376 research outputs found
Low-Complexity Saliency Detection Algorithm for Fast Perceptual Video Coding
A low-complexity saliency detection algorithm for perceptual video coding is proposed; low-level encoding information is adopted as the characteristics of visual perception analysis. Firstly, this algorithm employs motion vector (MV) to extract temporal saliency region through fast MV noise filtering and translational MV checking procedure. Secondly, spatial saliency region is detected based on optimal prediction mode distributions in I-frame and P-frame. Then, it combines the spatiotemporal saliency detection results to define the video region of interest (VROI). The simulation results validate that the proposed algorithm can avoid a large amount of computation work in the visual perception characteristics analysis processing compared with other existing algorithms; it also has better performance in saliency detection for videos and can realize fast saliency detection. It can be used as a part of the video standard codec at medium-to-low bit-rates or combined with other algorithms in fast video coding
Graph-based EEG approach for depression prediction: integrating time-frequency complexity and spatial topology
Depression has become the prevailing global mental health concern. The accuracy of traditional depression diagnosis methods faces challenges due to diverse factors, making primary identification a complex task. Thus, the imperative lies in developing a method that fulfills objectivity and effectiveness criteria for depression identification. Current research underscores notable disparities in brain activity between individuals with depression and those without. The Electroencephalogram (EEG), as a biologically reflective and easily accessible signal, is widely used to diagnose depression. This article introduces an innovative depression prediction strategy that merges time-frequency complexity and electrode spatial topology to aid in depression diagnosis. Initially, time-frequency complexity and temporal features of the EEG signal are extracted to generate node features for a graph convolutional network. Subsequently, leveraging channel correlation, the brain network adjacency matrix is employed and calculated. The final depression classification is achieved by training and validating a graph convolutional network with graph node features and a brain network adjacency matrix based on channel correlation. The proposed strategy has been validated using two publicly available EEG datasets, MODMA and PRED+CT, achieving notable accuracy rates of 98.30 and 96.51%, respectively. These outcomes affirm the reliability and utility of our proposed strategy in predicting depression using EEG signals. Additionally, the findings substantiate the effectiveness of EEG time-frequency complexity characteristics as valuable biomarkers for depression prediction
Apprenticeship Standard : Non-Destructive Testing Engineer
High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems
Modeling fuel-, vehicle-type-, and age-specific CO2 emissions from global on-road vehicles in 1970–2020
Vehicles are among the most important contributors to global anthropogenic CO2 emissions. However, the lack of fuel-, vehicle-type-, and age-specific information about global on-road CO2 emissions in existing datasets, which are available only at the sector level, makes these datasets insufficient for supporting the establishment of emission mitigation strategies. Thus, a fleet turnover model is developed in this study, and CO2 emissions from global on-road vehicles from 1970 to 2020 are estimated for each country. Here, we analyze the evolution of the global vehicle stock over 50 years, identify the dominant emission contributors by vehicle and fuel type, and further characterize the age distribution of on-road CO2 emissions. We find that trucks accounted for less than 5 % of global vehicle ownership but represented more than 20 % of on-road CO2 emissions in 2020. The contribution of diesel vehicles to global on-road CO2 emissions doubled during the 1970–2020 period, driven by the shift in the fuel-type distribution of vehicle ownership. The proportion of CO2 emissions from vehicles in developing countries such as China and India in terms of global emissions from newly registered vehicles significantly increased after 2000, but global CO2 emissions from vehicles that had survived more than 15 years in 2020 still originated mainly from developed countries such as the United States and countries in the European Union. The data are publicly available at https://doi.org/10.6084/m9.figshare.24548008 (Yan et al., 2024).</p
Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
The divergence between labeled training data and unlabeled testing data is a
significant challenge for recent deep learning models. Unsupervised domain
adaptation (UDA) attempts to solve such problem. Recent works show that
self-training is a powerful approach to UDA. However, existing methods have
difficulty in balancing the scalability and performance. In this paper, we
propose a hard-aware instance adaptive self-training framework for UDA on the
task of semantic segmentation. To effectively improve the quality and diversity
of pseudo-labels, we develop a novel pseudo-label generation strategy with an
instance adaptive selector. We further enrich the hard class pseudo-labels with
inter-image information through a skillfully designed hard-aware pseudo-label
augmentation. Besides, we propose the region-adaptive regularization to smooth
the pseudo-label region and sharpen the non-pseudo-label region. For the
non-pseudo-label region, consistency constraint is also constructed to
introduce stronger supervision signals during model optimization. Our method is
so concise and efficient that it is easy to be generalized to other UDA
methods. Experiments on GTA5 to Cityscapes, SYNTHIA to Cityscapes, and
Cityscapes to Oxford RobotCar demonstrate the superior performance of our
approach compared with the state-of-the-art methods.Comment: arXiv admin note: text overlap with arXiv:2008.1219
An Adaptive Motion Estimation Scheme for Video Coding
The unsymmetrical-cross multihexagon-grid search (UMHexagonS) is one of the best fast Motion Estimation (ME) algorithms in video encoding software. It achieves an excellent coding performance by using hybrid block matching search pattern and multiple initial search point predictors at the cost of the computational complexity of ME increased. Reducing time consuming of ME is one of the key factors to improve video coding efficiency. In this paper, we propose an adaptive motion estimation scheme to further reduce the calculation redundancy of UMHexagonS. Firstly, new motion estimation search patterns have been designed according to the statistical results of motion vector (MV) distribution information. Then, design a MV distribution prediction method, including prediction of the size of MV and the direction of MV. At last, according to the MV distribution prediction results, achieve self-adaptive subregional searching by the new estimation search patterns. Experimental results show that more than 50% of total search points are dramatically reduced compared to the UMHexagonS algorithm in JM 18.4 of H.264/AVC. As a result, the proposed algorithm scheme can save the ME time up to 20.86% while the rate-distortion performance is not compromised
Nonlinear terahertz metamaterials via field-enhanced carrier dynamics in GaAs
We demonstrate nonlinear metamaterial split ring resonators (SRRs) on GaAs at
terahertz frequencies. For SRRs on doped GaAs films, incident terahertz
radiation with peak fields of ~20 - 160 kV/cm drives intervalley scattering.
