This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on th...This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.展开更多
Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions.However,conventional static metrics have been found to be inadequate for modern obj...Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions.However,conventional static metrics have been found to be inadequate for modern object-oriented software due to the presence of object-oriented features such as polymorphism,dynamic binding,inheritance and unused code.This fact motivates us to focus on dynamic metrics in place of traditional static metrics.Moreover,dynamic metrics are more precise than static metrics as they are able to capture the dynamic behaviour of the software system during measurement.These dynamic metrics are usually obtained from the execution traces of the code or from the executable models.In this paper,advantages of dynamic metrics over static metrics are discussed and then a survey of the existing dynamic metrics is carried out.These metrics are characterized into different categories such as dynamic coupling metrics, dynamic cohesion metrics.Towards end of the paper,potential research challenges and opportunities in the field of dynamic metrics are identified.展开更多
The grouping of correlated classes into a package helps in better organization of modern object-oriented software. The quality of such packages needs to be measured so as to estimate their utilization. In this paper, ...The grouping of correlated classes into a package helps in better organization of modern object-oriented software. The quality of such packages needs to be measured so as to estimate their utilization. In this paper, new package coupling metrics are proposed, which also take into consideration the hierarchical structure of packages and direction of connections among package elements. The proposed measures have been validated theoretically as well as empirically using 18 packages taken from two open source software systems. The results obtained from this study show strong correlation between package coupling and understandability of the package which suggests that proposed metrics could be further used to represent other external software quality factors.展开更多
Background:Interleukin-28B (IL-28B) polymorphism is an important predictor for hepatitis C virus (HCV) treatment response.Whether IL-28b genotypes also influence other nontreatment related clinical parameters is uncle...Background:Interleukin-28B (IL-28B) polymorphism is an important predictor for hepatitis C virus (HCV) treatment response.Whether IL-28b genotypes also influence other nontreatment related clinical parameters is unclear.Methods:Patients with HCV-related chronic liver diseases who attended our department during 2012-2014 were retrospectively analyzed.The single nucleotide polymorphisms (SNPs) of rs12979860 (IL-28B) were correlated with various clinical parameters.We also compared these parameters in patients with and without overt diabetes to identify possible associations.Results:A total of 115 patients were included (median age 48,range 15-76 years;70% males).Overall,43/115(37%) patients had chronic hepatitis,while the remaining 72/115 (63%) had cirrhosis.The most common IL-28B genotype was CC,which was found in 53% of patients (61/115),while the remaining 47% were nonCC [CT 42% (48/115) and T-r 5% (6/115)].Clinical and laboratory parameters like Hb,white blood cell (WBC),platelets,bilirubin,transaminases,and albumin were similar in the CC and nonCC genotypes.Overt diabetes mellitus was present in 22% (25/115) of patients.Patients with nonCC genotype had significantly higher prevalence of overt diabetes mellitus than patients with CC genotype (31% [17/54] versus 13% [8/61];p < 0.05).When parameters were compared in patients with and without overt diabetes mellitus,only IL-28B and age were significantly associated with overt diabetes mellitus (p < 0.05).Conclusion:In HCV patients,overt diabetes mellitus was more commonly associated with the nonCC genotype of IL-28B than the CC genotype.Carriers of the T-allele of SNP rs12979860 were more likely to have insulin resistance than CC homozygotes,and this finding may explain the higher prevalence of diabetes in non-CC genotypes.Thus,an IL-28B test may be useful in patients of HCV in order to determine their likelihood of developing diabetes mellitus.展开更多
Optical imaging techniques provide low-cost,non-radiative images with high spatiotemporal resolution,making them advantageous for long-term dynamic observation of blood perfusion in stroke research and other brain stu...Optical imaging techniques provide low-cost,non-radiative images with high spatiotemporal resolution,making them advantageous for long-term dynamic observation of blood perfusion in stroke research and other brain studies compared to non-optical methods.However,high-resolution imaging in optical microscopy fundamentally requires a tight optical focus,and thus a limited depth of field(DOF).Consequently,large-scale,non-stitched,high-resolution images of curved surfaces,like brains,are difficult to acquire without z-axis scanning.To overcome this limitation,we developed a needle-shaped beam optical coherence tomography angiography(NB-OCTA)system,and for the first time,achieved a volumetric resolution of less than 8μm in a non-stitched volume space of 6.4 mm×4 mm×620μm in vivo.This system captures the distribution of blood vessels at 3.4-times larger depths than normal OCTA equipped with a Gaussian beam(GB-OCTA).We then employed NB-OCTA to perform long-term observation of cortical blood perfusion after stroke in vivo,and quantitatively analyzed the vessel area density(VAD)and the diameters of representative vessels in different regions over 10 days,revealing different spatiotemporal dynamics in the acute,sub-acute and chronic phase of post-ischemic revascularization.Benefiting from our NB-OCTA,we revealed that the recovery process is not only the result of spontaneous reperfusion,but also the formation of new vessels.This study provides visual and mechanistic insights into strokes and helps to deepen our understanding of the spontaneous response of brain after stroke.展开更多
文摘This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.
