Mathematics-I: migrated to the electrostatics-sandbox

docs.md

@@ -2,11 +2,22 @@
 
 The following page lists the documentation manuals provided by the _Electrostatic Sandbox Project_ in the context of embedded systems testing, simulation, and design.
 
-- [The AVR-Sandbox Project](https://electrostat-lab.github.io/Electrostatic-Sandbox/embedded-system-design/avr-sandbox/index): Includes documentation on basic C programming, digital electronics, AVR architecture, and embedded systems interfacing together with some circuit design.
+## Computing Guidelines:
 
 - [ACM Guidelines for Curricula](https://electrostat-lab.github.io/Electrostatic-Sandbox/embedded-system-design/acm-guidelines): Generalized guidelines for studying computer science utilized in approaching the self-taught route of embedded software/hardware co-design and engineering.
 
+- [IEEE-1516 HLA Specification](https://electrostat-lab.github.io/Electrostatic-Sandbox/embedded-system-design/ieee-1516): Provides a documentation manual for the _High-level Architecture_ IEEE specification.
+
+## Embedded Systems:
+
+- [The AVR-Sandbox Project](https://electrostat-lab.github.io/Electrostatic-Sandbox/embedded-system-design/avr-sandbox/index): Includes documentation on basic C programming, digital electronics, AVR architecture, and embedded systems interfacing together with some circuit design.
+
+
 - [Embedded Systems Design](https://electrostat-lab.github.io/Electrostatic-Sandbox/embedded-system-design/): WIP.
 
-- [IEEE-1516 HLA Specification](https://electrostat-lab.github.io/Electrostatic-Sandbox/embedded-system-design/ieee-1516): Provides a documentation manual for the _High-level Architecture_ IEEE specification.
+
+## Mathematics: 
+
+- [Mathematics-I](https://electrostat-lab.github.io/Electrostatic-Sandbox/embedded-system-design/mathematics-i/index): Housing useful resusable equations and formulas in calculus, discrete mathematics, and linear algebra.
+
 

embedded-system-design/mathematics-i/LICENSE

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+BSD 3-Clause License
+
+Copyright (c) 2024, Software~Hardware Co-design
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
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+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

embedded-system-design/mathematics-i/README.md

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+# Mathematics-I
+> Created by [Pavl_G](https://github.com/Scrappers-glitch)
+>> [Contribution Guidelines](https://github.com/Electrostat-Lab/.github/blob/main/CONTRIBUTING.md)
+
+This repository houses useful re-usable formulas and equations for discrete mathematics, linear algebra, and calculus together with an insightful addition of appendices that include proofs, analysis tools, and other useful life applications.
+
+> Utilizes Resources from:
+
+| _Discrete & Finite Mathematics_ | _Pure Mathematics_ |
+|----------|---------|
+| <img width=160 height=200 src="https://github.com/Electrostat-Lab/Mathematics-I/assets/60224159/c100afd6-459c-48f2-ae1d-5aa387b3eff5"/> | <img width=160 height=200 src="https://github.com/Electrostat-Lab/Mathematics-I/assets/60224159/8006dfe9-80fa-4668-be6f-93726a708ea0"/> |
+| <img width=160 height=200 src="https://github.com/Electrostat-Lab/Mathematics-I/assets/60224159/f3ce09d7-223a-46b1-a849-84271b15acc8"/> | <a href="https://link.springer.com/book/10.1007/978-3-319-91041-3"><img width=160 height=200 src="https://github.com/Electrostat-Lab/Mathematics-I/assets/60224159/04300248-da55-48f9-b9f8-23315d29535c"/></a> |
+| <img width=160 height=200 src="https://github.com/Electrostat-Lab/Mathematics-I/assets/60224159/4207ea21-1cc3-4c11-a64a-042947122a21"/> | <a href="https://link.springer.com/book/10.1007/978-3-540-72122-2"><img width=160 height=200 src="https://github.com/Electrostat-Lab/Mathematics-I/assets/60224159/bee1c1a4-7d55-4697-a340-456a5e7df950"/></a> |
+
+| _Practical Applications_ |
+|--------------------------|
+| <img width=160 height=200 src="https://github.com/Electrostat-Lab/Mathematics-I/assets/60224159/e589fc74-c6d4-428d-8941-c3857cee2d21"/> | 
+
+> Powered by:
+
+<a href="https://jekyllrb.com/"><img width=170 height=100 src="https://github.com/Electrostat-Lab/Mathematics-I/assets/60224159/e8f4ae7f-8dcf-498b-8856-661278875347"/></a> <a href="https://www.mathjax.org/"> <img width=220 height=70 src="https://github.com/Electrostat-Lab/Mathematics-I/assets/60224159/a3489889-5669-4a5b-ab94-a9a1155d85f5"/> </a>
+

