Monday, December 27, 2021

3D Audio Plugin : Console, PC, Mac, IOS, Android, Linux

Any Console Game Buffered VR Surround & 3D Audio Game development 2021 (c)RS

3D Audio Plugin : Console, PC, Mac, IOS, Android, Linux

Remember all the game needs is a virtual Output buffer before send to console audio output

7.1.2 output emulation layer, Is simply to write 7.1.2 Audio buffer before output & simply virtualise to any output buffer mode the console or Astro's or Creative logic USB or TV Has in E-AC3 Dolby Atmos

Yes 7.1.2 channel Audio Cache in Game SDK & Firmware is entirely possible,

Output Processing is unique to Game cartridge & E-AC3 & E-AC4 Processing Plugin Codec

To clarify : If the game buffers a 7.1.2 channel profile, Any output Audio Firmware is Compatible with 3D sound, Even Stereo E-AC3 Dolby

Any Firmware or Bios or GPU can accomplish this with willpower,

To any 3D Format:

Creative 3D EAX
Dolby
DTS
THX

Buffer & Plugin SDK Codec

Any game can process 7.1.2 audio into stereo with Virtual Dolby plugin in the source code of console

If a company:

Soundblaster : Creative logic
Dolby Atmos
DTS
THX

Create plugins for your GAME SDK

E-AC3 & E-AC4 & DTS

*

My version of AmbiSonics 3D Audio &+ Virtual Surround imbedded into the channels if required (Not always) (c)RS

Basically you can stream to server in 5.1 HQ & convert into ambersonics 2.1 
(Joint stereo with 3 way conversion, 
Which essentially means up to 7.3.3 Channel arrangement,

You can use more than 5 channels & subchannels,
But planning for Bluetooth means 2 joint Stereo channels per earbud,
Stereo headphones & Bluetooth headphones commonly utilise a Single Channel stream..
Joint 2.1 : 2 Channel & 1 Joined optimised for fidelity & speaker arrangement.

Theoretical if you use 2 joint stereo channels,
With Centric joint channel being : High, Low, Centric &or+ BASS

E-AC3 & E-AC4 & DTS & AAC & OPUS are most likely to work here

https://science.n-helix.com/2021/10/eccd-vr-3datmos-enhanced-codec.html

*

Amber Sonics (3D Spatial Averaging network Depth N to N8) & Spatial Audio : Unreal Engine Demonstration : THX, DTS, Dolby : Personalised HRTF Ear Profile ML Servers

AmbiSonic 360.. Float 32-bit Opus Codec
Progress in AmbiSonics 3D Audio Surround : New : AmbiSonic 360.. These guys are playing for your Samui game : 360 TokniOKI Blade!

Traditional Asiatic music (Calypso style) in at a very minimum; Very high quality Stereo > 
Channel layout Opus 32Bit float QUAD with a beautiful high quality sound; Video is basic..
Suitable for assessment & enjoyment.


Cyberpunk 2077 HDR : THX, DTS, Dolby : Haptic response so clear you can feel the 3D SOUND

Apex Legends : THX, DTS, Dolby

TERMINATOR Interview #Feeling https://www.youtube.com/watch?v=srksXVEkfAs & Yes you want that Conan to sound right in 3D HTRF

https://www.youtube.com/watch?v=d1OBJP7VcJs

Best Game Graphics of 2021 - PC, Xbox, PlayStation

Tuesday, November 30, 2021

MultiBit Serial & Parallel execution conversion inline of N*Bit -+

Multi Bit load operations for bitmap,Texture & Other tasks +ON+HighLowOP (c)RS

May take higher or lower bit depth & precisions: Rupert S 2021

2 16 Bit loads is 32Bit but takes 2 cycles...

16 Bit loads with 32 Bit Stores & Math unit:

Operation 1

16Bit , 16Bit , 16Bit , 16Bit Operation
\ / \ /

Inline Store

32Bit Store 32Bit Store
64Bit Store
\ /

32Bit ADD/DIV x 2 or 64Bit ADD/DIV x1

Operation 2

32Bit ADD/DIV x 2 or 64Bit ADD/DIV x1
\ /

4x 16Bit Store

4 x 16Bit Operation

MultiBit Serial & Parallel execution conversion inline of N*Bit -+

In the case of ADD -+ Signed for example:(c)RS
Plus & - Lines ADD or Subtract (Signed, Bit Depth Irrelevant)

Multiples of 16Bit works in place of 32Bit or 64Bit

V1: 16Bit Values composing a total 128Bit number
V2: 16Bit Values composing a total 128Bit number - (Value less than V1)
V3: Result

NBit: Bit Depth

4x16Bit operations in the same cycle >

If Value = 16Bit = Store
If Value = V3=Bit = Store * NBit

Stored 128Bit RAM or if remainder = less > 4x16Bit -1-1-1 ; 16Bit Value Store

RS https://bit.ly/DJ_EQ

*

*RAND OP Ubuntu

https://pollinate.n-helix.com/

(Rn1 *<>/ Rn2 *<>/ Rn3)

-+
VAR(+-) Var = Rn1 +- Rn8

(Rn5 *<>/ Rn6 *<>/ Rn7)

4 Samples over N * Sample 1 to 4

Input into pool 1 Low half -+
Input into pool 1 High half -+

*RAND OP Recycle It

RS
*

https://science.n-helix.com/2021/11/parallel-execution.html
https://science.n-helix.com/2021/11/monticarlo-workload-selector.html

Sunday, November 21, 2021

MontiCarlo Workload Selector

Cash_Bo_Montin Selector (c)Rupert S for Cache & System Operations Optimisation & Compute

CBoMontin Processor Scheduler - Good for consoles & RT Kernels (For HTTP+JS HyperThreading)

*
Monticarlo Workload Selector

CPU, GPU, APU, SPU, ROM, Kernel & Operating system :

CPU/GPU/Chip/Kernel Cache & Thread Work Operations management

In/out Memory operations & CU feature selection are ordered into groups based on:

CU Selection is preferred by Chip features used by code & Cache in-lining in the same group.

Global Use (In application or common DLL) Group Core CU
Localised Thread group, Sub prioritised to Sub CU in location of work use
Prioritised to local CU with Chip feature available & with lower utilisation (lowers latency)

{ Monticarlos In/Out }
System input load Predictable Statistic analysis }
Monticarlo Assumed averages per task }
System: IO, IRQ, DMA, Data Motion }

{ Process by Advantage }
{ Process By Task FeatureSet }
{ Process by time & Tick & Clock Cycle: Estimates }
{ Monticarlos Out/In }

Random task & workload optimiser ,
Task & Workload Assignment Requestor,
Pointer Allocator,
Cache RAM Allocation System.

Multithreaded pointer Cache Object tasks & management.

{SEV_TDL_TDX Kernel Interaction mount point: Input & Output by SSL Code Class}:
{Code Runtime Classification & Arch:Feature & Location Store: Kernel System Interaction Cache Flow Buffer}
https://is.gd/SEV_SSLSecureCore
https://is.gd/SSL_DRM_CleanKernel
*

Based upon the fact that you can input Monti Carlos Semi Random Ordered work loads into the core process:

*Core Process Instruction*

CPU, Cache, Light memory load job selector
Resident in Cache L3 for 256KB+- Cache list + Code 4Kb L2 with list access to L3

L2:L3 <> L1 Data + Instruction

*formula*


(c)RS 12:00 to 14:00 Haptic & 3D Audio : Group Cluster Thread SPU:GPU CU

Merge = "GPU+CPU SiMD" 3D Wave (Audio 93% * Haptic 7%)

Grouping selector
3D Wave selector

Group Property value A = Audio S=Sound G=Geometry V=Video H=Haptic B=Both BH=BothHaptic

CPU Int : ID+ (group of)"ASGVH"

Float ops FPU Light localised positioning 8 thread

Shader ID + Group 16 Blocks
SiMD/AVX Big Group 2 Cycle
GPU CU / Audio CU (Localised grouping MultiThreads)

https://www.youtube.com/watch?v=cJkx-OLgLzo

*

Task & Workload Assignment Requestor : Memory & Power


We have to bear in mind power requirements & task persistence in the :Task & Workload Assignment Requestor

knowledge of the operating systems requirements:
Latency list in groups { high processor load requirements > Low processor load requirements } : { latency Estimates }
Ram load , Store & clear {high burst : 2ns < 15ns } GB/s Ordered
Ram load , Store & clear {high burst : 5ns < 20ns } MB/s Disordered

GPU Ram load , Store & clear {high burst : 2ns < 15ns } GB/s Ordered
AUDIO Ram load , Store & clear {high burst : 1ns < 15ns } MB/s Disordered

AUDIO Ram load , Store & clear {high burst : 1ns < 15ns } MB/s Ordered
AUDIO Ram load , Store & clear {high burst : 1ns < 15ns } KB/s Disordered

Network load , Send & Receive {Medium burst : 2ns < 15ns } GB/s Ordered
Network load , Send & Receive {high burst : 1ns < 20ns } MB/s Disordered
Hard drive management & storage {medium : 15ns SSD < 40ns HDD}

*

Also Good for disassociated Asymmetric cores; Since these pose a significant challenge to most software,
However categorising by Processor function yields remarkable classification abilities:

Processor Advanced Instruction set
Core speed
Importance

Location in association with a group of baton passing & interthread messaging & cache,
Symmetry classed processes & threads.

