Abstract: A computer-implemented method for generating a training dataset for training an artificial intelligence for operating a measuring device, in particular a wall diagnostic device, includes (i) receiving unclassified sensor data of at least one sensor unit of a measuring device by a classifier module, (ii) classifying the unclassified sensor data and providing classified sensor data using a classifier module, and (iii) adding the classified sensor data to a training dataset.

Abstract: A method for performing an optimized Neural Network Architecture Search (NAS). The method includes: providing a technical problem definition, the technical problem to be solved by a to be searched set of candidate neuronal networks, a training dataset, a search space and preferably a seed neuronal network architecture; training, by the NAS-process, each one of the set of candidate neuronal networks based on the at least a part of the training dataset; adjusting and/or fine-tuning, by the NAS-process, a network architecture of each one of the set of trained candidate neuronal networks via Quantization-Aware Training; converting, by the NAS-process, the respectively adjusted and/or fine-tuned network architecture of each one of the set of candidate neuronal networks to a framework.

Abstract: A method of decoding a received SC-FDMA symbol in a receiver in a OFDM communication system is described. The method comprises calculating an approximate constellation energy {circumflex over (K)} from channel matrices Hi for all subcarriers i, a noise covariance matrix S, and a data signal power matrix C associated with the OFDM symbol, an approximate constellation energy {circumflex over (K)}. The approximate constellation energy {circumflex over (K)} is calculated according to: Q i = H i H ? S - 1 ? H i ; Q ^ = 1 N sc ? ? i = 0 N sc - 1 ? Q i ; K ^ = diag ? ( ( Q ^ + C - 1 ) - 1 ? Q ^ ) The method further comprises decoding the received symbol using at least the associated approximate constellation energy {circumflex over (K)}. Also, a receiver, an apparatus, an OFDM communication system and a computer program product for such decoding are described.

Abstract: An embodiment of the invention relates to a parking spot identification system and method for providing parking-related information to a user seeking parking, hereinafter referred to as a “seeker.” The system enables a seeker to reduce time and energy spent looking for a parking spot and generally reduces distance between seeker's destination and the parking spot. In an embodiment the method comprises receiving a request from a seeker to park in an area, the request having a parking constraint; receiving an indication of a possibility of a parking spot being vacated by an occupier whose vehicle is parked in a parking spot meeting the parking constraint, and providing information to the seeker regarding the occupier once the probability of an occupier spot being vacated has exceeded a predetermined level.

Abstract: A method of decoding a received SC-FDMA symbol in a receiver in a OFDM communication system is described. The method comprises calculating an approximate constellation energy {circumflex over (K)} from channel matrices Hi for all subcarriers i, a noise covariance matrix S, and a data signal power matrix C associated with the OFDM symbol, an approximate constellation energy {circumflex over (K)}. The approximate constellation energy {circumflex over (K)} is calculated according to: Q i = H i H ? S - 1 ? H i ; Q ^ = 1 N sc ? ? i = 0 N sc - 1 ? Q i ; K ^ = diag ? ( ( Q ^ + C - 1 ) - 1 ? Q ^ ) The method further comprises decoding the received symbol using at least the associated approximate constellation energy {circumflex over (K)}. Also, a receiver, an apparatus, an OFDM communication system and a computer program product for such decoding are described.