Abstract: A heat recovery apparatus and process for cracked gas are provided. The cracked gas comprises liquid feedstock cracked gas and gaseous feedstock cracked gas, and the apparatus comprises a heat recovery device for liquid feedstock cracked gas, a heat recovery device for gaseous feedstock cracked gas and a heavy component removal unit. The invention solves the problems in the art, i.e., incomplete heat recovery technology for cracked gas, insufficient control of viscosity of quench oil, high capital investment and large footprint of the equipment, as well as unstable operation and high energy consumption.

Abstract: A method for creating a dictionary for a magnetic resonance fingerprinting (MRF) reconstruction includes training a semi-supervised learning system based on at least a set of MRF data and a set of control variables and generating a plurality of signal evolutions using the trained semi-supervised learning system. The method also includes generating an MRF dictionary using the plurality of signal evolutions generated using the trained semi-supervised learning system and storing the MRF dictionary in a memory. In one embodiment, the semi-supervised learning system is a MRF generative adversarial network (GAN).

Abstract: A system and method is provided for generating a map of a tissue property in a subject using magnetic resonance fingerprinting (MRF) and a compressed MRF dictionary, where the compressed MRF dictionary has a significantly reduced memory requirement relative to a standard MRF dictionary. The method includes performing a randomized singular value decomposition (rSVD) on a MRF dictionary to produce the compressed MRF dictionary. MRF data is then acquired and compared to the MRF dictionary to identify the tissue property from the region of interest in the subject. A tissue property map is then generated based on the tissue in the region of interest of the subject.

Abstract: A method for creating a dictionary for a magnetic resonance fingerprinting (MRF) reconstruction includes training a semi-supervised learning system based on at least a set of MRF data and a set of control variables and generating a plurality of signal evolutions using the trained semi-supervised learning system. The method also includes generating an MRF dictionary using the plurality of signal evolutions generated using the trained semi-supervised learning system and storing the MRF dictionary in a memory. In one embodiment, the semi-supervised learning system is a MRF generative adversarial network (GAN).

Abstract: Apparatus for hyperspectral imaging, the apparatus including input optics that receive radiation reflected or radiated from a scene, a spatial modulator that spatially samples radiation received from the input optics to generate spatially sampled radiation, a spectral modulator that spectrally samples the spatially sampled radiation received from the spatial modulator to generate spectrally sampled radiation, a sensor that senses spectrally sampled radiation received from the spectral modulator and generates a corresponding output signal and at least one electronic processing device that controls the spatial and spectral modulators to cause spatial and spectral sampling to be performed, receives output signals and processes the output signals in accordance with performed spatial and spectral sampling to generate a hyperspectral image.

Abstract: A system and method is provided for generating a map of a tissue property in a subject using magnetic resonance fingerprinting (MRF) and a compressed MRF dictionary, where the compressed MRF dictionary has a significantly reduced memory requirement relative to a standard MRF dictionary. The method includes performing a randomized singular value decomposition (rSVD) on a MRF dictionary to produce the compressed MRF dictionary. MRF data is then acquired and compared to the MRF dictionary to identify the tissue property from the region of interest in the subject. A tissue property map is then generated based on the tissue in the region of interest of the subject.

Abstract: Apparatus for hyperspectral imaging, the apparatus including input optics that receive radiation reflected or radiated from a scene, a spatial modulator that spatially samples radiation received from the input optics to generate spatially sampled radiation, a spectral modulator that spectrally samples the spatially sampled radiation received from the spatial modulator to generate spectrally sampled radiation, a sensor that senses spectrally sampled radiation received from the spectral modulator and generates a corresponding output signal and at least one electronic processing device that controls the spatial and spectral modulators to cause spatial and spectral sampling to be performed, receives output signals and processes the output signals in accordance with performed spatial and spectral sampling to generate a hyperspectral image.