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4th International Conference on Green Communication, Network, and Internet of Things, CNIoT 2024

作者： Wang, Yuanting Wu, Guoliang Inner Mongolia Electric Power Co. Ltd. Baotou Power Supply Company Baotou China

ISBN: (纸本)9781510685710

Internet of Things (IoT) devices are vulnerable to various cyber-attacks. Therefore, it is significantly crucial to design an effective intrusion detection system (IDS) for IoT security. However, IoT devices have limited resources such as computing resources needing to handle a great of data, which further increases the difficulty of precise intrusion detection. Moreover, most IDSs lack transparency. This study develops a feature selection-based hidden Markov model (HMM) for intrusion detection. We first establish a modified approach based on a fuzzy rough set for feature selection to select optimal features so that the computational burden can be reduced. Furthermore, an interpretable intrusion detection model is established based on the HMM. In addition, intrusion detection is achieved in the presence of partially missing data through the expectation-maximization algorithm. According to the simulation results, the proposed method is able to detect intrusion detection in IoT networks effectively. © 2024 SPIE.

关键词： Internet of things Computer intrusion detection expectation maximization algorithms Data modeling Cyberattacks Fuzzy logic Network security Feature extraction Chromium Education and training

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Review of identification technologies for stray animal governance 5

Review of identification technologies for stray animal gover...

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5th International Conference on Signal Processing and Computer Science, SPCS 2024

作者： Han, Puze College of Science and Engineering University of Minnesota Twin Cities Minneapolis55455 United States

ISBN: (数字)9781510686731

ISBN: (纸本)9781510686724

In recent years, societal changes have led to a growing prominence of pets in people's lives. However, uncontrolled pet reproduction in urban areas has given rise to a significant issue of stray animals, posing serious threats to human health and the environment. Conventional manual methods for counting stray animals face challenges in terms of efficiency and the risk of disease transmission. With technological advancements, image recognition, and sound identification, among other techniques, have emerged as crucial tools to address this issue. Image recognition, leveraging intuitive statistics based on external features, combined with the low-power attributes of sound identification and the health assessment capabilities of thermal imaging, collectively provide comprehensive technological support for stray animal population statistics. In the realm of image algorithms, both traditional target detection algorithms and deep learning methods such as RCNN and Faster RCNN employ convolutional neural networks to accurately identify and locate stray animals. Regarding sound algorithms, traditional Gaussian mixture models and hidden Markov models, as well as deep learning techniques involving convolutional neural networks, have effectively enhanced the accuracy of stray animal sound recognition. The integration of image and audio in a hybrid method significantly enhances stray animal monitoring. Employing advanced techniques in video tracking and sound recognition, this approach offers an efficient and practical solution, crucial for wildlife ecosystem surveillance and conservation. Research indicates that the application of deep learning methods in the domains of image and sound has significantly advanced compared to traditional approaches. In terms of image processing, I utilized the YOLO algorithm to perform grid division, feature extraction, and loss computation steps to achieve stray animal detection, demonstrating outstanding performance. Through the application of t

关键词： Animals Detection and tracking algorithms Object detection Animal model studies Image processing Education and training Diseases and disorders Positron emission tomography Deep learning expectation maximization algorithms

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Maximum-Likelihood Estimation of Thickness with a Linear-Scatter Model for Mammography

Maximum-Likelihood Estimation of Thickness with a Linear-Sca...

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Smith, Bryce Dey, Joyoni Louisiana State Univ Dept Phys & Astron Baton Rouge LA 70808 USA

ISBN: (数字)9781510649385

ISBN: (纸本)9781510649385;9781510649378

The objective of this work was to create a MLEM software-based scatter correction algorithm for removing the effect of Compton Scatter from mammography images acquired without scatter grid or with analyzer-less interferometry. We developed an MLEM algorithm with an efficient linear scatter model to estimate the thickness of compressed breast and evaluated the algorithm with breast images acquired with the GEANT4 Monte Carlo software. The thicknesses estimated from the algorithm on the GEANT4 images were compared to the true geometric thicknesses of the ellipsoid for each pixel of the detector and matched to within ~2mm RMS error.

关键词： X-rays Monte Carlo methods X-ray detectors Mammography Sensors X-ray sources X-ray imaging Breast expectation maximization algorithms Tissues

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A Deep Kernel Method for PET Image Reconstruction

A Deep Kernel Method for PET Image Reconstruction

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Conference on Medical Imaging - Image Processing

作者： Li, Siqi Wang, Guobao Univ Calif Davis Med Ctr Dept Radiol Sacramento CA 95817 USA

ISBN: (数字)9781510649408

ISBN: (纸本)9781510649408;9781510649392

Image reconstruction for positron emission tomography (PET) is challenging because of the ill-conditioned tomographic problem and low counting statistics. Kernel methods address this challenge by using kernel representation to incorporate image prior information in the forward model of iterative PET image reconstruction. Existing kernel methods construct the kernels commonly using an empirical procedure, which may lead to suboptimal performance. In this paper, we describe the equivalence between the kernel representation and a trainable neural network model. A deep kernel method is proposed with the training process utilizing available image prior to seek the best way to form a set of robust kernels optimally rather than empirically. The results from computer simulations and a real patient dataset demonstrate that the proposed deep kernel method can outperform existing kernel method and neural network method for dynamic PET image reconstruction.

