Array auto-calibration using a generalized least-squares method

Arjomandi-Lari, Mostafa; Karimi, Mahmood

doi:10.1016/j.aeue.2019.04.022

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…We refer to our approach by EMASEF-NPA (Error-Margin aware Approach for non-uniform Segmentation of Elementary Functions using degree-N Polynomial Approximation). EMASEF-NPA approach passes through three steps: (i) first, we proposed an new Dichotomy-based non-uniform segmentation efficient algorithm followed by Least Square-based [23] degree-n polynomial approximation; (ii) second, we used the bit-width optimization (BWO) process to carry out the fixed point arithmetic [24]. This process guarantees a better representation of the integer and fractional part of the polynomial approximation's coefficients, and the x-extremes of each interval of the segments; (iii) finally, we implement the elementary function on an FPGA hardware platform [25,26] using the Content-Addressable Memory (CAM) principle [27], and Horner's rules [28].…”

Section: Design and Implementation Of An Error-margin Aware Approachmentioning

confidence: 99%

Design and Implementation of an Error‐Margin Aware Approach for Non‐Uniform Segmentation of Elementary Functions Using Degree‐N Polynomial Approximation

Tahir,

Boudraa,

Lamini

et al. 2023

IEEJ Transactions Elec Engng

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Elementary functions are widely integrated in many embedded applications such as speech recognition and signal processing. This paper deals with the issues related to the hardware implementation of those functions. Indeed, a novel efficient Error‐Margin aware Approach for non‐uniform Segmentation of Elementary Functions using degree‐N Polynomial Approximation (EMASEF‐NPA) has been proposed. The bit‐width optimization (BWO) process has been used to obtain a better representation of the approximation polynomial coefficients. The principle of content‐addressable memory (CAM) and Horner's rules have been used in the physical implementation. Functional verification, logical synthesis, and physical implementation were performed using the FPGA platform. The results obtained using EMASEF‐NPA approach show a significant reduction, up to 70%, in the number of segments compared to conventional approaches. The findings of the physical implementation show a considerable decrease in area and delay. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Design and Implementation Of An Error-margin Aware Approachmentioning

confidence: 99%

Design and Implementation of an Error‐Margin Aware Approach for Non‐Uniform Segmentation of Elementary Functions Using Degree‐N Polynomial Approximation

Tahir,

Boudraa,

Lamini

et al. 2023

IEEJ Transactions Elec Engng

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“…where Z denotes the fitted elevation of the conductor using a polynomial. The optimal parameters of Equation ( 7) are determined using the least-squares method [60], which is mathematically expressed as…”

Section: Catenary Equationmentioning

confidence: 99%

Power Line Extraction and Tree Risk Detection Based on Airborne LiDAR

Xi,

Zhang,

Niu

et al. 2023

Sensors

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Transmission lines are the basis of human production and activities. In order to ensure their safe operation, it is essential to regularly conduct transmission line inspections and identify tree risk in a timely manner. In this paper, a power line extraction and tree risk detection method is proposed. Firstly, the height difference and local dimension feature probability model are used to extract power line points, and then the Cloth Simulation Filter algorithm and neighborhood sharing method are creatively introduced to distinguish conductors and ground wires. Secondly, conductor reconstruction is realized by the approach of the linear–catenary model, and numerous non-risk points are excluded by constructing the tree risk point candidate area centered on the conductor’s reconstruction curve. Finally, the grading strategy for the safety distance calculation is used to detect the tree risk points. The experimental results show that the precision, recall, and F-score of the conductors (ground wires) classification exceed 98.05% (97.98%), 99.00% (99.14%), and 98.58% (98.56%), respectively, which presents a high classification accuracy. The Root-Mean-Square Error, Maximum Error, and Minimum Error of the conductor’s reconstruction are better than 3.67 cm, 7.13 cm, and 2.64 cm, respectively, and the Mean Absolute Error of the safety distance calculation is better than 6.47 cm, proving the effectiveness and rationality of the proposed tree risk points detection method.

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