| 41. |
- Abbaspour Asadollah, Sara
(författare)
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Cyberattacks : Modeling, Analysis, and Mitigation
- 2022
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Ingår i: Proceedings - 2022 6th International Conference on Computer, Software and Modeling. - : Institute of Electrical and Electronics Engineers Inc.. - 9781665454865 ; , s. 80-84
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Konferensbidrag (refereegranskat)abstract
- Industrial cybersecurity has risen as an important topic of research nowadays. The heavy connectivity by the Internet of Things (IoT) and the growth of cyberattacks against industrial assets cause this risen and attract attention to the cybersecurity field. While fostering current software applications and use-cases, the ubiquitous access to the Internet has also exposed operational technologies to new and challenging security threats that need to be addressed. As the number of attacks increases, their visibility decreases. An attack can modify the Cyber-Physical Systems (CPSs) quality to avoid proper quality assessment. They can disrupt the system design process and adversely affect a product’s design purpose. This working progress paper presents our approach to modeling, analyzing, and mitigating cyberattacks in CPS. We model the normal behavior of the application as well as cyberattacks with the help of Microsoft Security Development Lifecycle (SDL) and threat modeling approach (STRIDE). Then verify the application and attacks model using a model checking tool and propose mitigation strategies to decrease the risk of vulnerabilities. The results can be used to improve the system design to overcome the vulnerabilities.
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| 42. |
- Abbaspour Asadollah, Sara, et al.
(författare)
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Evaluation of surface EMG-based recognition algorithms for decoding hand movements
- 2019
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Ingår i: Medical and Biological Engineering and Computing. - : Springer. - 0140-0118 .- 1741-0444. ; 58:1, s. 83-100
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Tidskriftsartikel (refereegranskat)abstract
- Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins’ set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands. [Figure not available: see fulltext.].
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| 43. |
- Abbaspour Asadollah, Sara, et al.
(författare)
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Runtime Verification for Detecting Suspension Bugs in Multicore and Parallel Software
- 2017
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Ingår i: Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017. - 9781509066766 ; , s. 77-80
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Konferensbidrag (refereegranskat)abstract
- Multicore hardware development increases the popularity of parallel and multicore software, while testing and debugging the software become more difficult, frustrating and costly. Among all types of software bugs, concurrency bugs are both important and troublesome. This type of bugs is increasingly becoming an issue, particularly due to the growing prevalence of multicore hardware. Suspension-based-locking bug is one type of concurrency bugs. This position paper proposes a model based on runtime verification and reflection technique in the context of multicore and parallel software to monitor and detect suspension-based-locking bugs. The model is not only able to detect faults, but also diagnose and even repair them. The model is composed of four layers: Logging, Monitoring, Suspension Bug Diagnosis and Mitigation. The logging layer will observe the events and save them into a file system. The monitoring layer will detect the presents of bugs in the software. The suspension bug diagnosis will identify Suspension bugs by comparing the captured data with the suspension bug properties. Finally, the mitigation layer will reconfigure the software to mitigate the suspension bugs. A functional architecture of a runtime verification tool is also proposed in this paper. This architecture is based on the proposed model and is comprised of different modules.
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| 44. |
- Abbaspour Asadollah, Sara, et al.
(författare)
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Towards Classification of Concurrency Bugs Based on Observable Properties
- 2015
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Ingår i: Proceedings - 1st International Workshop on Complex Faults and Failures in Large Software Systems, COUFLESS 2015. - 9781479919345 ; , s. 41-47
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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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| 45. |
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| 46. |
- Abbaspour, Sara, et al.
(författare)
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A Combination Method for Electrocardiogram Rejection from Surface Electromyogram
- 2014
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Ingår i: Open Biomedical Engineering Journal. - Netherlands : Bentham Science Publishers. - 1874-1207. ; 8:1, s. 13-19
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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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| 47. |
- Abbaspour, Saadeh, et al.
