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Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering

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arXiv 2022年

作者： Hain, Antonia Jörgens, Daniel Moreno, Rodrigo Saarland University Faculty of Mathematics and Computer Science Campus E1.7 Saarbruecken Saarland66041 Germany Division of Brain Imaging and Behaviour Krembil Research Institute Toronto Western Hospital University Health Network TorontoON Canada KTH Royal Institute of Technology Department of Biomedical Engineering and Health Systems Halsovagen 11C Huddinge Stockholm14157 Sweden

Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, Spherical-deconvolution Informed Filtering of Tractograms (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable to judge the plausibility of individual streamlines since its results depend on the size and composition of the surrounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify streamlines with very consistent filtering results, which were used as pseudo ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of plausible and implausible streamlines with accuracy above 80%.MSC Codes 68T07, 68U10, 92C55, 94A12. © 2022, CC BY.

关键词： Magnetic resonance imaging

Resilience to denial-of-service and integrity attacks: A structured systems approach

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arXiv 2021年

作者： Ramasubramanian, Bhaskar Rajan, M.A. Chandra, M. Girish Cleaveland, Rance Marcus, Steven I. Network Security Lab Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Innovation Labs Tata Consultancy Services Karnataka Bangalore560066 India Institute for Systems Research University of Maryland College ParkMD20742 United States Department of Computer Science University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States

The resilience of cyberphysical systems to denial-of-service (DoS) and integrity attacks is studied in this paper. The cyberphysical system is modeled as a linear structured system, and its resilience to an attack is interpreted in a graph theoretical framework. The structural resilience of the system is characterized in terms of unmatched vertices in maximum matchings of the bipartite graph and connected components of directed graph representations of the system under attack. We first present conditions for the system to be resilient to DoS attacks when an adversary may block access or turn off certain inputs to the system. We extend this analysis to characterize resilience of the system when an adversary might additionally have the ability to affect the implementation of state-feedback control strategies. This is termed an integrity attack. We establish conditions under which a system that is structurally resilient to a DoS attack will also be resilient to a certain class of integrity attacks. Finally, we formulate an extension to the case of switched linear systems, and derive conditions for such systems to be structurally resilient to a DoS attack. © 2021, CC BY.

关键词： Embedded systems

TWIST-GAN: Towards wavelet transform and transferred GAN for spatio-temporal single image super resolution

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arXiv 2021年

作者： Dharejo, Fayaz Ali Deeba, Farah Zhou, Yuanchun Das, Bhagwan Jatoi, Munsif Ali Zawish, Muhammad Du, Yi Wang, Xuezhi Computer Network Information Center Chinese Academy of Sciences University of Chinese Academy of Sciences Bejing100190 China Department of Electronic Engineering Quaid-e-Awam University Engineering Science and Technology Nawabshah Pakistan Department of Biomedical Engineering Salim Habib University Karachi Pakistan Telecommunication Software and Systems Group Waterford Institute of Technology Rono-Hills Waterford Ireland

Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from a remotely sensed image with low spatial resolution. Recently, deep learning and generative adversarial networks (GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR). However, the generated image still suffers from undesirable artifacts such as, the absence of texture-feature representation and high-frequency information. We propose a frequency domain-based spatio-temporal remote sensing single image super-resolution technique to reconstruct the HR image combined with generative adversarial networks (GANs) on various frequency bands (TWIST-GAN).We have introduced a new method incorporating Wavelet Transform (WT) characteristics and transferred generative adversarial network. The LR image has been split into various frequency bands by using theWT, whereas, the transfer generative adversarial network predicts high-frequency components via a proposed architecture. Finally, the inverse transfer of wavelets produces a reconstructed image with super-resolution. The model is first trained on an external DIV2 K dataset and validated with the UC Merceed Landsat remote sensing dataset and Set14 with each image size of 256x256. Following that, transferred GANs are used to process spatio-temporal remote sensing images in order to minimize computation cost differences and improve texture information. The findings are compared qualitatively and qualitatively with the current state-of-art approaches. In addition, we saved about 43% of the GPU memory during training and accelerated the execution of our simplified version by eliminating batch normalization layers. © 2021, CC BY.