embedded-system-design/mathematics-i/calculus/appendix-e.md

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+# Calculus I: Appendix-E: Derivation of the dot product and the projection vectors 
+
+<div align=center><img src="https://electrostat-lab.github.io/Mathematics-I/calculus/archive/the-dot-product.jpg" width=550 height=850/></div>
+
+## 1) The dot product: 
+
+- Recall, 2 intersecting vectors $u$ and $v$ together with the resultant vetcor $w$; such that, $$w = u - v$$
+
+- From the "Law of Cosines":
+
+$$Since, ||w||^2 = ||u||^2 + ||v||^2 - 2 * ||u||^2 * ||v||^2 * cos(\theta)$$
+
+$$Then, cos(\theta) = (||w||^2 - ||u||^2 - ||v||^2) / -2 * ||u||^2 * ||v||^2\ Lemma\.01$$ 
+
+$$ ------- $$
+
+$$Since, ||w|| = \sqrt{(u_x - v_x)^2 + (u_y - v_y)^2 + (u_z - v_z)^2}$$
+
+$$||v|| = \sqrt{\{v_x}^2 + {v_y}^2 + {v_z}^2\}$$
+
+$$||u|| = \sqrt{\{u_x}^2 + {u_y}^2 + {u_z}^2\}$$
+
+$$||w|| = ||u-v|| = \sqrt{\{(u_x - v_x)}^2 + {(u_y - v_y)}^2 + {(u_z - v_z)}^2\}$$
+
+$$Then,\ Lemma.02:$$
+
+$$1)\ ||v||^2 = {v_x}^2 + {v_y}^2 + {v_z}^2$$
+
+$$2)\ ||u||^2 = {u_x}^2 + {u_y}^2 + {u_z}^2$$
+
+$$3)\ ||w||^2 = {u_x - v_x}^2 + {u_y - v_y}^2 + {u_z - v_z}^2$$
+
+$$= ({u_x}^2 -2{u_x}{v_x} + {v_x}^2) + ({u_y}^2 -2{u_y}{v_y} + {v_y}^2) + ({u_z}^2 -2{u_z}{v_z} + {v_z}^2)$$
+
+$$= ||u||^2 + ||v||^2 -2({u_x}{v_x} + {u_y}{v_y} + {u_z}{v_z})$$
+
+$$ ------- $$
+
+- By back-substitution in $Lemma.01$:
+
+$$cos(\theta) = (||w||^2 - ||u||^2 - ||v||^2) / -2 * ||u||^2 * ||v||^2 = {(R.H.S)}_1 / {(R.H.S)}_2$$
+
+$${(R.H.S)}_1 = (||w||^2 - ||u||^2 - ||v||^2)$$
+
+$$= -2({u_x}{v_x} + {u_y}{v_y} + {u_z}{v_z})$$
+
+$${(R.H.S)}_2 = -2 * ||u||^2 * ||v||^2$$
+
+$$Hence, cos(\theta) = {(R.H.S)}_1 / {(R.H.S)}_2$$
+
+$$= -2({u_x}{v_x} + {u_y}{v_y} + {u_z}{v_z}) / -2 * ||u||^2 * ||v||^2$$
+
+$$= ({u_x}{v_x} + {u_y}{v_y} + {u_z}{v_z}) / ||u||^2 * ||v||^2$$
+
+- And, by definition $({u_x}{v_x} + {u_y}{v_y} + {u_z}{v_z})$ yields $u.v$, the dot product or the inner product.
+
+$$Thence, cos(\theta) = u.v / ||u|| * ||v||$$
+
+$$And, u.v = ||u|| * ||v|| * cos(\theta)$$
+
+
+## 2) Projection vectors: 
+
+* Definition: The projection vector $\vec{proj_{\vec{v}}} \vec{u}$ of a vector $\vec{u}$, is the vector component of that vector, that is coincident to the projectile vector $\vec{v}$, multiplied by the unit vector of the 
+projectile vector (i.e., the direction).
+
+* For a productive proof, let vector $\vec{u}$ be our target vector, the one we would like to find its vector components in the direction of another contigous vectors, and vector $\vec{v}$ be the projectile (aka. base) vector
+the one we would like to utilize its direction to find the projection vector.
+
+1) Finding the norm of the vector component $||\vec{u_x}||\$ that is coincident to the projectile vector $\vec{v}\$:
+
+$$Since,cos(\theta)=||\vec{u_x}||/||\vec{u}||$$
