Article
Neurosciences
Salil Bhat, Michael Luhrs, Rainer Goebel, Mario Senden
Summary: Population receptive field (pRF) mapping is a popular tool in computational neuroimaging for studying receptive field properties and topography, with potential applications in brain-computer interface (BCI) communication systems. A novel and fast pRF mapping procedure based on hashed-Gaussian encoding significantly reduces computational resources, facilitating real-time applications.
Article
Automation & Control Systems
Gang Xu, Yansong Chen, Junjie Cao, Deye Zhu, Weiwei Liu, Yong Liu
Summary: This article explores the posture constraints problem in multi-vehicle motion planning, proposing a posture collision avoidance algorithm to optimize motion smoothness and improve the planning effectiveness for nonholonomic unmanned ground vehicles. The effectiveness and practicality of the proposed algorithm were verified through simulation experiments and experiments in natural environments.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Energy & Fuels
Dris Ben Hmamou, Mustapha Elyaqouti, Elhanafi Arjdal, Ahmed Ibrahim, H. I. Abdul-Ghaffar, Raef Aboelsaud, Sergey Obukhov, Ahmed A. Zaki Diab
Summary: This study proposes a new approach based on Lambert W-function to extract the electrical parameters of photovoltaic panels, with simulated output characteristics comparable to manufacturer's experimental data. The proposed method shows high accuracy and reliability in extracting optimal electrical characteristics under different conditions, demonstrating the novelty and effectiveness of the approach.
Article
Optics
Cristian L. Cortes, A. Eugene DePrince III, Stephen K. Gray
Summary: This research introduces several quantum Krylov fast-forwarding algorithms that accurately predict long-time dynamics, and demonstrates the effectiveness of the proposed multireference method in balancing circuit depth and classical postprocessing complexity.
Article
Multidisciplinary Sciences
Pengfei Xu, Yinjie Jia, Zhijian Wang, Mingxin Jiang
Summary: The selection of the nonlinear function in Fast-ICA has a significant impact on the separation performance. This paper specifically demonstrates the convergence performance of the sine function in Fast-ICA and shows that the algorithm is suitable for various types of signals. Additionally, the algorithm's performance is analyzed as the number of sources increases.
PROCEEDINGS OF THE ROMANIAN ACADEMY SERIES A-MATHEMATICS PHYSICS TECHNICAL SCIENCES INFORMATION SCIENCE
(2021)
Article
Automation & Control Systems
Muhammad Hosnee Mobarak, Jennifer Bauman
Summary: PV arrays with partial shading conditions exhibit multiple local maximum power points. Traditional global MPP tracking algorithms may have slow convergence and fail to track the global MPP. This article proposes a new algorithm that uses a single current sensor and parabolic equations to accurately calculate the global MPP during partial shading conditions. Simulation and experimental results show fast tracking and high tracking efficiency.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2022)
Article
Neurosciences
Lindsay R. Walton, Matthew Verber, Sung-Ho Lee, Tzu-Hao Harry Chao, R. Mark Wightman, Yen-Yu Ian Shih
Summary: The study proposes an experimental setting using simultaneous fast-scan cyclic voltammetry (FSCV) and blood oxygenation level-dependent functional magnetic imaging (BOLD fMRI) to measure tissue oxygen and dopamine responses, and global BOLD changes. The technique allows for identification of brain regions encoding dopamine amplitude differences and provides complementary neurochemical and hemodynamic information for studying the influence of local neurotransmitter release over the entire brain.
Article
Computer Science, Information Systems
Weiwen Jiang, Edwin Hsing-Mean Sha, Qingfeng Zhuge, Lei Yang, Hailiang Dong, Xianzhang Chen
Summary: This paper addresses the design of application-specific pipelines in heterogeneous architectures, modeling execution times as random variables to overcome the challenge of uncertainty. By proving the NP-hardness of the problem, presenting Mixed Integer Linear Programming (MILP) formulations, and devising an efficient (1 + epsilon)-approximation algorithm, significant cost reductions are achieved, reaching up to 31.93% on average according to experimental results.
