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Real-time quantitative PCR (RT-qPCR) is a widely applied technique for relative quantification of gene expression. In this context, the selection of a suitable reference gene (RG) is an essential step for obtaining reliable and biologically relevant RTqPCR results. The present study aimed to determine the expression stability of commonly used RGs in mouse skeletal muscle tissue. The expression pattern of eight RGs (ACTB, GAPDH, HPRT, YWHAZ, B2M, PPIA, TUBA and 18S) were evaluated by RT-qPCR in different sample groups classified based on genetic background, muscle tissue type, and growth stage, as well as in a C2C12 myoblast cell line model. Five computational programs were included in the study (comparative ΔCq value, NormFinder, BestKeeper, geNorm, RefFinder) to evaluate the expression stability of RGs. Furthermore, the normalization effects of RGs in soleus (SOL) and gastrocnemius (GAS) muscle tissue were evaluated. Collectively, ACTB, HPRT and YWHAZ were shown to be the most stable RGs, whileGADPHand 18S were the least stable. Therefore, the combined use of ACTB, HPRT and YWHAZ is recommended for the normalization of gene expression results in experiments with murine skeletal muscle. The results discussed herein provide a foundation for gene expression analysis by RT-qPCR in mammalian skeletal muscle. [ABSTRACT FROM AUTHOR]

A composite algorithm combined with fuzzy proportional-integral-derivative (PID) control and acceleration closed-loop control is proposed to address the defects of slow response speed, strong oscillation, and long adjustment time of the current height following control system for a laser cutting head. The fuzzy PID control can satisfy the different requirements for the control parameters in each stage of the height follow-up adjustment process for the laser cutting head via the adaptive adjustment of the PID parameters. Accordingly, the height following error can be attenuated to the set positioning accuracy range. The acceleration closed-loop control can improve the acceleration and deceleration performance of the system through the positive and negative feedback regulation of the motion acceleration of the laser cutting head to achieve high-speed servo. An experimental bench of height follow-up control system for laser fabrication is developed to verify the effectiveness of the algorithm. Through experimental verification, compared with the conventional digital PID incremental algorithm, this composite control algorithm can accelerate the response speed of height follow-up control system for the laser cutting head, improve the dynamic performance of the system, and realize fast and precise servo control of laser cutting head height under the premise of ensuring positioning accuracy. The proposed algorithm is expected to lay a foundation for the development of the new intelligent laser processing system. [ABSTRACT FROM AUTHOR]

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery, and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning. Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion. In addition, previous route planning solutions fail to exploit the appearance patterns of future requests hidden in historical data for optimization. In this paper, we present a unified formulation of route planning called URPSM. It has a well-defined parameterized objective function which eliminates the contradicted objectives in previous studies and enables flexible multi-objective route planning for shared mobility. We propose two insertion-based frameworks to solve the URPSM problem. The first is built upon the plain-insertion widely used in prior studies, which processes online requests only, whereas the second relies on a new insertion operator called prophet-insertion that handles both online and predicted requests. Novel dynamic programming algorithms are designed to accelerate both insertions to only linear time. Theoretical analysis shows that no online algorithm can have a constant competitive ratio for the URPSM problem under the competitive analysis model, yet our prophet-insertion-based framework can achieve a constant optimality ratio under the instance-optimality model. Extensive experimental results on real datasets show that our insertion-based solutions outperform the state-of-the-art algorithms in both effectiveness and efficiency by a large margin (e.g., up to 30× more effective in the objective and up to 20× faster). [ABSTRACT FROM AUTHOR]

