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    Using reo formalism for compliance checking of architecture evolution with evolutionary rules

    , Article 18th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2019, 23 September 2019 through 25 September 2019 ; Volume 318 , 2019 , Pages 725-738 ; 09226389 (ISSN); 9781643680125 (ISBN) Liaghat, Z ; Besharati, M. R ; Izadi, M ; Khamespanah, E ; Sharif University of Technology
    IOS Press  2019
    Assessment of architectural evolution is a challenge and plays a significant role in system evolution management. Although the evolution rules of the software architecture are defined by some expert engineers or architects, there is no guarantee that applying them will end to the desired change. So, having a reliable assessment technique promotes the overall accuracy and quality of the evolution process. Compliance checking with expert-defined rules is a well-known assessment approach that can be applied in architectural evolution. In this paper, an approach is proposed for compliance checking of evolution processes. To this end, evolution paths and processes are modeled by Reo coordination... 

    Dynamic iranian sign language recognition using an optimized deep neural network: An implementation via a robotic-based architecture

    , Article International Journal of Social Robotics ; 2021 ; 18754791 (ISSN) Basiri, S ; Taheri, A ; Meghdari, A. F ; Boroushaki, M ; Alemi, M ; Sharif University of Technology
    Springer Science and Business Media B.V  2021
    Sign language is a non-verbal communication tool used by the deaf. A robust sign language recognition framework is needed to develop Human–Robot Interaction (HRI) platforms that are able to interact with humans via sign language. Iranian sign language (ISL) is composed of both static postures and dynamic gestures of the hand and fingers. In this paper, we present a robust framework using a Deep Neural Network (DNN) to recognize dynamic ISL gestures captured by motion capture gloves in Real-Time. To this end, first, a dataset of fifteen ISL classes was collected in time series; then, this dataset was virtually augmented and pre-processed using the “state-image” method to produce a unique... 

    A new framework based on recurrence quantification analysis for epileptic seizure detection

    , Article IEEE Journal of Biomedical and Health Informatics ; Volume 17, Issue 3 , 2013 , Pages 572-578 ; 21682194 (ISSN) Niknazar, M ; Mousavi, S. R ; Vosoughi Vahdat, B ; Sayyah, M ; Sharif University of Technology
    This study presents applying recurrence quantification analysis (RQA) on EEG recordings and their subbands: delta, theta, alpha, beta, and gamma for epileptic seizure detection. RQA is adopted since it does not require assumptions about stationarity, length of signal, and noise. The decomposition of the original EEG into its five constituent subbands helps better identification of the dynamical system of EEG signal. This leads to better classification of the database into three groups: Healthy subjects, epileptic subjects during a seizure-free interval (Interictal) and epileptic subjects during a seizure course (Ictal). The proposed algorithm is applied to an epileptic EEG dataset provided... 

    Accurate detection and recognition of dirty vehicle plate numbers for high-speed applications

    , Article IEEE Transactions on Intelligent Transportation Systems ; Volume 18, Issue 4 , 2017 , Pages 767-779 ; 15249050 (ISSN) Panahi, R ; Gholampour, I ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2017
    This paper presents an online highly accurate system for automatic number plate recognition (ANPR) that can be used as a basis for many real-world ITS applications. The system is designed to deal with unclear vehicle plates, variations in weather and lighting conditions, different traffic situations, and high-speed vehicles. This paper addresses various issues by presenting proper hardware platforms along with real-Time, robust, and innovative algorithms. We have collected huge and highly inclusive data sets of Persian license plates for evaluations, comparisons, and improvement of various involved algorithms. The data sets include images that were captured from crossroads, streets, and... 

    A hybrid deep model for automatic arrhythmia classification based on LSTM recurrent networks

    , Article 15th IEEE International Symposium on Medical Measurements and Applications, MeMeA 2020, 1 June 2020 through 3 June 2020 ; 2020 Bitarafan, A ; Amini, A ; Baghshah, M. S ; Khodajou Chokami, H ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc  2020
    Electrocardiogram (ECG) recording of electrical heart activities has a vital diagnostic role in heart diseases. We propose to tackle the problem of arrhythmia detection from ECG signals totally by a deep model that does not need any hand-designed feature or heuristic segmentation (e.g., ad-hoc R-peak detection). In this work, we first segment ECG signals by detecting R-peaks automatically via a convolutional network, including dilated convolutions and residual connections. Next, all beats are aligned around their R-peaks as the most informative section of the heartbeat in detecting arrhythmia. After that, a deep learning model, including both dilated convolution layers and a Long-Short Term...