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作者： Natarajan, Lakshmi Prasad Krishnan, Prasad Lalitha, V. Dau, Hoang Department of Electrical Engineering Iit Hyderabad Kandi502285 India Signal Processing and Communications Research Center International Institute of Information Technology Hyderabad Hyderabad500032 India Discipline of Computer Science and Information Technology School of Science Rmit University MelbourneVIC3000 Australia

An index code for broadcast channel with receiver side information is locally decodable if each receiver can decode its demand by observing only a subset of the transmitted codeword symbols instead of the entire codeword. Local decodability in index coding is known to reduce receiver complexity, improve user privacy and decrease decoding error probability in wireless fading channels. Conventional index coding solutions assume that the receivers observe the entire codeword, and as a result, for these codes the number of codeword symbols queried by a user per decoded message symbol, which we refer to as locality, could be large. In this paper, we pose the index coding problem as that of minimizing the broadcast rate for a given value of locality (or vice versa) and designing codes that achieve the optimal trade-off between locality and rate. We identify the optimal broadcast rate corresponding to the minimum possible value of locality for all single unicast problems. We present new structural properties of index codes which allow us to characterize the optimal trade-off achieved by: vector linear codes when the side information graph is a directed cycle;and scalar linear codes when the minrank of the side information graph is one less than the order of the problem. We also identify the optimal trade-off among all codes, including non-linear codes, when the side information graph is a directed 3-cycle. Finally, we present techniques to design locally decodable index codes for arbitrary single unicast problems and arbitrary values of locality. © 1963-2012 IEEE.

关键词： Economic and social effects

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Statistically consistent frequency estimation using linear prediction

Statistically consistent frequency estimation using linear p...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： S. Oh R.L. Kashyap Signal Processing Speech and Image Understanding Laboratories Computer Science Center Texas Instruments Inc. Dallas TX USA School of Electrical Engineering Purdue University West Lafayette IN USA

A new statistically consistent frequency estimation method has been developed and presented using linear prediction (LP) method. The observed signal is assumed to be a sum of complex exponentials with white random noise process. The number of complex exponentials is assumed to be known. A statistically consistent estimator is an estimator that converges to the true value as the number of observations increases to infinite. It is shown that conventional LP-based estimation methods such as Prony's method are not consistent statistically. It is also proved that the new estimation method provides statistically consistent estimates and performs better than Prony's estimation method. Numerical simulation is provided to confirm the performance of the new estimation method and its statistical consistency.< >

关键词： Frequency estimation Polynomials signal processing Maximum likelihood estimation signal to noise ratio White noise Gaussian noise Speech processing computer science Instruments

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Optical Memory Elements Based on Hetero-nipi Superlattice Asymmetric Cavity Spatial Light Modulators

Optical Memory Elements Based on Hetero-nipi Superlattice As...

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Spatial Light Modulators and Applications, SLM 1993

作者： Hibbs-Brenner, M.K. Mukherjee, S.D. Lehman, J. Ruden, P.P. Liu, J.J. Sawchuk, A.A. Hsu, W.-F. Honeywell Systems and Research Center 10701 Lyndale Avenue South BloomingtonMN55420 United States Dept. of Electrical Engineering and Computer Science University of Minnesota MinneapolisMN55455 United States Signal and Image Processing Institute University of Southern California Los AngelesCA90089-2564 United States

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Uncertain Fault Diagnosis of Grid-Connected PV Systems based Improved Data-Driven Paradigms 19

Uncertain Fault Diagnosis of Grid-Connected PV Systems based...

