In this paper, more efficient, low-complexity and reliable region of interest (ROI) image codec for compressing smooth low texture remote sensing images is proposed. We explore the efficiency of the modified ROI codec with respect to the selected set of convenient wavelet filters, which is a novel method. Such ROI coding experiment analysis representing low bit rate lossy to high quality lossless reconstruction with timing analysis is useful for improving remote sensing ground truth surveillance efficiency in terms of time and quality. The subjective [i.e. fair, five observer (HVS) evaluations using enhanced 3D picture view Hyper memory display technology] and the objective results revealed that for faster ground truth ROI coding applications, the Symlet-4 adaptation performs better than Biorthogonal 4.4 and Biorthogonal 6.8. However, the discrete Meyer wavelet adaptation is the best solution for delayed ROI image reconstructions.

This paper proposes a new adaptive post-filtering algorithm to remove coding artifacts in block-based video coder. The proposed method concentrates on blocking and ringing artifacts removal. For de-blocking, the blocking strength is identified to determine the filtering range, and the maximum quantization parameter of the image is used to adapt the 1D fuzzy filter. For de-ringing, besides the edge detection, a complementary ringing detection method is proposed to locate the neglected ringing blocks, and the gradient threshold is adopted to adjust the parameter of 2D fuzzy filter. Experiments are performed on the MPEG-4 sequences. Compared with other methods, the proposed one achieves better detail preservation and artifacts removal performance with lower computational cost.

The Internet evolves to incorporate very-high-bandwidth optical links and more large-delay satellite links. TCP faces new challenges in this unique environment. Theory and experiments showed that TCP becomes inefficient and is prone to be unstable as the per-flow product of bandwidth and latency increases, regardless of the queuing scheme. Variable-structure congestion Control Protocol (VCP) is proposed to address these problems. However, VCP has problem in terms of convergence time, i.e., it takes a long time for a new VCP flow to achieve fair bandwidth allocation if the existing VCP flows have large congestion windows. This paper proposed an Extended Variable-structure congestion Control Protocol (EVCP), which adopted a convergence controller. The basic idea of convergence controller is that if a flow has larger window than its fair window, its congestion window should be decreased more aggressively than usual in Multiplicative Decrease (MD) phase. Simulations showed that EVCP has better performance in terms of convergence time while keeping the advantages of VCP.

Various index structures have recently been proposed to facilitate high-dimensional KNN queries, among which the techniques of approximate vector presentation and one-dimensional (1D) transformation can break the curse of dimensionality. Based on the two techniques above, a novel high-dimensional index is proposed, called Bit-code and Distance based index (BD). BD is based on a special partitioning strategy which is optimized for high-dimensional data. By the definitions of bit code and transformation function, a high-dimensional vector can be first approximately represented and then transformed into a 1D vector, the key managed by a B⁺-tree. A new KNN search algorithm is also proposed that exploits the bit code and distance to prune the search space more effectively. Results of extensive experiments using both synthetic and real data demonstrated that BD outperforms the existing index structures for KNN search in high-dimensional spaces.

This paper discribes a data representation for WordNet 2.1 based on Web Ontology Language (OWL). The main components of WordNet database are transformed as classes in OWL, and the relations between synsets or lexcial words are transformed as OWL properties. Our conversion is based on the data file of WordNet instead of the Prolog database. This work can be used to enrich the work in progress of standard conversion of WordNet to the RDF/OWL representation at W3C.

This paper explores the application of term dependency in information retrieval (IR) and proposes a novel dependency retrieval model. This retrieval model suggests an extension to the existing language modeling (LM) approach to IR by introducing dependency models for both query and document. Relevance between document and query is then evaluated by reference to the Kullback-Leibler divergence between their dependency models. This paper introduces a novel hybrid dependency structure, which allows integration of various forms of dependency within a single framework. A pseudo relevance feedback based method is also introduced for constructing query dependency model. The basic idea is to use query-relevant top-ranking sentences extracted from the top documents at retrieval time as the augmented representation of query, from which the relationships between query terms are identified. A Markov Random Field (MRF) based approach is presented to ensure the relevance of the extracted sentences, which utilizes the association features between query terms within a sentence to evaluate the relevance of each sentence. This dependency retrieval model was compared with other traditional retrieval models. Experiments indicated that it produces significant improvements in retrieval effectiveness.

