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A Comprehensive User Modeling Framework and a Recommender System for Personalizing Well-Being Related Behavior Change Interventions: Development and Evaluation

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IEEE ACCESS 2022年 10卷 116766-116783页

作者： Honka, Anita M. Nieminen, Hannu Simila, Heidi Kaartinen, Jouni Van Gils, Mark VTT Tech Res Ctr Finland Ltd Dept Smart Hlth Tampere 33101 Finland Tampere Univ Fac Med & Hlth Technol Tampere 33100 Finland VTT Tech Res Ctr Finland Ltd Dept Smart Hlth Oulu 90570 Finland

Health recommender systems (HRSs) have the potential to effectively personalize well-being related behavior change interventions to the needs of individuals. However, personalization is often conducted with a narrow perspective, and the underlying user features are inconsistent across HRSs. Particularly, theory-based determinants of behavior and the variety of lifestyle domains influencing well-being are poorly addressed. We propose a comprehensive theory-based framework of user features, the virtual individual (VI) model, to support the extensive personalization of digital well-being interventions. We introduce a prototype HRS (With-Me HRS) with knowledge-based filtering, which recommends behavior change objectives and activities from several lifestyle domains. With-Me HRS realizes a minimum set of important VI model features related to well-being, lifestyle, and behavioral intention. We report the preliminary validity and usefulness of the HRS, evaluated in a real-life health-coaching program with 50 participants. The recommendations were used in decision-making for half of the participants and were hidden for others. For 73% of the participants (85% with visible vs. 62% with hidden recommendations), at least one of the recommended activities was included into their coaching plans. The HRS reduced coaches' perceived effort in identifying appropriate coaching tasks for the participants (effect size: Vargha-Delaney (A) over cap = 0.71, 95% CI 0.59-0.84) but not in identifying behavior change objectives. From the participants' perspective, the quality of coaching improved (effect size for one of three quality metrics: (A) over cap = 0.71, 95% CI 0.57-0.83). These results provide a baseline for testing the influence of additional user model features on the validity of recommendations generated by knowledge-based multi-domain HRSs.

关键词： Behavioral sciences digital health behavior change interventions disease prevention eHealth filtering algorithms knowledge based systems recommender systems user evaluation user modeling

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Single-Frame Infrared Small-Target Detection: A Survey

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IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE 2022年第2期10卷 87-119页

作者： Zhao, Mingjing Li, Wei Li, Lu Hu, Jin Ma, Pengge Tao, Ran Beijing Inst Technol Beijing 100081 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Key Lab Fract Signals & Syst Beijing 100081 Peoples R China Beijing Informat Sci & Technol Univ Sch Automat Beijing 100101 Peoples R China Zhengzhou Univ Aeronaut Sch Intelligent Engn Zhengzhou 450015 Peoples R China

Compared with radar and visible light imaging, infrared imaging has its own unique advantages, and in recent years, it has become a topic of intense research interest. Robust small-target detection is one of the key techniques in infrared search and tracking (IRST) applications, and there is no doubt that it has become an investigatory hot spot. In real applications, targets and backgrounds usually change quickly with very high velocities. In addition, a rapidly moving sensor platform typically makes the motion traces of the targets inconsistent. These factors reduce the detection performance of spatiotemporal-based methods, and thus single-frame infrared small-target detection is even more essential. In this survey, existing single-frame infrared small-target detection methods are comprehensively reviewed.

关键词： Information filters Image edge detection Signal to noise ratio Radar imaging filtering algorithms Adaptive filters Infrared imaging

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Vehicle Tracking on Satellite Video Based on Historical Model

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 7784-7796页

作者： Chen, Shili Wang, Taoyang Wang, Hongshuo Wang, Yunming Hong, Jianzhi Dong, Tiancheng Li, Zhen Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Beijing Aerosp Automat Control Inst Beijing 100854 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan 430079 Peoples R China China Acad Space Technol Inst Remote Sensing Satellite Beijing 100094 Peoples R China

Vehicle tracking on satellite videos poses a challenge for the existing object tracking algorithms due to the few features, object occlusion, and similar objects appearance. To improve the performance of the object tracking algorithm, a historical-model-based tracker intended for satellite videos is proposed in this study. It updates the tracker by using the historical model of each frame in the video, which contains plenty of object information and background information, so as to improve tracking ability on few-feature objects. Furthermore, a historical model evaluation scheme is designed to obtain reliable historical models, which ensures that the tracker is sensitive to the object in the current frame, thus avoiding the impact caused by changes in object appearance and background. Besides, to solve the drift issue of the tracker caused by object occlusion and the appearance of similar objects, an antidrift tracker correction scheme is proposed as well. According to the comparative experiments conducted on satellite videos dataset SatSOT, our tracker produces an excellent performance. Moreover, sensitivity analysis, varying criteria comparative experiments, and ablation experiments are conducted to demonstrate that the proposed schemes are effective in improving the precision and success rate of the tracker.

