Article
Green & Sustainable Science & Technology
Jinshan Yu, Zhongyuan Zheng, Yamin Li, Haohui Wang, Ying Hao, Xiaoxia Liang, Jianzheng Gao
Summary: This study develops a real-time active noise control system and evaluates different ANC methods for low-frequency noise reduction. The results show that the ANC system is effective in attenuating substation noise.
Article
Automation & Control Systems
Ping Ma, Yongkai Chen, Xinlian Zhang, Xin Xing, Jingyi Ma, Michael W. Mahoney
Summary: In this article, we develop an asymptotic analysis to derive the distribution of RandNLA sampling estimators for the least-squares problem. We show that the sampling estimator is asymptotically normally distributed under mild regularity conditions and is asymptotically unbiased in both full sample approximation and model parameter inference settings. Based on our asymptotic analysis, we identify optimal sampling probabilities using two criteria and propose several new optimal sampling probability distributions. Our theoretical and empirical results provide insights on the role of leverage in the sampling process and demonstrate improvements over existing methods.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Computer Science, Information Systems
Jinwoo Yoo, Bum Yong Park, Won Il Lee, JaeWook Shin
Summary: In this paper, a novel normalized least mean squares (NLMS) algorithm is proposed for system identification applications. The mean squared deviation performance of the NLMS algorithm is analyzed using a random walk model to select two optimal parameters, the step size and regularization parameters, for rapid convergence of colored input signals. It is verified that the proposed algorithm exhibits faster convergence than existing algorithms, even in scenarios of sudden system changes.
Article
Engineering, Multidisciplinary
Awadhesh K. Pandey, G. N. Singh, Neveen Sayed-Ahmed, Hanaa Abu-Zinadah
Summary: The treatment of incomplete data is crucial in statistical data analysis, and missing values can create challenges for researchers. Utilizing imputation methods with ancillary information can lead to improved estimation accuracy.
ALEXANDRIA ENGINEERING JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Athanasios I. Salamanis, George A. Gravvanis, Sotiris Kotsiantis, Konstantinos M. Giannoutakis
Summary: Although existing missing data imputation methods mainly focus on either time series or tabular data, this paper proposes a generic sparse regression method that can handle missing data in both types of data. The method utilizes a preconditioned iterative approach based on generic approximate sparse pseudoinverse to solve a sparse least squares problem, and introduces sparsity by dummy encoding categorical features. Extensive experiments conducted on multiple datasets demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Mathematical & Computational Biology
Saskya Mary Soemartojo, Titin Siswantining, Yoel Fernando, Devvi Sarwinda, Herley Shaori Al-Ash, Sarah Syarofina, Noval Saputra
Summary: This article investigates the issue of missing value imputation in gene expression data and proposes a new imputation method called bi-BPCA-iLS. Experiments show that this method significantly improves upon existing methods and does not significantly increase computational time.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2022)
Article
Health Care Sciences & Services
Shen-Ming Lee, Phuoc-Loc Tran, Chin-Shang Li
Summary: This paper addresses the issue of model checking for logistic regression with covariates missing at random. Two goodness-of-fit tests, Pearson chi-squared and unweighted residual sum-of-squares tests, are proposed and their test statistics are centered using inverse probability weighting (IPW) and nonparametric multiple imputation (MI) methods to solve the missing value problem. The paper establishes the asymptotic properties of these test statistics and introduces the IPW method and bootstrap re-sampling approaches to estimate the variances of the proposed test statistics. Simulation studies and real data examples are conducted to evaluate the performance of the proposed tests.
STATISTICAL METHODS IN MEDICAL RESEARCH
(2022)
Article
Engineering, Electrical & Electronic
Foad Fereidoony, Ali Jishi, Maziar Hedayati, Yuanxun Ethan Wang, Sridhar Kowdley
Summary: This paper proposes the Magnitude-Delay Least Mean Squares Equalization (MD-LMSE) for super-resolution time delay estimation, offering high accuracy, super resolution, and low computational time. The experimental results show that the approach can improve the range resolution of a system by 95% with low error.
IEEE SENSORS JOURNAL
(2021)
Article
Mathematics, Applied
Liqi Xia, Xiuli Wang, Peixin Zhao, Yunquan Song
Summary: This paper focuses on the statistical inferences for varying coefficient partially nonlinear model with missing responses, utilizing profile nonlinear least squares estimation and empirical likelihood inferences. Simulation studies are conducted to examine the finite sample performance of the methods, which are further applied to a real data example.