(författare)
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A comparative analysis of hybrid deep learning models for human activity recognition
- 2020
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:19
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Tidskriftsartikel (refereegranskat)abstract
- Recent advances in artificial intelligence and machine learning (ML) led to effective methods and tools for analyzing the human behavior. Human Activity Recognition (HAR) is one of the fields that has seen an explosive research interest among the ML community due to its wide range of applications. HAR is one of the most helpful technology tools to support the elderly’s daily life and to help people suffering from cognitive disorders, Parkinson’s disease, dementia, etc. It is also very useful in areas such as transportation, robotics and sports. Deep learning (DL) is a branch of ML based on complex Artificial Neural Networks (ANNs) that has demonstrated a high level of accuracy and performance in HAR. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models widely used in the recent years to address the HAR problem. The purpose of this paper is to investigate the effectiveness of their integration in recognizing daily activities, e.g., walking. We analyze four hybrid models that integrate CNNs with four powerful RNNs, i.e., LSTMs, BiLSTMs, GRUs and BiGRUs. The outcomes of our experiments on the PAMAP2 dataset indicate that our proposed hybrid models achieve an outstanding level of performance with respect to several indicative measures, e.g., F-score, accuracy, sensitivity, and specificity. © 2020 by the authors.
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| 48. |
- Abbaspour, Sara, et al.
(författare)
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A Novel Approach for Removing ECG Interferences from Surface EMG signals Using a Combined ANFIS and Wavelet
- 2016
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Ingår i: Journal of Electromyography & Kinesiology. - : Elsevier BV. - 1050-6411 .- 1873-5711. ; 26, s. 52-59
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the removal of electrocardiogram (ECG) interferences from electromyogram (EMG) signals has been given large consideration. Where the quality of EMG signal is of interest, it is important to remove ECG interferences from EMG signals. In this paper, an efficient method based on a combination of adaptive neuro-fuzzy inference system (ANFIS) and wavelet transform is proposed to effectively eliminate ECG interferences from surface EMG signals. The proposed approach is compared with other common methods such as high-pass filter, artificial neural network, adaptive noise canceller, wavelet transform, subtraction method and ANFIS. It is found that the performance of the proposed ANFIS-wavelet method is superior to the other methods with the signal to noise ratio and relative error of 14.97 dB and 0.02 respectively and a significantly higher correlation coefficient (p < 0.05).
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| 49. |
- Abbaspour, Sara, 1984-, et al.
(författare)
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ECG Artifact Removal from Surface EMG Signal Using an Automated Method Based on Wavelet-ICA
- 2015
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Ingår i: Studies in Health Technology and Informatics, Volume 211. - 9781614995159 ; , s. 91-97
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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.
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| 50. |
- Abbaspour, Sara, 1984-
(författare)
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Electromyogram Signal Enhancement and Upper-Limb Myoelectric Pattern Recognition
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Losing a limb causes difficulties in our daily life. To regain the ability to live an independent life, artificial limbs have been developed. Hand prostheses belong to a group of artificial limbs that can be controlled by the user through the activity of the remnant muscles above the amputation. Electromyogram (EMG) is one of the sources that can be used for control methods for hand prostheses. Surface EMGs are powerful, non-invasive tools that provide information about neuromuscular activity of the subjected muscle, which has been essential to its use as a source of control for prosthetic limbs. However, the complexity of this signal introduces a big challenge to its applications. EMG pattern recognition to decode different limb movements is an important advancement regarding the control of powered prostheses. It has the potential to enable the control of powered prostheses using the generated EMG by muscular contractions as an input. However, its use has yet to be transitioned into wide clinical use. Different algorithms have been developed in state of the art to decode different movements; however, the challenge still lies in different stages of a successful hand gesture recognition and improvements in these areas could potentially increase the functionality of powered prostheses. This thesis firstly focuses on improving the EMG signal’s quality by proposing novel and advanced filtering techniques. Four efficient approaches (adaptive neuro-fuzzy inference system-wavelet, artificial neural network-wavelet, adaptive subtraction and automated independent component analysis-wavelet) are proposed to improve the filtering process of surface EMG signals and effectively eliminate ECG interferences. Then, the offline performance of different EMG-based recognition algorithms for classifying different hand movements are evaluated with the aim of obtaining new myoelectric control configurations that improves the recognition stage. Afterwards, to gain proper insight on the implementation of myoelectric pattern recognition, a wide range of myoelectric pattern recognition algorithms are investigated in real time. The experimental result on 15 healthy volunteers suggests that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) outperform other classifiers. The real-time investigation illustrates that in addition to the LDA and MLE, multilayer perceptron also outperforms the other algorithms when compared using classification accuracy and completion rate.
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