Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation...Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation properties and keep the advantage of quasi-orthogonal transform of the discrete wavelet, transform (DWT). The issue has been supported by computer simulations.展开更多
Online news recommendation systems aim to address the information explosion of news and make personalized recommendations for users. The key problem of personalized news recommendation is to model users' interests...Online news recommendation systems aim to address the information explosion of news and make personalized recommendations for users. The key problem of personalized news recommendation is to model users' interests and track their changes. A common way to deal with the user modeling problem is to build user profiles from observed behavior. However, the majority of existing methods make static representations of user profiles and little research has focused on effective user modeling that could dynamically capture user interests in news topics. To address this problem, in this paper, we propose UP-TreeRec, a news recommendation framework based on a user profile tree(UP-Tree), which is a novel framework combining content-based and collaborative filtering techniques. First, by exploiting a novel topic model namely UILDA, we obtain the representation vectors for news content in a topic space as the fundamental bridge to associate user interests with news topics. Next, we design a decision tree with a dynamically changeable structure to construct a user interest profile from the user's feedback. Furthermore, we present a clustering-based multidimensional similarity computation method to select the nearest neighbor of the UP-Tree efficiently. We also provide a Map-Reduce framework-based implemen-tation that enables scaling our solution to real-world news recommendation problems. We conducted several experiments compared to the state-of-the-art approaches on real-world datasets and the experimental results demonstrate that our approach significantly improves accuracy and effectiveness in news recommendation.展开更多
移动无线通信在新型电力系统的发电、输电、配电、变电、用电等环节中都有着广泛的应用场景,滤波器组多载波(Filter Bank Multi-carrier,FBMC)技术作为一种新型无线通信方式,相比4G应用的正交频分复用(Orthogonal Frequency Division Mu...移动无线通信在新型电力系统的发电、输电、配电、变电、用电等环节中都有着广泛的应用场景,滤波器组多载波(Filter Bank Multi-carrier,FBMC)技术作为一种新型无线通信方式,相比4G应用的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术有着频带利用率高、带外功率泄漏低、无须循环前缀等优点,但也存在计算复杂度高、虚部干扰难以消除等缺点,对信道估计环节接收信号的恢复造成影响。为高效解决FBMC系统信道估计问题,结合压缩感知思想,利用稀疏度自适应匹配追踪(Sparse Adaptive Match Pursuit,SAMP)算法与离散傅里叶变换(Discrete Fourier Transform,DFT)算法,设计并完成信号恢复实验以及FBMC系统信道估计仿真实验,随机信号恢复实验验证了SAMP算法的重构性能,在FBMC系统信道估计仿真实验中,将提出的算法与SAMP、子空间追踪(Subspace Pursuit,SP)、正交匹配追踪(Orthogonal Matching Pursuit,OMP)等常见压缩感知算法充分比较,结果证明该算法相比其他传统算法有更低的误码率和更低的均方误差。展开更多
基金Supported by the National Natural Science Foundation of China,no.69672039
文摘Based on the scale function representation for a function in L2(R), a new wavelet transform based adaptive system identification scheme is proposed. It can reduce the amount of computation by exploiting the decimation properties and keep the advantage of quasi-orthogonal transform of the discrete wavelet, transform (DWT). The issue has been supported by computer simulations.
基金supported by the Beijing Natural Science Foundation (No.4192008)the General Project of Beijing Municipal Education Commission (No. KM201710005023)
文摘Online news recommendation systems aim to address the information explosion of news and make personalized recommendations for users. The key problem of personalized news recommendation is to model users' interests and track their changes. A common way to deal with the user modeling problem is to build user profiles from observed behavior. However, the majority of existing methods make static representations of user profiles and little research has focused on effective user modeling that could dynamically capture user interests in news topics. To address this problem, in this paper, we propose UP-TreeRec, a news recommendation framework based on a user profile tree(UP-Tree), which is a novel framework combining content-based and collaborative filtering techniques. First, by exploiting a novel topic model namely UILDA, we obtain the representation vectors for news content in a topic space as the fundamental bridge to associate user interests with news topics. Next, we design a decision tree with a dynamically changeable structure to construct a user interest profile from the user's feedback. Furthermore, we present a clustering-based multidimensional similarity computation method to select the nearest neighbor of the UP-Tree efficiently. We also provide a Map-Reduce framework-based implemen-tation that enables scaling our solution to real-world news recommendation problems. We conducted several experiments compared to the state-of-the-art approaches on real-world datasets and the experimental results demonstrate that our approach significantly improves accuracy and effectiveness in news recommendation.
文摘移动无线通信在新型电力系统的发电、输电、配电、变电、用电等环节中都有着广泛的应用场景,滤波器组多载波(Filter Bank Multi-carrier,FBMC)技术作为一种新型无线通信方式,相比4G应用的正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)技术有着频带利用率高、带外功率泄漏低、无须循环前缀等优点,但也存在计算复杂度高、虚部干扰难以消除等缺点,对信道估计环节接收信号的恢复造成影响。为高效解决FBMC系统信道估计问题,结合压缩感知思想,利用稀疏度自适应匹配追踪(Sparse Adaptive Match Pursuit,SAMP)算法与离散傅里叶变换(Discrete Fourier Transform,DFT)算法,设计并完成信号恢复实验以及FBMC系统信道估计仿真实验,随机信号恢复实验验证了SAMP算法的重构性能,在FBMC系统信道估计仿真实验中,将提出的算法与SAMP、子空间追踪(Subspace Pursuit,SP)、正交匹配追踪(Orthogonal Matching Pursuit,OMP)等常见压缩感知算法充分比较,结果证明该算法相比其他传统算法有更低的误码率和更低的均方误差。
