Abstract: A method of compressing digital signal data obtained from a signal is described. The method includes: receiving digital signal data associated with a signal and/or generating digital signal data based on a signal; transforming the digital signal data into a transform domain, thereby generating transformed digital signal data; determining at least one characteristic parameter based on the transformed digital signal data by an artificial intelligence circuit; detecting and/or classifying at least one wanted signal portion based on the at least one characteristic parameter by the artificial intelligence circuit; and storing only a subset of the digital signal data that is associated with the at least one wanted signal portion. Further, a signal compressor circuit for compressing digital signal data obtained from a signal and a computer program are described.

Abstract: A radar target simulator for simulating a radar target is disclosed. The radar target simulator comprises a display module and an input module via which a user of the radar target simulator is enabled to define a target to be simulated. The radar target simulator also comprises a processing module that is connected with the display module and the input module in a signal transmitting manner. The display module and the processing module together provide a graphical user interface for the user of the radar target simulator. The graphical user interface provides a two-dimensional representation of an at least two-dimensional space. The processing module receives at least one input signal from the input module based on an input of the user. The input of the user is associated with input coordinates in the two-dimensional representation. The processing module processes the at least one input signal, thereby generating a symbol of the target defined.

Abstract: An unmanned aerial vehicle includes a main body and at least two rotor units configured to propel the unmanned aerial vehicle. The unmanned aerial vehicle includes at least two antenna units configured to receive a radio signal. The antenna units are located with respect to the main body such that the antenna units are assigned to different lateral sides of the main body. Further, a direction finding system is described.

Abstract: A test system for testing a device under test (DUT) with a graphical user interface (GUI) and a touch-sensitive display is described. The test system includes a stimulation layer configured to selectively generate a stimulus signal in at least one area of the touch-sensitive display. The stimulation layer is at least partially transparent. The test system further includes at least one camera configured to capture at least one image of the GUI displayed on the touch-sensitive display through the at least partially transparent stimulation layer. The test system further includes an artificial intelligence (AI) circuit connected with the at least one camera and the stimulation layer. The AI circuit is configured to recognize at least one activatable element of the GUI based on the at least one image captured, and is configured to control the stimulation layer to selectively generate the stimulus signal based on the at least one recognized activatable element, such that a touch and/or or a gesture is imitated.

Abstract: A signal analysis method is described. The signal analysis method comprises the following steps: An input signal function associated with a time domain is obtained. A window function is determined based on the input signal function via an artificial intelligence module. The artificial intelligence module comprises at least one computing parameter, wherein the window function is determined based on the at least one computing parameter. The input signal function and the window function are convolved, thereby generating a convolved signal. Further, a signal analysis module is described.

Abstract: A measurement apparatus comprising an acquisition memory adapted to store data sections of at least one acquired measurement signal; a processor adapted to calculate a measurement parameter vector, v, for each data section of the acquired measurement signal; and a trained autoencoder neural network adapted to process the measurement parameter vectors, v, applied as input data to the trained autoencoder neural network to provide at a middle layer of said autoencoder neural network an encoded vector, h, with characteristic signal features of the acquired measurement signal.

Abstract: An unmanned aerial vehicle for at least one of direction finding and spectrum monitoring includes a main body including at least one electronic circuit and at least one motor. The unmanned aerial vehicle also has at least one rotor associated with the motor. The unmanned aerial vehicle includes an outer housing surrounding the rotor partially, the outer housing including at least one antenna module configured to receive a radio signal.

Abstract: A method for direction finding of at least one stationary and/or moving transmitter comprises the following steps: measuring the signals emitted by each of the at least one transmitter at at least two different measurement points; determining the location of the measurements points at the time of the measurement; determining the bearings from the measurement points to each of the at least one transmitter; transferring the bearings to a pre-trained artificial neural network; and estimating the locations of the at least one transmitter by the artificial neural network. Further, a system for direction finding of at least one stationary and/or moving transmitter is shown.

Abstract: A method for generating an electromagnetic signal is disclosed. The method comprises the following steps: An image having pixels is at least one of received and generated, wherein each one of the pixels has one of at least two different pixel values. At least one of a spectrogram of an electromagnetic signal and characteristic parameters of the electromagnetic signal is determined based on at least one of the image and the pixel values. The electromagnetic signal is generated based on at least one of the spectrogram and the characteristic parameters.

Abstract: An apparatus for generating a score signal representing the quality of an audio or video signal supplied to the apparatus is proposed. The apparatus comprises: an input for supplying an audio or video signal, a computing unit implementing a neural network, the computing unit being supplied with the audio or video signal, and producing a score signal representing the quality of an audio or video signal supplied representing at least one predefined quality parameter of the audio or video signal, the neural network being set up by being trained with training data of a specific transmission standard and/or codec used for generating the audio or video data.

