A method is provided for decompressing images captured by a compressive imaging system in conjunction with a compressive imaging application. The method comprises determining an estimation error e.sup.t based on a difference between: (i) a measurement y captured by the compressive imaging system and (ii) a product of a sensing matrix H and a signal x.sup.t that has been estimated. The method comprises determining a gradient descent st+1 based on a sum of: (i) the signal x.sup.t that has been estimated and (ii) a product of a step size , a vector formulation of the sensing matrix H.sup.T and the estimation error e.sup.t. The method comprises projecting the gradient descent s.sup.t+1 comprising applying a compression function f and a decompression function g to estimate the signal x.sup.t+1. The method comprises iteratively repeating determining the estimation error e.sup.t; determining the gradient descent s.sup.t+1 and projecting the gradient descent s.sup.t+1 to provide a reconstructed signal following a final iteration; and providing the reconstructed signal for display or storage.

The present technology relates to a learning apparatus and method, and a program which allow speech recognition with sufficient recognition accuracy and response speed. A learning apparatus includes a model learning unit that learns a model for recognition processing, on the basis of output of a decoder for the recognition processing constituting a conditional variational autoencoder when features extracted from learning data are input to the decoder, and the features. The present technology can be applied to learning apparatuses.

A system includes an optical computing device having an optical multiplexer that receives a sample light generated by an optical interaction between a sample and an illumination light is provided. The system includes sensing elements that optically interact with the sample light to generate modified lights, and a detector that measures a property of the modified lights separately. Linear and nonlinear models for processing data collected with the above system to form high-resolution spectra are also provided. Methods for designing optimal optical multiplexers for optimal reconstruction of high-resolution spectra are also provided.

Among the various aspects of the present disclosure is the provision of systems and methods of compressed-sensing ultrafast spectral photography.

The present invention integrates a low power and high compression pixel-wise coded exposure (PCE) camera with advanced object detection, tracking, and classification algorithms into a real-time system. A PCE camera can control exposure time of every pixel in the camera and at the same time can compress multiple frames into a compressed frame. Consequently, it can significantly improve the dynamic range as well as reduce data storage and transmission bandwidth usage. Conventional approaches utilize PCE camera for object detection, tracking and classification, require the compressed frames to be reconstructed. These approaches are extremely time consuming and hence makes the PCE cameras unsuitable for real-time applications. The present invention presents an integrated solution that incorporates advanced algorithms into the PCE camera, saving reconstruction time and making it feasible to work in real-time applications.

Hyperspectral imaging spectrometers have applications in environmental monitoring, biomedical imaging, surveillance, biological or chemical hazard detection, agriculture, and minerology. Nevertheless, their high cost and complexity has limited the number of fielded spaceborne hyperspectral imagers. To address these challenges, the wide field-of-view (FOV) hyperspectral imaging spectrometers disclosed here use computational imaging techniques to get high performance from smaller, noisier, and less-expensive components (e.g., uncooled microbolometers). They use platform motion and spectrally coded focal-plane masks to temporally modulate the optical spectrum, enabling simultaneous measurement of multiple spectral bins. Demodulation of this coded pattern returns an optical spectrum in each pixel. As a result, these computational reconfigurable imaging spectrometers are more suitable for small space and air platforms with strict size, weight, and power constraints, as well as applications where smaller or less expensive packaging is desired.

Any one or both of an optical system with a structured lighting pattern and a structured detecting system having a plurality of regions with different optical characteristics are used. In addition, optical signals from an object to be observed through one or a small number of pixel detectors are detected while changing relative positions between the object to be observed and any one of the optical system and the detecting system, time series signal information of the optical signals are obtained, and an image associated with an object to be observed from the time series signal information is reconstructed.

According to embodiments, point cloud data transmission method may include encoding point cloud data, encapsulating a bitstream that includes the encoded point cloud data and signaling data into a file, and transmitting the file, the bitstream is stored either in a single track or in multiple tracks of the file, the signaling data include at least one parameter set, and the encoded point cloud data include a geometry bitstream containing geometry data and an attribute bitstream containing attribute data.

An electromagnetic wave phase/amplitude generation device includes a radiation unit configured to radiate electromagnetic waves of a random radiation pattern on a spatial frequency in which a state of the electromagnetic waves to be radiated for each divided region is determined to an imaging object, an imaging unit configured to generate a captured image by imaging scattered electromagnetic waves that are electromagnetic waves generated when the imaging object scatters the electromagnetic waves of the radiation pattern radiated by the radiation unit, and a generation unit configured to generate information indicating at least a phase and amplitude of the electromagnetic waves from the imaging object by performing an arithmetic sparsity constraint operation according to sparsity of the imaging object on the basis of the captured image generated by the imaging unit, information indicating the radiation pattern, and information indicating a signal of the imaging object.

Representative embodiments are disclosed for a rapid and highly parallel decompression of compressed executable and other files, such as executable files for operating systems and applications, having compressed blocks including run length encoded (RLE) data having data-dependent references. An exemplary embodiment includes a plurality of processors or processor cores to identify a start or end of each compressed block; to partially decompress, in parallel, a selected compressed block into independent data, dependent (RLE) data, and linked dependent (RLE) data; to sequence the independent data, dependent (RLE) data, and linked dependent (RLE) data from a plurality of partial decompressions of a plurality of compressed blocks, to obtain data specified by the dependent (RLE) data and linked dependent (RLE) data, and to insert the obtained data into a corresponding location in an uncompressed file. The representative embodiments are also applicable to other types of data processing for applications having data dependencies.