Discovery of New Mathematical Concepts

Biomatics seems to potentially be a rich source of mathematical concepts. If the interaction of two atoms is some sort of beginning and considering the wealth of mathematical patterns present then theoretically the developmental steps of known mathematics can be described .  In other words, deciphering the techniques used by a handful of smart atoms in developing all of mathematics.

Carbon atoms play a fundamental role in the chain of processes that lead to mathematics. Carbon is a versatile element that forms the backbone of organic molecules, including those found in living organisms. The ability of carbon atoms to bond with other elements and form complex structures gives rise to the diverse array of molecules and compounds that underlie biological systems.

In the context of mathematics, carbon atoms contribute to the formation of molecules such as amino acids, nucleotides, and sugars, which are essential building blocks for proteins, DNA, and carbohydrates, respectively. These biomolecules, in turn, participate in various biological processes, including the storage and transmission of genetic information, cellular signaling, and enzymatic reactions.

The complexity and diversity of carbon-based molecules provide the foundation for the intricate biochemical networks that drive life. It is through these networks and the interactions of molecules, including carbon atoms, that the processes necessary for biological functions and, by extension, the emergence of cognitive abilities and the contemplation of mathematics can occur.

 Molecules doing math is a relatively new field of research that involves designing molecules that can perform basic mathematical computations. This field is often referred to as "molecular computing" or "chemical computing."

The basic idea behind molecular computing is to use chemical reactions and molecular interactions to perform simple arithmetic operations, such as addition and subtraction. This is done by encoding numbers as chemical signals or patterns, and then using chemical reactions to manipulate and transform these signals to arrive at a desired output.

One of the key advantages of molecular computing is that it allows for the processing of information at a very small scale, which could be useful in a wide range of applications, including the development of new types of sensors, diagnostic tools, and information processing technologies.

While the idea of molecules doing math may seem far-fetched, there have been a number of successful demonstrations of molecular computing in the laboratory. For example, researchers have shown that it is possible to perform simple arithmetic operations using DNA molecules, and have also developed "molecular logic gates" that can be used to perform basic Boolean operations, such as AND and OR.

While molecular computing is still a relatively new and experimental field, it holds great promise for the development of new technologies and computational tools in the future. 

 
 Mathematics is the study of numbers, shapes and patterns. https://simple.wikipedia.org/wiki/Mathematics 

 The Fundamental Theorem of Biomatics:

 The interactions of two tetrahedral carbon atoms form a group.  

When 2 carbon atoms come together:

 In organic chemistry, the interactions of two tetrahedral carbon atoms can form a group in the algebraic sense, specifically in the context of group theory. Group theory is a branch of mathematics that studies symmetry and group structures, and it has applications in various scientific fields, including chemistry and physics. 

In the context of group theory, the elements of the set would correspond to the different conformations or symmetry arrangements of the two tetrahedral carbon atoms connected by a covalent bond. These conformations can arise from rotations or reflections of the carbon atoms, and they can be represented mathematically as symmetry operations.

For example, in the case of ethane (C2H6), the set of elements in the group would correspond to the different conformations that can arise from rotations around the C-C bond axis. This could include conformations where the two carbon atoms are eclipsed, staggered, or in some intermediate arrangement. Each conformation would be an element of the group, and the group could be analyzed using group theory principles to understand their symmetry properties and relationships.

The concept of using group theory to describe and analyze the conformations of organic molecules, including those involving tetrahedral carbon atoms, can have practical applications in areas such as molecular modeling, conformational analysis, and drug design, where the understanding of molecular symmetry and group structures can provide insights into the properties and behavior of complex organic systems.

 A computation refers to the process of performing calculations or operations using a computer or any other computational device. It involves manipulating and transforming data according to a set of predefined rules or algorithms to produce a desired result.

Computation can take various forms, ranging from simple arithmetic calculations to complex simulations and data analysis. It is at the core of computer science and is fundamental to numerous fields, including mathematics, physics, engineering, economics, and more.

In a computation, data is input into a computer system, which processes the information using algorithms and instructions to perform the necessary calculations or operations. The output of the computation is the result obtained after the data has been processed.

