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1.
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3.
  • Abbas, M., et al. (författare)
  • Common Fixed Points of Generalized Rational Type Cocyclic Mappings in Multiplicative Metric Spaces
  • 2015
  • Ingår i: Discrete dynamics in nature and society. - 1026-0226. ; 2015
  • Tidskriftsartikel (refereegranskat)abstract
    • The aim of this paper is to present fixed point result of mappings satisfying a generalized rational contractive condition in the setup of multiplicative metric spaces. As an application, we obtain a common fixed point of a pair of weakly compatible mappings. Some common fixed point results of pair of rational contractive types mappings involved in cocyclic representation of a nonempty subset of a multiplicative metric space are also obtained. Some examples are presented to support the results proved herein. Our results generalize and extend various results in the existing literature.
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4.
  • Abbaspour Asadollah, Sara, et al. (författare)
  • A Survey on Testing for Cyber Physical System
  • 2015
  • Ingår i: Testing Software and Systems : 27th IFIP WG 6.1 International Conference, ICTSS 2015, Sharjah and Dubai, United Arab Emirates, November 23-25, 2015, Proceedings. - 978-3-319-25944-4 ; s. 194-207
  • Konferensbidrag (refereegranskat)abstract
    • Cyber Physical Systems (CPS) bridge the cyber-world of computing and communications with the physical world and require development of secure and reliable software. It asserts a big challenge not only on testing and verifying the correctness of all physical and cyber components of such big systems, but also on integration of these components. This paper develops a categorization of multiple levels of testing required to test CPS and makes a comparison of these levels with the levels of software testing based on the V-model. It presents a detailed state-of-the-art survey on the testing approaches performed on the CPS. Further, it provides challenges in CPS testing.
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5.
  • Abbaspour Asadollah, Sara, et al. (författare)
  • Towards Classification of Concurrency Bugs Based on Observable Properties
  • 2015
  • Konferensbidrag (refereegranskat)abstract
    • In software engineering, classification is a way to find an organized structure of knowledge about objects. Classification serves to investigate the relationship between the items to be classified, and can be used to identify the current gaps in the field. In many cases users are able to order and relate objects by fitting them in a category. This paper presents initial work on a taxonomy for classification of errors (bugs) related to concurrent execution of application level software threads. By classifying concurrency bugs based on their corresponding observable properties, this research aims to examine and structure the state of the art in this field, as well as to provide practitioner support for testing and debugging of concurrent software. We also show how the proposed classification, and the different classes of bugs, relates to the state of the art in the field by providing a mapping of the classification to a number of recently published papers in the software engineering field.
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6.
  • Abbaspour, Sara, et al. (författare)
  • A Combination Method for Electrocardiogram Rejection from Surface Electromyogram
  • 2014
  • Ingår i: Open Biomedical Engineering Journal. - Netherlands : Bentham Science Publishers. - 1874-1207. ; 8:1, s. 13-19
  • Tidskriftsartikel (refereegranskat)abstract
    • The electrocardiogram signal which represents the electrical activity of the heart provides interference in the recording of the electromyogram signal, when the electromyogram signal is recorded from muscles close to the heart. Therefore, due to impurities, electromyogram signals recorded from this area cannot be used. In this paper, a new method was developed using a combination of artificial neural network and wavelet transform approaches, to eliminate the electrocardiogram artifact from electromyogram signals and improve results. For this purpose, contaminated signal is initially cleaned using the neural network. With this process, a large amount of noise can be removed. However, low-frequency noise components remain in the signal that can be removed using wavelet. Finally, the result of the proposed method is compared with other methods that were used in different papers to remove electrocardiogram from electromyogram. In this paper in order to compare methods, qualitative and quantitative criteria such as signal to noise ratio, relative error, power spectrum density and coherence have been investigated for evaluation and comparison. The results of signal to noise ratio and relative error are equal to 15.6015 and 0.0139, respectively.
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7.
