Search for: pattern-recognition
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    Pattern recognition analysis of gas chromatographic and infrared spectroscopic fingerprints of crude oil for source identification

    , Article Microchemical Journal ; Volume 153 , 2020 Hashemi Nasab, F. S ; Parastar, H ; Sharif University of Technology
    Elsevier Inc  2020
    In this study, a chemometric strategy was developed for analysis of gas chromatographic (GC) and infrared spectroscopic (FT-IR) fingerprints of nine crude oil samples from the main oil wells of Iran to classify them and to find their origins. In this regard, a fractionation method based on saturated, aromatic, resin, and asphaltene (SARA) test was used. Then, these fractions were analyzed by GC-FID and GC–MS. Also, nine crude oil samples were analyzed by FT-IR. The obtained GC fingerprints were aligned using correlation optimized warping (COW) and auto-scaled, and then analyzed using principal component analysis (PCA) and hierarchical cluster analysis (HCA). Evaluation of PCA scores plot... 

    A novel approach to recognize hand movements via sEMG patterns

    , Article 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, EMBC'07, Lyon, 23 August 2007 through 26 August 2007 ; 2007 , Pages 4907-4910 ; 05891019 (ISSN) ; 1424407885 (ISBN); 9781424407880 (ISBN) Khezri, M ; Jahed, M ; Sharif University of Technology
    Electromyogram signal (EMG) is an electrical manifestation of contractions of muscles. Surface EMG (sEMG) signal collected form surface of the skin has been used in diverse applications. One of its usages is exploiting it in a pattern recognition system which evaluates and synthesizes hand prosthesis movements. The ability of current prosthesis has been limited in simple opening and closing that decreases the efficacy of these devices in contrary to natural hand. In order to extend the ability and accuracy of prosthesis arm movements and performance, a novel approach for sEMG pattern recognizing system is proposed. In order to have a relevant comparison, present and recent research for... 

    An artificial immune system with partially specified antibodies

    , Article 9th Annual Genetic and Evolutionary Computation Conference, GECCO 2007, London, 7 July 2007 through 11 July 2007 ; Pages 57-62 , 2007 ; 9781595936974 (ISBN) Halavati, R ; Bagheri ShourakiS, S ; Jalali Heravi, M ; Jafari Jashmi, B ; Sharif University of Technology
    Artificial Immune System algorithms use antibodies which fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm can not make use of schemata or classes of partial solutions. This paper presents a symbiotic artificial immune system (SymbAIS) algorithm which is an extension of CLONALG algorithm. It uses partially specified antibodies and gradually builds up building blocks of suitable sub-antibodies. The algorithm is compared with CLONALG on multimodal function optimization and combinatorial optimization problems and it is shown that it can solve problems that CLONALG is unable to solve.... 

    Evaluation of the effect of organic pollutants exposure on the antioxidant activity, total phenolic and total flavonoid content of lettuce (Lactuca sativa L.) using UV–Vis spectrophotometry and chemometrics

    , Article Microchemical Journal ; Volume 170 , 2021 ; 0026265X (ISSN) Nikzad, N ; Parastar, H ; Sharif University of Technology
    Elsevier Inc  2021
    In the present contribution, the effects of different contaminants of emerging concerns (CECs), including parabens, drugs, and polycyclic aromatic hydrocarbons (PAHs) on antioxidant activity of Lactuca sativa L. in different concentration levels (10–500 µg L−1) were evaluated using ultraviolet–visible (UV–Vis) spectrophotometry combined with chemometric techniques. The extracts of lettuce samples were investigated for the antioxidant activity (AA), total phenolic content (TPC), and total flavonoid content (TFC) after 39 days of planting and 14 days of exposure. Then, the spectroscopic data were arranged in two different data matrices, including (i) the control lettuce samples and PAHs... 

