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International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)

作者： Radhia Fezai Majdi Mansouri Nasreddine Bouguila Hazem Nounou Mohamed Nounou Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha QATAR Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9781728151847

ISBN: (纸本)9781728151854

Effective detection of faults in Biological processes is essential to observe the continuity of good functioning of the system under typical circumstances for ensuring safety. Therefore, the first objective of this paper is to develop a machine learning based Gaussian process regression (GPR) technique that can accurately model biological processes and compute the monitored residuals. The second objective is to apply a generalized likelihood ratio test (GLRT) to the evaluated residuals for fault detection purposes. The detection performance of the GPR-based GLRT is evaluated using a biological process representing a Cad System in E. Coli (CSEC) model. The GPR-based GLRT is used to enhance monitoring of the Cad System in E. coli process through monitoring some of the key variables involved in this process, such as enzymes, lysine, and cadaverine.

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Reduced Gaussian process regression for fault detection of chemical processes

Reduced Gaussian process regression for fault detection of c...

引用

International Conference on Internet of Things, Embedded Systems and Communications (IINTEC)

ISBN: (数字)9781728151847

ISBN: (纸本)9781728151854

In this paper, a reduced Gaussian process regression (RGPR)-based generalized likelihood ratio test (GLRT) is proposed for fault detection in industrial systems. In contrast to the classical GPR technique, the RGPR model can handle domains with a large number of activities that require very large training sets. The developed RGPR-based GLRT method aims first to build a RGPR model, then, it consists to apply GLRT to the monitored residuals obtained from PGPR for fault detection purposes. The fault detection performance of the developed RGPR-based GLRT method is illustrated through the Tennessee Eastman process. The simulation results show that the RGPR-based GLRT method outperforms the conventional GPR-based GLRT technique in terms of miss detection rate and CPU-time.

关键词： Gaussian process regression (GPR) reduced Gaussian process regression (RGPR) Kmeans fault detection generalized likelihood ratio test (GLRT) chemical processes

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Three-dimensional Optical Coherence Tomography image denoising through Multi-Input Fully-Convolutional Networks

arXiv

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arXiv 2018年

作者： Abbasi, Ashkan Monadjemi, Amirhassan Fang, Leyuan Rabbani, Hossein Zhang, Yi Artificial Intelligence Department Faculty of Computer Engineering University of Isfahan Isfahan Iran College of Electrical and Information Engineering Hunan University Changsha China Department of Biomedical Engineering Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran College of Computer Science Sichuan University Chengdu China

In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Due to the coherent nature of the image formation process, optical coherence tomography (OCT) images are inevitably affected by noise. This paper proposes a new method named the multi-input fully-convolutional networks (MIFCN) for denoising of OCT images. In contrast to recently proposed natural image denoising CNNs, the proposed architecture allows the exploitation of high degrees of correlation and complementary information among neighboring OCT images through pixel by pixel fusion of multiple FCNs. The parameters of the proposed multi-input architecture are learned by considering the consistency between the overall output and the contribution of each input image. The proposed MIFCN method is compared with the state-of-the-art denoising methods adopted on OCT images of normal and age-related macular degeneration eyes in a quantitative and qualitative manner. Copyright © 2018, The Authors. All rights reserved.

关键词： Optical tomography

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FOCAL: A Cost-Aware Video Dataset for Active Learning

arXiv

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arXiv 2023年

作者： Kokilepersaud, Kiran Logan, Yash-Yee Benkert, Ryan Zhou, Chen Prabhushankar, Mohit AlRegib, Ghassan Corona, Enrique Singh, Kunjan Parchami, Mostafa OLIVES The Center for Signal and Information Processing CSIP United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Ford Motor Company DearbornMI United States

In this paper, we introduce the FOCAL (Ford-OLIVES Collaboration on Active Learning) dataset which enables the study of the impact of annotation-cost within a video active learning setting. Annotation-cost refers to the time it takes an annotator to label and quality-assure a given video sequence. A practical motivation for active learning research is to minimize annotation-cost by selectively labeling informative samples that will maximize performance within a given budget constraint. However, previous work in video active learning lacks real-time annotation labels for accurately assessing cost minimization and instead operates under the assumption that annotation-cost scales linearly with the amount of data to annotate. This assumption does not take into account a variety of real-world confounding factors that contribute to a nonlinear cost such as the effect of an assistive labeling tool and the variety of interactions within a scene such as occluded objects, weather, and motion of objects. FOCAL addresses this discrepancy by providing real annotation-cost labels for 126 video sequences across 69 unique city scenes with a variety of weather, lighting, and seasonal conditions. These videos have a wide range of interactions that are at the intersection of infrastructure-assisted autonomy and autonomous vehicle communities. We show through a statistical analysis of the FOCAL dataset that cost is more correlated with a variety of factors beyond just the length of a video sequence. We also introduce a set of conformal active learning algorithms that take advantage of the sequential structure of video data in order to achieve a better trade-off between annotation-cost and performance while also reducing floating point operations (FLOPS) overhead by at least 77.67%. We show how these approaches better reflect how annotations on videos are done in practice through a sequence selection framework. We further demonstrate the advantage of these approaches by introducing two

