Pattern Recognition
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Author(s):  
Santosh Dhaigude

Abstract: In todays world during this pandemic situation Online Learning is the only source where one could learn. Online learning makes students more curious about the knowledge and so they decide their learning path . But considering the academics as they have to pass the course or exam given, they need to take time to study, and have to be disciplined about their dedication. And there are many barriers for Online learning as well. Students are lowering their grasping power the reason for this is that each and every student was used to rely on their teacher and offline classes. Virtual writing and controlling system is challenging research areas in field of image processing and pattern recognition in the recent years. It contributes extremely to the advancement of an automation process and can improve the interface between man and machine in numerous applications. Several research works have been focusing on new techniques and methods that would reduce the processing time while providing higher recognition accuracy. Given the real time webcam data, this jambord like python application uses OpenCV library to track an object-of-interest (a human palm/finger in this case) and allows the user to draw bymoving the finger, which makes it both awesome and interesting to draw simple thing. Keyword: Detection, Handlandmark , Keypoints, Computer vision, OpenCV


2022 ◽  
Vol 206 ◽  
pp. 107778
Author(s):  
Fabricio Alves de Almeida ◽  
Estevão Luiz Romão ◽  
Guilherme Ferreira Gomes ◽  
José Henrique de Freitas Gomes ◽  
Anderson Paulo de Paiva ◽  
...  

2022 ◽  
Author(s):  
Antoine Grimaldi ◽  
Victor Boutin ◽  
Sio-Hoi Ieng ◽  
Ryad Benosman ◽  
Laurent Perrinet

<div> <div> <div> <p>We propose a neuromimetic architecture able to perform always-on pattern recognition. To achieve this, we extended an existing event-based algorithm [1], which introduced novel spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built from asynchronous events acquired by a neuromorphic camera, these time surfaces allow to code the local dynamics of a visual scene and to create an efficient event-based pattern recognition architecture. Inspired by neuroscience, we extended this method to increase its performance. Our first contribution was to add a homeostatic gain control on the activity of neurons to improve the learning of spatio-temporal patterns [2]. A second contribution is to draw an analogy between the HOTS algorithm and Spiking Neural Networks (SNN). Following that analogy, our last contribution is to modify the classification layer and remodel the offline pattern categorization method previously used into an online and event-driven one. This classifier uses the spiking output of the network to define novel time surfaces and we then perform online classification with a neuromimetic implementation of a multinomial logistic regression. Not only do these improvements increase consistently the performances of the network, they also make this event-driven pattern recognition algorithm online and bio-realistic. Results were validated on different datasets: DVS barrel [3], Poker-DVS [4] and N-MNIST [5]. We foresee to develop the SNN version of the method and to extend this fully event-driven approach to more naturalistic tasks, notably for always-on, ultra-fast object categorization. </p> </div> </div> </div>


MAUSAM ◽  
2022 ◽  
Vol 53 (4) ◽  
pp. 417-424
Author(s):  
SUTAPA CHAUDHURI ◽  
SURAJIT CHATTOPADHYAY

The concept of Multi Layer Perceptron and Fuzzy logic is introduced in this paper to recognize the pattern of surface parameters pertaining to forecast the occurrence of pre-monsoon thunderstorms over Kolkata (22 ° 32¢ , 88 ° 20¢ ).   The results reveal that surface temperature fluctuates significantly from Fuzzy Multi Layer Perceptron (FMLP) model values on thunderstorm days whereas on non-thunderstorm days FMLP model fits well with the surface temperature.   The results further indicate that no definite pattern could be made available with surface dew point temperature and surface pressure that can help in forecasting the occurrence of these storms.


2022 ◽  
Author(s):  
Jianning Wu ◽  
Qiaoling Tan ◽  
Xiaoyan Wu

Abstract Background: The deep learning techniques have been attracted increasing attention on wireless body sensor networks (WBSNs) gait pattern recognition that has a great contribution to monitoring gait change in clinical application. However, in existing studies, there are some challenging issues such as low generalization performance and no potential interpretation for gait variability. It is necessary to search for the advanced deep learning models to resolve these issues. Method: A public WARD database including acceleration and gyroscope data acquired from each subject wearing five sensors was selected, and the gait with different combination of on-body multi-sensors is considered as a WBSNs’ gait pattern. An advanced attention-enhanced hybrid deep learning model of DCNN and LSTM for WBSNs’ gait pattern recognition was proposed. In our proposed technique, the combination model of DCNN with LSTM is firstly to discover the spatial-temporary gait correlation features. And then the attention mechanism is introduced to exploit the more valuable intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability hidden in spatial-temporary gait space obtained. This significantly contributes to enhancing the generalization performance and taking insight on gait variability in a certain anatomical region. Results: The ten gait patterns are randomly selected from WARD database to evaluate the feasibility of our proposed method. Our experiments demonstrated the superior generalization ability of our method to some models such as CNN-LSTM, DCNN-LSTM. Our proposed model could classify ten gait patterns with the highest accuracy and F1-score of 91.48% and 91.46%, respectively. Moreover, we also found that the classification performance of a certain gait pattern was almost same best when the combinations of three or five on-body sensors were employed respectively, suggesting that our method possibly take insight on gait variability in a certain anatomical region. Conclusion: Our proposed technique could feasibly discover the more intrinsic nonlinear dynamic correlation gait characteristics associated with gait variability from on-body multi-sensors gait data, which greatly contributed to best generalization performance and potential clinical interpretation. Our proposed technique would hopefully become a powerful tool of monitoring gait change in clinical application.


