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Optimal Nonrecursive Noise Shaping Filters for Oversampling Data Converters Part 2: Applications 过采样数据转换器的最佳非递归噪声整形滤波器。第2部分:应用
S. Norsworthy
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引用次数: 2
Worst-case l1 system identification using perturbed ARMA models 用摄动ARMA模型进行最坏情况l1系统辨识
M. Milanese, N. Elia
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引用次数: 2
Double Edge Triggered Devices: Speed and Power Considerations 双边缘触发设备:速度和功率考虑
R. Hossain, L. Wronski, A. Albicki
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引用次数: 0
Systolic Array for 2-D Circular Convolution Using the Chinese Remainder Theorem 用中国剩余定理求解二维圆卷积的收缩阵列
L. Wang, I. Hartimo
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引用次数: 0
Linear-phase Filters Configured as a Combination of Sigma-delta Modulator, SC Transversal Filter and a Low-Q Biquad 线性相位滤波器配置为Sigma-delta调制器,SC横向滤波器和低q双通道的组合
Qiuting Huang
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引用次数: 3
Optimal Nonrecursive Noise Shaping Filters for Oversampling Data Converters Part 1: Theory 过采样数据转换器的最优非递归噪声整形滤波器第1部分:理论
S. Norsworthy
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引用次数: 13
Highly Linear Four Quadrant Analog BiCMOS Multiplier for ± 1.5 V Supply Operation 高线性四象限模拟BiCMOS乘法器±1.5 V供电操作
J. Ramírez-Angulo
A highly linear four-quadrant analogue BiCMOS multiplier is presented. It operates with ±1.5V supplies and with 2V peak to peak input and output signal wings. It is based on the use of crosscoupled, voltage biased differential pairs. Experimental results of a CMOS test chip are presented that confirm the proposed structure.
提出了一种高度线性的四象限模拟BiCMOS乘法器。它工作在±1.5V电源和2V峰值输入和输出信号翼。它是基于交叉耦合,电压偏置差分对的使用。最后给出了CMOS测试芯片的实验结果,验证了所提出的结构。
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引用次数: 32
Analog VLSI implementation of neural networks 模拟VLSI实现的神经网络
E. Vittoz
The potentialities of CMOS analog VLSI for the implementation of neural systems are demonstrated. It is shown how the various modes of operation of the transistor can be exploited to build very efficient neurons on a very small area with very low power consumption. The connectivity problem can be alleviated by selecting appropriate architectures. Various methods for implementing analog synaptic memories are discussed, and examples of working chips are given.<>
演示了CMOS模拟VLSI在实现神经系统方面的潜力。它展示了如何利用晶体管的各种操作模式,在非常小的面积上以非常低的功耗构建非常高效的神经元。通过选择合适的体系结构,可以缓解连接性问题。讨论了实现模拟突触记忆的各种方法,并给出了工作芯片的实例。
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引用次数: 75
Design of stable high order 1-bit sigma-delta modulators 稳定高阶1位σ - δ调制器的设计
T. Ritoniemi, T. Karema, H. Tenhunen
A method for designing stable 1-b high-order (>or=3) sigma-delta modulators is presented. The stability analysis is based on the root locus and modeling the quantizer for each clock period at a time. The quantizer's gain in the modulator at the present clock period determines the modulator's stability for the next clock period. If the modulator is stable during each clock period, it is unconditionally stable and behaves as a linear analog/digital converter. Examples with third-, fourth-, fifth-, and sixth-order sigma-delta modulators are given to explore the use of the proposed method in practice. With the designed sixth-order modulator it is possible to achieve 23-b signal-to-quantization noise ratio at the oversampling ratio of 64.<>
提出了一种设计稳定的1-b高阶(>或=3)σ - δ调制器的方法。稳定性分析基于根轨迹,并对每个时钟周期的量化器进行建模。在当前时钟周期,量化器在调制器中的增益决定了调制器在下一个时钟周期的稳定性。如果调制器在每个时钟周期内稳定,则它是无条件稳定的,并且表现为线性模拟/数字转换器。以三阶、四阶、五阶和六阶σ - δ调制器为例,探讨该方法在实际中的应用。利用所设计的六阶调制器,可以在过采样比为64的情况下实现23b的信量化噪声比。
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引用次数: 99
Constrained principal component analysis via an orthogonal learning network 基于正交学习网络的约束主成分分析
S. Kung
The regular principal components (PC) analysis of stochastic processes is extended to the so-called constrained principal components (CPC) problem. The CPC analysis involves extracting representative components which contain the most information about the original processes, The CPC solution has to be extracted from a given constraint subspace. Therefore, the CPC solution may be adopted to best recover the original signal and simultaneously avoid the undesirably noisy or redundant components. A technique for finding optimal CPC solutions via an orthogonal learning network (OLN) is proposed. The underlying numerical analysis for the theoretical proof of the convergency of OLN is discussed. The same numerical analysis provides a useful estimate of optimal learning rates leading to very fast convergence speed. Simulation and application examples are provided.<>
将随机过程的正则主成分分析推广到约束主成分问题。CPC分析涉及提取包含有关原始过程的最多信息的代表性组件,CPC解决方案必须从给定的约束子空间中提取。因此,可以采用CPC解决方案,以最好地恢复原始信号,同时避免不必要的噪声或冗余组件。提出了一种利用正交学习网络(OLN)寻找最优CPC解的方法。讨论了OLN收敛性理论证明的基本数值分析。同样的数值分析提供了一个有用的最优学习率估计,导致非常快的收敛速度。给出了仿真和应用实例。
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引用次数: 18
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IEEE International Symposium on Circuits and Systems proceedings. IEEE International Symposium on Circuits and Systems
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