关键词： Positron emission tomography Data modeling Image restoration Neural networks Reconstruction algorithms expectation maximization algorithms Signal to noise ratio Feature extraction Image processing Computer simulations

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Multi-Atlas-based Tissue Identification in the Lower Leg using pQCT

Multi-Atlas-based Tissue Identification in the Lower Leg usi...

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Medical Imaging Conference - Image Processing

作者： Makrogiannis, Sokratis Okorie, Azubuike Biswas, Taposh Ferrucci, Luigi Delaware State Univ Div Phys Engn Math & Comp Sci Dover DE 19901 USA NIA NIH Baltimore MD 21224 USA

ISBN: (纸本)9781510633940

Accurate and reproducible tissue identification techniques are essential for understanding structural and functional changes that either occur naturally with aging, or because of chronic disease, or in response to intervention therapies. These image analysis techniques are frequently utilized for characterization of changes in bone architecture to assess fracture risk, and for the assessment of loss of muscle mass and strength defined as sarcopenia. Peripheral quantitative computed tomography (pQCT) is widely employed for tissue identification and analysis. Advantages of pQCT scanners are compactness, portability, and low radiation dose. However, these characteristics imply limitations in spatial resolution and SNR. Therefore, there is still a need for segmentation methods that address image quality limitations and artifacts such as patient motion. In this paper, we introduce multi-atlas segmentation (MAS) techniques to identify soft and hard tissues in pQCT scans of the proximal tibia (similar to 66% of tibial length) and to address the above factors that limit delineation accuracy. To calculate the deformation fields, we employed multi-grid free-form deformation (FFD) models with B-splines and a symmetric extension of the log-domain diffeomorphic demons (SDD). We then applied majority voting and Simultaneous Truth And Performance Level Estimation (STAPLE) for label fusion. We compared the results of our MAS methodology for each deformable registration model and each label fusion method, using Dice similarity coefficient scores (DSC). The results show that our technique utilizing SDD with STAPLE produces very good accuracy (DSC mean of 0.868) over all tissues, even for scans with considerable quality degradations caused by motion artifacts.

关键词： Tissues Image segmentation Bone expectation maximization algorithms Image quality Motion models Clinical trials Signal to noise ratio Fusion energy Medical imaging

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The Effectiveness of a Probabilistic Principal Component Analysis Model and expectation Maximisation Algorithm in Treating Missing Daily Rainfall Data

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ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES 2020年第1期56卷 119-129页

作者： Chuan, Zun Liang Deni, Sayang Mohd Fam, Soo-Fen Ismail, Noriszura Univ Malaysia Pahang Fac Ind Sci & Technol Gambang Kuantan 26300 Pahang Dm Malaysia Univ Teknol MARA Fac Comp & Math Sci UiTM Shah Alam 40450 Malaysia Univ Tekn Malaysia Melaka Fac Technol Management & Technoprenetuship Durian Tunggal 76100 Melaka Malaysia Univ Kebangsaan Malaysia Fac Sci & Technol Sch Math Sci Bangi 43600 Selangor De Malaysia

The reliability and accuracy of a risk assessment of extreme hydro-meteorological events are highly dependent on the quality of the historical rainfall time series data. However, missing data in a time series such as this could result in lower quality data. Therefore, this paper proposes a multiple-imputation algorithm for treating missing data without requiring information from adjoining monitoring stations. The proposed imputation algorithms are based on the M-component probabilistic principal component analysis model and an expectation maximisation algorithm (MPPCA-EM). In order to evaluate the effectiveness of the MPPCA-EM imputation algorithm, six distinct historical daily rainfall time series data were recorded from six monitoring stations. These stations were located at the coastal and inland regions of the East-Coast Economic Region (ECER) Malaysia. The results of analysis show that, when it comes to treating missing historical daily rainfall time series data recorded from coastal monitoring stations, the 2-component probabilistic principal component analysis model and expectation-maximisation algorithm (2PPCA-EM) were found to be superior to the single- and multiple-imputation algorithms proposed in previous studies. On the contrary, the single-imputation algorithms as proposed in previous studies were superior to the MPPCA-EM imputation algorithms when treating missing historical daily rainfall time series data recorded from inland monitoring stations.

关键词： expectation maximization algorithms Missing daily rainfall Probabilistic principal component analysis model VIKOR technique

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An Assessment of PET Dose Reduction with Penalized Likelihood Image Reconstruction using a Computationally Efficient Model Observer

An Assessment of PET Dose Reduction with Penalized Likelihoo...