2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Ana Carpio ◽  
Alejandro Simón ◽  
Alicia Torres ◽  
Luis F. Villa

AbstractMedical data often appear in the form of numerical matrices or sequences. We develop mathematical tools for automatic screening of such data in two medical contexts: diagnosis of systemic lupus erythematosus (SLE) patients and identification of cardiac abnormalities. The idea is first to implement adequate data normalizations and then identify suitable hyperparameters and distances to classify relevant patterns. To this purpose, we discuss the applicability of Plackett-Luce models for rankings to hyperparameter and distance selection. Our tests suggest that, while Hamming distances seem to be well adapted to the study of patterns in matrices representing data from laboratory tests, dynamic time warping distances provide robust tools for the study of cardiac signals. The techniques developed here may set a basis for automatic screening of medical information based on pattern comparison.


2022 ◽  
Author(s):  
Antoine Grimaldi ◽  
Victor Boutin ◽  
Sio-Hoi Ieng ◽  
Ryad Benosman ◽  
Laurent Perrinet

<div> <div> <div> <p>We propose a neuromimetic architecture able to perform always-on pattern recognition. To achieve this, we extended an existing event-based algorithm [1], which introduced novel spatio-temporal features as a Hierarchy Of Time-Surfaces (HOTS). Built from asynchronous events acquired by a neuromorphic camera, these time surfaces allow to code the local dynamics of a visual scene and to create an efficient event-based pattern recognition architecture. Inspired by neuroscience, we extended this method to increase its performance. Our first contribution was to add a homeostatic gain control on the activity of neurons to improve the learning of spatio-temporal patterns [2]. A second contribution is to draw an analogy between the HOTS algorithm and Spiking Neural Networks (SNN). Following that analogy, our last contribution is to modify the classification layer and remodel the offline pattern categorization method previously used into an online and event-driven one. This classifier uses the spiking output of the network to define novel time surfaces and we then perform online classification with a neuromimetic implementation of a multinomial logistic regression. Not only do these improvements increase consistently the performances of the network, they also make this event-driven pattern recognition algorithm online and bio-realistic. Results were validated on different datasets: DVS barrel [3], Poker-DVS [4] and N-MNIST [5]. We foresee to develop the SNN version of the method and to extend this fully event-driven approach to more naturalistic tasks, notably for always-on, ultra-fast object categorization. </p> </div> </div> </div>


2022 ◽  
Vol 15 ◽  
Author(s):  
Xiangxin Li ◽  
Yue Zheng ◽  
Yan Liu ◽  
Lan Tian ◽  
Peng Fang ◽  
...  

Surface electromyogram-based pattern recognition (sEMG-PR) has been considered as the most promising method to control multifunctional prostheses for decades. However, the commercial applications of sEMG-PR in prosthetic control is still limited due to the ambient noise and impedance variation between electrodes and skin surface. In order to reduce these issues, a force-myography-based pattern recognition method was proposed. In this method, a type of polymer-based flexible film sensors, the piezoelectrets, were used to record the rate of stress change (RSC) signals on the muscle surface of eight able-bodied subjects for six hand motions. Thirteen time domain features and four classification algorithms of linear discriminant analysis (LDA), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM) were adopted to decode the RSC signals of different motion classes. In addition, the optimal feature set, classifier, and analysis window length were investigated systematically. Results showed that the average classification accuracy was 95.5 ± 2.2% by using the feature combination of root mean square (RMS) and waveform length (WL) for the classifier of KNN, and the analysis window length of 300 ms was found to obtain the best classification performance. Moreover, the robustness of the proposed method was investigated, and the classification accuracies were observed above 90% even when the white noise ratio increased to 50%. The work of this study demonstrated the effectiveness of RSC-based pattern recognition method for motion classification, and it would provide an alternative approach for the control of multifunctional prostheses.


2022 ◽  
Vol 3 (1) ◽  
Author(s):  
Erica Scarpa

Accounting is a routine activity. Through repetition, the scribes of the Ebla Archives (Syria, 24th cent. BCE) have been able to record thousands of transactions. They organized and stored accounting data referred to more than thirty years of the Palace G activities. The recurring textual patterns characterizing the administrative corpus are a byproduct of this routine-based approach. The ability to see recurring patterns in the textual record is fundamental when dealing with an administrative corpus: however, this ability fails when the patterns are buried in data. In this paper, I argue that theoretical aspects of data mining are not far from theoretical and methodological tenets of the historical approach. Data mining is a useful technique for the identification of document clusters and relevant information which would otherwise remain hidden. Furthermore, textual pattern recognition is critical to address topics such as the study of society: belonging to a category of complex problems, any socio-historical investigation requires dealing with multiple interconnected variables. However, not all research topics require such an approach. I define the line beyond which digital approaches are extremely useful (if not indispensable) as 'visibility threshold’. The position of this interface is relative and subjective.


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