Abstract: A test system comprises a device under test and a testing device. The device under test comprises a first initiation unit, wherein the first initiation unit is configured to generate a first wireless initiation signal. The initiation signal consists of at least one of electromagnetic waves and sound waves, wherein the initiation signal comprises a first test command. The testing device comprises a first sensor unit, wherein the first sensor unit is configured to receive the initiation signal via the first sensor unit. The testing device is configured to generate a first electromagnetic test signal based on the first test command.

Abstract: A radar target simulator for simulating a radar target is disclosed. The radar target simulator comprises a display module and an input module via which a user of the radar target simulator is enabled to define a target to be simulated. The radar target simulator also comprises a processing module that is connected with the display module and the input module in a signal transmitting manner. The display module and the processing module together provide a graphical user interface for the user of the radar target simulator. The graphical user interface provides a two-dimensional representation of an at least two-dimensional space. The processing module receives at least one input signal from the input module based on an input of the user. The input of the user is associated with input coordinates in the two-dimensional representation. The processing module processes the at least one input signal, thereby generating a symbol of the target defined.

Abstract: A method of compressing digital signal data obtained from a signal is described. The method includes: receiving digital signal data associated with a signal and/or generating digital signal data based on a signal; transforming the digital signal data into a transform domain, thereby generating transformed digital signal data; determining at least one characteristic parameter based on the transformed digital signal data by an artificial intelligence circuit; detecting and/or classifying at least one wanted signal portion based on the at least one characteristic parameter by the artificial intelligence circuit; and storing only a subset of the digital signal data that is associated with the at least one wanted signal portion. Further, a signal compressor circuit for compressing digital signal data obtained from a signal and a computer program are described.

Abstract: A method for detecting anomalies in a spectrogram, spectrum or signal using a detection module having a first machine learning submodule, a second machine learning submodule and a comparison submodule is provided. The method includes: receiving the spectrogram, spectrum or signal as measured data by the detection module; generating filtered data based on the measured data by the first submodule; generating predicted data based on the measured data and/or the filtered data by the second submodule; and determining that the spectrogram, spectrum or signal includes an anomaly if the filtered data deviates from the measured data and/or the predicted data deviates from the measured data and/or the filtered data by at least a predetermined amount.

Abstract: A test system comprises a device under test and a testing device. The device under test comprises a first microphone, wherein the first microphone is configured to receive sound waves. The testing device comprises a first speaker, wherein the first speaker is configured to generate sound waves. The testing device is configured to generate a first acoustic command signal via the first speaker, wherein the first acoustic command comprises a first test command. The device under test is configured to receive the first acoustic command signal via the first microphone. The device under test is configured to generate a first electromagnetic test signal based on the first test command.

Abstract: A signal analysis method is described. The signal analysis method comprises the following steps: An input signal function associated with a time domain is obtained. A window function is determined based on the input signal function via an artificial intelligence module. The artificial intelligence module comprises at least one computing parameter, wherein the window function is determined based on the at least one computing parameter. The input signal function and the window function are convolved, thereby generating a convolved signal. Further, a signal analysis module is described.

Abstract: A test system comprises a device under test and a testing device. The device under test comprises a first microphone, wherein the first microphone is configured to receive sound waves. The testing device comprises a first speaker, wherein the first speaker is configured to generate sound waves. The testing device is configured to generate a first acoustic command signal via the first speaker, wherein the first acoustic command comprises a first test command. The device under test is configured to receive the first acoustic command signal via the first microphone. The device under test is configured to generate a first electromagnetic test signal based on the first test command.

Abstract: An unmanned aerial vehicle includes a main body and at least two rotor units configured to propel the unmanned aerial vehicle. The unmanned aerial vehicle includes at least two antenna units configured to receive a radio signal. The antenna units are located with respect to the main body such that the antenna units are assigned to different lateral sides of the main body. Further, a direction finding system is described.

Abstract: A test system comprises a device under test and a testing device. The device under test comprises a first initiation unit, wherein the first initiation unit is configured to generate a first wireless initiation signal. The initiation signal consists of at least one of electromagnetic waves and sound waves, wherein the initiation signal comprises a first test command. The testing device comprises a first sensor unit, wherein the first sensor unit is configured to receive the initiation signal via the first sensor unit. The testing device is configured to generate a first electromagnetic test signal based on the first test command.

Abstract: A method for generating an electromagnetic signal is disclosed. The method comprises the following steps: An image having pixels is at least one of received and generated, wherein each one of the pixels has one of at least two different pixel values. At least one of a spectrogram of an electromagnetic signal and characteristic parameters of the electromagnetic signal is determined based on at least one of the image and the pixel values. The electromagnetic signal is generated based on at least one of the spectrogram and the characteristic parameters.