基于WebGL的3D立库可视化系统设计与实现

针对当前扩大化和普及化的仓储物流,提出了一种3D自动化立体仓库可视化系统的实现方案,用以解决仓储系统中遇到的数据不直观的问题。该系统采用JavaScript编程语言,以Three.js引擎为基础,构建了基于WebGL的三维自动化立体仓库的可视化系统,该系统能有效直观的进行仓储管理,适合现代化仓储企业的管理需求。     

In Vivo Simultaneous Analysis of Gene Expression by Dual-Color Luciferases in Caenorhabditis elegans

Both fluorescent and luminescent observation are widely used to examine real-time gene expression patterns in living organisms. Several fluuorescent and luminescent proteins with specific optical properties have been developed and applied for simultaneous, multi-color observation of more than two gene expression profiles. Compared to fluorescent proteins, however, the application of multi-color luminescent imaging in living organisms is still limited. In this study, we introduced two-color luciferases into the soil nematode C. elegans and performed simultaneous analysis of two gene expression profiles. Using a green-emitting luciferase Eluc (emerald luciferase) and red-emitting luciferase SLR (stable luciferase red), the expression patterns of two genes were simultaneously observed in single animals from embryonic to adult stages over its whole life span. In addition, dual gene activities were observed at the single embryo level, with the simultaneous observation of morphological changes. These are the first application of a two-color luciferase system into a whole animal and suggest that precise relationship of expression patterns of multiple genes of interest can be analyzed over the whole life of the animal, dependent on the changes in genetic and/or environmental conditions.     

AP2M1 Supports TGF-β Signals to Promote Collagen Expression by Inhibiting Caveolin Expression

The extracellular matrix (ECM) is important for normal development and disease states, including inflammation and fibrosis. To understand the complex regulation of ECM, we performed a suppressor screening using Caenorhabditis elegans expressing the mutant ROL-6 collagen protein. One cuticle mutant has a mutation in dpy-23 that encodes the μ2 adaptin (AP2M1) of clathrin-associated protein complex II (AP-2). The subsequent suppressor screening for dpy-23 revealed the lon-2 mutation. LON-2 functions to regulate body size through negative regulation of the tumor growth factor-beta (TGF-β) signaling pathway responsible for ECM production. RNA-seq analysis showed a dominant change in the expression of collagen genes and cuticle components. We noted an increase in the cav-1 gene encoding caveolin-1, which functions in clathrin-independent endocytosis. By knockdown of cav-1, the reduced TGF-β signal was significantly restored in the dpy-23 mutant. In conclusion, the dpy-23 mutation upregulated cav-1 expression in the hypodermis, and increased CAV-1 resulted in a decrease of TβRI. Finally, the reduction of collagen expression including rol-6 by the reduced TGF-β signal influenced the cuticle formation of the dpy-23 mutant. These findings could help us to understand the complex process of ECM regulation in organism development and disease conditions.     

Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing

Edge Computing is one of the radically evolving systems through generations as it is able to effectively meet the data saving standards of consumers, providers and the workers. Requisition for Edge Computing based items have been increasing tremendously. Apart from the advantages it holds, there remain lots of objections and restrictions, which hinders it from accomplishing the need of consumers all around the world. Some of the limitations are constraints on computing and hardware, functions and accessibility, remote administration and connectivity. There is also a backlog in security due to its inability to create a trust between devices involved in encryption and decryption. This is because security of data greatly depends upon faster encryption and decryption in order to transfer it. In addition, its devices are considerably exposed to side channel attacks, including Power Analysis attacks that are capable of overturning the process. Constrained space and the ability of it is one of the most challenging tasks. To prevail over from this issue we are proposing a Cryptographic Lightweight Encryption Algorithm with Dimensionality Reduction in Edge Computing. The t-Distributed Stochastic Neighbor Embedding is one of the efficient dimensionality reduction technique that greatly decreases the size of the non-linear data. The three dimensional image data obtained from the system, which are connected with it, are dimensionally reduced, and then lightweight encryption algorithm is employed. Hence, the security backlog can be solved effectively using this method.     

A globally convergent BFGS method for pseudo-monotone variational inequality problems

In this paper, we propose a globally convergent BFGS method to solve Variational Inequality Problems (VIPs). In fact, a globalization technique on the basis of the hyperplane projection method is applied to the BFGS method. The technique, which is independent of any merit function, is applicable for pseudo-monotone problems. The proposed method applies the BFGS direction and tries to reduce the distance of iterates to the solution set. This property, called Fejer monotonicity of iterates with respect to the solution set, is the basis of the convergence analysis. The method applied to pseudo-monotone VIP is globally convergent in the sense that subproblems always have unique solutions, and the sequence of iterates converges to a solution to the problem without any regularity assumption. Finally, some numerical simulations are included to evaluate the efficiency of the proposed algorithm.     

Multiple Empirical Kernel Learning with Majority Projection for imbalanced problems

Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound.     

A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

A Frame Breaking Based Hybrid Algorithm for UHF RFID Anti-Collision

Multi-tag collision imposes a vital detrimental effect on reading performance of an RFID system. In order to ameliorate such collision problem and to improve the reading performance, this paper proposes an efficient tag identification algorithm termed as the Enhanced Adaptive Tree Slotted Aloha (EATSA). The key novelty of EATSA is to identify the tags using grouping strategy. Specifically, the whole tag set is divided into groups by a frame of size F. In cases multiple tags fall into a group, the tags of the group are recognized by the improved binary splitting (IBS) method whereas the rest tags are waiting in the pipeline. In addition, an early observation mechanism is introduced to update the frame size to an optimum value fitting the number of tags. Theoretical analysis and simulation results show that the system throughput of our proposed algorithm can reach as much as 0.46, outperforming the prior Aloha-based protocols.