Search Results (151)

Time-series clustering is a prominent research area with extensive practical applications. Given the complexity and diversity of modern time-series data, this study proposes a novel time-series clustering method based on inter-network heterogeneity. First, each time-series is converted into a network by using two types of time-series segmentation techniques. Second, an inter-network clustering approach based on graph spectral theory is introduced: we calculate the total variation (TV) distance between the empirical spectral distributions of each network and identify distinct clusters using a hierarchical clustering algorithm. From the perspective of symmetry, networks constructed from similar time-series tend to exhibit comparable spectral structures, which reflect the underlying structural symmetries of their dynamics. Differences in spectral distributions correspond to symmetry breaking among networks, providing an effective mechanism for distinguishing heterogeneous time-series patterns. Our method effectively preserves more distinctive features inherent in the original time-series. To evaluate the performance of the proposed method, simulation studies are conducted, including the recognition of both stationary and non-stationary sequences. The method also performs well on real-world datasets, such as stock closing prices. These results demonstrate that our approach can handle non-stationary sequences and identify the intrinsic correlations in time-series. Full article

Volumetric modulated arc therapy-computed tomography (VMAT-CT), which is the CT reconstructed using the portal images collected during VMAT, can potentially be an effective onsite imaging tool. The goal of this study was to propose an iterative reconstruction algorithm that can further improve the image quality of VMAT-CT and reduce the number of failed reconstructions. An iterative algorithm combining total variation (TV) with block-matching and 3D filtering (BM3D) was proposed, addressing the L1-L2 regularization problem using the split Bregman method. We collected portal images from 67 VMAT cases including 50 phantom and 17 real-patient cases. Both Feldkamp–Davis–Kress (FDK) and TV-BM3D iterative algorithms were used to reconstruct VMAT-CT using the collected images. The preprocessing methods developed by our group previously were also used in this study. A total of 48 out of 50 phantom cases and 15 out of 17 real-patient cases were successfully reconstructed using the iterative algorithm together with image preprocessing. In contrast, 39 phantom cases and 8 patient cases could be reconstructed using the original FDK algorithm, and 44 phantom cases and 11 patient cases could be reconstructed using the FDK algorithm together with preprocessing. Compared with the FDK algorithm, the TV-BM3D iterative algorithm significantly improved the image quality of VMAT-CT at all treatment sites. To the best of our knowledge, this study is the first to develop an iterative VMAT-CT reconstruction algorithm. It can be used to reconstruct CT images locally, and is superior to FDK-based algorithms in terms of the success rate and reconstructed image quality. This strongly supports the use of VMAT-CT as a promising imaging tool for treatment monitoring and adaptive radiotherapy. Full article

Metal artifact reduction (MAR) remains a long-standing challenge in computed tomography (CT) reconstruction. Metallic implants introduce inconsistencies between the acquired projection data and the ideal Radon transform, resulting in severe streaking artifacts in images reconstructed using the conventional filtered back projection (FBP) algorithm. In this work, we propose a nonlinear weighted anisotropic total variation (NWATV) regularization method to mitigate metal artifacts and improve CT image quality. The effectiveness of the NWATV method is evaluated through three experiments, and the results demonstrate that it achieves superior reconstruction performance compared to the conventional linear interpolation method, the normalized metal artifact reduction method and the anisotropic total variation (TV) regularization method. Full article

Total variation (TV)-based image denoising effectively suppresses noise while preserving edges, but it often introduces staircase artifacts in flat regions. To address this limitation, we propose a novel denoising model that combines adaptive fractional-order total variation with a minimax-concave (MC) penalty in the regularization term. The adaptive fractional-order TV alleviates staircase effects in homogeneous areas while preserving fine details in textured regions. The MC penalty provides a more accurate estimation of image sparsity, improving restoration fidelity compared to traditional L1-based regularization. The resulting model, termed AFTVMC, is efficiently solved using an alternating direction method of multipliers (ADMM). Extensive numerical experiments on synthetic and natural images demonstrate that AFTVMC outperforms classical TV, higher-order LLT, adaptive ATV, and state-of-the-art MCFOTV models in both objective metrics—peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)—and subjective visual quality, particularly in suppressing staircase artifacts and preserving complex texture details. Full article

