Computational Complexity Research Articles

A reduced vector model predictive controller for a three-level neutral point clamped inverter with common-mode voltage suppression

This paper presents a novel, state-of-the-art predictive control architecture that addresses the computational complexity and limitations of conventional predictive control methodologies while enhancing the performance efficacy of predictive control techniques applied to three-level voltage source converters (NPC inverters). This framework's main goal is to decrease the number of filtered voltage lifespan vectors in each sector, which will increase the overall efficiency of the control system and allow for common mode voltage reduction in three-level voltage source converters. Two particular tactics are described in order to accomplish this. First, a statistical approach is presented for the proactive detection of potential voltage vectors, with an emphasis on selecting and including the vectors that are most frequently used. This method lowers the computational load by limiting the search space needed to find the best voltage vectors. Then, using statistical analysis, a plan is presented to split the sectors into two separate parts, so greatly limiting the number of voltage vectors. The goal of this improved predictive control methodology is to reduce computing demands and mitigate common mode voltage. The suggested strategy's resilience is confirmed in a range of operational scenarios using simulations and empirical evaluation. The findings indicate a pronounced enhancement in computational efficiency and a notable diminution in common mode voltage, thereby underscoring the efficacy of the proposed methodology. This increases their ability to incorporate renewable energy sources into the electrical grid.

Impact of phase selection on accuracy and scalability in calculating distributed energy resources hosting capacity

Hosting capacity (HC) and dynamic operating envelopes (DOEs), defined as dynamic, time-varying HC, are calculated using three-phase optimal power flow (OPF) formulations. Due to the computational complexity of such optimisation problems, HC and DOE are often calculated by introducing certain assumptions and approximations, including the linearised OPF formulation, which we implement in the Python-based tool ppOPF. Furthermore, we investigate how assumptions of the distributed energy resource (DER) connection phase impact the objective function value and computational time in calculating HC and DOE in distribution networks of different sizes. The results are not unambiguous and show that it is not possible to determine the optimal connection phase without introducing binary variables since, no matter the case study, the highest objective function values are calculated with mixed integer OPF formulations. The difference is especially visible in a real-world low-voltage network in which the difference between different scenarios is up to 14 MW in a single day. However, binary variables make the problem computationally complex and increase computational time to several hours in the DOE calculation, even when the optimality gap different from zero is set.

Distributed credible evidence fusion with privacy-preserving

Considering data safety in more and more applied peer-to-peer networks, such as wireless sensor networks, has become the focus of information fusion, this paper proposes the problem of credible evidence fusion (CEF) in a distributed system with privacy-preserving, where agent’s raw evidence is shared only with authenticated neighbors while access or inference by non-neighbors is prevented. This privacy-preserving in a distributed style brings out two challenges never met in centralized CEF. One is about credibility calculation consensus (CCC), where the local evidence difference measure matrix (EDMM) faces elements missing for evidence unavailability to non-neighbors. The other is about privacy-preserving fusion consensus (PFC), because fusion based on raw evidence may lead to a counter-intuitive result. In CCC, missing elements in EDMM are recovered via low-rank matrix completion, and credibility consistency is further guaranteed via diffusion gradient descent. In PFC, a new measure named privacy iterative compensation term (PICT) is constructed based on credibility and discounted evidence so that raw evidence is impossibly inferred. Furthermore, the linear average consensus based on PICT is presented and proved to be converging to the centralized CEF if credibility is accurately given. The computational complexity is proportional to the cube of the number of agents and the square of the number of elements in the power set of the framework of discrimination. Simulation results demonstrate that the proposed method achieves credibility errors below 0.1, leading to fusion results and classification accuracy approaching those of centralized CEF, with a 30% improvement compared to RANSAC-based and COF-based methods.

Optimal Extreme Learning Machine for Diagnosing Brain Tumor Based on Modified Sailfish Optimizer

