In recent advancements, hyperspectral image (HSI) classification through few-shot learning (FSL) has significantly progressed. Domain adaptation, integrated with FSL, effectively utilizes transferable knowledge from a source domain (SD) with abundant labeled data to excel in classification tasks within a target domain (TD) with scarce labels. However, most existing methods usually use traditional convolutional neural networks (CNNs) to extract local spatial information to characterize and mine feature and distribution information while ignoring the underlying topological relationships among feature classes. Therefore, we propose a topology graph perception cross-domain FSL (TGP-CFSL) framework that leverages graph information aggregation. Specifically, to construct the extended topological relationships of the target, we have designed a topological graph-based multiscale fusion (TGMF) feature extraction module, which is adept at fully mining the topological spatial neighborhood information of the target. Meanwhile, a dual-graph information perception (DGIP) module is designed, which is able to characterize and aggregate intradomain topological relationships in terms of both feature representations and interdomain distribution similarities and to extract higher order domain distribution information for realizing domain alignment. Experimental results on three public HSI datasets demonstrate that the proposed method outperforms existing methods.

A new decision-directed (DD) synchronization scheme is proposed for joint estimation of carrier frequency offset (CFO) and sampling clock frequency offset (SFO) in orthogonal frequency-division multiplexing (OFDM) systems. By exploiting the hard decisions, we report accurate estimators of residual CFO and small SFO. The performance analysis and simulation results indicate that the proposed novel DD scheme achieves much better performance than the conventional pilot-based schemes in both additive white Gaussian noise and frequency-selective channels.

A new decision-directed (DD) synchronization scheme is proposed for joint estimation of carrier frequency offset (CFO) and sampling clock frequency offset (SFO) in coded orthogonal frequency division multiplexing (OFDM) systems. By exploiting the decisions provided by a Viterbi decoder and the information available on all the modulated subcarriers, we report accurate estimators of residual CFO and small SFO without relying on pilots. The performance analysis and simulation results indicate that the proposed novel DD scheme achieves much better performance than the conventional pilot-based schemes in both AWGN and frequency-selective channels.

This letter deals with the compensation of the self-noise (jitter) in a nondata-aided (blind) feedforward symbol-timing estimator using two samples per symbol for linearly modulated waveforms transmitted through additive white Gaussian noise channels. The main contribution of this letter is the derivation of an appropriate prefilter to achieve nearly jitter-free digital blind feedforward timing recovery. Computer simulations illustrate that the proposed prefiltering scheme improves significantly the performance at mid and high signal-to-noise ratios.

We propose a fast and reduced complexity frame and carrier acquisition scheme for orthogonal frequency-division multiplex systems which assumes either a continuous or a burst mode operation in additive white Gaussian noise and frequency-selective channels. By exploiting the repetitive structure of a training symbol, a robust frame synchronizer is obtained and shown to resume to finding the peak of a certain correlation metric that is obtained by invoking maximum likelihood principles. A modified carrier estimator that can correct frequency offsets up to two subcarrier spacings is also proposed. The efficiency of the proposed synchronization algorithms is illustrated by both theoretical performance analysis and computer simulations.