Based on the Arnoldi method, we propose a simpler generalized minimal residual based iterative back propagation algorithm for polynomial nonlinear models (SGMR‑IBP). In contrast to the existing generalized minimal residual based iterative back propagation (GMR-IBP) algorithm, our new algorithm eliminates the need for Givens transformations and eigenvalue calculations. In addition, we introduce an adaptive step-size updating rule to strengthen the robustness and universality of the identification algorithm. Theoretical analysis is provided to prove the feasibility of the proposed SGMR-IBP, and two numerical examples are carried out to verify its effectiveness.
Automatic speech recognition (ASR) for dysarthric speech remains challenging due to substantial variability in speech articulation across speakers. While Low-Rank Adaptation (LoRA) offers excellent parameter efficiency, its fixed-rank configuration struggles to accommodate the wide spectrum of empirical fitting complexities. This limitation restricts the model’s capacity to capture complex, layer-wise feature variations under severe acoustic variations. To address this, we present Differentiated Expert Ensemble with Gradient-driven Adaptive Low-Rank Adaptation (DEEGoRA), a two-stage dual-expert training framework tailored to dysarthric speech. In the first stage, a gradient-driven static rank allocation strategy is integrated with loss-aware data partitioning to pre-allocate computational capacity, promoting expert specialization. In the second stage, lightweight collaborative scaling factors are introduced to fuse expert outputs, enabling the model to capture both universal phonetic patterns and speaker-specific pathological deviations. Experimental results on the CDSD, MDSC, and MSDM datasets demonstrate that DEEGoRA consistently outperforms all evaluated parameter-efficient fine-tuning (PEFT) baselines. Specifically, it achieves relative character error rate (CER) reductions of 5.7
Sliding Mode Control (SMC) is widely used in nonlinear control due to its robustness against model uncertainties and external disturbances, fast dynamic response, and strong tracking performance. Despite these advantages, conventional SMC methods face challenges, particularly in the design of reaching laws, which often rely on discontinuous functions such as the sign function. These discontinuities can cause severe chattering, reducing control accuracy and potentially exciting high-frequency dynamics, while many traditional reaching laws also show limited convergence speed, restricting performance under rapidly changing conditions. To address these issues, this paper proposes a novel reaching law based on a logarithmic function, eliminating the need for the common smoothing techniques such as saturation or hyperbolic tangent functions, providing smooth and continuous control while maintaining robustness. The practical finite-time convergence of the proposed law is rigorously analyzed using the Padé approximation, a method not previously applied in reaching-law studies. Comparative evaluations with four state-of-the-art reaching laws demonstrate superior robustness, reduced chattering, and faster dynamic response. Simulation results on a representative nonlinear system under various operating conditions confirm that the logarithmic-based SMC strategy enhances performance, offering a practical and efficient solution for modern nonlinear control applications.
The increasing demand for high-sensitivity and low-power ultra-wideband (UWB) receiver integrated circuits (ICs) in medical and biomedical sensing applications necessitates the development of low-noise amplifiers (LNAs) with wide bandwidth, low noise figure (NF), and low power consumption. To address these requirements, this paper proposes a UWB low-noise amplifier employing a novel two-stage noise cancellation-noise compensation (NC-NC) technique. In the first stage, a current-mirror-assisted noise-cancellation network suppresses thermal noise in the conventional wideband common-gate/common-source (CG–CS) architecture without increasing power consumption. In the second stage, a noise-compensation current path is introduced to further reduce the common-source transistor's noise contribution while preserving the noise-cancellation condition. The proposed LNA is implemented in 28-nm CMOS technology and occupies a compact active area of 0.014 mm2. Post-layout simulation results demonstrate a 3-dB bandwidth of 7.81 GHz, covering 2.8–10.61 GHz, with an approximately flat gain of 16.34 dB. The amplifier achieves a minimum NF of 1.8 dB across the UWB band while consuming only 1.6 mW from a 1-V supply. The proposed NC–NC technique effectively improves the noise performance without compromising gain or linearity, providing an attractive solution for low-power, high-sensitivity UWB medical receiver front-ends.