This study proposes a hierarchical automated methodology for detecting brain tumors in Magnetic Resonance Imaging (MRI), focusing on preprocessing images to improve quality and eliminate artifacts or noise. A modified Extreme Learning Machine is then used to diagnose brain tumors that are integrated with the Modified Sailfish optimizer to enhance its performance. The Modified Sailfish optimizer is a metaheuristic algorithm known for efficiently navigating optimization landscapes and enhancing convergence speed. Experiments were conducted using the “Whole Brain Atlas (WBA)” database, which contains annotated MRI images. The results showed superior efficiency in accurately detecting brain tumors from MRI images, demonstrating the potential of the method in enhancing accuracy and efficiency. The proposed method utilizes hierarchical methodology, preprocessing techniques, and optimization of the Extreme Learning Machine with the Modified Sailfish optimizer to improve accuracy rates and decrease the time needed for brain tumor diagnosis. The proposed method outperformed other methods in terms of accuracy, recall, specificity, precision, and F1 score in medical imaging diagnosis. It achieved the highest accuracy at 93.95%, with End/End and CNN attaining high values of 89.24% and 93.17%, respectively. The method also achieved a perfect score of 100% in recall, 91.38% in specificity, and 75.64% in F1 score. However, it is crucial to consider factors like computational complexity, dataset characteristics, and generalizability before evaluating the effectiveness of the method in medical imaging diagnosis. This approach has the potential to make substantial contributions to medical imaging and aid healthcare professionals in making prompt and precise treatment decisions for brain tumors.

AUV path planning in a three-dimensional marine environment based on a novel multiple swarm co-evolutionary algorithm

Autonomous underwater vehicles (AUVs) are rapidly advancing in ocean exploration. High-performance path planning techniques are essential for AUVs. Path planning for AUVs is a multi-faceted challenge, necessitating careful consideration of safety, energy consumption and the influence of sea currents to ensure the development of a high-quality trajectory that satisfies all operational criteria. Existing path planning algorithms have problems such as incomplete consideration of influencing factors, computational complexity and weak applicability. Therefore, we propose a swarm intelligence optimisation algorithm based on multiple swarm co-evolution (MCO) to address the issue of AUV path planning in a three-dimensional marine environment. First, a three-dimensional marine environment model and the corresponding path evaluation mechanism are established. Second, the MCO rule is established to ensure that the MCO has balanced exploration and exploitation capabilities. A shared dynamic optimal particle between populations is introduced to ensure information exchange between populations. In addition, the cross-integration mutation strategy has been proposed for promoting the fusion of two populations of superior genes to ensure the inheritance of superior paternal genes to the offspring. Finally, four comparison experiments are designed, and the experiments compare eight commonly used intelligent search algorithms and improved versions. The results of the experiments proved that the MCO has excellent three-dimensional marine environment path planning capability, with robustness and search capabilities superior to other swarm intelligence optimisation algorithms.

Robust adaptive learning framework for semi-supervised pattern classification

Hessian scatter regularized twin support vector machine (HSR-TSVM) employs hinge loss function and L2-distance metric, which makes it ineffective in dealing with outliers and noise data problems. Aiming to this problem, this paper a novel robust adaptive learning framework CL2,pHSR-TSVM is developed for semi-supervised classification tasks. In CL2,pHSR-TSVM, the generalized adaptive robust loss function Lδ(u) is first innovatively introduced to overcome the problem that hinge loss function is not sensitive to noise and outliers. Intuitively, Lδ(u) can improve the robustness of the model by selecting different robust loss functions for different learning tasks during the learning process via the adaptive parameter δ. Secondly, the robust distance metric capped L2,p-norm is introduced in CL2,pHSR-TSVM to reduce and eliminate the exaggerated influence of L2-distance metric on the learning process of outliers, especially when the outliers are far from the normal data distribution, by setting the appropriate parameters. Furthermore, to improve the computational efficiency of CL2,pHSR-TSVM, the fast CL2,pHSR-TSVM is presented for semi-supervised classification tasks. Finally, two effective algorithms are designed to solve our methods respectively, and the convergence and computational complexity are analyzed theoretically. Experimental results demonstrate the effectiveness and robustness of our methods.

DHBLSN: A diligent hierarchical broad learning system network for cogent polyp segmentation