This reduces the carrier mobility and enhances the SRR LC response due to a
conductivity decrease in the doped thin film. Above ~160 kV/cm, electric field
enhancement within the SRR gaps leads to efficient impact ionization,
increasing the carrier density and the conductivity which, in turn, suppresses
the SRR resonance. We demonstrate an increase of up to 10 orders of magnitude
in the carrier density in the SRR gaps on semi-insulating GaAs substrate.
Furthermore, we show that the effective permittivity can be swept from negative
to positive values with increasing terahertz field strength in the impact
ionization regime, enabling new possibilities for nonlinear metamaterials.Comment: 5 pages, 4 figure
Variations of China's emission estimates:Response to uncertainties in energy statistics
The accuracy of China's energy statistics is of great concern because it contributes greatly to the uncertainties in estimates of global emissions. This study attempts to improve the understanding of uncertainties in China's energy statistics and evaluate their impacts on China's emissions during the period of 1990-2013. We employed the Multi-resolution Emission Inventory for China (MEIC) model to calculate China's emissions based on different official data sets of energy statistics using the same emission factors. We found that the apparent uncertainties (maximum discrepancy) in China's energy consumption increased from 2004 to 2012, reaching a maximum of 646Mtce (million tons of coal equivalent) in 2011 and that coal dominated these uncertainties. The discrepancies between the national and provincial energy statistics were reduced after the three economic censuses conducted during this period, and converging uncertainties were found in 2013. The emissions calculated from the provincial energy statistics are generally higher than those calculated from the national energy statistics, and the apparent uncertainty ratio (the ratio of the maximum discrepancy to the mean value) owing to energy uncertainties in 2012 took values of 30.0, 16.4, 7.7, 9.2 and 15.6%, for SO2, NOx, VOC, PM2.5 and CO2 emissions, respectively. SO2 emissions are most sensitive to energy uncertainties because of the high contributions from industrial coal combustion. The calculated emission trends are also greatly affected by energy uncertainties - from 1996 to 2012, CO2 and NOx emissions, respectively, increased by 191 and 197% according to the provincial energy statistics but by only 145 and 139% as determined from the original national energy statistics. The energy-induced emission uncertainties for some species such as SO2 and NOx are comparable to total uncertainties of emissions as estimated by previous studies, indicating variations in energy consumption could be an important source of China's emission uncertainties
NOx Emission Trends over Chinese Cities Estimated from OMI Observations During 2005 to 2015
Satellite NO2 observations have been widely used to evaluate emission changes. To determine trends in NOx emission over China, we used a method independent of chemical transport models to quantify the NOx emissions from 48 cities and 7 power plants over China, on the basis of Ozone Monitoring Instrument (OMI) NO2 observations during 2005 to 2015. We found that NOx emissions over 48 Chinese cities increased by 52 from 2005 to 2011 and decreased by 21 from 2011 to 2015. The decrease since 2011 could be mainly attributed to emission control measures in power sector; while cities with different dominant emission sources (i.e. power, industrial and transportation sectors) showed variable emission decline timelines that corresponded to the schedules for emission control in different sectors. The time series of the derived NOx emissions was consistent with the bottom-up emission inventories for all power plants (r = 0.8 on average), but not for some cities (r = 0.4 on average). The lack of consistency observed for cities was most probably due to the high uncertainty of bottom-up urban emissions used in this study, which were derived from downscaling the regional-based emission data to cities by using spatial distribution proxies
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