关键词： Wavelet transforms

SoK: Cross-border Criminal Investigations and Digital Evidence

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arXiv 2022年

作者： Casino, Fran Pina, Claudia López-Aguilar, Pablo Batista, Edgar Solanas, Agusti Patsakis, Constantinos Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Spain Information Management Systems Institute Athena Research Center Artemidos 6 Marousi15125 Greece European Judicial Cybercrime Network Eurojust The Hague Netherlands Anti-Phishing Working Group-Europe Av. Diagonal 621-629 Barcelona08028 Spain Department of Informatics University of Piraeus 80 Karaoli & Dimitriou str. Piraeus18534 Greece

Digital evidence underpin the majority of crimes as their analysis is an integral part of almost every criminal investigation. Even if we temporarily disregard the numerous challenges in the collection and analysis of digital evidence, the exchange of the evidence among the different stakeholders has many thorny issues. Of specific interest are cross-border criminal investigations as the complexity is significantly high due to the heterogeneity of legal frameworks which beyond time bottlenecks can also become prohibiting. The aim of this article is to analyse the current state of practice of cross-border investigations considering the efficacy of current collaboration protocols along with the challenges and drawbacks to be overcome. Further to performing a legally-oriented research treatise, we recall all the challenges raised in the literature and discuss them from a more practical yet global perspective. Thus, this article paves the way to enabling practitioners and stakeholders to leverage horizontal strategies to fill in the identified gaps timely and accurately. © 2022, CC BY-NC-SA.

关键词： Crime

Frequency-Aware Physics-Inspired Degradation Model for Real-World Image Super-Resolution

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arXiv 2021年

作者： Dong, Zhenxing Cao, Hong Shen, Wang Gan, Yu Ling, Yuye Zhai, Guangtao Su, Yikai John Hopcroft Center for Computer Science Shanghai Jiao Tong University China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Department of Electrical and Computer Engineering University of Alabama TuscaloosaAL35401 United States State Key Lab of Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University China

Current learning-based single image super-resolution (SISR) algorithms underperform on real data due to the deviation in the assumed degradation process from that in the real-world scenario. Conventional degradation processes consider applying blur, noise, and downsampling (typically bicubic downsampling) on high-resolution (HR) images to synthesize low-resolution (LR) counterparts. However, few works on degradation modelling have taken the physical aspects of the optical imaging system into consideration. In this paper, we analyze the imaging system optically and exploit the characteristics of the real-world LR-HR pairs in the spatial frequency domain. We formulate a real-world physics-inspired degradation model by considering both optics and sensor degradation;The physical degradation of an imaging system is modelled as a low-pass filter, whose cut-off frequency is dictated by the object distance, the focal length of the lens, and the pixel size of the image sensor. In particular, we propose to use a convolutional neural network (CNN) to learn the cutoff frequency of real-world degradation process. The learned network is then applied to synthesize LR images from unpaired HR images. The synthetic HR-LR image pairs are later used to train an SISR network. We evaluate the effectiveness and generalization capability of the proposed degradation model on real-world images captured by different imaging systems. Experimental results showcase that the SISR network trained by using our synthetic data performs favorably against the network using the traditional degradation model. Moreover, our results are comparable to that obtained by the same network trained by using real-world LR-HR pairs, which are challenging to obtain in real scenes. © 2021, CC BY.

关键词： Cutoff frequency

Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles

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arXiv 2022年

作者： Liang, Siyuan Wu, Hao Zhen, Li Hua, Qiaozhi Garg, Sahil Kaddoum, Georges Hassan, Mohammad Mehedi Yu, Keping The Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi’an710122 China The Computer School Hubei University of Arts and Science Xiangyang441000 China The Electrical Engineering Department École de Technologie Supérieure MontréalQCH3C 1K3 Canada The Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia The Global Information Telecommunication Institute Waseda University Tokyo169-8050 Japan

Driven by the ever-increasing requirements of autonomous vehicles, such as traffic monitoring and driving assistant, deep learning-based object detection (DL-OD) has been increasingly attractive in intelligent transportation systems. However, it is difficult for the existing DL-OD schemes to realize the responsible, cost-saving, and energy-efficient autonomous vehicle systems due to low their inherent defects of low timeliness and high energy consumption. In this paper, we propose an object detection (OD) system based on edge-cloud cooperation and reconstructive convolutional neural networks, which is called Edge YOLO. This system can effectively avoid the excessive dependence on computing power and uneven distribution of cloud computing resources. Specifically, it is a lightweight OD framework realized by combining pruning feature extraction network and compression feature fusion network to enhance the efficiency of multi-scale prediction to the largest extent. In addition, we developed an autonomous driving platform equipped with NVIDIA Jetson for system-level verification. We experimentally demonstrate the reliability and efficiency of Edge YOLO on COCO2017 and KITTI data sets, respectively. According to COCO2017 standard datasets with a speed of 26.6 frames per second (FPS), the results show that the number of parameters in the entire network is only 25.67 MB, while the accuracy (mAP) is up to 47.3%. © 2022, CC BY.