In medical practice, polyp segmentation holds immense significance for early Colorectal Cancer diagnosis. Over the past decade, techniques based on Deep Learning (DL) have been used extensively for segmentation purposes, showcasing promising performance. However, the efficacy of DL methods comes with a trade-off as they require manipulating many parameters for better performance, which increases the computation cost and takes a large amount of training time. In this study, we unfold a new perspective on polyp segmentation based on a broad learning system (BLS) namely dHBLSN: A diligent Hierarchical Broad Learning System Network for Cogent Polyp Segmentation that does not require multiple layers for training which significantly reduces the computational cost. The proposed dHBLSN is characterized by multi-feature extraction and hierarchical structure. Firstly, the colonoscopy images were partitioned into non-overlapping patches, which was beneficial for extracting local spatial information. Subsequently, Multi-scale features namely “Single Iteration forward pass convolution (Sifp-conv)” feature and “2D Dual Tree-Complex Wavelet Transform (2D-DT-CWT)” feature were extracted from each patch and were used directly as the feature nodes and no explicit feature mapping was required, unlike other BLS variants. These multiscale features are then enhanced and trained separately within two parallel BLS, each with its own set of feature and enhancement nodes. A separate fusion BLS was used to stack the output of the two parallel BLS hierarchically. Additionally, we introduced diligence parameter (d), to regulate the possibility of sudden divergence of the already learned and settled network during incremental learning, ensuring network stability. We performed extensive experiments on two public datasets, namely CVC-Clinic and Kvasir-SEG. Our proposed framework achieved Dice Coefficient (DC)- 0.889, Precision-0.917, Recall-0.906, F1-0.911, and F2-0.908 on the CVC-Clinic dataset. For the Kvasir-SEG dataset, our method achieved Dice Coefficient (DC)- 0.909, Precision-0.924, Recall-0.909, F1-0.916, and F2-0.911. Cross-dataset validation further underscores the generalization capability of dHBLSN, affirming its clinical relevance. Comparative analysis against state-of-the-art models highlights dHBLSN’s superior balance between effectiveness and computational complexity.

Ingredient-guided multi-modal interaction and refinement network for RGB-D food nutrition assessment

RGB-D food nutrition assessment entails the direct prediction of nutritional content in pairs of RGB and depth food images using signal processing technology. However, existing methods present numerous challenges related to accuracy and computational complexity. In this paper, we introduce the Ingredient-guided Multi-modal Interaction and Refinement Network (IMIR-Net), a novel framework for RGB-D food nutrition assessment. We first utilize a visual grounding model to accurately identify the dish region containing food in the input image with complex semantics. The identified RGB-D dish regions are processed through a multi-modal interaction and refinement module to compute the fused embedding. Concurrently, we introduce an ingredient guidance module to effectively capture ingredient textual information, thereby facilitating the acquisition of the enhanced fused embedding. Finally, we design a decoupled nutrition predictor to predict nutrition from the fused RGB-D embedding. The IMIR-Net can capture the rich multi-modal semantics for RGB-D food nutrition assessment and achieves state-of-the-art performances on the Nutrition5k dataset. Specifically, the percentage of mean absolute error (PMAE) of Calories, Mass, Fat, Carb and Protein reached 14.5%, 10.4%, 21.8%, 20.4% and 20.0%, respectively. Compared with the existing best method, our IMIR-Net demonstrated superior prediction accuracy, with a mean PMAE value of 17.4%, an improvement of 1.1%. In terms of computational complexity, the floating-point operations (FLOPs) and parameters reached 63.87 and 105.11 via our method, which is improved by 5.2% and 7.4%, respectively. The codes, data, and model of our method are publicly available at https://github.com/nianfd/IMIR-Net-DSP.

RBF-based partition of unity methods for two-dimensional time-dependent PDEs: Numerical and theoretical aspects

This article discusses standard Radial Basis Function (RBF) partition of unity method and an enhanced version known as the direct RBF partition of unity method. We introduce the framework of these methods for the effective and accurate numerical analysis of certain two-dimensional time-dependent Partial Differential Equations (PDEs). Additionally, we conduct a convergence analysis of these methods and establish error bounds for local approximations. These error bounds are determined based on conditions associated with the eigenvalues of the Laplacian operator matrices generated by the proposed methods. To validate the accuracy and reliability of these approaches, we provide several numerical examples. We assess the consistency between theoretical and numerical results. The localized techniques introduced in this research demonstrate a significant reduction in computational cost and algorithmic complexity, achieved through the direct local procedure. Despite this computational advantage, both versions of the RBF partition of unity method exhibit a similar level of accuracy. Finally, we conduct a comparative analysis between the RBF partition of unity local methods and the RBF-generated finite differences method, offering valuable insights into the strengths and limitations of each approach.

