Parallel Globally Consistent Normal Orientation of Raw Unorganized Point Clouds

A mandatory component for many point set algorithms is the availability of consistently oriented vertex‐normals (e.g. for surface reconstruction, feature detection, visualization). Previous orientation methods on meshes or raw point clouds do not consider a global context, are often based on unrealistic assumptions, or have extremely long computation times, making them unusable on real‐world data. We present a novel massively parallelized method to compute globally consistent oriented point normals for raw and unsorted point clouds. Built on the idea of graph‐based energy optimization, we create a complete kNN‐graph over the entire point cloud. A new weighted similarity criterion encodes the graph‐energy. To orient normals in a globally consistent way we perform a highly parallel greedy edge collapse, which merges similar parts of the graph and orients them consistently. We compare our method to current state‐of‐the‐art approaches and achieve speedups of up to two orders of magnitude. The achieved quality of normal orientation is on par or better than existing solutions, especially for real‐world noisy 3D scanned data.     

Computation Controllable Mode Decision and Motion Estimation for Scalable Video Coding

This paper proposes an efficient computation‐aware mode decision and search point (SP) allocation algorithm for spatial and quality scalabilities in Scalable Video Coding. In our proposal, a linear model is derived to allocate the computation for macroblocks in enhancement layers by using the rate distortion costs of the base layer. In addition, an adaptive SP decision algorithm is proposed to decide the number of SPs for motion estimation under the constraint of the allocated computation. Experiment results demonstrate that the proposed algorithm allocates the computation resource efficiently and outperforms other works in rate distortion performance under the same computational availability constraint.     

入口速度分布对后台阶流动模拟的影响研究

为深入研究台阶入口速度分布情况对后台阶流动的影响,利用Fluent软件,分别使用S-A和标准κ-ε两种湍流模型,将不同的速度分布情况作为速度入口条件,对后台阶流动进行数值仿真计算,研究了入口条件差异导致的水平速度曲线在近壁面处的凸起情况,并将计算结果与试验数据和已有研究成果进行比较。结果表明,两种湍流模式下,在附着点距离和速度分布曲线上均存在普遍而明显的差异,且这种差异受入口条件影响较大,进而得出了合理的速度入口条件,能有效减小计算结果与试验结果的误差,对理论分析和工程应用均具有参考意义。     

Parkinson’s Disease Detection Using Biogeography-Based Optimization

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1- good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.     

一种量子衍生神经网络模型算法及应用

为提高神经网络的逼近和预测能力，提出一种各维输入为离散序列的量子衍生神经网络模型及算法。该模型为三层结构，隐层为量子衍生神经元，输出层为普通神经元。量子衍生神经元由量子旋转门和多位受控旋转门组成，利用多位受控旋转门中目标量子位的输出向输入端的反馈，实现对输入序列的整体记忆，利用受控旋转门输出中多位量子比特的纠缠获得量子衍生神经元的输出。基于量子计算理论设计了该模型的学习算法。该模型可从宽度和深度两方面获取输入序列的特征。仿真结果表明，当输入节点数和序列长度满足一定关系时，该模型明显优于普通神经网络。     

Influence of abnormal stress under a residual bearing coal pillar on the stability of a mine entry

Mine entries close to residual bearing coal pillars(RBCPs) will suffer large deformation that may cause rock burst. To better understand the deformation mechanism and develop safe and practical guidelines for entry design, most studies focus on the absolute size of the stress field in and around the pillar. In this paper, we present a new approach to analyze the abnormal stress field close to a RBCP that uses the stress concentration coefficient(SCC), stress gradient(SG), and coefficient of lateral pressure(CLP) to describe the stress state induced by the RBCP. Based on elastic theory and a mathematical model for the abutment stress in the RBCP, an analytical solution for the abnormal stress in the strata below the RBCP was derived and the characteristics of the abnormal stress for a case study of a coal mine in China were analyzed. The results show that the abnormal stress field around the pillar is characterized by four distinct zones: a zone of high SCC, high SG, and CLP less than 1, a zone of high SCC, low SG, and CLP less than 1, a zone of low SCC, SG close to 0, and CLP greater than 1, and a zone of SCC close to 1, SC close to 0, and CLP close to 1. Based on this zoning pattern, a numerical model was established to study the combined effects of the abnormal stress on the stability of the entry. The most stable zone was determined based on a model of the Xinrui coal mine and verified by field measurements at the mine. Our conclusions can be used as guidelines for designing safe entry layouts in similar geological and mining settings.     

