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- Read more about PROPER GUIDANCE IMAGE GENERATION BASED ON SALIENCY FACTOR FOR BETTER TRANSMISSION REFINEMENT IN IMAGE DEHAZING
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Guided image filter is one of the most commonly used ways to refine transmission maps. However, since this filter transfers the structures of the guidance image to the filtering output, when the guidance image is the input image itself, even small textures in the input image will cause the change of transmission, which is obviously contrary to the principle that transmission changes only when scene depth changes. In this paper, saliency detection, which simulates the way human eyes work, is introduced into haze removal to tackle the above issue.
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- Read more about Discovering Optimal Variable-length Time Series Motifs in Large-Scale Wearable Recordings of Human Bio-behavioral Signals
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Continuously-worn wearable sensors produce copious amounts of rich bio-behavioral time series recordings. Exploring recurring patterns, often known as motifs, in wearable time series offers critical insights into understanding the nature of human behavior. Challenges in discovering motifs from wearable recordings include noise removal, pattern generalization, and accounting for subtle variations between subsequences in one motif set.
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- Read more about AUDIO CODING BASED ON SPECTRAL RECOVERY BY CONVOLUTIONAL NEURAL NETWORK
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This study proposes a new method of audio coding based on spectral recovery, which can enhance the performance of transform audio coding. An encoder represents spectral information of an input in a time-frequency domain and transmits only a portion of it so that the remaining spectral information can be recovered based on the transmitted information. A decoder recovers the magnitudes of missing spectral information using a convolutional neural network. The signs of missing spectral information are either transmitted or randomly assigned, according to their importance.
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- Read more about Effective and Stable Neuron Model Optimization Based on Aggregated CMA-ES
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Computer simulations have facilitated our understanding of the dynamic behavior of the brain and the effect of the medical treatment such as deep brain stimulation. For improving the simulation model, it is essential to develop a method for optimizing parameters of a neuron model from available experimental data. In this paper, we apply Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) to the parameter optimization problem, and compare it with widely used conventional approaches including genetic algorithm (GA) and the Nelder-Mead method.
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- Read more about On Massive MIMO Cellular Systems Resilience to Radar Interference
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In this paper we consider a massive multiple-input multiple output (MIMO) communication system using 5G New Radio compliant
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- Read more about MAXIMALLY SEPARATED AVERAGES PREDICTION FOR HIGH FIDELITY REVERSIBLE DATA HIDING
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Recently pixel pairing and pixel sorting/selection have been used in prediction-error expansion based reversible data hiding schemes to generate low entropy prediction-error histograms (PEH) necessary for achieving high fidelity. Such schemes generally use the four-neighbor average rhombus predictor as it allows pixel sorting and flexible pixel pairing. In this paper, we propose the maximally separated averages (MSA) predictor that uses the four-neighborhood context.
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- Read more about CrowNN: Human-in-the-loop Network with Crowd-generated Inputs
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Input features are indispensable for almost all machine learning methods; however, their definitions themselves are sometimes too abstract to extract automatically. Human-in-the- loop machine learning is a promising solution to such cases where humans extract the feature values for machine learning models. We use crowdsourcing for feature value extraction and consider a problem to aggregate the feature values to improve machine learning classifiers.
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In the recent years, singing voice separation systems showed increased performance due to the use of supervised training. The design of training datasets is known as a crucial factor in the performance of such systems. We investigate on how the characteristics of the training dataset impacts the separation performances of state-of-the-art singing voice separation algorithms. We show that the separation quality and diversity are two important and complementary assets of a good training dataset. We also provide insights on possible transforms to perform data augmentation for this task.
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- Read more about Motion-Adapted Three-Dimensional Frequency Selective Extrapolation
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