Learning an interpretable end-to-end network for real-time acoustic beamforming

Recently, many forms of audio industrial applications, such as sound monitoring and source localization, have begun exploiting smart multi-modal devices equipped with a microphone array. Regrettably, model-based methods are often difficult to employ for such devices due to their high computational complexity, as well as the difficulty of appropriately selecting the user-determined parameters. As an alternative, one may use deep network-based methods, but these are often difficult to generalize, nor can they generate the desired beamforming map directly. In this paper, a computationally efficient acoustic beamforming algorithm is proposed, which may be unrolled to form a model-based deep learning network for real-time imaging, here termed the DAMAS-FISTA-Net. By exploiting the natural structure of an acoustic beamformer, the proposed network inherits the physical knowledge of the acoustic system, and thus learns the underlying physical properties of the propagation. As a result, all the network parameters may be learned end-to-end, guided by a model-based prior using back-propagation. Notably, the proposed network enables an excellent interpretability and the ability of being able to process the raw data directly. Extensive numerical experiments using both simulated and real-world data illustrate the preferable performance of the DAMAS-FISTA-Net as compared to alternative approaches.

A lightweight target tracking algorithm based on online correction for meta-learning

The traditional Siamese network based object tracking algorithms suffer from high computational complexity, making them difficult to run on embedded devices. Moreover, when faced with long-term tracking tasks, their success rates significantly decline. To address these issues, we propose a lightweight long-term object tracking algorithm called Meta-Master-based Ghost Fast Tracking (MGTtracker),which based on meta-learning. This algorithm integrates the Ghost mechanism to create a lightweight backbone network called G-ResNet, which accurately extracts target features while operating quickly. We design a tiny adaptive weighted fusion feature pyramid network (TiFPN) to enhance feature information fusion and mitigate interference from similar objects. We introduce a lightweight region regression network, the Ghost Decouple Net (GDNet) for target position prediction. Finally, we propose a meta-learning-based online template correction mechanism called Meta-Master to overcome error accumulation in long-term tracking tasks and the difficulty of reacquiring targets after loss. We evaluate the algorithm on public datasets OTB100, VOT2020, VOT2018LT, and LaSOT and deploy it for performance testing on Jetson Xavier NX. Experimental results demonstrate the effectiveness and superiority of the algorithm. Compared to existing classic object tracking algorithms, our approach achieves a faster running speed of 25 FPS on NX, and real-time correction enhances the algorithm’s robustness. Although similar in accuracy and EAO metrics, our algorithm outperforms similar algorithms in speed and effectively addresses the issues of significant cumulative errors and easy target loss during tracking. Code is released at https://github.com/ygh96521/MGTtracker.git.

Prestige bias in cultural evolutionary dynamics

If the traits of more successful individuals are more likely to be adopted, the resulting cultural transmission is described as success biased. In contrast, if the traits of ‘prestigious’ individuals—those who have already been copied many times—are more likely to be adopted, this is described as prestige-biased cultural transmission. In this case, prestige can be a convenient proxy for success. However, it is unclear how success and prestige biases interact to determine the outcome of cultural evolutionary dynamics. Here, we aim to clarify this using mathematical analysis and stochastic simulations. We find analytic approximations to the stochastic role-model choice process that facilitate the mathematical analysis and reduce the computational complexity of simulations. Approximations are given to the fixation probability and the fixation time of an invading cultural trait in different environments. Our results show that success bias effectively plays the role of natural selection, whereas prestige bias effectively plays the role of genetic drift. Prestige bias, which may be strong in highly social communities, also accelerates the evolutionary dynamics, as expected in a rich-get-richer process. These results signify a step forward in understanding how different cultural transmission biases interact.

Improved brain effective connectivity modelling by dynamic Bayesian networks

Background:If brain effective connectivity network modelling (ECN) could be accurately achieved, early diagnosis of neurodegenerative diseases would be possible. It has been observed in the literature that Dynamic Bayesian Network (DBN) based methods are more successful than others. However, DBNs have not been applied easily and tested much due to computational complexity problems in structure learning. New method:This study introduces an advanced method for modelling brain ECNs using improved discrete DBN (Improved- dDBN) which addresses the computational challenges previously limiting DBN application, offering solutions that enable accurate and fast structure modelling. Results:The practical data and prior sizes needed for the convergence to the globally correct network structure are proved to be much smaller than the theoretical ones using simulated dDBN data. Besides, Hill Climbing is shown to converge to the true structure at a reasonable iteration step size when the appropriate data and prior sizes are used. Finally, importance of data quantization methods are analysed. Comparison with existing methods:The Improved-dDBN method performs better and robust, when compared to the existing methods for realistic scenarios such as varying graph complexity, various input conditions, noise cases and non-stationary connections. The data used in these tests is the simulated fMRI BOLD time series proposed in the literature. Conclusions:Improved-dDBN is a good candidate to be used on real datasets to accelerate developments in brain ECN modelling and neuroscience. Appropriate data and prior sizes can be identified based on the approach proposed in this study for global and fast convergence.

