Perturbed Orthogonal Matching Pursuit

Cataloged from PDF version of article.Compressive Sensing theory details how a sparsely represented signal in a known basis can be reconstructed with an underdetermined linear measurement model. However, in reality there is a mismatch between the assumed and the actual bases due to factors such as discretization of the parameter space defining basis components, sampling jitter in A/D conversion, and model errors. Due to this mismatch, a signal may not be sparse in the assumed basis, which causes significant performance degradation in sparse reconstruction algorithms. To eliminate the mismatch problem, this paper presents a novel perturbed orthogonal matching pursuit (POMP) algorithm that performs controlled perturbation of selected support vectors to decrease the orthogonal residual at each iteration. Based on detailed mathematical analysis, conditions for successful reconstruction are derived. Simulations show that robust results with much smaller reconstruction errors in the case of perturbed bases can be obtained as compared to standard sparse reconstruction techniques

Comparison between in situ dry matter degradation and in vitro gas production of tannin-containing leaves from four tree species

Dry matter (DM) degradation of Glycrrhiza glabra L, Arbutus andrachne, Juniperus communis, and Pistica lentiscus was determined using two different techniques: (i) the in vitro gas production and (ii) the in situ nylon bag degradability technique. Samples were incubated in situ and in vitro for 3, 6, 12, 24, 48, 72 and 96 h. In situ and in vitro DM degradation kinetics were described using the equation y = a + b (1 - e ct). At all incubation times except 3 and 72 h the cumulative gas production of J. communis was significantly lower than that of G. glabra, A. andrachne and P. lentiscus. At 3, 6 and 12 h incubation times the DM disappearance of J. communis was only significantly lower than that of P. lentiscus. At 24 and 48 h incubation times DM disappearance of J. communis was significantly lower than that of A. andrachne and P. lentiscus. There were significant relationships between in vitro gas production and in situ DM disappearance at 24 h and 96 h incubation times. The gas productions at 24 and 96 h incubation explained 51.2 and 52.4% of variation of DM disappearance, respectively. Gas production from the insoluble fraction (b) alone explained 66.4% of the variation of effective DM degradability (EDMD). The inclusion of gas production from quickly soluble fraction (a) and rate constant (c) of gas production in the regression equation did not improve the accuracy of predicting EDMD. It was concluded that in situ DM disappearance parameters of tannin-containing tree leaves such as used in this present study may be predicted from in vitro gas production parameters. South African Journal of Animal Science Vol. 34(4) 2004: 233-24

Generalizable Embeddings with Cross-batch Metric Learning

Global average pooling (GAP) is a popular component in deep metric learning (DML) for aggregating features. Its effectiveness is often attributed to treating each feature vector as a distinct semantic entity and GAP as a combination of them. Albeit substantiated, such an explanation's algorithmic implications to learn generalizable entities to represent unseen classes, a crucial DML goal, remain unclear. To address this, we formulate GAP as a convex combination of learnable prototypes. We then show that the prototype learning can be expressed as a recursive process fitting a linear predictor to a batch of samples. Building on that perspective, we consider two batches of disjoint classes at each iteration and regularize the learning by expressing the samples of a batch with the prototypes that are fitted to the other batch. We validate our approach on 4 popular DML benchmarks.Comment: \c{opyright} 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Sparse ground-penetrating radar imaging method for off-the-grid target problem

Cataloged from PDF version of article.Spatial sparsity of the target space in subsurface or through-the-wall imaging applications has been successfully used within the compressive-sensing framework to decrease the data acquisition load in practical systems, while also generating high-resolution images. The developed techniques in this area mainly discretize the continuous target space into grid points and generate a dictionary of model data that is used in image-reconstructing optimization problems. However, for targets that do not coincide with the computation grid, imaging performance degrades considerably. This phenomenon is known as the off-grid problem. This paper presents a novel sparse ground-penetrating radar imaging method that is robust for off-grid targets. The proposed technique is an iterative orthogonal matching pursuit-based method that uses gradient-based steepest ascent-type iterations to locate the off-grid target. Simulations show that robust results with much smaller reconstruction errors are obtained for multiple off-grid targets compared to standard sparse reconstruction techniques. (c) 2013 SPIE and IS&

A robust compressive sensing based technique for reconstruction of sparse radar scenes