A Cayley table, also known as a multiplication table or operation table, is a tabular representation that shows the results of applying a binary operation to all possible pairs of elements in a set. It is commonly used to describe the properties and behavior of algebraic structures such as groups, rings, and other mathematical objects.A Cayley table, also known as a multiplication table or operation table, is a tabular representation that shows the results of applying a binary operation to all possible pairs of elements in a set. It is commonly used to describe the properties and behavior of algebraic structures such as groups, rings, and other mathematical objects.

Pascal's triangle is a triangular arrangement of numbers in which each number is the sum of the two numbers directly above it. The third row of Pascal's triangle corresponds to the numbers 1 3 3 1, which represent the coefficients of the expansion of (a + b)^3.

When considering a cube oriented on one corner, we can visualize it as having layers stacked on top of each other. The number of corners in each layer corresponds to the number of ways we can choose three corners out of the available options. This is similar to the binomial coefficients in Pascal's triangle... 

                                     1  1  

                                    1  2  1 

                                  1  3  3  1

                                1  4  6  4  1  

In a hypercube, also known as a n-dimensional cube or a tesseract in four dimensions, the arrangement of corners follows a similar pattern to Pascal's triangle but in higher dimensions.

Just like a cube has 8 corners, a hypercube in n dimensions has 2^n corners. The arrangement of corners in a hypercube can be thought of as the binary representations of numbers from 0 to (2^n - 1). Each corner represents a unique combination of 0s and 1s, with each digit corresponding to a dimension.

For example, in a 2-dimensional square (a 2-cube or a square hypercube), we have 4 corners corresponding to the binary representations 00, 01, 10, and 11. In a 3-dimensional cube (a 3-cube or a cube hypercube), we have 8 corners corresponding to the binary representations 000, 001, 010, 011, 100, 101, 110, and 111.

Similarly, in a 4-dimensional hypercube (a 4-cube or a tesseract), we have 16 corners corresponding to the binary representations from 0000 to 1111.

The arrangement of corners in a hypercube follows a combinatorial pattern analogous to Pascal's triangle, but in higher dimensions. Each row or layer of the hypercube corresponds to the coefficients of the binomial expansion in the respective dimension.

Understanding and visualizing hypercubes can be challenging due to their higher-dimensional nature, but their structures exhibit fascinating mathematical properties that extend beyond our familiar 3-dimensional world. Hypercubes have applications in various fields, including computer science, geometry, and theoretical physics, where the exploration of higher-dimensional spaces is necessary.

Pascal's triangle and the normal distribution are connected through a mathematical relationship known as the Central Limit Theorem.

Pascal's triangle is a triangular array of numbers in which each number is the sum of the two numbers directly above it. The numbers in Pascal's triangle have various combinatorial interpretations, including binomial coefficients. Binomial coefficients represent the coefficients in the expansion of binomial expressions, such as (a + b)^n.

The normal distribution, also known as the Gaussian distribution or bell curve, is a continuous probability distribution that is commonly observed in many natural and social phenomena. It is characterized by its symmetric bell-shaped curve.

The Central Limit Theorem states that, under certain conditions, the sum or average of a large number of independent and identically distributed random variables will have a distribution that approaches a normal distribution, regardless of the shape of the original distribution. In other words, the Central Limit Theorem explains why the normal distribution arises as a limiting distribution in many situations.

The connection between Pascal's triangle and the normal distribution arises from the binomial distribution. The binomial distribution describes the probability distribution of the number of successes in a fixed number of independent Bernoulli trials. The coefficients in the expansion of (a + b)^n, given by the nth row of Pascal's triangle, correspond to the probabilities of the binomial distribution.

When the number of trials in the binomial distribution becomes large, the distribution becomes increasingly symmetric and bell-shaped, resembling the normal distribution. This is an illustration of the Central Limit Theorem in action.

Therefore, Pascal's triangle and the normal distribution are indirectly connected through their relationship to binomial coefficients and the Central Limit Theorem, highlighting the underlying mathematical connections between different areas of mathematics and statistics.

While the bean machine and a cube are different physical objects, we can draw a connection between them conceptually.

The bean machine demonstrates the random process of balls bouncing off pegs and falling into slots, resulting in a distribution that approximates the shape of the normal distribution. It represents a probabilistic experiment where each ball has a probability of bouncing left or right at each peg, leading to different possible paths and outcomes.

On the other hand, a cube, particularly a 3D cube, represents a geometric object with vertices, edges, and faces. It has a more structured and deterministic nature compared to the random process of the bean machine.