*

Bo-Montin Workload Compute :&: Hardware Accelerated Audio : 3D Audio Dolby NR & DTS


Hardware Accelerated Audio : 3D Audio Dolby NR & DTS : Project Acoustics : Strangely enough ....
Be more positive about Audio Block : Dolby & DTS will use it & thereby in games!

Workload Compute : Where you optimise workload lists though SiMD Maths to HASH subtasks into new GPU workloads,

Simply utilize Direct ML to anticipate future motion vectors (As with video)

OpenCL & Direct Compute : Lists & Compute RAM Loads and Shaders to load...

DMA & Reversed DMA (From GPU to & from RAM)
ReBAR to vector compressed textures without intervention of one processor or another...

Compression Block :
KRAKEN & BC Compression & Decompression
&
SiMD Direct Compressed Load using the Cache Block per SiMD Work Group.

Shaders Optimised & compiled in FPU & SiMD Code form for GPU: Compiling Methods:

In advance load & compile : BRT : Before Runtime Time : task load optimised & ordered Task Executor : Bo-Montin Scheduler

GPU SiMD & FPU (micro 128KB Block encoder : decoder : compiler)
CPU SiMD & FPU (micro 128KB Block encoder : decoder : compiler)

JIT : Just in Time task load optimised & ordered Task Executor : Bo-Montin Scheduler

load & compile :

GPU SiMD & FPU (micro 128KB Block encoder : decoder : compiler)
CPU SiMD & FPU (micro 128KB Block encoder : decoder : compiler)


*

Task manager opportunistically &or Systematic Resource Allocation (c)RS


We also need a direct transport tunnel for data between GPU of different types,

Firstly my experience is as follows:

I have a RX280x & RX560 & Intel® Movidius™ Neural Compute SDK Python API v2 & both do Python work! When I have this configuration the RX280x is barely used unless clearly utilized independently!

The Task manager & Python needs to directly transfer workloads a processor tasks between each system processor,

Not limited to the primary Processor (4Ghz FX8320E) & the AVX supporting Movidius & to & from the RX280 & RX560, Both however supported direct Video rendering & Encoding though DX12,

However the RX6500 does not directly support the AMD Hardware Encode under DX12.1 (New Version 2022-04-21)

& That RX560 comes in handy! if the Video rendering work is directly transferred to RX560 or RX280x & Encoded there!

Therefore I clearly see 2 examples.. & there are more!

Clearly Movidius is advantaged for scaler work on behalf of the Python process & in addition the Upscaling RSR & Dynamic Resolution; We do however need directly to have the Task manager opportunistically or systematically plan the use of resources & Even the processor could offload AVX Work.

No-one has this planned & We DO.

(c)Rupert S https://science.n-helix.com

Kernel Computation Resources Management :

OpenCL, Direct Compute, Compute Shaders & MipMaps :

Optimisation of all system resource use & management 2022 HPC RS

On the matter of Asymmetric GPU / CPU configuration, As in when 2 GPU are not of the same Class or from different providers,

Such a situation is when the motherboard is NVidia & the GPU is AMD for example.

We need both to work, So how?

Firstly the kind of work matters: Operating System Managed Workload Scheduler : Open CL & Direct X as examples:

Firstly PCI 1+ has DMA Transfers of over 500MB/s so data transfer is not a problem,
Secondly DMA is card based; So a shader can transfer work.
Third the memory transfer can be compressed; Does not need to transition mainly though the CPU..
No Cache Issue; Same for Audio Bus

MipMaping is an example with a low PCI to PCI DMA Transfer cost,
But Shaders & OpenCL or Direct Compute are primary examples,
(Direct Compute & OpenCL workloads are cross compatible & convertible)

Exposing a systems potential does require that a DX11 card be utilized for MipMaps or Texture Storage & operations; Within the capacities of Direct 11, 12, 12.1 As and when compatible..

Optimisation of all system resource use & management 2022 HPC

Rupert S
*

https://science.n-helix.com/2021/11/monticarlo-workload-selector.html


 
*

EMS Leaf Allocations & Why we find them useful: (c)RS https://science.n-helix.com


Memory clear though page Voltage removal..

Systematic Cache randomisation flipping (On RAM Cache Directs syncobable (RAND Static, Lower quality RAND)(Why not DEV Write 8 x 16KB (Aligned Streams (2x) L2 CACHE Reasons)

Anyway in order to do this we Allocate Leaf Pages or Large Pages...
De Allocation invokes scrubbing or VOID Call in the case of a VM.

So in our case VT86 Instructions are quite useful in a Hypervisor;
&So Hypervisor from kernel = WIN!

(c)Rupert S Reference T Clear

*

Atomic: Add custom atomic.h implementation

Now we can use Statistic variance Atomic Counters inside loops with SivHASH 32Bit value hashes to add variances to dev/random & quite significantly increase motion in the pool,

But use Main thread interactions with average micro loops to reduce the overall HASH turnover rate..

Modification of the additional kind ADD's to the pre published value & additionally passes CPU Activity count numbers to the statistic pool; In the same loop main thread.

Rupert S

Atomics & Reference PID/TSC/LeafBlend

https://science.n-helix.com/2022/03/security-aspect-leaf-hash-identifiers.html
Atomics https://lkml.org/lkml/2022/4/12/84
RDPID https://lkml.org/lkml/2022/4/12/143
Opening Time Security Layering Reference PID with RDPID LeafHASH
https://lkml.org/lkml/2022/4/12/300

*

If you could "Decode" Win DLL & particularly the Compiler code, plug
in! you could use these on console :

https://bit.ly/DJ_EQ
https://bit.ly/VESA_BT

https://www.youtube.com/watch?v=cJkx-OLgLzo

High performance firmware:



https://is.gd/SEV_SSLSecureCore
https://is.gd/SSL_DRM_CleanKernel



*
More on HRTF 3D Audio

TERMINATOR Interview #Feeling https://www.youtube.com/watch?v=srksXVEkfAs & Yes you want that Conan to sound right in 3D HTRF

Cyberpunk 2077 HDR : THX, DTS, Dolby : Haptic response so clear you can feel the 3D SOUND




*

AES RAND*****


If we had a front door & a back door & we said that, "That door is only available exclusively to us "Someone would still want to use our code!
AES is good for one thing! Stopping Cyber Crime!
hod Save us from total anarchistic cynicism

Rupert S

/*
  * This function will use the architecture-specific hardware random
- * number generator if it is available.  The arch-specific hw RNG will
- * almost certainly be faster than what we can do in software, but it
- * is impossible to verify that it is implemented securely (as
- * opposed, to, say, the AES encryption of a sequence number using a
- * key known by the NSA).  So it's useful if we need the speed, but
- * only if we're willing to trust the hardware manufacturer not to
- * have put in a back door.
- *
- * Return number of bytes filled in.
+ * number generator if it is available. It is not recommended for
+ * use. Use get_random_bytes() instead. It returns the number of
+ * bytes filled in.
  */

https://lore.kernel.org/lkml/20220209135211.557032-1-Jason@zx2c4.com/t/


RAND : Callback & spinlock

Callback & spinlock are not just linux : Best we hash &or Encrypt several sources (if we have them)
If we have a pure source of Random.. we like the purity! but 90% of the time we like to hash them all together & keep the quality & source integrally variable to improve complexity.
Rupert S
https://www.spinics.net/lists/linux-crypto/msg61312.html

'function gets random data from the best available sourceThe current code has a sequence in several places that calls one or more of arch_get_random_long() or related functions, checks the return value(s) and on failure falls back to random_get_entropy().get_source long() is intended to replace all such sequences.This is better in several ways. In the fallback case it gives much more random output than random_get_entropy(). It never wasted effort by calling arch_get_random_long() et al. when the relevant config variables are not set. When it does usearch_get_random_long(), it does not deliver raw output from that function but masks it by mixing with stored random data.'

RAND : Callback & spinlock : Code Method


Spinlock IRQ Interrupted upon RAND Pool Transfer > Why not Use DMA Transfer & Memory Buffer Merge with SiMD : AVX Byte Swapping & Merge into present RAM Buffer or Future location with Memory location Fast Table.

