2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021
With the new overall COVID-19 pandemic, utilizing face veils have become a significant piece of o... more With the new overall COVID-19 pandemic, utilizing face veils have become a significant piece of our lives. To evade the spread of contamination Individuals are urged to cover their faces using a mask when in open territory. The utilization of these face veils makes the orthodox facial recognition system inadequate for authentication in school/office participation, face access control, facial participation, facial security checks at airport and train stations, etc. The recent progressed deep learning-based face acknowledgment approaches rely upon countless training samples and significant features of the entire face. This paper addresses the issue of recognition of face and veil using Gabor wavelet and deep transfer learning. Here the Gabor wavelet features are extracted from the non-masked region of the face and integrated with deep learned CNN features to generate a more robust feature vector for optimizing recognition. The proposed method is experimented on 4 benchmark datasets and a manually prepared dates and achieved averaged 97% recognition accuracy.
As digital multimedia works (video, audio and images) become more available for retransmission, r... more As digital multimedia works (video, audio and images) become more available for retransmission, reproduction, and publishing over the Internet, a real need for protection against unauthorized copy and distribution is increased. These concerns motivate researchers to find ways to forbid copyright violation and create content protection technique like watermarking, which is one of the most efficient ways to protect the digital properties in recent years. A number of digital watermarking techniques are proposed by many authors in the last several years. These techniques exploit different ways in order to embed a robust watermark and to maintain the original signal fidelity . However, there are not enough analysis and comparison on the previous researches. In this paper, for the understanding of the previous works and for the future related research, we try to classify and analyze the conventional watermark techniques from the several aspects and techniques. We concentrate our survey on...
The triangular geometry of the microstrip triangular patch antenna one of the most common shape f... more The triangular geometry of the microstrip triangular patch antenna one of the most common shape finds its application in wide frequency range from lumped circuit elements to recent trend wireless antennas. In this paper a novel design, implementation and testing of dual feed triangular microstrip patch antennas with tip truncation and triangular slot is proposed to achieve circular polarization, to increase the axial ratio bandwidth. The proposed antenna structure uses Tip-truncating to improve bandwidth [2] and by cutting a small equilateral triangular slot near the vertex of radiating patch antenna to provide both LHCP and RHCP to double the impedance and bandwidth with improved gain at frequency 1.3 GHz.
Microfluidic technologies have emerged as a potential tool for point of care — diagnostics and th... more Microfluidic technologies have emerged as a potential tool for point of care — diagnostics and therapeutics applications. Isolation of multi-targets (Cancer cells along with platelets, red blood cells (RBCs), white blood cells (WBCs), and antigen-presenting cells (APCs)) simultaneously is of great interest in drug discovery and medical diagnosis. By utilizing dielectrophoresis (DEP) effect inside the micro channel, several attempts were made to separate binary mixtures by precisely controlling and manipulating the motion of the particles. However, all of these methods limit its applicability for multi-target particle separation in a single run. In this paper, we attempt to develop a simulation model with novel electrode arrangements to isolate multiple particles using negative DEP. Our proposed model establishes criteria for separating micron-sized particle mixtures (3µm, 7µm, 15µm, 20µm, 25µm) with various electrode shapes, electrode potentials, inlet velocities, and channel widths. The device efficiency was evaluated for a triangular electrode, square-shaped electrode, and rectangular electrode under various practical design constraints. Our study demonstrates an optimum solution for effective separation of particle mixtures using triangular electrode arrangements (utilizing less voltage) and a wider channel of 300µm width that eventually avoid channel clogging issues due to cells inside main channel and collection channels. While evaluating the separation efficiency of the proposed design, we observe that platelets, RBCs, WBCs, APCs, and CTCs experienced distinct DEP force on each, allowing them to collect in different collection outlets without any cross-mixing. Hence our proposed design allows flexibility to the researchers working on DEP by using a wider channel with triangular electrode arrangements enabling them to fabricate the device under resource-limited constraints.
