The Iterative Closest Points (ICP) algorithm is the mainstream algorithm used in the process of accurate registration of 3D point cloud data. The algorithm requires a proper initial value and the approximate registration of two point clouds to prevent the algorithm from falling into local extremes, but in the actual point cloud matching process, it is difficult to ensure compliance with this requirement. In this paper, we proposed the ICP algorithm based on point cloud features (GF-ICP). This method uses the geometrical features of the point cloud to be registered, such as curvature, surface normal and point cloud density, to search for the correspondence relationships between two point clouds and introduces the geometric features into the error function to realize the accurate registration of two point clouds. The experimental results showed that the algorithm can improve the convergence speed and the interval of convergence without setting a proper initial value.

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A series of benzo[de][1,7]naphthyridin-7(8H)-ones possessing a functionalized long-chain appendage have been designed and evaluated as novel PARP1 inhibitors. The initial effort led to the first-generation PARP1 inhibitor 26 bearing a terminal phthalazin-1(2H)-one framework and showing remarkably high PARP1 inhibitory activity (0.31 nM) but only moderate potency in the cell. Further effort generated the second-generation lead 41, showing high potency against both the PARP1 enzyme and BRCA-deficient cells, especially for the BRCA1-deficient MDA-MB-436 cells (CC50 < 0.26 nM). Mechanistic studies revealed that the new PARP1 inhibitors significantly inhibited H2O2-triggered PARylation in SKOV3 cells, induced cellular accumulation of DNA double-strand breaks, and impaired cell-cycle progression in BRCA2-deficient cells. Significant potentiation on the cytotoxicity of Temozolomide was also observed. The unique structural character and exceptionally high potency of 41 made it stand out as a promising drug candidate worthy for further evaluation.

This paper presents a Hidden Markov Model (HMM) approach for real-time activity classification using signals from wearable wireless sensor networks. A wearable wireless sensor network can be used to continuously monitor the daily activities of a subject in real time. However, the wireless sensor nodes are constrained by limited battery and computing resources. The proposed HMM framework has been applied to find the most probable activity states series with low data transmission rate, which makes it highly suitable for daily activity classification applications. The performance was evaluated using a small sensor network consisting of three accelerometers. The activity detection rate is 95.82%, using a test set of 5 subjects with 11 activity series.

Time-of-Flight (ToF) cameras, a technology which has developed rapidly in recent years, are 3D imaging sensors providing a depth image as well as an amplitude image with a high frame rate. As a ToF camera is limited by the imaging conditions and external environment, its captured data are always subject to certain errors. This paper analyzes the influence of typical external distractions including material, color, distance, lighting, etc. on the depth error of ToF cameras. Our experiments indicated that factors such as lighting, color, material, and distance could cause different influences on the depth error of ToF cameras. However, since the forms of errors are uncertain, it’s difficult to summarize them in a unified law. To further improve the measurement accuracy, this paper proposes an error correction method based on Particle Filter-Support Vector Machine (PF-SVM). Moreover, the experiment results showed that this method can effectively reduce the depth error of ToF cameras to 4.6 mm within its full measurement range (0.5–5 m).

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Bone age assessment (BAA) based on hand X-ray imaging is a common clinical practice for investigating disorders and predicting the adult height of a child. However, the traditional manual method is time consuming and prone to obverse variability. There is an urgent need for a fully automatic framework based on deep learning with high performance and efficiency. We propose an end-to-end BAA model based on lossless image compression and a squeeze-and-excitation deep residual network (SE-ResNet). First, we apply the compression module to compress the raw image without losing important features. Second, the SE-ResNet-based model extracts features of the compressed images. Furthermore, the regression model with improved loss function predicts bone age. The experiments on a public dataset reveal that our method outperforms the baseline models. In conclusion, the presented method is a fully automatic and effective solution to process hand X-ray images for BAAs.INDEX TERMS bone age assessment, deep learning, convolutional neural network, residual network, regression.

Modern cloud computing platforms based on virtual machine monitors carry a variety of complex business that present many network security vulnerabilities. At present, the traditional architecture employs a number of security devices at front-end of cloud computing to protect its network security. Under the new environment, however, this approach can not meet the needs of cloud security. New cloud security vendors and academia also made great efforts to solve network security of cloud computing, unfortunately, they also cannot provide a perfect and effective method to solve this problem. We introduce a novel network security architecture for cloud computing (NetSecCC) that addresses this problem. NetSecCC not only provides an effective solution for network security issues of cloud computing, but also greatly improves in scalability, fault-tolerant, resource utilization, etc. We have implemented a proofof-concept prototype about NetSecCC and proved by experiments that NetSecCC is an effective architecture with minimal performance overhead that can be applied to the extensive practical promotion in cloud computing.