signal and image processing

Current remote sensing research lacks a systematic interdisciplinary integration framework. Method: This paper constructs a three-tiered fusion paradigm—“foundational technology embedding → methodological transfer → co-solving of problems”—using ecology, mathematical morphology, machine learning, and electronics as representative disciplines. It integrates optical imaging, CCD circuit design, morphological image processing, supervised/unsupervised classification, and ecological modeling to systematically analyze cross-disciplinary interactions. Contribution/Results: The study identifies four distinct interdisciplinary mechanisms and pinpoints critical knowledge-transfer nodes, achieving the first structured synthesis of remote sensing interdisciplinary integration. It provides both a theoretical taxonomy and practical guidelines for the autonomous evolution and collaborative innovation of remote sensing as a discipline.

This study addresses the limitations of conventional image enhancement, filtering, and pattern recognition—namely, heavy reliance on manual feature engineering and insufficient real-time performance—by proposing a theory-driven, end-to-end machine learning framework. Methodologically, it is the first to systematically integrate discrete Fourier transform (DFT), Z-transform, and continuous Fourier analysis into deep learning pipelines, synergistically coupling them with convolutional neural networks (CNNs) and classical digital filtering algorithms to enable frequency-domain-guided automated feature extraction and real-time joint signal–image processing. The key contributions include: (i) development of an extensible Python framework; (ii) average PSNR improvement of 3.2 dB in image enhancement and noise suppression tasks; and (iii) 40% acceleration in feature extraction efficiency. This work establishes a novel paradigm for AI-powered real-time computer vision that simultaneously ensures high performance and interpretability.

To address the challenge of detecting 6-kHz narrowband transient components in real-time signals, this paper proposes an FPGA-optimized wavelet spectral analysis method. Unlike conventional FFT-based approaches, which suffer from inherent trade-offs between time-frequency resolution and latency, our method fully hardware-implements the Daubechies wavelet transform—including fixed-point arithmetic design, pipelined convolution, and on-chip RAM caching—on a Xilinx Artix-7 FPGA. The architecture achieves both high precision and ultra-low latency: under a 250 MS/s input throughput, detection latency is below 5 μs, with total system power consumption under 1.2 W. This work overcomes the real-time detection bottleneck for narrowband transients on resource-constrained embedded platforms and establishes a reusable hardware acceleration paradigm for edge intelligence in high-frequency dynamic signal sensing.

Traditional discrete Fourier transform (DFT) is constrained by uniform sampling and fixed-length sequences, rendering it inadequate for non-uniformly sampled, missing-data, or ultra-long signals. To address this, we propose the Extended Discrete Fourier Transform (EDFT), which formulates spectral estimation as an optimization problem minimizing the Fourier integral residual. EDFT adaptively constructs frequency-domain basis functions without requiring equispaced time-domain sampling or identical sequence lengths. Our method integrates iterative optimization, explicit Fourier integral constraints, and adaptive inverse DFT-based signal reconstruction. It enables high-resolution spectral estimation, time-domain extrapolation, missing-data imputation, and direct processing of non-uniformly sampled signals. Compared to DFT, EDFT substantially broadens the applicability of Fourier analysis while preserving theoretical rigor and computational feasibility.

To address the challenge of rapidly and accurately identifying spectral shifts and distortions in pulse waveforms under low signal-to-noise ratio (SNR) conditions, this paper proposes an extended statistical signal representation method. The approach jointly exploits moments and cumulants applied to the original waveform, its first-order derivative, and its integral—yielding a 30-dimensional high-order statistical feature vector that significantly enhances sensitivity to dynamic spectral variations. Integrated with a single-layer feedforward backpropagation (BP) neural network, the method achieves high classification accuracy in distortion identification tasks for Sinc, Gaussian, and chirp pulses. Unlike conventional statistical representations operating solely on the raw waveform, our method extends high-order statistics into the derivative and integral domains, thereby broadening the dimensionality of statistical signal modeling. It offers both computational efficiency and robustness, making it well-suited as a lightweight preprocessing module for resource-constrained embedded systems.

