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IEEE International Ultrasonics Symposium (IEEE IUS)

作者： Khan, Christopher van Sloun, Ruud J. G. Byram, Brett Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands

ISBN: (纸本)9780738112091

Aperture Domain Model Image REconstruction (ADMIRE) is an advanced ultrasound beamforming method that uses a model-based approach to suppress sources of acoustic clutter and improve ultrasound image quality. However, although effective, ADMIRE requires solving an inverse problem that is ill-posed, which means that there are infinitely many solutions that can have different impacts on image quality. Currently, linear regression with elastic-net regularization is used to obtain a solution, but there are potentially better methods for performing model fitting. Therefore, in this work, we propose using a deep neural network sparse encoder for performing the model fits of ADMIRE. In particular, we unfold the iterations of the iterative shrinkage and thresholding algorithm (ISTA) as a feedforward neural network and train it using different training schemes to perform sparse coding. Test results using both simulated and in vivo data demonstrate that ADMIRE using a deep neural network sparse encoder has the potential to outperform conventional ADMIRE in terms of ultrasound image quality while still preserving the model-based intuition of ADMIRE.

关键词： ADMIRE Deep Learning sparse coding ISTA

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Structures in Physics and Neuroscience Structural Realism and the Unity of Science

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AXIOMATHES 2021年第4期31卷 479-495页

作者： Beni, Majid Davoody Northoff, Georg Amirkabir Univ Technol Dept Management Sci & Technol Tehran Iran Nazarbayev Univ Nur Sultan City Kazakhstan Univ Ottawa Inst Mental Hlth Res Ottawa ON Canada Univ Ottawa Inst Mental Hlth Res Royal Ottawa Mental Hlth Ctr Mind Brain Imaging & Neuroeth Res Unit 1145 Carling AveRm 6435 Ottawa ON K1Z 7K4 Canada Taipei Med Univ Grad Inst Humanities Med Taipei Taiwan Taipei Med Univ Shuang Ho Hosp Brain & Consciousness Res Ctr New Taipei Taiwan Natl Chengchi Univ Res Ctr Mind Brain & Learning Taipei Taiwan Natl Chengchi Univ Dept Psychol Taipei Taiwan Normal Univ Hangzhou Ctr Cognit & Brain Disorders CBBD Hangzhou Peoples R China

We offer to extend structural realism into the field of mind and brain studies. The naturalised metaphysics of structural realism has been defined in terms of unification of sciences. The unification program has been carried out nicely in fields of Quantum Mechanics and General Relativity. But for the structural realist metaphysics to receive a recommendation, the unification program needs to be extended to the fields of especial sciences. Our aim in the paper is twofold. On the one hand, we present a structural realist theory of the brain that is well-supported by the best theories of computational neurosciences. On the other hand, and at the same time, we provide further reason for being optimistic about the structural realism metaphysical claim which is based on the idea of unification in sciences.

关键词： Structural realism Unification Difference-based coding Neuronal structures sparse coding

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A Heterogeneous Spiking Neural Network for Computationally Efficient Face Recognition 53

A Heterogeneous Spiking Neural Network for Computationally E...

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Zhou, Xichuan Zhou, Zhenghua Zhong, Zhengqing Yu, Jianyi Wang, Tengxiao Tian, Min Jiang, Ying Shi, Cong Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Xianfeng Elect Inst Co Ltd Chongqing 400050 Peoples R China

ISBN: (纸本)9781728192017

Computational efficiency is critical to many mobile and always-on face recognition applications. To this end, a heterogeneous spiking neural network (SNN) is proposed for face recognition. To obtain high recognition accuracy at minimal computational overheads, the heterogeneous SNN consists of an encoding subnet for sparse image feature encoding and classification subnet for feature classification. The experimental results suggest that the proposed heterogeneous algorithm can achieve high recognition accuracy on small datasets of human face samples with labeled identities at a high computational efficiency with very low neuronal activities. The proposed SNN is promising for low-cost mobile or always-on systems with strictly constrained resource and energy budgets.

关键词： neuromorphic computing spiking neural networks face recognition sparse coding computational efficiency

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An exemplar selection algorithm for native-nonnative voice conversion 22

An exemplar selection algorithm for native-nonnative voice c...