    Optimizing allocation of two dimensional irregular shapes using an agent based approach

    , Article Wec 05: Fourth World Enformatika Conference, Istanbul, 24 June 2005 through 26 June 2005 ; Volume 6 , 2005 , Pages 241-244 ; 9759845857 (ISBN) Halavati, R ; Shouraki, S. B ; Noroozian, M ; Zadeh, S. H ; Ardil C ; Sharif University of Technology
    Packing problems arise in a wide variety of application areas. The basic problem is that of determining an efficient arrangement of different objects in a region without any overlap and with minimal wasted gap between shapes. This paper presents a novel population based approach for optimizing arrangement of irregular shapes. In this approach, each shape is coded as an agent and the agents' reproductions and grouping policies results in arrangements of the objects in positions with least wasted area between them. The approach is implemented in an application for cutting sheets and test results on several problems from literature are presented. COPYRIGHT © ENFORMATIKA  

    Gold-nanoparticle-based colorimetric sensor array for discrimination of organophosphate pesticides

    , Article Analytical Chemistry ; Volume 88, Issue 16 , 2016 , Pages 8099-8106 ; 00032700 (ISSN) Fahimi Kashani, N ; Hormozi Nezhad, M. R ; Sharif University of Technology
    American Chemical Society 
    There is a growing interest in developing high-performance sensors monitoring organophosphate pesticides, primarily due to their broad usage and harmful effects on mammals. In the present study, a colorimetric sensor array consisting of citrate-capped 13 nm gold nanoparticles (AuNPs) has been proposed for the detection and discrimination of several organophosphate pesticides (OPs). The aggregation-induced spectral changes of AuNPs upon OP addition has been analyzed with pattern recognition techniques, including hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). In addition, the proposed sensor array has the capability to identify individual OPs or mixtures of them in... 

    Fetal R-wave detection from multichannel abdominal ECG recordings in low SNR

    , Article Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; 2009 , Pages 344-347 ; 1557170X (ISSN) Kharabian, S ; Shamsollahi, M. B ; Sameni, R ; Sharif University of Technology
    Abdominal recordings of fetal ECG (fECG) have lower signal-to-noise ratio (SNR) as compared with invasive procedures. In this paper we have combined two previously proposed methods, one for extracting fECG, called piCA and the other, a transformation based on Hilbert transform to enhance the R-peaks. The combination of these methods seems to work well in situations of noisy data and fetal repositioning. Also a comparison is done by using ICA in order to extract the fetal signals. Performance of both methods is studied separately. Results show that applying the transformation on the components extracted with the use of piCA (after maternal ECG cancellation), had a very good performance. Also,... 

    MEG based classification of wrist movement

    , Article Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 ; 2009 , Pages 986-989 ; 1557170X (ISSN) ; 978-142443296-7 (ISBN) Montazeri, N ; Shamsollahi, M. B ; Hajipour, S ; Sharif University of Technology
    Neural activity is very important source for data mining and can be used as a control signal for brain-computer interfaces (BCIs). Particularly, Magnetic signals of neurons are enriched with information about the movement of different part of the body such as wrist movement. In this paper, we use MEG (Magneto encephalography) signals of two subjects recorded during wrist movement task in four directions. Data were prepared for BCI competition 2008 for multiclass classification. Our approach for this classification problem consists of PCA as a noise reduction method, ULDA for feature reduction and various linear classifiers such as Bayesian, KNN and SVM. Final results (58%-62% for subject 1... 

    Development of a colorimetric sensor array based on monometallic and bimetallic nanoparticles for discrimination of triazole fungicides

    , Article Analytical and Bioanalytical Chemistry ; April , 2021 ; 16182642 (ISSN) Kalantari, K ; Fahimi Kashani, N ; Hormozi Nezhad, M. R ; Sharif University of Technology
    Springer Science and Business Media Deutschland GmbH  2021
    Due to the widespread use of pesticides and their harmful effects on humans and wildlife, monitoring their residual amounts in crops is critically essential but still challenging regarding the development of high-throughput approaches. Herein, a colorimetric sensor array has been proposed for discrimination and identification of triazole fungicides using monometallic and bimetallic silver and gold nanoparticles. Aggregation-induced behavior of AgNPs, AuNPs, and Au-AgNPs in the presence of four triazole fungicides produced a fingerprint response pattern for each analyte. Innovative changes to the metal composition of nanoparticles leads to the production of entirely distinct response patterns... 

    Feature Extraction and Classification Using Sparse Represantation

    , M.Sc. Thesis Sharif University of Technology Joneid, Mohsen (Author) ; Ghaemmaghami, Shahrokh (Supervisor) ; Fatemizadeh, Emad (Co-Advisor)
    Sparse representation has attracted a lot of attention during the last few years. This adaptive representation has been used as an alternative of the classic transforms. In this kind of representation, signals are decomposed in terms of some basis functions. This basis functions are called “atoms” and their collection is called a “dictionary”. Dictionary learning should be such that signals have a sparse representation. Specified dictionary could apply some other properties except sparsity for the transform domain representation.In this thesis after study of methods convert the dictionary discriminative, we propose KLDA method for learning a discriminative dictionary. Also a new algorithm... 

