Article | 16-December-2013
Electrocardiogram (ECG) signal for human identity recognition is a new area on biometrics research. The ECG is a vital signal of human body, unique, robustness to attack, universality and permanence, difference to others traditional biometrics technic. This study also proposes Adaptive Multilayer Generalized Learning Vector Quantization (AMGLVQ), that integrating feature extraction and classification method. The experiments shown that AMGLVQ can improve the accuracy of classification better
Elly Matul Imah,
Wisnu Jatmiko,
T. Basaruddin
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 5, 1891–1917
Article | 01-December-2016
Jiang Xinhua,
Xue Heru,
Zhang Lina,
Zhou Yanqing
International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1991–2009
Article | 01-September-2014
Feature extraction and selection are the most important techniques for ultrasonic flaw signal classification. In this study, empirical mode decomposition (EMD) is first used to obtain the intrinsic mode functions (IMFs) of original ultrasonic signals. Such IMFs and traditional time as well as frequency domain based statistical parameters are extracted as the initial features of flaw signal. After that, spectral clustering method is used for feature value discretization so that rough set
Yu Wang
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 1401–1420
Article | 10-April-2018
equipment. This article discusses the hardware and software design methods in detail, as well as the complete process of the across-node communication module. In order to verify the principle of the design, we created a prototype test machine which consists of an ARM Cortex-M4 core microprocessor and 10 ARM Cortex-M0 core microprocessors through the UART serial interconnection to form a star network and carried out an experimental about the ECG feature extraction operation. Experimental results show
Xin Liu,
Zhiqiang Wei
International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 27–32
Research Article | 27-December-2017
as hand plays vital communication mode. Considering earlier reported work, various techniques available for hand tracking, segmentation, feature extraction and classification are listed. Vision based system have challenges over traditional hardware based approach; by efficient use of computer vision and pattern recognition, it is possible to work on such system which will be natural and accepted, in general.
Archana S. Ghotkar,
Dr. Gajanan K. Kharate
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 1, 96–115
research-article | 30-November-2019
objects. Moreover, traffic signs might be physically faded and damaged due to vandalism, making it harder to detect their color and shape. Segmentation is used to separate the color of traffic signs from the background, which is then continued with shape search to find candidates based on feature extraction. Researches on shape features mostly use Histogram of Oriented Gradient (HOG) and Pyramid Histogram of Oriented Gradient (PHOG). HOG uses blocks and cells to determine shape features. In order to
Aris Sugiharto,
Agus Harjoko,
Suharto Suharto
International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–15
Research Article | 01-September-2017
S. Srinivasan,
Dr. K. Rajakumar
International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 3, 567–593
Article | 05-September-2013
1Nishchal K. Verma,
Rahul K. Sevakula,
Jayesh K. Gupta,
Sumanik Singh,
Sonal Dixit,
Al Salour
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1763–1782
Article | 30-November-2018
=sgn{i=1n[aiKxi,x-sidi§]}, f (x)≠1 or f(x)≠− 1, The x vector does not belong to the support vector or the x vector belongs to the support vector.
An initial support vector library trained from known flows. After the known flow rate is trained by the data acquisition module, the feature extraction module, the data preprocessing module, and the training module, a support vector is generated to perform feature analysis, and its characteristic word information is added to the support vector library
Wang Zhongsheng,
Gao Jiaqiong
International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 3, 53–60
Article | 01-June-2015
Iris recognition and favor because of its high recognition rate, noninvasive and simple algorithm and other advantages, in a variety of biometric identification technology is very prominent. The iris texture feature extraction is the core of the iris recognition algorithm. Fractal geometry theory provides new ideas and methods to express nonlinear image information, the fractal dimension is an important parameter of fractal geometry, is a measure of complexity of irregular change, covering
Gao Xiaoxing,
Feng Sumin,
Cui Han
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 2, 1085–1103
Article | 07-May-2018
Ding Li,
Chong Jiao
International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 2, 99–103
Research Article | 15-February-2020
This paper reports on a series of experiments to evaluate the methods for feature extraction and denoising the digital signal from thin film zinc oxide-tin dioxide composite gas sensor devices. The aim was to find a method that not only cleaned the signal but also maintained the shape, precision and resolution of the signal. It was found that the Savitzky–Golay smoothing filter method gave the best, smoothed and cleaned, approximation of the sensor response regardless of the thin film
Enobong Bassey,
Jacqueline Whalley,
Philp Sallis,
Krishnamachar Prasad
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–5
Article | 05-September-2013
Prestressed Concrete Cylinder Pipe (PCCP) is a widely used water pipe all over the world. A major cause of PCCP failure is the internal wire break, which will emit acoustic signal. In this paper, a hydrophone-based PCCP real-time monitoring and failure-prediction system was proposed. By applying wavelet energy normalization analysis to signal feature extraction and Support Vector Machine (SVM) to signal recognition, a high prediction accuracy of 98.33% was achieved. The result showed that the
Yuan Zhang,
Yibo Li
International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 4, 1582–1598
Article | 13-July-2020
models. In the following years, CNN have made leaps and bounds in digital image recognition and processing with their powerful feature extraction capabilities.
II.
