View source for Nec corporation (20240125935). ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM simplified abstract

=ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM=


==Organization Name==

[[:Category:nec corporation|nec corporation]]


[[Category:nec corporation]]

==Inventor(s)==

[[:Category:Tsubasa Nakamura of Tokyo (JP)|Tsubasa Nakamura of Tokyo (JP)]][[Category:Tsubasa Nakamura of Tokyo (JP)]]

==ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240125935 titled 'ARITHMETIC OPERATION SYSTEM, TRAINING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM STORING TRAINING PROGRAM

==Simplified Explanation==

The abstract describes a system where an evaluation unit calculates the difference between a teaching signal and an estimated signal. The teaching signal is obtained by integrating spatial distribution signals observed by a sensor using emission waves in a region of interest, while the estimated signal is calculated by integrating estimated density from a spatial estimation model based on sample points.

* Evaluation unit calculates difference between teaching signal and estimated signal
* Teaching signal obtained by integrating spatial distribution signals observed by sensor using emission waves
* Estimated signal calculated by integrating estimated density from spatial estimation model based on sample points

== Potential Applications ==
This technology could be applied in various fields such as medical imaging, environmental monitoring, and industrial quality control.

== Problems Solved ==
This technology helps in accurately estimating signals in a spatial distribution, which can be crucial in fields where precise measurements are required.

== Benefits ==
The system provides a method for calculating differences between signals accurately, leading to improved data analysis and decision-making processes.

== Potential Commercial Applications ==
One potential commercial application of this technology could be in the development of advanced sensor systems for various industries, including healthcare and manufacturing.

== Possible Prior Art ==
Prior art in this field may include existing systems for signal estimation and analysis using spatial data, but the specific integration of emission waves and spatial estimation models as described in this patent application may be novel.

=== Unanswered Questions ===

=== How does this technology compare to existing signal estimation methods in terms of accuracy and efficiency? ===
This technology offers a unique approach to signal estimation by integrating emission waves and spatial estimation models. It would be interesting to compare its performance with traditional methods in terms of accuracy and efficiency.

=== What are the potential limitations or challenges in implementing this technology in real-world applications? ===
While the abstract outlines a sophisticated system for signal estimation, there may be practical challenges in implementing this technology in various industries. Understanding these limitations can help in refining the technology for practical use.


==Original Abstract Submitted==

in an arithmetic operation system, an evaluation unit calculates a difference amount between a teaching signal and an estimated signal. the teaching signal has a value that is obtained by integrating spatial distribution signals observed by a sensor using emission waves for a spatial structure along a region of interest in an emission wave region in which emission waves are emitted from a plurality of emission reference directions and reach the sensor. the region of interest is a curved line region or a curved surface region intersecting the plurality of emission reference directions. this estimated signal is calculated by integrating a plurality of pieces of estimated density of a plurality of sample points obtained from a spatial estimation model by having a sampling unit input information about a position of each of the plurality of sample points on the region of interest to the spatial estimation model.



[[Category:G01S17/89]]