Hyperspectral reflectance (350~2500 nm) data were recorded at two different sites of rice in two experiment fields including two cultivars, and three levels of nitrogen (N) application. Twenty-five Vegetation Indices (VIs) were used to predict the rice agronomic parameters including Leaf Area Index (LAI, m² green leaf/m² soil) and Green Leaf Chlorophyll Density (GLCD, mg chlorophyll/m² soil) by the traditional regression models and Radial Basis Function Neural Network (RBF). RBF emerged as a variant of Artificial Neural Networks (ANNs) in the late 1980’s. A large variety of training algorithms has been tested for training RBF networks. In this study, Original RBF (ORBF), Gradient Descent RBF (GDRBF), and Generalized Regression Neural Network (GRNN) were employed. Results showed that green waveband Normalized Difference Vegetation Index (NDVI_green) and TCARI/OSAVI have the best prediction power for LAI by exponent model and ORBF respectively, and that TCARI/OSAVI has the best prediction power for GLCD by exponent model and GDRBF. The best performances of RBF are compared with the traditional models, showing that the relationship between VIs and agronomic variables are further improved when RBF is used. Compared with the best traditional models, ORBF using TCARI/OSAVI improves the prediction power for LAI by lowering the Root Mean Square Error (RMSE) for 0.1119, and GDRBF using TCARI/OSAVI improves the prediction power for GLCD by lowering the RMSE for 26.7853. It is concluded that RBF provides a useful exploratory and predictive tool when applied to the sensitive VIs.

This paper presents an efficient and reliable genetic algorithm (GA) based particle swarm optimization (PSO) technique (hybrid GAPSO) for solving the economic dispatch (ED) problem in power systems. The non-linear characteristics of the generators, such as prohibited operating zones, ramp rate limits and non-smooth cost functions of the practical generator operation are considered. The proposed hybrid algorithm is demonstrated for three different systems and the performance is compared with the GA and PSO in terms of solution quality and computation efficiency. Comparison of results proved that the proposed algorithm can obtain higher quality solutions efficiently in ED problems. A comprehensive software package is developed using MATLAB.

A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper. Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information criterion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP) approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.

This study is aimed at assessing muscle fatigue during a static contraction using multifractal analysis and found that the surface electromyographic (SEMG) signals characterized multifractality during a static contraction. By applying the method of direct determination of the f(α) singularity spectrum, the area of the multifractal spectrum of the SEMG signals was computed. The results showed that the spectrum area significantly increased during muscle fatigue. Therefore the area could be used as an assessor of muscle fatigue. Compared with the median frequency (MDF)―the most popular indicator of muscle fatigue, the spectrum area presented here showed higher sensitivity during a static contraction. So the singularity spectrum area is considered to be a more effective indicator than the MDF for estimating muscle fatigue.

Objective: To study the regulation of blood pulse volume via photoplethysmography (PPG) signal detected from toe, while the lower limb is passively raised in different height positions. Methods: Use a modified non-invasive PPG technique to detect the blood pulse signal on toe with infrared (IR) photo sensor. A protocol consisting of two postures, i.e., supine and 45° reclining, was designed to conduct laboratory trial in this study. During the period of performing the protocol of these postures, the lower limb was passively raised from the heights of 10 cm to 60 cm randomly and individually with sponge blocks underneath the foot. Results: In the supine posture, the higher the foot was passively raised, the more the blood PPG signal decreased. In the 45° reclining posture, the blood PPG signal increased at the beginning and then decreased in the foot height position from 10 cm to 60 cm. In both postures the normalized AC signal changes significantly while the normalized DC signal changes little. Conclusion: The toe PPG signals can obviously indicate the regulated blood volume change with the designated postural procedures due to the heart level position.

Objective: To estimate the prevalence rates of neural tube defects (NTDs) in Heshun County, Shanxi Province, China by Bayesian smoothing technique. Methods: A total of 80 infants in the study area who were diagnosed with NTDs were analyzed. Two mapping techniques were then used. Firstly, the GIS software ArcGIS was used to map the crude prevalence rates. Secondly, the data were smoothed by the method of empirical Bayes estimation. Results: The classical statistical approach produced an extremely dishomogeneous map, while the Bayesian map was much smoother and more interpretable. The maps produced by the Bayesian technique indicate the tendency of villages in the southeastern region to produce higher prevalence or risk values. Conclusions: The Bayesian smoothing technique addresses the issue of heterogeneity in the population at risk and it is therefore recommended for use in explorative mapping of birth defects. This approach provides procedures to identify spatial health risk levels and assists in generating hypothesis that will be investigated in further detail.

Online shopping has already become the new mode that a lot of customers try to adopt. At the same time, the online shopping could not be successfully completed without logistics service. Logistics service quality (LSQ) has significant impact on revenue and profitability. This paper presents the issue from the perspective of the customer, and explores the initial factors of LSQ based on the online shopping through in-depth interview and the Delphi method. The survey uses a standard 7-point Likert-type scale to measure the LSQ. Empirical research results are shown in detail to confirm seven LSQ dimensions with Chinese characteristics, including timeliness quality, personal contact quality, order quality, order discrepancy handling, order condition and convenience. Statistical analyses of the investigation were conducted to test the reliability and validity of the LSQ evaluation model.