The aim of this study is to assess and compare two clustering algorithms, namely, the Wave-cluster and Density Based Spatial Clustering of Application with Noise (DBSCAN) algorithms, for landslide susceptibility assessment in Baota District, China. Based on historical reports, interpretation of aerial photographs and field survey reports, 293 were identified in the study area. Seven factors associated with landslide occurrence were prepared and used as inputs into the models for landslide susceptibility modelling. The models divided the study area grids into 478 and 465 subclasses, respectively, which were further grouped into five susceptibility levels using K-means algorithm, based on the landslide density values of the obtained subclasses. The resulting two models were validated and compared using area under the receiver operating characteristics (AUC-ROC) curve and statistical measures including sensitivity, specificity and accuracy. The analysis demonstrated that the Wave-cluster model outperformed the DBSCAN model with sensitivity, specificity and accuracy values of 88.05%, 89.2%, and 88.54%, respectively, while DBSCAN had sensitivity, specificity and accuracy values of 86.69%, 86.38%, and 86.56% respectively. In addition, the Wave-cluster model had higher value AUC of 0.823 than the DBSCAN model (0.808). This indicates that, the landslide susceptibility maps (LSMs) generated in this study had good performance for assessing landslide susceptibility in the study area and can be useful tools in assessing landslides, as the foundation for preventing and reducing landslide consequences for suitable social and economic development. [ABSTRACT FROM AUTHOR]

An objective normalization strategy is essential in any evolutionary multiobjective or many-objective optimization (EMO or EMaO) algorithm, due to the distance calculations between objective vectors required to compute diversity and convergence of population members. For the decomposition-based EMO/EMaO algorithms involving the Penalty Boundary Intersection (PBI) metric, normalization is an important matter due to the computation of two distance metrics. In this article, we make a theoretical analysis of the effect of instabilities in the normalization process on the performance of PBI-based MOEA/D and a proposed PBI-based NSGA-III procedure. Although the effect is well recognized in the literature, few theoretical studies have been done so far to understand its true nature and the choice of a suitable penalty parameter value for an arbitrary problem. The developed theoretical results have been corroborated with extensive experimental results on three to 15-objective convex and non-convex instances of DTLZ and WFG problems. The article, makes important theoretical conclusions on PBI-based decomposition algorithms derived from the study. [ABSTRACT FROM AUTHOR]

Context: Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking.Objectives: This work aimed to establish effective models to predict early GDM.Methods: Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Based on a machine learning (ML)-driven feature selection method, 17 variables were selected for early GDM prediction. To facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable data set and the 73-variable data set to build models predicting early GDM for different situations, respectively.Results: A total of 16 819 and 14 992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low body mass index (BMI) (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = .09). Total 3,3,5'-triiodothyronine (T3) and total thyroxin (T4) were superior to free T3 and free T4 in predicting GDM. Lipoprotein(a) was demonstrated a promising predictive value (AUC = 0.66).Conclusions: We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population. [ABSTRACT FROM AUTHOR]

In recent years, mobile crowdsourcing has emerged as a powerful computation paradigm to harness human power to perform spatial tasks such as collecting real-time traffic information and checking product prices in a specific supermarket. A fundamental problem of mobile crowdsourcing is: When both tasks and crowd workers appear in the platforms dynamically, how to assign an appropriate set of tasks to each worker. Most existing studies focus on efficient assignment algorithms based on bipartite graph matching. However, they overlook an important fact that crowd workers might be unreliable. Thus, their task assignment schemes cannot ensure the overall quality. In this article, we investigate the Quality-aware Online Task Assignment (QAOTA) problem in mobile crowdsourcing. We propose a probabilistic model to measure the quality of tasks and a hitchhiking model to characterize workers' behavior patterns. We model task assignment as a quality maximization problem and derive a polynomial-time online assignment algorithm. Through rigorous analysis, we prove that the proposed algorithm approximates the offline optimal solution with a competitive ratio of 10/7. Finally, we demonstrate the efficiency and effectiveness of our solution through intensive experiments. [ABSTRACT FROM AUTHOR]