Part of Bo-Montin Selector Code:

(CPU & Thread Synced & on same CPU)

(Thread 1 : cpu:1:2:3:4)
(RAND)
(Buffer 1) > SiMD cache & Function :

(Thread 2 : cpu:1:2:3:4)
(Memory Location Table : EMS:XMS:32Bit:64Bit)
(Selection Buffer & Transfer)

(Buffer 1) (Buffer 2) (Buffer 3)
(Entropy Sample : DieHARD : Small)

Rupert S

https://lore.kernel.org/all/20220211011446.392673-1-Jason@zx2c4.com/

Random Initiator : Linus' 50ee7529ec45


Linus' 50ee7529ec45 ("random: try to actively add entropy
rather than passively wait for it"), the RNG does a haveged-style jitter
dance around the scheduler, in order to produce entropy

The key is to initialize with a SEED key; To avoid the seed needing to be replaced too often we Encipher it in a set order with an additive key..

to create the perfect circumstances we utilize 2 seeds:
AES/SHA2/PolyCHA

Initiator math key CH1:8Bit to 32Bit High quality HASH Cryptic
& Key 2 CrH

8Bit to 256Bit : Stored HASH Cryptic

We operate maths on the differential and Crypro the HASH :
AES/SHA2/PolyCHA
CrH 'Math' CH1(1,2,3>)

AES/SHA2/PolyCHA > Save to /dev/random & use

We may also use the code directly to do unique HASH RAND & therefore keep crucial details personal or per application & MultiThreads &or CPU & GPU & Task.

Rupert S

(Spectra & Repoline Ablation) PreFETCH Statistical Load Adaptive CPU Optimising Task Manager ML(c)RS 2022


Come to think of it, Light encryption 'In State' may be possible in the Cache L3 (the main problem with repoline) & L2 (secondary) : How?

PFIO_Pol & GPIO Combined with PSLAC TaskManager (CBo_Montin) Processor, Kernel, UserSpace.
 
Byte Swapping for example or 16b instruction, If a lightly used instruction is used
(one that is under utilized)
Other XOR SiMD instructions can potentially be used to pre load L2 & L1 Instruction & Data.

Spectra & Repoline 1% CPU Hit : 75% improved Security : ALL CPU v& GPU Processor Type Compatible.

In Terms of passwords & SSL Certificate loads only, The Coding would take 20Minutes & consume only 0.1% of total CPU Time.

Also Good for disassociated Asymmetric cores; Since these pose a significant challenge to most software,
However categorising by Processor function yields remarkable classification abilities:

Processor Advanced Instruction set
Core speed
Importance

Location in association with a group of baton passing & interthread messaging & cache,
Symmetry classed processes & threads.

HASH Example

https://lkml.org/lkml/2022/3/17/120
https://lkml.org/lkml/2022/3/17/119
https://lkml.org/lkml/2022/3/17/116
https://lkml.org/lkml/2022/3/17/115
https://lkml.org/lkml/2022/3/17/118

https://science.n-helix.com/2022/02/interrupt-entropy.html
In reference to : https://science.n-helix.com/2021/11/monticarlo-workload-selector.html

CPU Statistical load debug 128 Thread :
https://lkml.org/lkml/2022/3/17/243

PFIO_Pol Generic Processor Function IO & Feature Statistics polling + CPUFunctionClass.h + VCache Memory Table Secure HASH

Also Good for disassociated Asymmetric cores; Since these pose a significant challenge to most software,
However categorising by Processor function yields remarkable classification abilities:

Processor Advanced Instruction set
Core speed
Importance

Location in association with a group of baton passing & interthread messaging & cache,
Symmetry classed processes & threads.

GPIO: Simple logic analyzer using polling : Prefer = Precise Core VClock + GPIO + Processor Function IO & Feature Statistics polling

https://lkml.org/lkml/2022/3/17/216
https://lkml.org/lkml/2022/3/17/215

Wednesday, November 17, 2021

iHM_TES - Interpretive Haptic Motion Time expression Sense-8é: iHM_TES: (c)RS

Interpretive Haptic Motion Time expression Sense-8é: iHM_TES: (c)RS

1 Introduce 3D Audio containerised packet for haptic,
2 Simplification of technique to allow WebAPI,
3 Meta Data for interaction use (Adaptation of geometry, Sound & feedback loop)
4 Backported API : Interaction is a packet; Not a form of MP3 or AAC or H264, H265, VP9, VVC
5 Interpreted loosely (Common goal, Many thiems.
6 Smell, Taste, Sound, Feel, Interaction, Choice : 5 Senses? Why not "Sense"ation 8
7 You can feel it, Taste it & Know what it thinks, How it's heart pulses.. Sense' At (E)ions
8 Properties in the bitstream notify Audio & Video & Expressions of Sense to the meaning to be transferred & meant. the Sense-ATE Property Packet is flexible & multiple endpoint.
9 Transference one expression of experience into another, Convoluted networks transfer one sense into another.
10 Meshes Sense(tm) Combined low latency packets merge sense expression into one cohesive low latency experience by notifying your BT, HDMI, Audio, AMP & TV of the TIME & Sync of each play or motion or move.


(Haptic Is a 3D Sound Waveform of 3D Geometry) ,
Can be visual but not guaranteed to need that complication So:

SBC, AAC, AptX prove virtually indistinct from, Visual waveform geometry Profiled haptic.

Both methods work with localised packet container format..

Game Database loaded waveforms.

Game geometry in the form of waves:

Simple
Colorful
Complex

Rupert Summerskill 2021

https://bit.ly/DJ_EQ

https://science.n-helix.com/2019/06/vulkan-stack.html

https://science.n-helix.com/2017/02/open-gaming.html

https://science.n-helix.com/2016/04/3d-desktop-virtualization.html

https://science.n-helix.com/2020/04/render.html

MPEG Standardisation of haptic feedback: 2 missions: SDK + Client Build + Size & Latency. (c)RS

https://www.marketscreener.com/quote/stock/IMMERSION-CORPORATION-9670/news/Immersion-MPEG-Standardization-is-a-Watershed-Moment-for-Haptics-37048471/

Saturday, November 13, 2021

Wave-Focus-ANC

Sound-focusing & Wave-Focus-ANC & WF_AnANC (c)RS

Sound Violation & Noise + Digital + Electronic noise reduction in harmonic failure.

Applicable to HDMI, VESA, Bluetooth, Radio, DAB Radio & TV, WIFI & all energy technology though licenced technology (c)RS

By applying wave sampling to waveforms & compression waveforms (Wavelets) we can either
Subtract or add to the wave, By applying Noise suppression or noise shaping or noise boosting..

To the electronic, Light or energy or Data, Image or audio we can shape that wave so that the value displayed or utilised is:

*

Dr ANC Table: Applies to:


Sound
Electronics
Light
LED
Laser
Processing
File compression
File Accuracy
Noise levels
Power & amplification

Sensors &+ Noise
Sharpening & Enhancing
Processing, Isolating or Extrapolating Data
Video process
Audio Process
Data Process

+

More or less

Accurate
Colourful
Sharper
Distinct
Uniform or ordered
Chaotic
Complex
Simple
Cleaner or Original
Unique or the Same as the Master

*

Anti +- Wave-Focus-ANC : ANC Applied to invert frequencies in:RS

NE Noise Enhancement }for a purpose
NR Noise Reduction }
Shaping & Direction }
Sharpening & Enhancing }
Isolating or extrapolating Data }
Resultant Manipulation }
Resultant Clarification or Simplification }

Speakers & Display Systems : TV, Monitor, VR, Motion sensation & Haptic Feedback
Sensors & Camera or Video & motion etcetera
Signal &+- noise data with statistical & or dynamic data
Motion
Rockets
Mechanical motion enhancement
Mechanical vibration
Electrical noise & Static
Cars & Aeroplanes & space ships
Fan blades
Motors

Application of a static vibrator (Physical, Electrical, Energy & force)
For common noise reduction or enhancement or filter..
Beside the application though automatic reduction such as:

Foam
Static foam
Metal & polymer & Resin

Component for common vibration of a statistically normalize level & Dynamic NR + Dynamic NE
*

To direct sound through computational variance of sound wave profile so that it varies or vibrates the cone in different ways to reflect:

A 3 Dimensional shape over the cone that will reproduce a sound varied over a 3D space such as an eardrum or ear tunnel or a room..

Or otherwise shape sound though ANC Noise Cancelling calculation Sin, Cos & Tan Waves varied over time to modulate audio or filter Audio

To Shape audio and enhance it though Inverted ANC & thus subtly or greatly boost & direct audio in subtle ways that reflect across surfaces & angles ...

Both to boost waves in the Sense of EQ or to enhance or modify measured Fidelity of a speaker or relay:

Examples of inverted &+ ANC:

Electric cables carry noise (Remove it) or use noise to enhance audio boosting.
(principally like jiu Jitsu: To use momentum to advantage)

To shape waves & to make clean & precise, Sharp, Angular or otherwise shape.

In AMP's, Power converters, Cables and other energy systems such as:
Cameras, Lenses, Lasers, Emitters & receivers.

Image systems, Sensors & File save formats & HDD, SSD..
Application in principle enhances or destroys or shapes noise..
As we know Noise shaping also involves wavelets:

Both applicable second layer modifiers +-
& Wave co-modifiers.

