Gamma adjustment is one of the simplest global tone reproduction operators. If an image is too bright or too dark the image can be made pleasing by applying a gamma greater than one (leading to a darker image) or less than one (leading to a brighter image) respectively. In recent theoretical work, the ’optimal’ gamma in an information theoretic sense has been derived. The starting point of this...

The reactive diffracting trees are known efficient distributed data structures for supporting synchronization. They not only distribute a set of processes to smaller groups accessing different parts of the memory in a global coordinated manner, but also adjust their size in order to attain efficient performance across different levels of contention. However, the existing reactive adjustment pol...

The particle swarm, which optimizes neural networks, has overcome its disadvantage of slow convergent speed and shortcoming of local optimum. The parameter that the particle swarm optimization relates to is not much. But it has strongly sensitivity to the parameter. In this paper, we applied PSO-BP to evaluate the environmental effect of an agricultural project, and researched application and P...

Affinity propagation clustering (AP) has two limitations: it is hard to know what value of parameter ‘preference’ can yield an optimal clustering solution, and oscillations cannot be eliminated automatically if occur. The adaptive AP method is proposed to overcome these limitations, including adaptive scanning of preferences to search space of the number of clusters for finding the optimal clus...

Independence estimating equations (IEEs) are commonly used for the analysis of clustered data. Here, we use the properties of IEEs to adjust the “independence” log-likelihood function in the presence of clustering. The proposed adjustment relies on the robust sandwich estimator of the parameter covariance matrix, which is easy to calculate. We show that the adjusted log-likelihood competes favo...

A prototype Problem Solving Environment (PSE) is presented for problems in interactive modelling of multiway data. Multiway data result from measurements as a function of two or more independent variables. The PSE comprises a parameter estimation loop and a model adjustment loop. The model can be specified hierarchically using mathematically described building blocks which encapsulate the model...