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Gifford, Howard C. Schmidtlein, C. Ross Krol, Andrzej Xu, Yuesheng Univ Houston Houston TX 77004 USA Mem Sloan Kettering Canc Ctr 1275 York Ave New York NY 10021 USA SUNY Upstate Med Univ Syracuse NY 13210 USA Old Dominion Univ Norfolk VA USA

ISBN: (纸本)9781510633926

Developing PET reconstruction algorithms with improved low-count capabilities may provide a timely and cost-effective means of reducing radiation dose in promising clinical applications such as immuno-PET that require long-lived radiotracers. For many PET clinics, the reconstruction protocol consists of postsmoothed ordered-sets expectation-maximization (OSEM) reconstruction, but penalized likelihood methods based on total-variation (TV) regularization could substantially reduce dose. We performed a task-based comparison of postsmoothed OSEM and higher-order TV (HOTV) reconstructions using simulated images of a contrast-detail phantom. An anthropomorphic visual-search model observer read the images in a location-known receiver operating characteristic (ROC) format. Acquisition counts, target uptake, and target size were study variables, and the OSEM postfiltering was task-optimized based on count level. A psychometric analysis of observer performance for the selected task found that the HOTV algorithm allowed a two-fold reduction in dose compared to the optimized OSEM algorithm.

关键词： Positron emission tomography Reconstruction algorithms Data modeling expectation maximization algorithms Algorithm development Cancer Visual process modeling Image quality

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Tests of Projection and Reconstruction Domain Equivalence for a Feature-driven Model Observer

Tests of Projection and Reconstruction Domain Equivalence fo...

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Conference on Medical Imaging: Image Perception, Observer Performance, and Technology Assessment

作者： Gifford, Howard C. Karbaschi, Zohreh Univ Houston Houston TX 77004 USA

ISBN: (纸本)9781510625525

Nonlinear visual-search (VS) observers have shown an ability to model humans for realistic detection, localization, and classification tasks with tomographic reconstructions. As reconstruction studies test the joint effects of data acquisition and postprocessing, applying observers for similar tasks in the projection domain is also of interest. To help investigate what information a nonideal model can provide in this role, we have developed an analytical method for assessing acquisition quality in the reconstruction domain. This approach is most useful for assessing acquisition quality based on lesion-localization tasks. Observer studies with simulated Ga-67 SPECT data were conducted to test our method. The data were acquired for different numbers of projection angles. The results illustrate how image reconstruction processes can improve on acquisition quality as measured for an anthropomorphic model observer.

关键词： expectation maximization algorithms Reconstruction algorithms Data acquisition Feature extraction Image processing Image quality Data modeling Image restoration Visual process modeling Visualization

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A Probabilistic Approach for the Registration of Images with Missing Correspondences

A Probabilistic Approach for the Registration of Images with...

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Conference on Medical Imaging: Image Processing

作者： Krueger, Julia Ehrhardt, Jan Schultz, Sandra Handels, Heinz Univ Lubeck Inst Med Informat Lubeck Germany

ISBN: (纸本)9781510625464

The registration of two medical images is usually based on the assumption that corresponding regions exist in both images. If this assumption is violated by e. g. pathologies, most approaches encounter problems. The here proposed registration method is based on the use of probabilistic correspondences between sparse image representations, leading to a robust handling of potentially missing correspondences. A maximum-a-posteriori framework is used to derive the optimization criterion with respect to deformation parameters that aim to compensate not only spatial differences between the images but also appearance differences. A multi-resolution scheme speeds-up the optimization and increases the robustness. The approach is compared to a state-of-theart intensity-based variational registration method using MR brain images. The comprehensive quantitative evaluation using images with simulated stroke lesions shows a significantly higher accuracy and robustness of the proposed approach.

关键词： Image registration 3D image processing expectation maximization algorithms Pathology Image segmentation Brain Medical imaging Statistical modeling

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A Bootstrapped PMHT with Feature Measurements and a New Way to Derive Its Information Matrix 26

A Bootstrapped PMHT with Feature Measurements and a New Way ...

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Conference on Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI

作者： Lu, Qin Domrese, Katherine Willett, Peter Bar-Shalom, Yaakov Pattipati, Krishna Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9781510609013;9781510609020

The probabilistic multiple-hypothesis tracker (PMHT), a tracking algorithm of considerable theoretical elegance based on the expectation-maximization (EM) algorithm, will be considered for the problem of multiple target tracking (MTT) with multiple sensors in clutter. Aside from position observations, continuous measurements associated with the unique and constant feature of each target are incorporated to jointly estimate the states and feature of the targets for the sake of tracking and classification, leading to a bootstrapped implementation of the PMHT. In addition, we rederived the information matrix for the big state vector stacking states for all the targets at all the time steps during the observation time. Simulation results have been conducted for both closely spaced and well separated scenarios with and without feature measurements. The normalized estimation error squared (NEES) calculated using the information matrix for both scenarios with and without feature measurements are within the 95% probability region. In other words, the estimates are consistent with the corresponding covariances.

关键词： expectation maximization algorithms Detection and tracking algorithms Sensors Feature extraction Motion measurement Motion models Algorithm development Matrices algorithms Time metrology

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