Abstract: A multi-microphone hands-free device distinguishes between non-steady noise and speech and adapts the de-noising to the presence and characteristics of the detected non-steady noise without spoiling any speech that is present. In the frequency domain, the method comprises calculating a first noise reference by analyzing spatial coherence of signals picked up, calculating a second noise reference by analyzing directions of incidence of signals picked up, estimating a main direction of incidence of signals picked up, selecting as a referent noise signal noise references as a function of estimated main direction, combining signals picked up into a noisy combined signal, calculating probability that speech is absent in the noisy combined signal on basis of respective spectral energy levels of the noisy combined signal and of the referent noise signal, and selectively reducing noise by applying variable gain that is specific to each frequency band and to each time frame.

Abstract: The concentrator couples a plurality of digital and analog sources to an audio/video reader system suitable for reading one of the sources. Each available source is associated with a source identifier and an activated or deactivated state flag, as a result of the user taking action on a source. A register (42) contains an identifier of the current source being read, and a multiple-level LIFO stack (44) stores an ordered list of identifiers of other sources on standby waiting to be read. When a new source is activated, automatic means replace the content of the register with the identifier of the new source so as to substitute the new source for the current source in order to enable it to be read by the system, and concurrently said automatic means add the identifier of the current source to the stack. The inverse operation is performed when the current source is deactivated, with the most recent source contained in the stack being substituted for the current source so as to enable it to be read by the system.

Abstract: This method implements a transition from i) moving state in which the drone is flying at speed and tilt angle that are not zero to ii) hovering state in which the drone has speed and tilt angle that are both zero. The method comprises: a) measuring horizontal linear speed, tilt angles, and angular speeds at the initial instant; b) setting stopping time value; c) on the basis of initial measurements and set stopping time, parameterizing a predetermined predictive function that models optimum continuous decreasing variation of horizontal linear speed as a function of time; d) applying setpoint values to a loop for controlling motors of the drone, which values correspond to target horizontal linear speed precalculated from said parameterized predictive function; and e) once hovering state has been reached, activating a hovering flight control loop for maintaining drone at speed and tilt angle that are zero relative to the ground.

Abstract: An automatic gain control system applied to a audio signal as a function of the ambient noise, the system comprising: an ambient noise estimator module suitable for establishing a current noise value estimated at least from a signal provided by a microphone; and an automatic gain control module suitable for applying to the audio signal gain of a value that is determined as a function of the current noise value received from the ambient noise estimator module. According to the invention, the ambient noise estimator module comprises an MCRA estimator suitable for establishing the current noise value from a signal provided by the microphone picking up the real noise, the echo of the music, and where appropriate speech. The system also includes a module for estimating the power of the audio signal and suitable for providing the automatic gain control module with a current power value for the audio signal.

Abstract: The method operates by estimating the differential movement of the scene picked up by a vertically-oriented camera. Estimation includes periodically and continuously updating a multiresolution representation of the pyramid of images type modeling a given picked-up image of the scene at different, successively-decreasing resolutions. For each new picked-up image, an iterative algorithm of the optical flow type is applied to said representation. The method also provides responding to the data produced by the optical-flow algorithm to obtain at least one texturing parameter representative of the level of microcontrasts in the picked-up scene and obtaining an approximation of the speed, to which parameters a battery of predetermined criteria are subsequently applied. If the battery of criteria is satisfied, then the system switches from the optical-flow algorithm to an algorithm of the corner detector type.

Abstract: The present invention relates to a system (1) of remotely controlled drones (10, 12) fitted with respective cameras (14) enabling a virtual shot from an assailant drone (12) at a target drone (10) to be validated by recognizing the target drone (10) in a video image (17) supplied by the camera (14) of the assailant drone (12) while firing a virtual shot. The recognition means comprise a beacon (15, 16) arranged on the target drone (10) and covered in two first strips (18) of a first color reflecting light at a first wavelength lying in the range 590 nm to 745 nm, situated on either side of at least one second strip (19, 20) of a second color reflecting light at a second wavelength, lying in a range 445 nm to 565 nm. It is thus possible to identify very reliably drones flying in an open space whether outdoors or indoors, in spite of the very great variety of interfering details present in the background images that are likely to be picked up by the camera.

Abstract: Each motor is controlled by a microcontroller and the set of microcontrollers is driven by a central controller. According to the invention, said method comprises: a preliminary step consisting at least in establishing an asynchronous serial communications link over a line between the central controller and each of the microcontrollers, and in allocating an address parameter to each microcontroller; and in operation, at least a control step proper consisting: i) for the central controller, in sending simultaneously on each link line a message containing at least one instruction specified by the address parameter of a destination microcontroller that is to execute said instruction; and ii) for each destination microcontroller, in extracting the instruction addressed thereto from said message, and executing it.

