Neural networks are extensively used for determining different characteristics of optical burst switching networks. The main disadvantage of optical burst switching network is burst drop and burst contention, which occurs because of burst getting blocked. Using neural network approaches, blocking probability can be pre-determined for the upcoming traffic. In this paper, Log-incremental modified extreme learning machine trained generalized radial basis function neural network (MELM-GRBFNN) model is used for training and predicting burst contention or burst blocking probability. From the obtained results, it is inferred that the prediction accuracy of our proposed model is more accurate and faster than the contemporary approaches. It is observed that our proposed method is competent in predicting the burst blocking probability with higher accuracy and indicates a reduction in the burst loss. Thus, it will help network designers to have a preliminary idea about the performance of the network model under specific configurations.

Instruments onboard spacecraft acquire large amounts of data which is to be transmitted over a very low bandwidth. Consequently for some missions, the volume of data collected greatly exceeds the volume that can be downlinked before the next orbit. This necessitates the introduction of an intelligent autonomous decision making module that maximizes the return of the most scientifically relevant dataset over the low bandwidth for experts to analyze further. We propose an iterative rule based approach, guided by expert knowledge, to represent scientifically interesting geological landforms with respect to expert selected attributes. The rules are utilized to assign a priority based on how novel a test instance is with respect to its rule. High priority instances from the test set are used to iteratively update the learned rules. We then determine the effectiveness of the proposed approach on images acquired by a Mars orbiter and observe an expert-acceptable prioritization order generated by the rules that can potentially increase the return of scientifically relevant observations.

Data analysis and machine learning methods have great potential to aid in planetary exploration. Spacecraft often operate at great distances from the Earth, and the ability to autonomously detect features of interest onboard can enable content-sensitive downlink prioritization to increase mission science return. We describe algorithms that we designed to assist in three specific scientific investigations to be conducted during flybys of Jupiter's moon Europa: the detection of thermal anomalies, compositional anomalies, and plumes of icy matter from Europa's subsurface ocean. We also share the unique constraints imposed by the onboard computing environment and several lessons learned in our collaboration with planetary scientists and mission designers.

Normalized Difference Vegetation Index (NDVI) time series is used to study different land cover dynamics such as change, compare vegetation dynamics between years and analyze intra-annual components. A nonlinear cosine model of the NDVI time series with a constant frequency is used to account for the time-varying nature of the land cover parameters due to seasonality or change. The Extended Kalman Filter (EKF) is used to estimate these parameters, which introduces linearization and negatively impacts the state estimation accuracy. This paper proposes using a Particle Filter (PF) for state estimation to better address nonlinearity in the model. The cosine model is modified to capture frequency variations to account for changes in the vegetation growth cycle caused by abrupt phenomenon such as forest fires. PF obtains better state estimates than EKF, capturing the intra-annual components and time-varying frequency of the model accurately.