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19th IEEE International Multi-Conference on Systems, signals and Devices, SSD 2022

作者： Dhibil, Khaled Mansouri, Majdi Bouzrara, Kais Nounou, Hazem Nounou, Mohamed National Engineering School of Monastir Research Laboratory of Automation Signal Processing and Image 5019 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha23874 Qatar Prince Sultan Univer-sity Department of Mathematics and Sciences Riyadh11586 Saudi Arabia Chemical Engineering Program Texas A&m University at Qatar Doha23874 Qatar

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

The main idea behind this work is to diagnose Grid-Connected Photovoltaic (PV) systems. The uncertainty was treated by using the interval-valued data representation. The main interventions are threefold: first, interval ensemble techniques based on the combination of several models (SVM, KNN, and tree) into one improved model are proposed in order to isolate the different PV systems operating modes using interval raw data. Then, feature extraction and selection steps are proposed to improve the fault diagnosis results. Therefore, the interval KPCA (IKPCA) method is performed in order to extract and select the important characteristics. The proposed techniques were used to diagnose the GCPV system under different operating modes. The results demonstrated the superiority of the proposed methods. © 2022 IEEE.

关键词： Support vector machines

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UNSUPERVISED UNCERTAINTY ANALYSIS FOR VIDEO SALIENCY DETECTION

UNSUPERVISED UNCERTAINTY ANALYSIS FOR VIDEO SALIENCY DETECTI...

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Asilomar Conference on signals, Systems and computers

作者： Tariq Alshawi Zhiling Long Ghassan AlRegib Center for Signal and Information Processing (CSIP) School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 USA

ISBN: (纸本)9781467385770

This paper presents a new unsupervised uncertainty estimation method for video saliency detection using spatial cues of the saliency map. The algorithm exploits the relationship between a pixel and its spatial neighbours in saliency maps to estimate the uncertainty of the saliency detected at the pixel location. Unlike supervised methods that fits uncertainty model to available training data, the proposed algorithm is based on very simple observation of the eye fixation map, which is largely influenced by human visual attention mechanisms. Thus, the proposed method is data independent. The performance of the proposed algorithm is evaluated using the challenging CRCNS video dataset and quantified using Receiver Operating Characteristics (ROC). The results are promising and could lead to robust uncertainty estimation using eye-fixation neighbourhood modeling.

关键词： Unsupervised Uncertainty analysis Video Saliency detection Attention framework Spatial correlation

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High SNR flexible top hat monopole probe for 1.5 T MRI

High SNR flexible top hat monopole probe for 1.5 T MRI

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作者： Mohammadzadeh, M. Shahabadi, M. Soltanian-Zadeh, H. Tavakoli, A. Rafiei, B. Javidnia, D. Oghabian, M.A. Department of Physics Amir-Kabir University of Technology Tehran Iran School of Electrical and Computer Engineering University of Tehran Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Department of Radiology Henry Ford Health System Detroit MI United States Department of Electrical Engineering Amir-Kabir University of Technology Tehran Iran Medical Imaging Center Imam Khomeyni Hospital Tehran Iran Research Center for Science and Technology in Medicine Tehran University of Medical Sciences Tehran Iran

In this paper, design principles and application of a thin and flexible intravascular top hat monopole probe with increased signal-to-noise ratio (SNR) and improved longitudinal and radial coverage are described and compared with the conventional monopole probe. The top hat probe is constructed by removing a segment of the shield of a coaxial cable. A flexible thin copper foil in contact with the inner conductor of the cable is added to the end section of the cable. The copper foil acts as an antenna top hat load increasing the uniformity of the current distribution on the probe and establishes a uniform region of SNR distribution around the probe. Improvement of the SNR distribution of the top hat probe relative to the conventional monopole probe is evaluated using quantitative indices. Experimental results show that for the top hat probe, the ratio of the maximum image SNR to its relative standard deviation (SR) is 2.73 times higher than that of a conventional probe. © 2006 IOP Publishing Ltd.

关键词： Medical imaging

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Distorted representation space characterization through backpropagated gradients

arXiv

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arXiv 2019年

作者： Kwon, Gukyeong Prabhushankar, Mohit Temel, Dogancan AlRegib, Ghassan Center for Signal and Information Processing School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332-0250 United States

In this paper, we utilize weight gradients from backpropagation to characterize the representation space learned by deep learning algorithms. We demonstrate the utility of such gradients in applications including perceptual image quality assessment and out-of-distribution classification. The applications are chosen to validate the effectiveness of gradients as features when the test image distribution is distorted from the train image distribution. In both applications, the proposed gradient based features outperform activation features. In image quality assessment, the proposed approach is compared with other state of the art approaches and is generally the top performing method on TID 2013 and MULTI-LIVE databases in terms of accuracy, consistency, linearity, and monotonic behavior. Finally, we analyze the effect of regularization on gradients using CURE-TSR dataset for out-of-distribution classification. Copyright © 2019, The Authors. All rights reserved.

