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High-Level Synthesis
High-level synthesis (HLS) is the process of compiling a software program into a digital circuit. This chapter provides a view into the HLS design...
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Saliency detection based on color descriptor and high-level prior
The existing saliency detection methods calculate the Euclidean distance in CIElab color space as similarity degrees between image pixels or patches,...
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High-Level Data Flow
Given our self-imposed constraint of using GraphQL, in this chapter, we’ll consider the high-level data flow of a system that utilizes it to back the...
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TLP-LDPC: Three-Level Parallel FPGA Architecture for Fast Prototy** of LDPC Decoder Using High-Level Synthesis
Low-Density Parity-heck Codes (LDPC) with excellent error-correction capabilities have been widely used in both data communication and storage...
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Rapid Prototy** of Complex Micro-architectures Through High-Level Synthesis
Register-Transfer Level (RTL) design has been a traditional approach in hardware design for several decades. However, with the growing complexity of...
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Prior-SSL: A Thickness Distribution Prior and Uncertainty Guided Semi-supervised Learning Method for Choroidal Segmentation in OCT Images
Choroid structure is crucial for the diagnosis of ocular diseases, and deep supervised learning (SL) techniques have been widely applied to segment...
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Mining Top-K constrained cross-level high-utility itemsets over data streams
Cross-Level High-Utility Itemsets Mining (CLHUIM) aims to discover interesting relationships between hierarchy levels by introducing the taxonomy of...
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Population-level comparisons of gene regulatory networks modeled on high-throughput single-cell transcriptomics data
Single-cell technologies enable high-resolution studies of phenotype-defining molecular mechanisms. However, data sparsity and cellular heterogeneity...
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High-Level Features for Human Activity Recognition and Modeling
High-Level Features (HLF) are a novel way of describing and processing human activities. Each feature captures an interpretable aspect of activities,...
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Prior Knowledge-Based Intelligent Model for Lithology Classification
Vegetation coverage can weaken the lithology information and increases inter-class similarity, making it difficult to effectively extract key feature...
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meTMQI: multi-task and exposure-prior learning for Tone-Mapped Quality Index
With limited dynamic range in consumer-level photographs and electronic displays, high dynamic range images can be rendered as the standard dynamic...
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Towards adaptive graph neural networks via solving prior-data conflicts
Graph neural networks (GNNs) have achieved remarkable performance in a variety of graph-related tasks. Recent evidence in the GNN community shows...
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Improving Semantic Map** with Prior Object Dimensions Extracted from 3D Models
Semantic map** in mobile robotics has gained significant attention recently for its important role in equip** robots with a comprehensive...
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Prior-combined dehazing network based on mutual learning
Single-image dehazing is an important problem for high-level computer vision tasks since the existence of haze severely degrades the recognition...
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Coding Prior-Driven JPEG Image Artifact Removal
Image priors play an important role in JPEG image artifact removal. However, most existing methods ignore the use of coding priors. This paper...
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Shape generation via learning an adaptive multimodal prior
Significant interest and progress have been drawn to the recent advancements in image creation using deep generative model, but the field of...
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Neural Network and Prior Knowledge Ensemble for Whistle Recognition
Whistle recognition is becoming an increasingly crucial aspect of RoboCup. Therefore neural networks are being utilized in this field more...
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Extracting Top-k High Utility Patterns from Multi-level Transaction Databases
Several approaches have been introduced to solve the problem of high utility pattern mining (HUPM). However, the proposed algorithms require a...
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Detecting and diagnosing prior and likelihood sensitivity with power-scaling
Determining the sensitivity of the posterior to perturbations of the prior and likelihood is an important part of the Bayesian workflow. We introduce...
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Low-dose CT image denoising based on edge prior and high-frequency sensitive feature fusion network
Low-dose CT (LDCT) is a feasible method to reduce the radiation dose to the patient. However, both artifacts and noise appear in the reconstructed...