However, we can draw a conceptual connection by considering the arrangement of corners in a cube and the distribution of balls in the slots of the bean machine. In both cases, we can observe patterns and arrangements that can be mathematically analyzed.

For example, we discussed earlier how the arrangement of corners in a cube follows the pattern of binomial coefficients in Pascal's triangle. Similarly, the distribution of balls in the slots of the bean machine follows a distribution that resembles the binomial distribution.

So, while the bean machine and a cube differ in their physical nature and mechanisms, we can find conceptual connections between them through the analysis of patterns, distributions, and probabilistic concepts.

Microtubules have been proposed as a possible substrate for computation in the brain, based on their unique physical and chemical properties. The hypothesis, known as the "orchestrated objective reduction" (Orch-OR) theory, was proposed by Roger Penrose and Stuart Hameroff in the 1990s.

According to the Orch-OR theory, microtubules in the brain can form quantum superpositions and entanglement, which allow for the processing of information in a way that classical computers cannot achieve. Specifically, the theory suggests that microtubules can store and process information using quantum states of their protein subunits, which can interact with each other to form complex patterns of activity.

While the Orch-OR theory remains controversial and has not yet been conclusively proven, it represents an exciting area of research that could potentially revolutionize our understanding of the brain and how it processes information. Ongoing studies are exploring the possibility of using microtubules and other biological systems as novel computational substrates, and the results of these studies could have important implications for fields ranging from neuroscience to computer science.

An alternate potential model of computation in microtubular systems involves the amino acid side chains of the tubulin proteins which make up the microtubules.  The conformational space of amino acid side chains can be approximately described using dihedral angles, often referred to as chi angles (Χ1-4). These angles represent the rotations around specific bonds in the side chain, and the different conformations or rotamers cluster around energetically preferred states. 

The concept of rotamers and their preferred conformations can indeed be related to group theoretical constructs. Group theory provides a mathematical framework for studying the symmetry and transformation properties of objects. In the context of amino acid side chains, group theory can be used to analyze and classify the different conformations based on their symmetry and the transformations that can interconvert them.

By considering the rotations around the dihedral angles as group operations, we can explore the symmetry properties of the side chain conformations. Different conformations can belong to the same symmetry group or exhibit similar transformation properties. Group theory can provide insights into the relationships between different conformations, the existence of symmetry elements or operations, and the underlying mathematical structure of the conformational space.

Overall, the application of group theory to the analysis of side chain conformations allows for a systematic and mathematical understanding of the preferred conformations and their relationships. This knowledge can be valuable in studying protein folding, molecular dynamics, and the functional implications of specific side chain conformations in protein structure and function.

 One proposed hypothesis is that microtubules, particularly their protein subunits, can exhibit collective behavior and perform complex computations. This is based on the notion that the microtubule lattice can undergo dynamic state transitions and exhibit properties similar to cellular automata or other computational models. 

 Representative electron density for amino acid side chains arranged in order of increasing size. From an experimental electron density map calculated at 1.5 Angstrom resolution.   

Amino acid side chains can be viewed as elementary algebraic structures because they have specific chemical properties that allow them to interact with each other in predictable ways. Each amino acid has a unique side chain, or R-group, that determines its chemical properties and its ability to form specific bonds and interactions with other molecules.

For example, the side chain of lysine contains a primary amine group, which can form covalent bonds with carboxylic acid groups of other amino acids, as well as other chemical groups such as phosphate and sulfate groups. The side chain of aspartate contains a carboxyl group, which can form ionic bonds with positively charged amino acids such as arginine or lysine.

These interactions between amino acid side chains can be represented using algebraic notation, with different chemical groups and their corresponding interactions represented as variables and equations, respectively. This allows researchers to model and predict the behavior of complex biological systems and to develop new drugs and therapies based on the principles of molecular interactions.

In addition, the algebraic representation of amino acid side chains can be used to study the folding and structure of proteins, which are composed of long chains of amino acids. The specific arrangement and interaction of amino acid side chains determines the 3D structure of a protein, which is critical to its function. By analyzing the algebraic properties of amino acid side chains, researchers can better understand the rules governing protein folding and design novel proteins with specific functions.