(JPG & ALAC, AAC & SBC + Other file compression systems)

Enhancement, Sharpening & improvements..
Quality, Colour, Sound, Energy, Waveforms.

(c)Rupert S

Combined with:
https://science.n-helix.com/2021/10/the-principle-of-inversion-sign-sign-crs.html
https://science.n-helix.com/2021/11/expand-formula-sonarus.html
https://science.n-helix.com/2021/09/temporal-aliasing-image-shaping-polygon.html
https://science.n-helix.com/2021/03/upscaling-enhancement.html

Thursday, November 4, 2021

*Expand Formula* SonaRuS : Form & Shape - Codec Wavelet Complimentary cross conversion (c)RS 2021

Form & Shape - Codec Wavelet Complimentary cross conversion (c)RS 2021


Full support on all Hardware architectures & platforms + CPU & GPU.
Full support on all Bluetooth Devices, HDMI Devices, S/PDIF & TOSLink Devices.

Though Hardware Accelerated Conversion & Enhancement or otherwise optimisation for Data Bandwidth & Quality of content; QoS

More like most GPU in the NVidia & AMD (& qualcomm & ARM) lineup,I really need both of you to support : SBC, AAC, LC3 & AptX as potential HDMI connection options.


You see as you know, largely upscaled MP3 & MP4 Content barely benefact;
From Conversion to a final PCM, Maybe LPCM?


But benefact massively from cross conversion into an upscaled form of the same codec type!

They also benefit from quick low latency conversion with the same WAVE Shapes (Wavelets)..
Scaled to higher precision.

principally in audio analogue from digital convergence; higher precision output from compressed waves command the following:

Audio compression & expansion formula :


*Expand Formula* SonaRuS


D = Distance
T = Time period

X = (Angle X Over D) / T
Y = (Angle X Over D) / T²

Expand = (D/T) * (D/T²)


*UP*


(CoSin X) = (CoSin Y) * Expand | Replace

*Down*


(CoSin Y) * Expand = (CoSin X) | Replace

(c)Rupert S

https://bit.ly/VESA_BT

Sunday, October 10, 2021

The principle of inversion, Sign+ & Sign+ (c)RS

The principle of inversion, Sign+ & Sign+ (c)RS


In principle one uses a wave inverter circuit to invert a Waveform,
Such a circuit is called a wave inverter or converter; A diode matrix <>

Maths of a positive integer form allow all positive values, Invert the wave & All Negative.

So what do you propose is the purpose of wave inverting a waveform on an Integer Processor?

All Integer values of Positive Value subtracted + Inversion = Remainder Negative value,

In our case we are using the number to compute the differential in a L - R = Center Value.

Larger number - Smaller number = All values positive, Inversion leaves us with the remainder on the positive & Negative Value Set..

Rather than invert we sign (1 Bit) & therefore can subtract the value from a larger value without using Negative Values in a whole line,

Principally A Sin, Cosine pair are both positive; Or we can invert with a single Bit.

NON-FPU, All Integer & Hence for a codec & Display unit we can use all positive Values in out maths & most objectives involving subtracting waves;

We can displace for 0.0> Values of a sub 0 value such as 0.001

https://science.n-helix.com/2018/01/integer-floats-with-remainder-theory.html

https://science.n-helix.com/2021/02/multi-operation-maths.html

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

Useful here:
https://science.n-helix.com/2021/10/he-aacsbc-overlapping-wave-domains.html
https://science.n-helix.com/2021/10/eccd-vr-3datmos-enhanced-codec.html

*****

Precision & Inversion - Waveform delta, Timing, Scaling, Inversion & reversion in timing circuits & Computation, Audio, Video & Visual Systems (c)RS

Usage cases include : Defining Audio , World beating power grids , Computer Chips, AMP's & PreAMPS & Power chargers & power supply or packages.

*

Such is called an inverted 4/3 Analog or digital Wave converter, Where in a Wave of high density is converted into a low frequency; Mostly about timing and precision,

Low to high conversion is mostly about smooth wave modulation and specifically for situations commanding Very precise tight waveforms of Factored precision on lower bit order processors & principally is used for timing clocks;

For example Red Laser light amplitude modulators & timers of the slow & thus high precision simple function with an almost impossible to beat TIME Precision,

Usage cases include : Defining Audio , World beating power grids , Computer Chips, AMP's & PreAMPS & Power chargers & power supply or packages.

"Inverted Driver Geometry (IDG), with the bass/mid driver sited above the treble unit rather than below. This aids time-alignment."

(c)RS

Examples :

Mission(tm) Accomplished A Classic British Speaker Brand : Wireless : https://www.forbes.com/sites/marksparrow/2021/09/30/mission-accomplished-as-classic-british-speaker-brand-goes-wireless/

(Principally, Because hay! in Napoli we like a good price)

*

Sub-Banding Audio compression document 


https://science.n-helix.com/2021/10/he-aacsbc-overlapping-wave-domains.html
Example use of -+ Signed Data Arrays: 

SiMD 16Bit, 2 workflows+ exist:

16Bit positive 16Bit Negative, Use cases:

Antialiasing
Sharpening
Noise subtraction (Image+ -Noise, Quick) ANC
HDR, Low & high field arrays
HDR, High Pass & Low Pass, Light & Shadow (Light)

*
Integers in Low frequency band Clean waveform deltoids.
Integer -+ Signed Data Arrays Example banding for lower frequency audio channel sub-banding.

Integer is a good clean vibrant Bing sound,
With clean sounds; Sin waves & Low wave frequency;
A clean FP16b or 16Bit is a good way to go!

If we have plenty of FP16b we can still convert to float, but this way integer has low data rate + high efficiency in CPU & GPU + AVX 
*
Sub-Band Fractioning Signed : Camera CMOS, Sensor & Codec Example:
Sub-Band Fractioning +- Array Line Input SiMD FastMath

By using sub-banding fractions (For Example SBC Codec)
Small values can be subtracted or added to values & interpolated:

16Bit value, -+ small value & Interpolate
(Interpolate + 32Bit In/Out Cache Memory value storage Array) processor instruction set

Example 16Bit Arrays + 32Bit Array processor instruction set
16Bit Value, -+ Sub-Band of lower or higher frequency + Interpolate in 32Bit,
Merging & Super-Sampling & filtering.

Example 16Bit Operations of -+ Sign Code: CPU, FPU, GPU with Sub-Banding Maths (c)RS


Firstly a - Signed integer does not need to be a - Value if we apply a Table with Value Band:

Variable Table Vectored Database Variable (c)RS

Definition Table: B = Sub-Band (Defined as a value of a valid 16Bit Value; That represents a High or Low bit of a Bit Depth
32Bit Value / 2 = (2 * 16Bit : -+ Signed &or B1 + B2)
or 2 = (2 * 16Bit : -+ Signed &or B1 + B2)
or 3 = ( 3 * 80Bit : -+ Signed &or B1 + B2 + B3)
or 4 = (4 * 16Bit = 64Bit : -+ Signed &or B1 + B2 + B3 + B4)

B0 +- Signed Line in Variable Table 16bit
B1 +- Signed Line in Variable Table 16bit

V1(16Bit) + V2(16Bit) line = V3(32Bit)

V1 & V2 Make 16Bit transfer & Store possible.
V3 can use a 32Bit Store & Math processor or 32Bit SiMD Unit.

This process is called : Value Banding Table : VBT (c)RS
We can obviously use this procedure with all BitDepths: 8Bit, 16Bit, 32Bit, 64Bit, 80Bit, 128Bit, 256Bit <>

Rupert S

*

Math operations
Stereo audio (Single process)
Quickly inversion Sin, Cos, Tan Subtraction or addition, 
Possible use : Single Array storage of lines of + & - Values 
(For cache read (Quick) or storage space)

MP3, AAC, SBC, AptX Audio decompression, 
Conversion & Storage or play with, Low Processor processing usage requirements.

Tiny DAC & Audio processor arrays for : 
Bluetooth, 
WiFi, 
Headphones, 
Radio DAB+
Clocks; etcetera.

Saturday, October 9, 2021

ECCD-VR-3DAtmos - Enhanced Codec Compression Digital VR

ECCD-VR-3DAtmos - Enhanced Codec Compression Digital VR 7(+16) 1(+2) (L + R) With combined Bitrate Centre channel (c)RS


For TOSLink, ARC, eARC, Bluetooth

TOSLink specifics are 384Kb/s , If we can manage 1Mb/s Many Codecs work as is.
Bluetooth has the same issues with Data rate & ARC also.

Bluetooth has a specific capacity of 10Mb,
But often 1Mb/s is Codec maximum with reasonable CPU usage.

Specifically, the Encoder & Decoder rate of 1mb/s capacity can do 7.1 with Atmos VR Channels from 16 to 38.

The VR channel capacity is achieved by combining Extended channels (L + R) With combined Bitrate Center channel,

The capacity to decode with interpolation &or Mathematical Dithering of the (L + R) Center,
Therefore extending virtual channels within the Dolby Atmos + DTS Standards.