Purpose: In spite of the vehicle, magnetic field interference can be reduced by some measures and techniques in ammunition design and manufacturing stage, the corruption of the vehicle magnetic field can still reach hundreds to thousands of nanoteslas. Besides, the magnetic field that the ferromagnetic materials generate in response to the strong magnetic field in the vicinity of the body. So, a real-time and accurate vehicle magnetic field calibration method is needed to improve the real-time measurement accuracy of the geomagnetic field for spinning projectiles. Design/methodology/approach: Unlike the past two-step calibration method, the algorithm uses a linear model to calibrate the magnetic measurement error in real-time. In the method, the elliptical model of magnetometer measurement is established to convert the coefficients of hard and soft iron errors into the parameters of the elliptic equation. Then, the parameters are estimated by recursive least square estimator in real-time. Finally, the initial conditions for the estimator are established using prior knowledge method or static calibration method. Findings: Studies show the proposed algorithm has remarkable estimation accuracy and robustness and it realizes calibration the magnetic measurement error in real-time. A turntable experiments indicate that the post-calibration residuals approximate the measurement noise of the magnetometer and the roll accuracy is better than 1°. The algorithm is restricted to biaxial magnetometers' calibration in real-time as expressed in this paper. It, however, should be possible to broaden this method's applicability to triaxial magnetometers' calibration in real-time. Originality/value: Unlike the past two-step calibration method, the algorithm uses a linear model to calibrate the magnetic measurement error in real-time and the calculation is small. Besides, it does not take up storage space. The proposed algorithm has remarkable estimation accuracy and robustness and it realizes calibration the magnetic measurement error in real time. The algorithm is restricted to biaxial magnetometers' calibration in real-time as expressed in this paper. It, however, should be possible to broaden this method's applicability to triaxial magnetometers' calibration in real-time. [ABSTRACT FROM AUTHOR]

Scene recognition is a popular open problem in the computer vision field. Among lots of methods proposed in recent years, Convolutional Neural Network (CNN) based approaches achieve the best performance in scene recognition. We propose in this paper an advanced feature fusion algorithm using Multiple Convolutional Neural Network (Multi-CNN) for scene recognition. Unlike existing works that usually use individual convolutional neural network, a fusion of multiple different convolutional neural networks is applied for scene recognition. Firstly, we split training images in two directions and apply to three deep CNN model, and then extract features from the last full-connected (FC) layer and probabilistic layer on each model. Finally, feature vectors are fused with different fusion strategies in groups forwarded into SoftMax classifier. Our proposed algorithm is evaluated on three scene datasets for scene recognition. The experimental results demonstrate the effectiveness of proposed algorithm compared with other state-of-art approaches. [ABSTRACT FROM AUTHOR]

Borda sorting algorithm is a kind of improvement algorithm based on weighted position sorting algorithm, it is mainly suitable for the high duplication of search results, for the independent search results, the effect is not very good and the computing method of relative score in Borda sorting algorithm is according to the rule of the linear regressive, but position relationship cannot fully represent the correlation changes. aimed at this drawback, the new sorting algorithm is proposed in this paper, named PMS-Sorting algorithm, firstly the position score of the returned results is standardized processing, and the similarity retrieval word string with the query results is combined into the algorithm, the similarity calculation method is also improved, through the experiment, the improved algorithm is superior to traditional sorting algorithm. [ABSTRACT FROM AUTHOR]

Although lane detection technology has been developed for decades, however, many challenging issues remain unresolved. In this paper, we propose a robust vanishing point-based lane detection method. Due to the perspective effect, the two parallel straight lines in 3-D space intersect at the 2-D plane. For the lane line extraction, we use lane shape features to extract lane lines. Firstly, we use LSD (line segment detectors) to extract the straight line segments in each frame of image. Secondly, we use a direction priority search method to remove the most of the interference information. This algorithm makes reference to the directional and shape features of lane lines in 2-D images. Finally, using the remaining straight line segments after filtering out the noise straight line to calculate the vanishing point of the lane line, we use a score function to remove non-candidate lane markings, the score function is constructed using the shape features, direction of the line segments. The experimental results show that the validity and robustness of our new algorithm under complex structured road scenes. [ABSTRACT FROM AUTHOR]