关键词： Satellites filtering algorithms Correlation Object tracking Feature extraction Monitoring Remote sensing Correlation filter (CF) high-confidence tracking motion estimation object tracking satellite video

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An Intermittent FxLMS Algorithm for Active Noise Control Systems With Saturation Nonlinearity

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2022年 30卷 2347-2356页

作者： Tian, Xing Huang, Jie Feng, Xuelei Shen, Yong Nanjing Univ Key Lab Modern Acoust Nanjing 210093 Peoples R China Nanjing Univ Inst Acoust Nanjing 210093 Peoples R China

In practical active noise control systems, saturation nonlinearity is one of the most common nonlinear effects, which can occur at the electronic circuits in the secondary path. Adaptive algorithms designed for linear systems, such as the filtered-x least mean square (FxLMS) algorithm, may diverge in nonlinear systems with a saturated secondary path and result in limited noise reduction performance. Various nonlinear algorithms with improved stability have been proposed;however, a trade-off between noise reduction performance and computational complexity exists when selecting a satisfactory algorithm. This paper therefore proposes a simple algorithm that can achieve significant noise reduction with low computational complexity. The proposed algorithm adopts the same update equation as the FxLMS algorithm, but uses an intermittent update strategy to avoid unnecessary fluctuations of the weight vector when the output of the control filter lies in the saturation region. Simulation results under different conditions demonstrate the effectiveness and advantages of the proposed algorithm.

关键词： Simulation Noise reduction Process control filtering algorithms Control systems Stability analysis Computational complexity Active noise control filtered-x least mean square algorithm intermittent update saturation nonlinearity

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Visual SLAM Based on Semantic Segmentation and Geometric Constraints for Dynamic Indoor Environments

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IEEE ACCESS 2022年 10卷 69636-69649页

作者： Yang, Shiqiang Zhao, Cheng Wu, Zhengkun Wang, Yan Wang, Guodong Li, Dexin Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China China Aerosp Sci & Ind Corp 210 Res Inst 6 Xian 710048 Peoples R China

Simultaneous localization and mapping (SLAM), a core technology of mobile robots and autonomous driving, has received more and more attention in recent years. However, most of the existing visual SLAM algorithms do not consider the impact of dynamic objects on the visual SLAM system, resulting in significant system positioning errors and high map redundancy. Based on the ORB-SLAM2 algorithm, this paper combines semantic information and a geometric constraint algorithm based on feature point homogenization to improve the positioning accuracy of the SLAM system. Aiming at the problem that the feature points extracted by the ORB-SLAM2 algorithm are easily concentrated, and the extraction rate is low in the weak texture area, a feature point homogenization algorithm based on quadtree and adaptive threshold is proposed to improve the uniformity of feature point extraction. In addition, in view of the impact of dynamic targets on the SLAM system, the dynamic information in the scene is filtered out through the semantic segmentation network and the motion consistency detection algorithm, and the static feature points obtained after filtering are used to estimate the camera pose. Then, a semantic map is constructed after filtering out the dynamic point cloud according to the semantic information. Finally, the test results on Oxford and TUM datasets show that the uniformity of feature points extracted by the improved algorithm is increased by 56.3%. The positioning error of visual SLAM is reduced by 68.8% on average, and the constructed semantic map has rich semantic information and less redundancy.

