plant diseases may badly affect the quality of food,production,and ultimately the *** plant disease detection and management,agriculturalists spend a huge amount of ***,the manual detection method of plant diseases is complicated and ***,automated systems for plant disease detection using machine learning(ML)approaches are ***,most of the existing ML techniques of plants diseases recognition are based on handcrafted features and they rarely deal with huge amount of input *** address the issue,this article proposes a fully automated method for plant disease detection and recognition using deep neural *** the proposed method,AlexNet and VGG19 CNNs are considered as pre-trained *** is capable to obtain the feature extraction of the given data with fine-tuning *** convolutional neural network feature extraction,it selects the best subset of features through the correlation coefficient and feeds them to the number of classifiers including K-Nearest Neighbor,Support Vector Machine,Probabilistic Neural Network,Fuzzy logic,and Artificial Neural *** validation of the proposed method is carried out on a self-collected dataset generated through the augmentation *** achieved average accuracy of our method is more than 96%and outperforms the recent techniques.

关键词： Plants diseases wavelet transform fast algorithm deep learning feature extraction classification

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Efficient Simulation of Cuboid Object in Multiplanar Layered Medium by Improved Spectrum Integral Method

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2021年第8期59卷 7086-7097页

作者： Ren, Yi Zhao, Xunwang Lin, Zhongchao Ren, Qiang Chen, Yongpin Liu, Yanhui Hu, Hao Xidian Univ Sch Elect Engn Xian 710071 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Elect Sci & Technol China Sch Elect Sci & Engn Chengdu 611731 Peoples R China Beijing Inst Control & Elect Technol Syst Simulat Ctr Beijing 100038 Peoples R China

An efficient improved spectrum integral method (ISIM) is proposed to accelerate the scattering simulation of cuboid object by method of moment (MoM) in multiplanar layered medium (LM). The layered medium Green's functions (LMGFs) are studied and decomposed into transmission and reflection terms, which hold the translational invariance and reverse translational invariance, respectively. By virtue of the (reverse) translational invariance of LMGE from the uniform square grids on the surface of the cuboid object, most of the coupling between the expanding basis functions and the testing basis functions can be represented by two kinds of submatrices, i.e., the Toeplitz matrix when transmission term is involved, and the conjugate Toeplitz matrix when reflection term is involved. Moreover, the submatrix coupling the expanding/testing basis functions defined on the edges with those defined on the parallel surfaces/edges is also extended to the (conjugate) Toeplitz matrixes. This will significantly alleviate the heavy computation and memory burden from the conventional SIM. With the improved operations on the basis functions associated with the edges of the cuboid object, the proposed ISIM in LM can reach the computational complexity as low as O(N-1.5 log(N)) in the matrix rector production, while the computational complexity of matrix filling and the memory requirement are both O(N-1.5). Moreover, compared with conventional SIM, both theoretical analysis and numerical results demonstrate that the memory requirement and matrix filling time are dramatically decreased.

关键词： fast algorithm layered medium Green's function (LMGF) spectrum integral method (SIM) surface integral equation (SIE)

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Video Content Features Based fast Intra Prediction algorithm on HEVC 5

Video Content Features Based Fast Intra Prediction Algorithm...

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5th International Conference on Fuzzy Systems and Data Mining (FSDM)

作者： Feng, Guanxiong Huang, Bo Chen, Jian Wu, Linhuang Zhuo, Deshan Fuzhou Univ Coll Phys & Informat Engn Fuzhou 350108 Peoples R China

ISBN: (纸本)9781643680194;9781643680187

High Efficiency Video Coding (HEVC) is the mainstream international standard of video compression. HEVC introduces a more flexible division structure based on quadtree coding block and 35 intra prediction modes, and results in high video compression performance for intra prediction of HEVC. However, in order to get the best prediction mode, traditional encoders usually traverse all intra prediction modes, which have high complexities. To reduce the complexity of the coding of intra prediction, we propose a fast algorithm of intra prediction based on video content features. The fast algorithm of intra prediction proposed in this paper is implemented on the HM 16.7 which is a reference software of HEVC, and the results of experiment show that the average coding time is reduced by about 28.13% while the BD rate increases by only 0.92% under the all-intra conguration.

关键词： HEVC Intra prediction fast algorithm video content features

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Point Cloud Normal Estimation by fast Guided Least Squares Representation

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IEEE ACCESS 2020年 8卷 101580-101590页

作者： Zhang, Jie Duan, Jiahui Tang, Kewei Cao, Junjie Liu, Xiuping Liaoning Normal Univ Sch Math Dalian 116029 Peoples R China Dalian Univ Technol Sch Math Sci Dalian 116024 Peoples R China

Normal estimation is an essential task for scanned point clouds in various CAD/CAM applications. The method (GLSRNE) based on guided least squares representation (GLSR) balances speed with quality well among state-of-the-art methods. First, it segments each neighborhood into multiple sub-neighborhoods. For some neighborhoods, the segmentation is obtained by GLSR which is an efficient subspace segmentation model and widely applied in other applications. The segmentation of the rest neighborhoods is inferred via the subspace structure propagation (SSP) algorithm. Then, each sub-neighborhood is fitted by a plane. The plane achieving the minimum distance with the current point is selected for the final normal estimation. We make improvements for effectiveness and efficiency in the following three aspects. First, to improve the speed of GLSR, we propose a novel iterative algorithm to reduce the computation complexity from O(n(3)) to O(n(2)) represents the number of the data points. Moreover, this proposed algorithm will also be useful for other applications. Second, we add a normal constraint for SSP to improve accuracy. Third, when selecting one plane to estimate the final normal, we consider the match between the plane and all neighbors, whereas GLSRNE only considers the match between the plane and the current point. The experiments exhibit that our method is faster than GLSRNE and more effective than GLSRNE and other state-of-the-art methods.