    Learning Improvement in Phase Oscillator Models

    , M.Sc. Thesis Sharif University of Technology Aghighi, Meysam (Author) ; Jalili, Mahdi (Supervisor)
    In the recent years, the problem of modeling a cognitive task using phase oscillators has been receiving a significant attention. In this view, single neurons are no longer elementary computational units. Rather, coherent oscillating groups of neurons are seen as nodes of networks performing cognitive tasks. From this assumption, we develop a model of stimulus-response learning and recognition. The most significant part of our work is defining learning methods for natural frequencies and coupling weights in a coupled phase oscillator network under Kuramoto conditions. In this thesis, we improved the previous models by not only emphasizing on the frequency of the oscillators but also taking... 

    Wavelet Applications in Pattern Recognition

    , M.Sc. Thesis Sharif University of Technology Piri Pishekloo, Bijan (Author) ; Mahdavi Amiri, Nezameddin (Supervisor)
    We study wavelet transformation for use in pattern recognition. Feature extraction is a crucial processing step for pattern recognition. Feature extraction is based on finding mathematical methods for reducing dimensionality of pattern representation. Two most important features to be extracted are edges and textures. Edges and textures are not absolute concepts and change with scale. Moreover, local characteristics of edges are needed. In recent decades, wavelet transformation as a mathematical tool has captured the attention of many scientists in signal processing and mathematics. Wavelet basis can provide efficient and useful description of a function or signal since it has local... 

    Efficient kernel learning from constraints and unlabeled data

    , Article Proceedings - International Conference on Pattern Recognition, 23 August 2010 through 26 August 2010, Istanbul ; 2010 , Pages 3364-3367 ; 10514651 (ISSN) ; 9780769541099 (ISBN) Soleymani Baghshah, M ; Bagheri Shouraki, S ; Sharif University of Technology
    Recently, distance metric learning has been received an increasing attention and found as a powerful approach for semi-supervised learning tasks. In the last few years, several methods have been proposed for metric learning when must-link and/or cannot-link constraints as supervisory information are available. Although many of these methods learn global Mahalanobis metrics, some recently introduced methods have tried to learn more flexible distance metrics using a kernel-based approach. In this paper, we consider the problem of kernel learning from both pairwise constraints and unlabeled data. We propose a method that adapts a flexible distance metric via learning a nonparametric kernel... 

    Data mining with a simulated annealing based fuzzy classification system

    , Article Pattern Recognition ; Volume 41, Issue 5 , 2008 , Pages 1824-1833 ; 00313203 (ISSN) Mohamadi, H ; Habibi, J ; Saniee Abadeh, M ; Saadi, H ; Sharif University of Technology
    Elsevier Ltd  2008
    In this paper, the use of simulated annealing (SA) metaheuristic for constructing a fuzzy classification system is presented. In several previous investigations, the capability of fuzzy systems to solve different kinds of problems has been demonstrated. Simulated annealing based fuzzy classification system (SAFCS), hybridizes the learning capability of SA metaheuristic with the approximate reasoning method of fuzzy systems. The objective of this paper is to illustrate the ability of SA to develop an accurate fuzzy classifier. The use of SA in classification is an attempt to effectively explore and exploit the large search space usually associated with classification problems, and find the... 

    An effective manipulator trajectory planning with obstacles using virtual potential field method

    , Article 2007 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2007, Montreal, QC, 7 October 2007 through 10 October 2007 ; January , 2007 , Pages 1573-1578 ; 1062922X (ISSN); 1424409918 (ISBN); 9781424409914 (ISBN) Haghshenas Jaryani, M ; Sharif University of Technology
    This paper presents a new approach for trajectory planning of manipulator robots based on Virtual Potential Field (VPF) in presence of static obstacles. In this method, a series of via points between starting point and goal point is obtained by traverse end-effector of manipulator in affected by different VPF while avoiding obstacles in the Cartesian space. An optimum trajectory is generated by using pattern search algorithm which determines strength of potential fields to minimize the value of desired objective function. Cubic splines are used to generate a smooth trajectory through path points in joint space that are obtained by inverse kinematics solution of corresponding points in the... 

