How Does Non-locality Influence Our Perception of Remote Sensing Images?

In summary, the conversation discusses the important concepts of metrics, dimensions, and DNA in relation to remote sensing and the understanding of images. It delves into the theories of non-locality and perception, and how they relate to the multidimensional continuum. The discussion also explores the definitions and correlations of dimensions, both in terms of space-time and spectral density. The concept of metrical dimensions and their role in creating coherent images is also discussed. Finally, the conversation briefly touches on the topic of intronal-exonal DNA/RNA sequences and their relationship to dimensional continuums.
  • #1
Ramanujan12
Metrics, Dimensions, DNA

In order to understand the images processed and viewed in remote sensing it is important to understand where these images are located, as products, and what the scaling for these domains actually constitutes in the multidimensional time-series. Non-locality theory suggests that all information that determines our sensing of the real world cannot be justifiably located by the given vectors of our sensing. Perception of information in the posterior space must be only the synthetic response product from a prior space. Priority and locality define the original reality and the location which it synthetically appears. Non-local space/time has a metrical structure that prioritizes the projection of positions and locational domains for sense objects. Such a concept as this non-local model is very dynamical and can be used as a metaphor for describing how the basic order follows from a remotely sensed image to the product space (product measure) where the dimensional product resides. All phenomena that is sensed through remote channels is eidetic in character and represents the multidimensional continuum. Remote sensing of an eidetic residuum for instance always corresponds to it’s memory phase in the multidimensional continuum. Eidetic here relates to memory, the recognition and sensing of deep non-local memory that is primarily optical in nature, say 70%, and can carry various other synesthesia overtones (combined multi-senses).

What this section seeks to provide is a correlary definition of dimension. Dimensions correlate through a definitive relation of various conceptual domains that really define the same thing. Various meanings express the idea of dimensionality and these structures are enfolded yet dependant on a singular prerequisite that is the original source of the dimensional concept. Dimensions are areas of space-time/matter extensions that constitute the domain of a dimensional universe. This is what we refer to as the posterior density model for defining dimensionality, a local definition of space, cosmology, and universality. On the other definition of dimension we refer the areas of space, cosmology, and universality as a spectral density. Both posterior density and spectral density are functions, the former is for distributions ie. the distribution of space as material extensions, and the latter is for the measure of spectra ie. measurements of color, frequency and wave numbers. Even though the external (posterior) definition of dimension has it’s density in material extension, such as 1 dimensional point, 2-dimensional plane, 3-dimensional sphere, 4-dimensional time; the posterior density is a distribution function. The derivation of numerical dimensions such as 3 dimensional space are based on the axis or number of extensions that space is capable of distributing for material objects. And how does this matter and relate to the definition of spectral density? It matters and relates because distributions themselves are image measures and spectral density is a function of frequency measurement for color waves. Posterior density therefore is rooted, prior to having it’s density, inside the image-measure which relates to color measure eg. chromodynamical morphology. Light geometry itself is conical on large scale but perhaps cylindrical on small scale. Color is the response to the large scale evolution in the human optical receptors for cones in the human eye. Cones for color reception facilitate the spectral units of measurement representing 3-dimensional objects that we call color.

Here the derived concept of dimensionaliy used commonly to express the universe we inhabit and the space-time continuum which capacitates the extensions of material objects. This is the posterior or externally local measure for density. Density itself can be more than just a physical value for extended/distributed objects in space, it can also be interpreted as frequency and color range eg. spectral density. Dimensions are also commonly referred to as units of measurement, often confused as lengths or volumes, they are the meter, the gallon, the centigrade, the pound etc. Units of measurement we will formally call “metrical dimensions”. Metrical dimensions in their pure form are dimensions without transformation of distortion. In other words they are spectral densities of frequency measurement that have not been sensed as posterior. What this means is that the metrical dimensions undergo posteriorization into an extended image or distribution from their transformation by the senses. Knowing this we can see that the underlying non-local interference pattern of an extended dimension for space-time and matter is a projective structure of image measures prioritized by the purely metrical dimensions of spectral density. Hence frequency measure, units of measurement, and therefor color itself determines the localizability of distributed quantities in space. Purely metrical dimensions are sensed as “distortions” or “interference patterns”, they are transformed by the observer or the apparatus of sense into coherent images. This is what is called a coherency pattern. When fundamental dimensions (metrical dimensions) are made to be coherent, the derived or derivational dimensions (space-time extensions) are combinations of fundamental metric dimensions. In other words it requires a combinatorial sequence of metrical dimensions for a single unit of measure, for a single dimension or interference pattern to cohere within an extended universe of space. At the the same time it is the metrical dimensions ‘meta-language’ or syntactical sequencing of it’s measures which combine to form a coherency pattern that represents this function. The function itself is a kind of linear transformation not unlike the formalizing of grammar and syntax into meaningful language, a Laplace or a Fourier transform. It is this sequencing, this combining of purely measure-valued dimensions that localizes sense information, makes interference patterns coherent, distributes material reality into a dimensional system of extensions, and from here we turn to the next section.


Intronal-Exonal DNA/RNA Sequences and Dimensional Continuums

To Be Continued-



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  • #2
Wow Raman, your writing "Metrics, Dimensions, DNA" has been a huge boon to my mathematics comprehension and understanding. Thank you Man!

Avron
 
  • #3
Wow Raman, your writing "Metrics, Dimensions, DNA" has been a huge boon to my mathematics comprehension and understanding. Thank you Man!

Avron
 

What are metrics?

Metrics are quantifiable measures used to track and evaluate the performance or progress of a system, process, or project. They are often used to make data-driven decisions and improve efficiency.

What are dimensions in the context of data analysis?

Dimensions refer to the different characteristics or attributes of data that can be analyzed, such as time, location, or customer demographics. They provide context and help to categorize and organize data for analysis.

What is DNA?

DNA (deoxyribonucleic acid) is a molecule that contains the genetic instructions for the development, functioning, and reproduction of all living organisms. It is made up of four chemical bases (adenine, guanine, cytosine, and thymine) and carries the genetic information that determines an organism's traits.

How are metrics and dimensions related in data analysis?

Metrics and dimensions are both used in data analysis to gain insights and make informed decisions. Metrics provide the quantifiable values, while dimensions provide the context and categorization for those values. Together, they help to paint a full picture of the data being analyzed.

What is the role of DNA in genetics and biology?

DNA is the fundamental building block of all living organisms and plays a vital role in genetics and biology. It contains the instructions for the development, functioning, and reproduction of all living things, and is responsible for passing on traits from one generation to the next through the process of genetic inheritance.

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