关键词： Budget control

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Enhancing Fault Diagnosis of Uncertain Grid-Connected Photovoltaic Systems using Deep GRU-based Bayesian optimization

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IFAC-PapersOnLine 2024年第4期58卷 449-454页

作者： Zahra Yahyaoui Mansour Hajji Majdi Mansouri Abdelmalek Kouadri Kais Bouzrara Hazem Nounou Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine 1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Signals and Systems Laboratory Institute of Electrical and Electronics Engineering University M Hamed Bougara of Boumerdes Boumerdes Algeria Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir Tunisia

The efficacy of photovoltaic systems is significantly impacted by electrical production losses attributed to faults. Ensuring the rapid and cost-effective restoration of system efficiency necessitates robust fault detection and diagnosis (FDD) procedures. This study introduces a novel interval-gated recurrent unit (I-GRU) based Bayesian optimization framework for FDD in grid-connected photovoltaic (GCPV) systems. The utilization of an interval-valued representation is proposed to address uncertainties inherent in the systems, the GRU is employed for fault classification, while the Bayesian algorithm optimizes its hyperparameters. Addressing uncertainties through the proposed approach enhances monitoring capabilities, mitigating computational and storage costs associated with sensor uncertainties. The effectiveness of the proposed approach for FDD in GCPV systems is demonstrated using experimental application.

关键词： Bayesian optimization fault detection fault diagnosis gated recurrent units grid-connected PV systems interval-data representation uncertainties

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Personalized Multimedia Recommendation Systems Using Higher-Order Tensor Singular-Value-Decomposition

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IEEE Transactions on Broadcasting 2024年第1期70卷 148-160页

作者： Chang, Shih Yu Wu, Hsiao-Chun Yan, Kun Chen, Xinjia Huang, Scott Chih-Hao Wu, Yiyan San Jose State University Department of Applied Data Science San JoseCA95192 United States Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Department of Information and Telecommunication Guilin541004 China Northwestern State University Department of Engineering Technology NatchitochesLA71497 United States National Tsing Hua University Department of Electrical Engineering Hsinchu300 Taiwan Communications Research Centre Canada OttawaK2H 8S2 Canada

Because of rapid growth of multimedia data over the Internet, the infobesity has been emerging in recent years. Many recommender systems (RSs) have been proposed using a variety of techniques, including artificial intelligence, machine learning, statistical analysis, and data mining. Nowadays, E-commerce companies such as Amazon, eBay, Netflix, etc. have applied RSs conveniently to provide better customer services than before by enabling customized multimedia-content recommendations. It is well known that classical RSs are almost all based on the user-item attribute matrices. However, this two-fold user-item relationship should be extended to the emerging many-fold relationship among an arbitrary number of attribute groups so that the corresponding new RSs may lead to better performances. Therefore, in this paper, we introduce a novel personalized multimedia recommender system (PMRS) according to our proposed new parallel higher-order tensor singular-value-decomposition (PHOTSVD) algorithm. This PHOTSVD algorithm is capable of accommodating an arbitrary number of entities such as social-media channels, users, queries, webpages, etc. in a robust RS. Furthermore, we also investigate the cold start and data bias problems critical to RSs. Finally, we compare the performances of our proposed new PMRS and three other existing tensor-based RSs over realworld data with respect to the well-adopted metric, namely normalized root-mean-square error (NRMSE). Our proposed new PMRS greatly outperforms the other three existing tensor-based RSs in terms of NRMSE. © 1963-12012 IEEE.

关键词： Singular value decomposition

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Quantification of Artifacts and image Distortions in 1.5 Tesla Magnetic Resonance images of a Commercial Multi-Channel Vaginal Cylinder Brachytherapy Applicator Set

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Journal of Biomedical Physics and engineering 2023年第6期13卷 523-534页

作者： Kanani, Abolfazl Fatemi-Ardakani, Ali Owrangi, Amir M. Yazdi, Mehran Baghbani, Hadi Mosleh-Shirazi, Mohammad Amin Ionizing and Non-Ionizing Radiation Protection Research Center INIRPRC School of Paramedical Sciences Shiraz University of Medical Sciences Shiraz Iran Department of Physics Jackson State University JSU JacksonMS United States SpinTecx JacksonMS United States Department of Radiation Oncology Community Health Systems CHS Cancer Network JacksonMS United States Department of Radiation Oncology UT Southwestern Medical Center 2280 Inwood Rd EC2.242 DallasTX75235 United States Signal and Image Processing Lab SIPL School of Electrical and Computer Eng Shiraz University Shiraz Iran Department of Radiology Namazi Hospital Shiraz University of Medical Sciences Shiraz Iran Physics Unit Department of Radio-Oncology School of Medicine Shiraz University of Medical Sciences Shiraz Iran

