Image Deblurring According to Facially Recognized Locations Within the Image

This publication describes techniques for image deblurring according to a facially recognized locations within the image. An algorithm may use facial detection and recognition to selectively sharpen aspects of faces within an image and the surrounding area associated with the facial detection. In one or more aspects, the selectivity of sharpening improves the computational load and other aspects of image provision to improve overall computer function, power consumption, and user experience. Individual faces within the image may be cropped or thumbnailed, providing portions of the image that include the faces. Counterpart images associated with the individual faces may be found within a database having a repository of sharp features associated with the counterpart images. As such, the features may be integrated with the blurred faces of the original image to sharpen an image output

Rate Compatible LDPC Neural Decoding Network: A Multi-Task Learning Approach

Deep learning based decoding networks have shown significant improvement in decoding LDPC codes, but the neural decoders are limited by rate-matching operations such as puncturing or extending, thus needing to train multiple decoders with different code rates for a variety of channel conditions. In this correspondence, we propose a Multi-Task Learning based rate-compatible LDPC ecoding network, which utilizes the structure of raptor-like LDPC codes and can deal with multiple code rates. In the proposed network, different portions of parameters are activated to deal with distinct code rates, which leads to parameter sharing among tasks. Numerical experiments demonstrate the effectiveness of the proposed method. Training the specially designed network under multiple code rates makes the decoder compatible with multiple code rates without sacrificing frame error rate performance

Exploring the mechanism of coordinated development with multi-source data fusion: a case study in Beijing-Tianjin-Hebei Region, China

Measuring the degree of regional coordinated development and analyzing the factors affecting regional coordinated development are of great significance for assessing the status of regional coordinated development and formulating regional coordinated development strategies. The advancement of remote sensing data and big data provide the possibility to measure the degree of regional coordinated development on a more precise scale. The aim of this study is to use multiple sources of data to construct the evaluation indicator system of coordinated development level and to analyze the mechanism of regional development. All the 200 counties (districts) in Beijing-Tianjin-Hebei Region are selected as the study area. By measuring the level of coordinated development of 200 county units in Beijing-Tianjin-Hebei region from 2012 to 2017, it is found that although the synergy degree at the county level of Beijing, Tianjin and Hebei is increased, the overall regional synergy degree is still at a low level. The spatial panel model is further used to analyze the factors that influence the coordinated development of all counties. The conclusion is that with the promotion of the Beijing-Tianjin-Hebei coordinated development policy, the excessive government intervention, and the imbalance of market allocation in the past in Beijing-Tianjin-Hebei region have been alleviated. However, the weaknesses in the process of the Beijing-Tianjin-Hebei coordinated development are still obvious. The population density and urban size have become the constraints in the process of coordinated development. The degree of opening to the outside world and the investment in the ecological environment construction is still far from enough. These findings enable us to have a clearer assessment of the level of coordinated development and a deeper understanding of the influence mechanism of the regional development in Beijing-Tianjin-Hebei region. Furthermore, this study might benefit regional development strategy research

Does the introduction of index futures stabilize stock markets? Further evidence from emerging markets

We examine how the introduction of index futures affects the stability of stock markets in seven emerging countries by studying the existence and the impact of positive feedback trading in both pre- and post-futures periods. Consistent with the findings in advanced markets, we find that positive feedback traders are already prevalent before the introduction of index futures in six out of the seven markets studied. After the introduction of index futures, signs of positive feedback trading emerge in only two markets (India and Poland). In contrast to the evidence in developed markets, positive feedback traders migrate from spot to futures markets in four markets, which suggests that the introduction of index futures may destabilize some emerging stock markets. Another interesting finding is that positive feedback trading becomes more intense when there is a market decline in the majority of the markets

Bright Soliton Solution of (1+1)-Dimensional Quantum System with Power-Law Dependent Nonlinearity

We study the nonlinear dynamics of (1+1)-dimensional quantum system in power-law dependent media based on the nonlinear Schrödinger equation (NLSE) incorporating power-law dependent nonlinearity, linear attenuation, self-steepening terms, and third-order dispersion term. The analytical bright soliton solution of this NLSE is derived via the F-expansion method. The key feature of the bright soliton solution is pictorially demonstrated, which together with typical analytical formulation of the soliton solution shows the applicability of our theoretical treatment

Power generation expansion optimization model considering multi-scenario electricity demand constraints: a case study of Zhejiang Province, China

Reasonable and effective power planning contributes a lot to energy efficiency improvement, as well as the formulation of future economic and energy policies for a region. Since only a few provinces in China have nuclear power plants so far, nuclear power plants were not considered in many provincial-level power planning models. As an extremely important source of power generation in the future, the role of nuclear power plants can never be overlooked. In this paper, a comprehensive and detailed optimization model of provincial-level power generation expansion considering biomass and nuclear power plants is established from the perspective of electricity demand uncertainty. This model has been successfully applied to the case study of Zhejiang Province. The findings suggest that the nuclear power plants will contribute 9.56% of the total installed capacity, and it will become the second stable electricity source. The lowest total discounted cost is 1033.28 billion RMB and the fuel cost accounts for a large part of the total cost (about 69%). Different key performance indicators (KPI) differentiate electricity demand in scenarios that are used to test the model. Low electricity demand in the development mode of the comprehensive adjustment scenario (COML) produces the optimal power development path, as it provides the lowest discounted cost

Hybrid PolyLingual Object Model: An Efficient and Seamless Integration of Java and Native Components on the Dalvik Virtual Machine

Copyright © 2014 Yukun Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded. 1