Dear Experts,
Dictionaries are arranged alphabetically and that is a big cause of wrong reasoning. Fallacies , biases ... ambiguities occur because we don't classify the vocabulary through their concept dependency tree structures.
Obviously Tekla has Hierarchical objects. That helps us ideate the concept. Tekla has numbering feature (Which is used to classify the things (Sometimes topologically sometime deep geometrically/some times spatially / sometimes as per erections sequences /sometimes due to purchase planning......Advanced Bills of materials etc... ...) PURPOSE DECIDES NUMBERING SCHEMES ... Hierarchy is important for mental maps
For long time it is running in mind that when we write any compiler (Say framework for Tekla API ... or Say When Tekla software was under development... the most fundamental logical building blocks were classified , concretized compiled ... and then only next stage of classes are developed. Until we model the grids we don't proceed to place columns nor beams...Until we have concrete concept of UP or Down ... we cant have concrete concept of Lock up or Lock Down...until we have clarity on the concepts of left and right ... we cannot have the concrete concept of copy left and copy right...
Human mind is also like a c++ compiler(Or c# compiler) But so many times our surrounding worlds push fallacies into our concepts tree and our reasoning systems get corrupt
While reading design documents or tender documents we never get the dependency tree like indexing and for that reason our mind get boggled. We need a Qhenomenology reasoning system (automated Natural language processing systems which is very light weight simple dictionary / vocabulary based system (no need of deep neural network to implement such reasoning systems but we need a Roslyn Like Compiler on Wordnet (and technical terms Concept Hierarchy like things to classify the missing informations or first focusable informations in these documents.
Just a Thought...
But made a framework (testing inhouse with our limited sets of keywords , Wordnet and Roslyn static verifier...)
Sanjoy Nath's Qhenomenology offers a radical paradigm for wave analysis, fundamentally rejecting conventional DSP and Fourier transforms. It posits that a wave's essence, particularly its musical qualities, lies in its topological and combinatorial structure, which remains invariant under scaling.
The theory views a wave as a "Building Information Model" (BIM) composed of discrete "Atomic Building Blocks" (AABB). A core component is the ContainerAABB, which represents a complete wave cycle. Its topological identity is defined by scale-invariant properties like local maxima/minima counts and monotonic segments. Crucially, a ContainerAABB isn't a simple sine wave segment but a sophisticated, ordered "queue" or "assembly" of multiple CrestAABB and TroughAABB objects.
Frequency, in Qhenomenology, is redefined not by spectral content but as the "combinatorial packing factor" of these topologically classified ContainerAABB objects along the timeline. This is computed by counting valid ContainerAABB instances within a given duration.
A key departure from traditional filtering is the unique amplitude management. Qhenomenology proposes "silencing" (enforcing zero amplitudes for) low-amplitude samples (e.g., below MEAN + 1 * STD DEV). This radical reduction is claimed to preserve listenability and understanding, acting as a topology-preserving "denoising" by focusing on significant structural information rather than minute amplitude variations.
Zero-crossing points are re-conceptualized as critical "junctions" or "connectors" between wave components, each with its own scale-invariant topological classification (e.g., Crest-to-Trough, Trough-to-Crest, Silence). These classifications, along with analysis of "interfacing neighborhoods," are crucial for forming the ContainerAABBs and deriving the wave's underlying "grammar."
The ultimate goal is a direct mapping from wave data to musical semantics. This includes extracting MIDI notes, managing complex pitch bends by identifying monotonically changing notes within a semitone range, and classifying timbre and tonality directly from the topological numbering of the AABB objects. Qhenomenology offers a unique computational approach to understand sound's intrinsic structure beyond traditional signal decomposition.
___________________
Sanjoy Nath’s Qhenomenological Wave Theory: A Topology-Based Alternative to Fourier Analysis
Traditional Fourier analysis decomposes signals into infinite sinusoidal components, assuming periodicity and requiring windowed smoothing. In contrast, Qhenomenology rejects harmonic superposition entirely. It models waveforms as discrete sequences of topologically classified geometric primitives, notably crest AABB, trough AABB, and container AABB objects, each defined strictly by scale-invariant properties.