+
+$$Then,||\vec{u_x}||=||\vec{u}||*cos(\theta)$$
+
+2) Finding the vector norm $||\vec{v}||$ of the projectile vector $\vec{v}$:
+
+$$||\vec{v}||=\sqrt{\{v_x}^2+{v_y}^2+{v_z}^2\}$$
+
+3) Finding the normalization ratio using the scalar division property $\vec{v_{unit}}=\vec{v}/||\vec{v}||$ of the projectile vector (base vector).
+
+4) Using the scalar multiplication property $||\vec{u_x}||*\vec{v_{unit}}$:
+
+> $Lemma.01$
+
+$$||\vec{u_x}||*\vec{v_{unit}}=(||\vec{u}||*cos(\theta))\*\vec{v_{unit}}$$
+
+$$Since,\vec{u}.\vec{v}=||\vec{u}||*||\vec{v}||\*cos(\theta)$$
+
+> $Lemma.02$
+
+$$Then,||\vec{u}||*cos(\theta)=(\vec{u}.\vec{v})/||\vec{v}||$$
+
+5) Then, from $Lemma.01$ and $Lemma.02$, we can deduce the projection vector formula in terms of the dot product between 2 vectors as follows: 
+
+$$\vec{proj_{\vec{v}}}\vec{u}=[({\vec{u}}.{\vec{v}})*{\vec{v_{unit}}}]/||\vec{v}||$$
+
+6) Another formula when $\vec{v_{unit}}$ is broken: 
+
+$$\vec{proj_{\vec{v}}}\vec{u}=[\vec{u}.\vec{v}*\vec{v}]/||\vec{v}||^2$$
+
+> Note:
+> * This is could be applied to other components of vector $\vec{u}$, the $\vec{u_y}$, and the $\vec{u_z}$, and any vector could be utilized as the base or the projectile vector.
+> * The projection vector of vector $\vec{u}$ on itself $\vec{proj_{\vec{u}}} \vec{u}$ is the vector $\vec{u}$ itself scaled with the length of one of its vector components.
+
+## 3) Usages Review:
+1) Finding the work done by a force vector $(F)$ to move an object a displacement $(D)$ with an inscribed angle $(a)$, formula (Physics):
+
+$$W = F.D = ||F|| * ||D|| * cos(a) = \sum_{i=0}^{n} u_i v_i = u_0 v_0+u_1 v_1+u_2 v_2+...+ u_{n-1} v_{n-1} + u_n v_n$$
+
+2) Finding the inscribed angle (<a) between 2 intersecting vectors, formula:
+
+$$m(a) = acos(u.v/(||u|| * ||v||))$$
+
+> where u.v can be evaluated using the Riemann's sum formula (Trigo./Physics).
+  
+3) Finding whether 2 intersecting vectors are orthogonal, formula: $u.v = ||u|| * ||v|| * cos(PI/2) = ZERO.$ (Geometry).
+
+4) Finding projection vectors, formula: "the vector projection of $u$ onto $v$", formula:
+
+$$proj_{v}^{u} = (||u|| * cos(a)) * (v/||v||) = (u.v / ||v||^2) * v$$ 
+
+> where $(||u|| * cos(a))$ is the length of the triangle base, and $(v/||v||)$ is the unit vector form (normalized) of $v$.
+
+5) Finding the total electromotive force (EMF) in a closed circuit loop, formula (aka. Ohm's Law):
+
+$$V = I * R * cos(0)$$
+
+6) Finding the driving arterial blood pressure in a closed arterial circuitry, formula (Hemodynamics):
+
+$$BP = CO * SVR * cos(0)$$
+
+> References: 
+> * Thomas' Calculus $14^{th}e$: Ch.12 Vectors & Geometry of Space: Section.12.3. (The Dot Product).
+> * Guyton and Hall Textbook of Medical Physiology $13^{th}e$
+> * Applied Linear Algebra $2^{nd}e$ Springer
+

