Abstract: A method of acoustic echo suppression using a recurrent neural network, performed by at least one processor, is provided. The method includes receiving a microphone signal and a far-end reference signal, estimating an echo suppressed signal and an echo signal based on the microphone signal and the far-end reference signal, estimating enhancement filters for the microphone signal based on the echo suppressed signal and the echo signal, generating an enhanced signal based on the enhancement filters, and adjusting the enhanced signal using automatic gain control (AGC) and outputting the adjusted signal.

Abstract: Facilitation of determination of detailed location of a source signal is provided. In one embodiment, a device comprises a memory that stores computer executable components; and a processor that executes computer executable components stored in the memory. The computer executable components can comprise: a low-resolution computation logic component that implements a coarse algorithm and determines an approximate direction of arrival (DOA) of a source signal of an input signal, wherein the coarse algorithm uses both a coarse spatial grid and input data received from the input signal to determine the approximate DOA; and an error estimation logic component that estimates an estimation error of the coarse algorithm, and wherein the error estimation logic component uses the estimation error and the approximate DOA to determine a spatial interval range.

Abstract: An adjustable hyperspectral detection chip enhanced by a multi-resonance plasmonic mechanism. The detection chip consists of an array of metal nanonail resonator detection units. Each detection unit (1) comprises: a bottom electrode (2), a semiconductor material layer (3), a spacer layer (4), a nanonail array (5), a control material layer (6), a top electrode (7), a peripheral control signal (8), and a driving circuit (9). The positional relationship from top to bottom is the top electrode (7), the control material layer (6), the nanonail array (5), the spacer layer (4), the semiconductor material layer (3), and the bottom electrode (2). The nanonail array (5) is loaded inside the control material layer (6), and the peripheral control signal (8) and the driving circuit (9) are connected to both sides of the control material layer (6).

Abstract: An adjustable hyperspectral detection chip enhanced by a multi-resonance plasmonic mechanism. The detection chip consists of an array of metal nanonail resonator detection units. Each detection unit (1) comprises: a bottom electrode (2), a semiconductor material layer (3), a spacer layer (4), a nanonail array (5), a control material layer (6), a top electrode (7), a peripheral control signal (8), and a driving circuit (9). The positional relationship from top to bottom is the top electrode (7), the control material layer (6), the nanonail array (5), the spacer layer (4), the semiconductor material layer (3), and the bottom electrode (2). The nanonail array (5) is loaded inside the control material layer (6), and the peripheral control signal (8) and the driving circuit (9) are connected to both sides of the control material layer (6).

Abstract: Facilitation of determination of detailed location of a source signal is provided. In one embodiment, a device comprises a memory that stores computer executable components; and a processor that executes computer executable components stored in the memory. The computer executable components can comprise: a low-resolution computation logic component that implements a coarse algorithm and determines an approximate direction of arrival (DOA) of a source signal of an input signal, wherein the coarse algorithm uses both a coarse spatial grid and input data received from the input signal to determine the approximate DOA; and an error estimation logic component that estimates an estimation error of the coarse algorithm, and wherein the error estimation logic component uses the estimation error and the approximate DOA to determine a spatial interval range.