GBT Tokenize Is Developing Real Time Object Detection Algorithms And Techniques For Kirlian Research
GBT Technologies Inc. (OTC PINK: GTCH) is developing real-time algorithms for Kirlian imaging, aiming to analyze this vast graphical data for potential health insights. The company's research involves machine learning to enhance object detection and classification within Kirlian images, focusing on characteristics like shape and color. However, GBT faces challenges including the need for substantial capital and regulatory approvals to implement this technology successfully. The project could provide rapid analysis of health-related data, but its success remains uncertain.
- Development of advanced algorithms for real-time Kirlian imaging analysis.
- Focus on machine learning to enhance pattern recognition and detection.
- Success of technology development is uncertain with no guaranteed outcomes.
- Requirement for significant capital investment to support research.
SAN DIEGO, Feb. 23, 2021 (GLOBE NEWSWIRE) -- GBT Technologies Inc. (OTC PINK: GTCH) ("GBT” or the “Company”), together with GBT Tokenize Corp (“GBT/Tokenize”) is developing real time algorithms and techniques for Kirlian research. A Kirilian image includes vast amount of graphical data. GBT/Tokenize research is aiming to develop a system and method to analyze this data as a potential of health-related information source. These algorithms and methods are pattern detection and recognition, based on unique principles. An image’s objects are categorized according to their physical characteristics like shape, color, texture, and more. A machine learning based flow is targeted to operate as an object’s classifier and analytics processor. We believe that the object detection and analytics method can be used for processing Kirlian images for their characteristics as another stage of GBT/Tokenize’s Kirlian Electrophotography research. Kirlian photography method introduces a series of techniques that are based on the phenomenon known as electrical coronal discharge. This technique produces an object’s energy related images with a colorful representation called aura. When performed on human organs, although not scientifically proven, some believe that these images can be interpreted to analyze health conditions. GBT/Tokenize is researching the development of imaging related techniques to further investigate the data generated from these images for possible health related conclusions. The research is not medical but technical and targeted to conclude a real time analysis of Kirilian images that may be related to human’s health conditions.
The goal of our research is to enable a machine learning algorithm to decide as to whether an image’s object is of interest or not, pointing a possible health related conclusion. A Kirlian image contains a huge amount of data. The detection and analytics of a Kirlian image requires major computational capabilities in a real time operation. A neural network analysis is done to ensure a reliable object’s classification and to train for the detection of objects of interest within a Kirlian image. The approach performs an image based color-based pre-processing, to reach a conclusion about certain pattern and color presented in the image. The goal is to reach a real-time Kirilian image processing with the use of deep learning algorithms and supporting computational hardware resources, achieving advanced imaging conclusions that may provide health related information.
Since a Kirlian image includes a vast amount of information, a private, custom real-time algorithms is planned to be developed. This approach is planned to utilize the analysis of an image object’s neighboring positions, utilizing this data to increase the overall detection speed. The method is contemplated to process a computation of the original objects and their neighboring information to formulate an efficient flow that can be repeatedly executed, achieving a true real-time processing. Finally, advanced algorithmic and hardware related architectures will exploit parallel processing to accelerate the complex computations operations. Data parallelism can be achieved using a CPU that performs programmatic instruction, which is efficient to accelerate large amount of data processing. The company targets this development to achieve a high speed, real time Kirilian imaging processing that may be of use for health-related advice.
“As we continue with the Kirlian Imaging research, we encountered the need for a fast processing of a Kirlian image. A Kirlian image contains a huge amount of data to be analyzed, especially if we are interested in a real time processing. An image that is produced by Kirlian technique includes a colorful, object’s energy representation, called Aura. We are aiming to analyze this Aura, using computational geometry algorithms and neural network algorithms. Each image’s Aura is targeted to be analyzed according to its color, size, shape and pattern. This type of processing may take a long time even with advanced computational geometry approaches. Due to the vast amount of image’s data, we are in the need to develop advanced pattern recognition algorithms that will enable real-time results. We are researching a new detection and analysis approach to categorize each image’s Aura characteristics and features. The method will analyze geometrical objects, their relation to their neighbored objects and to the overall image. Each object will be processed through a module, which we call a classifier to catalog it within similar set of objects. It is our goal to have a neural network-based algorithm performing a parallel processing per objects group to achieve rapid, real-time results. In order to further enhance this processing, we will consider parallel processing of the data by software and supporting hardware, i.e. CPUs, GPUs. We are going to invest significant efforts to develop real-time object detection and pattern recognition methods to reach our goal, which is a speed lightning, real time analysis. This stage of the research is aimed to assist with providing the data on-the-fly, possibly advising into health-related information can be identify underline conditions and symptoms” stated Danny Rittman, the Company’s CTO.
There is no guarantee that the Company will be successful in researching, developing or implementing this technology. In order to successfully implement this technology, the Company will need to raise adequate capital to support its research and, if successfully researched, developed and granted regulatory approval, the Company would need to enter into a strategic relationship with a third party that has experience in manufacturing, selling and distributing this product. There is no guarantee that the Company will be successful in any or all of these critical steps.
Kirlian Research Rendering Analysis sample:
A photo accompanying this announcement is available at: https://www.globenewswire.com/NewsRoom/AttachmentNg/51a2a5ea-ea62-425b-b8bc-fd5541f12c4c
About Us
GBT Technologies, Inc. (OTC PINK: GTCH) (“GBT”) (http://gbtti.com) is a development stage company which considers itself a native of Internet of Things (IoT), Artificial Intelligence (AI) and Enabled Mobile Technology Platforms used to increase IC performance. GBT has assembled a team with extensive technology expertise and is building an intellectual property portfolio consisting of many patents. GBT’s mission, to license the technology and IP to synergetic partners in the areas of hardware and software. Once commercialized, it is GBT’s goal to have a suite of products including smart microchips, AI, encryption, Blockchain, IC design, mobile security applications, database management protocols, with tracking and supporting cloud software (without the need for GPS). GBT envisions this system as a creation of a global mesh network using advanced nodes and super performing new generation IC technology. The core of the system will be its advanced microchip technology; technology that can be installed in any mobile or fixed device worldwide. GBT’s vision is to produce this system as a low cost, secure, private-mesh-network between any and all enabled devices. Thus, providing shared processing, advanced mobile database management and sharing while using these enhanced mobile features as an alternative to traditional carrier services.
Forward-Looking Statements
Certain statements contained in this press release may constitute "forward-looking statements". Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements as a result of various important factors as disclosed in our filings with the Securities and Exchange Commission located at their website ( http://www.sec.gov). In addition to these factors, actual future performance, outcomes, and results may differ materially because of more general factors including (without limitation) general industry and market conditions and growth rates, economic conditions, governmental and public policy changes, the Company’s ability to raise capital on acceptable terms, if at all, the Company’s successful development of its products and the integration into its existing products and the commercial acceptance of the Company’s products. The forward-looking statements included in this press release represent the Company's views as of the date of this press release and these views could change. However, while the Company may elect to update these forward-looking statements at some point in the future, the Company specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing the Company's views as of any date subsequent to the date of the press release.
Contact:
Dr. Danny Rittman, CTO
press@gopherprotocol.com
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