embedded-system-design/mathematics-i/calculus/archive/README.md

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+# Calculus I: Archive
+
+This folder archives supportive assets other than the main text and materials utilized for the website deployment.

embedded-system-design/mathematics-i/discrete-maths/appendix-a.md

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+# Discrete Mathematics I: Appendix-A: Symbolic Designation for mathematical analysis
+
+<div align=center><img src="https://electrostat-lab.github.io/Mathematics-I/discrete-maths/archive/algorithm-analysis-using-machines.jpg" width=550 height=850/></div>
+
+## The following is the symbolic designation legend to aid in the subsequent mathematical analysis: 
+- $N_c$: the number of times of execution of the closure $c$ imposed by the enclosing executing environment $E_e$ or the superclosure.
+- $E_{e}$: the superclosure of the current closure that is of interest; the superclosure defines the executing environment that imposes the syntactical interpretation of iterations and machine transitions.
+- $C_c$: the clock-complexity function; defines the number of cycles needed by the CPU to execute a set of machines inside an environment.
+- ${\tau\}$: the transition-complexity function; defines the approximate clock-complexity taken by some machinery transitions among a set of specified machinery states (e.g., $M_{\alpha} = [\{\mu}_n, \{\mu}\_{n+1}, ..., \{\mu}\_{N-1}, \{\mu}\_{N}]$), and it follows that the transition-complexity function formula is the same as the clock-complexity function formula (i.e., ${\{\tau\}\'\}\_n = {C^{''}}_c$).
+- $t_c$: the physical time-complexity function; defines the approximate time taken in seconds to execute the specified runnable set of machines in seconds unit.
+- ${\epsilon}$: the error rate complexity function; defines the error rate as a result of calculating the exact physical time taken to execute some machines.
+
+## The following is the generalized formula: 
+
+$$Since, N_c = \prod_{i=1}^I E_{e_i}$$
+
+$$C_c = N_c * \sum_{n=1}^N {\tau}\_n$$
+
+- Such that, ${\tau}\_n = C'_c$, and ${\{\tau\}\'\}\_n = {C^{''}}_c$, and so on; as it represents the transition between machinery states, so this is a recursive formula re-evaluating on the most inner closures.
+
+$$Then, C_c = \prod_{i=1}^I E_{e_i} * \sum_{n=0}^N {\tau}\_n = (E_{e_1} * E_{e_2} * ... * E_{e_{I-1}} * E_{e_{I}}) * ({\tau}\_{1} + {\tau}\_{2}  + ... + {\tau}\_{(N-1)} + {\tau}\_{(N)})$$
+
+$$And, t_c = (C_c/F_{CPU}) + {\epsilon}$$

