End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning

Sketch-based face recognition is an interesting task in vision and multimedia research, yet it is quite challenging due to the great difference between face photos and sketches. In this paper, we propose a novel approach for photo-sketch generation, aiming to automatically transform face photos into detail-preserving personal sketches. Unlike the traditional models synthesizing sketches based on a dictionary of exemplars, we develop a fully convolutional network to learn the end-to-end photo-sketch mapping. Our approach takes whole face photos as inputs and directly generates the corresponding sketch images with efficient inference and learning, in which the architecture are stacked by only convolutional kernels of very small sizes. To well capture the person identity during the photo-sketch transformation, we define our optimization objective in the form of joint generative-discriminative minimization. In particular, a discriminative regularization term is incorporated into the photo-sketch generation, enhancing the discriminability of the generated person sketches against other individuals. Extensive experiments on several standard benchmarks suggest that our approach outperforms other state-of-the-art methods in both photo-sketch generation and face sketch verification.Comment: 8 pages, 6 figures. Proceeding in ACM International Conference on Multimedia Retrieval (ICMR), 201

A Modified Direct Allocation Algorithm with Application to Redundant Actuators

AbstractControl allocation considers the problem of controlling instruction distribution for control systems with multiple and redundant actuators. This paper focuses on the direct allocation method, making the time requirement of the algorithm analogous compared with modified pseudoinverse redistribution methods, linear programming methods solved by simplex method, and sub-gradient optimization method. To reduce off-line computations of constructing the attainable moment set of actuators, a new approach based on the null space of the control effectiveness matrix is proposed, which is superior when the number of actuators is less than 10 compared with traditional method. To decrease on-line computations, an improvement method of searching the facet that is aligned with the desired moment is presented, shortening the search time by checking only the facets that lie around the desired moment. To find such facets, the vertices of the attainable moment set are normalized and saved during off-line computations. Simulation results show that at least 32.22% of off-line computation time would be saved using null space-based construction when the number of actuators is less than 10. In on-line computations, the modified method performs superiorly compared with the three aforementioned methods. Furthermore, it may solve the problem of control allocation efficiently when a remarkable large number of redundant actuators are configured

Research on Key Quality Characteristics of Electromechanical Product Based on Meta-Action Unit

Electromechanical products have many quality characteristics, representing their quality. In addition, there are long-existed quality problems of electromechanical products, such as poor accuracy, short precision life, large fluctuations in performance, frequently failing, and so on. Based on meta-action unit (MU) for electromechanical products, this book chapter proposes a key quality characteristic control method, which provides theoretical and technical support for essentially guaranteeing the complete machine’s quality. The formation mechanisms of MU’s four key quality characteristics (precision, precision life, performance stability, and reliability) are studied. Moreover, we introduce an overview of key quality characteristic control methods based on MU. The complex large system research method of “decomposition-analysis-synthesis” is adopted to study these key science problems

Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification

In this paper, we propose a logistic regression classification method based on the integration of a statistical learning model with linearized kernel pre-processing. The single Gaussian kernel and fusion of Gaussian and cosine kernels are adopted for linearized kernel pre-processing respectively. The adopted statistical learning models are the generalized linear model and the generalized additive model. Using a generalized linear model, the elastic net regularization is adopted to explore the grouping effect of the linearized kernel feature space. Using a generalized additive model, an overlap group-lasso penalty is used to fit the sparse generalized additive functions within the linearized kernel feature space. Experiment results on the Extended Yale-B face database and AR face database demonstrate the effectiveness of the proposed method. The improved solution is also efficiently obtained using our method on the classification of spectra data

Intracoronary Sarcoplasmic Reticulum CalciumATPase Gene Therapy in Advanced Heart Failure Patients with reduced Ejection Fraction: A Prospective Cohort Study

OBJECTIVE: Heart failure is a progressive and debilitating disease. Intracoronary sarcoplasmic reticulum calciumATPase gene therapy may improve the function of cardiac muscle cells. This study aimed to test the hypothesis that intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy can improve outcomes and reduce the number of recurrent and terminal events in advanced heart failure patients with reduced ejection fraction. METHODS: A total of 768 heart failure patients with reduced ejection fraction and New York Heart Association classification II to IV were included in this prospective cohort study. Patients either underwent intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy (CA group, n=384) or received oral placebo (PA group; n=384). Data regarding recurrent and terminal event(s), treatment-emergent adverse effects, and outcome measures were collected and analyzed. RESULTS: After a follow-up period of 18 months, intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy reduced the number of hospital admissions (p=0.001), ambulatory treatments (p=0.0004), and deaths (p=0.024). Additionally, intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy improved the left ventricular ejection fraction (po0.0001) and Kansas City Cardiomyopathy Questionnaire score (po0.0001). The number of recurrent and terminal events/patients were higher in the PA group than in the CA group after the follow-up period of 18 months (p=0.015). The effect of the intracoronary sarcoplasmic reticulum calciumATPase gene therapy was independent of the confounding variables. No new arrhythmias were reported in the CA group. CONCLUSIONS: Intracoronary sarcoplasmic reticulum calcium-ATPase gene therapy reduces the number of recurrent and terminal events and improves the clinical course of advanced heart failure patients with reduced ejection fraction

Geometric Distribution Weight Information Modeled Using Radial Basis Function with Fractional Order for Linear Discriminant Analysis Method

Fisher linear discriminant analysis (FLDA) is a classic linear feature extraction and dimensionality reduction approach for face recognition. It is known that geometric distribution weight information of image data plays an important role in machine learning approaches. However, FLDA does not employ the geometric distribution weight information of facial images in the training stage. Hence, its recognition accuracy will be affected. In order to enhance the classification power of FLDA method, this paper utilizes radial basis function (RBF) with fractional order to model the geometric distribution weight information of the training samples and proposes a novel geometric distribution weight information based Fisher discriminant criterion. Subsequently, a geometric distribution weight information based LDA (GLDA) algorithm is developed and successfully applied to face recognition. Two publicly available face databases, namely, ORL and FERET databases, are selected for evaluation. Compared with some LDA-based algorithms, experimental results exhibit that our GLDA approach gives superior performance

Mobile E-business Platform: Collaboration System of Wi-Fi and WAVE Based on Cognitive Radio

The new international standard IEEE802.11p (WAVE) defined enhancements to IEEE802.11 (Wi-Fi). A new mobile e-business platform of Wi-Fi and WAVE is described. Based on cognitive radio, the popular wireless network Wi-Fi can collaborate with WAVE in their common unlicensed spectrum band and licensed spectrum band. As a core technology in the cognitive radio field, OFDM is introduced, and a novel frequency estimation algorithm for them is presented

Recent Advances in Morphological Cell Image Analysis

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed