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1. Thiran Filters for Wideband DSP-Based Multi-Beam True Time Delay RF Sensing Applications.

Author

Perera, Sirani M., Rathnasekara, Gayani, and Madanayake, Arjuna

Subjects
*APPLICATION-specific integrated circuits, *CIRCUIT complexity, *RADIO frequency, *VANDERMONDE matrices, *IMPULSE response, *DIGITAL signal processing, *MATRIX decomposition, *FREQUENCY modulation transmitters
Abstract

The ability to sense propagating electromagnetic plane waves based on their directions of arrival (DOAs) is fundamental to a range of radio frequency (RF) sensing, communications, and imaging applications. This paper introduces an algorithm for the wideband true time delay digital delay Vandermonde matrix (DVM), utilizing Thiran fractional delays that are useful for realizing RF sensors having multiple look DOA support. The digital DVM algorithm leverages sparse matrix factorization to yield multiple simultaneous RF beams for low-complexity sensing applications. Consequently, the proposed algorithm offers a reduction in circuit complexity for multi-beam digital wideband beamforming systems employing Thiran fractional delays. Unlike finite impulse response filter-based approaches which are wideband but of a high filter order, the Thiran filters trade usable bandwidth in favor of low-complexity circuits. The phase and group delay responses of Thiran filters with delays of a fractional sampling period will be demonstrated. Thiran filters show favorable results for sample delay blocks with a temporal oversampling factor of three. Thiran fractional delays of orders three and four are mapped to Xilinx FPGA RF-SoC technologies for evaluation. The preliminary results of the APF-based Thiran fractional delays on FPGA can potentially be used to realize DVM factorizations using application-specific integrated circuit (ASIC) technology. [ABSTRACT FROM AUTHOR]

Published
2024
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2. Noise tolerant drift detection method for data stream mining.

Author

Wang, Pingfan, Jin, Nanlin, Woo, Wai Lok, Woodward, John R., and Davies, Duncan

Subjects
*DATA mining, *NOISE, *INTERNET of things, *TEST methods
Abstract

• To detect drifts in data stream when noise presents. • A novel tolerant drift detection method is designed to detect and validate. • A sparse sliding window strategy is proposed to reduce false drift detection. • Our method outperforms the existing six established drift detection methods in both accuracy and timeliness. Drift detection methods identify changes in data streams. Such changes are called concept drifts. Existing drift detection methods often assume that the input is a noise-free data stream. However, in real world applications, for example, data streams generating from internet of things are normally contaminated with noise. (noise, i.e. class noise and/or attribute noise). In this paper, we propose a Noise Tolerant Drift Detection Method (NTDDM), which is based on two-step detection and validation function to detect drifts, and filters out the false drifts caused by the noise. The NTDDM is compared with six well-known drift detection methods and tested on four benchmarks having different levels. Three performance indicators are proposed to determine whether the drift detection is made within a reasonable time, and the length of time to the known drift starting point. The comparative studies demonstrate that NTDDM outperforms the existing methods, over these performance indicators. Our proposed method has achieved a statistically significant improvement on drift detection compared to the methods in experiment. The proposed NTDDM makes it possible to efficiently and effectively detect drift in a noisy data stream. [ABSTRACT FROM AUTHOR]

Published
2022
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