The necessity of recognizing handwritten characters is increasing day by day because of its various applications. The objective of this paper is to provide a sophisticated, effective and efficient way to recognize and classify Bangla handwritten characters. Here an extended convolutional neural network (CNN) model has been proposed to recognize Bangla handwritten characters. Our CNN model is tested on "BanglalLekha-Isolated" dataset where there are 10 classes for digits, 11 classes for vowels and 39 classes for consonants. Our model shows accuracy of recognition as: 99.50% for Bangla digits, 93.18% for vowels, 90.00% for consonants and 92.25% for combined classes.
Objective of this paper is to identify a person taking fingerprint as a biometric parameter using wavelet packet transform. Here both conventional discrete wavelet transform (DWT) and discrete wavelet packet transform (WPT) are used considering special basis function/matrix to extract the coefficients of basis functions those convey the most of the energy of the signal or image. Here top 5% coefficients are chosen which actually convey the characteristics of an image. The outcome of the paper is to determine the set of energetic coefficients of basis functions which carry the features of an image hence storage required to preserve the template of images will be reduced considerably.
The structure of any Bangla numerical character is more complex compared to English numerical character. Two pairs of numerical character in Bangla resembles to be closed and they are: "one and nine" and "five and six". We found that, handwritten Bangla numerical character cannot be recognized using single machine learning algorithm or discrete wavelet transform (DWT). Above phenomenon motivated us to use combination of DWT, Fuzzy Inference System (FIS) and Principal Component Analysis (PCA) to recognize numerical characters of Bangla in handwritten format. The four lowest spectral components of a preprocessed image are taken using DWT, which is considered as the feature vector to recognize the digits in first phase. The feature vector is then applied to FIS and PCA separately. The combined method provides recognition accuracy of 95.8% whereas application of individual method gives less rate of accuracy. Instead of storing the images itself in a folder, if we can store the feature vector of images achieved from DWT in tabular form. The records of table can be applied in FIS, PCA or other object detection algorithm. Although the technique used in the paper can detect objects with moderate rate of accuracy but can save huge storage against a benchmark database of images. If a tradeoff is made between storage requirements and accuracy of recognition, the model of the paper is preferable compared to other present state-of-art. Another finding of the paper is that, the spectral components of images acquired by DWT only matched with FIS and PCA for classification but do not match properly with unsupervised (K-mean clustering) and supervised (support vector machine) learning.
The aim of the paper is to detect object using the
combination of three algorithms: convolutional neural network
(CNN) and extended speeded up robust features (SUFR) and
Fuzzy inference system (FIS). Here three types of objects are
considered: first, we consider RGB images of hundred different
types of objects (for example anchor, laptop airplane, car etc.)
taken from benchmark database; second, we take grayscale
images of human fingerprint from recognized database; third,
Bangla handwritten alphabet from standard database. In this
paper we extend the SURF algorithm then the result of the
extended SURF is applied in FIS to enhance accuracy of
detection. Finally, three algorithms are combined and the
accuracy of detection of combined technique is found better than
individual one. The combined algorithm provides the average
recognition rate for objects of first case as 94.21%, for human
finger print as 92.17%%, for Bangla letter as 92.38% and for the
Bangla digit as 93.69%.
Recently power saving is a vital issue for wireless devices of 4G and 5G networks. A device enters in sleeping mode (short and long sleep cycle) when there is no arrival of traffic but wakeup once the arrival of traffic. Before wakeup, the UE user equipment (UE) spends the rest of the sleeping cycle which incurs a delay of service. There is a tradeoff between the length of a sleep cycle (power saving factor ids higher for longer sleep cycle) and mean delay of service. In this paper, a Markov chain is designed including timer inactivity, short sleep, and long sleep and active service states. The closedform solution of the chain is performed using node equations hence comparison of performance is made with previous work in the context of power-saving factor and mean delay. Both the power saving factor and mean delay of this paper are found marginally better than the previous work at lower packet arrival rate but at higher arrival rate performance are almost the same but claims some explanations.
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