Development of an early detection and automatic targeting system for cotton weeds using an improved lightweight YOLOv8 architecture on an edge device

Traditional means of weed removal, such as human work or the use of pesticides, frequently require significant amounts of effort, incur high expenses, and can negatively impact the environment. This study introduces a modified version of the YOLOv8 nano architecture that is suitable for running on edge devices for real-time applications. The proposed model uses an augmented version of the well-known CottonWeedDet12 dataset consisting of a total of 16,944 images with characteristic annotations to develop a model capable of correctly distinguishing 12 different cotton weed classes with an increased mean average precision of 97.6 % that is about 1.2 % more than the model trained using original, unaugmented dataset. The final selected model uses a convolutional block attention module (CBAM) and a unique C3Ghost block within the YOLOv8 backbone, which together increase the model's reliability for more accurate predictions with reduced computational complexity. Upon training with the augmented dataset, the proposed model with only 3.6 million parameters was able to achieve an mAP@50 score of 97.6 %, which surpasses all previous studies conducted using this dataset. Additionally, a high F1 score of 94.4 % proves that the model has a good balance between recall and precision. Class Activation Map (CAM) approaches such as EigenCAM, Grad-CAM++, and LayerCAM explainable AI (XAI) showed promising results for each of the customized models upon testing their interpretability for cotton weed detection. Furthermore, based on this model, a fast and cost-efficient targeting system was developed using a yaw-pitch mechanism for automatic weed tracking and herbicide spraying.

Transparent boundary condition and its effectively local approximation for the Schrödinger equation on a rectangular computational domain

The transparent boundary condition for the free Schrödinger equation on a rectangular computational domain requires implementation of an operator of the form ∂t−i△Γ where △Γ is the Laplace-Beltrami operator. It is known that this operator is nonlocal in time as well as space which poses a significant challenge in developing an efficient numerical method of solution. The computational complexity of the existing methods scale with the number of time-steps which can be attributed to the nonlocal nature of the boundary operator. In this work, we report an effectively local approximation for the boundary operator such that the resulting complexity remains independent of number of time-steps. At the heart of this algorithm is a Padé approximant based rational approximation of certain fractional operators that handles corners of the domain adequately. For the spatial discretization, we use a Legendre-Galerkin spectral method with a new boundary adapted basis which ensures that the resulting linear system is banded. A compatible boundary-lifting procedure is also presented which accommodates the segments as well as the corners on the boundary. The proposed novel scheme can be implemented within the framework of any one-step time marching schemes. In particular, we demonstrate these ideas for two one-step methods, namely, the backward-differentiation formula of order 1 (BDF1) and the trapezoidal rule (TR). For the sake of comparison, we also present a convolution quadrature based scheme conforming to the one-step methods which is computationally expensive but serves as a golden standard. Finally, several numerical tests are presented to demonstrate the effectiveness of our novel method as well as to verify the order of convergence empirically.

Algorithm-Dependent Generalization of AUPRC Optimization: Theory and Algorithm.

Stochastic optimization of the Area Under the Precision-Recall Curve (AUPRC) is a crucial problem for machine learning. Despite extensive studies on AUPRC optimization, generalization is still an open problem. In this work, we present the first trial in the algorithm-dependent generalization of stochastic AUPRC optimization. The obstacles to our destination are three-fold. First, according to the consistency analysis, the majority of existing stochastic estimators are biased with biased sampling strategies. To address this issue, we propose a stochastic estimator with sampling-rate-invariant consistency and reduce the consistency error by estimating the full-batch scores with score memory. Second, standard techniques for algorithm-dependent generalization analysis cannot be directly applied to listwise losses. To fill this gap, we extend the model stability from instance-wise losses to listwise losses. Third, AUPRC optimization involves a compositional optimization problem, which brings complicated computations. In this work, we propose to reduce the computational complexity by matrix spectral decomposition. Based on these techniques, we derive the first algorithm-dependent generalization bound for AUPRC optimization. Motivated by theoretical results, we propose a generalization-induced learning framework, which improves the AUPRC generalization by equivalently increasing the batch size and the number of valid training examples. Practically, experiments on image retrieval and long-tailed classification speak to the effectiveness and soundness of our framework.