A ContainerAABB represents a full wave cycle, bounded by two successive zero crossings. Unlike Fourier cycles, these are not harmonic assumptions, but physically observed transitions. Within each container, crests and troughs are identified via local maxima and minima, and characterized using counts of monotonic segments, area ratios, and rank-based shape ordering. These features remain invariant under amplitude or time scaling, thereby ensuring topological stability.
Frequency is not imposed, but emerges from counting valid ContainerAABB cycles per time window. The resulting "frequency" reflects structural repetition, not spectral energy. Crucially, this avoids all RC-style filters or amplitude-based silencing. Instead, neighborhood-scale checks around zero crossings confirm the topological consistency of each wave unit. Thus, the wave is treated as a queue of grammar tokens, enabling symbolic parsing, MIDI conversion, and semantic audio analysis.
The Qhenomenology method enables robust detection of pitch bends (via monotonic glides across crests) and timbre (via repeated topological structures). It preserves waveform character without resorting to FFTs, spectral smoothing, or any artificial windowing. This grammar-based model provides a deterministic, bias-free alternative to frequency domain reasoning. It treats signal understanding as a form of discrete geometry and compiler-like class instantiation rather than spectral estimation.
Hence, Qhenomenology redefines waveform analysis not through energy decomposition, but through topological parsing, structural consistency, and discrete symbolic sequencing—offering a transformative alternative to Fourier-based signal science.
________________
Here is a condensed, 3000-character version of Sanjoy Nath’s Qhenomenology Theory of Waves:
Qhenomenology of Waves: A Topological Framework for Signal Analysis
Sanjoy Nath’s Qhenomenology is a novel, non-Fourier approach to waveform analysis. It models signals not as superpositions of sinusoids, but as queues of topologically classified geometric primitives—specifically, Axis-Aligned Bounding Boxes (AABBs) representing crests and troughs. These AABBs are analyzed using scale-invariant topological features, enabling a new form of frequency and structure detection.
1. AABB-Based Modeling
- Crest AABB: Encloses positive waveform segments.
- Trough AABB: Encloses negative segments.
- Container AABB: Encloses a full wave cycle, possibly containing multiple crests and troughs.
Each AABB is characterized by:
- Local maxima/minima count
- Monotonic segments
- Area under curve
- Center of Gravity (CG)
- Percentile-based amplitude ranks
These features are topologically invariant under scaling.
2. Frequency as Packing Factor
Frequency is defined as:
Frequency = Number of Valid Container AABBs / Time Window
This redefines frequency as a packing metric—how many topologically valid cycles fit in a second—rather than a harmonic decomposition.
3. Zero Crossing Topology
Zero crossings are classified into types (CT, TC, CC, TT, etc.) based on amplitude transitions and neighborhood topology. Each crossing is assigned a topology classification number, enabling symbolic parsing of waveform structure.
4. Stack-Based Spectral Analysis
Instead of FFT, Qhenomenology uses stacking of transparent AABB rectangles aligned by left margins. When viewed from the top, CG points align vertically, forming spectral lines. This reveals frequency content without sin/cos decomposition.
5. Topology Classification Metric
Each AABB is assigned a Topology Classification Sensitiveness Number (TCSN):
This allows grouping of similar AABBs and identification of repeating structural motifs, akin to Building Information Modeling (BIM).
6. Philosophical Foundation
Qhenomenology treats all signal components as concrete C++-like classes. No abstract classes or neural networks are allowed. Reasoning is compiler-like, where each concept must be concretely defined before use. This ensures bias-free, deterministic reasoning.
7. Applications
- Audio: MIDI note extraction, pitch bend modeling
- Vibration: Structural health monitoring
- Cognition: Symbolic reasoning over waveform strings
Conclusion
Qhenomenology redefines waveform analysis through combinatorial, topological, and geometric reasoning. It avoids Fourier transforms entirely, offering a scale-invariant, structure-preserving, and visually interpretable alternative for signal understanding.
Let me know if you'd like this formatted into a PDF, LaTeX paper, or visual diagram!
Sanjoy Nath's Qhenomenology offers a radical paradigm for wave analysis, fundamentally rejecting conventional DSP and Fourier transforms. It posits that a wave's essence, particularly its musical qualities, lies in its topological and combinatorial structure, which remains invariant under scaling.