Abstract Deep Convolutional neural networks (CNN) have been among the utmost competitive neural n... more Abstract Deep Convolutional neural networks (CNN) have been among the utmost competitive neural network architectures and have set the state-of-the-art in various fields of computer vision. In this paper, we present OCR-Nets, variants of (AlexNet & GoogleNet) for recognition of handwritten Urdu characters through transfer learning. Our proposed networks are experimented using an integrated dataset. To compare the recognition rate with traditional character recognition methods and to confirm the fairness of the experiment an additional Urdu character dataset is manually generated with different fonts and size. The experimental result shows that OCR-AlexNet and OCR-GoogleNet produce significant performance gains of 96.3% and 94.7% averaged success rate respectively.
2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 2019
Character recognition is developed for various patterns of handwritten or optical characters to b... more Character recognition is developed for various patterns of handwritten or optical characters to be recognized digitally. There are many Tamil literatures in undigitized form. Using deep learning the undigitized Tamil literatures can be converted into readable format. Many researches were carried on character recognition using deep learning for languages like Arabic, Devanagari, Telugu, etc… Due to the larger category set and confusion in similarities between handwritten characters Tamil character recognition is a challenge. In this paper, we propose a character recognition system for handwritten Tamil characters using deep learning. Here, VGG 16 approaches is carried out. The proposed work gives efficiency of 94.52% on our datasets.
Deep learning has achieved magnificent success in the field of pattern recognition. In recent yea... more Deep learning has achieved magnificent success in the field of pattern recognition. In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network. Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document. In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents. Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the formal Urdu text document. In this work, first, we generated a dataset by extracting the recurrent ligatures from an ancient Urdu fatawa book. Secondly, we categorized and augment the ligatures to generate batches of augmented images that improvise the training efficiency and classification accuracy. Finally, we proposed a multi-domain deep Convolutional Neural Network which integrates a spatial domain and a frequency domain CNN to learn the modular relations between features originating from the two different domain networks to train and improvise the classification accuracy. The experimental results show that the proposed network with the augmented dataset achieves an averaged accuracy of 97.8% which outperforms the other CNN models in this class. The experimental results also show that for the recognition of ancient Urdu literature, well-known benchmark datasets are not appropriate which is also verified with our prepared dataset.
2021 5th International Conference on Computing Methodologies and Communication (ICCMC), 2021
With the new overall COVID-19 pandemic, utilizing face veils have become a significant piece of o... more With the new overall COVID-19 pandemic, utilizing face veils have become a significant piece of our lives. To evade the spread of contamination Individuals are urged to cover their faces using a mask when in open territory. The utilization of these face veils makes the orthodox facial recognition system inadequate for authentication in school/office participation, face access control, facial participation, facial security checks at airport and train stations, etc. The recent progressed deep learning-based face acknowledgment approaches rely upon countless training samples and significant features of the entire face. This paper addresses the issue of recognition of face and veil using Gabor wavelet and deep transfer learning. Here the Gabor wavelet features are extracted from the non-masked region of the face and integrated with deep learned CNN features to generate a more robust feature vector for optimizing recognition. The proposed method is experimented on 4 benchmark datasets and a manually prepared dates and achieved averaged 97% recognition accuracy.
As digital multimedia works (video, audio and images) become more available for retransmission, r... more As digital multimedia works (video, audio and images) become more available for retransmission, reproduction, and publishing over the Internet, a real need for protection against unauthorized copy and distribution is increased. These concerns motivate researchers to find ways to forbid copyright violation and create content protection technique like watermarking, which is one of the most efficient ways to protect the digital properties in recent years. A number of digital watermarking techniques are proposed by many authors in the last several years. These techniques exploit different ways in order to embed a robust watermark and to maintain the original signal fidelity . However, there are not enough analysis and comparison on the previous researches. In this paper, for the understanding of the previous works and for the future related research, we try to classify and analyze the conventional watermark techniques from the several aspects and techniques. We concentrate our survey on...
The triangular geometry of the microstrip triangular patch antenna one of the most common shape f... more The triangular geometry of the microstrip triangular patch antenna one of the most common shape finds its application in wide frequency range from lumped circuit elements to recent trend wireless antennas. In this paper a novel design, implementation and testing of dual feed triangular microstrip patch antennas with tip truncation and triangular slot is proposed to achieve circular polarization, to increase the axial ratio bandwidth. The proposed antenna structure uses Tip-truncating to improve bandwidth [2] and by cutting a small equilateral triangular slot near the vertex of radiating patch antenna to provide both LHCP and RHCP to double the impedance and bandwidth with improved gain at frequency 1.3 GHz.