A weighted full-reference image quality assessment based on visual saliency

In full reference image quality assessment (IQA), the images without distortion are usually employed as reference, while the structures in both reference images and distorted images are ignored and all pixels are equally treated. In addition, the role of human visual system (HVS) is not taken account into subjective IQA metric. In this paper, a weighted full-reference image quality metric is proposed, where a weight imposed on each pixel indicates its importance in IQA. Furthermore, the weights can be estimated via visual saliency computation, which can approximate the subjective IQA via exploiting the HVS. In the experiments, the proposed metric is compared with several objective IQA metrics on LIVE release 2 and TID 2008 database. The results demonstrate that SROCC and PLCC of the proposed metric are 0.9647 and 0.9721, respectively,which are higher than other methods and it only takes 427.5 s, which is lower than that of most other methods.     

Hybrid multiobjective artificial bee colony for multiple sequence alignment

In the bioinformatics community, it is really important to find an accurate and simultaneous alignment among diverse biological sequences which are assumed to have an evolutionary relationship. From the alignment, the sequences homology is inferred and the shared evolutionary origins among the sequences are extracted by using phylogenetic analysis. This problem is known as the multiple sequence alignment (MSA) problem. In the literature, several approaches have been proposed to solve the MSA problem, such as progressive alignments methods, consistency-based algorithms, or genetic algorithms (GAs). In this work, we propose a Hybrid Multiobjective Evolutionary Algorithm based on the behaviour of honey bees for solving the MSA problem, the hybrid multiobjective artificial bee colony (HMOABC) algorithm. HMOABC considers two objective functions with the aim of preserving the quality and consistency of the alignment: the weighted sum-of-pairs function with affine gap penalties (WSP) and the number of totally conserved (TC) columns score. In order to assess the accuracy of HMOABC, we have used the BAliBASE benchmark (version 3.0), which according to the developers presents more challenging test cases representing the real problems encountered when aligning large sets of complex sequences. Our multiobjective approach has been compared with 13 well-known methods in bioinformatics field and with other 6 evolutionary algorithms published in the literature.     

MRoCS: A new multi-robot communication system based on passive action recognition

Multi-robot search-and-rescue missions often face major challenges in adverse environments due to the limitations of traditional implicit and explicit communication. This paper proposes a novel multi-robot communication system (MRoCS), which uses a passive action recognition technique that overcomes the shortcomings of traditional models. The proposed MRoCS relies on individual motion, by mimicking the waggle dance of honey bees and thus forming and recognising different patterns accordingly. The system was successfully designed and implemented in simulation and with real robots. Experimental results show that, the pattern recognition process successfully reported high sensitivity with good precision in all cases for three different patterns thus corroborating our hypothesis.     

Fast Two-Output Secure Computation with Optimal Error Probability

Cut-and-choose paradigm makes Yao's protocol for two-party computation secure in malicious model with an error probability.In CRYPTO 2013,based on multi-phase cut-and-choose,Lindell reduced this probability to the optimal value.However,this work can only compute single-output functions with optimal error probability.We transform multi-phase cut-and-choose for singleoutput case into one that can deal with two-output functions,meanwhile maintaining the optimal error probability.Based on this new paradigm,we propose an efficient two-output secure computation protocol.Besides,by utilizing the specific property of the output garbled keys,we solve the authenticity issue of the generator's output with only symmetric cryptographic operations linear in the output length of the generator,which is the most efficient method so far in standard model without Random oracle (Ro).