Abstract: An electronic device may be provided with an antenna having a resonating element and a light source module mounted to a flexible printed circuit and a metal cowling. The module may emit light through a rear housing wall. The printed circuit may be interposed between the metal cowling and a conductive support plate in the rear housing wall. The printed circuit may include a ground trace coupled to the resonating element. A dimpled pad may couple the ground trace to the support plate. Compressive foam may be used to exert a force against the flexible printed circuit that presses the dimpled pad against the conductive support plate. The ground trace and the dimpled pad may form a return path to ground for the resonating element. The dimpled pad may occupy less height within the device than other structures such as metal springs.

Abstract: A modular furniture has a first modular furniture unit. The first modular unit has a first frame. A pair of front legs is attached to the first frame, wherein a first of the pair of front legs supports and holds one of a first adjacent modular furniture unit or a first arm rest and a second of the pair of front legs supports and holds one of a second adjacent modular furniture unit or a first arm rest. A pair of rear legs is coupled to the first frame, wherein a first of the pair of rear legs supports and holds one of the first adjacent modular furniture unit or the first arm rest and a second of the pair of rear legs supports and holds one of the second adjacent modular furniture unit or the second arm rest.

Abstract: An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. Each training geospatial image may include pixels. The processor may be configured to operate a training model to identify a given feature from each of the training geospatial images, and to iteratively generate a probability distribution function based upon a number of pixels corresponding to the given feature and also based upon a bias factor being reduced with each iteration.

Abstract: A modular furniture has a first modular furniture unit. The first modular unit has a first frame. A pair of front legs is attached to the first frame, wherein a first of the pair of front legs supports and holds one of a first adjacent modular furniture unit or a first arm rest and a second of the pair of front legs supports and holds one of a second adjacent modular furniture unit or a first arm rest. A pair of rear legs is coupled to the first frame, wherein a first of the pair of rear legs supports and holds one of the first adjacent modular furniture unit or the first arm rest and a second of the pair of rear legs supports and holds one of the second adjacent modular furniture unit or the second arm rest.

Abstract: An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. The processor may be configured to operate a training model to identify a given feature from each of the training geospatial images, and to iteratively apply a bias factor to a loss function based upon a number of incorrectly identified pixels for the given feature. The bias factor may be reduced with each iteration.

Abstract: An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. The processor may be configured to blur each of the training geospatial images. The processor may also be configured to iteratively operate a training model to identify a given feature from each of the blurred training geospatial images so that the blurring is reduced with each iteration.

Abstract: An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. The processor may be configured to blur each of the training geospatial images. The processor may also be configured to iteratively operate a training model to identify a given feature from each of the blurred training geospatial images so that the blurring is reduced with each iteration.

Abstract: An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. Each training geospatial image may include pixels. The processor may be configured to operate a training model to identify a given feature from each of the training geospatial images, and to iteratively generate a probability distribution function based upon a number of pixels corresponding to the given feature and also based upon a bias factor being reduced with each iteration.

Abstract: An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. The processor may be configured to operate a training model to identify a given feature from each of the training geospatial images, and to iteratively apply a bias factor to a loss function based upon a number of incorrectly identified pixels for the given feature. The bias factor may be reduced with each iteration.