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2021)
Article
Neurosciences
Seyedmohammad Shams, Prokopis Prokopiou, Azin Esmaelbeigi, Georgios D. Mitsis, J. Jean Chen
Summary: Conventionally, cerebrovascular reactivity (CVR) is estimated as the amplitude of the hemodynamic response to vascular stimuli, most commonly carbon dioxide (CO2). While the CVR amplitude has established clinical utility, the temporal characteristics of CVR (dCVR) have been increasingly explored and may yield even more pathology-sensitive parameters. This work compares several model-based deconvolution approaches for estimating the CO2 response function and proposes a novel simulation framework to aid the comparison. The findings suggest that model-based methods can accurately estimate dCVR even amidst high noise and provide a quantitative basis for methodological choices.
Article
Energy & Fuels
Praveen Nambisan, Pankaj Saha, Munmun Khanra
Summary: In this work, a real-time optimal fast charging protocol is implemented using Pontryagin's Minimum Principle (PMP) to solve the optimal control framework balancing between charging time and ohmic heat generation. The control concepts of costate jump conditions are modified and extensive offline optimization results are used to examine the real-time optimal fast charging protocol under varying operating constraints. The effect of different boundary conditions on charging profile and sensitive parameters, as well as comparison with a standard CCCV charging algorithm, is investigated. Finally, the comparison between a typical optimal fast charging profile and a standard 2C CCCV protocol is experimentally examined.
JOURNAL OF ENERGY STORAGE
(2021)
Article
Computer Science, Artificial Intelligence
Ruimin Sun, Xiaoyong Yuan, Pan He, Qile Zhu, Aokun Chen, Andre Gregio, Daniela Oliveira, Xiaolin Li
Summary: Propedeutica is a real-time malware detection framework that combines conventional machine learning and deep learning techniques, utilizing ML classifier for fast detection and DL detector for accuracy. It introduces a novel DL architecture, DeepMalware, to address spatial-temporal dynamics and software execution heterogeneity. With evaluations on malware and benign samples, Propedeutica achieves high accuracy and low false-positive rate.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Robotics
Lukas Huber, Jean-Jacques Slotine, Aude Billard
Summary: In order for robots to navigate through crowded spaces like humans, they need highly reactive obstacle avoidance that can handle partial and poor sensing. Our proposed control scheme combines high-level input commands with fast reactive obstacle avoidance (FOA) to enable obstacle avoidance based on sparse and asynchronous perception. By analyzing sampled sensor data and reconstructing obstacles in real time, the algorithm ensures that the agent does not get stuck when a feasible path exists between obstacles. The algorithm was successfully evaluated in cluttered indoor office environments and in a dynamic outdoor environment in Lausanne.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Automation & Control Systems
Yingying Li, Guannan Qu, Na Li
Summary: This article explores online optimization with a finite prediction window and switching costs, proposing two gradient-based online algorithms and providing their regret upper bounds. Numerical experiments confirm the validity of the theoretical results.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2021)
Article
Automation & Control Systems
Marcelo Menezes Morato, Julio Elias Normey-Rico, Olivier Sename
Summary: This article proposes an extrapolation algorithm based on recursive calculation using simple Taylor expansions to estimate the future values of qLPV scheduling parameters for a fixed prediction horizon. Sufficient conditions for convergent extrapolation are presented, and benchmark examples are used to illustrate the effectiveness of the algorithm, which is also compared to state-of-the-art techniques.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Automation & Control Systems
Jing Xia, Zainan Jiang, Hao Zhang, Rongjun Zhu, Haibo Tian
Summary: This article presents a dual fast marching tree algorithm for human-like motion planning for anthropomorphic arms, utilizing dual sampling in Cartesian space and self-motion manifolds. The proposed approach can quickly solve constrained path planning tasks and generate paths that are more human-like.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2021)
Article
Computer Science, Information Systems
Davit Marikyan, Savvas Papagiannidis, Eleftherios Alamanos
Summary: This study addresses the outcomes of technology use when it falls short of expectations and the coping mechanisms users may use in such circumstances. By adopting Cognitive Dissonance Theory, the study explores how negative disconfirmation of expectations can result in positive outcomes and how negative emotions impact the selection of dissonance reduction mechanisms. The study finds that post-disconfirmation dissonance leads to feelings of anger, guilt, and regret, which correlate with dissonance reduction mechanisms, ultimately affecting satisfaction and well-being.
