This paper presents an information-theoretic framework for unifying active learning problems: level set estimation (LSE), Bayesian optimization (BO), and their generalized variant. We first introduce a novel active le...
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Optimizing the design, performance, and resource efficiency of wireless networks (WNs) necessitates the ability to discern Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios across diverse applications and env...
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ISBN:
(数字)9798350362244
ISBN:
(纸本)9798350362251
Optimizing the design, performance, and resource efficiency of wireless networks (WNs) necessitates the ability to discern Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios across diverse applications and environments. Unmanned Aerial Vehicles (UAVs) exhibit significant potential in this regard due to their rapid mobility, aerial capabilities, and payload characteristics. Particularly, UAVs can serve as vital nonterrestrial base stations (NTBS) in the event of terrestrial base station (TBS) failures or downtime. In this paper, we propose CNN autoencoder resizer (CAR) as a framework that improves the accuracy of LoS/NLoS detection without demanding extra power consumption. Our proposed method increases the mean accuracy of detecting LoS/NLoS signals from 66% to 86%, while maintaining consistent power consumption levels. In addition, the resolution provided by CAR shows that it can be employed as a preprocessing tool in other methods to enhance the quality of signals.
Although built-in self-repair (BISR) techniques have been widely used to improve memory yield, their applications to the testing of 3D systems-on-chip (SoC) remained primarily unexplored. In this manuscript, we presen...
Although built-in self-repair (BISR) techniques have been widely used to improve memory yield, their applications to the testing of 3D systems-on-chip (SoC) remained primarily unexplored. In this manuscript, we present a multi-stage approach to implement BISR in 3D SoCs with an aim to (i) reduce test time by proposing a test scheduling technique satisfying given power constraints, (ii) reduce the number of BISR modules, and (iii) to place BISR circuitry in suitable layers for facilitating thermal dissipation. Experimental results on several SoC benchmarks show that our approach reduces both test time as well as the cost of BISR architecture in most cases.
Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate po...
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AI is considered as most disruptive language and revolutionized various sectors with its ability analyze data with its large language model. incapability of other AI to read PDF and accept prompt and do text generatio...
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ISBN:
(数字)9798350354218
ISBN:
(纸本)9798350354225
AI is considered as most disruptive language and revolutionized various sectors with its ability analyze data with its large language model. incapability of other AI to read PDF and accept prompt and do text generation using their large language models .The work is based on proposing a new technique to integrate it into current AI. It will eliminate the need manual work of copy pasting Portable Document Format data to AI for prompt . In this can rely on Technologies Like Optical character recognition for reading images to text but challenging part is Portable Document Format to image and then image to text. this work is advancement of Optical character recognition which can eliminate the drawback of already existing technology.
Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people world...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Recognition of Bengali sign language characters is crucial for facilitating communication for the deaf and hard-of-hearing population in Bengali-speaking regions, which encompass approximately 430 million people worldwide. Despite the significant number of individuals requiring this support, research on Bengali sign language character recognition remains underdeveloped. This article presents a novel approach to categorize Bengali sign language characters using the Ishara-Lipi dataset, based on convolutional neural networks (CNNs) and pretrained models. We evaluated our approach using metrics such as accuracy, precision, recall, F1-score, and confusion matrices. Our findings indicate that the CNN model achieved the highest performance with an accuracy of 98%, followed by VGG19 with $\mathbf{94\%}$ and ResNet variants achieving around $\mathbf{88\%}$. The proposed model demonstrates robust and efficient classification capabilities, significantly bridging the gap in existing literature. This study holds substantial promise for enhancing assistive technology, thereby improving social inclusion and quality of life for Bengalispeaking deaf and hard-of-hearing individuals.
Bayesian optimization (BO) has recently been extended to the federated learning (FL) setting by the federated Thompson sampling (FTS) algorithm, which has promising applications such as federated hyperparameter tuning...
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The movement changes the underlying spatial representation of the participated mobile objects or nodes. In real world scenario, such mobile nodes can be part of any biological network, transportation network, social n...
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While the availability of multispectral imagery (MSI) is increasing, there is still much to be learned regarding the spectral sensitivity of deep neural networks (DNNs). This work presents and analyzes DNNs trained to...
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
While the availability of multispectral imagery (MSI) is increasing, there is still much to be learned regarding the spectral sensitivity of deep neural networks (DNNs). This work presents and analyzes DNNs trained to perform object detection in high-resolution multispectral, satellite imagery. These DNNs are first trained and evaluated on variations of the xView dataset with varied bit-dept.s and spectral band inclusion. Inference is also performed on test imagery with different bit-dept.s or with missing spectral information in order to analyze their importance for object detection. Initial experiments show an MSI-trained DNN had an average optimal F1-score 3.7 points higher than the best performing RGB network. Additional experiments suggest significant DNN sensitivity to changes in bit-dept. and an even greater sensitivity to the loss of any given spectral band. The results given here highlight the need for future research regarding spectral biases of DNNs for object detection in high-resolution MSI.
The scheduling community has long been interested in educational timetabling. Particularly in academia, since timetabling dictates the day to day operation of Universities, great effort has been exercised to produce h...
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