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2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

作者： Fezai, Radhia Mansouri, Majdi Bouguila, Nasreddine Nounou, Hazem Nounou, Mohamed Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir 5019 Tunisia Electrical and Computer Engineering Program Texas AM University at Qatar Doha Qatar Chemical Engineering Program Texas AM University at Qatar Doha Qatar

ISBN: (纸本)9781728151847

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. © 2019 IEEE.

关键词： Fault detection

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Machine learning based Gaussian process regression for fault detection of Biological Systems

Machine learning based Gaussian process regression for fault...

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2019 International Conference on Internet of Things, Embedded Systems and Communications, IINTEC 2019

ISBN: (纸本)9781728151847

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. © 2019 IEEE.

关键词： Fault detection

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Optimizing Space Communications Using Deep Learning

Optimizing Space Communications Using Deep Learning

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2021 IEEE Cognitive Communications for Aerospace Applications Workshop, CCAAW 2021

作者： Robertson, Brianna I. Smith, Aaron Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA United States Nasa Glenn Research Center Cognitive Signal Processing Branch ClevelandOH United States

ISBN: (纸本)9781665412582

The paradigm shift from standard communication models to artificially intelligent solutions continues to be an exciting avenue of research. Through applying neural networks to a communication-learning model, the autonomous development of robust signal constellations emerges;however, optimal training architectures for these algorithms have not been determined. This work focuses on improving the modulation and coding solutions produced by deep neural networks by evaluating various training methods and configurations. The communications system is modeled with an autoencoder, an auto-associative neural network that learns how to transmit and recover input data, representing transmitter and receiver. Effective training of an autoencoder-based system encourages the model to learn modulation and coding schemes through optimizing symbol placement, which is achieved in this work through proper network normalization and adding channel noise. A normalization layer bounds symbol energy in a learned constellation, but these layers can impair the reproducibility of the training processes. This work assesses the effect of various normalization layers on the minimum distance between symbols in a learned constellation. Furthermore, a nonlinear noise technique, which intentionally mislabels symbols during training, is implemented to encourage symbol separation. This work concludes with a comparison between bit-wise versus symbol-wise training to determine if the learned constellation can achieve gray coding-like results. © 2021 IEEE.

关键词： Deep neural networks

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A statistical shape model using 2D-principal component analysis from few medical samples and its evaluation

A statistical shape model using 2D-principal component analy...

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2nd International Conference on Software engineering and Data Mining, SEDM 2010

作者： Tateyama, Tomoko Tanaka, Taishi Kohara, Shinya Foruzan, Amir Hossein Furukawa, Akira Chen, Yen-Wei Intelligent Image Processing Lab College of Information Science and Engineering Ritsumeikan University Shiga Japan Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Radiology Shiga University of Medical Science Shiga Japan

ISBN: (纸本)9788988678213

Since the medical training samples are very limited, it is difficult to construct a statistical shape model with good generalization using few samples. In this paper, we propose a novel statistical shape modeling method using 2D PCA. The 3D shape is represented as a matrix by spherical parameterization. The experiments showed that our proposed method can reconstruct statistical shape model with good generalization even using fewer samples.

关键词： Principal component analysis

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A novel approach for increasing security and data embedding capacity in images for data hiding applications

A novel approach for increasing security and data embedding ...

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International Conference on Information Technology: Coding and Computing (ITCC)

作者： F. Alturki R. Mersereau Department of Electrical and Computer Engineering Center of Signal and Image Processing Georgia Institute of Technology Atlanta GA USA

ISBN: (纸本)0769510620

Data hiding in multimedia is the process of secretly embedding information into data sources such as image, video, or audio signals without changing the perceptual quality of the data source. We present a novel blind data hiding technique for hiding information in still images. This technique is based on embedding the information in the transform domain, after decorrelating the samples in the spatial domain. This results in a significant increase in the number of transform coefficients that can be used to transmit the hidden information. The technique is suitable for a variety of data hiding applications such as steganography, data authentication and captioning. The technique achieves a higher and more secure data embedding rate than existing data embedding transform domain techniques developed for these particular applications.

关键词： Data security Data encapsulation Decorrelation Information security signal processing Copyright protection Steganography Frequency Quantization Application software

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Noise can speed Markov chain Monte Carlo estimation and quantum annealing

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Physical Review E 2019年第5期100卷 053309-053309页

作者： Brandon Franzke Bart Kosko Center for Quantum Information Science and Technology Signal and Image Processing Institute Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90089 USA

Carefully injected noise can speed the average convergence of Markov chain Monte Carlo (MCMC) estimates and simulated annealing optimization. This includes quantum annealing and the MCMC special case of the Metropolis-Hastings algorithm. MCMC seeks the solution to a computational problem as the equilibrium probability density of a reversible Markov chain. The algorithm must cycle through a long burn-in phase until it reaches equilibrium because the Markov samples are statistically correlated. The special injected noise reduces this burn-in period in MCMC. A related theorem shows that it reduces the cooling time in simulated annealing. Simulations showed that optimal noise gave a 76% speed-up in finding the global minimum in the Schwefel optimization benchmark. The noise-boosted simulations found the global minimum in 99.8% of trials compared with only 95.4% of trials in noiseless simulated annealing. Simulations also showed that the noise boost is robust to accelerated cooling schedules and that noise decreased convergence times by more than 32% under aggressive geometric cooling. Molecular dynamics simulations showed that optimal noise gave a 42% speed-up in finding the minimum potential energy configuration of an eight-argon-atom gas system with a Lennard-Jones 12-6 potential. The annealing speed-up also extends to quantum Monte Carlo implementations of quantum annealing. Noise improved ground-state energy estimates in a 1024-spin simulated quantum annealing simulation by 25.6%. The quantum noise flips spins along a Trotter ring. The noisy MCMC algorithm brings each Markov step closer on average to equilibrium if an inequality holds between two expectations. Gaussian or Cauchy jump probabilities reduce the noise-benefit inequality to a simple quadratic inequality. Simulations show that noise-boosted simulated annealing is more likely than noiseless annealing to sample high probability regions of the search space and to accept solutions that increase the search bre

