A system and method for hologram synthesis and processing capable of synthesizing holographic 3D data and displaying (or reconstructing) a full 3D image at high speed using a deep learning engine. The system synthesizes or generates a digital hologram from a light field refocus image input using the deep learning engine. That is, RGB-depth map data is acquired at high speed using the deep learning engine, such as a convolutional neural network (CNN), from real 360° multi-view color image information and the RGB-depth map data is used to produce hologram content. In addition, the system interlocks hologram data with user voice recognition and gesture recognition information to display the hologram data at a wide viewing angle and enables interaction with the user.

The LIDAR system includes a first transform component configured to perform a complex mathematical transform on first signals. The LIDAR system also includes a second transform component configured to perform a real mathematical transform on second signals. Electronics are configured to use an output of the first transform component in combination with an output of the second transformation component to generate LIDAR data. The electronics are further configured to use a peak in the output of the first transform component to identify the peak in the output of the second transform component that is located at the beat frequency of the second signals.

The LIDAR system includes a first transform component configured to perform a complex mathematical transform on first signals. The LIDAR system also includes a second transform component configured to perform a real mathematical transform on second signals. Electronics are configured to use an output of the first transform component in combination with an output of the second transformation component to generate LIDAR data. The electronics are further configured to use a peak in the output of the first transform component to identify the peak in the output of the second transform component that is located at the beat frequency of the second signals.

Disclosed is an operation device. The operation device includes a memory storing at least one instruction; and a processor configured to execute the at least one instruction, and the processor, by executing the at least one instruction, may perform encoding or decoding for an approximate homomorphic ciphertext using a predetermined matrix having only a half of an element of a matrix corresponding to a canonical embedding function.

A method for predicting variables of interest related to a system includes collecting one or more sensor streams over a time period from sensors in the system and generating one or more anomaly streams for the time period based on the sensor streams. Values for variables of interest for the time period are determined based on the sensor streams and the anomaly streams. Next, a time-series predictive algorithm is applied to the (i) the sensor streams, (ii) the anomaly streams, and (iii) the values for the variables of interest to generate a model for predicting new values for the variables of interest. The model may then be used to predict values for the variables of interest at a time within a new time period based on one or more new sensor streams.

A method for predicting variables of interest related to a system includes collecting one or more sensor streams over a time period from sensors in the system and generating one or more anomaly streams for the time period based on the sensor streams. Values for variables of interest for the time period are determined based on the sensor streams and the anomaly streams. Next, a time-series predictive algorithm is applied to the (i) the sensor streams, (ii) the anomaly streams, and (iii) the values for the variables of interest to generate a model for predicting new values for the variables of interest. The model may then be used to predict values for the variables of interest at a time within a new time period based on one or more new sensor streams.

A quantum circuit for Daubechies-6 wavelet transform includes: a B quantum circuit configured to receive a first part of n-dimensional data and generate a first intermediate result; a Q.sub.2.sub.n.Math.Q.sub.2.sub.n quantum circuit configured to receive a second part of the n-dimensional data, and the Q.sub.2.sub.n.Math.Q.sub.2.sub.n quantum circuit coupled to the B quantum circuit to receive the first intermediate result, and the Q.sub.2.sub.n.Math.Q.sub.2.sub.n quantum circuit generating a second intermediate result corresponding to the first intermediate result and a first result corresponding to the second part; and an A quantum circuit coupled to the Q.sub.2.sub.n.Math.Q.sub.2.sub.n quantum circuit to receive the second intermediate result and to generate a second result according to the second intermediate result. The present disclosure further discloses a manufacturing method of a quantum circuit for Daubechies-6 wavelet transform and a quantum circuit for Daubechies-6 wavelet inverse transform corresponding to the aforementioned quantum circuit for Daubechies-6 wavelet transform.

A signal analysis method is described. The signal analysis method comprises the following steps: An input signal function associated with a time domain is obtained. A window function is determined based on the input signal function via an artificial intelligence module. The artificial intelligence module comprises at least one computing parameter, wherein the window function is determined based on the at least one computing parameter. The input signal function and the window function are convolved, thereby generating a convolved signal. Further, a signal analysis module is described.

A signal analysis method is described. The signal analysis method comprises the following steps: An input signal function associated with a time domain is obtained. A window function is determined based on the input signal function via an artificial intelligence module. The artificial intelligence module comprises at least one computing parameter, wherein the window function is determined based on the at least one computing parameter. The input signal function and the window function are convolved, thereby generating a convolved signal. Further, a signal analysis module is described.

A method and apparatus are disclosed which enable the analysis of a break in a vehicle glazing panel without the attendance of a technician, the method and apparatus utilize capturing an image of the break and processing the image of the break to enable the suitability for repair or replacement of the glazing panel to be determined.