Sound event detection (SED) requires simultaneous event classification and precise temporal localization. Existing methods face three orthogonal challenges: frequency‑axis translation equivariance in standard convolution, difficulty in capturing events of vastly different durations, and imbalance between local and long‑range dependency modeling in recurrent or Transformer models. Additionally, strong label scarcity limits generalization. To address these issues, we propose a convolutional recurrent neural network (CRNN)‑based model, Slide‑ATST‑FDy‑Hybrid‑SED (Audio Teacher‑Student Transformer, Frequency Dynamic Convolution, and Hybrid‑minGRU), with four synergistic innovations: an ATST frame‑wise module providing 40‑ms resolution features; frequency dynamic convolution that adapts kernel weights to frequency positions, breaking the physically invalid equivariance; a slide‑window module fusing global and local temporal contexts for multi‑scale perception; and a hybrid‑minGRU module that parallelizes lightweight minGRU with self‑attention to capture both local patterns and long‑range dependencies efficiently. A mean teacher semi‑supervised framework with two‑stage training leverages weakly labeled and unlabeled data. On the DESED 2023 Task 4 dataset, our model achieves an Event‑based F1 of 0.636, PSDS1 and PSDS2 (Polyphonic Sound Detection Score) of 0.589 and 0.824—relative improvements of 46.9
This paper presents a floating three-terminal CFOA-based memtransistor emulator and investigates its suitability for artificial synaptic plasticity applications. The proposed three-terminal emulator is realized using six current-feedback operational amplifiers (CFOAs), one analog multiplier, eight resistors, and a capacitor, and is formulated to reproduce gate-controlled, memory-dependent drain–source behavior under a near-zero gate-leakage assumption. A theoretical analysis is developed from the constitutive mem-element framework to derive the terminal relations of the emulator and to clarify the role of the internal state path in establishing memory-dependent conductance. PSpice simulations show that the proposed circuit exhibits the pinched hysteresis behavior expected from a memtransistor emulator. To demonstrate functional relevance beyond terminal characterization, the proposed emulator is further employed in an artificial synaptic plasticity application.
Nowadays, the diffusion algorithms have been widely used in processing large-scale data because of the advantage of parameter estimation of multiple nodes at different locations. Considering the poor convergence of existing diffusion algorithms in the errors-in-variables (EIV) model containing generalized Gaussian noise, the diffusion generalized maximum total correntropy (DGMTC) algorithm is proposed in this paper. This algorithm significantly enhances the robustness against generalized Gaussian noise by introducing the generalized maximum correntropy criterion (GMCC) into the diffusion-based distributed adaptive estimation utilizing gradient-descent total least-squares (DGDTLS). In addition, the DGMTC algorithm is analyzed for local mean stability and steady state mean square performance. Finally, the excellence of the DGMTC algorithm compared with other algorithms and the correctness of the theoretical analysis are demonstrated by simulation.
The development of the discrete fractional Hankel transform (DFRHT) depends fundamentally on the generation of orthonormal eigenvectors of the symmetric kernel matrix of the discrete Hankel transform (DHT). For the DFRHT to approximate its continuous counterpart, namely, the fractional Hankel transform (FRHT), the eigenvectors in question should approximate samples of the eigenfunctions of the Hankel transform (HT), which are the products of generalized Laguerre polynomials, a Gaussian function, and a power function. Consequently, the target eigenvectors will have the descriptor “Laguerre-Gaussian-power-like (LGPL)”. The recently developed techniques for generating optimal eigenvectors of any unitary kernel matrix can be classified as either indirect (in the sense of first generating initial eigenvectors as a prerequisite for generating the final optimal ones) or direct (in the sense of not requiring initial eigenvectors). Moreover, the direct techniques can be viewed as either batch (in the sense of generating a partition of the modal matrix corresponding to a single distinct eigenvalue as a whole) or sequential (in the sense of generating the columns of the partition one after another). The present paper aims to assess the performance of the recently developed direct batch and sequential generation algorithms of optimal orthonormal eigenvectors when applied to the real symmetric kernel matrix of the DHT. This research endeavor has not been undertaken before. The assessment will demonstrate the relative merits of each algorithm.