In this paper, a algorithm which is used for carrier frequency offset (CFO) estimation is proposed in coherent optical communications system with binary phase-shift keying (BPSK) modulation. The proposed algorithm utilizes the frequency divider to broaden the range of the estimation algorithm. In a 5-GBaud coherent BPSK system, simulation results show that when 12dB signal-to-noise ratio (SNR) and 512 samples are utilized, a 8 frequency divider can expand the range of the frequency offset estimation from [-0.5GHz, +0.5GHz] to [-4GHz, +4GHz] and the CFO estimation error can be obtained less than 5MHz. The paper presents a novel algorithm that features the large estimation range for carrier frequency offset while estimation error is not enlarged. The experimental results verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Considering that generally frequency instability problems occur due to abrupt variations in load demand growth and power variations generated by different renewable energy sources (RESs), the application of superconducting magnetic energy storage (SMES) may become crucial due to its rapid response features. In this paper, liquid hydrogen with SMES (LIQHYSMES) is proposed to play a role in the future energy internet in terms of its combination of the SMES and the liquid hydrogen storage unit, which can help to overcome the capacity limit and high investment cost disadvantages of SMES. The generalized predictive control (GPC) algorithm is presented to be appreciatively used to eliminate the frequency deviations of the isolated micro energy grid including the LIQHYSMES and RESs. A benchmark micro energy grid with distributed generators (DGs), electrical vehicle (EV) stations, smart loads and a LIQHYSMES unit is modeled in the Matlab/Simulink environment. The simulation results show that the proposed GPC strategy can reschedule the active power output of each component to maintain the stability of the grid. In addition, in order to improve the performance of the SMES, a detailed optimization design of the superconducting coil is conducted, and the optimized SMES unit can offer better technical advantages in damping the frequency fluctuations. [ABSTRACT FROM AUTHOR]

For the problem of low accuracy, time-consuming and quantified faults cannot be provided that the current methods have, a fault recognition method based on affinity propagation clustering algorithm and least square curve fitting is proposed. Firstly, discontinuous points of horizons were located by the connected component labeling method and used as control points of the faults. Then, the discontinuous points were clustered by the affinity propagation clustering algorithm and the points of the same cluster were used to determine a fault. The number of the faults was determined by the number of classes after clustering. At the end, the discontinuous points of the same cluster were fitted by the least square curve fitting, and the curves were the desired faults. By the proposed method, the time consuming cross correlation calculation of the traditional method was discarded, the computing process was simplified and the faults were quantified. To validate the effectiveness, the proposed algorithm was compared with the traditional and the latest methods on the real seismic data and the experimental results confirmed that the accuracy of the proposed algorithm is better and the time consumption is reduced greatly. [ABSTRACT FROM AUTHOR]