To address the challenges of strong electromechanical coupling, nonlinear friction, and poor disturbance rejection in semi-direct-drive scraper conveyor systems under complex coal mining conditions, this paper aims to propose a high-performance drive control strategy that balances dynamic response speed with steady-state operational smoothness. First, an integrated electromechanical coupling dynamic model incorporating Permanent Magnet Synchronous Motor (PMSM) vector control and the time-varying meshing stiffness of a two-stage planetary gear train is established. Subsequently, a Sliding Mode Control (SMC) strategy optimized with a saturation boundary layer is designed and compared with traditional Proportional-Integral (PI) control under multiple operating conditions. Time-frequency domain analysis indicates that SMC significantly enhances the dynamic stiffness of the drive system. Under sudden load change conditions, the speed recovery time is shortened by approximately 76%, and the steady-state error is reduced by 37% compared to PI control. Microscopic characteristic evaluation based on FFT and Total Variation (TV) metrics reveals that SMC achieves active disturbance rejection through spectral broadening of the electromagnetic torque. Crucially, the steady-state cumulative control effort of SMC is equivalent to that of PI, implying no additional mechanical stress burden, while the equivalent dynamic transmission force fluctuation in the mechanical chain is reduced by about 3%. The study confirms that the proposed strategy successfully achieves a synergistic optimization of “macroscopic rapid response” and “microscopic smooth operation,” providing a theoretical basis for the high-precision control of heavy-duty underground transmission equipment. Full article

Bell–CHSH is an inequality about unconditional expectations: under measurement independence, Bell locality, and bounded outcomes, the CHSH value satisfies $S\le 2$. Experimental correlators, however, are often computed on an accepted subset of trials defined by detection logic, coincidence matching, quality cuts, and analysis windows. We model this by an acceptance probability $\gamma (a,b,\lambda )\in [0,1]$ and the resulting accepted hidden-variable law ${\nu }_{ab}$ obtained by weighting the measurement-independent prior $\rho$ by $\gamma$ and renormalizing. If ${\nu }_{ab}$ depends on the setting pair then the four correlators entering CHSH are expectations under four different measures, and a Bell-local measurement-independent model can yield $S_{\mathrm{obs}}>2$ by selection alone. We quantify the required setting dependence in total variation (TV) distance. For any reference law $\mu$ we prove the sharp bound $S_{\mathrm{obs}}\le 2+2{\sum }_{q\in Q}\mathrm{TV}({\nu }_q,\mu )$ for a CHSH quartet Q. Optimizing over $\mu$ yields the intrinsic dispersion bound $S_{\mathrm{obs}}\le 2+2{\Delta }_Q$, and, in particular, $S_{\mathrm{obs}}\le min\{4,2+6D_Q\}$, where $D_Q$ is the quartet TV diameter. The constants are optimal. Consequently, reproducing Tsirelson’s value $2\sqrt{2}$ within Bell-local measurement-independent models via setting-dependent acceptance requires ${\Delta }_Q\ge \sqrt{2}-1$ (hence, $D_Q\ge (\sqrt{2}-1)/3$). We then propose a two-lane experimental audit protocol: (i) prior-relative fair-sampling diagnostics using tags recorded on all trials, and (ii) prior-free dispersion diagnostics using accepted-tag distributions across settings, with ${\Delta }_{Q,X}$ computable by linear programming on finite tag alphabets. Full article