Nighttime image flare removal is a challenging task critical for autonomous driving and surveillance. Unlike global degradations, flare exhibits locality, diversity, and nonuniformity, with halos, streaks, and diffuse artifacts often co-occurring and coupling with background scenes. Existing methods suffer from incomplete removal and noticeable artifacts. To address these issues, this paper proposes a Multi-Scale Spatial-Frequency Synergistic Network (MSFNet), which adopts an explicit parallel architecture. A spatial-frequency synergistic module adaptively separates low-frequency halos from high-frequency details, enabling synergistic fusion with spatial features. A differential gated fusion module, driven by feature differences, integrates shallow details and deep semantics via spatial gating and channel attention, effectively suppressing artifacts. Extensive experiments on Flare7K++ validate that MSFNet achieves a PSNR of 27.71 dB on real-world images and 31.33 dB on synthetic images, outperforming the next-best method by 0.12 dB and 0.39 dB, respectively. It surpasses state-of-the-art algorithms in terms of both global and local metrics, which demonstrates the effectiveness of spatial-frequency synergistic learning for flare removal and provides a new technical pathway for nighttime image restoration.
Underwater image enhancement is complicated by spatially uneven optical degradation. Current methods often struggle to reconcile the dual goals of restoring local textures and removing distant haze within a single global mapping. To address this, we propose a depth-guided dual-stream network that explicitly decouples the enhancement process. Rather than applying uniform processing, our framework utilizes depth priors to extract pure geometric contexts and generate soft attention masks via a feature refinement mechanism, explicitly differentiating between foreground and background regions. Adopting a divide-and-conquer strategy, one stream adaptively restores high-frequency details in the near field, while the other employs physical transmission cues to correct non-linear scattering and color distortion in the far field. Furthermore, to rectify global color casts, we introduce an illumination-color refinement stage that embeds a color constancy mechanism during feature decoding to simulate the stability of human vision. Experiments across multiple datasets demonstrate superior performance in both quantitative metrics and visual quality, effectively restoring natural color balance and vivid details. Additionally, such high-quality restoration provides a solid foundation for downstream visual applications. This provides compelling evidence that explicitly decoupling spatial heterogeneity via depth priors is an effective strategy for addressing complex underwater image degradation.
This paper investigates the qualitative behavior of semilinear Hilfer-nabla fractional discrete time systems characterized by inherent memory effects. The considered model employs the Hilfer-type fractional difference operator, which interpolates between the Riemann-Liouville and Caputo nabla operators and provides additional flexibility through the order and type parameters. By transforming the system into an equivalent fractional Volterra summation equation, an explicit representation of the solution is obtained via discrete multi-parameter Mittag-Leffler functions using the Picard’s successive approximation method. Based on this representation, sufficient conditions ensuring the existence, uniqueness, finite-time stability, and attractive stability of the system are derived. Finally, two numerical examples are presented to validate the theoretical findings and illustrate the effectiveness of the proposed stability criteria.
In this paper, an adaptive correlated Gaussian approximation filter (A-CGAF) is introduced for nonlinear state estimation problems when there are correlated noises of process and measurement. Classical statistics-fixed filtering techniques have to rely on a priori knowledge of static noise correlation; otherwise, their performance will be degraded. This paper seeks to improve the problem by developing a new algorithm that takes advantage of a neural network to estimate the noise cross-correlation in an online fashion based on a correlated Gaussian approximation filter structure. The effectiveness of the approach is demonstrated through simulations conducted under static, sudden, and gradually changing noise cross-correlation environments. Simulation results show that the proposed A-CGAF has superior estimation accuracy, robustness, and fast convergence performance compared with the fixed statistics-based competitors. More specifically, it delivers a 20.43
In this paper, we introduce the working of signals, systems, and signal processing on totally weighted graphs. We discuss some real-world systems on graphs by considering sensing points as vertices, connection between sensing points as edges, assigning signal values as vertex weight, measure of connection between vertices as edge weight, and try to ensure the efficiency of the system by assigning a signal condition on each vertex. By suggesting a matrix representation for this system, we try to connect the matrix energy as a measure of the energy of the signal and system. In addition, signal processing is represented on totally weighted graphs considering a different type of edge weight. Also, discussed are the sampling of analog signal and representation of the digitalized signal on totally weighted graph. We also consider the energy of signal, system, and signal processing matrix as a measure of efficiency.