Conventional semi-infinite solution for extracting blood flow index (BFI) from diffuse correlation spectroscopy (DCS) measurements may cause errors in estimation of BFI (αDB) in tissues with small volume and large curvature. We proposed an algorithm integrating Nth-order linear model of autocorrelation function with the Monte Carlo simulation of photon migrations in tissue for the extraction of αDB. The volume and geometry of the measured tissue were incorporated in the Monte Carlo simulation, which overcome the semi-infinite restrictions. The algorithm was tested using computer simulations on four tissue models with varied volumes/geometries and applied on an in vivo stroke model of mouse. Computer simulations shows that the high-order (N≥5) linear algorithm was more accurate in extracting αDB (errors<±2%) from the noise-free DCS data than the semi-infinite solution (errors: -5.3% to -18.0%) for different tissue models. Although adding random noises to DCS data resulted in αDB variations, the mean values of errors in extracting αDB were similar to those reconstructed from the noise-free DCS data. In addition, the errors in extracting the relative changes of αDB using both linear algorithm and semi-infinite solution were fairly small (errors<±2.0%) and did not rely on the tissue volume/geometry. The experimental results from the in vivo stroke mice agreed with those in simulations, demonstrating the robustness of the linear algorithm. DCS with the high-order linear algorithm shows the potential for the inter-subject comparison and longitudinal monitoring of absolute BFI in a variety of tissues/organs with different volumes/geometries. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity and transition dynamics between different operating points of the high purity distillation column process, it is difficult to use a single model for modeling such a process. Therefore, the multiple model based approach is introduced for modeling the high purity distillation column plant under the framework of the expectation maximization (EM) algorithm. In this paper, autoregressive exogenous (ARX) models are adopted to construct the local models of this chemical process at different operating points, and the EM algorithm is used for identification of local models as well as the probability that each local model takes effect. The global model is obtained by aggregating the local models using an exponential weighting function. Finally, the simulation performed on the high purity distillation column demonstrates the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In the field of wireless communications the idea of cognitive radio is of utmost interest. Due to its advantageous propagation properties, the TV white space can be considered to become the first commercial application of cognitive radio. It allows the usage of secondary communication systems at non-occupied frequency bands. Within this paper, spectrum sensing algorithms are introduced for the three predominant Chinese TV standards DTMB, CMMB, and PAL-D/K. A prototype platform is presented and its underlying architecture based on a combination of DSP and FPGA is illustrated including the setup of the cognitive radio application. Furthermore, the performance of the sensing algorithms implemented on the prototype platform is shown in comparison to simulation results. [ABSTRACT FROM AUTHOR]

Abstract: A lot of work has been done in the field of multi-valued model checking home and abroad. But some research works are not perfect. Some problems in the semantic definitions of temporal operators and corresponding model checking algorithms are found in the paper “Model Checking Using Partial Kripke Structure with 3-Valued Temporal Logic”. Some counterexamples are given to show these errors. On the basis of the discussion, some sound semantic definitions of temporal operators and correct algorithms of model checking for some temporal operators are given. [Copyright &y& Elsevier]

The importance of difference semantics (e.g., "similar" or "dissimilar") has been recently recognized for declaring dependencies among various types of data, such as numerical values or text values. We propose a novel form of Differential Dependencies (DDs), which specifies constraints on difference, called differential functions, instead of identification functions in traditional dependency notations like functional dependencies. Informally, a differential dependency states that if two tuples have distances on attributes X agreeing with a certain differential function, then their distances on attributes Y should also agree with the corresponding differential function on Y. For example, [date(⩽ 7)] → [price(< 100)] states that the price difference of any two days within a week length should be no greater than 100 dollars. Such differential dependencies are useful in various applications, for example, violation detection, data partition, query optimization, record linkage, etc. In this article, we first address several theoretical issues of differential dependencies, including formal definitions of DDs and differential keys, subsumption order relation of differential functions, implication of DDs, closure of a differential function, a sound and complete inference system, and minimal cover for DDs. Then, we investigate a practical problem, that is, how to discover DDs and differential keys from a given dataset. Due to the intrinsic hardness, we develop several pruning methods to improve the discovery efficiency in practice. Finally, through an extensive experimental evaluation on real datasets, we demonstrate the discovery performance and the effectiveness of DDs in several real applications. [ABSTRACT FROM AUTHOR]

Background: Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and money. Results: To deal with redundant information and improve classification, we propose a gene selection method, Recursive Feature Addition, which combines supervised learning and statistical similarity measures. To determine the final optimal gene set for prediction and classification, we propose an algorithm, Lagging Prediction Peephole Optimization. By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others. Conclusions: On average, with the use of popular learning machines including Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier and Random Forest, Recursive Feature Addition outperformed other methods. Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF. [ABSTRACT FROM AUTHOR]