关键词： Fluctuations & noise Noise

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Partial shape recognition: a landmark-based approach

Partial shape recognition: a landmark-based approach

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IEEE International Conference on Systems, Man and Cybernetics

作者： N. Ansari E.J. Delp Center for Communications and Signal Processing Electrical and Computer Engineering Department New Jersey Institute of Technology Newark NJ USA Computer Vision and Image Processing Laboratory School of Electrical Engineering Purdue University IN USA

A technique to guide landmark matching known as hopping dynamic programming is described. The location of the model in the scene is estimated with a least-squares fit. A heuristic measure is then computed to decide if the model is in the scene. The shape features of an object are the landmarks associated with the object. The landmarks of an object are defined as the points of interest of the object that have important shape attributes. Examples of landmarks are corners, holes, protrusions, and high-curvature points.< >

关键词： Shape measurement Layout computer vision Pattern recognition signal processing image processing Laboratories Performance evaluation Least squares approximation Feature extraction

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FAST NON-LINEAR METHODS FOR DYNAMIC TEXTURE PREDICTION

FAST NON-LINEAR METHODS FOR DYNAMIC TEXTURE PREDICTION

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IEEE International Conference on image processing

作者： Sameeksha Katoch Pavan Turaga Andreas Spanias Cihan Tepedelenlioglu School of Electrical Computer Energy Engineering Sensor Signal and Information Processing (SenSIP) Center

This paper aims to develop a fast dynamic-texture prediction method, using tools from non-linear dynamical modeling, and fast approaches for approximate regression. We consider dynamic textures to be described by patch-level non-linear processes, thus requiring tools such as delay-embedding to uncover a phase-space where dynamical evolution can be more easily modeled. After mapping the observed time-series from a dynamic texture video to its recovered phase-space, a time-efficient approximate prediction method is presented which utilizes locality-sensitive hashing approaches to predict possible phase-space vectors, given the current phase- space vector. Our experiments show the favorable performance of the proposed approach, both in terms of prediction fidelity, and computational time. The proposed algorithm is applied to shading prediction in utility scale solar arrays.

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A multirate pilot-symbol-assisted channel estimator for OFDM transmitter diversity systems

A multirate pilot-symbol-assisted channel estimator for OFDM...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： K.F. Lee D.B. Williams Center for Signal and Image Processing School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA Motorola Inc. Boynton Beach Florida USA Center for Signal and Image Processing Georgia Institute of Technology Atlanta Georgia

Orthogonal frequency division multiplexing (OFDM) transmitter diversity techniques have been shown to be efficient means of achieving near optimal diversity gain in frequency-selective fading channels. For these systems, knowledge of the channel parameters is required at the receivers for diversity combining and decoding. In this paper, we propose a low complexity, bandwidth efficient, pilot-symbol-assisted channel estimator for multiple transmitter OFDM systems. The pilot symbols are constructed to be non-overlapping in frequency to allow for the simultaneous sounding of the multiple channels. The time-varying channel responses are tracked by interpolating a set of estimates obtained through periodically transmitted pilot symbols. The effectiveness and limitations of the proposed estimator are verified by simulations.

关键词： OFDM Transmitters Diversity reception Discrete Fourier transforms Transmitting antennas Channel estimation Data communication Diversity methods Frequency-selective fading channels Bandwidth

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A two-piece R-D model for hybrid video coding and its application in fast mode decision

A two-piece R-D model for hybrid video coding and its applic...

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2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012

作者： Aminlou, Alireza Fahim-Hashemi, Hana Hashemi, Mahmoud Reza Gabbouj, Moncef Fatemi, Omid Multimedia Processing Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Signal Processing Tampere University of Technology Tampere Finland Viterbi School of Engineering University of Southern California Los Angeles CA United States Nano-Electronic Center of Excellence University of Tehran Tehran Iran

ISBN: (纸本)9781467316590

The mode decision process has a significant effect on the quality and complexity of a video encoder. The conventional method that fully codes each macro block for different modes results in the best quality performance, but it suffers from high computational complexity. On the other hand, some other methods ignore the residual part and use the prediction data, or adopt early mode selection approaches in order to reduce the computational cost. These approaches have a negative impact on the coding performance. In this paper, we have used a simple model for the residual coding part and proposed a two-piece R-D model for a macro block. Based on this model, we have introduced a mode decision algorithm that reduces the bit-rate by up to 11.62% at the expense of just 0.5% computational overhead. © 2012 IEEE.

关键词： Cost reduction

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