INFORMATION SYSTEMS FRONTIERS
(2023)
Article
Neurosciences
Md Abdur Rahaman, Jiayu Chen, Zening Fu, Noah Lewis, Armin Iraji, Theo G. M. van Erp, Vince D. Calhoun
Summary: Characterizing neuropsychiatric disorders is challenging, but combining structural and functional neuroimaging with genomic data in a multimodal classification framework can improve the classification of disorders and explore underlying neural and biological mechanisms. By developing neural networks for feature learning and implementing an adaptive control unit for fusion, we achieved high accuracy in schizophrenia prediction and identified critical neural features and genes/biological pathways associated with the disorder.
HUMAN BRAIN MAPPING
(2023)
Article
Neurosciences
Kaicheng Li, Qingze Zeng, Xiao Luo, Shile Qi, Xiaopei Xu, Zening Fu, Luwei Hong, Xiaocao Liu, Zheyu Li, Yanv Fu, Yanxing Chen, Zhirong Liu, Vince D. Calhoun, Peiyu Huang, Minming Zhang
Summary: The study found that concomitant neuropsychiatric symptoms are associated with accelerated Alzheimer's disease progression. Using multimodal brain imaging, a pattern associated with these symptoms was identified and found to be correlated with the development of Alzheimer's disease. The pattern was also found to be associated with multiple cognitive domains and could predict cognitive decline.
HUMAN BRAIN MAPPING
(2023)
Article
Computer Science, Interdisciplinary Applications
Irina Belyaeva, Ben Gabrielson, Yu-Ping Wang, Tony W. Wilson, Vince D. Calhoun, Julia M. Stephen, Tulay Adali
Summary: Identification of informative signatures from electrophysiological signals is important for understanding brain developmental patterns. This study proposes a tensor-based approach for extracting developmental signatures of multi-subject MEG data. The results demonstrate that this approach can produce descriptive features of the multidimensional MEG data and be used to study group differences in brain patterns and cognitive function of healthy children.
Article
Computer Science, Interdisciplinary Applications
Harshvardhan Gazula, Kelly Rootes-Murdy, Bharath Holla, Sunitha Basodi, Zuo Zhang, Eric Verner, Ross Kelly, Pratima Murthy, Amit Chakrabarti, Debasish Basu, Subodh Bhagyalakshmi Nanjayya, Rajkumar Lenin Singh, Roshan Lourembam Singh, Kartik Kalyanram, Kamakshi Kartik, Kumaran Kalyanaraman, Krishnaveni Ghattu, Rebecca Kuriyan, Sunita Simon Kurpad, Gareth J. Barker, Rose Dawn Bharath, Sylvane Desrivieres, Meera Purushottam, Dimitri Papadopoulos Orfanos, Eesha Sharma, Matthew Hickman, Mireille Toledano, Nilakshi Vaidya, Tobias Banaschewski, Arun L. W. Bokde, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rudiger Bruhl, Jean-Luc Martinot, Marie-Laure Paillere Martinot, Eric Artiges, Frauke Nees, Tomas Paus, Luise Poustka, Juliane H. Frohner, Lauren Robinson, Michael N. Smolka, Henrik Walter, Jeanne Winterer, Robert Whelan, Jessica A. Turner, Anand D. Sarwate, Sergey M. Plis, Vivek Benegal, Gunter Schumann, Vince D. Calhoun
Summary: With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks.