This work addresses the statistical modeling and application of spectrogram zeros of noisy signals. Specifically, it investigates the random point process formed by spectrogram zeros in the complex plane—a fundamental object in time-frequency analysis—and establishes, for the first time, a rigorous theoretical connection between these zeros and those of Gaussian analytic functions, thereby bridging time-frequency analysis, random analytic function theory, and spatial point process theory. Building upon this foundation, we develop a statistically principled model for zero-point distributions and design novel signal detection and adaptive denoising algorithms grounded in spatial statistical inference. The proposed methods enjoy strong theoretical guarantees—including consistency and asymptotic optimality—and demonstrate robustness and interpretability even at low signal-to-noise ratios. By recasting time-frequency signal processing through the lens of stochastic geometry and random zero sets, this work introduces a new paradigm for analyzing and processing nonstationary signals in the time-frequency domain.

This study addresses the challenge of effectively uncovering hidden patterns in non-uniform image data by proposing an enhanced approach based on the inverse-square mean shift algorithm, extended to accommodate heterogeneous data distributions. The method integrates three-dimensional fast Fourier transform (3D FFT) to explore latent structures in the image frequency domain, jointly leveraging spatial distribution priors and spectral features. This synergistic strategy enables the detection of subtle patterns that are typically missed by conventional techniques. Experimental results demonstrate the effectiveness and robustness of the proposed framework on non-uniform image datasets, offering a novel perspective for analyzing complex image structures through frequency-domain representations.

This work addresses the challenge of achieving real-time performance, high accuracy, and energy efficiency in embedded vision systems operating on resource-constrained hardware. The authors propose an algorithm-hardware co-design methodology tailored for DSP/FPGA platforms, optimizing edge, corner, and blob detection operators through hardware-aware algorithmic refinements and quantization techniques. To further enhance throughput without compromising image quality, the approach incorporates inter-frame redundancy elimination and adaptive frame averaging strategies. Experimental results demonstrate that, compared to conventional solutions, the proposed method delivers significantly improved processing speed and energy efficiency, enabling scalable and highly effective real-time embedded vision across diverse applications such as automotive systems, surveillance, and robotics.

This study addresses the challenge of detecting and classifying echolocation click signals from marine mammals in complex underwater environments, where low signal-to-noise ratios and reverberation severely degrade performance. To overcome the limitations of conventional short-time Fourier transform–based spectrograms, the authors propose a time–frequency image representation derived from the continuous wavelet transform, which offers improved resolution across both high and low frequency bands. Building on this representation, they introduce CLICK-SPOT, an end-to-end image-based object detection model tailored for click signal identification. Experimental results on a real-world dataset of Norwegian killer whale recordings demonstrate that the proposed approach significantly outperforms traditional spectrogram-based methods, achieving markedly higher accuracy and robustness in detecting and classifying clicks under low signal-to-noise conditions.

This work addresses the lack of a unified theoretical foundation for classical and modern signal transforms, which has hindered systematic understanding and automated selection. By leveraging group representation theory, the authors unify a broad class of transforms—including the DFT, DCT, Walsh–Hadamard, Haar wavelets, KLT, spherical harmonics, and fractional Fourier transform—as eigenbases of covariance matrices that are covariant under specific group actions. Central to this framework is the identification of a “matching group” that leaves the signal covariance invariant, combined with the Peter–Weyl theorem and the Algebraic Diversity (AD) formalism. The study further introduces a novel, data-driven polynomial-time algorithm to automatically discover the optimal matching group without expert intervention, enabling automatic transform selection. This approach naturally extends to cutting-edge domains such as massive MIMO systems, graph neural networks, and Transformer attention mechanisms.

This work addresses the lack of effective online experimental platforms in signal processing education and engineering talent development. To bridge this gap, the authors developed and have continuously refined J-DSP, a web-based simulation environment that pioneered the migration of the original Java-based DSP toolkit to an HTML5 architecture, enabling cross-platform— including mobile—accessibility. The platform integrates advanced topics such as digital filter design, FFT-based spectral analysis, machine learning for signal classification, and quantum Fourier transform. Having operated reliably for 25 years, J-DSP has been widely adopted in university courses and National Science Foundation–funded programs, including REU, IRES, and RET initiatives, significantly advancing the modernization of signal processing pedagogy and fostering STEM workforce development.