关键词： Object detection

A predictive machine learning approach for early fault identification in solar photovoltaic systems

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Electric Power systems Research 2025年 247卷

作者： Nora EL-Rashidy Zainab H. Ali Yara A. Sultan Department of Machine Learning and Information Retrieval Faculty of Artificial Intelligence Kafrelsheikh University El-Geish st Kafrelsheikh 33516 Egypt Department of Embedded Network Systems and Technology Faculty of Artificial Intelligence Kafrelsheikh University El-Geish st Kafrelsheikh 33516 Egypt Department of Electronics and Computer Engineering School of Engineering and Applied Sciences at Nile University Giza Egypt Mechatronics Department Faculty of Engineering Horus University-Egypt New Damietta Egypt

The reliability of photovoltaic (PV) panels plays a pivotal role in optimizing energy output, particularly as solar energy becomes an indispensable pillar in global initiatives aimed at achieving sustainable and decarbonized energy infrastructures. Since faults in PV panels can significantly impair system performance and reduce energy yield, early detection is essential to maintaining operational efficiency. This study proposes an advanced fault detection framework leveraging contemporary machine learning techniques to identify anomalies early, thereby preventing performance degradation and minimizing downtime. The proposed model incorporates single- and multi-objective feature selection strategies to enhance fault identification accuracy. Specifically, single-objective techniques utilize Information Gain (IG), Gini Ratio (GR), and Gini Importance (GI). At the same time, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is employed as a multi-objective method to select an optimal subset of fault-relevant features. The model was trained on a dataset comprising 600 instances. Results indicate that gradient boosting achieved 97.6 % accuracy using single-objective selection, whereas the NSGA-II-based approach reduced the feature set to 15 optimal variables and improved accuracy to 99.3 %. Implementing this model enhances the operational reliability of solar PV systems and supports the broader goal of advancing the sustainability of solar energy production.

关键词： Machine learning Optimization techniques Photovoltaic fault detection algorithms Photovoltaic systems

Application of spectral computing technics for color vision testing using virtual reality devices 18

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Application of spectral computing technics for color vision ...

18th Computational Imaging Conference, CI 2020

作者： Cwierz, Halina C. Diaz-Barrancas, Francisco Pardo, Pedro J. Perez, Angel Luis Suero, Maria Isabel Department of Computer and Network Systems Engineering University of Extremadura Centro Universitario de Mérida C/Santa Teresa de Jornet 38 MéridaE06800 Spain Department of Physics University of Extremadura Facultad de Ciencias Avda. de Elvas s/n BadajozE06071 Spain

Color deficiency tests are well known all over the world. However, there are not applications that attempt to simulate these tests with total color accuracy in virtual reality using spectral color computing. In this work a study has been made of the tools that exist in the market in VR environments to simulate the experience of users suffering from color vision deficiencies (CVD) and the VR tools that detect CVD. A description of these tools is provided and a new proposal is presented, developed using Unity Game Engine software and HTC Vive VR glasses as Head Mounted Display (HMD). The objective of this work is to assess the ability of normal and defective observers to discriminate color by means of a color arrangement test in a virtual reality environment. The virtual environment that has been generated allows observers to perform a virtual version of the Farnsworth-Munsell 100 Hue (FM 100) color arrangement test. In order to test the effectiveness of the virtual reality test, experiments have been carried out with real users, the results of which we will see in this paper. © 2020, Society for Imaging Science and Technology.

关键词： Color

Visual fidelity improvement in virtual reality through spectral textures applied to lighting simulations 18

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Visual fidelity improvement in virtual reality through spect...

18th Computational Imaging Conference, CI 2020

作者： Diaz-Barrancas, Francisco Cwierz, Halina C. Pardo, Pedro J. Perez, Angel Luis Suero, Maria Isabel Department of Computer and Network Systems Engineering University of Extremadura Centro Universitario de Mérida C/Santa Teresa de Jornet 38 MéridaE06800 Spain Department of Physics University of Extremadura Facultad de Ciencias Avda. de Elvas s/n BadajozE06071 Spain

Virtual reality has experienced a strong advance in recent years, its use in industries such as video games, automotive and medicine is now a reality. Previous work has determined that color is one of the most important characteristics when it comes to creating a sense of realism in the user. Therefore, through the use of spectral techniques we have found that it is possible to improve the fidelity in the reproduction of a real scene inside a virtual reality scene. In order to do this, we have developed a workflow that allows the user to compare real scenes with virtual scenes. The results of this test show that there exists an improvement of the visual fidelity in the reproduction of real scenes inside a virtual reality system compared with previous works. © 2020, Society for Imaging Science and Technology.

关键词： Virtual reality