Background: The BEBIG Portio multi-channel applicator provides better target dose coverage and sparing organs-at-risk compared to a single-channel cylinder. However, artifacts and distortions of Portio in magnetic resonance images (MRI) have not yet been reported. Objective: We aimed to quantify the artifacts and distortions in its 1.5-Tesla MR images before clinical use. Material and Methods: In this experimental study, we employed a gelatinfilled phantom to conduct our measurements. T2-weighted (T2W) images were examined for artifacts and distortions. Computed tomography (CT) images were used as a reference to assess image distortions. Artifact severity was measured by recording the full-width-at-half-maximum (FWHM) image pixel values at various positions along the length of the applicator/channels. CT and MRI-based applicator reconstruction accuracy were then compared, and signal-to-noise ratio (SNR) and contrast were also determined for the applicator images. Results: The applicator distortion level for the Portio applicator was less than the image spatial resolution (0.5±0.5 pixels). The average FWHM for the tandem applicator images was 5.23±0.39 mm, while it was 3.21±0.37 mm for all channels (compared to their actual diameters of 5.0 mm and 3.0 mm, respectively). The average applicator reconstruction difference between CT and MR images was 0.75±0.30 mm overall source dwell positions. The image SNR and contrast were both acceptable. Conclusion: These findings indicate that the Portio applicator has a satisfactory low level of artifacts and image distortions in 1.5-Tesla, T2W images. It may, therefore, be a promising option for MRI-guided multi-channel vaginal brachytherapy. © Journal of Biomedical Physics and engineering.

关键词： Magnetic resonance imaging

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Audio signal processing using graphics processing units

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AES: Journal of the Audio engineering Society 2011年第1-2期59卷 3-19页

作者： Savioja, Lauri Välimäki, Vesa Smith, Julius O. NVIDIA Research and Department of Media Technology Aalto University School of Science Espoo Finland Department of Signal Processing and Acoustics Aalto University School of Electrical Engineering Espoo Finland Center for Computer Research in Music and Acoustics Stanford University Stanford CA 94305 United States

Current graphics processing units (GPUs) are massively parallel computing environments offering remarkable performance boosts in parallelizable tasks. Audio signal processing is a potential application area. Three different cases for GPU implementation are studied: additive synthesis, fast Fourier transforms (FFT), and time-domain convolution. For additive synthesis nearly two million sine waves were computable in real time on the GPU, giving a factor of 250-3000 performance gain over CPU implementation. Similarly, the GPU was able to perform an FFT eight times longer than the CPU version in the same time. For a stereo signal the GPU was able to compute a two-million-point FFT and inverse FFT in real time with an input buffer size of 1024 samples at a sampling rate of 48 kHz with 50% overlap. Finally the GPU could compute approximately 130 times longer FIR filters than the CPU in the same time. For stereo input and output, requiring four filters altogether, the GPU processing was able to implement FIR filters of length 376 000 taps in real time. The latency in all these tasks is tolerable, since the performance is nearly optimal with a hundred-sample buffer, which corresponds to a few milliseconds. In summary the results show that GPUs are highly useful for computationally intensive audio signal processing tasks.

关键词： Graphics processing unit

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Universal Steganalysis Based on Local Prediction Error in Wavelet Domain

Universal Steganalysis Based on Local Prediction Error in Wa...

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International Conference on Intelligent Information Hiding and Multimedia signal processing (IIHMSP)

作者： Anahita Shojaei-Hashemi Mostafa Mehdipour-Ghazi Shahrokh Ghaemmaghami Hamid Soltanian-Zadeh School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Electronics Research Institute Sharif University of Technology Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI USA Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

A passive universal image steganalysis method is proposed that is shown to be of higher detection accuracy than existing truly blind steganalysis methods including Farid's and the WAM. This is achieved by improving some weaknesses of Farid's steganalysis scheme in feature extraction, that is, instead of deriving an over-determined equation system for each sub band of the wavelet decomposition, the sub bands are divided into overlapping blocks and an over-determined equation system is constructed for each block. To guarantee the existence of finite answers, the over-determined equation systems are solved in a way different from Farid's by using Moore-Penrose pseudo-inverse concept. Further improvement to the performance is achieved by adding diagonal directions and increasing the number of moments. The comparative evaluations confirm the superiority of the proposed method over prevalent blind steganalysis schemes.

关键词： Equations Mathematical model Payloads Transform coding Correlation Educational institutions Reliability

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Multipath Effects in GPS Receivers: A Primer

引用

2015年

作者： Steven Miller Steven Miller Xue Zhang Andreas Spanias

ISBN: (数字)9781627054140

Autonomous vehicles use global navigation satellite systems (GNSS) to provide a position within a few centimeters of truth. Centimeter positioning requires accurate measurement of each satellite"s direct path propagation time. Multipath corrupts the propagation time estimate by creating a time-varying bias. A GNSS receiver model is developed and the effects of multipath are investigated. MATLABtm code is provided to enable readers to run simple GNSS receiver simulations. More specifically, GNSS signal models are presented and multipath mitigation techniques are described for various multipath conditions. Appendices are included in the booklet to derive some of the basics on early minus late code synchronization methods. Details on the numerically controlled oscillator and its properties are also given in the appendix.

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