Emission tomography, including single-photon emission computed tomography (SPECT), requires image reconstruction from noisy and incomplete projection data. The maximum-likelihood expectation maximization (MLEM) algorithm is widely used due to its statistical foundation and non-negativity preservation, but it is highly sensitive to noise, particularly in low-count conditions. Although total variation (TV) regularization can reduce noise, it often oversmooths structural details and requires careful parameter tuning. We propose a Graph-Enhanced Expectation Maximization (GREM) algorithm that incorporates graph-based neighborhood information into an MLEM-type multiplicative reconstruction scheme. The method is motivated by a penalized formulation combining a Kullback–Leibler divergence term with a graph Laplacian regularization term, promoting local structural consistency while preserving edges. The resulting update retains the multiplicative structure of MLEM and preserves the non-negativity of the image estimates. Numerical experiments using synthetic phantoms under multiple noise levels, as well as clinical ^99mTc-GSA liver SPECT data, demonstrate that GREM consistently outperforms conventional MLEM and TV-regularized MLEM in terms of PSNR and MS-SSIM. These results indicate that GREM provides an effective and practical approach for edge-preserving noise suppression in emission tomography without relying on external training data. Full article

Conventional total variation (TV) regularization methods based on Laplacian or fixed-scale Hyper-Laplacian priors impose uniform sparsity penalties on gradients. These uniform penalties fail to capture the heterogeneous sparsity characteristics across different regions and directions, often leading to the over-smoothing of edges and loss of fine details. To address this limitation, we propose a novel regularization Hyper-Laplacian Adaptive Weighted Total Variation (HLAWTV). The proposed regularization employs a proportional mixture of Hyper-Laplacian distributions to dynamically adapt the sparsity decay rate based on image structure. Simultaneously, the adaptive weights can be adjusted based on local gradient statistics and exhibit strong robustness in texture preservation when facing different datasets and noise. Then, we propose an hyperspectral image (HSI) denoising method based on the HLAWTV regularizer. Extensive experiments on both synthetic and real hyperspectral datasets demonstrate that our denoising method consistently outperforms state-of-the-art methods in terms of quantitative metrics and visual quality. Moreover, incorporating our adaptive weighting mechanism into existing TV-based models yields significant performance gains, confirming the generality and robustness of the proposed approach. Full article

Satellite videos are widely applied for large-scale surveillance. Existing low-rank matrix decomposition-based methods produce promising results under simple and stationary backgrounds. However, these methods suffer a severe performance drop on satellite videos with complex and dynamic backgrounds. To address these challenges, we propose a matrix-based total variation regularized robust PCA (TV-RPCA) approach for moving-vehicle detection. Specifically, our TV-RPCA uses the partial sum of singular values to model the low-rank background. Moreover, a ${\mathit{\ell }}_p$ norm and a spatial–temporal TV regularization are adopted to encourage the spatial–temporal continuity of foregrounds. The optimization of our TV-RPCA is carried out using the augmented Lagrangian multiplier framework combined with the alternating direction minimization approach. Comprehensive experiments conducted on SkySat and Jilin-1 video data verify the effectiveness of the proposed approach. Full article

In infrared detectors, the readout circuits usually cause horizontal or vertical streak noise, whereas the infrared focal plane arrays experience triangular nonuniform fixed-pattern noise. In addition, imaging devices suffer from optically relevant fixed-pattern noise owing to the temperature. When the infrared camera is in orbit, it is affected by the photon effect, temperature change, and time drift. This makes the nonuniformity correction coefficients pertaining to the ground no longer applicable, resulting in the degradation of the nonuniformity correction effect. The existing methods are not fully applicable to triangular fixed-pattern noise or the fixed-pattern noise caused by detector optics. To address this situation, this paper proposes a nonuniformity correction method, namely infrared image sequences based on the optimization of $L_{2,1}$ group sparsity in the spatiotemporal domain. We established a nonuniformity correction model of differential operators in the spatiotemporal domain for infrared image sequences by applying the time-domain differential operator constraints to the images to denoise the image. This enables the adaptive correction of the nonuniformity of the above types of noise. We demonstrate that the proposed method is effective for triangular nonuniform and optically induced fixed-pattern noises. The proposed method was extensively evaluated using publicly available datasets and datasets containing image sequences of different scenes captured by a high-resolution infrared camera of the Qilu-2 satellite. The method has high robustness and good processing results with effective ghost suppression and significant reduction of nonuniform noise. Full article

