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  • Articles  (96)
  • Hindawi  (96)
  • American Meteorological Society
  • Blackwell Publishing Ltd
  • Copernicus
  • Elsevier
  • Institute of Electrical and Electronics Engineers
  • Molecular Diversity Preservation International
  • Public Library of Science
  • Springer Nature
  • Springer Science + Business Media
  • 2020-2022
  • 2010-2014  (96)
  • 1985-1989
  • 1970-1974
  • 1960-1964
  • 2013  (96)
  • 1964
  • ISRN Signal Processing  (75)
  • 163739
  • Electrical Engineering, Measurement and Control Technology  (96)
  • Mathematics
  • Natural Sciences in General
  • Architecture, Civil Engineering, Surveying
Collection
  • Articles  (96)
Source
Publisher
  • Hindawi  (96)
  • American Meteorological Society
  • Blackwell Publishing Ltd
  • Copernicus
  • Elsevier
    • +
Years
  • 2020-2022
  • 2010-2014  (96)
  • 1985-1989
  • 1970-1974
  • 1960-1964
Year
Journal
Topic
  • Electrical Engineering, Measurement and Control Technology  (96)
  • Mathematics
  • Natural Sciences in General
  • Architecture, Civil Engineering, Surveying
  • 1
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    Design of Adjustable Square-Shaped 2D IIR Filters (2013)
    Hindawi
    Details
    Publication Date: 2013-09-12
    Description: This paper proposes an analytical design method for two-dimensional square-shaped IIR filters. The designed 2D filters are adjustable since their bandwidth and orientation are specified by parameters appearing explicitly in the filter matrices. The design relies on a zero-phase low-pass 1D prototype filter. To this filter a frequency transformation is next applied, which yields a 2D filter with the desired square shape in the frequency plane. The proposed method combines the analytical approach with numerical approximations. Since the prototype transfer function is factorized into partial functions, the 2D filter also will be described by a factorized transfer function, which is an advantage in implementation.
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    Electronic ISSN: 2090-505X
    Topics: Electrical Engineering, Measurement and Control Technology
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  • 2
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    A Novel Neuron in Kernel Domain (2013)
    Hindawi
    Details
    Publication Date: 2013-09-19
    Description: Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.
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    Topics: Electrical Engineering, Measurement and Control Technology
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  • 3
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    DCT Watermarking Approach for Security Enhancement of Multimodal System (2013)
    Hindawi
    Details
    Publication Date: 2013-02-26
    Description: We have addressed a novel watermarking algorithm to support the capacity demanded by the multimodal biometric templates. Proposed technique embeds watermark in low frequency AC coefficients of selected 8 × 8 DCT blocks. Selection of blocks accomplishes perceptual transparency by exploiting the masking effects of human visual system (HVS). Embedding is done by modulating the coefficient magnitude as a function of its estimated value. Neighborhood estimation is used for the weighted DC coefficients from eight neighboring DCT blocks. The weights of the DC coefficients are calculated from local image intrinsic property. For our experimentation we have used iris and finger prints as the two templates which are watermarked into standard test images. The robustness of the proposed algorithm is compared with the few state-of-the-art literature when watermarked image is subjected to common channel attacks.
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  • 4
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    Online Boosting Algorithm Based on Two-Phase SVM Training (2013)
    Hindawi
    Details
    Publication Date: 2013-02-26
    Description: We describe and analyze a simple and effective two-step online boosting algorithm that allows us to utilize highly effective gradient descent-based methods developed for online SVM training without the need to fine-tune the kernel parameters, and we show its efficiency by several experiments. Our method is similar to AdaBoost in that it trains additional classifiers according to the weights provided by previously trained classifiers, but unlike AdaBoost, we utilize hinge-loss rather than exponential loss and modify algorithm for the online setting, allowing for varying number of classifiers. We show that our theoretical convergence bounds are similar to those of earlier algorithms, while allowing for greater flexibility. Our approach may also easily incorporate additional nonlinearity in form of Mercer kernels, although our experiments show that this is not necessary for most situations. The pre-training of the additional classifiers in our algorithms allows for greater accuracy while reducing the times associated with usual kernel-based approaches. We compare our algorithm to other online training algorithms, and we show, that for most cases with unknown kernel parameters, our algorithm outperforms other algorithms both in runtime and convergence speed.
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  • 5
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    Cochlear Implant Speech Processing Using Wavelet Transform (2013)
    Hindawi
    Details
    Publication Date: 2013-02-26
    Description: We present a method for coding speech signals for the simulation of a cochlear implant. The method is based on a wavelet packet decomposition strategy. We used wavelet packet db4 for 7 levels, generated a series of channels with bandwidths exactly the same as nucleus device, and applied an input stimulus to each channel. The processed signal was then reconstructed and compared to the original signal, which preserved the contents to a high percentage. Finally, performance of the wavelet packet decomposition in terms of computational complexity was compared to other commonly used strategies in cochlear implants. The results showed the power of this method in processing of the input signal for implant users with less complexity than other methods, while maintaining the contents of the input signal to a very good extent.
