A fast symbolic SNR computation method and its Verilog-A implementation for Sigma-Delta modulator design optimization

Zhang, Ailin; Shi, Guoyong

doi:10.1016/j.vlsi.2017.09.007

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Its main advantage is that the algorithm is simple and fast. If the resulting clusters are dense and the diference between clusters is obvious, it works best [10]. For processing large datasets, the algorithm is relatively scalable and efcient.…”

Section: Time Series Symbolization Methods Based On the K-means Algor...mentioning

confidence: 99%

Time Series Symbolization Method for the Data Mining K-Means Algorithm

Wang

2023

Discrete Dynamics in Nature and Society

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Time series is a data type frequently encountered in data analysis. With the current depth and breadth of the data and the improvement in computer processing capabilities, the dimensionality and the complexity of time series are getting higher and higher. Time series symbolization is to cluster and assign complex and lengthy time series in the form of symbols to achieve the purpose of reducing the dimensionality of the sequence or making the sequence easier to process. Considering the excellent performance of the K-means algorithm in data mining and processing, as well as in the allocation algorithm for clustering, we plan to develop a simple method for the symbolization of time series for the K-means algorithm and hope that this method can realize the high-dimensional time series dimensionality reduction, processing of the special points in time series, and so on. Based on this, this article proposes an improved sans algorithm based on the K-means algorithm and discusses the representation method and the data processing of time series symbolization. Experimental results show that this method can effectively reduce the dimensionality of high-dimensional time series. After dimensionality reduction, the information retention rate contained in the elevation of the sequence can reach more than 90%, which is very effective for the detection of outliers in low-dimensional sequences.

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Section: Time Series Symbolization Methods Based On the K-means Algor...mentioning

confidence: 99%

Time Series Symbolization Method for the Data Mining K-Means Algorithm

Wang

2023

Discrete Dynamics in Nature and Society

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“…Equation (20) demonstrates that the proposed model mitigates the IQ imbalance given the fact that such imbalance is compensated in terms of phase . When substituting I = cos( b t) and Q = sin( b t), Equation (23) is obtained:…”

Section: Methods 1: the Trigonometrical Approachmentioning

confidence: 96%

“…A practical digital signal generation using

M

‐QAM equivalent with complex real valued set of data frames reported in Zhang and Shi and Cavers is implemented; thus, the circuit block designed includes M‐ary cases by using the principles of binary amplitude shift keying (BASK) and binary phase shift keying (BPSK) modulations. Therefore, the

M

‐QAM modulator comprises a complex signal generation for a single input single output (SISO) transmitter where both amplitude and phase convey orthogonally as

x false(t false) = I false(t false) \cos false(2 π f_{0} t false) - Q false(t false) \sin false(2 π f_{0} t false),

where

x false(t false)

represents the transmitted signal, with the components of

I

in‐phase and

Q

phase quadrature providing characteristics of constant‐envelope signals

([], E^{2} = I^{2} false(t false) + Q^{2} false(t false) = normalc o n s t a n t)

.…”

Section: Digital Design Of Rf Blocks System Applicationmentioning

confidence: 99%

FPGA‐based system for effective IQ imbalance mitigation of RF power amplifiers

Pérez¹,

Cazares²,

Galaviz-Aguilar

et al. 2020

Circuit Theory & Apps

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Summary To provide an adequate signal integrity to a power amplifier (PA), we propose a digital system for the degradation at the transmitter path, and it is implemented on a field‐programmable gate array (FPGA) board. The proposed system offers the following features: A M‐ary quadrature amplitude modulation (QAM) digital signal generation and in‐phase/quadrature (IQ) imbalance mitigation, and by default, it performs as a predistortion model extraction from PA‐measured data. The simulations and tests provided are performed to effectively verify the PA linearity by using 256‐QAM signals. The nonlinearities are predicted as a reliable solution for linearizing the PA from measurements of AM/AM and AM/PM conversion curves. The performance is evaluated in terms of linearity, computation complexity, and FPGA hardware synthesis according to a dependability compliance of digital signal processing. Finally, the model is validated with input/output data observations to linearize the model with a fitting normalized mean squared error (NMSE) of around −35 dB, a spurious free dynamic range of 40 dBm, and an adjacent channel power ratio reduction by −20 dBm, for a class‐AB broadband radio frequency PA GaN HEMT of 10 W working at 2.34 GHz.

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RETRACTED ARTICLE: An enhanced design and random optimization for oversampling ∆∑ modulator

Kalaipoonguzhali

Kannan

2020

J Ambient Intell Human Comput

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A fast symbolic SNR computation method and its Verilog-A implementation for Sigma-Delta modulator design optimization

Cited by 5 publications

References 26 publications

Time Series Symbolization Method for the Data Mining K-Means Algorithm

Time Series Symbolization Method for the Data Mining K-Means Algorithm

FPGA‐based system for effective IQ imbalance mitigation of RF power amplifiers

RETRACTED ARTICLE: An enhanced design and random optimization for oversampling ∆∑ modulator

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