In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking...
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In existing remote sensing image retrieval(RSIR)datasets,the number of images among different classes varies dramatically,which leads to a severe class imbalance *** studies propose to train the model with the ranking‐based metric(e.g.,average precision[AP]),because AP is robust to class ***,current AP‐based methods overlook an important issue:only optimising samples ranking before each positive sample,which is limited by the definition of AP and is prone to local *** achieve global optimisation of AP,a novel method,namely Optimising Samples after positive ones&AP loss(OSAP‐Loss)is proposed in this ***,a novel superior ranking function is designed to make the AP loss differentiable while providing a tighter upper ***,a novel loss called Optimising Samples after Positive ones(OSP)loss is proposed to involve all positive and negative samples ranking after each positive one and to provide a more flexible optimisation strategy for each ***,a graphics processing unit memory‐free mechanism is developed to thoroughly address the non‐decomposability of AP *** experimental results on RSIR as well as conventional image retrieval datasets show the superiority and competitive performance of OSAP‐Loss compared to the state‐of‐the‐art.
This paper proposes an Internet of Medical Things (IoMT) Seizure Detection Algorithm that uses smartphone acceleration sensors to detect early seizures. The propose algorithm used based on MATLAB Mobile, which seamles...
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This work aims to transform beach garbage management by developing an autonomous rover that utilizes deep learning and computer vision. The main goal is to enable the rover to traverse coastal environments on its own ...
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We report, for the first time, electric field periodic poling of single-crystal thin film barium titanate grown using pulsed-laser-deposition on dysprosium scandate substrate. Uniform domains with periods 5-7 μm are ...
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This article proposes a new single-stage three-phase buck-boost inverter and control scheme, which remarkably reduces both the low and high-frequency ripple components in the input current. The low-frequency ripple co...
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This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon c...
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In wireless sensor networks (WSNs), the coverage hole problem is one of the challenging problems that needs an effective solution. Data routing protocols in WSNs aim to disseminate the sensors' data to the central...
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Aside from pure intellectual interest, why do we teach our students parallel computing? Most people would agree that the primary goal is to produce greater application performance. Yet students frequently parallelize ...
ISBN:
(数字)9798350364606
ISBN:
(纸本)9798350364613
Aside from pure intellectual interest, why do we teach our students parallel computing? Most people would agree that the primary goal is to produce greater application performance. Yet students frequently parallelize code only to discover that it runs disappointingly slower because they don't understand performance. To exploit parallelism effectively, it must operate synergistically with a host of other techniques, including caching, vectorization, algorithms, bit tricks, loop unrolling, using compiler switches, tailoring code to the architecture, exploiting sparsity, changing data representation, metaprogramming, etc. Software performance engineering, which encompasses these techniques, is the science and art of making code run fast or otherwise limiting its consumption of resources, such as energy, memory footprint, network utilization, response time, etc. In this talk, I will argue that the end of Moore's Law makes software performance engineering a critical skill for our students to learn.
We investigate secondary electron yield (SEY) reduction in high porosity surfaces consisting of periodic through-holes. Using Vaughan's empirical model of SEY [1], [2], we perform two-dimensional Monte Carlo (MC) ...
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The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal...
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The non-stationary of the motor imagery electroencephalography(MI-EEG)signal is one of the main limitations for the development of motor imagery brain-computer interfaces(MI-BCI).The nonstationary of the MI-EEG signal and the changes of the experimental environment make the feature distribution of the testing set and training set deviates,which reduces the classification accuracy of *** this paper,we propose a Kullback–Leibler divergence(KL)-based transfer learning algorithm to solve the problem of feature transfer,the proposed algorithm uses KL to measure the similarity between the training set and the testing set,adds support vector machine(SVM)classification probability to classify and weight the covariance,and discards the poorly performing *** results show that the proposed algorithm can significantly improve the classification accuracy of the testing set compared with the traditional algorithms,especially for subjects with medium classification ***,the algorithm based on transfer learning has the potential to improve the consistency of feature distribution that the traditional algorithms do not have,which is significant for the application of MI-BCI.
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