关键词： Classification (of information)

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Object recognition under multifarious conditions: A reliability analysis and a feature similarity-based performance estimation

arXiv

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arXiv 2019年

作者： Temel, Dogancan Lee, Jinsol AlRegib, Ghassan Center for Signal and Information Processing School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30332-0250 United States

In this paper, we investigate the reliability of online recognition platforms, Amazon Rekognition and Microsoft Azure, with respect to changes in background, acquisition device, and object orientation. We focus on platforms that are commonly used by the public to better understand their real-world performances. To assess the variation in recognition performance, we perform a controlled experiment by changing the acquisition conditions one at a time. We use three smartphones, one DSLR, and one webcam to capture side views and overhead views of objects in a living room, an office, and photo studio setups. Moreover, we introduce a framework to estimate the recognition performance with respect to backgrounds and orientations. In this framework, we utilize both handcrafted features based on color, texture, and shape characteristics and data-driven features obtained from deep neural networks. Experimental results show that deep learningbased image representations can estimate the recognition performance variation with a Spearman's rank-order correlation of 0.94 under multifarious acquisition conditions. Copyright © 2019, The Authors. All rights reserved.

关键词： Reliability analysis

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Targeting Negative Flips in Active Learning using Validation Sets

arXiv

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arXiv 2024年

作者： Benkert, Ryan Prabhushankar, Mohit AlRegib, Ghassan OLIVES The Center for Signal and Information Processing CSIP School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States

The performance of active learning algorithms can be improved in two ways. The often used and intuitive way is by reducing the overall error rate within the test set. The second way is to ensure that correct predictions are not forgotten when the training set is increased in between rounds. The former is measured by the accuracy of the model and the latter is captured in negative flips between rounds. Negative flips are samples that are correctly predicted when trained with the previous/smaller dataset and incorrectly predicted after additional samples are labeled. In this paper, we discuss improving the performance of active learning algorithms both in terms of prediction accuracy and negative flips. The first observation we make in this paper is that negative flips and overall error rates are decoupled and reducing one does not necessarily imply that the other is reduced. Our observation is important as current active learning algorithms do not consider negative flips directly and implicitly assume the opposite. The second observation is that performing targeted active learning on subsets of the unlabeled pool has a significant impact on the behavior of the active learning algorithm and influences both negative flips and prediction accuracy. We then develop ROSE - a plug-in algorithm that utilizes a small labeled validation set to restrict arbitrary active learning acquisition functions to negative flips within the unlabeled pool. We show that integrating a validation set results in a significant performance boost in terms of accuracy, negative flip rate reduction, or both. © 2024, CC BY.

关键词： Active learning

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ReSIFT: RELIABILITY-WEIGHTED SIFT-BASED image QUALITY ASSESSMENT

ReSIFT: RELIABILITY-WEIGHTED SIFT-BASED IMAGE QUALITY ASSESS...

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IEEE International Conference on image processing

作者： Dogancan Temel Ghassan AlRegib Center for Signal and Information Processing (CSIP) School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 USA

ISBN: (纸本)9781467399623

This paper presents a full-reference image quality estimator based on SIFT descriptor matching over reliability-weighted feature maps. Reliability assignment includes a smoothing operation, a transformation to perceptual color domain, a local normalization stage, and a spectral residual computation with global normalization. The proposed method ReSIFT is tested on the LIVE and the LIVE Multiply Distorted databases and compared with 11 state-of-the-art full-reference quality estimators. In terms of the Pearson and the Spearman correlation, ReSIFT is the best performing quality estimator in the overall databases. Moreover, ReSIFT is the best performing quality estimator in at least one distortion group in compression, noise, and blur category.

关键词： Reliability Databases Correlation Standards image quality image color analysis Distortion

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