DHTs are scalable, self-organizing, and adaptive underlying topology changes, thus being a promising infrastructure for realizing autonomic communications in distributed systems. To provide the above advantages, however, DHTs sacrifice flexibility, that is, all messages are routed by using a common algorithm in a DHT on the assumption that participant nodes are homogeneous. In practice, nodes in large-scale systems might be heterogeneous with respect to their capabilities, reputations, affiliations of administrative domains, and so on, which consequently makes it preferable to distinguish the heterogeneity of participant nodes and enable flexible control of routing destinations and paths. To achieve this, in this paper we propose a novel approach that supports organizing nodes groups and enables routing control in a DHT. The effectiveness of our proposals is demonstrated through theoretical analysis and extensive simulations. [ABSTRACT FROM AUTHOR]

Supervised classifiers, such as artificial neural network, partition trees, and support vector machines, are often used for the prediction and analysis of biological data. However, choosing an appropriate classifier is not straightforward because each classifier has its own strengths and weaknesses, and each biological dataset has its own characteristics. By integrating many classifiers together, people can avoid the dilemma of choosing an individual classifier out of many to achieve an optimized classification results (Rahman et al., Multiple Classifier Combination for Character Recognition: Revisiting the Majority Voting System and Its Variation, Springer, Berlin, 2002, 167–178). The classification algorithms come from Weka (Witten and Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, 2005) (a collection of software tools for machine learning algorithms). By integrating many predictors (classifiers) together through simple voting, the correct prediction (classification) rates are 65.21% and 65.63% for a basic training dataset and an independent test set, respectively. These results are better than any single machine learning algorithm collected in Weka when exactly the same data are used. Furthermore, we introduce an integration strategy which takes care of both classifier weightings and classifier redundancy. A feature selection strategy, called minimum redundancy maximum relevance (mRMR), is transferred into algorithm selection to deal with classifier redundancy in this research, and the weightings are based on the performance of each classifier. The best classification results are obtained when 11 algorithms are selected by mRMR method, and integrated together through majority votes with weightings. As a result, the prediction correct rates are 68.56% and 69.29% for the basic training dataset and the independent test dataset, respectively. The web-server is available at http://chemdata.shu.edu.cn/protein_st/. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2009 [ABSTRACT FROM AUTHOR]

Abstract  Neural networks have been successfully applied to many applications due to their approximation capability. However, complicated network structures and algorithms will lead to computational and time-consuming burdens. In order to satisfy demanding real-time requirements, many fast learning algorithms were explored in the past. Recently, a fast algorithm, Extreme Learning Machine (ELM) (Huang et al. 70:489–501, 2006) was proposed. Unlike conventional algorithms whose neurons need to be tuned, the input-to-hidden neurons of ELM are randomly generated. Though a large number of experimental results have shown that input-to-hidden neurons need not be tuned, there lacks a rigorous proof whether ELM possesses the universal approximation capability. In this paper, based on the universal approximation property of an orthonormal method, we firstly illustrate the equivalent relationship between ELM and the orthonormal method, and further prove that neural networks with ELM are also universal approximations. We also successfully apply ELM to the identification of QoS violation in the multimedia transmission. [ABSTRACT FROM AUTHOR]

In this paper, a Neuro-Predictive (NP) controller was first designed and implemented on a highly non-linear system, a model helicopter in a constrained situation. It was observed that the closed loop system with the NP controller had a significant overshoot and a long settling time in comparison to the same system with an fuzzy controller. In order to improve the system's performance, a Sugeno-type fuzzy compensator, having only two rules, is added to the control loop to adjust control input. The newly designed Neuro-Predictive control with the Fuzzy Compensator (NPFC) improves the system's performance. Furthermore, it is shown that the NPFC controlled system is robust to disturbance and system parameter changes. [ABSTRACT FROM AUTHOR]

