We report the excitation of optical Tamm states (OTS) in inverse opal (IO) - based three-dimensional photonic crystal on a flat metal substrate, validated through both numerical simulations and experimental observatio...
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We report the existence of optical Tamm states (OTS) in inverse opal (IO) - based three-dimensional photonic crystal on a flat metal substrate, validated through both numerical simulations and experimental observation...
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MIMO-OFDM systems are known for high data rate and lower bit error rate (BER) in wireless communication system. These systems helps to yield high data rate and spectral efficiency over multipath and fading channels. O...
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In this work we present a voice based mobile application for dissemination of agricultural commodity procurement and consumer prices. Disbursed information is crawled at daily basis from government authorized websites...
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The following topics are dealt with: learning (artificial intelligence); feature extraction; image classification; convolutional neural nets; face recognition; medical image processing; object detection; image segment...
The following topics are dealt with: learning (artificial intelligence); feature extraction; image classification; convolutional neural nets; face recognition; medical image processing; object detection; image segmentation; computer vision; support vector machines.
In today's high-speed networks, network flow monitoring has become prevalent method for monitoring network traffic. Flow based monitoring system involves tasks like packet classification, flow generation and manag...
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In today's high-speed networks, network flow monitoring has become prevalent method for monitoring network traffic. Flow based monitoring system involves tasks like packet classification, flow generation and management, flow export and flow analysis. We have proposed a hardware-assisted architecture for flow monitoring for high speed networks. Our architecture maps packet classification and flow generation tasks into FPGA hardware. Remaining tasks are performed in software. By doing this bifurcation we have tried to exploit best of both hardware and software domains. The proposed architecture is implemented on Altera Stratix-IV FPGA board and the hardware details for the same are provided here. The implemented architecture was thoroughly tested and maximum performance of 20.2 Gbps was achieved.
Real world Automatic Speech Recognition (ASR) system development requires rigorous performance review under varying real world conditions. This paper reports our effort on ASR resource creation, transcription, system ...
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Today, the digital photos especially identity images are used in almost every form filling application. As user is uploading his/her photographs, the image quality issues will come up. Especially, if some automatic pr...
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Today, the digital photos especially identity images are used in almost every form filling application. As user is uploading his/her photographs, the image quality issues will come up. Especially, if some automatic processing like face recognition is happening at back end. The image quality parameters include resolution/dimensions, size and blur. Apart form blur, rest can be checked by simple conditions. But, blur detection cannot be solved in a trivial way. Here we are proposing a deep learning based approach for detecting the blur in an image. We have prepared data comprising 250,000 identity images. Our aim is to provide a comprehensive list of clear and blur images before learning happens. Apart from naturally blurred images, we have added five types of artificial blur into the images for better generalization. We have done comparative analysis of our approach to statistical feature extractor i.e. BRISQUE which was trained on SVM. We have empirically selected the convolutional network with 4 layers. Performance evaluation shows the proposed approach is giving 98.05% on 113,000 images, and thus outperforming BRISQUE using SVM.
Classical machine learning, extensively utilized across diverse domains, faces limitations in speed, efficiency, parallelism, and processing of complex datasets. In contrast, quantum machine learning algorithms offer ...
Classical machine learning, extensively utilized across diverse domains, faces limitations in speed, efficiency, parallelism, and processing of complex datasets. In contrast, quantum machine learning algorithms offer significant advantages, including exponentially faster computations, enhanced data handling capabilities, inherent parallelism, and improved optimization for complex problems. In this study, we used the entanglement enhanced quantum kernel in a quantum support vector machine to train complex respiratory datasets. Compared to classical algorithms, our findings reveal that quantum support vector machine (QSVM) performs better with higher accuracy (45%) for complex respiratory datasets while maintaining comparable performance with linear datasets in contrast to their classical counterparts executed on a 2-qubit system. Through our study, we investigate the efficacy of the QSVM-Kernel algorithm in harnessing the enhanced dimensionality of the quantum Hilbert space for effectively training complex datasets.
Twitter is a vast store of textual data that can be helpful in determining disaster related crisis. In this research the aim is to implement a system that can acquire tweets from Twitter using the keyword `earthquake&...
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ISBN:
(纸本)9781728134567
Twitter is a vast store of textual data that can be helpful in determining disaster related crisis. In this research the aim is to implement a system that can acquire tweets from Twitter using the keyword `earthquake' and determine the disaster location soon after the disaster. For prefiltering the tweets, a statistical approach has been developed. For determining out of context tweets a fuzzy matching approach has been used. To further categorize those tweets as relevant and irrelevant, Multinomial Naïve Bayes classifier has been used. Relevant tweets were further analyzed to determine probable location of the disaster using a clustering-based approach. After obtaining the probable location of the disaster; it is then visualized on map.
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