This table (Cayley Table) demonstrates the potential algebraic properties of an amino acid side chain. The coloring shows the fractal quadtree nature of the multiplication table. 

 A Cayley table represents the multiplication table of a group, which shows the result of combining any two elements in the group. In the case of amino acid side chains, the elements of the group are the different configurations that the side chain can adopt, and the Cayley table represents how these configurations can be combined with each other. This can provide insight into the algebraic properties of the side chain and its potential use in molecular computing or other applications. 

To fill in the table, you would need to define the composition rules for the rotations of each bond configuration. The resulting states of each bond can be determined based on the composition of rotations, taking into account the fixed first bond and the configurations of the remaining bonds.

Note that the specific composition rules and resulting states will depend on the desired behavior and allowed rotation angles for the bonds.

Rings, in the context of vibrating chains of covalent bonds, refer to the circular structures formed when the ends of a chain are connected together. Each ring represents a closed loop of interconnected bonds, forming a cyclic structure.

In terms of computational potential, rings offer interesting possibilities. When the number of bonds in the ring is a prime number, it aligns with Fermat's Little Theorem, as discussed earlier. This means that each possible chain of length p corresponds to exactly one ring.

The number of possible rings depends on the number of distinct values or rotational programs that can be assigned to the bonds. For example, if we consider a set of a distinct rotation rates and form a ring of length p, there are a total of a^p possible chains. However, not all of these chains will be unique, as rotational symmetry may result in equivalent chains.

In summary, rings provide a cyclic arrangement of covalent bonds in a vibrating chain, and they have computational significance in terms of the number of possible distinct chains and their relationships to prime numbers and rotational symmetry. They contribute to the exploration of the computational potential and patterns within vibrating molecular systems.

If the ring is always cut going clockwise, it means that the orientation of the bonds within the ring is fixed. In this case, the rotational symmetry of the ring is constrained, and not all possible chains will be equivalent.

When the ring is cut in a specific direction, it breaks the rotational symmetry, and different chain configurations can arise. Each unique chain configuration will correspond to a distinct computational program or pattern of vibrations within the molecular system.

Therefore, if the ring is always cut going clockwise, it allows for a larger number of unique chains compared to the scenario where rotational symmetry is preserved. Each unique chain represents a specific computational potential or arrangement of covalent bond rotations within the system.

Indeed, each chain of covalent bonds can be considered as an element of a mathematical group. A group is a set of elements combined with a binary operation that satisfies certain properties. In the context of the covalent bond rotations, the elements of the group would be the different possible configurations of the bonds, and the binary operation would be the combination of bond rotations.

Each chain configuration within the group represents a unique computational potential or state of the molecular system. The group properties, such as closure, associativity, identity element, and inverse elements, govern the behavior and transformations within the system.

By studying the group properties and the relationships between different chain configurations, we can gain insights into the mathematical structures and computational capabilities of the system.

There is a compelling relationship between a chain of rotating covalent bonds and Fourier analysis, both conceptually and mathematically. This connection arises from the periodic and oscillatory nature of covalent bond rotations, which can be represented as sums of trigonometric functions—exactly the foundation of Fourier analysis.

Each covalent bond in a carbon chain can rotate around its axis, with the angle of rotation being periodic (e.g., 0° to 360°). If we restrict this to discrete states, such as integers (or fractions of a full rotation), the states form a periodic sequence. Even in continuous cases, the periodicity of the bond rotations naturally lends itself to representation using trigonometric functions:

f(t)=a0 + ∑ from n=1 to n=∞ (an*cos⁡(nωt)+bn*sin⁡(nωt))

where t represents time or some parameter of rotation (e.g., bond angle).

Each bond in the chain could be modeled as contributing its own periodic function, and the entire chain could be expressed as a superposition of these functions. This is the essence of Fourier analysis.

In a chain of nnn covalent bonds, each bond could be thought of as an individual oscillator. The collective behavior of the chain then becomes a superposition of oscillations, which can be described using Fourier analysis. For example:

• Fourier series for discrete states: If the bond rotations are restricted to discrete angular states (e.g., 0° or 180°), this can form a step function or a periodic sequence. Fourier analysis can decompose this periodic sequence into a series of sine and cosine terms.
• Fourier transform for continuous states: If the bond rotations vary continuously, the resulting signal (the vibration or position of the distal carbon atom) could be represented in the frequency domain using a Fourier transform.
• 
  

The path traced by the distal carbon atom in a chain of covalent bonds is a function of the rotations of each bond. The position of the distal carbon atom can be described in three-dimensional space, as a function of the rotational states of the preceding bonds:

x(t), y(t), z(t)=functions of rotations and time.