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Interspeech Conference

作者： Liberatore, Christopher Gutierrez-Osuna, Ricardo Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9781713836902

We present an algorithm for selecting exemplars for native-to-nonnative voice conversion (VC) using a sparse, Anchor-Based Representation of speech (SABR). The algorithm uses phoneme labels and clustering to learn optimal exemplars when source and target speakers are affected by poor time alignment, as is common in in native-to- nonnative voice conversion. We evaluate the method on speech from the ARCTIC and L2-ARCTIC corpora and compare it to a baseline exemplar-based VC algorithm. The proposed algorithm significantly improves synthesis quality and more than doubles that of a baseline exemplar-based VC system while using two orders of magnitude fewer atoms. Additionally, the proposed algorithm significantly reduces the VC error and improves the synthesis quality as compared to unoptimized SABR models. We discuss the implications of both optimization algorithms for SABR and broader exemplar-based VC *** terms should be included as shown below.

关键词： sparse coding voice conversion dictionary learning exemplar-based

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ADAPTIVE APPROACH FOR sparse REPRESENTATIONS USING THE LOCALLY COMPETITIVE ALGORITHM FOR AUDIO 31

ADAPTIVE APPROACH FOR SPARSE REPRESENTATIONS USING THE LOCAL...

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IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Bahadi, Soufiyan Rouat, Jean Plourde, Eric Univ Sherbrooke NECOTIS Res Lab Sherbrooke PQ Canada

ISBN: (纸本)9781728163383

Gammachirp filterbank has been used to approximate the cochlea in sparse coding algorithms. An oriented grid search optimization was applied to adapt the gammachirp's parameters and improve the Matching Pursuit (MP) algorithm's sparsity along with the reconstruction quality. However, this combination of a greedy algorithm with a grid search at each iteration is computationally demanding and not suitable for real-time applications. This paper presents an adaptive approach to optimize the gammachirp's parameters but in the context of the Locally Competitive Algorithm (LCA) that requires much fewer computations than MP. The proposed method consists of taking advantage of the LCA's neural architecture to automatically adapt the gammachirp's filterbank using the backpropagation algorithm. Results demonstrate an improvement in the LCA's performance with our approach in terms of sparsity, reconstruction quality, and convergence time. This approach can yield a significant advantage over existing approaches for real-time applications.

关键词： Gammachirp sparse coding Locally Competitive Algorithm Spikegram Backpropagation algorithm Real-time application

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NMF-SAE: AN INTERPRETABLE sparse AUTOENCODER FOR HYPERSPECTRAL UNMIXING

NMF-SAE: AN INTERPRETABLE SPARSE AUTOENCODER FOR HYPERSPECTR...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Xiong, Fengchao Zhou, Jun Ye, Minchao Lu, Jianfeng Qian, Yuntao Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China Griffith Univ Sch Informat & Commun Technol Brisbane Qld Australia China Jiliang Univ Coll Informat Engn Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9781728176055

Hyperspectral unmixing is an important tool to learn the material constitution and distribution of a scene. Model-based unmixing methods depend on well-designed iterative optimization algorithms, which is usually time consuming. Learning-based methods perform unmixing in a data-driven manner but heavily rely on the quality and quantity of the training samples due to the lack of physical interpretability. In this paper, we combine the advantages of both model-based and learning-based methods and propose a nonnegative matrix factorization (NMF) inspired sparse autoencoder (NMF-SAE) for hyperspectral unmixing. NMF-SAE consists of an encoder and a decoder, both of which are constructed by unrolling the iterative optimization rules of L-1 sparsity-constrained NMF for the linear spectral mixture model. All parameters in our method are obtained by end-to-end training in a data-driven manner. Our network is not only physically interpretable and flexible but also has higher learning capacity with fewer parameters. Experimental results on both synthetic and real-world data demonstrate that our method is capable of producing desirable unmixing results when compared against several alternative approaches.

关键词： Hyperspectral unmixing model-based neural network autoencoder sparse coding

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MODEL-INSPIRED DEEP LEARNING FOR LIGHT-FIELD MICROSCOPY WITH APPLICATION TO NEURON LOCALIZATION

MODEL-INSPIRED DEEP LEARNING FOR LIGHT-FIELD MICROSCOPY WITH...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Song, Pingfan Jadan, Herman Verinaz Howe, Carmel L. Quicke, Peter Foust, Amanda J. Dragotti, Pier Luigi Imperial Coll London Dept Elect & Elect Engn London England Imperial Coll London Dept Bioengn London England Imperial Coll London Ctr Neurotechnol London England

ISBN: (纸本)9781728176055

Light-field microscopes are able to capture spatial and angular information of incident light rays. This allows reconstructing 3D locations of neurons from a single snap-shot. In this work, we propose a model-inspired deep learning approach to perform fast and robust 3D localization of sources using light-field microscopy images. This is achieved by developing a deep network that efficiently solves a convolutional sparse coding (CSC) problem to map Epipolar Plane Images (EPI) to corresponding sparse codes. The network architecture is designed systematically by unrolling the convolutional Iterative Shrinkage and Thresholding Algorithm (ISTA) while the network parameters are learned from a training dataset. Such principled design enables the deep network to leverage both domain knowledge implied in the model, as well as new parameters learned from the data, thereby combining advantages of model-based and learning-based methods. Practical experiments on localization of mammalian neurons from light-fields show that the proposed approach simultaneously provides enhanced performance, interpretability and efficiency.