A fully digital timing background calibration algorithm based on first-order auto-correlation for time-interleaved ADCs

This paper presents a fully digital background calibration method for time-interleaved (TI) analog-to-digital converters (ADCs). The timing detector employs a timing detection method based on an enhanced autocorrelation function, combined with matrix operations, to improve the accuracy of timing mismatch acquisition. The algorithm makes full use of the autocorrelation functions of each channel and proposes a more precise method for obtaining autocorrelation derivatives. Furthermore, the algorithm features a simple structure, low computational complexity, and can support timing calibration for any number of channels without requiring additional channels. The algorithm was validated using an ADC + FPGA combined system, with a 8-channel TI-ADC manufactured in 28 nm technology and a sampling rate of 20 GS/s. Results demonstrate that compared to the uncalibrated state, the algorithm significantly improves the SNDR and SFDR from 27.98 dB and 30.76 dB to 39.38 dB and 42.13 dB, respectively when the inputs are near the Nyquist frequency.

Adaptive Masked Autoencoder Transformer for image classification

Vision Transformers (ViTs) have exhibited exceptional performance across a broad spectrum of visual tasks. Nonetheless, their computational requirements often surpass those of prevailing CNN-based models. Token sparsity techniques have been employed as a means to alleviate this issue. Regrettably, these techniques often result in the loss of semantic information and subsequent deterioration in performance. In order to address these challenges, we propose the Adaptive Masked Autoencoder Transformer (AMAT), a masked image modeling-based method. AMAT integrates a novel adaptive masking mechanism and a training objective function for both pre-training and fine-tuning stages. Our primary objective is to reduce the complexity of Vision Transformer models while concurrently enhancing their final accuracy. Through experiments conducted on the ILSVRC-2012 dataset, our proposed method surpasses the original ViT by achieving up to 40% FLOPs savings. Moreover, AMAT outperforms the efficient DynamicViT model by 0.1% while saving 4% FLOPs. Furthermore, on the Places365 dataset, AMAT achieves a 0.3% accuracy loss while saving 21% FLOPs compared to MAE. These findings effectively demonstrate the efficacy of AMAT in mitigating computational complexity while maintaining a high level of accuracy.

State of health estimation for battery modules with parallel-connected cells under cell-to-cell variations

State of health (SOH) estimation for lithium-ion battery modules with cells connected in parallel is a challenging problem, especially with cell-to-cell variations. Incremental capacity analysis (ICA) and differential voltage analysis (DVA) are effective at the cell level, but a generalizable method to extend them to module-level SOH estimation remains missing, when only module-level measurements are available. This paper proposes a new method and demonstrates that, with multiple features systematically selected from the module-level ICA and DVA, the module-level SOH can be estimated with high accuracy and confidence in the presence of cell-to-cell variations. First, an information theory-based feature selection algorithm is proposed to find an optimal set of features for module-level SOH estimation. Second, a relevance vector regression (RVR)-based module-level SOH estimation model is proposed to provide both point estimates and three-sigma credible intervals while maintaining model sparsity. With more selected features incorporated, the proposed method achieves better estimation accuracy and higher confidence at the expense of higher model complexity. When applied to a large experimental dataset, the proposed method and the resulting sparse model lead to module-level SOH estimates with a 0.5% root-mean-square error and a 1.5% average three-sigma value. With all the training processes completed offboard, the proposed method has low computational complexity for onboard implementations.

A fully digital timing background calibration algorithm based on first-order auto-correlation for time-interleaved ADCs

This paper presents a fully digital background calibration method for time-interleaved (TI) analog-to-digital converters (ADCs). The timing detector employs a timing detection method based on an enhanced autocorrelation function, combined with matrix operations, to improve the accuracy of timing mismatch acquisition. The algorithm makes full use of the autocorrelation functions of each channel and proposes a more precise method for obtaining autocorrelation derivatives. Furthermore, the algorithm features a simple structure, low computational complexity, and can support timing calibration for any number of channels without requiring additional channels. The algorithm was validated using an ADC + FPGA combined system, with a 8-channel TI-ADC manufactured in 28 nm technology and a sampling rate of 20 GS/s. Results demonstrate that compared to the uncalibrated state, the algorithm significantly improves the SNDR and SFDR from 27.98 dB and 30.76 dB to 39.38 dB and 42.13 dB, respectively when the inputs are near the Nyquist frequency.