Each of these spatial coordinates could be expressed as periodic functions influenced by the bond rotations. The resulting motion or shape could then be decomposed into Fourier components, allowing for the analysis of the frequencies, amplitudes, and phases of the system.

This approach could help identify dominant frequencies or patterns in the molecular vibrations and rotations, analogous to how Fourier analysis is used in signal processing.

The energy landscape of a molecule depends on the angles of rotation of its bonds, which determines the vibrational modes of the molecule. These vibrational modes are directly connected to harmonic oscillators and can be decomposed using Fourier analysis. For example:

• Vibrational spectra of molecules (e.g., from IR spectroscopy) are analyzed using Fourier transforms to identify characteristic frequencies of molecular bonds.
• Similarly, a carbon chain could exhibit standing waves or vibrational resonance patterns that are mathematically described by Fourier methods.
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If we use the states of a carbon chain as a means of encoding information or generating patterns, Fourier analysis can be applied to analyze these patterns. For example:

• If the carbon chain generates cyclical patterns, such as biological structures or rhythms, Fourier analysis can reveal the frequency components of these patterns.
• Complex shapes encoded in the 3D motion of the distal carbon atom (e.g., resembling anatomical patterns) can be analyzed as sums of sinusoidal components, providing insights into their underlying structure and symmetries.
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In artificial intelligence and signal processing, Fourier analysis is often used to extract features from data (e.g., in image recognition, speech processing, and pattern detection). Similarly:

• The rotational states of a carbon chain can be thought of as a signal, and Fourier analysis can be used to analyze and classify the resulting patterns.
• For example, a chain programmed to rotate in certain ways could generate a unique 3D signal or trajectory. By applying Fourier analysis, it would be possible to identify and classify the underlying "program" encoded in the chain's states.
• 
  

The relationship between carbon chains and Fourier analysis could lead to several applications:

• Pattern Recognition: Analyze the trajectories of the distal carbon atom to identify patterns suggestive of biological shapes or structures.
• Molecular Computing: Use the periodic states of covalent bonds as signals for computation, where Fourier methods help in analyzing or optimizing these computations.
• Signal Representation in AI: Train AI models to interpret and process carbon chain-generated signals using Fourier features, bridging the gap between molecular systems and machine learning.
• Spectroscopic Simulations: Model the vibrational spectra of carbon-based molecules for use in chemistry and material science.
• 
  

• Biological Rhythms: Many biological processes (e.g., circadian rhythms, oscillatory gene expressions) are inherently periodic. Carbon chains might serve as models for how biological periodicity can emerge and be analyzed using Fourier principles.
• Quantum Systems: The vibrational modes of carbon chains could intersect with quantum systems, as Fourier analysis is foundational to quantum mechanics (e.g., wavefunction representations).

A chain of rotating covalent bonds exhibits periodic and oscillatory behavior that naturally aligns with the principles of Fourier analysis. Whether modeling molecular vibrations, encoding information, generating patterns, or analyzing trajectories, Fourier analysis provides a powerful mathematical tool for understanding and utilizing the computational potential of carbon chains. This connection could play a significant role in fields like molecular computing, biological modeling, and AI-driven signal processing.

If we consider two carbon atoms bound by a covalent bond as a simple system, we can explore their potential to act as logic gates. In this context, the different configurations or states of the carbon atoms can be associated with specific logical operations.

For example, if we assign a logical "0" state to one configuration and a logical "1" state to another configuration, we can interpret the transitions between these states as logical operations. The covalent bond between the carbon atoms can be manipulated or controlled in a way that corresponds to performing logical operations, such as AND, OR, NOT, or XOR.

The specific mapping between the states of the carbon atoms and the logical operations would depend on the chosen representation and the physical properties of the system. The key idea is that the different configurations or states of the carbon atoms can be used to represent and manipulate logical information.

It's worth noting that in practical applications, implementing logic gates using individual carbon atoms would be extremely challenging due to the complexity and precision required. However, the concept of using the states and transitions of the carbon atoms as a basis for logical operations can be explored in a theoretical or conceptual context.