关键词： Feature extraction Semantics Heuristic algorithms Simultaneous localization and mapping Information filters filtering algorithms Data mining Visual SLAM ORB-SLAM2 dynamic feature filtering semantic map

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Extraction of Fetal ECG From Abdominal and Thorax ECG Using a Non-Causal Adaptive Filter Architecture

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IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022年第5期16卷 981-990页

作者： Dhas, Edwin D. Suchetha, M. Vellore Inst Technol Ctr Healthcare Adv Innovat & Res SENSE Chennai 600127 Tamil Nadu India Vellore Inst Technol Ctr Healthcare Adv Innovat & Res Chennai 600127 Tamil Nadu India

Extracting the Electrocardiogram (ECG) of a fetus from the ECG signal of the maternal abdomen is a challenging task due to different artifacts. The paper proposes a N-tap non-causal adaptive filter (NC-AF) that update the weight by considering the N number of past weights and N - 1 number of the reference signal and error signal samples after the processing sample number n. Using the maternal abdominal signal as the primary signal and thorax signal as the reference input, the output e(n) is obtained from the mean of N number of errors. The filtering performance of NC-AF was evaluated using the Synthetic dataset and Daisy dataset with the metrics such as correlation coefficient (gamma), peak root mean square difference (PRD), the output signal to noise ratio (SNR), root mean square error (RMSE), and fetal R-peak detection accuracy (FRPDA). The NC-AF provides a maximum correlation coefficient, PRD, SNR, RMSE and FRPDA of 0.9851, 83.04%, 8.52 dB, 0.208 and 97.09% respectively with filter length N = 38. The paper also proposes the architecture of NC-AF that can be implemented in hardware like FPGA. Further, the NC-AF was implemented on Virtex-7FPGA and its performance is evaluated in terms of resource utilization, throughput, and power consumption. For filter length N = 38 and wordlength L = 24, the maximum performance of the filter can be attained with a power consumption of 1.287W and a maximum clock frequency of 139.47 MHz.

关键词： Adaptive filters Electrocardiography filtering algorithms Fetus Field programmable gate arrays Thorax Echocardiography Fetal ECG extraction Field programmable gate array (FPGA) Filter architecture Least mean square (LMS) adaptive filter and Non-Causal systems

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Functional Parcellation of Human Brain Using Localized Topo-Connectivity Mapping

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2022年第10期41卷 2670-2680页

作者： Zhao, Yu Gao, Yurui Li, Muwei Anderson, Adam W. Ding, Zhaohua Gore, John C. Vanderbilt Univ Inst Imaging Sci Nashville TN 37232 USA Vanderbilt Univ Dept Radiol & Radiol Sci Nashville TN 37232 USA Vanderbilt Univ Dept Biomed Engn Nashville TN 37232 USA Vanderbilt Univ Dept Elect & Comp Engn Nashvile TN 37232 USA Vanderbilt Univ Dept Comp Sci Nashvile TN 37232 USA

The analysis of connectivity between parcellated regions of cortex provides insights into the functional architecture of the brain at a systems level. However, the derivation of functional structures from voxel-wise analyses at finer scales remains a challenge. We propose a novel method, called localized topo-connectivity mapping with singular-value-decomposition-informed filtering (or filtered LTM), to identify and characterize voxel-wise functional structures in the human brain from resting-state fMRI data. Here we describe its mathematical formulation and provide a proof-of-concept using simulated data that allow an intuitive interpretation of the results of filtered LTM. The algorithm has also been applied to 7T fMRI data acquired as part of the Human Connectome Project to generate group-average LTM images. Generally, most of the functional structures revealed by LTM images agree in the boundaries with anatomical structures identified by T1-weighted images and fractional anisotropy maps derived from diffusion MRI. In addition, the LTM images also reveal subtle functional variations that are not apparent in the anatomical structures. To assess the performance of LTM images, the subcortical region and occipital white matter were separately parcellated. Statistical tests were performed to demonstrate that the synchronies of fMRI signals in LTM-derived functional parcels are significantly larger than those with geometric perturbations. Overall, the filtered LTM approach can serve as a tool to investigate the functional organization of the brain at the scale of individual voxels as measured in fMRI.