embedded-system-design/mathematics-i/discrete-maths/appendix-b.md

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+# Discrete Mathematics I - Appendix-B: Algorithm Analysis
+
+## Algorithms Manual Analysis (Closures Analysis): 
+### 1) N-order looping algorithms:
+- Recall, _Closure A_:
+```java
+ A {
+   FOR I = 1 TO N:
+       command()
+   END
+ }
+ ```
+- Then, it follows that the dummy `command()` with the clock-complexity $N_c$ will be executed $N$ times, so $$f(N) = N * (N_c)$$
+- Hence, the complexity of the closure execution can be represented as a finite-sum by Riemann's sum formula:
+  $$f(N) = \sum_{n=1}^{N} N_{c_n} = {(N_c)}_1 + {(N_c)}_2 + {(N_c)}_3 +...+ ({N_c})\_{N-2} + ({N_c})\_{N-1} + ({N_c})\_{N} = N(N_c)$$
+- As a matter of multi-variable equations, as well as the total clock-complexity of the execution depends on the number of iterations, it also depends on what's inside the loop closures, or in other words the `command()` complexity. If, the `command()`'s clock-complexity can be evaluated to $N_c=1$ as a matter of simple command-execution opcode, then the total clock-complexity for this loop closure is $$f(N) = \sum_{n=1}^{N} 1 = N(1) = N$$.
+- Since, Riemann's sums can be applied for a finite-set _S_ of closures execution, so $$f(I) = \sum_{i=1}^{I} f(N_{i}) = f(N_{i}) + f(N_{i+1}) + f(N_{i+2}) + ... + f(N_{I-2}) + f(N_{I-1}) + f(N_{I})$$
+- Then, a specific notation of Riemann's sums can be applied for a finite-set $S_L$ of loop closures execution: $$f(I) = \sum_{i=1}^{I} f(L_{i}) = f(L_{i}) + f(L_{i+1}) + f(L_{i+2}) + ... + f(L_{I-2}) + f(L_{I-1}) + f(L_{I})$$
+                 $$= L_i + L_{i+1} + L_{i+2} + ... + L_{I-2} + L_{I-1} + L_{I}$$ ;where $I$ is the total number of closures, and it represents the index of the finite-item in the set.
+ 
+### 2) Conditional closures algorithms:
+- Recall, _Closure B_:
+```java
+B {
+   IF ({C_i} = {VALUE}) THEN
+       command()
+   END
+ }
+```
+- Where, $C_i$ is the condition tag, and _i_ is the number of conditions, in this case, it's 1 times.
+- Then, it follows that the dummy `command()` will be executed $1$ times, so $$f(n) = 1 * N_c$$ ;where $N_c$ represents the clock-complexity for the involved `command()` to be executed by this execution.
+- Since, Riemann's sums can be applied for a finite-set _S_ of closures execution, so $$f(I) = \sum_{i=1}^{I} f(N_{i}) = f(N_{i}) + f(N_{i+1}) + f(N_{i+2}) + ... + f(N_{I-2}) + f(N_{I-1}) + f(N_{I})$$
+- Then, a specific notation of Riemann's sums can be applied for a finite-set $S_C$ of conditional closures execution, so $$f(I) = \sum_{i=1}^{I} f(C_{i}) = f(C_{i}) + f(C_{i+1}) + f(C_{i+2}) + ... + f(C_{I-2}) + f(C_{I-1}) + f(C_{I})$$
+                $$={(N_c)}\_1 + {(N_c)}\_2 + {(N_c)}\_3 +...+ ({N_c})\_{I-2} + ({N_c})\_{I-1} + ({N_c})\_{I} $$ ;where $I$ is the total number of closures, and it represents the index of the finite-item in the set, and $f(C_{i})$ is the complexity of execution of a conditional command `command()` (notice, how this function is very abstract, as the `command()` could be another algorithm of another complexity, see `compound complexities section`).
+
+### 3) The scientific basis behind compositing closures (defining a transcendental formula for closures):
+
