Analysis and design of the true piecewise approximation logarithmic amplifiers

Shaterian, Mostafa; Abrishamifar, Adib; Shamsi, Hossein

doi:10.1007/s10470-011-9820-5

Cited by 13 publications

(3 citation statements)

References 24 publications

(22 reference statements)

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“…Logarithmic characteristics [ 16 ]: In KPI time series, there are many outliers related to zero value. When the relevant data of the system suddenly become zero, there is likely to be an exception.…”

Section: Time Series Anomaly Detection Modelmentioning

confidence: 99%

Time Series Anomaly Detection Model Based on Multi-Features

Tang

Wang

Jiang

2022

Computational Intelligence and Neuroscience

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For Internet information services, it is very important to closely monitor a large number of key time series data generated by core business for anomaly detection. Although there have been many anomaly detection models in recent years, its practical application is still a big challenge. The model usually needs repeated iteration and parameter adjustment; and for different types of time series data, we need to select different models. Therefore, this paper proposes an anomaly detection model based on time series. The model first designs the statistical features, fitting features, and time-frequency domain features for the time series, and then uses the random forest integration model to automatically select the appropriate features for anomaly classification. In addition, this paper presents an anomaly evaluation index ADC score with timeliness window, which adds the time delay factor of anomaly detection on the basis of F1-score. We use the KPI time series, a representative key performance index in the industry, as the experimental data. It is found that the ADC score of the anomaly detection model in this paper reaches the level of 0.7–0.8, which can meet the needs of practical application.

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Section: Time Series Anomaly Detection Modelmentioning

confidence: 99%

Time Series Anomaly Detection Model Based on Multi-Features

Tang

Wang

Jiang

2022

Computational Intelligence and Neuroscience

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“…Each LLA is a non-linear amplifier with two output voltages [ 35 ], namely, a pseudo-logarithmic output, V Log , which represents a (pseudo) logarithmic conversion of the corresponding input signal, and a limited output, V LA , which varies linearly with the input voltage (linear operation) for sufficiently low input levels and is a saturated signal for higher input levels, which represent most of the amplifier input dynamic range. To achieve the necessary input dynamic range, the LLA is implemented with a successive-detection architecture, specifically using a parallel-summation technique [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

Wearable and Noninvasive Device for Integral Congestive Heart Failure Management in the IoMT Paradigm

Ausin

Ramos²,

Lorido³

et al. 2023

Sensors

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Noninvasive remote monitoring of hemodynamic variables is essential in optimizing treatment opportunities and predicting rehospitalization in patients with congestive heart failure. The objective of this study is to develop a wearable bioimpedance-based device, which can provide continuous measurement of cardiac output and stroke volume, as well as other physiological parameters for a greater prognosis and prevention of congestive heart failure. The bioimpedance system, which is based on a robust and cost-effective measuring principle, was implemented in a CMOS application specific integrated circuit, and operates as the analog front-end of the device, which has been provided with a radio-frequency section for wireless communication. The operating parameters of the proposed wearable device are remotely configured through a graphical user interface to measure the magnitude and the phase of complex impedances over a bandwidth of 1 kHz to 1 MHz. As a result of this study, a cardiac activity monitor was implemented, and its accuracy was evaluated in 33 patients with different heart diseases, ages, and genders. The proposed device was compared with a well-established technique such as Doppler echocardiography, and the results showed that the two instruments are clinically equivalent.

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“…The dynamic range of amplifier is (A + 1) N . Equation (8) can be simplified as :

\begin{matrix} center \\ center V_{o} = K_{y} {log}_{10} \frac{V_{i}}{K_{x}} & center K_{y} = \frac{V_{L}}{{log}_{10} A + 1} & center K_{x} = \frac{V_{L}}{A {(A + 1)}^{N + \frac{1}{A}}} \end{matrix}

…”

Section: Circuit Designmentioning

confidence: 99%

Low‐noise low‐power front‐end logarithmic amplifier for neural recording system

Derafshi¹,

Frounchi²

2014

Circuit Theory & Apps

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SUMMARY In this work, a low‐power, low‐noise logarithmic preamplifier for biopotential and neural recording application is presented. The amplifier is based on a linear limit logarithmic amplifier technique, and an active filter as a DC cancellation filter has been included to its input in order to eliminate DC offsets, which are produced at the electrode–tissue interface. This system has been simulated in a UMC standard 90‐nm 1P9M CMOS process. Five dual gain stages are used to produce the required linear limit logarithmic amplifier. The dynamic range of the amplifier is measured to be 48 dB which covers the signals with amplitude from 20 μV to 5 mV. The amplifier consumes 23.5 μW from a 1.2‐V power supply and has a maximum gain of 69.8 dB. The simulated input referred noise is 5.3 μV over 0.1 Hz to 20 kHz. Copyright © 2012 John Wiley & Sons, Ltd.

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Analysis and design of the true piecewise approximation logarithmic amplifiers

Cited by 13 publications

References 24 publications

Time Series Anomaly Detection Model Based on Multi-Features

Time Series Anomaly Detection Model Based on Multi-Features

Wearable and Noninvasive Device for Integral Congestive Heart Failure Management in the IoMT Paradigm

Low‐noise low‐power front‐end logarithmic amplifier for neural recording system

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