The growing deployment of unmanned aerial vehicles in sensitive fields such as military operations demands high levels of image security and authentication. To address the increasing risk of tampering, this study proposes a robust watermarking and encryption framework designed to ensure both the integrity and confidentiality of unmanned aerial vehicles images. The proposed hybrid technique integrates non-subsampled shearlet transform, QR decomposition and multi-level singular value decomposition for robust and imperceptible watermark embedding. To further secure the data, double random phase encoding is applied for encryption, followed by compression using compressive sensing to enable efficient storage and transmission. The framework is evaluated using standard image quality and security metrics under various attack conditions. Experimental results demonstrate that the method preserves the visual quality of unmanned aerial vehicles images after decryption and offers strong resilience to common signal processing and noise attacks. The watermark remains reliably detectable, with minimal loss of image fidelity. The proposed framework ensures secure transmission and authentication of unmanned aerial vehicles images while maintaining high image quality. Its layered architecture shows significant potential for deployment in real-time unmanned aerial vehicles—based applications, especially where security and data integrity are critical.
Robust adaptive filtering is a key technique for sparse system identification. However, traditional algorithms struggle to balance robustness, convergence speed, and steady-state accuracy under impulsive and heavy-tailed noise. The logarithmic Student’s t-based filtering method offers good robustness to impulsive noise due to its heavy-tailed error modeling, but its fixed zero-centered symmetric assumption introduces steady-state bias under nonzero-mean noise and does not fully leverage the system’s sparsity prior. To address this, we propose the Logarithmic Student’s t-based Generalized Maximum Variable-Center Correntropy (LSGMVCC) algorithm, which dynamically adjusts the error distribution center using a sliding-window median to compensate for nonzero-mean bias. Additionally, we introduce a sparsity-enhanced variant, SN-LSGMVCC, incorporating a sparsity-aware regularization term for improved convergence and steady-state accuracy in sparse system identification. Simulation results demonstrate that the proposed algorithms achieve faster convergence and lower steady-state mean square deviation under various non-Gaussian noise environments, significantly enhancing the robust identification performance of sparse systems.
Digital signal processors predominantly use Carry Select Adder (CSLA) arithmetic units. This paper presents the design and implementation of multiply and accumulate (MAC) units through five proposed multistage hybrid CSLAs to reduce computational complexity and to improve performance metrics. Five different CSLA structures are proposed with novel power, area optimized carry generation and propagation blocks, i.e., Proposed Block-1 and Proposed Block-2. Based on these optimized blocks, two multistage square-root (SQRT)-based CSLAs, two SQRT-based hybrid CSLAs with fast add-on multiplexing (FAM), and one combined hybrid CSLA are developed. The proposed architectures are functionally verified using a System Verilog test bench in Xilinx-Vivado and implemented in the Cadence system design tool using 90 nm technology to obtain performance parameters such as area, delay, and power. In comparison with the existing state-of-the-art CSLA architectures, Proposed-I, III, and V achieved area reductions of up to 35–53
Maximum-likelihood (ML) direction-of-arrival (DOA) estimation for vector hydrophone arrays provides strong statistical performance. However, multidimensional nonlinear search still causes a heavy computational burden. To address this problem, this paper proposes an ML-DOA estimation method based on an improved velvet worm optimization (IVWO) algorithm. The original velvet worm optimization (VWO) algorithm balances global exploration and local exploitation, but its direct application to complex ML-DOA objective functions exhibits insufficient initial search coverage, an abrupt exploration–exploitation transition, and limited late-stage local search. Accordingly, IVWO introduces randomized Hammersley initialization, proposes an iteration-dependent three-stage smooth quota allocation strategy, and incorporates logarithmic-spiral local search to improve initial coverage, transition smoothness, and local search, respectively. Simulations under different SNRs, numbers of snapshots, population sizes, and numbers of sources show that IVWO-ML achieves faster convergence than the Improved Secretary Bird Optimization Algorithm (IMSBOA), Improved Invasive Weed Optimization (IIWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), the original VWO, and Grey Wolf Optimizer (GWO), while also attaining lower RMSE and better run-to-run stability, especially in closely spaced multi-source scenarios. Compared with the traditional Multiple Signal Classification (MUSIC) and Alternating Projection Maximum Likelihood (AP-ML) baselines, IVWO-ML also provides higher estimation accuracy and stronger robustness under low-SNR and closely spaced multi-source scenarios.
