A Modified inverse integer Cholesky decorrelation method and the performance on ambiguity resolution

One of the research focuses in the integer least squares problem is the decorrelation technique to reduce the number of integer parameter search candidates and improve the efficiency of the integer parameter search method. It remains as a challenging issue for determining carrier phase ambiguities and plays a critical role in the future of GNSS high precise positioning area. Currently, there are three main decorrelation techniques being employed: the integer Gaussian decorrelation, the Lenstra–Lenstra–Lovász (LLL) algorithm and the inverse integer Cholesky decorrelation (IICD) method. Although the performance of these three state-of-the-art methods have been proved and demonstrated, there is still a potential for further improvements. To measure the performance of decorrelation techniques, the condition number is usually used as the criterion. Additionally, the number of grid points in the search space can be directly utilized as a performance measure as it denotes the size of search space. However, a smaller initial volume of the search ellipsoid does not always represent a smaller number of candidates. This research has proposed a modified inverse integer Cholesky decorrelation (MIICD) method which improves the decorrelation performance over the other three techniques. The decorrelation performance of these methods was evaluated based on the condition number of the decorrelation matrix, the number of search candidates and the initial volume of search space. Additionally, the success rate of decorrelated ambiguities was calculated for all different methods to investigate the performance of ambiguity validation. The performance of different decorrelation methods was tested and compared using both simulation and real data. The simulation experiment scenarios employ the isotropic probabilistic model using a predetermined eigenvalue and without any geometry or weighting system constraints. MIICD method outperformed other three methods with conditioning improvements over LAMBDA method by 78.33% and 81.67% without and with eigenvalue constraint respectively. The real data experiment scenarios involve both the single constellation system case and dual constellations system case. Experimental results demonstrate that by comparing with LAMBDA, MIICD method can significantly improve the efficiency of reducing the condition number by 78.65% and 97.78% in the case of single constellation and dual constellations respectively. It also shows improvements in the number of search candidate points by 98.92% and 100% in single constellation case and dual constellations case.

Computed success rates of various carrier phase integer estimation solutions and their comparison with statistical success rates

The success rate of carrier phase ambiguity resolution (AR) is the probability that the ambiguities are successfully fixed to their correct integer values. In existing works, an exact success rate formula for integer bootstrapping estimator has been used as a sharp lower bound for the integer least squares (ILS) success rate. Rigorous computation of success rate for the more general ILS solutions has been considered difficult, because of complexity of the ILS ambiguity pull-in region and computational load of the integration of the multivariate probability density function. Contributions of this work are twofold. First, the pull-in region mathematically expressed as the vertices of a polyhedron is represented by a multi-dimensional grid, at which the cumulative probability can be integrated with the multivariate normal cumulative density function (mvncdf) available in Matlab. The bivariate case is studied where the pull-region is usually defined as a hexagon and the probability is easily obtained using mvncdf at all the grid points within the convex polygon. Second, the paper compares the computed integer rounding and integer bootstrapping success rates, lower and upper bounds of the ILS success rates to the actual ILS AR success rates obtained from a 24 h GPS data set for a 21 km baseline. The results demonstrate that the upper bound probability of the ILS AR probability given in the existing literatures agrees with the actual ILS success rate well, although the success rate computed with integer bootstrapping method is a quite sharp approximation to the actual ILS success rate. The results also show that variations or uncertainty of the unit–weight variance estimates from epoch to epoch will affect the computed success rates from different methods significantly, thus deserving more attentions in order to obtain useful success probability predictions.

Innovation networks in the construction arena : the strategic management of mixed enterprises

The building and construction sector is one of the five largest contributors to the Australian economy and is a key performance component in the economy of many other jurisdictions. However, the ongoing viability of this sector is increasingly reliant on its ability to foster and transfer innovated products and practices. Interorganisational networks, which bring together key industry stakeholders and facilitate the flows of information, resources and trust necessary to secure innovation, have emerged as a key growth strategy within this and other arenas. The blending of organisations, resources and purposes creates new, hybrid institutional forms that draw on a mix of contract, structure and interpersonal relationship as integration processes. This paper argues that hybrid networked arrangements, because they incorporate relational elements, require management strategies and techniques that not always synonymous with conventional management approaches, including those used within the building and construction sector. It traces the emergence of the Construction Innovation Project in Australia as a hybrid institutional arrangement moulding public, private and academic stakeholders of the building and construction industry into a coherent collective force aimed at fostering innovation and its application within all levels of the industry. Specifically, the paper examines the Construction Innovation Project to ascertain the impact of relational governance and its management to harness and leverage the skills, resources and capacities of members to secure innovative outcomes. Finally, the paper offers some prospects to guide the ongoing work of this body and any other charged with a similar integrative responsibility.

Tangible programming elements for young children

Tangible programming elements offer the dynamic and programmable properties of a computer without the complexity introduced by the keyboard, mouse and screen. This paper explores the extent to which programming skills are used by children during interactions with a set of tangible programming elements: the Electronic Blocks. An evaluation of the Electronic Blocks indicates that children become heavily engaged with the blocks, and learn simple programming with a minimum of adult support.

