Effect of q-nonextensive hot electrons on bifurcations of nonlinear and supernonlinear ion-acoustic periodic waves

Nonlinear and supernonlinear ion-acoustic periodic waves are investigated in a three-component unmagnetized plasma which consists of mobile fluid cold ions, Maxwellian cold electrons and q-nonextensive hot electrons employing phase plane analysis. Using the traveling wave transformation, the plasma system is reduced to a planar autonomous dynamical system. Utilizing phase plane analysis of planar dynamical systems, all possible phase portraits including nonlinear homoclinic orbit, nonlinear periodic orbit, supernonlinear homoclinic orbit and supernonlinear periodic orbit are presented depending on physical parameters $q,\alpha ,\sigma$ and V. Using numerical simulations, nonlinear and supernonlinear ion-acoustic periodic waves are shown for different conditions. It is found that the nonextensive parameter q plays a crucial role in the bifurcations of nonlinear and supernonlinear ion-acoustic periodic waves. Our study may be applicable to understand the nonlinear and supernonlinear periodic features in auroral plasma.     

Demodulation of a Continuous PM Signal Contaminated with Incidental AM Discrete Phase-Lock

This paper analyzes the problem of communication from phase-modulated (PM) satellite transmitters which usually exhibit some degree of incidental amplitude modulation (AM). Hybrid analog-digital demodulator structures for this type of signal are presented using results of nonlinear estimation theory. It is shown that the structure of the optimal demodulator is nonlinear in nature. Hence, first- and second-order approximations are employed to yield physically realizable phase-lock loop structures. Such algorithms are applicable at sampling instants and linear predictory relationships between sampling instants are derived.     

Track-to-track fusion with dissimilar sensors

An analysis is described of a kinematic state vector fusion algorithm when tracks are obtained from dissimilar sensors. For the sake of simplicity, it is assumed that two dissimilar sensors are equipped with nonidentical two-dimensional optimal linear Kalman filters. It is shown that the performance of such a track-to-track fusion algorithm can be improved if the cross-correlation matrix between candidate tracks is positive. This cross-correlation is introduced by noise associated with target maneuver that is common to the tracking filters in both sensors and is often neglected. An expression for the steady state cross-correlation matrix in closed form is derived and conditions for positivity of the cross-correlation matrix are obtained. The effect of positivity on performance of kinematic track-to-track fusion is also discussed     

An efficient algorithm for multisensor track fusion

An efficient algorithm for track-to-track fusion by incorporating cross-covariance between tracks created by dissimilar sensors is described. An analytical solution of this problem is complicated if cross-correlation between sensors tracking the same target is taken into account. An explicit solution of the cross-covariance matrix at steady state is derived in terms of an integral. It is shown that solution of this integral involves inversion of a matrix whose elements are functions of parameters of individual trackers. Structure of this matrix is analyzed. An efficient analytical solution for inversion of this matrix is obtained. For fusion of similar sensors, it is shown that this matrix is reduced to the Routh-Hurwitz matrix which arises in the study of steady state stability of linear systems. Numerical results showing the amount of reduction of fused track covariance by taking into account the effects of cross-correlation between candidate tracks for fusion is also presented     

On optimal track-to-track fusion

Track-to-track fusion is an important part in multisensor fusion. Much research has been done in this area. Chong et al. (1979, 1986, 1990) among others, presented an optimal fusion formula under an arbitrary communication pattern. This formula is optimal when the underlying systems are deterministic, i.e., the process noise is zero, or when full-rate communication (two sensors exchange information each time they receive new measurements) is employed. However, in practice, the process noise is not negligible due to target maneuvering and sensors typically communicate infrequently to save communication bandwidth. In such situations, the measurements from two sensors are not conditionally (given the previous target state) independent due to the common process noise from the underlying system, and the fusion formula becomes an approximate one. This dependence phenomena was also observed by Bar-Shalom (1981) where a formula was derived to compute the cross-covariance of two track estimates obtained by different sensors. Based on this results a fusion formula was subsequently derived (1986) to combine the local estimates which took into account the dependency between the two estimates. Unfortunately, the Bayesian derivation made an assumption that is not met. This work points out the implicit approximation made and shows that the result turns out to be optimal only in the ML (maximum likelihood) sense. A performance evaluation technique is then proposed to study the performance of various track-to-track fusion techniques. The results provide performance bounds of different techniques under various operating conditions which can be used in designing a fusion system.     

