Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural-similarity (SSIM) index brings IQA from pixel-based stage to structure-based stage. In this paper, a novel feature-similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.

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Image segmentation is a fundamental problem in computer vision. Despite many years of research, general purpose image segmentation is still a very challenging task because segmentation is inherently ill-posed. Among different segmentation schemes, graph theoretical ones have several good features in practical applications. It explicitly organizes the image elements into mathematically sound structures, and makes the formulation of the problem more flexible and the computation more efficient. In this paper, we conduct a systematic survey of graph theoretical methods for image segmentation, where the problem is modeled in terms of partitioning a graph into several sub-graphs such that each of them represents a meaningful object of interest in the image. These methods are categorized into five classes under a uniform notation: the minimal spanning tree based methods, graph cut based methods with cost functions, graph cut based methods on Markov random field models, the shortest path based methods and the other methods that do not belong to any of these classes. We present motivations and detailed technical descriptions for each category of methods. The quantitative evaluation is carried by using five indices -Probabilistic Rand (PR) index, Normalized Probabilistic Rand (NPR) index, Variation of Information (VI), Global Consistency Error (GCE) and Boundary Displacement Error (BDE) -on some representative automatic and interactive segmentation methods.

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The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-to-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch effect removal framework, called iMAP, based on both deep autoencoders and generative adversarial networks. Compared with current methods, iMAP shows superior, robust, and scalable performance in terms of both reliably detecting the batch-specific cells and effectively mixing distributions of the batch-shared cell types. Applying iMAP to tumor microenvironment datasets from two platforms, Smart-seq2 and 10x Genomics, we find that iMAP can leverage the powers of both platforms to discover novel cell-cell interactions.

As the current research of face gear drive cannot realize fluctuating gear ratio, a design method of orthogonal fluctuating gear ratio face gear drive is proposed. The mathematical model of orthogonal fluctuating gear ratio face gear drive is found based on the space engagement theory. The equation of the pitch curve, addendum curve and dedendum curve of face gear are derived. The design method of tooth surface of the face gear is available based on the envelope method. The conversion relationship of enveloping coordinate systems is obtained during the enveloping process of orthogonal fluctuating gear ratio face gear drive after the establishment of enveloping coordinate systems. Then combining with equation of generating surface, the tooth surface equation of the face gear is obtained. The three-dimensional model of orthogonal fluctuating gear ratio face gear is acquired on the basis of a modeling program, which is developed under the environment of VB and Solidworks (API). Furthermore, localization of the bearing contact is achieved by the manufacturing method and it is justified by the finite element method analysis result. Finally, the kinematics of the orthogonal fluctuating gear ratio face gear drive is analyzed, and the change laws of transmission ratio, angular displacement and angular acceleration of the face gear are acquired.

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