A control and surveillance ITS location model to improve safety

Traffic safety in rural highways can be considered as a constant source of concern in many countries. Nowadays, transportation professionals widely use Intelligent Transportation Systems (ITS) to address safety issues. However, compared to metropolitan applications, the rural highway (non-urban) ITS applications are still not well defined. This paper provides a comprehensive review on the existing ITS safety solutions for rural highways. This research is mainly focused on the infrastructure-based control and surveillance ITS technology, such as Crash Prevention and Safety, Road Weather Management and other applications, that is directly related to the reduction of frequency and severity of accidents. The main outcome of this research is the development of a ‘ITS control and surveillance device locating model’ to achieve the maximum safety benefit for rural highways. Using cost and benefits databases of ITS, an integer linear programming method is utilized as an optimization technique to choose the most suitable set of ITS devices. Finally, computational analysis is performed on an existing highway in Iran, to validate the effectiveness of the proposed locating model.

A vibrational spectroscopic study of the mixed anion mineral sanjuanite Al2(PO4)(SO4)(OH)•9H2O

The mineral sanjuanite Al2(PO4)(SO4)(OH)•9H2O has been characterised by Raman spectroscopy complimented by infrared spectroscopy. The mineral is characterised by an intense Raman band at 984 cm-1, assigned to the (PO4)3- ν1 symmetric stretching mode. A shoulder band at 1037 cm-1 is attributed to the (SO4)2- ν1 symmetric stretching mode. Two Raman bands observed at 1102 and 1148 cm-1 are assigned to (PO4)3- and (SO4)2- ν3 antisymmetric stretching modes. Multiple bands provide evidence for the reduction in symmetry of both anions. This concept is supported by the multiple sulphate and phosphate bending modes. Raman spectroscopy shows that there are more than one non-equivalent water molecules in the sanjuanite structure. There is evidence that structural disorder exists, shown by the complex set of overlapping bands in the Raman and infrared spectra. At least two types of water are identified with different hydrogen bond strengths. The involvement of water in the sanjuanite structure is essential for the mineral stability.

Learning the kernel matrix with semidefinite programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive semidefinite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space - classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -using the labeled part of the data one can learn an embedding also for the unlabeled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.

Learning the Kernel Matrix with Semi-Definite Programming

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and positive definite matrix that encodes the relative positions of all points. Specifying this matrix amounts to specifying the geometry of the embedding space and inducing a notion of similarity in the input space -- classical model selection problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semi-definite programming (SDP) techniques. When applied to a kernel matrix associated with both training and test data this gives a powerful transductive algorithm -- using the labelled part of the data one can learn an embedding also for the unlabelled part. The similarity between test points is inferred from training points and their labels. Importantly, these learning problems are convex, so we obtain a method for learning both the model class and the function without local minima. Furthermore, this approach leads directly to a convex method to learn the 2-norm soft margin parameter in support vector machines, solving another important open problem. Finally, the novel approach presented in the paper is supported by positive empirical results.

Optimistic linear programming gives logarithmic regret for irreducible MDPs

We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). OLP uses its experience so far to estimate the MDP. It chooses actions by optimistically maximizing estimated future rewards over a set of next-state transition probabilities that are close to the estimates, a computation that corresponds to solving linear programs. We show that the total expected reward obtained by OLP up to time T is within C(P) log T of the reward obtained by the optimal policy, where C(P) is an explicit, MDP-dependent constant. OLP is closely related to an algorithm proposed by Burnetas and Katehakis with four key differences: OLP is simpler, it does not require knowledge of the supports of transition probabilities, the proof of the regret bound is simpler, but our regret bound is a constant factor larger than the regret of their algorithm. OLP is also similar in flavor to an algorithm recently proposed by Auer and Ortner. But OLP is simpler and its regret bound has a better dependence on the size of the MDP.

The mixed anion mineral parnauite Cu9[(OH)10|SO4|(AsO4)2]•7H2O – a Raman spectroscopic study

The mixed anion mineral parnauite Cu9[(OH)10|SO4|(AsO4)2].7H2O from two localities namely Cap Garonne Mine, Le Pradet, France and Majuba Hill mine, Pershing County, Nevada, USA has been studied by Raman spectroscopy. The Raman spectrum of the French sample is dominated by an intense band at 975 cm-1 assigned to the ν1 (SO4)2- symmetric stretching mode and Raman bands at 1077 and 1097 cm-1 may be attributed to the ν3 (SO4)2- antisymmetric stretching mode. Two Raman bands 1107 and 1126 cm-1 are assigned to carbonate CO32- symmetric stretching bands and confirms the presence of carbonate in the structure of parnauite. The comparatively sharp band for the Pershing County mineral at 976 cm-1 is assigned to the ν1 (SO4)2- symmetric stretching mode and a broad spectral profile centered upon 1097 cm-1 is attributed to the ν3 (SO4)2- antisymmetric stretching mode. Two intense bands for the Pershing County mineral at 851 and 810 cm-1 are assigned to the ν1 (AsO4)3- symmetric stretching and ν3 (AsO4)3- antisymmetric stretching modes. Two Raman bands for the French mineral observed at 725 and 777 cm-1 are attributed to the ν3 (AsO4)3- antisymmetric stretching mode. For the French mineral, a low intensity Raman band is observed at 869 cm-1 and is assigned to the ν1 (AsO4)3- symmetric stretching vibration. Chemical composition of parnauite remains open and the question may be raised is parnauite a solid solution of two or more minerals such as a copper hydroxy-arsenate and a copper hydroxy sulphate.