The Experts below are selected from a list of 80124 Experts worldwide ranked by ideXlab platform

Peng Hua - One of the best experts on this subject based on the ideXlab platform.

Antonio Ortega - One of the best experts on this subject based on the ideXlab platform.

  • Practical graph Signal Sampling with log-linear size scaling
    arXiv: Signal Processing, 2021
    Co-Authors: Ajinkya Jayawant, Antonio Ortega
    Abstract:

    Graph Signal Sampling is the problem of selecting a subset of representative graph vertices whose values can be used to interpolate missing values on the remaining graph vertices. Optimizing the choice of Sampling set can help minimize the effect of noise in the input Signal. While many existing Sampling set selection methods are computationally intensive because they require an eigendecomposition, existing eigendecompostion-free methods are still much slower than random Sampling algorithms for large graphs. In this paper, we propose a Sampling algorithm that can achieve speeds similar to random Sampling, while reaching accuracy similar to existing eigendecomposition-free methods for a broad range of graph types.

  • ICASSP - Robust Graph Signal Sampling
    ICASSP 2019 - 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019
    Co-Authors: Basak Guler, Ajinkya Jayawant, A. Salman Avestimehr, Antonio Ortega
    Abstract:

    This paper considers the graph Signal Sampling problem when some of the selected samples are lost or unavailable due to sensor failures or adversarial erasures. We formulate a robust graph Signal Sampling problem where only a subset of selected samples are received, and the goal is to maximize the worst-case performance. We propose a novel greedy robust sample selection algorithm and study its performance guarantees. Our numerical results demonstrate the performance improvement of the proposed algorithm over the existing schemes.

  • efficient sensor position selection using graph Signal Sampling theory
    International Conference on Acoustics Speech and Signal Processing, 2016
    Co-Authors: Akie Sakiyama, Yuichi Tanaka, Toshihisa Tanaka, Antonio Ortega
    Abstract:

    We consider the problem of selecting optimal sensor placements. The proposed approach is based on the Sampling theorem of graph Signals. We choose sensors that maximize the graph cut-off frequency, i.e., the most informative sensors for predicting the values on unselected sensors. We study the existing methods in the context of graph Signal processing and clarify the relationship between these methods and the proposed approach. The effectiveness of our approach is verified through numerical experiments, showing advantages in prediction error and execution time.

Tianming Liu - One of the best experts on this subject based on the ideXlab platform.

  • A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data.
    Frontiers in neuroinformatics, 2018
    Co-Authors: Xi Jiang, Yifei Sun, Tianming Liu
    Abstract:

    The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a Signal Sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain’s fMRI data. We compared the proposed structurally guided Sampling method with no Sampling, random Sampling and uniform Sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.

  • Signal Sampling for efficient sparse representation of resting state fmri data
    International Symposium on Biomedical Imaging, 2015
    Co-Authors: Jin Wang, Xi Jiang, Shu Zhang, Shijie Zhao, Wei Zhang, Qinghua Zhao, Junwei Han, Lei Guo, Tianming Liu
    Abstract:

    As brain imaging data such as fMRI is growing explosively, how to reduce its size but not to lose much information becomes a pressing problem. To address this problem, this work aims to represent resting state fMRI (rs-fMRI) Signals of a whole brain via a statistical Sampling based sparse representation. Specifically, we improve the online dictionary learning and sparse coding algorithm by adding a Sampling step before the whole-brain sparse representation. Our comparison experiments demonstrated that this Sampling-enabled sparse representation method can speedup by ten times without losing much information. In particular, our results showed that anatomical landmark-guided Sampling is substantially better than statistical random Sampling in reconstructing concurrent functional brain networks from the Human Connectome Project (HCP) rs-fMRI data.

Andrzej Marek Borys - One of the best experts on this subject based on the ideXlab platform.

  • Spectrum Aliasing Does Occur Only in Case of Non-ideal Signal Sampling
    International Journal of Electronics and Telecommunications, 2021
    Co-Authors: Andrzej Marek Borys
    Abstract:

    In this paper, it has been shown that the spectrum aliasing and folding effects occur only in the case of non-ideal Signal Sampling. When the duration of the Signal Sampling is equal to zero, these effects do not occur at all. In other words, the absolutely necessary condition for their occurrence is just a nonzero value of this time. Periodicity of the Sampling process plays a secondary role.