Sparsity-driven regularization has undergone significant development in single-image restoration, particularly with the transition from handcrafted priors to trainable deep architectures. In this work, a geometric prior-enhanced deep image prior (DIP) framework, termed DIP-MC, is proposed that integrates mean curvature (MC) regularization to promote natural smoothness and structural coherence in reconstructed images. To strengthen the representational capacity of DIP, a self-attention module is incorporated between the encoder and decoder, enabling the network to capture long-range dependencies and preserve fine-scale textures. In contrast to total variation (TV), which frequently produces piecewise-constant artifacts and staircasing, MC regularization leverages curvature information, resulting in smoother transitions while maintaining sharp structural boundaries. DIP-MC is evaluated on standard grayscale and color image denoising and deblurring tasks using benchmark datasets including BSD68, Classic5, LIVE1, Set5, Set12, Set14, and the Levin dataset. Quantitative performance is assessed using peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) metrics. Experimental results demonstrate that DIP-MC consistently outperformed the DIP-TV baseline with 26.49 PSNR and 0.9 SSIM. It achieved competitive performance relative to BM3D and EPLL models with 28.6 PSNR and 0.87 SSIM while producing visually more natural reconstructions with improved detail fidelity. Furthermore, the learning dynamics of DIP-MC are analyzed by examining update-cost behavior during optimization, visualizing the best-performing network weights, and monitoring PSNR and SSIM progression across training epochs. These evaluations indicate that DIP-MC exhibits superior stability and convergence characteristics. Overall, DIP-MC establishes itself as a robust, scalable, and geometrically informed framework for high-quality single-image restoration. Full article

In this study, we introduce a novel adaptive fault-tolerant sliding mode control strategy for the finite-time control of symmetric robotic manipulators subjected to uncertainties, disturbances and actuator failures. Firstly, we design a novel type of sliding mode manifold termed Practical Fast Terminal Sliding Mode (P-FTSM). P-FTSM exhibits the capability to accelerate convergence speed while ensuring the finite-time convergence of the system. Subsequently, the P-FTSM is integrated with the super-twisting algorithm (STA) to mitigate the chattering of control input. Additionally, a novel ${\mathcal{K}}_{\infty }$ function is introduced to serve as the gain of the STA. This strategy, which does not require knowledge of the upper bound of the disturbance and fault information, ensures that the gain is tuned according to the disturbance and fault variations, mitigating the adverse effects of high gain and further weakening of the chattering. Simulation results on a two-link symmetric manipulator verify that the proposed method achieves outstanding quantitative performance. The proposed method achieves convergence times of 0.22 and 0.12 s for the joint errors, with root mean square errors (RMSE) of 0.036 and 0.095. The integral absolute errors (IAE) are 0.049 and 0.086, and the total control energy is 943.46. The total variations (TV) of the control signals are $2.86\times {10}^3$ and $1.64\times {10}^3$, indicating effectively suppressed chattering. Overall, the proposed strategy ensures high precision, rapid convergence, and strong fault-tolerant capability. Full article

Streak Tube Imaging Lidar (STIL) offers significant advantages in long-range sensing and ultrafast diagnostics by encoding spatial-temporal information as streaks, and hence decodes 3D images using tailored algorithm. However, under low-photon conditions that caused either long-range or reduced exposure time, the reconstructed image suffer from low contrast, strong noise and blurring, hindering the application in various scenarios. To address this challenge, we propose a Multi-Sparsity Constraints and Adaptive Regularization (MSC-AR) algorithm based on the Maximum a Posteriori (MAP) framework, which jointly denoises and deblurs degraded streak images and efficiently solved using the Alternating Direction Method of Multipliers (ADMM). MSC-AR considers gradient sparsity, intensity sparsity, and an adaptively weighted Total Variation (TV) regularization along the temporal dimension of the streak image which collaboratively optimizing image quality and structural detail, thus better 3D restoration results in low-photon conditions. Experimental results demonstrate that MSC-AR significantly outperforms existing approaches under low-photon conditions. At an exposure time of 300 ms, it achieves millimeter-level RMSE and over 88% SSIM in depth image reconstruction, while maintaining robustness and generalization across different reconstruction strategies and target types. Full article