(L + R) With combined Bitrate Center channel,

(L + /VR\ + R) Sub (L + /VR\ + R)

(L + /VR\ + R) VRCenterSub (L + /VR\ + R)

(L + /VR\ + R) Sub (L + /VR\ + R)

Dolby:DTS : (c)RS 2021 : 

The way for 72 subchannel 7.1.2 to satisfy Console Working 1024 Variance Pure 3D Positional Audio, 
Is to sub-filter sound profiles provided to 7.1.2+72 Subchannels.. 
As this is a LOT of processing, 
Try not to go too heavy on EGO. 
But Sub-processing Sound field with AA & Anti-isotropic For Audio Waveforms;  like sub-pixel for GPU Screen display WILL Work, Mark my words "Wisdom WORKS".

(c)Rupert Summerskill

*

SBC — 200 to 328kbps
AAC — 128 to 256kbps
LC3 — 160 to 345kbps
LDAC — 300kbps, 660kbps, 990kbps
aptX — 352kbps
aptX HD — 576kbps
aptX Adaptive — 279 to 420kbps
aptX Lossless — 120kbps to >1Mbps

*

https://science.n-helix.com/2021/10/he-aacsbc-overlapping-wave-domains.html

https://science.n-helix.com/2021/09/temporal-aliasing-image-shaping-polygon.html

https://bit.ly/VESA_BT

https://is.gd/BT_ANC_3DShapedAudio

Dolby Atmos 3D Audio in production
https://www.youtube.com/watch?v=Bmq1Zj2Z0-8

https://www.soundguys.com/the-ultimate-guide-to-bluetooth-headphones-aac-20296/

https://hdbluetooth.com/bluetooth-audio-codecs-explained/

https://www.nextpit.com/bluetooth-audio-codecs

Useful codec speed improvement:
https://science.n-helix.com/2021/10/the-principle-of-inversion-sign-sign-crs.html

#ASIO Produces lower latency from audio Input/Output Cycles https://is.gd/FasterAudioASIO
ASIO #FasterAudio : but a lot faster, like in gaming or production https://www.asio4all.org/

*
For hardware developers of HDMI, VESA, Bluetooth:
ASIO:DSD:SACD:22.5792 MHz (512 times that of CD): 
As https://en.wikipedia.org/wiki/Direct_Stream_Digital 
States only ASIO can playback DSD https://bit.ly/FasterAudioASIO
*  

Samples for Codec & Sound optimisation, Recorded on 2D Mic in 3D


DJ Bobby laser sample , 2 min sample 3D Audio + MC Vocal by JN 
https://is.gd/BobbyLaserJN_EchoZ313 
https://is.gd/BobbyLaserJN_AtmosEchoZ313
https://is.gd/DJPolyEstervsJN7_1

Buddhist Sentience Laboratory
https://is.gd/BuddhistTempleRune3D

https://is.gd/Z313EchoDOT7_1_3D

Monday, October 4, 2021

HE-AAC+SBC overlapping wave domains Quadratic Quantifier with wavelets (c)RS 2021

HE-AAC+SBC overlapping wave domains Quadratic Quantifier with wavelets (c)RS 2021


Wavelet Quantification of 4 Band Harmonic overlay for Radio, DAB, Bluetooth & WIFI & VESA Codec, Display Port & HDMI & TOS-Link

For application of all MP3 & AAC & DSC Standard Wave Compression techniques & Quadratic Banding.

SBC overlapping wave domains: (C)Rupert Summerskill

Principally in Radio harmonics : 4 Band SBC : HE-AAC Quadratic filter mask overlay quantifier

Long wave

Medium Wave

Short wave

UHF

4 bands

Signed =

Reflection Stereo (Simple reflection mapping, binaural)

Dynamic range from centric (2x Bits : Detailed)

8 Bit signed quantizers (Implemented in 16Bit F16b Signed) Long wave

9 to 12 : 12 to 14 : 9 to 14 Bit Quantifiers : Medium & Short Wave

14 to 16Bit & F12b to F16b Signed with 4Bit Remainder float : UHF

For application of all MP3 & AAC & DSC Standard Wave Compression techniques & Quadratic Banding.

Wavelet Quantification of 4 Band Harmonic overlay for Radio, DAB, Bluetooth & WIFI & VESA Codec, Display Port & HDMI & TOS-Link

Dolby Atmos : 7.1 SBC, HE-AAC, AptX F16b-Signed : HE-DA-SBCTank(tm) (c)RS

7.1 HE-SBC (3 Synced streams of R+L+Center Float : F16b+Signed, Combined Global 3D Aria Center mapped over 7.1/7.2 Channels) (c)RS

*

Harmonic beautification : JN

Observe an Earth Quake that is 85% or more within the harmonics:

Of the 2hz to 8Khz range if capable.. Tremble like a kit to Ibiza; Dark metal sings..

5Khz to 14Khz Hum the world like a tiger, Sing like a bee..; Be dark & ruthfull like "HE"; Be flexible!

11Khz to 18Khz Rings like a bell!; Sing for eternity of thy child..

16Khz to 22Kzh Sing like a harp...; The sonnet of the angels!

21Kzh to 24Khz you know the scream of metal harps! Electro ARC!

22Khz to 35Khz You know the world of the atom; The singing of a mid day sun... The heat of a pan.

*

Optimum Dynamic Direct Routing Table : ODDRT : RS


Reaching straight for Encode AAC, AptX, SBCn Dolby, DTX from the decode is the most logical choice, Needs encode path to H265, H264, VVC with as few deviations from source.

Direct container AC3, AAC 8 Support 64Bit Integer (For CPU Pure Code) Or+ AVX &@ SiMD..
Directly supports Driver Encode path though Float from GPU & CPU Driver layer,
Latency is minimal with a rout of:

Optimum Dynamic Direct Routing Table : ODDRT : Direct Encode Rout List of method: 6 V 5 or 4

Older: 6 Path : 6 Cycles
Encode, Decode, Re-encode PCM, Recode Dolby 7.1, DTS, Encode AAC or AptX or SBC or DTS

Newer: 5 Path 4 Cycles (Including System Driver functions: Virtual, Loudness, EQ etcetera)

Encode, Decode, Re-encode Dolby 7.1, DTS, Encode AAC or AptX or SBC or DTS

*

Bearing in mind SBC is a default AAC Encoder & Decoder we can see from the following link,
Conversion of Quality DTS & Dolby Content too an optimal Surround format at highest quality.
(c)RS

https://appuals.com/how-to-modify-bluetooth-stacks-on-android-for-greatly-enhanced-bluetooth-audio-quality/

That we can commonly set a data rate of 528Kb/s and that the rate works with most common..
Bluetooth headphones; So what can we do with this information on SBC?

SBC, AAC, HE-AAC, LAAC, ALAC, Dolby  are core MP3 Code base codecs; With a data rate of 384Kb/s we can already see the use for slower TOSLink connections.

Under specifications for TOSLink between 1Mb/s & 127Mb/s can be achieved; We need to have codec set their recompression size by the Speed & Bitrate of the connection after the Transfer rate is tested & reconfirmed upon preinitiations.

However List:

TOSLink : 384Kb/s to 127Mb/s (Cable quality; Glass Fibre, Plastic Fibre, High Quality, Quality, Base)

ARC : 384Kb/s to 1Mb/s & up to 37Mb/s (eARC Theoretical Future proofing in ROM) (Capacity of the receiver & Modern Cable can improve data capacity)

eARC : 384Kb/s to 1Mb/s & up to 37Mb/s <> 127Mb/s (Extended ROM) (eARC Theoretical Future proofing in ROM) (Capacity of the receiver & Modern Cable can improve data capacity)

Conversion of Quality DTS & Dolby Content too an optimal Surround format at highest quality.

