The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed wo...The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.展开更多
In this study, we present a miniOS kernel implemented via analysis of the context switching, the scheduler, and the memory management of the original OS kernel for an embedded system based on ARM core. Since this is a...In this study, we present a miniOS kernel implemented via analysis of the context switching, the scheduler, and the memory management of the original OS kernel for an embedded system based on ARM core. Since this is a large subject, we have limited our scope to them only that made up an embedded operating system. The implemented miniOS kernel is composed only by them, to the exclusion of all other functions of the original kernel. Our goal is to modify the OS kernel depending on the product function. The implementation method of the miniOS kernel can be applicable to any OS being mounted based on the ARM core. Modifying the kernel depending on the product function can improve the OS booting speed as well as save the system memory. The functions of the scheduler, the context switching, and the memory management are described with the source in each section. The miniOS kernel was implemented in the Assembly and C language and was verified through the build and the test. The results are shown in the Section 5.展开更多
The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a...The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.展开更多
Based on the in-depth research of the embedded device management system,analyze the process of the driver parameters delivery on kernel level particularly.According to this,expound the device driver's working prin...Based on the in-depth research of the embedded device management system,analyze the process of the driver parameters delivery on kernel level particularly.According to this,expound the device driver's working principle,structure and design methods especially.Finally realize the drivers of the character-based devices which can be dynamically loaded.Actual result shows that mastering the realization of the device driver mechanism and the process of parameters delivery with kernel can improve the embedded device driver development efficiency and reduce error probability effectively,thus saving the development cost and development cycle of embedded products.展开更多
In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity mea...In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.展开更多
Little Kernel(lk)是被Android系统接受进入源码树的Bootloader程序,并被多款智能手机和平板电脑所采用。论文介绍了lk的主要功能,分析了lk的源码结构,并在此基础上详细说明了lk移植的方法和过程。将移植后的lk进行编译并下载至TCC8801 ...Little Kernel(lk)是被Android系统接受进入源码树的Bootloader程序,并被多款智能手机和平板电脑所采用。论文介绍了lk的主要功能,分析了lk的源码结构,并在此基础上详细说明了lk移植的方法和过程。将移植后的lk进行编译并下载至TCC8801 DEMO板上,lk能够正常启动并引导linux内核。展开更多
To address the problem of underwater multi-sensor multi-target passive tracking in clutter,a distributed kernel mean embedding-based Gaussian belief propagation(DKME-GaBP)algorithm is proposed.First,a joint posterior ...To address the problem of underwater multi-sensor multi-target passive tracking in clutter,a distributed kernel mean embedding-based Gaussian belief propagation(DKME-GaBP)algorithm is proposed.First,a joint posterior probability density function(PDF)is established and factorized,and it is represented by the corresponding factor graph.Then,the GaBP algorithm is executed on this factor graph to reduce the computational complexity of data association.The factor graph of the GaBP consists of inner and outer loops.The inner loop is responsible for local track estimation and data association.The outer loop fuses information from different sensors.For the inner loop,the kernel mean embedding(KME)with a Gaussian kernel is designed to transform the strong nonlinear problem of local estimation into a linear problem in a high-dimensional reproducing kernel Hilbert space(RKHS).For the outer loop,a multi-sensor distributed fusion method based on KME is proposed to improve fusion accuracy by accounting for the distance among different PDFs in RKHS.The effectiveness and robustness of the DKME-GaBP are validated in the simulations.展开更多
当前机器学习技术已经在大量领域得到广泛应用,然而仍面临许多亟待解决的问题:依赖大量的训练数据和训练技巧、难以适应环境变化、数据隐私/所有权的保护、灾难性遗忘等等.最近,学件范式使得上述问题同时得到系统性地解决成为可能.在该...当前机器学习技术已经在大量领域得到广泛应用,然而仍面临许多亟待解决的问题:依赖大量的训练数据和训练技巧、难以适应环境变化、数据隐私/所有权的保护、灾难性遗忘等等.最近,学件范式使得上述问题同时得到系统性地解决成为可能.在该范式下,用户面临新的机器学习任务时可以通过学件基座系统方便地复用他人的结果,而不必从头开始.学件范式的核心在于规约,规约使得学件基座系统在不接触原始数据的情况下,可以根据用户的需求快速识别出对用户任务有帮助的学件.近期研究均通过缩略核均值嵌入(Reduced Kernel Mean Embedding,RKME)为模型构造规约,并通过构建学件原型系统验证了范式的有效性.在实际中,学件基座系统中往往包含在各种领域任务、数据类型上构建的机器学习模型,而传统的RKME规约面临维度灾难的问题,难以适用于高维数据,例如图像场景.为了拓展RKME规约的适用范围,本文引入神经切线核进行RKME规约构造.为提升方法的高效性,本文进一步通过神经网络高斯过程与随机特征近似,快速为各种模型生成RKME规约.最后,本文在真实数据构建的销量预测、图像分类场景的学件基座系统中进行大量实验验证了所提出方法的有效性和高效性,所提出方法相比于传统RKME规约查搜准确率显著提升近9%,且实验结果表明改进后的规约在图像任务上具有良好的隐私保护性质.代码见:.展开更多
文摘The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.