关键词： Estimation Three-dimensional displays Principal component analysis Task analysis Computational complexity Shape Iterative methods Normal estimation feature preserving least squares representation fast algorithm

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DeepSCC: Deep Learning-Based fast Prediction Network for Screen Content Coding

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2020年第7期30卷 1917-1932页

作者： Kuang, Wei Chan, Yui-Lam Tsang, Sik-Ho Siu, Wan-Chi Hong Kong Polytech Univ Dept Elect & Informat Engn Hong Kong Peoples R China

Screen content coding (SCC) is an extension of high efficiency video coding (HEVC), and it is developed to improve the coding efficiency of screen content videos by adopting two new coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the flexible quadtree-based coding tree unit (CTU) partitioning structure and various mode candidates make the fast algorithms of the SCC extremely challenging. To efficiently reduce the computational complexity of SCC, we propose a deep learning-based fast prediction network DeepSCC that contains two parts: DeepSCC-I and DeepSCC-II. Before feeding to DeepSCC, incoming coding units (CUs) are divided into two categories: dynamic CTUs and stationary CTUs. For dynamic CTUs having different content as their collocated CTUs, DeepSCC-I takes raw sample values as the input to make fast predictions. For stationary CTUs having the same content as their collocated CTUs, DeepSCC-II additionally utilizes the optimal mode maps of the stationary CTU to further reduce the computational complexity. Compared with the HEVC-SCC reference software SCM-8.3, the proposed DeepSCC reduces the encoding time by 48.81% on average with a negligible Bjontegaard delta bitrate increase of 1.18% under all-intra configuration.

关键词： Encoding Copper Partitioning algorithms High efficiency video coding Deep learning Videos Computational complexity Screen content coding (SCC) high efficiency video coding (HEVC) fast algorithm convolutional neural network (CNN) deep learning

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fast Implementation of Signal Reconstruction based on Sparse Bayesian Learning with Fourier Dictionary

Fast Implementation of Signal Reconstruction based on Sparse...

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2021 CIE International Conference on Radar, Radar 2021

作者： Wang, Yuanyuan Jin, Shuojing Dai, Fengzhou Xidian University National Laboratory of Radar Signal Processing Xi'an China

ISBN: (纸本)9781665498142

Sparse Bayesian learning (SBL) is a robust algorithm for sparse signal reconstruction (SSR) and widely applied in many fields. Unfortunately, the conventional SBL algorithm suffers from heavy computational complexity due to the matrix inversion involved in each iteration, which is proportional to the cube of the observed data length and thus prevents it solving large-scale data. In this paper, based on the Fourier Dictionary (FD)-based SBL, abbreviated as FD-SBL, we propose an efficient FD-SBL method to implement SSR for uniformly sampled one-dimensional (1D) case. By utilizing the property that the matrix to be inverted is a Toeplitz matrix, we can exploit the Gohberg-Semencul (G-S)-type factorization to reduce the computational complexity of the FD-SBL algorithm. To be noted, there is no approximation in our proposed method, and the computational cost is reduced by several orders of magnitude compared with the conventional SBL algorithm. Finally, the experimental results verify the effectiveness of the proposed method. © 2021 IEEE.

关键词： fast algorithm Fourier dictionary Gohberg-Semencul Factorization Sparse Bayesian learning

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A class of efficient time-stepping methods for multi-term time-fractional reaction-diffusion-wave equations

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APPLIED NUMERICAL MATHEMATICS 2021年 165卷 56-82页

作者： Yin, Baoli Liu, Yang Li, Hong Zeng, Fanhai Inner Mongolia Univ Sch Math Sci Hohhot 010021 Peoples R China Shandong Univ Sch Math Jinan 250100 Peoples R China

A family of novel time-stepping methods for the fractional calculus operators is presented with a shifted parameter. The truncation error with second-order accuracy is proved under the framework of the shifted convolution quadrature. To improve the efficiency, two aspects are considered, that i) a fast algorithm is developed to reduce the computation complexity from O(N-t(2)) to O(N-t log N-t) and the memory requirement from O(N-t) to O(log N-t), where N-t denotes the number of successive time steps, and ii) correction terms are added to deal with the initial singularity of the solution. The stability analysis and error estimates are provided in detail where in temporal direction the novel time-stepping methods are applied and the spatial variable is discretized by the finite element method. Numerical results for d-dimensional examples (d = 1, 2, 3) confirm our theoretical conclusions and the efficiency of the fast algorithm. (C) 2021 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Fractional reaction-diffusion-wave equation Shifted convolution quadrature fast algorithm Correction terms Time-stepping methods

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