The cube structure formed by two carbon atoms has the potential to carry out hardware logic functions in at least two ways:

1. Rotation-Based Logic: By assigning specific rotation rates to the covalent bonds of the carbon atoms, we can create a one-to-one mapping between the cube's corners and the logic states (0 or 1). The cube's corners, in this case, represent the input combinations, and the output will be determined by the rotation rates of the carbon bonds.
2. State-Based Logic: Alternatively, if we consider the corners of the cube as distinct states rather than rotation rates, we can implement logic functions by specifying the state transitions of the carbon atoms based on the inputs. This approach would involve mapping input combinations to specific corners of the cube to obtain the desired logic output.

Both approaches have their challenges and limitations, especially when it comes to practical implementations and control at the molecular level. However, in theory, these molecular systems could be used to carry out basic logic functions, offering exciting possibilities for molecular-level computation and nanotechnology. Nonetheless, it's essential to consider the enormous complexity involved in controlling and manipulating molecular systems for practical computing applications.

In mathematics, an orthonormal basis is a set of vectors in a vector space that are mutually orthogonal (perpendicular) to each other and have unit length. It is a fundamental concept in linear algebra and plays a crucial role in various areas of mathematics and physics.

More formally, let V be a vector space over a field F with an inner product (e.g., dot product). A set of vectors {v1, v2, ..., vn} in V is called an orthonormal basis if:

1. Orthogonality: The vectors are pairwise orthogonal, meaning that for any i ≠ j, the inner product of vi and vj is zero: ⟨vi, vj⟩ = 0.
2. Unit length: Each vector has a length of 1, meaning that for each i, the inner product of vi with itself is 1: ⟨vi, vi⟩ = 1.

The orthonormal basis provides a convenient way to express any vector in the vector space as a linear combination of the basis vectors. The coefficients of this linear combination, called the coordinates or components of the vector with respect to the basis, can be determined using inner products and the properties of the basis vectors.

Orthonormal bases have several important properties and applications. They simplify computations involving inner products, orthogonality, and projections. They are particularly useful in solving systems of linear equations, diagonalizing matrices, and representing signals in signal processing and Fourier analysis.

In summary, an orthonormal basis is a set of vectors in a vector space that are orthogonal to each other and have unit length. It provides a convenient and powerful framework for representing vectors and performing calculations in linear algebra and related fields.

Orthonormal bases have numerous applications across various fields of mathematics, physics, and engineering. Here are some notable applications:

1. Signal Processing: Orthonormal bases play a central role in signal processing, especially in Fourier analysis. The Fourier basis, consisting of complex exponentials, forms an orthonormal basis for representing signals in the frequency domain. This allows for efficient signal analysis, filtering, compression, and reconstruction.
2. Quantum Mechanics: In quantum mechanics, orthonormal bases are used to describe the state space of quantum systems. The basis states, known as eigenstates, provide a complete representation of the system's states, and their orthogonality ensures the uniqueness of quantum measurements.
3. Linear Transformations: Orthonormal bases are useful in studying linear transformations and operators. They allow for easy representation of linear operators as matrices, simplifying computations such as matrix multiplication, eigenvalue/eigenvector analysis, and diagonalization.
4. Image and Video Compression: Orthonormal bases, such as the Discrete Cosine Transform (DCT) basis used in JPEG compression, are employed to efficiently represent and compress images and videos. The orthogonality of the basis allows for energy compaction, concentrating most of the signal's energy into a small number of coefficients.
5. Optimization: Orthonormal bases are employed in optimization algorithms, such as gradient descent, to find optimal solutions in a more efficient manner. They can help transform the optimization problem into a coordinate system where the optimization is simplified.
6. Quantum Computing: Orthonormal bases are fundamental in quantum computing for representing and manipulating quantum states. Quantum gates, which perform quantum operations, are typically defined with respect to an orthonormal basis.
7. Error Correction Codes: Orthonormal bases, such as the Walsh-Hadamard basis, are used in error correction codes to detect and correct errors in transmitted data. The orthogonality of the basis ensures minimal interference between codewords.

These are just a few examples of the wide-ranging applications of orthonormal bases. Their usefulness stems from their ability to simplify computations, provide efficient representations, and capture important properties of the underlying systems.