文摘In this study, we present a miniOS kernel implemented via analysis of the context switching, the scheduler, and the memory management of the original OS kernel for an embedded system based on ARM core. Since this is a large subject, we have limited our scope to them only that made up an embedded operating system. The implemented miniOS kernel is composed only by them, to the exclusion of all other functions of the original kernel. Our goal is to modify the OS kernel depending on the product function. The implementation method of the miniOS kernel can be applicable to any OS being mounted based on the ARM core. Modifying the kernel depending on the product function can improve the OS booting speed as well as save the system memory. The functions of the scheduler, the context switching, and the memory management are described with the source in each section. The miniOS kernel was implemented in the Assembly and C language and was verified through the build and the test. The results are shown in the Section 5.
文摘The Neighborhood Preserving Embedding(NPE) algorithm is recently proposed as a new dimensionality reduction method.However, it is confined to linear transforms in the data space.For this, based on the NPE algorithm, a new nonlinear dimensionality reduction method is proposed, which can preserve the local structures of the data in the feature space.First, combined with the Mercer kernel, the solution to the weight matrix in the feature space is gotten and then the corresponding eigenvalue problem of the Kernel NPE(KNPE) method is deduced.Finally, the KNPE algorithm is resolved through a transformed optimization problem and QR decomposition.The experimental results on three real-world data sets show that the new method is better than NPE, Kernel PCA(KPCA) and Kernel LDA(KLDA) in performance.
文摘Based on the in-depth research of the embedded device management system,analyze the process of the driver parameters delivery on kernel level particularly.According to this,expound the device driver's working principle,structure and design methods especially.Finally realize the drivers of the character-based devices which can be dynamically loaded.Actual result shows that mastering the realization of the device driver mechanism and the process of parameters delivery with kernel can improve the embedded device driver development efficiency and reduce error probability effectively,thus saving the development cost and development cycle of embedded products.
文摘In this paper, a recently proposed dimensional-ity reduction method called Twin Kernel Em-bedding (TKE) [10] is applied in 2-dimensional visualization of protein structure relationships. By matching the similarity measures of the input and the embedding spaces expressed by their respective kernels, TKE ensures that both local and global proximity information are preserved simultaneously. Experiments conducted on a subset of the Structural Classification Of Pro-tein (SCOP) database confirmed the effective-ness of TKE in preserving the original relation-ships among protein structures in the lower di-mensional embedding according to their simi-larities. This result is expected to benefit sub-sequent analyses of protein structures and their functions.
基金supported by the National Natural Science Foundation of China(Nos.62371173,U22A2044,and U22A2047)the Stable Supporting Fund of Acoustic Science and Technology Laboratory(NO.JCKYS2024604SSJS009)。
文摘To address the problem of underwater multi-sensor multi-target passive tracking in clutter,a distributed kernel mean embedding-based Gaussian belief propagation(DKME-GaBP)algorithm is proposed.First,a joint posterior probability density function(PDF)is established and factorized,and it is represented by the corresponding factor graph.Then,the GaBP algorithm is executed on this factor graph to reduce the computational complexity of data association.The factor graph of the GaBP consists of inner and outer loops.The inner loop is responsible for local track estimation and data association.The outer loop fuses information from different sensors.For the inner loop,the kernel mean embedding(KME)with a Gaussian kernel is designed to transform the strong nonlinear problem of local estimation into a linear problem in a high-dimensional reproducing kernel Hilbert space(RKHS).For the outer loop,a multi-sensor distributed fusion method based on KME is proposed to improve fusion accuracy by accounting for the distance among different PDFs in RKHS.The effectiveness and robustness of the DKME-GaBP are validated in the simulations.
文摘当前机器学习技术已经在大量领域得到广泛应用,然而仍面临许多亟待解决的问题:依赖大量的训练数据和训练技巧、难以适应环境变化、数据隐私/所有权的保护、灾难性遗忘等等.最近,学件范式使得上述问题同时得到系统性地解决成为可能.在该范式下,用户面临新的机器学习任务时可以通过学件基座系统方便地复用他人的结果,而不必从头开始.学件范式的核心在于规约,规约使得学件基座系统在不接触原始数据的情况下,可以根据用户的需求快速识别出对用户任务有帮助的学件.近期研究均通过缩略核均值嵌入(Reduced Kernel Mean Embedding,RKME)为模型构造规约,并通过构建学件原型系统验证了范式的有效性.在实际中,学件基座系统中往往包含在各种领域任务、数据类型上构建的机器学习模型,而传统的RKME规约面临维度灾难的问题,难以适用于高维数据,例如图像场景.为了拓展RKME规约的适用范围,本文引入神经切线核进行RKME规约构造.为提升方法的高效性,本文进一步通过神经网络高斯过程与随机特征近似,快速为各种模型生成RKME规约.最后,本文在真实数据构建的销量预测、图像分类场景的学件基座系统中进行大量实验验证了所提出方法的有效性和高效性,所提出方法相比于传统RKME规约查搜准确率显著提升近9%,且实验结果表明改进后的规约在图像任务上具有良好的隐私保护性质.代码见:.