+- Closures can be designated as special types of _Sets_; where operations, a specific sort of relations, are being monitored in an execution environment, hence all types of closures creates $$f(N) = C(N) * \sum_{c=1}^{C} N_c = C(N) * (N_1 + N_2 + N_3 + ... + N_{C-2} + N_{C-1} + N_C)$$ ;where $f(N)$ is the total clock-complexity of the closure execution (execution of commands inside the closure), $C(N)$ is the clock-complexity of the closure itself by its class (e.g., first-order loops use $C(N)=N$), and $N_c$ represents the clock-complexity of the single command, in which their Riemann's sum yields the total clock-complexity of execution of the enclosed commands.
+
+- It follows that this could be also represented using the _integral function_, aka. _Leibniz's notation_, the integral function integrates the time complexities of the stack of the function in the form $f(x).dx=C(N).N_c$:
+$$Since, f(x) = F(x) = \int_a^x{f'(x).dx}$$
+$$Hence, F(x_1) - F(x_0) = \int_a^{x_1}{f'(x).dx} - \int_a^{x_0}{f'(x).dx} = \int_{x_0}^{x_1}{f'(x).dx} = f(x_1) - f(x_0)$$
+$$Then, F(x) = f(N) = C(N) * \sum_{c=1}^{C} N_c = \sum_{c=1}^{C} N_c * C(N) = \int_1^C{f'(x).dx} = f(C) - f(1)$$
+ 
+- Almost all properties of _Sets_ could be applied to closures, hence if the super-closure (superset) has a simple complexity of constant functional execution (i.e., of $C(N) = 1$), then the generalized Riemann's sum can be narrowed down to: $$f(N) = C(N) * \sum_{c=1}^{C} N_c = (1) * \sum_{c=1}^{C} N_c$$
+
+- While, if the super-closure (superset) has a loop complexity of transcendental functional execution (i.e., of $C(N) = c*N^e$), then the generalized Riemann's sum can be obtained as follows: $$f(N) = C(N) * \sum_{c=1}^{C} N_c = c * N^e * \sum_{c=1}^{C} N_c$$ ;where $c$ is a constant co-efficient, and $e$ is the exponent representing nested loop closures.
+   
+### 4) Compound (or Nested) closures algorithms:
+- Recall, a super-closure $S_c$; such that:
+```java
+S_c: {
+  command()
+}
+```
+- Then, it follows that this closure executes the `command()` in (N) times the clock-complexity of the command $N_c$, hence $$f(N) = N*N_c$$
+- Hence, if the `command()` holds the following closure as its stack:
+```java
+command(): {
+  FOR I = 1 TO N:
+       command1();
+  END
+}
+```
+- Then, the total clock-complexity of execution will be: $$f(N) = N*N_c$$
+- However, if the `command()` holds a simple closure as the follows:
+ ```java
+command(): {
+   IF ({C_i} = {VALUE}) THEN
+       command1()
+   END
+}
+```
+- Then, it follows that the total clock-complexity of execution will be: $$f(N) = N*N_c = (1) * N_c = N_c$$
+- Now, if the `command()` holds a nested loop closure as follows:
+```java
+command(): {
+  FOR I = 1 TO N:
+     FOR J = 1 TO N:
+       command1();
+     END
+  END
+}
+```
+- Then, it follows that the total clock-complexity can be evaluated to: $$f(N) = N*N_c = N * (N * N_c') = N^2 * N_c'$$ ;which means that the `command1()` where $N_c'$ will be executed $N^2$ times, in a product set fashion.
+
+- Now, the $N_c$ can represent any type of clock-complexity ranging from complexity to compund complexity involving finite-sets, the general formula utilizes Riemann's sum, and can be also represented as an integral function using _Leibniz's notation_.
+
+
+
+
+