Clustering is a popular technique for analyzing microarray data sets, with n genes and m experimental conditions. As explored by biologists, there is a real need to identify coregulated gene clusters, which include both positive and negative regulated gene clusters. The existing pattern-based and tendency-based clustering approaches cannot directly be applied to find such coregulated gene clusters, because they are designed for finding positive regulated gene clusters. In this paper, in order to cluster coregulated genes, we propose a coding scheme that allows us to cluster two genes into the same cluster if they have the same code, where two genes that have the same code can be either positive or negative regulated. Based on the coding scheme, we propose a new algorithm for finding maximal subspace coregulated gene clusters with new pruning techniques. A maximal subspace coregulated gene cluster clusters a set of genes on a condition sequence such that the cluster is not included in any other subspace coregulated gene clusters. We conduct extensive experimental studies. Our approach can effectively and efficiently find maximal subspace coregulated gene clusters. In addition, our approach outperforms the existing approaches for finding positive regulated gene clusters. [ABSTRACT FROM AUTHOR]

Three important parts of a portable phase-shifting interferometer with optical zoom system are described. The first is a phase-shifter whose driver is integrated in small size with milli-voltage resolution and kHz dynamic-frequency response. Second, a seed-algorithm is proposed that some spatial points are picked up as seeds, and the wavefronts with obscuration or irregular edges can be retrieved from these seeds as they grow and spread. Third, the measurement of a corner-cube reflector is given in detail. © 2004 Wiley Periodicals, Inc. Microwave Opt Technol Lett 43: 417–419, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20487 [ABSTRACT FROM AUTHOR]

Rule reduction is one of the research objectives in numerous successful rule-based systems. In some analyses, too many useless rules may be a concern in a rule-based system. Although rule reduction has already attracted wide attention to optimise the performance of the rule-based system, the extended belief-rule-based system (EBRBS), which is an advanced rule-based system developed from the belief-rule-based system (BRBS) recently, still lacks methods to reduce rules. This study focuses on the rule reduction of EBRBS and introduces data envelopment analysis (DEA) to evaluate the efficiency of each rule in an extended belief-rule-based (EBRB). However, two challenges must be addressed. First, a measure of the extended belief rule's efficiency value must be given because it is the foundation of rule reduction. Second, a novel decision-making-unit (DMU) must be constructed using the efficiency value of the extended belief rules to build a bridge for EBRBS and DEA. Therefore, the concepts of contribution degree and the extended belief rule-based DMU are introduced in the present study for the first time to propose a DEA-based rule reduction method. Moreover, the classic CCR model, which is identification engine of the rule reduction method, is applied to calculate the efficiency value of the extended belief rule and finally achieve the compact structure of an EBRB. Two case studies on regression and classification problems are performed to illustrate how efficiency of the DEA-based rule reduction method in promoting the performance of EBRBS. Comparison results demonstrate that the proposed rule reduction can downsize the EBRB and improve the accuracy of EBRBS. [ABSTRACT FROM AUTHOR]

In this paper, a novel method based on nonsubsampled shearlet transform (NSST) and multi-scale top-hat transform (MTHT) is proposed to fuse the SAR image, infrared image and visible light image, which will produce a fused image with higher contrast and better edges. First, three original images are decomposed into low frequency subband coefficients and the bandpass direction subband coefficients by NSST. Second, the effective bright and dark image features from low-frequency coefficients of three original images are extracted by MTHT, then by using the pixel-wise maximum operation, the salient fused bright and dark features are obtained and joined into the combined low frequency coefficients. To reduce the influence of noises of SAR and infrared image on the fused image, the significant factor of local directional information entropy, which can distinguish meaningful image characteristics and noises, is employed as the measurement to select the bandpass direction subband coefficients. Finally, the fused image is obtained by the inverse NSST on the combined coefficients. The experiment results demonstrate the superiority of our method compared with conventional methods in terms of visual inspection and objective measures. [ABSTRACT FROM AUTHOR]