(c)Rupert Summerskill
*

(c)RS

https://bit.ly/VESA_BT

https://www.androidauthority.com/lossless-bluetooth-audio-2740550/

*
https://www.ffmpeg.org/index.html#news

FFMPG Outputs the Bluetooth Codecs we need http://soundexpert.org/articles/-/blogs/audio-quality-of-bluetooth-aptx

AAC, SBC, AptX Codec With legal representation for all standards submission by Google, Cloudflare & CB for all operating systems & devices (c)RS

*

Improved 3D Audio Containers : Codec's as vehicles for Audio & Video Enhancement: AV.En,

With minimum Processing (CPU+SiMD) on devices such as monitors & AMP's & Bluetooth headsets,

Powerful &(small)

https://science.n-helix.com/2021/09/temporal-aliasing-image-shaping-polygon.html

https://www.androidauthority.com/lossless-bluetooth-audio-2740550/

https://hdbluetooth.com/bluetooth-audio-codecs-explained/

https://www.nextpit.com/bluetooth-audio-codecs

https://www.trustedreviews.com/news/sound-and-vision-does-aptx-lossless-herald-new-era-for-bluetooth-streaming-4161529

https://www.trustedreviews.com/opinion/sound-and-vision-is-3d-audio-the-next-battleground-for-headphones-4151733

Useful codec speed improvement:
https://science.n-helix.com/2021/10/the-principle-of-inversion-sign-sign-crs.html

Dolby Atmos 3D Audio in production

https://www.youtube.com/watch?v=Bmq1Zj2Z0-8

Codec's For Audio & Video



*

Echo DOT

[CODEC#1 supported by device]
CODEC Type: SBC, Sampling Frequency: [16]/[32]/44.1/48kHz, Channel Mode: Mono/Dual Channel/Stereo/Joint Stereo, Block Length: 4/8/12/16, Subbands: 4/8, Allocation Method: SNR/Loudness, Min/Max Bitpool: 2/250

[CODEC selected by Windows]
CODEC Type: SBC, Sampling Frequency: 44.1kHz, Channel Mode: Joint Stereo, Block Length: 16, Subbands: 8, Allocation Method: Loudness, Min/Max Bitpool: 2/53

AUDOM ANC8

[CODEC#1 supported by device]
CODEC Type: SBC, Sampling Frequency: 16/32/44.1/48kHz, Channel Mode: Mono/Dual Channel/Stereo/Joint Stereo, Block Length: 4/8/12/16, Subbands: 4/8, Allocation Method: SNR/Loudness, Min/Max Bitpool: 2/53

[CODEC selected by Windows]
CODEC Type: SBC, Sampling Frequency: 44.1kHz, Channel Mode: Joint Stereo, Block Length: 16, Subbands: 8, Allocation Method: Loudness, Min/Max Bitpool: 2/53

*

BT Codec Bitpool & Data Rate examination of codecs (Needs fine tuning)

Based upon simple analysis of the Bitpool :
Min/Max Bitpool: 2/250 Versus Min/Max Bitpool: 2/53

(One imagines around 5x the data processing & Maybe 4x the data, So 350Kb/s X 4 1400Kb/s)

16Bit + 48Khz + SubBands 8 (Maybe 16) +
50 Bitpool (2.1 Audio)
100 Bitpool (4.1 Audio)
150 Bitpool (5.1 Audio HQ)
200 Bitpool (5.1 Audio eHQ)

Suitable for:

TOSLink, S/PDIF
HDMI
WiFi

Bluetooth
16Bit + 48Khz + SubBands 8 (Maybe 16) +
50 Bitpool (2.1 Audio)
100 Bitpool (4.1 Audio HQ)
100 Bitpool (5.1 Audio Logical quality)
*

Pearl Codec Method(tm)

Pearling a codec

For SBC, AAC, AptX, Dolby, DTS

Pearling is where you take Bitpool such as: 2/250
Divide the Bitpool into segments such as 35/35 with 15 overlap so 25 pure & 15 Shared : 65 total,

Representing 65 Stereo shared Centre channel on Left & 65 Right, 6 Channels effective by direct measure,

However it is in-fact L Front L Centre (additive to F+R) L Back
Single channel total process L or R

Left & Right shared pool FLB(FCB)FRB

Surround with Signal induced Dolby Atmos
Expected data rates:

240Kb/s
to 2Mb/s
Average 570Kb/s
*

Sub-Banding Audio compression document 

https://science.n-helix.com/2021/10/he-aacsbc-overlapping-wave-domains.html

Example use of -+ Signed Data Arrays: 

SiMD 16Bit, 2 workflows+ exist:

16Bit positive 16Bit Negative, Use cases:

Antialiasing
Sharpening
Noise subtraction (Image+ -Noise, Quick) ANC
HDR, Low & high field arrays
HDR, High Pass & Low Pass, Light & Shadow (Light)

*
Integers in Low frequency band Clean waveform deltoids.
Integer -+ Signed Data Arrays Example banding for lower frequency audio channel sub-banding.

Integer is a good clean vibrant Bing sound,
With clean sounds; Sin waves & Low wave frequency;
A clean FP16b or 16Bit is a good way to go!

If we have plenty of FP16b we can still convert to float, but this way integer has low data rate + high efficiency in CPU & GPU + AVX 
*
Sub-Band Fractioning Signed : Camera CMOS, Sensor & Codec Example:
Sub-Band Fractioning +- Array Line Input SiMD FastMath

By using sub-banding fractions (For Example SBC Codec)
Small values can be subtracted or added to values & interpolated:

16Bit value, -+ small value & Interpolate
(Interpolate + 32Bit In/Out Cache Memory value storage Array) processor instruction set

Example 16Bit Arrays + 32Bit Array processor instruction set
16Bit Value, -+ Sub-Band of lower or higher frequency + Interpolate in 32Bit,
Merging & Super-Sampling & filtering.




*

Challenge accepted : Testing the Codecs Qualification 'Levels" :

Higher Dynamic Range Qualification Audio Test cases 5.1 HD 24Bit Stereo Mic's 



Sing 3D


#ASIO Produces lower latency from audio Input/Output Cycles https://bit.ly/FasterAudioASIO
ASIO #FasterAudio : but a lot faster, like in gaming or production https://www.asio4all.org/
#WAX it live & WAX it FAST #ASIO for production https://www.asio4all.org/

*
For hardware developers of HDMI, VESA, Bluetooth:
ASIO:DSD:SACD:22.5792 MHz (512 times that of CD): 
As https://en.wikipedia.org/wiki/Direct_Stream_Digital 
States only ASIO can playback DSD https://bit.ly/FasterAudioASIO
*  

Samples for Codec & Sound optimisation, Recorded on 2D Mic in 3D

DJ Bobby laser sample , 2 min sample 3D Audio + MC Vocal by JN
https://is.gd/BobbyLaserJN_EchoZ313
https://is.gd/BobbyLaserJN_AtmosEchoZ313
https://is.gd/DJPolyEstervsJN7_1

Buddhist Sentience Laboratory
https://is.gd/BuddhistTempleRune3D

https://is.gd/Z313EchoDOT7_1_3D

Friday, October 1, 2021

Noise VIOLATION Technology Bluetooth : Noise Reduction Technology & Noise Enhancement Technology in use (c)RS

Noise VIOLATION Technology Bluetooth : Noise Reduction Technology & Noise Enhancement Technology in use (c)RS

But ANC Bluetooth technology is provably viable for Noise cancelation!
& Also for noise enhancement telemetry..

But also for Screen, Video, Sound & File..


Hard Drive & SSD Storage..

LED Screens make a tiny bit of noise & we can profile that noise & remove it though processing.

Noise VIOLATION Technology Bluetooth : Noise Reduction Technology & Noise Enhancement:
Technology in use

Laser Mouse is sensitive enough to track sound, As in Sonic vibration that move a mouse over a mad & thus earth quake detections..

Mic can if low pass filtering is examined...

Obviously TV stations have used vibration on cameras, Stabilizers may interfere..

But analysis of low Long wave distortions makes these a network of machines to examine common issues..

We can likewise use Electronic exhaustive examination on electric noise to examine most longwave telescope issues...

Electric Cells process & filter noise so average light is what we have!

(c)Rupert S

QUAKE CON \\//_ entions 2021 - SOUND VIOLATION : QE

https://is.gd/VESA_HDMI_HDR

Wednesday, September 29, 2021

Temporal Aliasing - Image shaping Polygon Resolve Precision Enhancement (c)RS

Temporal Aliasing - Image shaping Polygon Resolve Precision Enhancement - The Vector Scope (c)RS Definition 2021-09


Vector Scope Polygon precision enhancement with temporal shaping matrix (c)Rupert Summerskill

Improve resolution of WebP, Audio & Video elements, Games & 3D & 2D & in principle Geometry, Fonts & Systems, Research faster & better but quicker, Bluetooth & earbuds,  TOSLink, HDMI, Video Playback , Audio Encoding & Decoding  , Video Encoding & Decoding, Audio Tracks, DVD & BlueRay Players, TV's & Monitors & Hardware requiring precision.

Definition of usage:

Audio Codecs
Video Codecs

Polygon Math

Resolution Enhancement
Processor Cycle Usage Reduction
Compression & Storage of object math

Complementary work on subject: RS

https://science.n-helix.com/2021/03/upscaling-enhancement.html
https://science.n-helix.com/2019/06/vulkan-stack.html

https://science.n-helix.com/2019/06/kernel.html

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

*****

The Vector Scope : Definition : RS


*

To Vector scope is to examine an original output of a Vector unit math..
In order to render the results in a higher resolution..
Or in order to upscale a rendered frame buffer (Letterbox) Into a higher resolution frame buffer,

In games that are mode set to a specific resolution (Sonic for example)..

We can install a layer after the game renders the screen that will interpret the resolution to a higher detail.

We can also Render a game in 1920 to the primary frame buffer & resolve additional details in a higher resolution.


***** Resolving material for Vector Precision Enhancement of math objects: RS

High resolution polygon translation table (matrix)

We can save a polygon table that defines higher precision mathematical representations of polygones to decompress & render in higher detail packed float polygons,

F8 definition polygons in F16b

A polygon defined in 8Bit converted though table into 32Bit polygons..
Result ? we do not have to mathematically interpolate in FPU; We load higher precision.