Abstract: A device comprising a circuit for picking up acoustic signals with the microphone; a circuit for playing back audio signals with an amplifier and a loudspeaker; an interface circuit for interfacing with a telephone network or a cell phone; and a circuit for digitally processing audio signals and reducing the acoustic echo that results from interaction of the loudspeaker with the microphone. The circuit for digitally processing audio signals comprises: an echo cancellation stage with an adaptive linear filter suitable for subtracting from the signal picked up by the microphone a reference signal that is derived from the signal received at the output from the interface; a gain control stage for suppressing the residual echo, and a stage for selectively reducing the background noise present in the signal received at the output from the echo suppression stage.

Abstract: The device (10) for piloting a drone (8) comprises a housing having a tilt detector (12) for detecting tilts of the housing, and a touchpad (16) displaying a plurality of touch zones (30, 32, 34, 36, 38, 40, 42). The drone is provided with a self-contained stabilizer system to stabilize it in hovering flight in the absence of any user commands. The device comprises means controlled by a touch zone (30) forming an activation/deactivation button to cause the drone piloting mode to switch in alternation between: i) an activation mode in which the self-contained stabilizer system of the drone is activated, in which mode said piloting commands transmitted to the drone result from transforming signals delivered by the touch zones; and ii) a deactivation mode in which the self-contained stabilizer system of the drone is deactivated, in which mode said piloting commands transmitted to the drone result from transforming signals emitted by the tilt detector of the housing.

Abstract: The device comprises a microphone for detecting a speech signal from a near speaker, and a loudspeaker for reproducing a speech signal from a remote speaker. The processing for canceling the interfering acoustic echo implements an adaptive linear filtering algorithm. Double talk situations are detected by: evaluating an index representative of the convergence or divergence of the algorithm; assessing a predetermined condition for detecting a double talk situation; and if the condition is satisfied, modifying at least one parameter of the algorithm in response to the detection. The representative index may be the norm of the gradient vector describing the adaptation of the filter from one iteration of the algorithm to the next, the conditions being a comparison between the gradient and a threshold. The parameter that is modified double talk situation may be the adaptation stepsize of the algorithm, and also the gain control of an echo suppression stage.

Abstract: The appliance comprises a touch screen and wireless data transmission means for communicating with the drone. Drone piloting commands are activated by one or two fingers making contact with and/or moving over the locations of corresponding piloting symbols displayed on the screen. The method proceeds by: a) detecting finger contact at an arbitrary contact point in at least one predefined zone of the screen on which piloting symbols are not already displayed; b) displaying a piloting icon on the screen at said contact point, the piloting icon comprising a movable icon displayed at a position that tracks any movement of the finger contact point over the screen from an initial position to an offset position; c) detecting the movement of the movable icon; d) on detecting said movement, analyzing the direction and/or the amplitude of the movement relative to the initial position; and e) activating a piloting command as a function of the result of said analysis.

Abstract: The system comprises a master device (10) and at least one slave device (20). Each device has wireless transceiver and interface means (12, 22), a digital signal processor (14, 24), and a clock (16, 26). The master device transmits signals at a rate defined by its clock (16). The slave device has means for determining the frequency offset between the clocks and for correcting accordingly the rate at which signals are delivered internally to its processor. At the slave end, the frequency of the external clock(s) (26) is controllable about its nominal value, and means are provided for extracting a frequency offset value from a register (50) and for responding thereto by adjusting the frequency of the second clock in the direction tending to minimize the offset.

Abstract: The assembly comprises: a car radio with a housing and a removable half front plate; a mounting sheath; means for securing the housing in the sheath; and means for unlocking the housing, said means comprising a tool suitable for being inserted in at least two orifices formed in the front face of the housing. These two orifices are formed in a central zone of the housing that is hidden by the front plate when it is mounted on the housing, and that is visible when the front plate is separated from the housing. The assembly has essentially no peripheral cover fitted thereto. The U-shaped tool makes it easy to extract the car radio using one hand in a single movement.

Abstract: A method of analyzing time coherence in the noisy signal including the steps of: a) determining a reference signal from the noisy signal by applying treatment (10, 18) to the noisy signal that is suitable for attenuating speech components more strongly than the noise component, in particular by an adaptive recursive predictive algorithm of the LMS type; b) determining (24) a probability of speech being present/absent on the basis of the respective energy levels in the spectral domain of the noisy signal and of the reference signal; and c) deriving (26) a denoised estimate of the speech signal from the noise signal as a function of the probability of the speech being present/absent as determined in this way.

Abstract: In the frequency domain, the method comprises the following steps: a) calculating a first noise reference by analyzing the spatial coherence of the signals picked up; b) calculating a second noise reference by analyzing the directions of incidence of the signals picked up; c) estimating a main direction of incidence of the signals picked up; d) selecting as a referent noise signal one or the other of the noise references as a function of the estimated main direction; e) combining the signals picked up into a noisy combined signal; f) calculating a probability that speech is absent in the noisy combined signal on the basis of the respective spectral energy levels of the noisy combined signal and of the referent noise signal; and g) selectively reducing the noise by applying variable gain that is specific to each frequency band and to each time frame.