In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has pr...In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has proposed a high-performance dimension reduction parallel matching algorithm that integrates Principal Component Analysis(PCA)and Dual-Heap Filtering(DHF).The algorithm employs PCA to map the feature points into the lower-dimensional space and employs the square of Euclidean distance for feature matching,which significantly reduces computational complexity.To ensure the accuracy of feature matching,the algorithm utilizes Dual-Heap Filtering to filter and refine matched point pairs.To further enhance matching speed and make optimal use of computational resources,the algorithm introduces a multi-core parallel matching strategy,greatly elevating the efficiency of feature matching.Compared to Scale-Invariant Feature Transform(SIFT)and Speeded Up Robust Features(SURF),the proposed algorithm reduces matching time by 77%to 80%and concurrently enhances matching accuracy by 5%to 15%.Experimental results demonstrate that the proposed algorithmexhibits outstanding real-time matching performance and accuracy,effectivelymeeting the feature-matching requirements of automotive panoramic surround view systems.展开更多
In modern motoring, many factors are considered to realize driving convenience and achieving safety at a reasonable cost. A drive towards effective management of traffic and parking space allocation in urban centres u...In modern motoring, many factors are considered to realize driving convenience and achieving safety at a reasonable cost. A drive towards effective management of traffic and parking space allocation in urban centres using intelligent software applications is currently being developed and deployed as GPS enabled service to consumers in automobiles or smartphone applications for convenience, safety and economic benefits. Building a fuzzy logic inference for such applications may have numerous approaches such as algorithms in Pascal or C-languages and of course using an effective fuzzy logic toolbox. Referring to a case report based on IrisNet project analysis, in this paper Matlab fuzzy logic toolbox is used in developing an inference for managing traffic flow and parking allocation with generalized feature that is open for modification. Being that modifications can be done within any or all among the tool’s universe of discourse, increment in the number of membership functions and changing input and output variables etc, the work here is limited within changes at input and output variables and bases of universe of discourse. The process implications is shown as plotted by the toolbox in surface and rule views, implying that the inference is flexibly open for modifications to suit area of application within reasonable time frame no matter how complex. The travel time to the parking space being an output variable in the current inference is recommended to be substituted with distance to parking space as the former is believed to affect driving habits among motorist, whom may require the inference to as well cover other important locations such as nearest or cheapest gas station, hotels, hospitals etc.展开更多
Introduction Primary liver cancer, the second most common cause of cancer related death worldwide, presents ethnic, etiological, sex, and geographical diversity2 （Figure 1A）. At the histological level, liver cancer ...Introduction Primary liver cancer, the second most common cause of cancer related death worldwide, presents ethnic, etiological, sex, and geographical diversity2 （Figure 1A）. At the histological level, liver cancer includes two major types： hepatocellular carcinoma （HCC, about 80%） and cholangiocarcinoma （CCA, about 15%）. Many etiological factors contribute to HCC development, such as hepatitis B virus （HBV）, hepatitis C virus （HCV）, aflatoxin B1 （AFB1）, alcohol, and metabolic diseases3. By contrast, the major risk factors for CCA are liver flukes （Opisthorchis viverrini and Clonorchis sinensis） and primary sclerosing cholangitis4,展开更多
基金the National Natural Science Foundation of China(61803206)the Key R&D Program of Jiangsu Province(BE2022053-2)the Nanjing Forestry University Youth Science and Technology Innovation Fund(CX2018004)for partly funding this project.
文摘In response to the challenges posed by insufficient real-time performance and suboptimal matching accuracy of traditional feature matching algorithms within automotive panoramic surround view systems,this paper has proposed a high-performance dimension reduction parallel matching algorithm that integrates Principal Component Analysis(PCA)and Dual-Heap Filtering(DHF).The algorithm employs PCA to map the feature points into the lower-dimensional space and employs the square of Euclidean distance for feature matching,which significantly reduces computational complexity.To ensure the accuracy of feature matching,the algorithm utilizes Dual-Heap Filtering to filter and refine matched point pairs.To further enhance matching speed and make optimal use of computational resources,the algorithm introduces a multi-core parallel matching strategy,greatly elevating the efficiency of feature matching.Compared to Scale-Invariant Feature Transform(SIFT)and Speeded Up Robust Features(SURF),the proposed algorithm reduces matching time by 77%to 80%and concurrently enhances matching accuracy by 5%to 15%.Experimental results demonstrate that the proposed algorithmexhibits outstanding real-time matching performance and accuracy,effectivelymeeting the feature-matching requirements of automotive panoramic surround view systems.
文摘In modern motoring, many factors are considered to realize driving convenience and achieving safety at a reasonable cost. A drive towards effective management of traffic and parking space allocation in urban centres using intelligent software applications is currently being developed and deployed as GPS enabled service to consumers in automobiles or smartphone applications for convenience, safety and economic benefits. Building a fuzzy logic inference for such applications may have numerous approaches such as algorithms in Pascal or C-languages and of course using an effective fuzzy logic toolbox. Referring to a case report based on IrisNet project analysis, in this paper Matlab fuzzy logic toolbox is used in developing an inference for managing traffic flow and parking allocation with generalized feature that is open for modification. Being that modifications can be done within any or all among the tool’s universe of discourse, increment in the number of membership functions and changing input and output variables etc, the work here is limited within changes at input and output variables and bases of universe of discourse. The process implications is shown as plotted by the toolbox in surface and rule views, implying that the inference is flexibly open for modifications to suit area of application within reasonable time frame no matter how complex. The travel time to the parking space being an output variable in the current inference is recommended to be substituted with distance to parking space as the former is believed to affect driving habits among motorist, whom may require the inference to as well cover other important locations such as nearest or cheapest gas station, hotels, hospitals etc.
基金supported,in part,by the Precision Medical Research Program from Ministry of Science and Technology of China(Grant No.YL 2017YFC0908400)National Science and Technology Major Project for Infectious Disease and Funding(Grant No.YL 17-163-12-ZT-005-095-01)+2 种基金Science and Technology Commission in Ministry of National Defense of China(Grant No.YL 17-163-12-ZT-005-095-01)Xinwei Wang was supported by the intramural research program of the Center for Cancer Research,National Cancer Institute of the United StatesJunfang Ji was supported by the Thousand Young Talents Plan of China,National Natural Science Foundation of China(Grant No.81672905)
文摘Introduction Primary liver cancer, the second most common cause of cancer related death worldwide, presents ethnic, etiological, sex, and geographical diversity2 （Figure 1A）. At the histological level, liver cancer includes two major types： hepatocellular carcinoma （HCC, about 80%） and cholangiocarcinoma （CCA, about 15%）. Many etiological factors contribute to HCC development, such as hepatitis B virus （HBV）, hepatitis C virus （HCV）, aflatoxin B1 （AFB1）, alcohol, and metabolic diseases3. By contrast, the major risk factors for CCA are liver flukes （Opisthorchis viverrini and Clonorchis sinensis） and primary sclerosing cholangitis4,
