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    • 71. 发明申请
    • Estimation method of flat fading channel in cdma communication system and apparatus for the same
    • cdma通信系统中平坦衰落信道的估计方法及其设备
    • US20060062284A1
    • 2006-03-23
    • US10474192
    • 2001-04-16
    • Gang LiYu JinSheng Liu
    • Gang LiYu JinSheng Liu
    • H04B1/707
    • H04L25/025H04B1/71057H04B1/7117H04L25/0212H04L25/03178
    • The invention provides a method and apparatus for estimating flat fading channel in CDMA communication system, said method is implemented by using an adaptive forward prediction technique based on lattice filter and maximum likelihood technique of Viterbi algorithm. The adaptive lattice filter is used to carry out prediction of LS criteria on channel fading, and a maximum likelihood detection technique completes Viterbi algorithm in accordance with a channel fading value obtained by the prediction, thus obtaining final estimation and decision about the transmitting signals. The present invention has the advantages that it can obtain accurate result for channel estimation and sequence decision when it operates in the fast fading channel, and overcome fast fading influence due to motion speed up of mobile station, thereby satisfying mobile station speed and corresponding receiving performance required in 3G mobile communication.
    • 本发明提供了一种用于估计CDMA通信系统中的平坦衰落信道的方法和装置,所述方法通过使用基于维特比算法的网格滤波器和最大似然技术的自适应前向预测技术来实现。 自适应网格滤波器用于对信道衰落的LS标准进行预测,最大似然检测技术根据通过预测获得的信道衰落值完成维特比算法,从而获得关于发送信号的最终估计和决策。 本发明的优点在于它可以在快速衰落信道中操作时获得信道估计和序列确定的准确结果,并克服移动台运动加速引起的快速衰落影响,从而满足移动台速度和相应的接收性能 需要3G移动通信。
    • 74. 发明授权
    • Rare earth magnet and method for making same
    • 稀土磁铁及其制作方法
    • US06527874B2
    • 2003-03-04
    • US09901104
    • 2001-07-10
    • Gang Li
    • Gang Li
    • H01F1053
    • B82Y25/00H01F1/0573H01F1/0577H01F1/0579H01F1/058
    • A rapidly solidified alloy is produced by quenching and solidifying a melt of an alloy having a general formula represented by (Fe1-mTm)100-x-y-zQxRyMz where T denotes at least one kind of element selected from the group consisting of Co and Ni, Q denotes at least one kind of element selected from the group consisting of B and C, R denotes at least one kind of rare earth element, and M denotes at least one kind of element selected from the group consisting of Nb and Mo, and the mole fractions x, y, z, and m respectively satisfy 2≦x≦28 (atom %), 8≦y≦30 (atom %), 0.1 ≦z
    • 通过淬火和固化具有由(Fe1-mTm)100-xy-zQxRyMz表示的通式的合金的熔体来制造快速凝固的合金,其中T表示选自Co和Ni中的至少一种元素, Q表示选自B和C中的至少一种元素,R表示至少一种稀土元素,M表示选自Nb和Mo中的至少一种元素, 摩尔分数x,y,z和m分别满足2 <= x <= 28(原子%),8 <= y <= 30(原子%),0.1 <= z <1.0(原子%), = m <= 0.5(原子%)。 然后将快速凝固的合金粉碎并烧结以制造稀土永磁体。 将冷却速度控制在102K /秒至104K /秒的范围内,使得合金结构均匀,并且添加的元素M均匀分散。
    • 78. 发明授权
    • Discriminative pretraining of deep neural networks
    • 深层神经网络的辨别性预训练
    • US09235799B2
    • 2016-01-12
    • US13304643
    • 2011-11-26
    • Dong YuLi DengFrank Torsten Bernd SeideGang Li
    • Dong YuLi DengFrank Torsten Bernd SeideGang Li
    • G10L15/16G06N3/08
    • G06N3/08G06N3/04
    • Discriminative pretraining technique embodiments are presented that pretrain the hidden layers of a Deep Neural Network (DNN). In general, a one-hidden-layer neural network is trained first using labels discriminatively with error back-propagation (BP). Then, after discarding an output layer in the previous one-hidden-layer neural network, another randomly initialized hidden layer is added on top of the previously trained hidden layer along with a new output layer that represents the targets for classification or recognition. The resulting multiple-hidden-layer DNN is then discriminatively trained using the same strategy, and so on until the desired number of hidden layers is reached. This produces a pretrained DNN. The discriminative pretraining technique embodiments have the advantage of bringing the DNN layer weights close to a good local optimum, while still leaving them in a range with a high gradient so that they can be fine-tuned effectively.
    • 提出了预先训练深层神经网络(DNN)的隐藏层的识别性预训练技术实施例。 一般来说,首先使用带有误差反向传播(BP)的标签对标签进行单层隐藏层神经网络的训练。 然后,在丢弃前一个隐藏层神经网络中的输出层之后,将另一个随机初始化的隐藏层与先前训练过的隐藏层一起添加,并将其代表表示用于分类或识别的目标的新输出层。 然后使用相同的策略对所得到的多隐层DNN进行鉴别性训练,等等,直到达到所需数量的隐藏层。 这产生了一个预训练的DNN。 鉴别预培训技术实施例具有使DNN层权重接近良好的局部最优值的优点,同时仍将它们留在具有高梯度的范围内,使得它们可以被有效地微调。