This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algori...
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We study the problem of repairing erasures in locally repairable codes beyond the code locality under the rack-aware model. We devise two repair schemes to reduce the repair bandwidth for Tamo-Barg codes under the rac...
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ISBN:
(数字)9798350382846
ISBN:
(纸本)9798350382853
We study the problem of repairing erasures in locally repairable codes beyond the code locality under the rack-aware model. We devise two repair schemes to reduce the repair bandwidth for Tamo-Barg codes under the rack-aware model, by setting each repair set as a rack. The first repair scheme provides optimal repair bandwidth for one rack erasure. We then establish a cut-set bound for locally repairable codes under the rack-aware model. Using this bound we show that our second repair scheme is optimal. Furthermore, we consider the partial-repair problem for locally repairable codes under the rack-aware model, and introduce both repair schemes and bounds for this scenario.
Learning High-Resolution representations is essential for semantic segmentation. Convolutional neural network (CNN) architectures with downstream and upstream propagation flow are popular for segmentation in medical d...
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A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,...
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A significant number and range of challenges besetting sustainability can be traced to the actions and inter actions of multiple autonomous agents(people mostly)and the entities they create(e.g.,institutions,policies,social network)in the corresponding social-environmental systems(SES).To address these challenges,we need to understand decisions made and actions taken by agents,the outcomes of their actions,including the feedbacks on the corresponding agents and *** science of complex adaptive systems-complex adaptive sys tems(CAS)science-has a significant potential to handle such *** address the advantages of CAS science for sustainability by identifying the key elements and challenges in sustainability science,the generic features of CAS,and the key advances and challenges in modeling *** intelligence and data science combined with agent-based modeling promise to improve understanding of agents’behaviors,detect SES struc tures,and formulate SES mechanisms.
Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and cause millions of lifelong disability worldwide yearly. However, specialists' manual scree...
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Malaria is a potentially fatal plasmodium parasite injected by female anopheles mosquitoes that infect red blood cells and cause millions of lifelong disability worldwide yearly. However, specialists' manual screening in clinical practice is laborious and prone to error. Therefore, a novel Deep Boosted and Ensemble Learning (DBEL) framework, comprising the stacking of new Boosted-BR-STM convolutional neural networks (CNN) and the ensemble ML classifiers, is developed to screen malaria parasite images. The proposed Boosted-BR-STM is based on a new dilated-convolutional block-based Split Transform Merge (STM) and feature-map Squeezing-Boosting (SB) ideas. Moreover, the new STM block uses regional and boundary operations to learn the malaria parasite's homogeneity, heterogeneity, and boundary with patterns. Furthermore, the diverse boosted channels are attained by employing Transfer Learning-based new feature-map SB in STM blocks at the abstract, medium, and conclusion levels to learn minute intensity and texture variation of the parasitic pattern. Additionally, to enhance the learning capacity of Boosted-BR-STM and foster a more diverse representation of features, boosting at the final stage is achieved through TL by utilizing multipath residual learning. The proposed DBEL framework implicates the stacking of prominent and diverse boosted channels and provides the generated discriminative features of the developed Boosted-BR-STM to the ensemble of ML classifiers. The proposed framework improves the discrimination ability and generalization of ensemble learning. Moreover, the deep feature spaces of the developed Boosted-BR-STM and customized CNNs are fed into ML classifiers for comparative analysis. The proposed DBEL framework outperforms the existing techniques on the NIH malaria dataset that are enhanced using discrete wavelet transform to enrich feature space. The proposed DBEL framework achieved Accuracy (98.50%), Sensitivity (0.9920), F-score (0.9850), and AUC (0.
This article presents an enhanced JavaScript feature-injection based framework that obstructs the execution of cross-site scripting (XSS) worms from the virtual machines of cloud-based online social network (OSN). It ...
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Growing demands in today's industry results in increasingly stringent performance and throughput specifications. For accurate positioning of high-precision motion systems, feedforward control plays a crucial role....
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Rockets and missiles fired from a tube usually have aerodynamic surfaces that are packed when the rocket is in the tube. One of the common fins folding solutions is the wrap-around fins. The wrap-around fins are usual...
Rockets and missiles fired from a tube usually have aerodynamic surfaces that are packed when the rocket is in the tube. One of the common fins folding solutions is the wrap-around fins. The wrap-around fins are usually separated from the rocket’s body by a bearing; thus the characteristics and the type of bearings used will have an effect on roll stabilization and the performance of roll stabilization autopilot. Also, during the flight, many forces act upon the missile thus affecting its exposed components. In turn, this may also affect the performance of the bearings that allow the wrap-around fins to rotate independently around the missile’s body. This dual-spin concept increases roll stabilization efficiency and reduces induced roll from lateral control. For this design to be effective and achieve the desired performance, it is critical to analyze how the movements of the missile affect the bearing separating the body from the wrap-around fins. Since the sections are separated by the bearing, various imperfections of the joint such as friction, misalignment, etc. combined with the acceleration of the missile may have a transitional influence on the performance of the wrap-around fins and thus roll stabilization. In this paper, we will first identify and explain the problem of acceleration influence on friction in bearings. Next, the laboratory equipment and experimental procedures for examining friction in bearings will be described in detail. We will then present and analyze the results obtained from the experiments. Finally, we will draw conclusions and present the design modification this investigation has led to as well as the improved roll autopilot performance achieved by using the appropriate bearing.
This study proposes a method that can easily grasp the relationship between the actual machine and the graphs. In recent years, there has been a lot of research on augmented reality displays. The fields of research ra...
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This study proposes a method that can easily grasp the relationship between the actual machine and the graphs. In recent years, there has been a lot of research on augmented reality displays. The fields of research range from education to welfare. In the development of control systems, when evaluating the performance of a system by simulation or experiment, the results are often checked as graphs. Since the graphs are checked on a PC using CAD or other means, it is difficult to know which part of the actual machine each graph corresponds to. Therefore, we developed a tool that displays graphs in augmented reality around the actual machine through a camera on a mobile terminal. To display graphs in augmented reality, it is important to obtain the coordinates of the actual machine and display them in a location associated with the device. Therefore, a USD model with the same shape and size as the actual machine is used. This is achieved by displaying the USD model in augmented reality so that it is superimposed on the actual machine. The accuracy of the tool was also examined and its usefulness was evaluated.
Aggressively underscaling the supply voltage $(V_{dd})$ below the safe voltage $(V_{min})$ margin is an effective solution to attain substantial energy savings. Unfortunately, operating at such low voltages is cha...
Aggressively underscaling the supply voltage $(V_{dd})$ below the safe voltage $(V_{min})$ margin is an effective solution to attain substantial energy savings. Unfortunately, operating at such low voltages is challenging due to the high number of permanent faults as a result of variations in the manufacturing process of current technology nodes. This work characterizes the impact of permanent faults on the accuracy of a Convolutional Neural Network (CNN) inference accelerator with on-chip activation memories supplied at low $V_{dd}$ below $V_{min}$ . Based on these observations, this paper proposes a couple of low-cost microarchitectural techniques, referred to as flipping and patching, that ensure the accuracy of CNN applications despite the presence of permanent faults. Contrary to prior work, the proposed techniques are transparent to the programmer and do not depend on application characteristics. Experimental results show that the proposed techniques maintain the original CNN accuracy with a minimal impact on system performance (less than 0.05%), while reducing the energy consumption of activation memories by 11.2% and 46.7% compared to those of a conventional accelerator operating at safe and nominal supply voltages, respectively.
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