关键词： Light-field microscopy model-inspired deep learning algorithm unrolling sparse coding

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Fast and Efficient Union of sparse Orthonormal Transform for Image Compression 18

Fast and Efficient Union of Sparse Orthonormal Transform for...

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18th International Conference on Signal Processing and Multimedia Applications (SIGMAP)

作者： Lee, Gihwan Choe, Yoonsik Yonsei Univ Dept Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9789897585258

sparse coding has been widely used in image processing. Overcomplete-based sparse coding is powerful to represent data as a small number of bases, but with time-consuming optimization methods. Orthogonal sparse coding is relatively fast and well-suitable in image compression like analytic transforms with better performance than the existing analytic transforms. Thus, there have been many attempts to design image transform based on orthogonal sparse coding. In this paper, we introduce an extension of sparse orthonormal transform (SOT) based on unions of orthonormal bases (UONB) for image compression. Different from UONB, we allocate image patches to one orthonormal dictionary according to their direction. To accelerate the method, we factorize our dictionaries into the discrete cosine transform matrix and another orthonormal matrix. In addition, for more effective implementation, calculation of direction is also conducted in DCT domain. As expected, our framework fulfills the goal of improving compression performance of SOT with fast implementation. Through experiments, we verify that proposed method produces similar performance to overcomplete dictionary outperforms SOT in compression with rather faster speed. The proposed methods are from twice to four times faster than the SOT and hundreds of times faster than UONB.

关键词： sparse coding Dictionary Learning Orthogonal sparse coding Image Compression Image Transform sparse Transform Union of Orthonormal Bases

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Biomedical time series clustering based on non-negative sparse coding and probabilistic topic model

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COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013年第3期111卷 629-641页

作者： Wang, Jin Liu, Ping She, Mary F. H. Nahavandi, Saeid Kouzani, Abbas Deakin Univ Ctr Intelligent Syst Res Waurn Ponds Vic 3217 Australia Univ S Carolina Dept Comp Sci Columbia SC 29205 USA Deakin Univ Inst Frontier Mat Waurn Ponds Vic 3217 Australia Deakin Univ Sch Engn Waurn Ponds Vic 3217 Australia

Biomedical time series clustering that groups a set of unlabelled temporal signals according to their underlying similarity is very useful for biomedical records management and analysis such as biosignals archiving and diagnosis. In this paper, a new framework for clustering of long-term biomedical time series such as electrocardiography (ECG) and electroencephalography (EEG) signals is proposed. Specifically, local segments extracted from the time series are projected as a combination of a small number of basis elements in a trained dictionary by non-negative sparse coding. A Bag-of-Words (BoW) representation is then constructed by summing up all the sparse coefficients of local segments in a time series. Based on the BoW representation, a probabilistic topic model that was originally developed for text document analysis is extended to discover the underlying similarity of a collection of time series. The underlying similarity of biomedical time series is well captured attributing to the statistic nature of the probabilistic topic model. Experiments on three datasets constructed from publicly available EEG and ECG signals demonstrates that the proposed approach achieves better accuracy than existing state-of-the-art methods, and is insensitive to model parameters such as length of local segments and dictionary size. (c) 2013 Elsevier Ireland Ltd. All rights reserved.

关键词： Unsupervised learning Bag-of-Words Probabilistic topic model sparse coding

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Discriminative sparse coding on multi-manifolds

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KNOWLEDGE-BASED SYSTEMS 2013年 54卷 199-206页

作者： Wang, Jim Jing-Yan Bensmail, Halima Yao, Nan Gao, Xin KAUST Comp Elect & Math Sci & Engn Div Thuwal 239556900 Saudi Arabia Qatar Comp Res Inst Doha 5825 Qatar Shanghai Jiao Tong Univ Dept Instrumentat Engn Shanghai 200240 Peoples R China Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China KAUST Computat Biosci Res Ctr Thuwal 239556900 Saudi Arabia

sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. (C) 2013 The Authors. Published by Elsevier B.V. All rights reserved.

关键词： Data representation sparse coding Multi-manifolds Large margins

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