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  • 6
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    An Efficient Adaptive Technique with Low Complexity for Reducing PAPR in OFDM-Based Cognitive Radio (2013)
    Hindawi
    Details
    Publication Date: 2013-02-26
    Description: Cognitive radio (CR) is considered nowadays as a strong candidate solution for the spectrum scarcity problem. On standards level, many cognitive radio standards have chosen Non-Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM) as their modulation scheme. Similar to OFDM, NC-OFDM suffers from the problem of having a high Peak to Average Power Ratio (PAPR). If not solved, either the transmitted signal will be distorted, which will cause interference to primary (licensed) users, or the effeciency of the power amplifier will be seriously degraded. The effect of the PAPR problem in NC-OFDM based cognitive radio networks is worse than normal OFDM systems. In this paper, we propose enhanced techniques to reduce the PAPR in NC-OFDM systems. We start by showing that combining two standard PAPR reduction techniques (interleaver-based and selective mapping) results in a lower PAPR than using them individually. Then, an “adaptive number of interleavers” will be proposed that achieves the same performance of conventional interleaver-based PAPR reduction while reducing the CPU time by 41.3%. Finally, adaptive joint interleaver with selective mapping is presented, and we show that it gives the same performance as conventional interleaver-based technique, with reduction in CPU time by a factor of 50.1%.
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  • 7
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    Observability of Spectral Components beyond Nyquist Limit in Nonuniformly Sampled Signals (2013)
    Hindawi
    Details
    Publication Date: 2013-02-26
    Description: Identification of a signal component with the frequency exceeding the Nyquist limit is a challenging problem in signal theory as well as in some specific applications areas like astronomy and biosciences. A consequence of the well-known sampling theorem for a uniformly sampled signal is that the spectral component above the Nyquist limit is aliased into lower frequency range, making a distinction between true and aliased components impossible. The nonuniform sampling, however, offers a possibility to reduce aliased components and uncover information above the Nyquist limit. In this paper, we provide a theoretical analysis of the aliased components reduction in the nonparametric periodogram for two sampling schemes: the random sampling pattern and the sampling pattern generated by the integral pulse frequency modulation (IPFM), the latter widely accepted as the heart rate timing model. A general formula that relates the variance of timing deviations from a regular scheme with the aliased component suppression is proposed. The derived relation is illustrated by Lomb-Scargle periodograms applied on simulated data. Presented experimental data consisting of the respiration signal derived from the electrocardiogram and the heart rate signal also support possibility to detect frequencies above the Nyquist limit in the condition known as the cardiac aliasing.
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  • 8
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    An Overview on Image Forensics (2013)
    Hindawi
    Details
    Publication Date: 2013-02-26
    Description: The aim of this survey is to provide a comprehensive overview of the state of the art in the area of image forensics. These techniques have been designed to identify the source of a digital image or to determine whether the content is authentic or modified, without the knowledge of any prior information about the image under analysis (and thus are defined as passive). All these tools work by detecting the presence, the absence, or the incongruence of some traces intrinsically tied to the digital image by the acquisition device and by any other operation after its creation. The paper has been organized by classifying the tools according to the position in the history of the digital image in which the relative footprint is left: acquisition-based methods, coding-based methods, and editing-based schemes.
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  • 9
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    Instantaneous Granger Causality with the Hilbert-Huang Transform (2013)
    Hindawi
    Details
    Publication Date: 2013-02-26
    Description: Current measures of causality and temporal precedence have limited frequency and time resolution and therefore may not be viable in the detection of short periods of causality in specific frequencies. In addition, the presence of nonstationarities hinders the causality estimation of current techniques as they are based on Fourier transforms or autoregressive model estimation. In this work we present a combination of techniques to measure causality and temporal precedence between stationary and nonstationary time series, that is sensitive to frequency-specific short episodes of causality. This methodology provides a highly informative time-frequency representation of causality with existing causality measures. This is done by decomposing each time series into intrinsic oscillatory modes with an empirical mode decomposition algorithm and, subsequently, calculating their complex Hilbert spectrum. At each time point the cross-spectrum is calculated between time series and used to measure coherency and compute the transfer function and error covariance matrices using the Wilson-Burg method for spectral factorization. The imaginary part of coherency can then be computed as well as several Granger causality measures in the previous matrices. This work covers the most important theoretical background of these techniques and tries to prove the usefulness of this new approach while pointing out some of its qualities and drawbacks.
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  • 10
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    A Probabilistic Approach to Computerized Tracking of Arterial Walls in Ultrasound Image Sequences (2013)
    Hindawi
    Details
    Publication Date: 2013-02-26
    Description: Tracking of arterial walls in ultrasound image sequences is useful for studying the dynamics of arteries. Manual delineation is prohibitively labour intensive and existing methods of computerized segmentation are limited in terms of applicability and availability. This paper presents a probabilistic approach to the computerized tracking of arterial walls that is effective and easy to implement. In the probabilistic approach, given a point B with a probability of being in an arterial lumen of interest, the probability that a neighbouring point A is also a part of the same lumen is proportional to with a Gaussian fall in probability with increasing grayscale contrast between the two points. Efficacy of the probabilistic algorithm was evaluated by testing it on ultrasound images and image sequences of the carotid arteries and the abdominal aorta and various laboratory, ultrasound test objects. The results showed that the probabilistic algorithm produced robust and effective lumen segmentation in the majority of cases encountered. Comparison with a conventional region growing technique based on intensity thresholding with a running, regional intensity average identified the main benefits of the probabilistic approach as increased immunity to speckle noise within the arterial lumen and a reduced susceptibility to region overflowing at boundary imperfections.
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