The paper presents a prototype computer system that uses an algorithmic complexity program for storing and transmission of input data into like-natured subgroups from both random and non-random linear sequential strings. Finite and infinite state machines are used to test this computer system. This new computer system has the shortest list of operational commands known in computing.
Inhaltsverzeichnis (Table of Contents)
- Abstract
- Introduction
- Example #1
- Example #2
- Example #2 Specific Compression
- Note
- Foundations
- Finite State Machine
- Infinite State Machine
- List of Instructions
- Universal Qualities
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
The objective of this paper is to examine the design and functionality of a novel computer system based on the compression of binary sequential strings. This system offers a unique approach to data storage and retrieval, applicable to both finite and infinite state machines.
- Compression of binary sequential strings
- Application to finite and infinite state machines
- Algorithmic complexity and data compression
- Minimal weight of semantic aspects to character types
- Development of a proto-type computer system based on successive addition of like-natured characters.
Zusammenfassung der Kapitel (Chapter Summaries)
Abstract: This paper introduces a prototype computer system built upon the principle of sequentially adding similar characters in a binary string. Arabic numerals represent each character, which are summed as input, grouped for storage, and decompressed as output. The system functions for both random and non-random strings, modeling finite and infinite state machines.
Introduction: This section details the computer system's design, originating from research in algorithmic complexity. It processes binary strings, grouping identical characters for efficient storage and exact reconstruction of the original input upon decompression. The system's efficiency in storing data is highlighted, demonstrating space-saving potential for transmission and storage.
Example #1: This example illustrates the compression of a non-random binary string (e.g., [10101010101010101010]). The regular pattern allows for significant compression, represented as [10] x 10, showcasing the system's effectiveness in handling predictable sequences.
Example #2: This section demonstrates the compression of a random binary string ([00011000010111000111]). The string is divided into subgroups of like characters, which are then compressed using a notation system that indicates the repeated character and its count. This approach reduces storage space while enabling perfect decompression.
Example #2 Specific Compression: This elaborates on the compression of Example #2, specifying the compression of each subgroup. The original 20-character string is reduced, highlighting the space-saving capabilities of the system while ensuring accurate data retrieval.
Note: This clarifies that the characters in the examples represent distinct symbols (like A and B), not their numerical values, emphasizing a qualitative and quantitative distinction as per Shannon's work. This distinction is crucial for understanding the system's underlying logic.
Foundations: This section lays the theoretical groundwork for the computer system, referencing the contributions of Post, Kleene, and Minsky, and the author's prior work on algorithmic complexity. It highlights the integration of concepts from algorithmic complexity and Shannon's information theory.
Finite State Machine: This explains how the system fulfills the criteria of a finite state machine through its components (Input, Reader/Scanner, Storage, Output) and their time-sensitive and discrete operations.
Infinite State Machine: This section adapts the system to function as an infinite state machine. It addresses the challenge of infinite storage by employing a closed-loop input system, allowing for continuous operation despite finite storage at any given time. The concept of an indefinitely large finite set approximating infinity is also discussed.
List of Instructions: This section simplifies the system's functionality to only two instructions: Read/Scan (with implied storage) and Decompression/Output, referencing Minsky's work on minimal instruction sets.
Schlüsselwörter (Keywords)
Binary sequential strings, data compression, algorithmic complexity, finite state machine, infinite state machine, data storage, data retrieval, Shannon's information theory, proto-type computer system.
Frequently Asked Questions about: A Novel Computer System Based on the Compression of Binary Sequential Strings
What is the main objective of this paper?
The paper's main objective is to examine the design and functionality of a novel computer system built upon the principle of compressing binary sequential strings. This system offers a unique approach to data storage and retrieval, applicable to both finite and infinite state machines.
What are the key themes explored in this paper?
Key themes include the compression of binary sequential strings, its application to finite and infinite state machines, algorithmic complexity and data compression, minimal weight of semantic aspects to character types, and the development of a prototype computer system based on successive addition of like-natured characters.
How does the computer system work?
The system compresses binary strings by grouping identical consecutive characters. Arabic numerals represent each character. These are summed as input, grouped for storage, and decompressed as output. The system functions for both random and non-random strings.
What are the chapter summaries?
The paper includes an abstract, introduction, examples illustrating compression of both random and non-random strings, a note clarifying the symbolic nature of the characters, sections on the theoretical foundations (referencing Post, Kleene, Minsky, and Shannon), explanations of how the system functions as both a finite and infinite state machine, a list of instructions, and a conclusion.
How does the system handle both finite and infinite state machines?
The system models a finite state machine through its discrete operations and components (input, reader/scanner, storage, output). It adapts to an infinite state machine by employing a closed-loop input system, handling potentially infinite input despite finite storage at any given time, approximating infinity with an indefinitely large finite set.
What are the theoretical foundations of this system?
The system's foundations are rooted in algorithmic complexity and Shannon's information theory, integrating concepts from the work of Post, Kleene, and Minsky. The author's prior work on algorithmic complexity is also referenced.
What is the significance of the "Note" section?
The "Note" section clarifies that the characters used in the examples represent distinct symbols, not their numerical values. This distinction, crucial for understanding the system, highlights a qualitative and quantitative difference, referencing Shannon's work.
What are the system's instructions?
The system's functionality is simplified to only two instructions: Read/Scan (with implied storage) and Decompression/Output, referencing Minsky's work on minimal instruction sets.
What are the keywords associated with this paper?
Keywords include: Binary sequential strings, data compression, algorithmic complexity, finite state machine, infinite state machine, data storage, data retrieval, Shannon's information theory, and prototype computer system.
Where can I find more information about this computer system?
This FAQ summarizes the provided document preview. For complete details, please refer to the full paper.
- Quote paper
- Professor Bradley Tice (Author), 2012, A Universal Archetype Computer System, Munich, GRIN Verlag, https://www.hausarbeiten.de/document/198631