OVERVIEW OF CNN
CNN, compared to other network models, are better able to adapt their structures to image structures while extracting features and classifying them, with outstanding performance in image processing. In addition, its weight sharing feature educes the training parameters of the network, which makes the network structure
Gao Zhiyu,
Liu Bailin,
Gu Hongxian,
Mu Jing
International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 2, 31–38
Research Article | 01-September-2011
A.-M. Cretu,
P. Payeur
International Journal on Smart Sensing and Intelligent Systems, Volume 4 , ISSUE 3, 402–423
Research Article | 01-September-2017
Missing fastener detection is a critical task due to its similar characteristics with surrounding environments. In this paper, a machine vision based fully automatic detection and classification of missing fastener detection system is proposed using Support Vector Machine (SVM) classifier. This proposed system consists of preprocessing, transformation, feature extraction and classifications. Image resizing is performed as preprocessing step and Gabor transform is used as transformation
R. Manikandan,
M. Balasubramanian,
S. Palanivel
International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 574–589
Research Article | 27-December-2017
Because of the functionality of an index finger, the disability of its motion in the modern age can decrease the person’s quality of life. As a part of rehabilitation therapy, the recognition of the index finger motion for rehabilitation purposes should be done properly. This paper proposes a novel recognition system of the index finger motion suing a cutting-edge method and its improvements. The proposed system consists of combination of feature extraction method, a dimensionality reduction
Khairul Anam,
Adel Al Jumaily,
Yashar Maali
International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 2, 644–657
Article | 01-March-2015
With the development of society and science technology, the information security becomes more and more important for people and nation. In this paper, we focus on the technology of iris-face fusion in multi-mode sense in order to find an algorithm to get a better recognition performance. Firstly, we introduce the feature extraction of iris and face, and fusion the features using different algorithms. By simulation, we find out the recognition performance with different fusion methods, which
Qihui Wang,
Bo Zhu,
Yo Liu,
Lijun Xie,
Yao Zheng
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 387–407
research-article | 30-November-2018
outlier removal process can be seen in Figure 6.
Figure 5:
Outlier removal using IQR.
Figure 6:
Beat features before and after outlier removal using IQR.
Feature extraction
Feature extraction is one of the key aspects of pre-processing techniques, as this process will select the features that will be used by the classification process later on. Therefore, it is essential to extract the best features from the data set, as selecting better features will lead to better outcome during the
Wisnu Jatmiko,
M. Anwar Ma’sum,
Hanif Arief Wisesa,
Hadaiq Rolis Sanabila
International Journal on Smart Sensing and Intelligent Systems, Volume 12 , ISSUE 1, 1–28
Article | 02-April-2020
He Li,
Yubian Wang
International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 1, 44–53
Article | 14-October-2020
classification can be achieved.
II.
RELATED WORK
A.
Traditional object detection algorithm
Traditional object detection algorithms include primary feature extraction methods such as HOG feature extraction of objects and training SVM classifiers for recognition. Their algorithms are generally divided into three stages (see Figure 1.):
Figure 1.
Traditional object detection algorithm
1)Select different sliding window frames according to the size of the object, and use the sliding window to select a part
Wei Zhang,
Hongge Yao,
Yuxing Tan
International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 3, 65–72
research-article | 30-November-2020
includes four parts, i.e., (i) the proposed system, (ii) signal acquisition and preprocessing, (iii) feature extraction and algorithms, and (iv) command translations. The third section presents experimental results and discussions to demonstrate the efficiency of the proposed system and algorithm from the second section for online testing. In the last section, the outcome and outlook of the proposed system will be presented as a conclusion, and future work will be suggested.
Research methods
Theerat Saichoo,
Poonpong Boonbrahm,
Yunyong Punsawad
International Journal on Smart Sensing and Intelligent Systems, Volume 14 , ISSUE 1, 1–10
Research Article | 01-September-2017
are trained into the databases using machine learning algorithm. The tracking of individuals can be achieved by capturing their images while on the move and comparing them with the values stored in the databases. The detection of facial structure is done with Viola-Jones algorithm which though older is easy and efficient to use and Kanade-Lucas-Tomasi(KLT) algorithm is used for feature extraction . The HOG (Histogram of Oriented Gradients) features are extracted for training.
P.J Leo Evenss,
Jennings Mcenroe .S,
A.Prabhu Chakkaravarthy
International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 163–173
Research Article | 05-September-2018
Fusion of multiple algorithms utilizes as much information as possible from each algorithm for enhancing the performance of the biometric authentication system. It is a big challenge to formulate a single algorithm for any biometric authentication system to addresses the problem of illumination, orientations and pose variations. The palmprint features are extracted using two feature extraction algorithms namely contourlet transform with principal component analysis and dual-tree complex wavelet
C. Murukesh,
G. Arul Elango
International Journal on Smart Sensing and Intelligent Systems, Volume 11 , ISSUE 1, 1–11
Article | 01-September-2015
Automatic facial expression recognition from video sequence is an essential research area in the field of computer vision. In this paper, a novel method for recognition facial expressions is proposed, which includes two stages of facial expression feature extraction and facial expression recognition. Firstly, in order to exact robust facial expression features, we use Active Appearance Model (AAM) to extract the global texture feature and optical flow technique to characterize facial expression
Shaoping Zhu
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1464–1483
Article | 01-March-2015
Human facial expressions detection plays a central role in pervasive health care and it is an active research field in computer vision. In this paper, a novel method for facial expression detection from dynamic facial images is proposed, which includes two stages of feature extraction and facial expression detection. Firstly, Active Shape Model (ASM) is used to extract the local texture feature, and optical flow technique is determined facial velocity information, which is used to characterize
Shaoping Zhu,
Yongliang Xiao
International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 581–601