Traditional X-ray fluorescence computed tomography (XFCT) suffers from issues such as low photon collection efficiency, slow data acquisition, severe noise interference, and poor imaging quality due to the limitations of mechanical collimation. This study proposes to design an X-ray fluorescence imaging system based on bilateral Compton cameras and to develop an optimized reconstruction algorithm to achieve high-quality 2D/3D imaging of low-concentration samples (0.2% gold nanoparticles). A system equipped with bilateral Compton cameras was designed, replacing mechanical collimation with “electronic collimation”. The traditional LM-MLEM algorithm was optimized through improvements in data preprocessing, system matrix construction, iterative processes, and post-processing, integrating methods such as Total Variation (TV) regularization (anisotropic TV included), filtering, wavelet-domain constraints, and isosurface rendering. Successful 2D and 3D reconstruction of 0.2% gold nanoparticles was achieved. Compared with traditional algorithms, improvements were observed in convergence, stability, speed, quality, and accuracy. The system exhibited high detection efficiency, angular resolution, and energy resolution. The Compton camera-based XFCT overcomes the limitations of traditional methods; the optimized algorithm enables low-noise imaging at ultra-low concentrations and has potential applications in early cancer diagnosis and material analysis. Full article

Objective: The accurate visualization of root canal systems on periapical radiographs is critical for successful endodontic treatment. This study aimed to evaluate and compare the effectiveness of several image enhancement algorithms—including a novel Total Variation–Contrast-Limited Adaptive Histogram Equalization (TV-CLAHE) technique—in improving the detectability of root canal configurations in mandibular incisors, using cone-beam computed tomography (CBCT) as the gold standard. A null hypothesis was tested, assuming that enhancement methods would not significantly improve root canal detection compared to original radiographs. Method: A retrospective analysis was conducted on 60 periapical radiographs of mandibular incisors, resulting in 420 images after applying seven enhancement techniques: Histogram Equalization (HE), Contrast-Limited Adaptive Histogram Equalization (CLAHE), CLAHE optimized with Pelican Optimization Algorithm (CLAHE-POA), Global CLAHE (G-CLAHE), k-Caputo Fractional Differential Operator (KCFDO), and the proposed TV-CLAHE. Four experienced observers (two radiologists and two dentists) independently assessed root canal visibility. Subjective evaluation was performed using an own scale inspired by a 5-point Likert scale, and the detection accuracy was compared to the CBCT findings. Quantitative metrics including Peak Signal-to-Noise Ratio (PSNR), Signal-to-Noise Ratio (SNR), image entropy, and Structural Similarity Index Measure (SSIM) were calculated to objectively assess image quality. Results: Root canal detection accuracy improved across all enhancement methods, with the proposed TV-CLAHE algorithm achieving the highest performance (93–98% accuracy), closely approaching CBCT-level visualization. G-CLAHE also showed substantial improvement (up to 92%). Statistical analysis confirmed significant inter-method differences (p < 0.001). TV-CLAHE outperformed all other techniques in subjective quality ratings and yielded superior SNR and entropy values. Conclusions: Advanced image enhancement methods, particularly TV-CLAHE, significantly improve root canal visibility in 2D radiographs and offer a practical, low-cost alternative to CBCT in routine dental diagnostics. These findings support the integration of optimized contrast enhancement techniques into endodontic imaging workflows to reduce the risk of missed canals and improve treatment outcomes. Full article