Water exchange between the different compartments of a heterogeneous specimen can be characterized via diffusion magnetic resonance imaging(dMRI).Many analysis frameworks using dMRI data have been proposed to describe...Water exchange between the different compartments of a heterogeneous specimen can be characterized via diffusion magnetic resonance imaging(dMRI).Many analysis frameworks using dMRI data have been proposed to describe exchange,often using a double diffusion encoding(DDE)stimulated echo sequence.Techniques such as diffusion exchange weighted imaging(DEWI)and the filter exchange and rapid exchange models,use a specific subset of the full space DDE signal.In this work,a general representation of the DDE signal was employed with different sampling schemes(namely constant b1,diagonal and anti-diagonal)from the data reduction models to estimate exchange.A near-uniform sampling scheme was proposed and compared with the other sampling schemes.The filter exchange and rapid exchange models were also applied to estimate exchange with their own subsampling schemes.These subsampling schemes and models were compared on both simulated data and experimental data acquired with a benchtop MR scanner.In synthetic data,the diagonal and near-uniform sampling schemes performed the best due to the consistency of their estimates with the ground truth.In experimental data,the shifted diagonal and near-uniform sampling schemes outperformed the others,yielding the most consistent estimates with the full space estimation.The results suggest the feasibility of measuring exchange using a general representation of the DDE signal along with variable sampling schemes.In future studies,algorithms could be further developed for the optimization of sampling schemes,as well as incorporating additional properties,such as geometry and diffusion anisotropy,into exchange frameworks.展开更多
Finding all occurrences of a twig query in an XML database is a core operation for efficient evaluation of XML queries. It is important to effiectively handle twig queries with wildcards. In this paper, a novel path-p...Finding all occurrences of a twig query in an XML database is a core operation for efficient evaluation of XML queries. It is important to effiectively handle twig queries with wildcards. In this paper, a novel path-partitioned encoding scheme is proposed for XML documents to capture paths of all elements, and a twig query is modeled as an XPattern extended from tree pattern. After definition, simplification, normalization, verification and initialization of the XPattern, both work sets and a join plan are generated. According to these measures, an effiective algorithm to answer for a twig query, called DMTwig, is designed without unnecessary elements and invalid structural joins. The algorithm can adaptively deal with twig queries with branch ([ ]), child edge (/), descendant edge (//), and wildcard (*) synthetically. We show that path-partitioned encoding scheme and XPattern guarantee the I/O and CPU optimality for twig queries. Experiments on representative data set indicate that the proposed solution performs significantly.展开更多
In this paper,a binary-extensible quality status encoding scheme,named IQSCT(IoT quality status code table),is proposed for the PCB-based product with available recovery options in remanufacturing.IQSCT is achieved by...In this paper,a binary-extensible quality status encoding scheme,named IQSCT(IoT quality status code table),is proposed for the PCB-based product with available recovery options in remanufacturing.IQSCT is achieved by code evolution based on binary logic,in which the product flow and the quality information flow are integrated,and three key features of PCB-based product(PCB-module association,assembly-disassembly logic,and disassembly risk)are involved in production costing.With IQSCT,the manufacturer can have better decisions to reduce remanufacturing cost and improve resource utilization,which is verified by a case study based on the real data from BOM cost and corresponding estimation of Apple iPhone 11 series.展开更多
A colored object encoding scheme in a ghost imaging （GI） system using orbital angular momentum is in- vestigated. A colored object is decomposed into three components and then each component is obtained in the idler...A colored object encoding scheme in a ghost imaging （GI） system using orbital angular momentum is in- vestigated. A colored object is decomposed into three components and then each component is obtained in the idler arm using a multiple grayscale encoding scheme. Afterward, we synthesize the three reconstructed components into a colored image. The scheme is conducted and then presented through numerical simula- tions and experiments. The simulation result shows that the average peak signal-to-noise ratio （PSNR） is at 21.636 for the reconstructed color of the ＂Lena＂ image with 255 gray scales. The experiment also shows that the PSNR is 8.082 for the reconstructed color of the ＂NUPT＂ characters. The successful imaging of colored obiects extends the further use of the GI technique展开更多
Data-driven machine learning, especially deep learning technology, is becoming an important tool for handling big data issues in bioinformatics. In machine learning, DNA sequences are often converted to numerical valu...Data-driven machine learning, especially deep learning technology, is becoming an important tool for handling big data issues in bioinformatics. In machine learning, DNA sequences are often converted to numerical values for data representation and feature learning in various applications. Similar conversion occurs in Genomic Signal Processing(GSP), where genome sequences are transformed into numerical sequences for signal extraction and recognition. This kind of conversion is also called encoding scheme. The diverse encoding schemes can greatly affect the performance of GSP applications and machine learning models. This paper aims to collect,analyze, discuss, and summarize the existing encoding schemes of genome sequence particularly in GSP as well as other genome analysis applications to provide a comprehensive reference for the genomic data representation and feature learning in machine learning.展开更多
基金the Swedish Foundation for International Cooperation in Research and Higher Education(STINT),and the Swedish Research Council(Dnr 2022e04715).