Cataloged from PDF version of article.Pulse-Doppler radar has been successfully applied to surveillance and tracking of both moving and stationary targets. For efficient processing of radar returns, delay–Doppler plane is discretized and FFT techniques are employed to compute matched filter output on this discrete grid. However, for targets whose delay–Doppler values do not coincide with the computation grid, the detection performance degrades considerably. Especially for detecting strong and closely spaced targets this causes miss detections and false alarms. This phenomena is known as the off-grid problem. Although compressive sensing based techniques provide sparse and high resolution results at sub-Nyquist sampling rates, straightforward application of these techniques is significantly more sensitive to the off-grid problem. Here a novel parameter perturbation based sparse reconstruction technique is proposed for robust delay– Doppler radar processing even under the off-grid case. Although the perturbation idea is general and can be implemented in association with other greedy techniques, presently it is used within an orthogonal matching pursuit (OMP) framework. In the proposed technique, the selected dictionary parameters are perturbed towards directions to decrease the orthogonal residual norm. The obtained results show that accurate and sparse reconstructions can be obtained for off-grid multi target cases. A new performance metric based on Kullback–Leibler Divergence (KLD) is proposed to better characterize the error between actual and reconstructed parameter spaces. Increased performance with lower reconstruction errors are obtained for all the tested performance criteria for the proposed technique compared to conventional OMP and 1 minimization techniques. © 2013 Elsevier Inc. All rights reserve

SAR image reconstruction by expectation maximization based matching pursuit

Cataloged from PDF version of article.Synthetic Aperture Radar (SAR) provides high resolution images of terrain and target reflectivity. SAR systems are indispensable in many remote sensing applications. Phase errors due to uncompensated platform motion degrade resolution in reconstructed images. A multitude of autofocusing techniques has been proposed to estimate and correct phase errors in SAR images. Some autofocus techniques work as a post-processor on reconstructed images and some are integrated into the image reconstruction algorithms. Compressed Sensing (CS), as a relatively new theory, can be applied to sparse SAR image reconstruction especially in detection of strong targets. Autofocus can also be integrated into CS based SAR image reconstruction techniques. However, due to their high computational complexity, CS based techniques are not commonly used in practice. To improve efficiency of image reconstruction we propose a novel CS based SAR imaging technique which utilizes recently proposed Expectation Maximization based Matching Pursuit (EMMP) algorithm. EMMP algorithm is greedy and computationally less complex enabling fast SAR image reconstructions. The proposed EMMP based SAR image reconstruction technique also performs autofocus and image reconstruction simultaneously. Based on a variety of metrics, performance of the proposed EMMP based SAR image reconstruction technique is investigated. The obtained results show that the proposed technique provides high resolution images of sparse target scenes while performing highly accurate motion compensation. (C) 2014 Elsevier Inc. All rights reserved

Assessing Structural Health Monitoring Alternatives Utilizing a Value-Focused Thinking Model

Current Air Force operations are undergoing significant changes necessitated by increasing fiscal constraints, increasing aircraft age, and recent drawdown in personnel to perform maintenance, repair, and other necessary functions. In order to deal with these challenges, the Air Force must effectively improve current operations. This paper explores potential structural health monitoring (SHM) solutions to some of the challenges facing aircraft maintenance and repair operations. As with any problem, a variety of solutions exist and this paper explores the potential solutions and limitations of various options. Aircraft SHM is an intriguing concept with potential capability to revolutionize current Air Force maintenance operations. However, this capability needs to be balanced with the total life cycle cost associated with training personnel, and with developing, integrating, maintaining, and disposing of the SHM system. This thesis develops and implements a value-focused thinking model as a decision-making tool to analyze several potential solutions to SHM problems

Deep Metric Learning with Chance Constraints

Deep metric learning (DML) aims to minimize empirical expected loss of the pairwise intra-/inter- class proximity violations in the embedding image. We relate DML to feasibility problem of finite chance constraints. We show that minimizer of proxy-based DML satisfies certain chance constraints, and that the worst case generalization performance of the proxy-based methods can be characterized by the radius of the smallest ball around a class proxy to cover the entire domain of the corresponding class samples, suggesting multiple proxies per class helps performance. To provide a scalable algorithm as well as exploiting more proxies, we consider the chance constraints implied by the minimizers of proxy-based DML instances and reformulate DML as finding a feasible point in intersection of such constraints, resulting in a problem to be approximately solved by iterative projections. Simply put, we repeatedly train a regularized proxy-based loss and re-initialize the proxies with the embeddings of the deliberately selected new samples. We apply our method with the well-accepted losses and evaluate on four popular benchmark datasets for image retrieval. Outperforming state-of-the-art, our method consistently improves the performance of the applied losses. Code is available at: https://github.com/yetigurbuz/ccp-dmlComment: Under review at IEEE Transactions on Neural Networks and Learning System

Feature Embedding by Template Matching as a ResNet Block

Convolution blocks serve as local feature extractors and are the key to success of the neural networks. To make local semantic feature embedding rather explicit, we reformulate convolution blocks as feature selection according to the best matching kernel. In this manner, we show that typical ResNet blocks indeed perform local feature embedding via template matching once batch normalization (BN) followed by a rectified linear unit (ReLU) is interpreted as arg-max optimizer. Following this perspective, we tailor a residual block that explicitly forces semantically meaningful local feature embedding through using label information. Specifically, we assign a feature vector to each local region according to the classes that the corresponding region matches. We evaluate our method on three popular benchmark datasets with several architectures for image classification and consistently show that our approach substantially improves the performance of the baseline architectures.Comment: Accepted at the British Machine Vision Conference 2022 (BMVC 2022