Optimal trajectory design of a probe for soft landing on the Moon from a lunar parking orbit by minimizing the fuel required is obtained. The problem is formulated as an optimal control problem with the thrust direction being the control variable. Using the maximum principle of Pontryagin, the control variable is expressed as a function of co-state variables and the problem is converted into a two-point boundary value problem. The two-point boundary value problem is solved using an optimization technique, i.e., controlled random search. The strategies such as • direct landing from a lunar parking orbit using powered braking • direct landing from an intermediate orbit using powered braking • by executing powered braking in two phases: through horizontal braking and vertical landing are analyzed and an optimal strategy that achieves the goals is suggested. Also, appropriate design parameters are selected using this analysis.

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The integration of Orthogonal Frequency Division Multiplexing (OFDM) technique along with Multiple Input Multiple Output (MIMO) systems seems to be under focus and also serves as a challenging research in the field of broadband wireless communication during topical dates. The resulting MIMO-OFDM system maneuvers the following high data rate wireless transmission of OFDM as well as maximized system capacity of MIMO. Even with these advantages, a foremost issue that influence the MIMO-OFDM system is the hardware perplexity aroused due to the mounted quantity of transmits and receives antennae. In prior works, a technique on the basis of Genetic Algorithm (GA) along with adaptive mutation is being proposed; still, the technique undergoes computational complexity. To surmount these disadvantages, in this paper, a hybrid technique is proposed to choose the optimal transmit antenna subset. The proposed hybrid technique is developed by blending of Artificial Neural Network (ANN) and GA with adaptive mutation. In the work, a training set is generated using GA with adaptive mutation and the generated training set is used to train the ANN using Back Propagation (BP) algorithm. The well-trained ANN selects the optimal transmit antennas when the required number of antennas to be selected and the desired Signal to Noise Ratio (SNR) level are given. The implementation results show that the proposed hybrid technique effectively selects the optimal transmit antennas with good ergodic capacity and less computational complexity.