Two-stage approach to image compression using wavelet and piecewise-linear transforms

Abstract Baseline drift caused by slowly changing environment and other instability factors affects significantly the performance of gas sensors, resulting in reduced accuracy of gas classification and quantification of the electronic nose. In this work, a two-stage method is proposed for real-time sensor baseline drift compensation based on estimation theory and piecewise linear approximation. In the first stage, the linear information from the baseline before exposure is extracted for prediction. The second stage continuously predicts changing linear parameters during exposure by combining temperature change information and time series information, and then the baseline drift is compensated by subtracting the predicted baseline from the real sensor response. The proposed method is compared to three efficient algorithms and the experiments are conducted towards two simulated datasets and two surface acoustic wave sensor datasets. The experimental results prove the effectiveness of the proposed algorithm. Moreover, the proposed method can recover the true response signal under different ambient temperatures in real-time, which can guide the future design of low-power and low-cost rapid detection systems.

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Two-Stage Multiview Image Compression Using Interview SIFT Matching

2014 Data Compression Conference ◽

10.1109/dcc.2014.14 ◽

2014 ◽

Cited By ~ 1

Author(s):

Huihui Bai ◽

Mengmeng Zhang ◽

Meiqin Liu ◽

Anhong Wang ◽

Yao Zhao

Keyword(s):

Image Compression ◽

Two Stage

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Textual image compression: two-stage lossy/lossless encoding of textual images

Proceedings of the IEEE ◽

10.1109/5.286192 ◽

1994 ◽

Vol 82 (6) ◽

pp. 878-888 ◽

Cited By ~ 31

Author(s):

I.H. Witten ◽

T.C. Bell ◽

H. Emberson ◽

S. Inglis ◽

A. Moffat

Keyword(s):

Image Compression ◽

Two Stage ◽

Textual Image ◽

Textual Image Compression

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Two-stage superresolution technique using a piecewise-linear model

10.1117/12.158635 ◽

1993 ◽

Cited By ~ 1

Author(s):

Alan H. Lettington ◽

Qi H. Hong

Keyword(s):

Linear Model ◽

Piecewise Linear ◽

Two Stage ◽

Piecewise Linear Model

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An Improved Linearization Circuit Used for Optical Rotary Encoders

Measurement Science Review ◽

10.1515/msr-2017-0029 ◽

2017 ◽

Vol 17 (5) ◽

pp. 241-249 ◽

Cited By ~ 2

Author(s):

Jelena Jovanović ◽

Dragan Denić ◽

Uglješa Jovanović

Keyword(s):

Piecewise Linear ◽

Angular Position ◽

Absolute Measurement ◽

Analog To Digital Converter ◽

Digital Converter ◽

Optical Encoder ◽

Two Stage ◽

Analog To Digital ◽

Linear Analog ◽

Linear Signal

Abstract Optical rotary encoders generate nonlinear sine and cosine signals in response to a change of angular position that is being measured. Due to the nonlinear shape of encoder output signals, encoder sensitivity to very small changes of angular position is low, causing a poor measurement accuracy level. To improve the optical encoder sensitivity and to increase its accuracy, an improved linearization circuit based on pseudo-linear signal generation and its further linearization with the two-stage piecewise linear analog-to-digital converter is presented in this paper. The proposed linearization circuit is composed of a mixed-signal circuit, which generates analog pseudo-linear signal and determines the first four bits of the final digital result, and the two-stage piecewise linear analog-to-digital converter, which performs simultaneous linearization and digitalization of the pseudo-linear signal. As a result, the maximal value of the absolute measurement error equals to 3.77168·10−5 [rad] (0.00216°) over the full measurement range of 2π [rad].

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Flexible piecewise linear model for investigating dose-response relationship in meta-analysis: methodology, examples, and comparison

10.7287/peerj.preprints.27277 ◽

2018 ◽

Cited By ~ 1

Author(s):

Chang Xu ◽

Lehana Thabane ◽

Tong-Zu Liu ◽

Ling Li ◽

Sayem Borhan ◽

...

Keyword(s):

Linear Model ◽

Dose Response ◽

Linear Trend ◽

Piecewise Linear ◽

Meta Analysis ◽

Continuous Exposure ◽

Response Relationship ◽

Dose Response Relationship ◽

Two Stage ◽

One Stage

Objectives: Dose-response meta-analysis (DRMA) is widely employed to establishing the potential dose-response relationship between continuous exposures and disease outcomes. However, no method is readily available for exploring the relation between a discrete exposure and a binary or continuous outcome. We proposed a piecewise linear (PL) DRMA model as a solution to this issue. Methods: We illustrated the methodology of PL model in both one-stage DRMA approach and two-stage DRMA approach. The method by testing the equality of slopes of each piecewise was employed to judge if there is “piecewise effect” against simple linear trend. We then used sleep (continuous exposure) and parity (discrete exposure) data as examples to illustrate how to apply PL model in DRMA using the Stata code attached. We also empirically compared the slopes of PL model with simple linear as well as restricted cubic spline (RCS) model. Results: Both one-stage and two-stage PL DRMA model fitted well in our examples, and the results were similar. Obvious “piecewise effects” were detected in both the two examples by the method we used. In our example, the PL model showed better fitting effect and practical reliable results compared to simple linear model, while similar results for to RCS model. Conclusion: Piecewise linear function is a simple and valid method for DRMA and can be used for discrete exposures. It also represents a superior model to linear model in DRMA and may be an alternative model to non-linear model.

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A New Separable Piecewise Linear Learning Algorithm for the Stochastic Empty Container Repositioning Problem

Mathematical Problems in Engineering ◽

10.1155/2020/4762064 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Shaorui Zhou ◽

Xiaopo Zhuo ◽

Zhiming Chen ◽

Yi Tao

Keyword(s):

Cost Function ◽

Programming Model ◽

Learning Algorithm ◽

Piecewise Linear ◽

Two Stage ◽

Empty Container ◽

Expected Cost ◽

Stochastic Programs ◽

Empty Container Repositioning ◽

Empty Containers

A common challenge faced by liner operators in practice is to effectively allocate empty containers now in a way that minimizes the expectation of costs and reduces inefficiencies in the future with uncertainty. To incorporate uncertainties in the operational model, we formulate a two-stage stochastic programming model for the stochastic empty container repositioning (ECR) problem. This paper proposes a separable piecewise linear learning algorithm (SPELL) to approximate the expected cost function. The core of SPELL involves learning steps that provide information for updating the expected cost function adaptively through a sequence of piecewise linear separable approximations. Moreover, SPELL can utilize the network structure of the ECR problem and does not require any information about the distribution of the uncertain parameters. For the two-stage stochastic programs, we prove the convergence of SPELL. Computational results show that SPELL performs well in terms of operating costs. When the scale of the problem is very large and the dimensionality of the problem is increased, SPELL continues to provide consistent performance very efficiently and exhibits excellent convergence performance.

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