Microfluidic technologies have emerged as a potential tool for point of care — diagnostics and th... more Microfluidic technologies have emerged as a potential tool for point of care — diagnostics and therapeutics applications. Isolation of multi-targets (Cancer cells along with platelets, red blood cells (RBCs), white blood cells (WBCs), and antigen-presenting cells (APCs)) simultaneously is of great interest in drug discovery and medical diagnosis. By utilizing dielectrophoresis (DEP) effect inside the micro channel, several attempts were made to separate binary mixtures by precisely controlling and manipulating the motion of the particles. However, all of these methods limit its applicability for multi-target particle separation in a single run. In this paper, we attempt to develop a simulation model with novel electrode arrangements to isolate multiple particles using negative DEP. Our proposed model establishes criteria for separating micron-sized particle mixtures (3µm, 7µm, 15µm, 20µm, 25µm) with various electrode shapes, electrode potentials, inlet velocities, and channel widths. The device efficiency was evaluated for a triangular electrode, square-shaped electrode, and rectangular electrode under various practical design constraints. Our study demonstrates an optimum solution for effective separation of particle mixtures using triangular electrode arrangements (utilizing less voltage) and a wider channel of 300µm width that eventually avoid channel clogging issues due to cells inside main channel and collection channels. While evaluating the separation efficiency of the proposed design, we observe that platelets, RBCs, WBCs, APCs, and CTCs experienced distinct DEP force on each, allowing them to collect in different collection outlets without any cross-mixing. Hence our proposed design allows flexibility to the researchers working on DEP by using a wider channel with triangular electrode arrangements enabling them to fabricate the device under resource-limited constraints.
Abstract Deep Convolutional neural networks (CNN) have been among the utmost competitive neural n... more Abstract Deep Convolutional neural networks (CNN) have been among the utmost competitive neural network architectures and have set the state-of-the-art in various fields of computer vision. In this paper, we present OCR-Nets, variants of (AlexNet & GoogleNet) for recognition of handwritten Urdu characters through transfer learning. Our proposed networks are experimented using an integrated dataset. To compare the recognition rate with traditional character recognition methods and to confirm the fairness of the experiment an additional Urdu character dataset is manually generated with different fonts and size. The experimental result shows that OCR-AlexNet and OCR-GoogleNet produce significant performance gains of 96.3% and 94.7% averaged success rate respectively.
2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 2019
Character recognition is developed for various patterns of handwritten or optical characters to b... more Character recognition is developed for various patterns of handwritten or optical characters to be recognized digitally. There are many Tamil literatures in undigitized form. Using deep learning the undigitized Tamil literatures can be converted into readable format. Many researches were carried on character recognition using deep learning for languages like Arabic, Devanagari, Telugu, etc… Due to the larger category set and confusion in similarities between handwritten characters Tamil character recognition is a challenge. In this paper, we propose a character recognition system for handwritten Tamil characters using deep learning. Here, VGG 16 approaches is carried out. The proposed work gives efficiency of 94.52% on our datasets.
Deep learning has achieved magnificent success in the field of pattern recognition. In recent yea... more Deep learning has achieved magnificent success in the field of pattern recognition. In recent years Urdu character recognition system has significantly benefited from the effectiveness of the deep convolutional neural network. Majority of the research on Urdu text recognition are concentrated on formal handwritten and printed Urdu text document. In this paper, we experimented the Challenging issue of text recognition in Urdu ancient literature documents. Due to its cursiveness, complex word formation (ligatures), and context-sensitivity, and inadequate benchmark dataset, recognition of Urdu text from the literature document is very difficult to process compared to the formal Urdu text document. In this work, first, we generated a dataset by extracting the recurrent ligatures from an ancient Urdu fatawa book. Secondly, we categorized and augment the ligatures to generate batches of augmented images that improvise the training efficiency and classification accuracy. Finally, we proposed a multi-domain deep Convolutional Neural Network which integrates a spatial domain and a frequency domain CNN to learn the modular relations between features originating from the two different domain networks to train and improvise the classification accuracy. The experimental results show that the proposed network with the augmented dataset achieves an averaged accuracy of 97.8% which outperforms the other CNN models in this class. The experimental results also show that for the recognition of ancient Urdu literature, well-known benchmark datasets are not appropriate which is also verified with our prepared dataset.
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