Abstract: A Photonic Crystal Fiber (PCF) a method of its production and a supercontinuum light source comprising such PCF. The PCF has a longitudinal axis and includes a core extending along the length of said longitudinal axis and a cladding region surrounding the core. At least the cladding region includes a plurality of microstructures in the form of inclusions extending along the longitudinal axis of the PCF in at least a microstructured length section. In at least a degradation resistant length section of the microstructured length section the PCF includes hydrogen and/or deuterium. In at least the degradation resistant length section the PCF further includes a main coating surrounding the cladding region, which main coating is hermetic for the hydrogen and/or deuterium at a temperature below Th, wherein Th is at least about 50° C., preferably 50° C.<Th<250° C.

Abstract: Examples relate to an apparatus, a method and a computer program for detecting an anomaly in input data, to a camera device and a system comprising such an apparatus, and to a method and computer program for training a sequence of machine-learning models for use in anomaly detection. The apparatus for detecting an anomaly in input data is configured to process the input data using a sequence of machine-learning models. The sequence of machine-learning models comprising a first machine-learning model configured to pre-process the input data to provide pre-processed input data and a second machine-learning model configured to process the pre-processed input data to provide output data. The first machine-learning model is trained to transform the input data such, that the pre-processed input data comprises a plurality of sub-components being statistically independent with a known probability distribution. The second machine-learning model is an auto-encoder.

Abstract: Examples relate to a concept for an entry-exit matching system, and in particular to an evaluation device, a method and a computer program for person re-identification for entry-exit matching in a transportation system. The evaluation device comprises processing circuitry configured to obtain a plurality of re-identification codes. Each re-identification code represents a person being recorded by at least one camera when entering or exiting at least a section of the transportation system. The processing circuitry is configured to match the plurality of re-identification codes using a global matching scheme to obtain a plurality of matched pairs of re-identification codes, such that each matched pair of re-identification codes comprises a re-identification code of a person entering and a re-identification code of a person exiting.

Abstract: In various embodiments, a system includes a mesh layer including a plurality of LEDs, where at least a subset of the LEDs is selectively controllable to control lighting of the mesh layer. The system further includes a screen layer at least partially aligned with the mesh layer, wherein the screen layer is configured to: be illuminated from behind by the mesh layer, and at least partially absorb light projected onto the screen layer.

Abstract: Various examples relate to a utility vehicle, and to a corresponding apparatus, method and computer program for a utility vehicle. The apparatus comprises at least one interface for obtaining video data from one or more cameras of the utility vehicle. The apparatus further comprises one or more processors. The one or more processors are configured to process, using a machine-learning model, the video data to determine pose information of a person being shown in the video data. The machine-learning model is trained to generate pose-estimation data based on video data. The one or more processors are configured to detect at least one pre-defined pose based on the pose information of the person. The one or more processors are configured to control the utility vehicle based on the detected at least one pre-defined pose.

Abstract: Various examples relate to a utility vehicle, and to a corresponding apparatus, method and computer program for a utility vehicle. The apparatus comprises at least one interface for obtaining video data from one or more cameras of the utility vehicle. The apparatus further comprises one or more processors. The one or more processors are configured to identify or re-identify one or more persons shown in the video data. The one or more processors are configured to determine an infraction of the one or more persons on one or more safety areas surrounding the utility vehicle based on the identification or re-identification of the one or more persons shown in the video data. The one or more processors are configured to provide at least one signal indicating the infraction of the one or more persons on the one or more safety areas to an output device.

Abstract: Examples relate to a concept for generating training data and training a machine-learning model for use in re-identification. A computer system for generating training data for training a machine-learning model for use in re-identification comprising processing circuitry configured to obtain media data, the media data comprising a plurality of samples representing a person, an animal or an object. The processing circuitry is configured to process the media data to identify tuples of samples that represent the same person, animal or object. The processing circuitry is configured to generate the training data based on the identified tuples of samples that represent the same person, animal or object.

Abstract: Examples relate to an evaluation device for re-identification and to a corresponding method, system and computer program. The evaluation device comprises processing circuitry being configured to obtain a plurality of transformed re-identification codes, each transformed re-identification code being associated with a timestamp and location information. Each transformed re-identification code is based on a similarity-preserving transformation of a re-identification code that represents at least a portion of a sample of media data, the media data originating from two or more different sources located in two or more different locations. The processing circuitry is configured to match transformed re-identification codes among the plurality of transformed re-identification codes using a similarity metric to generate one or more tuples of transformed re-identification codes that are similar according to the similarity metric.

Abstract: Examples relate to a concept for anonymous re-identification, more specifically, but not exclusively, to systems, apparatuses, methods and computer programs for performing anonymous re-identification and for training a machine-learning model for use in anonymous re-identification. An apparatus for re-identification comprises processing circuitry configured to obtain media data via an interface. the processing circuitry is configured to generate a re-identification code representing at least a portion of the media data using a hashing algorithm. The processing circuitry is configured to transform the re-identification code using a transformation functionality to obtain a transformed re-identification code.

Abstract: A well advisor system and console for monitoring and managing cementing operations in a well. The system may be accessed through one or more workstations, or other computing devices, which may be located at a well site or remotely. The system is in communication with and receives input from various sensors. It collects real-time sensor data sampled during operations at the well site. The system processes the data, and provides nearly instantaneous numerical and visual feedback through a variety of graphical user interfaces (“GUIs”), which are presented in the form of an operation-specific console.

Abstract: A well advisor system and console for monitoring and managing installation of casing and tubular goods in a well. The system may be accessed through one or more workstations, or other computing devices, which may be located at a well site or remotely. The system is in communication with and receives input from various sensors. It collects real-time sensor data sampled during operations at the well site. The system processes the data, and provides nearly instantaneous numerical and visual feedback through a variety of graphical user interfaces (“GUIs”), which are presented in the form of an operation-specific console.

Abstract: A well advisor system for monitoring and managing well drilling and production operations. The system may be accessed through one or more workstations, or other computing devices, which may be located at a well site or remotely. The system is in communication with and receives input from various sensors. It collects real-time sensor data sampled during operations at the well site, which may include drilling operations, running casing or tubular goods, completion operations, or the like. The system processes the data, and provides nearly instantaneous numerical and visual feedback through a variety of graphical user interfaces (“GUIs”), which are presented in the form of operation-specific consoles.

Abstract: A well advisor system for monitoring and managing well drilling and production operations. The system may be accessed through one or more workstations, or other computing devices, which may be located at a well site or remotely. The system is in communication with and receives input from various sensors. It collects real-time sensor data sampled during operations at the well site, which may include drilling operations, running casing or tubular goods, completion operations, or the like. The system processes the data, and provides nearly instantaneous numerical and visual feedback through a variety of graphical user interfaces (“GUIs”), which are presented in the form of operation-specific consoles.