SAN DIEGO, Jan. 05, 2021 (GLOBE NEWSWIRE) — GBT Technologies Inc. (OTC PINK: GTCH) (“GBT”, or the “Company”), announced that GBT Tokenize (“GBT/Tokenize) is developing new methods for health analysis based on its Kirlian Electrophotography research. GBT/Toeknize is developing a set of methods and algorithms to analyze imaging made by Kirlian Electrophotography.
Kirlian photography introduces a series of techniques that are based on the phenomenon known as electrical coronal discharge. Images that are produced using these techniques present a colorful so-called aura. Although not scientifically proven, some believe that these images can be interpreted to analyze health conditions. GBT/Tokenize reiterates that the claim that a medical conclusion can be reached based on analysis of the image (whether through AI or in person) has not been scientifically established. GBT/Tokenize is performing open research from a technological point of view, which cannot be considered medical research or portrayed to be as such.
It is believed by some that the Kirlian imaging process is made by placing an object on a photographic plate that is connected to a source of high-voltage current. A more modern way is using low voltage hand and head sensors to produce visual, interactive data that may represent health energy information. Kirlian imaging can produce organs energetic visualization such as graphical protuberances, halos, and discharge patterns, which can be analyzed by computer program to identify unique patterns. GBT is now developing a set of computational geometry algorithms targeted to be the base for a further AI analysis. Computational geometry is a mathematical field that involves the design, analysis and implementation of efficient algorithms for solving geometric problems. Advanced applications that typically use computational geometry methods are pattern recognition, computer vision, animation and graphics, (CAD) computer-aided design, robotics, and similar especially when require real-time speeds. We are developing a private, derived set of algorithms in order to classify the combinatorial and numerical computational geometry of Kirlian images. Each set is designed to identify, analyze and categorize images according to its parametric surfaces and curves, for example, spline curves and Bezier curves. This information will be fed to a machine learning program. Our algorithms are identifying and evaluating the image’s surfaces and parts of surfaces from particular viewing angles in order to categorize and classify anomalies.
“Kirlian imaging of living tissue may exhibit energy levels that can be visualized as auras. We are now developing a set of geometrical computation methods and algorithms to graphically analyze these images further feeding the data into our machine learning programs. The target is to reach consistent and reliable conclusions with respect to the analysis of the image and any anomalies. Computational geometry is a field of computer science that is aimed to solve problems stated in terms of geometry. Here we are implementing these techniques for Kirilian images to find unique patterns. The development of these geometrical algorithms is part of our image processing long term plan and will be further used during next year for AI imaging analytics. For example, we develop a specific version of Scanline method which is an algorithm that typically works on a row-by-row basis rather pixel-by-pixel basis. We created a new flow to identify critical vertexes only and then analyze pixel-by-pixel. This enables light speed image analysis in order to search and classify image’s points of interest. The main advantage of our version is that the sorting of crucial vertices only, is done in parallel with the normal scan, significantly shortening the overall scan time and reducing objects-of-interest detection time. We develop plane sweep based algorithms that works via a conceptual sweep line to analyze Kirilian images as an infrastructure for our AI program. The main idea is to scan across the image, stopping at object of interest vertexes (points) to identify patterns and consistencies. These geometric operations will become geometric objects that will intersect with others to form a human organ aura. By looking for full and partial similarities, repetitions, or atypical auras patterns, our goal is to be able to detect dynamic images changes in time. In addition, another set of computational geometry algorithms will search object’s boundaries in order to detect specific patterns and finding images correlations. The geometrical computation engine will be the first step to extract the base data from the Kirilian images and later processed by the AI engine.”
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.
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Dr. Danny Rittman, CTO
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