Article
Computer Science, Information Systems
Zhen Zhang, Lijuan Jia, Ran Tao, Yue Wang
Summary: Two new blind adaptive identification and equalization algorithms are proposed in this paper, based on second-order statistics and using antennas array technology. The algorithms tackle the issues of unknown noise statistics in transmission channels and stability with a variable step-size NLMS approach.
SCIENCE CHINA-INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Aiguo Wang, Jing Yang, Ning An
Summary: This study formalizes the problem of missing values in microarray data under a regularized sparse framework and proposes local learning-based imputation models with elastic net regularization to accurately estimate missing entries in gene expression profiles. Experimental results demonstrate the superiority of elastic net over other methods in terms of statistical analysis metrics.
Article
Computer Science, Artificial Intelligence
Xingchen Hu, Witold Pedrycz, Keyu Wu, Yinghua Shen
Summary: Granular Computing is a human-centric approach to discovering the fundamental structure of data sets, with information granules being used to organize knowledge and reveal data descriptions in classification problems. The focus of the study is on developing a novel information granule-based classification method for incomplete data, representing missing entities as information granules in a unified framework. Experimental studies demonstrated the advantages of the proposed methods on incomplete data classification and representation.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Yun Liu, Wen Yang, Jiayu Zhou, Yue Luo
Summary: This article studies the H-8 filter design for discrete-time periodic piecewise systems with missing measurements. A Bernoulli process is used to characterize missing measurements. Sufficient conditions are obtained to ensure the exponential mean-squared stability and H-8 estimation performance of the periodic piecewise filtering error system (PPFES) by constructing the continuous Lyapunov function with discrete time-scheduling periodic parameters under missing measurements. Moreover, a nominal H-8 filter with superior performance is developed for the case of complete transmission.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Acoustics
Sridhar Chintala, Jaisingh Thangaraj, Damodar Reddy Edla
Summary: A novel adaptive algorithm, based on a new step size, is proposed to eliminate ocular artifacts from recorded raw EEG signals. By using second and fourth-order power optimization algorithms, reference signals are processed and subtracted to obtain true EEG signals.
Article
Engineering, Biomedical
Jose Henrique Ferreira de Souza, Tiago Zanotelli, Leonardo Bonato Felix, Felipe Antunes
Summary: Auditory Steady-State Responses (ASSR) are evoked potentials used for estimating hearing thresholds. This study proposes an alternative technique to the discrete Fourier transform (DFT) called the least squares method with phase compensation. Results showed a small calibration error in the dataset and demonstrated the performance degradation of the magnitude-squared coherence (MSC) when using either very small or very large epoch lengths in real data. The proposed method allows for analysis with varying epoch lengths and frequencies, which was not possible with DFT.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Biology
Seyyed Bahram Borgheai, Alyssa Hillary Zisk, John McLinden, James Mcintyre, Reza Sadjadi, Yalda Shahriari
Summary: This study proposed a novel personalized scheme using fNIRS and EEG as the main tools to predict and compensate for the variability in BCI systems, especially for individuals with severe motor deficits. By establishing predictive models, it was found that there were significant associations between the predicted performances and the actual performances.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hongliang Guo, Hanbo Liu, Ahong Zhu, Mingyang Li, Helong Yu, Yun Zhu, Xiaoxiao Chen, Yujia Xu, Lianxing Gao, Qiongying Zhang, Yangping Shentu
Summary: In this paper, a BDSMA-based image segmentation method is proposed, which improves the limitations of the original algorithm by combining SMA with DE and introducing a cooperative mixing model. The experimental results demonstrate the superiority of this method in terms of convergence speed and precision compared to other methods, and its successful application to brain tumor medical images.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jingfei Hu, Linwei Qiu, Hua Wang, Jicong Zhang
Summary: This study proposes a novel semi-supervised point consistency network (SPC-Net) for retinal artery/vein (A/V) classification, addressing the challenges of specific tubular structures and limited well-labeled data in CNN-based approaches. The SPC-Net combines an AVC module and an MPC module, and introduces point set representations and consistency regularization to improve the accuracy of A/V classification.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Juan Antonio Valera-Calero, Dario Lopez-Zanoni, Sandra Sanchez-Jorge, Cesar Fernandez-de-las-Penas, Marcos Jose Navarro-Santana, Sofia Olivia Calvo-Moreno, Gustavo Plaza-Manzano
Summary: This study developed an easy-to-use application for assessing the diagnostic accuracy of digital pain drawings (PDs) compared to the classic paper-and-pencil method. The results demonstrated that digital PDs have higher reliability and accuracy compared to paper-and-pencil PDs, and there were no significant differences in assessing pain extent between the two methods. The PAIN EXTENT app showed good convergent validity.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Biao Qu, Jialue Zhang, Taishan Kang, Jianzhong Lin, Meijin Lin, Huajun She, Qingxia Wu, Meiyun Wang, Gaofeng Zheng