The theory views a wave as a "Building Information Model" (BIM) composed of discrete "Atomic Building Blocks" (AABB). A core component is the ContainerAABB, which represents a complete wave cycle. Its topological identity is defined by scale-invariant properties like local maxima/minima counts and monotonic segments. Crucially, a ContainerAABB isn't a simple sine wave segment but a sophisticated, ordered "queue" or "assembly" of multiple CrestAABB and TroughAABB objects.
Frequency, in Qhenomenology, is redefined not by spectral content but as the "combinatorial packing factor" of these topologically classified ContainerAABB objects along the timeline. This is computed by counting valid ContainerAABB instances within a given duration.
A key departure from traditional filtering is the unique amplitude management. Qhenomenology proposes "silencing" (enforcing zero amplitudes for) low-amplitude samples (e.g., below MEAN + 1 * STD DEV). This radical reduction is claimed to preserve listenability and understanding, acting as a topology-preserving "denoising" by focusing on significant structural information rather than minute amplitude variations.
Zero-crossing points are re-conceptualized as critical "junctions" or "connectors" between wave components, each with its own scale-invariant topological classification (e.g., Crest-to-Trough, Trough-to-Crest, Silence). These classifications, along with analysis of "interfacing neighborhoods," are crucial for forming the ContainerAABBs and deriving the wave's underlying "grammar."
The ultimate goal is a direct mapping from wave data to musical semantics. This includes extracting MIDI notes, managing complex pitch bends by identifying monotonically changing notes within a semitone range, and classifying timbre and tonality directly from the topological numbering of the AABB objects. Qhenomenology offers a unique computational approach to understand sound's intrinsic structure beyond traditional signal decomposition.
___________________
Here is a condensed, 3000-character version of Sanjoy Nath’s Qhenomenology Theory of Waves:
Qhenomenology of Waves: A Topological Framework for Signal Analysis
Sanjoy Nath’s Qhenomenology is a novel, non-Fourier approach to waveform analysis. It models signals not as superpositions of sinusoids, but as queues of topologically classified geometric primitives—specifically, Axis-Aligned Bounding Boxes (AABBs) representing crests and troughs. These AABBs are analyzed using scale-invariant topological features, enabling a new form of frequency and structure detection.
1. AABB-Based Modeling
- Crest AABB: Encloses positive waveform segments.
- Trough AABB: Encloses negative segments.
- Container AABB: Encloses a full wave cycle, possibly containing multiple crests and troughs.
Each AABB is characterized by:
- Local maxima/minima count
- Monotonic segments
- Area under curve
- Center of Gravity (CG)
- Percentile-based amplitude ranks
These features are topologically invariant under scaling.
2. Frequency as Packing Factor
Frequency is defined as:
Frequency = Number of Valid Container AABBs / Time Window
This redefines frequency as a packing metric—how many topologically valid cycles fit in a second—rather than a harmonic decomposition.
3. Zero Crossing Topology
Zero crossings are classified into types (CT, TC, CC, TT, etc.) based on amplitude transitions and neighborhood topology. Each crossing is assigned a topology classification number, enabling symbolic parsing of waveform structure.
4. Stack-Based Spectral Analysis
Instead of FFT, Qhenomenology uses stacking of transparent AABB rectangles aligned by left margins. When viewed from the top, CG points align vertically, forming spectral lines. This reveals frequency content without sin/cos decomposition.
5. Topology Classification Metric
Each AABB is assigned a Topology Classification Sensitiveness Number (TCSN):
This allows grouping of similar AABBs and identification of repeating structural motifs, akin to Building Information Modeling (BIM).
6. Philosophical Foundation
Qhenomenology treats all signal components as concrete C++-like classes. No abstract classes or neural networks are allowed. Reasoning is compiler-like, where each concept must be concretely defined before use. This ensures bias-free, deterministic reasoning.
7. Applications
- Audio: MIDI note extraction, pitch bend modeling
- Vibration: Structural health monitoring
- Cognition: Symbolic reasoning over waveform strings
Conclusion
Qhenomenology redefines waveform analysis through combinatorial, topological, and geometric reasoning. It avoids Fourier transforms entirely, offering a scale-invariant, structure-preserving, and visually interpretable alternative for signal understanding.
Let me know if you'd like this formatted into a PDF, LaTeX paper, or visual diagram!
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