A method to enhance the deep learning in an aerial image

© 2017 IEEE. In this paper, we propose a kind of pre-processing method which can be applied to the depth learning method for the characteristics of aerial image. This method combines the color and spatial information to do the quick background filtering. In addition to increase execution speed, but also to reduce the rate of false positives

DNA-GA: A new approach of network performance analysis

© 2016 IEEE. In this paper, we propose a new approach of network performance analysis, which is based on our previous works on the deterministic network analysis using the Gaussian approximation (DNA-GA). First, we extend our previous works to a signal-to-interference ratio (SIR) analysis, which makes our DNA-GA analysis a formal microscopic analysis tool. Second, we show two approaches for upgrading the DNA-GA analysis to a macroscopic analysis tool. Finally, we perform a comparison between the proposed DNA-GA analysis and the existing macroscopic analysis based on stochastic geometry. Our results show that the DNA-GA analysis possesses a few special features: (i) shadow fading is naturally considered in the DNA-GA analysis; (ii) the DNA-GA analysis can handle non-uniform user distributions and any type of multi-path fading; (iii) the shape and/or the size of cell coverage areas in the DNA-GA analysis can be made arbitrary for the treatment of hotspot network scenarios. Thus, DNA-GA analysis is very useful for the network performance analysis of the 5th generation (5G) systems with general cell deployment and user distribution, both on a microscopic level and on a macroscopic level

Biological impacts of 'hot-spot' mutations of hepatitis B virus X proteins are genotype B and C differentiated

AIM: To investigate the biological impacts of “hot-spot” mutations on genotype B and C HBV X proteins (HBx). METHODS: Five types of “hot-spot” mutations of genotype B or C HBV X genes, which sequentially lead to the amino acid substitutions of HBx as I127T, F132Y, K130M+V131I, I127T+K130M+V131I, or K130M+V131I+F132Y, respectively, were generated by means of site-directed mutagenesis. To evaluate the anti-proliferative effects, HBx or related mutants’ expression vectors were transfected separately to the Chang cells by lipofectamine, and the cells were cultured in hygromycin selective medium for 14 d, drug-resistant colonies were fixed with cold methanol, stained with Giemsa dyes and scored (increase of the colonies indicated the reduction of the anti-proliferation activity, and vice versa). Different types of HBx expression vectors were co-transfected separately with the reporter plasmid pCMVβ to Chang cells, which were lysed 48 h post-transfection and the intra-cellular β-galactosidase activities were monitored (increase of the β-galactosidase activities indicated the reduction of the transactivation activity, and vice versa). All data obtained were calculated by paired-samples t-test. RESULTS: As compared to standard genotype B HBx, mutants of I127T and I127T+K130M+V131I showed higher transactivation and anti-proliferative activities, while the mutants of F132Y, K130M+V131I, and K130M+V131I+F132Y showed lower activities. As compared to standard genotype C HBx, I127T mutant showed higher transactivation activity, while the other four types of mutants showed no differences. With regard to anti-proliferative activity, compared to standard genotype C HBx, F132Y and K130M+V131I mutants showed lower activities, and K130M+V131I +F132Y mutant, on the other hand, showed higher activity, while the mutants of I127T and I127T+K130M+V131I showed no differences. CONCLUSION: “Hot-spot” mutations affect the anti-proliferation and transactivation activities of genotype B and/or C HBx, and the biological impacts of most “hot-spot” mutations on HBx are genotype B and C differentiated.published_or_final_versio

Robust Feature-Based Automated Multi-View Human Action Recognition System

© 2013 IEEE. Automated human action recognition has the potential to play an important role in public security, for example, in relation to the multiview surveillance videos taken in public places, such as train stations or airports. This paper compares three practical, reliable, and generic systems for multiview video-based human action recognition, namely, the nearest neighbor classifier, Gaussian mixture model classifier, and the nearest mean classifier. To describe the different actions performed in different views, view-invariant features are proposed to address multiview action recognition. These features are obtained by extracting the holistic features from different temporal scales which are modeled as points of interest which represent the global spatial-temporal distribution. Experiments and cross-data testing are conducted on the KTH, WEIZMANN, and MuHAVi datasets. The system does not need to be retrained when scenarios are changed which means the trained database can be applied in a wide variety of environments, such as view angle or background changes. The experiment results show that the proposed approach outperforms the existing methods on the KTH and WEIZMANN datasets

Multi-view Vehicle Detection based on Part Model with Active Learning

© 2018 IEEE. Nowadays, most ofthe vehicle detection methods aim to detect only single-view vehicles, and the performance is easily affected by partial occlusion. Therefore, a novel multi-view vehicle detection system is proposed to solve the problem of partial occlusion. The proposed system is divided into two steps: Background filtering and part model. Background filtering step is used to filter out trees, sky and other road background objects. In the part model step, each of the part models is trained by samples collected by using the proposed active learning algorithm. This paper validates the performance of the background filtering method and the part model algorithm in multi-view car detection. The performance of the proposed method outperforms previously proposed methods

Automatic age estimation system for face images

Humans are the most important tracking objects in surveillance systems. However, human tracking is not enough to provide the required information for personalized recognition. In this paper, we present a novel and reliable framework for automatic age estimation based on computer vision. It exploits global face features based on the combination of Gabor wavelets and orthogonal locality preserving projections. In addition, the proposed system can extract face aging features automatically in real-time. This means that the proposed system has more potential in applications compared to other semi-automatic systems. The results obtained from this novel approach could provide clearer insight for operators in the field of age estimation to develop real-world applications. © 2012 Lin et al

Robust Facial Alignment for Face Recognition

© 2017, Springer International Publishing AG. This paper proposes a robust real-time face recognition system that utilizes regression tree based method to locate the facial feature points. The proposed system finds the face region which is suitable to perform the recognition task by geometrically analyses of the facial expression of the target face image. In real-world facial recognition systems, the face is often cropped based on the face detection techniques. The misalignment is inevitably occurred due to facial pose, noise, occlusion, and so on. However misalignment affects the recognition rate due to sensitive nature of the face classifier. The performance of the proposed approach is evaluated with four benchmark databases. The experiment results show the robustness of the proposed approach with significant improvement in the facial recognition system on the various size and resolution of given face images

Soft-Boosted Self-Constructing Neural Fuzzy Inference Network

© 2013 IEEE. This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets

Designing Mamdani-Type Fuzzy Reasoning for Visualizing Prediction Problems Based on Collaborative Fuzzy Clustering

In this paper a collaborative fuzzy c-means (CFCM) is used to generate fuzzy rules for fuzzy inference systems to evaluate the time series model. CFCM helps system to integrate two or more different datasets having similar features which are collected at the different environment with the different time period and it integrates these datasets together in order to visualize some common patterns among the datasets. In order to do any mode of integration between datasets, there is a necessity to define the common features between datasets by using some kind of collaborative process and also need to preserve the privacy and security at higher levels. This collaboration process gives a common structure between datasets which helps to define an appropriate number of rules for structural learning and also improve the accuracy of the system modeling