54 research outputs found
Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition
Recognition of handwritten numerals has gained much interest in recent years due to its various application
potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, a CNN based method has been
investigated for Bangla handwritten numeral recognition. A
moderated pre-processing has been adopted to produce patterns from handwritten scan images. On the other hand, CNN has been trained with the patterns plus a number of artificial patterns. A simple rotation based approach is employed to generate artificial patterns. The proposed CNN with artificial pattern is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset
Multiple convolutional neural network training for Bangla handwritten numeral recognition
Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high-dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, three different CNNs with same architecture are trained with different training sets and combined their decisions for Bangla handwritten numeral recognition. One CNN is trained with ordinary training set prepared from handwritten scan images; and training sets for other two CNNs are prepared with fixed (positive and negative, respectively) rotational angles of original images. The proposed multiple CNN based approach is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset
Bangla handwritten numeral recognition using convolutional neural network
Recognition of handwritten numerals has gained much interest in recent years due to its various application
potentials. Although Bangla is a major language in Indian
subcontinent and is the first language of Bangladesh study
regarding Bangla handwritten numeral recognition (BHNR) is
very few with respect to other major languages such Roman.
The existing BHNR methods uses distinct feature extraction
techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. It also automatically provides some degree of translation invariance. In this paper, a CNN based BHNR is investigated. The proposed BHNR-CNN normalizes the written numeral images and then employ CNN to classify individual numerals. It does not employ any feature extraction method like other related works. 17000 hand written numerals with different shapes, sizes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed other prominent exiting methods
Hot-spot traffic pattern on hierarchical 3D mesh network
A Hierarchical 3D-Mesh (H3DM) Network s a
2D-mesh network of multiple basic modules (BMs), in
which the basic modules are 3D-torus networks that are
hierarchically interconnected for higher-level networks. In
this paper, we evaluate the dynamic communication performance of a H3DM network under hot-spot traffic pattern
using a deadlock-free dimension order routing algorithm
with minimum number of virtual channels. We have also
evaluated the dynamic communication performance of the
mesh and torus networks. It is shown that under most
imbalance hot-spot traffic pattern H3DM network yields
high throughput and low average transfer time than that
of mesh and torus networks, providing better dynamic
communication performance compared to those networks
Convolutional neural network training incorporating rotation-based generated patterns and handwritten numeral recognition of major Indian scripts
Handwritten numeral recognition has gained much interest in recent times because of its diverse application potentials. Bangla and Hindi are the two major languages in Indian subcontinent and a large number of population in vast land scape uses Bangla and Devnagari numeral scripts of these two languages. Well-performed handwritten numeral recognition system for Bangla and Devnagari is challenging because of similar shaped numerals in both scripts; few numerals differ from their similar ones with a very few variation even in printed form. In this study, convolutional neural network (CNN) based two different methods have been investigated for better recognition of Bangla and Devnagari handwritten numerals. Both the methods use rotation-based generated patterns along with ordinary patterns to train CNN but in two different modes. In multiple CNN case, three different training sets (one with ordinary patterns and two with clockwise and anti-clockwise rotation-based generated patterns) are prepared; three different CNNs are trained individually with each of these training sets; and their decisions are combined for final system decision. On the other hand, in the case of single CNN, combination of above three training sets is used to train one CNN. A moderated pre-processing is also employed while generating patterns from the scanned images. The proposed methods have been tested on prominent benchmark handwritten numeral datasets and have achieved remarkable recognition accuracies. The achieved recognition accuracies are found better than reported recognition accuracies of prominent existing methods; and such outperformance mounted proposed methods as better recognition systems. Moreover, CNN's performance improvement due to use of generated patterns has also been clearly identified from the presented experimental results
Initiatives of Tropical Agroforestry to Sustainable Agriculture: A Case Study of Capasia Village, Northern Bangladesh
A relatively large percentage of the population in Bangladesh lives under the poverty line and is affected by the country's degrading natural resources. Agroforestry has been seen as one of the few options to lift people out of poverty. Research into the costs and benefits of agroforestry was undertaken in Capasia Village in Northern Bangladesh. Initial results indicate that agroforestry may not only be an optimal livelihood solution for poor farmers, biodiversity conservation and environmental sustainability but agroforestry systems also provide good economic rates of return. Thus the farmers who engage in agroforestry are benefited in different ways
Architecture and network-on-chip implementation of a new hierarchical interconnection network
A Midimew-connected Mesh Network (MMN) is a minimal distance mesh with wrap-around links network of multiple basic modules (BMs), in which the BMs are 2D-mesh networks
that are hierarchically interconnected for higher-level networks. In this paper, we present the architecture of the MMN, addressing of node, routing of message, and evaluate the static network performance of MMN, TESH, mesh and torus networks. In addition, we propose the network-on-chip (NoC) implementation of MMN. With innovative combination of diagonal and hierarchical structure, the MMN possesses several attractive features, including constant degree, small diameter, low cost, small average distance, moderate bisection width and high fault tolerant performance than that of other conventional and hierarchical interconnection
networks. The simple architecture of MMN is also highly suitable for NoC implementation. To implement all the links of level-3 MMN, only four layers are needed which is feasible with current and future VLSI technologies
Time-cost effective factor of a Midimew connected Mesh network
Hierarchical Interconnection Network (HIN) is indispensable
for the practical implementation of future generation
massively parallel computer systems which consists of hundred thousands nodes or even millions of nodes. Because it yields good performance with low cost due to reduction of communication links and by exploring the locality in the communication & traffic patterns. A Midimew connected Mesh Network (MMN) is an HIN comprised of numerous basic modules, where the basic modules are 2D-mesh networks and they are hierarchically interconnected using midimew network to construct the higher level networks. In this paper, we present the architecture of a MMN and evaluate
the time-cost effective factor of MMN, TESH, mesh, and torus
networks. It is found that the proposed MMN yields slightly high time-cost effectiveness factor with small diameter and average distance as compared to other networks. Overall, performance with respect to time-cost effectiveness factor with small diameter and average distance suggests that the proposed MMN will be a indispensable choice for the next generation massively parallel computer systems
Cost effective factor of a midimew connected mesh network
Background and Objective: Hierarchical Interconnection Network (HIN) is very much essential for the practical implementation of future
generation Massively Parallel Computers (MPC) systems which consists of millions of nodes. It yields better performance with low cost
due to reduction of wires and by exploring the locality in the communication\and traffic patterns. The main objective of this paper is to
analyze the static cost effective factor of Midimew connected Mesh Network (MMN). Materials and Methods: A Midimew connected Mesh
Network (MMN) is a HIN comprised of numerous basic modules, where the basic modules are 2D-mesh networks and they are
hierarchically interconnected using midimew network to assemble the higher level networks. Results: This study, present the architecture
of a MMN and evaluate the cost effective factor of MMN, TESH (Tori-connected Mesh), mesh and torus networks. The results shows that
the cost effective factor of MMN was trivially higher than that of mesh and torus network. Conclusion: It was revealed that the proposed
MMN yields a little bit high cost effectiveness factor with small diameter and average distance. Overall, performance with respect to cost
effective factor with small diameter and average distance suggests that the MMN will be a promising choice for next generation MPC
system
- โฆ