375 research outputs found
Biorthogonal radial multiresolution in dimension three
AbstractIn this paper, we present the definition and the relative theorems of the biorthogonal radial multiresolution in dimension three. Unlike the orthogonal case, there exist real-valued dual radial scaling functions with compact support in the biorthogonal case. The associated Mallat algorithm can be simply performed in terms of classical biorthogonal filters
High-Resolution Remotely Sensed Small Target Detection by Imitating Fly Visual Perception Mechanism
The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method
Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing
The limited over-the-air (OTA) pilot symbols in
multiple-input-multiple-output orthogonal-frequency-division-multiplexing
(MIMO-OFDM) systems presents a major challenge for detecting transmitted data
symbols at the receiver, especially for machine learning-based approaches.
While it is crucial to explore effective ways to exploit pilots, one can also
take advantage of the data symbols to improve detection performance. Thus, this
paper introduces an online attention-based approach, namely RC-AttStructNet-DF,
that can efficiently utilize pilot symbols and be dynamically updated with the
detected payload data using the decision feedback (DF) mechanism. Reservoir
computing (RC) is employed in the time domain network to facilitate efficient
online training. The frequency domain network adopts the novel 2D multi-head
attention (MHA) module to capture the time and frequency correlations, and the
structural-based StructNet to facilitate the DF mechanism. The attention loss
is designed to learn the frequency domain network. The DF mechanism further
enhances detection performance by dynamically tracking the channel changes
through detected data symbols. The effectiveness of the RC-AttStructNet-DF
approach is demonstrated through extensive experiments in MIMO-OFDM and massive
MIMO-OFDM systems with different modulation orders and under various scenarios.Comment: Accepted to IEEE Transactions on Communication
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