*

Definitions of triganomic objects for precision enhancement of stored Polygon data & maths >

*****

RSR APK Vrc:Lrc:Hrc : Advanced direct RAM Cached Pipe (c)RS


APK Formatic RSR App upscaling & in frame buffer Texture Super-Sampling at the sub pixel level..

Injected into the frame buffer from alternate middle buffer (c)RS

Virtual Screen Buffer : Low latency High precision Frame Cache : Output HDMI Render layer and DSC Compression link with VRR Direct to screen.

*****

Triganomic curvatures in glTF : (c)RS


Are you now using Tragicomic curvatures ..

Using ARC,SIN,TAN instead of polygons for curves?
You only need to map distance along the curve for a polygon point..

That point can be perfect; For there is no such thing as a limit to a curve except that defined by bit precision..

Being 16Bit or 32Bit SiMD can represent a perfection in 16K HDR..
Even more so a Float unit 186Bit! or divisions thereof for Multiplication & fraction boosted Threading.

Curvature modelling is a plan in which we need no points of a polygon
& thus we can compress the data..

A: b16Float for example because we need lower precision sub pixel rendering..

We use this for glTF

(c)Rupert S

https://www.youtube.com/watch?v=rf4yxkB3t4o

High precision FFT Examples : https://is.gd/ProcessorLasso in the SiMD Folder...

Advanced FFT & 3D Audio functions for CPU & GPU https://gpuopen.com/true-audio-next/
https://www.kfr.dev/

*****

High resolution polygon translation table (matrix)


We can save a polygon table that defines higher precision mathematical representations of polygons to decompress & render in higher detail packed float polygons,

F8 definition polygons in F16b

A polygon defined in 8Bit converted though table into 32Bit polygons..

Result ? we do not have to mathematically interpolate in FPU; We load higher precision.

*****

The Vector Scope : Definition


*

To Vector scope is to examine an original output of a Vector unit math..
In order to render the results in a higher resolution..

Or in order to upscale a rendered frame buffer (Letterbox) Into a higher resolution frame buffer,

In games that are mode set to a specific resolution (Sonic for example)..
We can install a layer after the game renders the screen that will interpret the resolution to a higher detail.

We can also Render a game in 1920 to the primary frame buffer & resolve additional details in a higher resolution.

*

We can still scope SiMD & FPU Maths precision outside of understanding the SDK they used..
For precise representation of our desired output virtualisation,

Either:

Original resolution x upscale + SiMD+FPU Vector Scope (the code run by the application or game)

Original resolution x upscale + SiMD+FPU Vector Scope; Into Virtual resolution


To Vector Scope: To understand the maths processes run by the program..

In order to improve precision of the output; We Know that the SiMD+FPU is a lot higher precision..
Than the output Display Resolution,
We can therefore promote the resolutions of all elements in Float values to vector quality.

Vector scope (the code run by the application or game)

We can then Machine Learn from Scope & that equals superior results,
But we can also directly apply those results though SiMD+FPU maths.

GBuffers are indeed a source of SiMD, Float results & we use all the details that we need.

Example method 3D Shaped screens & surfaces, Vector Scope:

Sample the 3D image of the surface & prove the following postulate:

Surface area N +- (Height + Contour Array bFloat16 = (Layer Surface requirement + Layer-N2)

contoured displays & dimples in wafers handled though VectorScope Maths.

(c)Rupert S / DukeThrust

https://is.gd/ProcessorLasso

***** Temporal Precision Enhancement : (c)RS

For Console Games : RCFedra 0.5x4:2:0.5x4 Frame method ML Bilinear & Trilinear Multi Cache Interpolation temporal matrix (c)RS

Alternative 1

One proposes 5 High Resolution frames 15 half frames Interlaced 20 Full frames as a 120 Solution..

Alternative 2

One proposes 5 High Resolution frame sets of :

1 = full frame, 0.5 = Interlace Frame Interpolated across 0.5:1:0.5 frame,

Can be 0.75; Less or more...

The matrix uses Time Interpolated slices across 3 pixels :

Centre = Full frame info and sides X + (shape) the interpolated.

Using bilinear & trilinear Interpolation / Filtering & Simple Association Machine Learning Matrix..
The basic matrix is 16 Node groups from a single dynamic Cache source...

2 Refresh Cycles learning advanced & 1 Behind :

*Localised independent based on DMA Access patterns combined with Data load & refresh.

(c)Rupert Summerskill

https://www.theverge.com/2021/7/1/22558816/ratchet-and-clank-rift-apart-fidelity-mode-40fps-120hz-30fps-refresh-rate

*****

FSR_DL 2 Motion vector+ with DSC:

Digital Signal Compression VESA Standard with Vector Prediction

1 plus 2 or rather Np1 + Np2 = Npr | N = Vector | Pixel 1 & Pixel 2

Pixel 2 is a vector direction from Np2 compared to Np1 from 8 locations , Ir rather 8 pixel squares surrounding Np1,

Processing the input Vector (lowering processing latency)

Obviously we take advantage of the fact that we have the keyboard & mouse or Input vector in low latency input mode & are processing the input Vector & therefore..
We can KNOW the Motion vector

Processing External input Vectors (lowering processing latency)

Obviously we take advantage of the fact that we have the Server or Input vector (Video for example with Predict; In low latency input mode & are processing the input Vector & therefore..
We can KNOW the Motion vector,

The 2 point motion Vector Frame


The 2 point motion & frame vector does have a clear advantage in that the DATA path is 100% 3D!
Indeed we do have a completely 3D Frame with:

Input Vector & 2 dislocated view point, The result is a 3D Frame with mathematically provable 3D Isometric Data, Also visible & processable,
Including by visual goggles & Red,Blue/Greed Differentiation (Classic Red & Green/Blue Glasses),

A simple SiMD Threaded examination of tells in the frame allows 3D Rendering,
Even with a single frame & we may provide 2+ different viewpoint frames...

Directly rendering that output To 3D Glass

https://www.youtube.com/watch?v=97JIldpUGE4

*****

ML Progress statement 2021 : RS


9:02 You know FSR Virtual Screen Resolution with Dynamic letterboxing & Machine Learning..

Requires some core function,

For example Vulkan DirectML..

Such a feature is a survival trait of core function:


Core function list ML:

Adaptable Tessellation

Adaptable Sharpening

Adaptable Image Enhancement

Adaptable Resolution improvements : Vertex,Polygons,Textures & Shaders

Core function is essential for adaptation of each game engine,

Core ML function is essential for progress & improvement..

Machine learning is in essence : Cognition, Brain function & Development..

Therefore required for improvement to be made.

(c)Rupert S

https://www.youtube.com/watch?v=fzu9oT2JaK8

https://is.gd/ProcessorLasso


*
Dolby Atmos 3D Audio in production
https://www.youtube.com/watch?v=Bmq1Zj2Z0-8


Challenge accepted : Testing the Codecs Qualification 'Levels" :

Higher Dynamic Range Qualification Audio Test cases 5.1 HD 24Bit Stereo Mic's 

Live Thunder 7.1HQ https://is.gd/7_1BiNeuralSample

Sing 3D
https://is.gd/7_1_24BitTuna
https://is.gd/AngelsLove

https://is.gd/3DAtmos7_1_DJ_Jamluca

ASIO #FasterAudio : but a lot faster, like in gaming or production https://www.asio4all.org/



https://science.n-helix.com/2022/03/fsr-focal-length.html
https://science.n-helix.com/2021/09/temporal-aliasing-image-shaping-polygon.html
https://science.n-helix.com/2022/03/simd-render.html
https://science.n-helix.com/2019/06/vulkan-stack.html

https://github.com/GPUOpen-Effects/FidelityFX-FSR2/releases/tag/v2.0.1a
https://github.com/GPUOpen-Effects/FidelityFX-FSR/releases/tag/v1.0.2

Thursday, March 25, 2021

Upscaling Enhancement

Super resolution API Photo & Video Enhance & upscaling demonstrations

Upscaling Enhancement For Telescopes, Space & Research Aviation Photography & Video
Photo Enhance & upscaling:


Photographic Enhancers:


BloodBorne : "Why not shoot for 4K too? Thus began a week of experiments using a tool called Topaz Video Enhance AI, which uses a number of different AI upscaling models - and it turned out that most of them could deliver appreciably higher detail."