  • Spectrum Aliasing Does not Occur in Case of Ideal Signal Sampling
    International Journal of Electronics and Telecommunications, 2021
    Co-Authors: Andrzej Marek Borys
    Abstract:

    A new model of ideal Signal Sampling operation is developed in this paper. This model does not use the Dirac comb in an analytical description of sampled Signals in the continuous time domain. Instead, it utilizes functions of a continuous time variable, which are introduced in this paper: a basic Kronecker time function and a Kronecker comb (that exploits the first of them). But, a basic principle behind this model remains the same; that is it also a multiplier which multiplies a Signal of a continuous time by a comb. Using a concept of a Signal object (or utilizing equivalent arguments) presented elsewhere, it has been possible to find a correct expression describing the spectrum of a sampled Signal so modelled. Moreover, the analysis of this expression showed that aliases and folding effects cannot occur in the sampled Signal spectrum, provided that the Signal Sampling is performed ideally.

  • Filtering Property of Signal Sampling in General and Under-Sampling as a Specific Operation of Filtering Connected with Signal Shaping at the Same Time
    International Journal of Electronics and Telecommunications, 2020
    Co-Authors: Andrzej Marek Borys
    Abstract:

    In this paper, we show that Signal Sampling operation can be considered as a kind of all-pass filtering in the time domain, when the Nyquist frequency is larger or equal to the maximal frequency in the spectrum of a Signal sampled. We demonstrate that this seemingly obvious observation has wide-ranging implications. They are discussed here in detail. Furthermore, we discuss also Signal shaping effects that occur in the case of Signal under-Sampling. That is, when the Nyquist frequency is smaller than the maximal frequency in the spectrum of a Signal sampled. Further, we explain the mechanism of a specific Signal distortion that arises under these circumstances. We call it the Signal shaping, not the Signal aliasing, because of many reasons discussed throughout this paper. Mainly however because of the fact that the operation behind it, called also the Signal shaping here, is not a filtering in a usual sense. And, it is shown that this kind of shaping depends upon the Sampling phase. Furthermore, formulated in other words, this operation can be viewed as a one which shapes the Signal and performs the low-pass filtering of it at the same time. Also, an interesting relation connecting the Fourier transform of a Signal filtered with the use of an ideal low-pass filter having the cut frequency lying in the region of under-Sampling with the Fourier transforms of its two under-sampled versions is derived. This relation is presented in the time domain, too.

Maria Minunni - One of the best experts on this subject based on the ideXlab platform.

  • Surface plasmon resonance imaging (SPRi)-based sensing: a new approach in Signal Sampling and management.
    Biosensors & bioelectronics, 2010
    Co-Authors: Simona Scarano, Cosimo Scuffi, Marco Mascini, Maria Minunni
    Abstract:

    Surface plasmon resonance imaging (SPRi) is at the forefront of optical sensing, allowing real-time and label free simultaneous multi-analyte measurements. It represents an interesting technology for studying a broad variety of affinity interactions with impact in chemistry, both in fundamental and applied research. Signal Sampling and management is a key step in SPRi measurements to achieve successful performances. This work aims to develop a strategy for selecting the sensing areas, called Regions of Interest (ROIs), to be sampled for recording SPRi Signals that could results in improved sensor performances. The approach has been evaluated using antigen-antibody interaction: anti-human IgGs are immobilized on the chip surface in an array format, while the specific ligand (hIgG antigen) is in solution. This approach has general applicability and demonstrates that rational selection of sensitive areas and standard management of SPRi data has dramatic impact on sensor behaviour. The criteria of the method are: (a) creation of high density maps of ROIs, (b) evaluation of the SPRi binding Signals on all the ROIs during a pre-analysis step, (c) 3D elaboration of the results, and (d) ranking of the ROIs for their final selection in further biosensor analysis. Using standard solution of antigen, three different ROIs selection approaches have been compared for their analytical performances. The proposed innovative method results to be the best one for SPRi-based sensing applications.