关键词： Functional magnetic resonance imaging Image reconstruction White matter Three-dimensional displays filtering algorithms Biomedical engineering Anatomical structure Graph signal processing functional MRI localized topo-connectivity mapping functional connectivity functional parcellation

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A Binary Multi-Objective Chimp Optimizer With Dual Archive for Feature Selection in the Healthcare Domain

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IEEE ACCESS 2022年 10卷 1756-1774页

作者： Piri, Jayashree Mohapatra, Puspanjali Pradhan, Manas Ranjan Acharya, Biswaranjan Patra, Tapas Kumar Int Inst Informat Technol Dept CSE Bhubaneswar 751029 India Skyline Univ Coll Sharjah U Arab Emirates Kalinga Inst Ind Technol Deemed Univ Bhubaneswar 751024 India Coll Engn & Technol Bhubaneswar 751029 India

Medical datasets frequently include vast feature sets with numerous features that are related to one another. As a result, the curse of dimensionality affects learning from a medical dataset to discover significant characteristics, making it necessary to minimize the feature set. Feature selection (FS) is a major step in classification and also in reducing the dimension. This study attempts a novel Binary Multi-objective Chimp Optimization Algorithm (BMOChOA) with dual archive and k-nearest neighbors (KNN) classifier for mining relevant aspects from medical data. In this research, 12 versions of BMOChOA are implemented based on the group information and types of chaotic functions used. The best Pareto front obtained from suggested BMOChOA variations is compared with three benchmark multi-objective FS methods by taking 14 popular medical datasets of variable dimensions. By analyzing the experimental outputs using four multi-objective performance evaluators, it is found that the proposed FS method is superior in finding the best trade-off between the two objective functions: the number of features and classification performance.

关键词： Optimization Task analysis Medical diagnostic imaging Medical services filtering theory Licenses filtering algorithms Chimp optimization classification healthcare data mining feature selection multi-objective

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Target Capacity Filter Pruning Method for Optimized Inference Time Based on YOLOv5 in Embedded Systems

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IEEE ACCESS 2022年 10卷 70840-70849页

作者： Jeon, Jihun Kim, Jaemyung Kang, Jin-Ku Moon, Sungtae Kim, Yongwoo Inha Univ Dept Elect & Comp Engn Incheon 22212 South Korea Korea Univ Technol & Educ Dept Comp Sci & Engn Cheonan Si 31253 South Korea Sangmyung Univ Dept Syst Semicond Engn Cheonan Si 31066 South Korea

Recently, convolutional neural networks (CNNs), which exhibit excellent performance in the field of computer vision, have been in the spotlight. However, as the networks become wider for higher accuracy, the number of parameters and the computational costs increase exponentially. Therefore, it is challenging to use deep learning networks in embedded environments with limited resources, computational performance, and power. Moreover, CNNs consume a great deal of time for inference. To solve this problem, we propose a practical method for filter pruning to provide an optimal network architecture for target capacity and inference acceleration. After revealing the correlation between the inference time and the FLOPs, we proposed a method to generate a network with the desired inference time. Various object detection datasets were used to evaluate the performance of the proposed filter pruning method. The inference time of the pruned network was measured and analyzed using the NVIDIA Jetson Xavier NX platform. As a result of pruning the number of parameters and FLOPs of the YOLOv5 network in the PASCAL VOC dataset by 30%, 40%, and 50%, the mAP decreased by 0.6%, 2.3%, and 2.9%, respectively, while the inference time was improved by 14.3%, 26.4%, and 34.5%, respectively.

关键词： Object detection Detectors Proposals Information filters Convolution filtering algorithms Embedded systems CNN filter pruning FLOPs inference time YOLOv5

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Simultaneous Optimization of Mismatched Filters and Controlled Amplitude Signals for Long-Range MIMO Radars

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IEEE ACCESS 2022年 10卷 108251-108260页

作者： Bezousek, Pavel Karamazov, Simeon Univ Pardubice Fac Elect Engn & Informat Pardubice 53210 Czech Republic

This study presents a new signal and filter design procedure for long-range multiple-input multiple-output (MIMO) radars with a pulse length shorter than the return time of signals reflected by the most distant targets. The proposed algorithm adopts and radically improves the method of alternating the optimization of filters and signals, which was recently published by the same authors. It introduces full control of the signal envelope variation during the pulse, preserving acceptable peak-to-average power ratio (PAR) and signal-to-noise ratio (SNR) loss values. This state-of-the-art method allows calculation simplicity and is important for high-speed computations in adaptive applications. A gradient algorithm was used for the signal amplitude and phase optimization. The signal amplitude was controlled using a special signal construction consisting of two complex exponentials.

关键词： Optimization filtering algorithms Crosstalk Radar Matched filters MIMO radar Signal to noise ratio Iterative methods Quadratic programming Noise measurement Crosstalk Doppler effect iterative optimization gradient algorithm MIMO radar peak to average power ratio quadratic programming separation filter side lobe signal envelope signal to noise ratio

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