Summary: This study proposes a deep unrolled neural network, pFISTA-DR, for radial MRI image reconstruction, which successfully preserves image details using a preprocessing module, learnable convolution filters, and adaptive threshold.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Alireza Rafiei, Milad Ghiasi Rad, Andrea Sikora, Rishikesan Kamaleswaran
Summary: This study aimed to improve machine learning model prediction of fluid overload by integrating synthetic data, which could be translated to other clinical outcomes.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jinlian Ma, Dexing Kong, Fa Wu, Lingyun Bao, Jing Yuan, Yusheng Liu
Summary: In this study, a new method based on MDenseNet is proposed for automatic segmentation of nodular lesions from ultrasound images. Experimental results demonstrate that the proposed method can accurately extract multiple nodules from thyroid and breast ultrasound images, with good accuracy and reproducibility, and it shows great potential in other clinical segmentation tasks.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Jiabao Sheng, SaiKit Lam, Jiang Zhang, Yuanpeng Zhang, Jing Cai
Summary: Omics fusion is an important preprocessing approach in medical image processing that assists in various studies. This study aims to develop a fusion methodology for predicting distant metastasis in nasopharyngeal carcinoma by mitigating the disparities in omics data and utilizing a label-softening technique and a multi-kernel-based neural network.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Zhenxiang Xiao, Liang He, Boyu Zhao, Mingxin Jiang, Wei Mao, Yuzhong Chen, Tuo Zhang, Xintao Hu, Tianming Liu, Xi Jiang
Summary: This study systematically investigates the functional connectivity characteristics between gyri and sulci in the human brain under naturalistic stimulus, and identifies unique features in these connections. This research provides novel insights into the functional brain mechanism under naturalistic stimulus and lays a solid foundation for accurately mapping the brain anatomy-function relationship.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qianqian Wang, Mingyu Zhang, Aohan Li, Xiaojun Yao, Yingqing Chen
Summary: The development of PARP-1 inhibitors is crucial for the treatment of various cancers. This study investigates the structural regulation of PARP-1 by different allosteric inhibitors, revealing the basis of allosteric inhibition and providing guidance for the discovery of more innovative PARP-1 inhibitors.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Qing Xu, Wenting Duan
Summary: In this paper, a dual attention supervised module, named DualAttNet, is proposed for multi-label lesion detection in chest radiographs. By efficiently fusing global and local lesion classification information, the module is able to recognize targets with different sizes. Experimental results show that DualAttNet outperforms baselines in terms of mAP and AP50 with different detection architectures.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Kaja Gutowska, Piotr Formanowicz
Summary: The primary aim of this research is to propose algorithms for identifying significant reactions and subprocesses within biological system models constructed using classical Petri nets. These solutions enable two analysis methods: importance analysis for identifying critical individual reactions to the model's functionality and occurrence analysis for finding essential subprocesses. The utility of these methods has been demonstrated through analyses of an example model related to the DNA damage response mechanism. It should be noted that these proposed analyses can be applied to any biological phenomenon represented using the Petri net formalism. The presented analysis methods extend classical Petri net-based analyses, enhancing our comprehension of the investigated biological phenomena and aiding in the identification of potential molecular targets for drugs.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Hansle Gwon, Imjin Ahn, Yunha Kim, Hee Jun Kang, Hyeram Seo, Heejung Choi, Ha Na Cho, Minkyoung Kim, Jiye Han, Gaeun Kee, Seohyun Park, Kye Hwa Lee, Tae Joon Jun, Young-Hak Kim
Summary: Electronic medical records have potential in advancing healthcare technologies, but privacy issues hinder their full utilization. Deep learning-based generative models can mitigate this problem by creating synthetic data similar to real patient data. However, the risk of data leakage due to malicious attacks poses a challenge to traditional generative models. To address this, we propose a method that employs local differential privacy (LDP) to protect the model from attacks and preserve the privacy of training data, while generating medical data with reasonable performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Biology
Siwei Tao, Zonghan Tian, Ling Bai, Yueshu Xu, Cuifang Kuang, Xu Liu
Summary: This study proposes a transfer learning-based method to address the phase retrieval problem in grating-based X-ray phase contrast imaging. By generating a training dataset and using deep learning techniques, this method improves image quality and can be applied to X-ray 2D and 3D imaging.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