Corporate restructuring was identified as a new industrial force that has great impact on economic values and that therefore has become central in daily financial decision making. This article investigates the optimal restructuring strategies under different dynamic factors and their numerous impacts on firm value. The concept of quasi-leverage is introduced and valuation models are built for corporate debt and equity under imperfect market conditions. The model’s input variables include the quasi-leverage and other firm-specific parameters, the output variables include multiple corporate security values. The restructuring cost is formulated in the form of exponential function, which allows us to observe the sensitivity of the variation in security values. The unified model and its analytical solution developed in this research allow us to examine the continuous changes of security values by dynamically changing the coupon rates, riskless interest rate, bankruptcy cost, quasi-leverage, personal tax rate, corporate taxes rate, transaction cost, firm risk, etc., so that the solutions provide useful guidance for financing and restructuring decisions.

A module M is called generalized extending if for any submodule N of M, there is a direct summand K of M such that N≤K and K/N is singular. Any extending module and any singular module are generalized extending. Any homomorphic image of a generalized extending module is generalized extending. Any direct sum of a singular (uniform) module and a semi-simple module is generalized extending. A ring R is a right Co-H-ring if and only if all right R modules are generalized extending modules.

Zhao (2003a) first established a congruence for any odd prime p>3, S(1,1,1;p)≡−2B_p−3 (mod p), which holds when p=3 evidently. In this paper, we consider finite triple harmonic sum S(α,β,γ;p) (mod p) is considered for all positive integers α,β,γ. We refer to w=α+β+γ as the weight of the sum, and show that if w is even, S(α,β,γ;p)≡0 (mod p) for p≥w+3; if w is odd, S(α,β,γ;p)≡rB_p−w (mod p) for p≥w, here r is an explicit rational number independent of p. A congruence of Catalan number is obtained as a special case.

In this paper, we describe several stationary conditions on weak solutions to the inhomogeneous Landau-Lifshitz equation, which ensure the partial regularity. For certain class of proper stationary weak solutions, a compactness result of the solutions, a finite Hausdorff measure result of the t-slice energy concentration sets and an asymptotic limit result of the Radon measures are proved. We also present a subtle rectifiability result for the energy concentration set of certain sequence of strong stationary weak solutions.

In this paper, we consider some polynomial (α,β)-metrics, and discuss the sufficient and necessary conditions for a Finsler metric in the form F=α+a₁β+a₂β²/α+a₄β⁴/α³ to be projectively flat, where a_i (i=1,2,4) are constants with a₁≠0, α is a Riemannian metric and β is a 1-form. By analyzing the geodesic coefficients and the divisibility of certain polynomials, we obtain that there are only five projectively flat cases for metrics of this type. This gives a classification for such kind of Finsler metrics.

In this work, we study the Asanov Finsler metric F=α(β²/α²+gβ/α+1)^1/2exp{(G/2)arctan[β/(hα)+G/2]}, where α=(α_ijyⁱy^j)^1/2 is a Riemannian metric and β=b_iy^j is a 1-form, g∈(−2,2), h=(1−g²/4)^1/2, G=g/h. We give the necessary and sufficient condition for Asanov metric to be locally projectively flat, i.e., α is projectively flat and β is parallel with respect to α. Moreover, we proved that the Douglas tensor of Asanov Finsler metric vanishes if and only if β is parallel with respect to α.

Proximal point algorithms (PPA) are attractive methods for solving monotone variational inequalities (MVI). Since solving the sub-problem exactly in each iteration is costly or sometimes impossible, various approximate versions of PPA (APPA) are developed for practical applications. In this paper, we compare two APPA methods, both of which can be viewed as prediction-correction methods. The only difference is that they use different search directions in the correction-step. By extending the general forward-backward splitting methods, we obtain Algorithm I; in the same way, Algorithm II is proposed by spreading the general extra-gradient methods. Our analysis explains theoretically why Algorithm II usually outperforms Algorithm I. For computation practice, we consider a class of MVI with a special structure, and choose the extending Algorithm II to implement, which is inspired by the idea of Gauss-Seidel iteration method making full use of information about the latest iteration. And in particular, self-adaptive techniques are adopted to adjust relevant parameters for faster convergence. Finally, some numerical experiments are reported on the separated MVI. Numerical results showed that the extending Algorithm II is feasible and easy to implement with relatively low computation load.

The convergence criterion of Newton’s method to find the zeros of a map f from a Lie group to its corresponding Lie algebra is established under the assumption that f satisfies the classical Lipschitz condition, and that the radius of convergence ball is also obtained. Furthermore, the radii of the uniqueness balls of the zeros of f are estimated. Owren and Welfert (2000) stated that if the initial point is close sufficiently to a zero of f, then Newton’s method on Lie group converges to the zero; while this paper provides a Kantorovich’s criterion for the convergence of Newton’s method, not requiring the existence of a zero as a priori.