Mobile multi-view object image search

High user interaction capability of mobile devices can help improve the accuracy of mobile visual search systems. At query time, it is possible to capture multiple views of an object from different viewing angles and at different scales with the mobile device camera to obtain richer information about the object compared to a single view and hence return more accurate results. Motivated by this, we propose a new multi-view visual query model on multi-view object image databases for mobile visual search. Multi-view images of objects acquired by the mobile clients are processed and local features are sent to a server, which combines the query image representations with early/late fusion methods and returns the query results. We performed a comprehensive analysis of early and late fusion approaches using various similarity functions, on an existing single view and a new multi-view object image database. The experimental results show that multi-view search provides significantly better retrieval accuracy compared to traditional single view search.     

A system for efficient motor learning using multimodal augmented feedback

Numerous studies have established that using various forms of augmented feedback improves human motor learning. In this paper, we present a system that enables real-time analysis of motion patterns and provides users with objective information on their performance of an executed set of motions. This information can be used to identify individual segments of improper motion early in the learning process, thus preventing improperly learned motion patterns that can be difficult to correct once fully learned. The primary purpose of the proposed system is to serve as a general tool in the research on impact of different feedback modalities on the process of motor learning, for example, in sports or rehabilitation. The key advantages of the system are high-speed and high-accuracy tracking, as well as its flexibility, as it supports various types of feedback (auditory and visual, concurrent or terminal). The practical application of the proposed system is demonstrated through the example of learning a golf swing.     

基于肿瘤细胞断层图像轮廓的三维极坐标插值方法

利用“细胞CT”——激光扫描共聚焦显微镜可以获得肿瘤细胞三维断层图像,实现肿瘤细胞三维可视化,具有重要临床及科研意义。本文提出了一种针对细胞断层图像轮廓的三维极坐标插值方法,以图形形态学中心为极坐标原点,将轮廓点转换为极坐标表示。按极角采样获得离散轮廓点数据,综合多层轮廓信息,依照表面轮廓变化趋势,进行均匀三次B样条拟合完成插值。并针对肿瘤细胞核常见的分支核结构,利用曲率角点检测和数学形态学相结合的方法实现轮廓分割,并采用基于阈值半径的轮廓匹配方法实现分支轮廓对应,继而实现细胞分支核插值。结果显示,本方法可实现细胞及细胞核的三维插值,插值后轮廓表面三维重建表现出良好的曲面光滑性和连续性。极坐标插值数据可为细胞三维形态结构测量提供便利。     

基于工程管理的南海地质数据库

从南海地质数据资源、工程实例出发,结合高级语言设计和开发了基于工程管理的南海地质信息数据库系统.该系统设计实现了南海地质信息数据共享、数据融合、数据分析,简单快捷的结合到工程实例中,并应用ADO、DAO、OLE等计算机技术实现的数据资源快速导入、导出便于工程应用的目的.对工程中土的性质做详细的介绍,包括随深度的下降有效重度、抗剪强度、单位表面摩阻力、单位桩端阻力的变化情况.可以按层号、深度、土体性质等不同的条件进行查找.汇总功能可以绘制曲线,比较同一工程土体参数和不同工程之间的参数等,对工程设计和施工进行指导.     

Decoupled approach to integrated optimum design of structures and robust control systems

Integrated optimum design of structures and control systems is studied by using H ₂ and robust control formulations. It is derived that conventional simultaneous optimization approach by using these robust control laws can be approximated by a decoupled optimization approach in which the structures are optimized by shaping the structural singular values and then the controllers can be designed, namely, decoupled, sequential or successive design approach. It is shown that the proposed decoupled optimization approach can be used to design optimum robust structures and has certain advantages over the conventional simultaneous optimization procedures such as it avoids the drawbacks of pure robust control laws and faster, especially if the number of degrees of freedom (DOF) of the associated structure is large. The bounds for achievable robustness measures are also obtained. Following, simultaneous and decoupled optimization approaches are applied to active control of two structures. The optimization results are presented, and it is concluded that the proposed decoupled optimization approach yields the achieved global minimum much faster than the simultaneous optimization approach.     

A new supervised classification algorithm in artificial immune systems with its application to carotid artery Doppler signals to diagnose atherosclerosis

Because of its self-regulating nature, immune system has been an inspiration source for usually unsupervised learning methods in classification applications of Artificial Immune Systems (AIS). But classification with supervision can bring some advantages to AIS like other classification systems. Indeed, there have been some studies, which have obtained reasonable results and include supervision in this branch of AIS. In this study, we have proposed a new supervised AIS named as Supervised Affinity Maturation Algorithm (SAMA) and have presented its performance results through applying it to diagnose atherosclerosis using carotid artery Doppler signals as a real-world medical classification problem. We have employed the maximum envelope of the carotid artery Doppler sonograms derived from Autoregressive (AR) method as an input of proposed classification system and reached a maximum average classification accuracy of 98.93% with 10-fold cross-validation method used in training-test portioning. To evaluate this result, comparison was done with Artificial Neural Networks and Decision Trees. Our system was found to be comparable with those systems, which are used effectively in literature with respect to classification accuracy and classification time. Effects of system's parameters were also analyzed in performance evaluation applications. With this study and other possible contributions to AIS, classification algorithms with effective performances can be developed and potential of AIS in classification can be further revealed.     

Quantum chemical studies on protonation of some substituted thiazole derivatives

The acidity constants, pK(a) values for protonation of some substituted thiazole derivatives were calculated by using AM1 and PM3 basis sets of semi-empirical methods and B3LYP/6-31G(d) basis sets of density functional theory (DFT) calculated physical and thermodynamic parameters. Correlation search among the experimental and calculated acidity constants, pK(a) values, revealed that the best correlation exist between the experimental and ab initio calculated pK(a) values with a regression of R(2)=0.98.     

Efficient storage of healthcare data in XML-based smart cards

In this paper we present storage structures for efficiently processing XML path queries on healthcare data stored in smart cards using XML format. In the design of the storage structures that we present, properties of healthcare data and fundamental types of queries on the healthcare data are taken into account as well as the constraints of the smart cards. We compare the space and time efficiency of the proposed storage structures for various types of queries.     

Algorithms for the Homogeneous Set Sandwich Problem

A homogeneous set is a non-trivial module of a graph, i.e. a non-empty, non-unitary, proper subset of a graph's vertices such that all its elements present exactly the same outer neighborhood. Given two graphs the Homogeneous Set Sandwich Problem (HSSP) asks whether there exists a sandwich graph which has a homogeneous set. In 2001 Tang et al. published an all-fast algorithm which was recently proven wrong, so that the HSSP's known upper bound would have been reset thereafter at the former determined by Cerioli et al. in 1998. We present, notwithstanding, new deterministic algorithms which have it established at We give as well two even faster randomized algorithms, whose simplicity might lend them didactic usefulness. We believe that, besides providing efficient easy-to-implement procedures to solve it, the study of these new approaches allows a fairly thorough understanding of the problem.     

Clustering of high throughput gene expression data

High throughput biological data need to be processed, analyzed, and interpreted to address problems in life sciences. Bioinformatics, computational biology, and systems biology deal with biological problems using computational methods. Clustering is one of the methods used to gain insight into biological processes, particularly at the genomics level. Clearly, clustering can be used in many areas of biological data analysis. However, this paper presents a review of the current clustering algorithms designed especially for analyzing gene expression data. It is also intended to introduce one of the main problems in bioinformatics – clustering gene expression data – to the operations research community.