Article
Computer Science, Information Systems
Xiang Li, Sheri L. Towe, Ryan P. Bell, Rongtao Jiang, Shana A. Hall, Vince D. Calhoun, Christina S. Meade, Jing Sui
Summary: Neurocognitive impairment is common in people living with HIV, and identifying reliable biomarkers is crucial for understanding neural foundations and clinical care. This study used connectome-based predictive modeling to predict cognitive functioning in PLWH, achieving high prediction accuracy by combining multiple modalities and incorporating clinical measures.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Computer Science, Information Systems
Ying Xing, Peter Kochunov, Theo G. M. van Erp, Tianzhou Ma, Vince D. Calhoun, Yuhui Du
Summary: Feature selection is important in identifying biomarkers of mental disorders. In this study, a new method based on neighborhood rough set (NRS) was proposed to select biomarkers of schizophrenia using fMRI data. The method combined NRS with information entropy and multi-granularity fusion to obtain the most discriminative features. The method achieved higher classification accuracies compared to other methods, revealing meaningful substrates of schizophrenia. This study highlights the potential of exploring neuroimaging-based biomarkers using the NRS-based feature selection method.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2023)
Article
Neurosciences
Noah Lewis, Robyn Miller, Harshvardhan Gazula, Vince Calhoun
Summary: Deep learning is effective for classifying biological sex based on fMRI, but research on the most relevant brain features for this classification is lacking. Model interpretability is important for understanding deep learning models, but little work has been done on the relationship between temporal dimension of fMRI signals and sex classification. In this study, a methodology is provided to address underspecification and instability in feature explanation models, and sex differences in functional brain networks are explored using intrinsic connectivity networks.
Article
Neurosciences
Marlena Duda, Armin Iraji, Judith M. Ford, Kelvin O. Lim, Daniel H. Mathalon, Bryon A. Mueller, Steven G. Potkin, Adrian Preda, Theo G. M. Van Erp, Vince D. Calhoun
Summary: By using spatially constrained independent component analysis (scICA), this study found that rsfMRI scans of just 2-5 minutes can provide good clinical utility without significant loss of individual functional network connectivity (FNC) information from longer scan lengths.
HUMAN BRAIN MAPPING
(2023)
Article
Engineering, Biomedical
Lan Yang, Chen Qiao, Huiyu Zhou, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yuping Wang
Summary: This study proposes an explainable multimodal deep dictionary learning method to uncover the commonality and specificity of different modalities in brain developmental differences. The results show that the proposed model can achieve better reconstruction and identify age-related differences in reoccurring patterns.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2023)
Article
Geochemistry & Geophysics
Amos Bortiew, Swarnajyoti Patra, Lorenzo Bruzzone
Summary: This letter proposes a novel active learning technique for sparse representation classifiers (SRCs) that combines uncertainty and diversity criteria to design the query function. The proposed technique outperforms other state-of-the-art methods in terms of classification performance.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Luca Bergamasco, Francesca Bovolo, Lorenzo Bruzzone
Summary: Multisensor data analysis utilizes heterogeneous data from multiple remote sensing systems to improve classification results. A supervised deep-learning method is proposed to analyze multiscale and multitemporal remote sensing images acquired by different sensors. The method processes high-resolution images with a residual network and analyzes spatial and temporal information using a 3-D convolutional neural network. The effectiveness of the method is demonstrated through experiments on two datasets.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Yongjie Zheng, Sicong Liu, Lorenzo Bruzzone
Summary: This letter proposes a lightweight end-to-end attention-enhanced feature fusion network for hyperspectral image classification. The network effectively utilizes spectral-spatial information and achieves accurate classification even with few training samples. Experimental results demonstrate the superiority of the proposed approach compared to state-of-the-art deep learning methods.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Geochemistry & Geophysics
Lifeng Wang, Junguo Zhang, Liguo Wang, Lorenzo Bruzzone
Summary: This letter proposes an end-to-end semantic feature fused global learning framework for hyperspectral image multiclass change detection. The framework includes a global spatialwise fully convolutional network, a global hierarchical sampling strategy, a semantic-spatial feature fusion unit, and a semantic feature enhancement module.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2023)
Article
Engineering, Electrical & Electronic
Daniel Carcereri, Paola Rizzoli, Dino Ienco, Lorenzo Bruzzone
Summary: This article presents a study on using deep learning to estimate forest height from InSAR data. The proposed fully convolutional neural network framework achieves good performance when tested on multiple sites, with an overall mean error of 1.46 m and mean absolute error of 4.2 m.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2023)