    Evolution of multiple states machines for recognition of online cursive handwriting

    , Article 2006 World Automation Congress, WAC'06, Budapest, 24 June 2006 through 26 June 2006 ; 2006 ; 1889335339 (ISBN); 9781889335339 (ISBN) Halavati, R ; Shouraki, S. B ; Hassanpour, S ; Sharif University of Technology
    IEEE Computer Society  2006
    Recognition of cursive handwritings such as Persian script is a hard task as there is no fixed segmentation and simultaneous segmentation and recognition is required. This paper presents a novel comparison method for such tasks which is based on a Multiple States Machine to perform robust elastic comparison of small segments with high speed through generation and maintenance of a set of concurrent possible hypotheses, The approach is implemented on Persian (Farsi) language using a typical feature set and a specific tailored genetic algorithm and the recognition and computation time is compared with dynamic programming comparison approach. Copyright - World Automation Congress (WAC) 2006  

    Assessment of preprocessing on classifiers used in the P300 speller paradigm

    , Article 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'06, New York, NY, 30 August 2006 through 3 September 2006 ; 2006 , Pages 1319-1322 ; 05891019 (ISSN); 1424400325 (ISBN); 9781424400324 (ISBN) Mirghasemi, H ; Shamsollahi, M. B ; Fazel Rezai, R ; Sharif University of Technology
    Artifact removal is an essential part in electroencephalogram (EEG) recording and the raw EEG signals require preprocessing before feature extraction. In this work, we implemented three filtering methods and demonstrated their effects on the performance of different classifiers. Bandpass digital filtering, median filtering and facet method are three preprocessing approaches investigated in this paper. We used data set lib from the BCI competition 2003 for training and testing phase. Our accuracy varied between 80% and 96%. In our work, we demonstrated that the problems of choosing the classifier and preprocessing methods are not independent of each other. Two of our approaches could achieve... 

    Contributive representation-based reconstruction for online 3d action recognition

    , Article International Journal of Pattern Recognition and Artificial Intelligence ; Volume 35, Issue 2 , 2021 ; 02180014 (ISSN) Tabejamaat, M ; Mohammadzade, H ; Sharif University of Technology
    World Scientific  2021
    Recent years have seen an increasing trend in developing 3D action recognition methods. However, despite the advances, existing models still suffer from some major drawbacks including the lack of any provision for recognizing action sequences with some missing frames. This significantly hampers the applicability of these methods for online scenarios, where only an initial part of sequences are already provided. In this paper, we introduce a novel sequence-To-sequence representation-based algorithm in which a query sample is characterized using a collaborative frame representation of all the training sequences. This way, an optimal classifier is tailored for the existing frames of each query... 

    A novel approach to persian online hand writing recognition

    , Article Wec 05: Fourth World Enformatika Conference, Istanbul, 24 June 2005 through 26 June 2005 ; Volume 6 , 2005 , Pages 232-236 ; 9759845857 (ISBN) Halavati, R ; Jamzad, M ; Soleymani, M ; Sharif University of Technology
    Persian (Farsi) script is totally cursive and each character is written in several different forms depending on its former and later characters in the word. These complexities make automatic handwriting recognition of Persian a very hard problem and there are few contributions trying to work it out. This paper presents a novel practical approach to online recognition of Persian handwriting which is based on representation of inputs and patterns with very simple visual features and comparison of these simple terms. This recognition approach is tested over a set of Persian words and the results have been quite acceptable when the possible words where unknown and they were almost all correct in... 

    A novel approach to spinal 3-D kinematic assessment using inertial sensors: towards effective quantitative evaluation of low back pain in clinical settings

    , Article Computers in Biology and Medicine ; Volume 89 , 2017 , Pages 144-149 ; 00104825 (ISSN) Ashouri, S ; Abedi, M ; Abdollahi, M ; Dehghan Manshadi, F ; Parnianpour, M ; Khalaf, K ; Sharif University of Technology
    This paper presents a novel approach for evaluating LBP in various settings. The proposed system uses cost-effective inertial sensors, in conjunction with pattern recognition techniques, for identifying sensitive classifiers towards discriminate identification of LB patients. 24 healthy individuals and 28 low back pain patients performed trunk motion tasks in five different directions for validation. Four combinations of these motions were selected based on literature, and the corresponding kinematic data was collected. Upon filtering (4th order, low pass Butterworth filter) and normalizing the data, Principal Component Analysis was used for feature extraction, while Support Vector Machine...