文摘Software metrics help us to make meaningful estimates for software products and guide us in taking managerial and technical decisions.However,conventional static metrics have been found to be inadequate for modern object-oriented software due to the presence of object-oriented features such as polymorphism,dynamic binding,inheritance and unused code.This fact motivates us to focus on dynamic metrics in place of traditional static metrics.Moreover,dynamic metrics are more precise than static metrics as they are able to capture the dynamic behaviour of the software system during measurement.These dynamic metrics are usually obtained from the execution traces of the code or from the executable models.In this paper,advantages of dynamic metrics over static metrics are discussed and then a survey of the existing dynamic metrics is carried out.These metrics are characterized into different categories such as dynamic coupling metrics, dynamic cohesion metrics.Towards end of the paper,potential research challenges and opportunities in the field of dynamic metrics are identified.
文摘The grouping of correlated classes into a package helps in better organization of modern object-oriented software. The quality of such packages needs to be measured so as to estimate their utilization. In this paper, new package coupling metrics are proposed, which also take into consideration the hierarchical structure of packages and direction of connections among package elements. The proposed measures have been validated theoretically as well as empirically using 18 packages taken from two open source software systems. The results obtained from this study show strong correlation between package coupling and understandability of the package which suggests that proposed metrics could be further used to represent other external software quality factors.
文摘Background:Interleukin-28B (IL-28B) polymorphism is an important predictor for hepatitis C virus (HCV) treatment response.Whether IL-28b genotypes also influence other nontreatment related clinical parameters is unclear.Methods:Patients with HCV-related chronic liver diseases who attended our department during 2012-2014 were retrospectively analyzed.The single nucleotide polymorphisms (SNPs) of rs12979860 (IL-28B) were correlated with various clinical parameters.We also compared these parameters in patients with and without overt diabetes to identify possible associations.Results:A total of 115 patients were included (median age 48,range 15-76 years;70% males).Overall,43/115(37%) patients had chronic hepatitis,while the remaining 72/115 (63%) had cirrhosis.The most common IL-28B genotype was CC,which was found in 53% of patients (61/115),while the remaining 47% were nonCC [CT 42% (48/115) and T-r 5% (6/115)].Clinical and laboratory parameters like Hb,white blood cell (WBC),platelets,bilirubin,transaminases,and albumin were similar in the CC and nonCC genotypes.Overt diabetes mellitus was present in 22% (25/115) of patients.Patients with nonCC genotype had significantly higher prevalence of overt diabetes mellitus than patients with CC genotype (31% [17/54] versus 13% [8/61];p < 0.05).When parameters were compared in patients with and without overt diabetes mellitus,only IL-28B and age were significantly associated with overt diabetes mellitus (p < 0.05).Conclusion:In HCV patients,overt diabetes mellitus was more commonly associated with the nonCC genotype of IL-28B than the CC genotype.Carriers of the T-allele of SNP rs12979860 were more likely to have insulin resistance than CC homozygotes,and this finding may explain the higher prevalence of diabetes in non-CC genotypes.Thus,an IL-28B test may be useful in patients of HCV in order to determine their likelihood of developing diabetes mellitus.
基金supported by the National Key R&D Program of China(No.2022YFB4702902)National Natural Science Foundation of China(Nos.61831014,62275023,and 32021002)+2 种基金Beijing Municipal Natural Science Foundation(No.4232077)Overseas Expertise Introduction Project for Discipline Innovation(No.B18005)STI2030-Major Projects(No.2022ZD0212000).
文摘Optical imaging techniques provide low-cost,non-radiative images with high spatiotemporal resolution,making them advantageous for long-term dynamic observation of blood perfusion in stroke research and other brain studies compared to non-optical methods.However,high-resolution imaging in optical microscopy fundamentally requires a tight optical focus,and thus a limited depth of field(DOF).Consequently,large-scale,non-stitched,high-resolution images of curved surfaces,like brains,are difficult to acquire without z-axis scanning.To overcome this limitation,we developed a needle-shaped beam optical coherence tomography angiography(NB-OCTA)system,and for the first time,achieved a volumetric resolution of less than 8μm in a non-stitched volume space of 6.4 mm×4 mm×620μm in vivo.This system captures the distribution of blood vessels at 3.4-times larger depths than normal OCTA equipped with a Gaussian beam(GB-OCTA).We then employed NB-OCTA to perform long-term observation of cortical blood perfusion after stroke in vivo,and quantitatively analyzed the vessel area density(VAD)and the diameters of representative vessels in different regions over 10 days,revealing different spatiotemporal dynamics in the acute,sub-acute and chronic phase of post-ischemic revascularization.Benefiting from our NB-OCTA,we revealed that the recovery process is not only the result of spontaneous reperfusion,but also the formation of new vessels.This study provides visual and mechanistic insights into strokes and helps to deepen our understanding of the spontaneous response of brain after stroke.
