Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms ...
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This paper presents an advanced approach in skin disease classification using a modified ResNet-50 architecture, applied to a specific subset from the ISIC 2019 Dataset focusing on Benign Keratosis, Basal Cell Carcino...
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Random packed beds are often employed in chemical reactors as a means to increase the contact surface between reactants or a catalyst. The present work proposes a helical flow deflector placed within the bed and numer...
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The Purpose of travel plan website is to provide the users with an interactive experience for planning the travels with the help of the chatbot that assists users in finding information about destinations and helping ...
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Women's mortality rate from breast cancer is currently greater than other types of cancer, and the rate is rising day by day. As a result, it is vital to analyze breast cancer at an early stage to avoid tragic dea...
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Massive computational complexity and memory requirement of artificial intelligence models impede their deploy-ability on edge computing devices of the Internet of Things(IoT).While Power-of-Two(PoT)quantization is pro...
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Massive computational complexity and memory requirement of artificial intelligence models impede their deploy-ability on edge computing devices of the Internet of Things(IoT).While Power-of-Two(PoT)quantization is pro-posed to improve the efficiency for edge inference of Deep Neural Networks(DNNs),existing PoT schemes require a huge amount of bit-wise manipulation and have large memory overhead,and their efficiency is bounded by the bottleneck of computation latency and memory *** tackle this challenge,we present an efficient inference approach on the basis of PoT quantization and model *** integer-only scalar PoT quantization(IOS-PoT)is designed jointly with a distribution loss regularizer,wherein the regularizer minimizes quantization errors and training ***,two-stage model compression is developed to effectively reduce memory requirement,and alleviate bandwidth usage in communications of networked heterogenous learning *** product look-up table(P-LUT)inference scheme is leveraged to replace bit-shifting with only indexing and addition operations for achieving low-latency computation and implementing efficient edge ***,comprehensive experiments on Residual Networks(ResNets)and efficient architectures with Canadian Institute for Advanced Research(CIFAR),ImageNet,and Real-world Affective Faces Database(RAF-DB)datasets,indicate that our approach achieves 2×∼10×improvement in the reduction of both weight size and computation cost in comparison to state-of-the-art methods.A P-LUT accelerator prototype is implemented on the Xilinx KV260 Field Programmable Gate Array(FPGA)platform for accelerating convolution operations,with performance results showing that P-LUT reduces memory footprint by 1.45×,achieves more than 3×power efficiency and 2×resource efficiency,compared to the conventional bit-shifting scheme.
Our paper focuses on assistive technology based on human-computer interaction principles providing simultaneously that products are user-friendly and meet specific user needs. Accessibility engineering, or designing a...
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The process of removing wet pixels from a picture is known as image deraining. As the image is being captured from different angles, the wet pixels add character with it. Many filtering techniques are employed in the ...
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In recent times, the spotlight has been on understanding and forecasting water quality, owing to the variety of pollutants that pose potential harm. This research aims to advance strategies for managing and minimizing...
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Recent advancements in font generation models include GANs, diffusion models, and transformers. This study introduces a GAN-based model, zi2zi self-attention, which enhances the zi2zi model by incorporating Residual B...
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