文摘Water exchange between the different compartments of a heterogeneous specimen can be characterized via diffusion magnetic resonance imaging(dMRI).Many analysis frameworks using dMRI data have been proposed to describe exchange,often using a double diffusion encoding(DDE)stimulated echo sequence.Techniques such as diffusion exchange weighted imaging(DEWI)and the filter exchange and rapid exchange models,use a specific subset of the full space DDE signal.In this work,a general representation of the DDE signal was employed with different sampling schemes(namely constant b1,diagonal and anti-diagonal)from the data reduction models to estimate exchange.A near-uniform sampling scheme was proposed and compared with the other sampling schemes.The filter exchange and rapid exchange models were also applied to estimate exchange with their own subsampling schemes.These subsampling schemes and models were compared on both simulated data and experimental data acquired with a benchtop MR scanner.In synthetic data,the diagonal and near-uniform sampling schemes performed the best due to the consistency of their estimates with the ground truth.In experimental data,the shifted diagonal and near-uniform sampling schemes outperformed the others,yielding the most consistent estimates with the full space estimation.The results suggest the feasibility of measuring exchange using a general representation of the DDE signal along with variable sampling schemes.In future studies,algorithms could be further developed for the optimization of sampling schemes,as well as incorporating additional properties,such as geometry and diffusion anisotropy,into exchange frameworks.
基金supported by the National High-Tech Research and Development Plan of China (Grant No.2005AA4Z3030)
文摘Finding all occurrences of a twig query in an XML database is a core operation for efficient evaluation of XML queries. It is important to effiectively handle twig queries with wildcards. In this paper, a novel path-partitioned encoding scheme is proposed for XML documents to capture paths of all elements, and a twig query is modeled as an XPattern extended from tree pattern. After definition, simplification, normalization, verification and initialization of the XPattern, both work sets and a join plan are generated. According to these measures, an effiective algorithm to answer for a twig query, called DMTwig, is designed without unnecessary elements and invalid structural joins. The algorithm can adaptively deal with twig queries with branch ([ ]), child edge (/), descendant edge (//), and wildcard (*) synthetically. We show that path-partitioned encoding scheme and XPattern guarantee the I/O and CPU optimality for twig queries. Experiments on representative data set indicate that the proposed solution performs significantly.
基金the National Natural Science Foundation of China(Grant Nos.71871058 and 71531010).
文摘In this paper,a binary-extensible quality status encoding scheme,named IQSCT(IoT quality status code table),is proposed for the PCB-based product with available recovery options in remanufacturing.IQSCT is achieved by code evolution based on binary logic,in which the product flow and the quality information flow are integrated,and three key features of PCB-based product(PCB-module association,assembly-disassembly logic,and disassembly risk)are involved in production costing.With IQSCT,the manufacturer can have better decisions to reduce remanufacturing cost and improve resource utilization,which is verified by a case study based on the real data from BOM cost and corresponding estimation of Apple iPhone 11 series.
基金supported by the National Natural Science Foundation of China(No.61271238)the Natural Science Research Foundation of Jiangsu Province(No.11KJA510002)+4 种基金the Open Research Fund Program of the National Laboratory of Solid State Microstructures(Nos.M25020 and M25022)the Foundation for Jiangsu Returned Chinese Scholar(No.NJ210002)the Open Research Fund of the Key Lab of Broadband Wireless Communication and Sensor Network Technology,the Ministry of Education(No.ZD035001NYKL01)the Priority Academic Program Development of Jiangsu Higher Education Institutionsthe Jiangsu Key Laboratory of Image Processing and Image Communication
文摘A colored object encoding scheme in a ghost imaging （GI） system using orbital angular momentum is in- vestigated. A colored object is decomposed into three components and then each component is obtained in the idler arm using a multiple grayscale encoding scheme. Afterward, we synthesize the three reconstructed components into a colored image. The scheme is conducted and then presented through numerical simula- tions and experiments. The simulation result shows that the average peak signal-to-noise ratio （PSNR） is at 21.636 for the reconstructed color of the ＂Lena＂ image with 255 gray scales. The experiment also shows that the PSNR is 8.082 for the reconstructed color of the ＂NUPT＂ characters. The successful imaging of colored obiects extends the further use of the GI technique
基金supports from the Department of Computing Sciences, State University of New York College at Brockport
文摘Data-driven machine learning, especially deep learning technology, is becoming an important tool for handling big data issues in bioinformatics. In machine learning, DNA sequences are often converted to numerical values for data representation and feature learning in various applications. Similar conversion occurs in Genomic Signal Processing(GSP), where genome sequences are transformed into numerical sequences for signal extraction and recognition. This kind of conversion is also called encoding scheme. The diverse encoding schemes can greatly affect the performance of GSP applications and machine learning models. This paper aims to collect,analyze, discuss, and summarize the existing encoding schemes of genome sequence particularly in GSP as well as other genome analysis applications to provide a comprehensive reference for the genomic data representation and feature learning in machine learning.
