This work investigates the impact of mutual coupling among antennas in a uniform linear array (ULA). Previous studies have addressed the typically nondiagonal mutual coupling matrix (MCM), its influence on the array manifold, and strategies for mitigating its adverse effects. This study builds on the principle that a linear system combining amplified and delayed sinusoidal signals of the same frequency results in a single sinusoid. By modeling these effects using complex weights in the phasor domain, which account for the initial amplitudes and phases, we propose an equivalent diagonal matrix to represent mutual coupling. We present this formulation, demonstrate how to derive the equivalent diagonal MCM for a ULA, and provide a circuit-theoretic interpretation. The main advantages of the proposed method are its simplicity, physical interpretation, and reduced computational complexity. The simulation results support our hypothesis and confirm the effectiveness of the proposed strategy.

This paper addresses the problem of localizing a sound source in strong reverberating and conceivably noisy environments and its relevant potential applications, e.g., target location in interior room acoustic environments, in- and outdoor navigation, and robotics. Available audio emitter location techniques, especially those based exclusively on Time Difference of Arrival, do not perform well in unfriendly environments like those discussed herein. Recent works propose using neural networks to solve the emitter location problem but do not adequately address the issue of severe reverberation. In this work, we propose a solution that uses a two-layer feedforward neural network supported by signal processing techniques to extract features to train the neural network. Indeed, using features incorporating information from the reverberation fingerprint of the environment to train the network results in an efficient system featuring reduced training time and relatively low mean squared error values.

This paper proposes an algorithm to optimize the walking of humanoid robots based on the inverse kinematic model combined with a Genetic Algorithm. The objectives are to improve the sagittal displacement of the robot and reduce possible lateral deviations during a predetermined path. The foot of the humanoid performs a tapered motion, an approximate ellipse. Horizontal and vertical speeds and the angulation of the humanoid trunk are the input parameters of the algorithm. The algorithm utilizes the input information to calculate the inverse kinematics, and then it submits the obtained result to an evaluation function. We develop a virtual simulator and a robotic platform with 14 degrees of freedom to validate the proposed algorithm. We then test a prototype using the best result obtained in the simulations.