In supervised speaker diarization, speaker misclassification errors remain a significant challenge despite advances in embedding representations and deep learning architectures. This work introduces a voting-based centroid–distance post-processing framework that refines diarization outputs by reassigning ambiguous speaker embeddings through consensus across multiple distance measures. Specifically, Mahalanobis, geodesic, fast dynamic time warping (DTW), and Euclidean distance measures are used to determine cluster affiliation, enabling more reliable correction of misclassified segments. The proposed approach is evaluated using ECAPA-TDNN and x-vector embeddings in conjunction with CNN, Bi-LSTM, and Conformer models, with ECAPA-TDNN consistently outperforming x-vectors across all configurations. On the CallHome dataset, agglomerative hierarchical clustering (AHC) yields a diarization error rate (DER) of 29.28
Accurate direction of arrival (DOA) estimation in impulsive noise remains a critical challenge in array signal processing. To address this issue, this paper proposes TSUC-Net, a novel two-stage deep learning framework that integrates a U-Net architecture with a convolutional neural network (CNN). In the first stage, a U-Net-based denoising network is designed with a learnable complex soft-clipping (LCSC) module placed at the input front-end. Unlike fixed nonlinear transformations, the LCSC module adaptively suppresses alpha-stable distributed impulsive noise by learning an optimal amplitude threshold while preserving phase information critical for spatial processing. Since the LCSC limiter’s parameters are fully adaptively learned, the burden of parameter tuning associated with traditional methods is eliminated. The denoised signals are then fed into the second-stage CNN for robust DOA estimation. Simulation results demonstrate that the proposed framework outperforms existing algorithms, including traditional subspace methods and recently proposed nonlinear transformation-based techniques, in terms of accuracy and generalization capability under various impulsive noise conditions.
Reconstructing a signal from a magnitude-only short-time Fourier transform (STFT) remains challenging because missing phase information introduces audible and structural artifacts. This paper proposes Time-domain Enhanced Spectrogram Alignment (TESA), a gradient-based framework that directly optimizes temporal samples so that the STFT magnitude of the reconstruction matches a specified target spectrogram. Unlike Griffin–Lim that updates phase implicitly through alternating projections between time and time–frequency domains, TESA performs sample-level optimization using a closed-form gradient that maps magnitude mismatch to an inverse STFT-based update, and employs adaptive moment estimation optimizer to stabilize and accelerate convergence. We conduct two experiments under controlled oracle conditions by providing the clean target spectrogram to isolate phase-retrieval capability from magnitude estimation errors. We additionally report performance when the target spectrogram is estimated from the observation to quantify practical impact. Across denoising and source separation benchmarks, TESA improves reconstruction quality relative to representative baselines (Griffin–Lim, optimal-transport alignment, deep neural network, and alternating direction method of multipliers), yielding average gains of 1.94 dB signal-to-noise ratio and 3.86 dB signal-to-distortion ratio under oracle condition, with consistent advantages in time–frequency mean square error. Runtime–resolution analysis further shows that TESA supports reduced execution time through lower-resolution STFT settings and fewer iterations. These results indicate that TESA provides a flexible optimization framework for magnitude-constrained reconstruction, with performance that remains competitive when moving from oracle to non-oracle evaluations.