Performance evaluation of track fusion with information matrixfilter

In a multisensor environment, each sensor detects multiple targets and creates corresponding tracks. Fusion of tracks from these, possibly dissimilar, sensors yields more accurate kinematic and attribute information regarding the target. Two methodologies have been employed for such purpose, which are: measurement fusion and state vector fusion. It is well known that the measurement fusion approach is optimal but computationally inefficient and the state vector fusion algorithms are more efficient but suboptimal, in general. This is so because the state vector estimates to be fused obtained from two sensors, are not conditionally independent in general due to the common process noise from the target being tracked. It is to be noted that there are three approaches to state vector fusion, which are: weighted covariance, information matrix, and pseudomeasurement. This research is restricted solely to performance evaluation of the information matrix form of state vector fusion. Closed-form analytical solution of steady state fused covariance has been derived as a measure of performance using this approach. Note that the results are derived under the assumptions that the two sensors are synchronized and no misassociation or merged measurement is considered in the study. Results are compared with those using Monte Carlo simulation, which was used in the past to predict fusion system performance by various authors. These results provide additional insight into the mechanism of track fusion and greatly simplify evaluation of fusion performance. In addition, availability of such a solution facilitates the trade-off studies for designing fusion systems under various operating conditions     

Precursory signature of several major earthquakes studied using 40 kHz low frequency signal

To investigate the precursory signature of earthquakes on low frequency (LF) signal propagation, six earthquakes, having magnitude greater than equal to 6.5 and depth less than equal to 30 km, are being studied. The base line level of 40 kHz signal, transmitted from JJY station, Japan, is analysed with respect to V_d statistical parameter. Results show that the V_d parameter values starts fluctuating from its ambient levels before and during the days of the earthquakes, with significant variation starting 1–3 days prior to the earthquake concerned. This present study is an approach for identifying the precursory signatures of earthquakes on LF signal propagation using a new methodology with V_d parameter.     

An analytical model of prominence dynamics

Solar prominences are magnetic structures incarcerating cool and dense gas in an otherwise hot solar corona. Prominences can be categorized as quiescent and active. Their origin and the presence of cool gas ($～{10}^4$?K) within the hot ($～{10}^6\text{K}$) solar corona remains poorly understood. The structure and dynamics of solar prominences was investigated in a large number of observational and theoretical (both analytical and numerical) studies. In this paper, an analytic model of quiescent solar prominence is developed and used to demonstrate that the prominence velocity increases exponentially, which means that some gas falls downward towards the solar surface, and that Alfvén waves are naturally present in the solar prominences. These theoretical predictions are consistent with the current observational data of solar quiescent prominences.     

Integration of artificial intelligence with meta classifiers for the gully erosion susceptibility assessment in Hinglo river basin,Eastern India

The main aim of this study is to evaluate the gully erosion susceptibility coupling the artificial intelligence and machine learning ensemble approaches. In the present study, the multilayer perceptron neural network (MLP) was used as the base classifier and the hybrid ensemble machine learning methods i.e. Bagging and Dagging were used as the functional classifiers. The Hinglo river basin, an important tributary of the Ajay River was selected as the study area, consists with the parts of Chhotonagpur plateau and Rarh lateritic region. The study area is facing the gully erosion problems which are interrupted the growth of the agriculture. The gully erosion susceptibility maps (GESMs), prepared by MLP, MLP-Bagging and MLP-Dagging were classified into four classes such as low, moderate, high and very high susceptibility classes with the help of natural break method (NBM) in GIS environment. The very high susceptibility class covered 19.41% (MLP), 13.52% (MLP-Bagging) and 15.30% (MLP-Dagging) areas of the basin. For the evaluation and comparison of the models, receiver operating characteristics (ROC), accuracy, mean absolute error (MAE) and root mean square error (RMSE) were applied. Overall, all the gully erosion susceptibility models were performed as excellent. Integration of hybrid ensemble models with MLP has increase the accuracy of the MLP models. Among these models MLP-Dagging has achieved the highest accuracy in compare to the other models. The importance of the selected factors in the present study was assessed by the Relief-F method. The results show that the soil type factor has the highest predictive performance. Sensitivity analysis also showed soil type as most important factor. The gully erosion susceptibility maps (GESMs) are considered as the efficient tool which could be used to take the necessary steps for mitigating and controlling the soil erosion problem and sustainable environmental management and development.     

The nutritional adequacy of a limited vegan diet for a Controlled Ecological Life-Support System.

Purdue University, as well as the Johnson and Kennedy Space Centers and NASA Ames Research Center, are investigating approximately 5-10 plants that will be grown hydroponically to provide not only the energy and nutrients, but also the oxygen for humans habitating in Mars and lunar bases. The growth and nutritional status of rats fed either a control diet (adequate in all macro- and micronutrients) or a strict vegetarian diet consisting of 5 (vegan-5) or 10 (vegan-10) candidate crop species were investigated. In addition, vegan-10 diets were supplemented with mineral and/or vitamin mix at a level similar to the control diets to assess the effect of supplementation on nutrient status. The assessment of inedible plant material as an alternative food source was also investigated. Results of this study demonstrated that consumption of the vegan-10 diet significantly improved weight gain of rats compared to that for rats fed the vegan-5 diet. Mineral supplementation, at a level present in the control diet, to the vegan-10 diet improved growth and nutrient status, but growth was significantly lower compared to the control-fed rats. Inclusion of inedible plant material, high in ash content, improved some indices of nutrient status, without improving growth.