  • Abbaspour, Sara, et al. (författare)
  • A comparison of adaptive filter and artificial neural network results in removing electrocardiogram contamination from surface EMG
  • 2012
  • Ingår i: ICEE 2012 - 20th Iranian Conference on Electrical Engineering. - IEEE. - 9781467311489 ; s. 1554-1557
  • Konferensbidrag (refereegranskat)abstract
    • Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment. These recordings are critically often contaminated by cardiac artifact. The purpose of this article was to evaluate the performance of an adaptive filter and artificial neural network (ANN) in removing electrocardiogram (ECG) contamination from surface EMGs recorded from the pectoralismajor muscles. Performance of these methods was quantified by power spectral density, coherence, signal to noise ratio, relative error and cross correlation in simulated noisy EMG signals. In between these two methods the ANN has better results.
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8.
  • Abbaspour, Sara, et al. (författare)
  • A comparison of adaptive neuro-fuzzy inference system and real-time filtering in cancellation ECG artifact from surface EMG
  • 2012
  • Ingår i: ICEE 2012 - 20th Iranian Conference on Electrical Engineering. - IEEE. - 978-146731148-9 ; s. 1558-1561
  • Konferensbidrag (refereegranskat)abstract
    • Electromyogram (EMG) is used in different applications such as diagnosis and treatment of diseases. Recorded EMG signals from upper trunk muscles are contaminated by electrocardiogram (ECG). Removal of the ECG artifact from surface EMG is not simple because their frequency content is much overlap. In this paper, we compare the results of adaptive Neuro fuzzy inference system (ANFIS) and real time filtering techniques. Finally performance of these methods is evaluated using qualitative criteria, power spectrum density and coherence and Quantitative criteria signal to noise ratio, relative error and cross correlation. The result of signal to noise ratio, relative error and cross correlation for better results (ANN) is equal to 13.274, 0.03 and %97respectively.
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9.
  • Abbaspour, Sara, 1984-, et al. (författare)
  • ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA
  • 2015
  • Ingår i: Studies in Health Technology and Informatics, Volume 211. - 978-1-61499-515-9 ; s. 91-97
  • Konferensbidrag (refereegranskat)abstract
    • This study aims at proposing an efficient method for automated electrocardiography (ECG) artifact removal from surface electromyography (EMG) signals recorded from upper trunk muscles. Wavelet transform is applied to the simulated data set of corrupted surface EMG signals to create multidimensional signal. Afterward, independent component analysis (ICA) is used to separate ECG artifact components from the original EMG signal. Components that correspond to the ECG artifact are then identified by an automated detection algorithm and are subsequently removed using a conventional high pass filter. Finally, the results of the proposed method are compared with wavelet transform, ICA, adaptive filter and empirical mode decomposition-ICA methods. The automated artifact removal method proposed in this study successfully removes the ECG artifacts from EMG signals with a signal to noise ratio value of 9.38 while keeping the distortion of original EMG to a minimum.
10.
  • Abbaspour, Sara, et al. (författare)
  • Evaluation of wavelet based methods in removing motion artifact from ECG signal
  • 2015
  • Ingår i: IFMBE Proceedings. - 978-3-319-12966-2 ; s. 1-4
  • Konferensbidrag (refereegranskat)abstract
    • Accurate recording and precise analysis of the electrocardiogram (ECG) signals are crucial in the pathophysiological study and clinical treatment. These recordings are often corrupted by different artifacts. The aim of this study is to propose two different methods, wavelet transform based on nonlinear thresholding and a combination method using wavelet and independent component analysis (ICA), to remove motion artifact from ECG signals. To evaluate the performance of the proposed methods, the developed techniques are applied to the real and simulated ECG data. The results of this evaluation are presented using quantitative and qualitative criteria. The results show that the proposed methods are able to reduce motion artifacts in ECG signals. Signal to noise ratio (SNR) of the wavelet technique is equal to 13.85. The wavelet-ICA method performed better with SNR of 14.23.
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