Research on Color Matching Model for Color QR Code

Objective To provide a faster and more effective way for consumers to obtain the additional information of product, the color quick response (QR) code is printed on product packaging. To improve decoding accuracy of color QR codes. This paper constructs a color matching model with good performance, which restrains aliasing between two color encoding modules of color QR code. Methods By comparing and analyzing the character of several typical color spaces, the HSV color space was chosen for its favorable attributes. Based on the enlarging capacity principle of color QR code and the maximum rule of relative Euclidean distance, 2n-1 base-color points are set in the HSV space, and 2n-1 pair-color points are calculated by using color distance equation, resulting in a formula capable of encoding 2n colors. Results Results show that the color gamut of QR code pictures generated using our proposed model is wider than that generated using another color matching model. Color aliasing between two encoding modules was greatly restrained. Conclusion The proposed color matching model offers good performance, based upon wider color gamut and reduced aliasing, for generating color QR codes in the HSV color space. Consumers can scan the printed color QR code on product packaging with their electronic devices to obtain more exhaustive information of product effectively

From Decolonial to the Postcolonial: Trauma of an Unfinished Agenda

Expression stability of the candidate reference genes under different conditions. (DOCX 13 kb

Improving yield and water use efficiency of apple trees through intercrop-mulch of crown vetch (Coronilla varia L.) combined with different fertilizer treatments in the Loess Plateau

Improving water use efficiency (WUE) and soil fertility is relevant for apple production in drylands. The effects of intercrop-mulch (IM) of crown vetch (Coronilla varia L.) combined with different fertilizer treatments on WUE of apple trees and soil fertility of apple orchards were assessed over three years (2011, 2013 and 2014). A split-plot design was adopted, in which the main treatments were IM and no intercrop-mulch (NIM). Five sub-treatments were established: no fertilization (CK); nitrogen and phosphorus fertilizer (NP); manure (M); N, P and potassium fertilizer (NPK); and NPK fertilizer combined with manure (NPKM). Due to mowing and mulching each month during July–September, the evapotranspiration for IM was 17.3% lower than that of NIM in the dry year of 2013. Additionally, the soil water storage of NPKM treatment was higher than that of CK during the experimental period. Thus, single fruit weight and fruit number per tree increased with IM and NPKM application. Moreover, applying NPKM with IM resulted in the highest yield (on average of three years), which was 73.25% and 130.51% greater than that of CK in IM and NIM, respectively. The WUE of NPKM combined with IM was also the highest in 2013 and 2014 (47.69 and 56.95% greater than applying IM alone). In addition, due to application of IM combined with NPKM, soil organic matter was increased by 25.8% compared with that of CK (in NIM). Additionally, application of IM combined with NPKM obtained more economic net return, compared to other combinations. Therefore, applying NPKM with IM is recommended for improving apple production in this rain-fed agricultural area

Development of FEB Configuration Test Board for ATLAS NSW Upgrade

The FEB(front end board) configuration test board is developed aiming at meeting the requirement of testing the new generation ASIC(application-specific integrated circuit) chips and its configuration system for ATLAS NSW(New Small Wheel) upgrade, In this paper, some functions are developed in terms of the configurations of the key chips on the FEB, VMM3 and TDS2 using GBT-SCA. Additionally, a flexible communication protocol is designed, verifying the whole data link. It provides technical reference for prototype FEB key chip configuration and data readout, as well as the final system configuration

When will you have a new mobile phone? An empirical answer from big data

When and why people change their mobile phones are important issues in mobile communications industry, because it will impact greatly on the marketing strategy and revenue estimation for both mobile operators and manufactures. It is a promising way to take use of big data to analyze and predict the phone changing event. In this paper, based on mobile user big data, first through statistical analysis, we find that three important probability distributions, i.e., power-law, log-normal, and geometric distribution, play an important role in the user behaviors. Second, the relationships between eight selected attributes and phone changing are built, for example, young people have greater intention to change their phones if they are using the phones belonging to the low occupancy phones or feature phones. Third, we verified the performance of four prediction models on phone changing event under three scenarios. Information gain ratio was used to implement attribute selection and then sampling method, cost-sensitive together with standard classifiers were used to solve imbalanced phone changing event. Experiment results show our proposed enhanced backpropagation neural network in the undersampling scenario can attain better prediction performance

A new textile sizing prepared by the hemp core cellulose ethers

As the key process of weaving, sizing is one of the processes which are of great costly and serious pollution in the whole cotton-textile production. Great efforts have been made to study new kinds of sizing to reduce costs and environment pollution. In this paper, hemp cellulose ethers of Methyl cellulose (MC) ether and Hydroxypropyl methyl (HPMC) cellulose ether further were used as a kind of textile sizing. The results indicated that the MC and HPMC blended by 1:1 of sizing efficiency was similar to thos e of PVA. After sizing with this new kind of size, the breaking strength and elongation at break could replace sizing property of PVA, even extending beyond PVA. Compared to COD in the desizing wastewater of 30240 mg/L of PVA, the COD of hemp cellulose ether sizing was very small of 13235.2 mg/L. Therefore, it is concluded that the hemp core ether is a new and environment-friendly textile sizing, which has low cost and contributes a healthier ecosyste

Atrial Septal Defect Detection in Children Based on Ultrasound Video Using Multiple Instances Learning

Purpose: Congenital heart defect (CHD) is the most common birth defect. Thoracic echocardiography (TTE) can provide sufficient cardiac structure information, evaluate hemodynamics and cardiac function, and is an effective method for atrial septal defect (ASD) examination. This paper aims to study a deep learning method based on cardiac ultrasound video to assist in ASD diagnosis. Materials and methods: We select two standard views of the atrial septum (subAS) and low parasternal four-compartment view (LPS4C) as the two views to identify ASD. We enlist data from 300 children patients as part of a double-blind experiment for five-fold cross-validation to verify the performance of our model. In addition, data from 30 children patients (15 positives and 15 negatives) are collected for clinician testing and compared to our model test results (these 30 samples do not participate in model training). We propose an echocardiography video-based atrial septal defect diagnosis system. In our model, we present a block random selection, maximal agreement decision and frame sampling strategy for training and testing respectively, resNet18 and r3D networks are used to extract the frame features and aggregate them to build a rich video-level representation. Results: We validate our model using our private dataset by five-cross validation. For ASD detection, we achieve 89.33 AUC, 84.95 accuracy, 85.70 sensitivity, 81.51 specificity and 81.99 F1 score. Conclusion: The proposed model is multiple instances learning-based deep learning model for video atrial septal defect detection which effectively improves ASD detection accuracy when compared to the performances of previous networks and clinical doctors