Department of Energy - RGB_Color-Seal_Green-Mark_SC_Vertical V2 Helix.jpg (2.08 MB) https://mirrorace.org/m/3Jz5o

JOE Science Workshop V1 - DcXw0jCU8AA-Jdk.jpg (1.09 MB) https://mirrorace.org/m/3Jz5p

SuperNova image_2144_1e-SN-1993J.jpg (1.74 MB) https://mirrorace.org/m/3Jz5q

XC50 Cray Met Data Test DQ-aoSpUQAAXby-.png (6.72 MB) https://mirrorace.org/m/3Jz5r

**

deadpool V2 3000.jpg (2.81 MB) https://mirrorace.org/m/5LrrU

Such wow art V2 3000 tGi0Ap74NwbRC.jpg (4.09 MB) https://mirrorace.org/m/4pwxi


https://bit.ly/DJ_EQ

Friday, March 12, 2021

Brain Bit Precision Int32 FP32, Int16 PF16, Int8 FP8, Int6 FP6, Int4? Idealness of Computational Machine Learning ML TOPS for the human brain

Brain Bit Precision Int32 FP32, Int16 PF16, Int8 FP8, Int6 FP6, Int4? Idealness of Computational Machine Learning ML TOPS for the human brain:

Brain level Int/Float inferencing is ideally in Int8/7 with error bits or float remainders

Comparison List : RS

48Bit Int+Float Int48+FP48 (many connections, Eyes for example) HDR Vison

40BitInt+Float Int40+FP40 HDR Basic

Int16 FP32

Int8 Float16(2Channel, Brain Node)(3% Brain Study)

Int7 (20% Brain Study)

Int6 (80% Brain Study)

Int5 (Wolves (some are 6+))

Int4 (Sheep & worms)

Int3 (Germ biosystems)


Statistically a science test stated 80% of brains in man quantify Bit at 6 20% to 7Bit

XBox X & PlayStation 5 do down to INT4Bit (quite likely for quick inferencing)

Be aware that using 4 bit Int instructions .. potentially means more instructions used per clock cycle & more micro data transfers..

Int8 is most commonly liable to quantify data with minimum error in 8Bit like the Atari STE or the Nintendo 8Bit..

Colour perception for example is many orders of magnitude higher! Or 8bit colours EGA is all we would use..


16Bit was not good enough.. But 32Bit suites most people! But 10Bit(x4) 40Bit & Dolby 12Bit(x4) 48Bit is a luxury & we love it!


(c)Rupert S https://is.gd/ProcessorLasso


Restricted Boltzmann ML Networks : Brain Efficient

I propose that SIMD of large scale width & depth can implement the model :
Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a variety of unsupervised machine learning applications

Restricted Boltzmann Machines utilize a percentage correctness based upon energy levels of multiple node values; That represent a percentage chance of a correct solution,

My impression is that Annealer machine simply utilise more hidden values per node on a neural network,
Thus i propose that SIMD of large scale width & depth can implement the model..

A flexible approach is to experiment with percentages from a base value...
100 or 1000; We can therefore attempt to work with percentiles in order to adapt classical computation to the theory of multiplicity.

SiMD in parallel can; As we know with RISC Architecture .. 
Attempt to run an ideal network composing many times Factor & regression learning model..

Once the rules are set; Millions of independent IO OPS can be performed in cyclic learning,

Without sending or receiving data in a way that interferes with the main CPU & GPU Function..

Localised DMA.

"Adaptive hyperparameter updating for training restricted Boltzmann machines on quantum annealers"

Adaptive hyperparameter updating for training restricted Boltzmann machines on:
Quantum annealers
Wide Path SiMD




"Restricted Boltzmann Machines (RBMs) have been proposed for developing neural networks for a
variety of unsupervised machine learning applications such as image recognition, drug discovery,
and materials design. The Boltzmann probability distribution is used as a model to identify network
parameters by optimizing the likelihood of predicting an output given hidden states trained on
available data. Training such networks often requires sampling over a large probability space that
must be approximated during gradient based optimization. Quantum annealing has been proposed
as a means to search this space more efficiently which has been experimentally investigated on
D-Wave hardware. D-Wave implementation requires selection of an effective inverse temperature
or hyperparameter (β) within the Boltzmann distribution which can strongly influence optimization.
Here, we show how this parameter can be estimated as a hyperparameter applied to D-Wave
hardware during neural network training by maximizing the likelihood or minimizing the Shannon
entropy. We find both methods improve training RBMs based upon D-Wave hardware experimental
validation on an image recognition problem. Neural network image reconstruction errors are
evaluated using Bayesian uncertainty analysis which illustrate more than an order magnitude
lower image reconstruction error using the maximum likelihood over manually optimizing the
hyperparameter. The maximum likelihood method is also shown to out-perform minimizing the
Shannon entropy for image reconstruction."

(c)Rupert S

Example ML Statistic Variable Conversion : Super Sampling Virtual Resolutions : Talking about machine learning & Hardware functions to use it/Run it; To run within the SiMD & AVX feature-set.

For example this works well with fonts & web browsers & consoles or standard input display hubs or User Interfaces, UI & JS & Webpage code.

In the old days photo applications did exist to use ML Image enhancement on older processors..
So how do they exploit Machine Learning on hardware with MMX for example ?

Procedural process data analytics:

Converting large statistics data bases; On general Tessellation/Interpolation of images
The procedural element is writing the code that interpolates data based upon the statistics database...

Associated colours..
Face identity...
Linearity or curvature...
Association of grain & texture...

Databases get large fast & a 2 MB to 15MB Database makes the most sense...
Averages have to be categorized by either being worthy of 2 Places in the database or an average..

You can still run ML on a database object & then the points in the table are called nodes!

Indeed you can do both, However database conversion makes datasets way more manageable to run within the SiMD & AVX feature-set.

However the matter of inferencing then has to be reduced to statistical averages & sometimes ML runs fine inferencing this way.

Both ways work, Whatever is best for you & the specific hardware.

(c)Rupert S

**

DL-ML slide : Machine Learning DL-ML


By my logic the implementation of a CPU+GPU model would be fluid to both..

Machine Learning : Scientific details relevant to DL-ML slide (CPU,GPU,SiMD Hash table(M1 Vector Matrix-table +Speed)

The vector logic is compatible to both CPU+GPU+SiMD+AVX.

Relevant because we use Vector Matrix Table hardware.. and in notes the Matrix significantly speeds up the process.
(Quantum Light Matrix)

The relevance to us is immense with world VM servers
DL-ML Machine Learning Model compatible with our hardware
By my logic the implementation of a CPU+GPU model would be fluid to both..
The vector logic is compatible with both CPU+GPU.

However this is a model we can use & train..
For common core : Rupert S https://is.gd/ProcessorLasso



"State-of-the-art approaches such as OpenMP and OpenCL"
https://is.gd/LEDSource

High performance ML Versatile Compute chips
https://www.xilinx.com/content/dam/xilinx/support/documents/data_sheets/ds950-versal-overview.pdf

https://science.n-helix.com/2022/10/ml.html
https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

Saturday, February 13, 2021

Multi Operation Maths - CPU,GPU Computation

Multi Operation Maths - CPU,GPU Computation (c)RS

Performing multiple 4,8,16,32 operations on a 64Bit integer core (The example)



Kind of an F16 operation & Integer 16 or Int8 if you need it, With careful management and special libraries ..
Capable of speeding up PC,Mac & Consoles :HPC:
Requires specially compiled libraries so compiled codes can be managed & roll ops assessed.

Rules:

All operations need to be by the same multiplication

Rolls usable to convert value for example Mul & Division


For example :


451 722 551 834 x 6


In the case of non base factor roll numbers

We have to fraction the difference between the value and our base roll number,

10 for example and 6, So the maths is convoluted & may not be worth it,

Could do 6 + rolls & then -Rolls

On a 10 processor the first factor would be 10x because we could compensate by placement

But we still need space to expand the result to the right or left

0451072205510834 x 10 =

4510722055108340

or 4510 roll -12
7220 roll -8
5510 roll -4
8340 no roll

Converting base 10 to & from hex may make sense

Depending on the cost of roll; This operation may be worth it!

This operation is in Base 10 & 8Bit makes more sense mostly for common operations in hex..

But 8 is not a very big number for larger maths & 16Bit makes more sense; Because it has a larger number.

Performing numeric expansion:

consoles in particular and FPU where expansion is required for emergence mathematics

Performing numeric expansion for circumstances where we require larger numbers for example:

To fill the 187 FPU buffer..

To do that we will roll to the left & expand the number, although we may need multiple operations..

Like i say : Roll + or Roll -

1447000
-Roll 3 = 1447
or
+Roll 3 = 1447000000

That way we can potentially exceed the Bit Depth 32Bit for example.

Rupert S https://science.n-helix.com


*****

Packed F16C & F16 Values in use on CPU & GPU - RS

F16C & F16 : lower precision values that are usable to optimise GPU & CPU operation that involve; Less Detailed values like Hashes or game data Metadata or Machine Learning : RS

Firstly the F16C is the FX 8320E supported instruction so the CPU can potentially use packed F16 Float instructions directly from the CPU,
As quoted F16 carefully managed produces a pipeline that is 100% F16..

Packed F16 instructions use 2 data sets per 32Bit storage register...

Data is converted if the array of instructions includes F32 & commonly all F16 should be present first; Before group conversion or alternatively...

Allocating an additional 16Bits of data for example 0000x4 or subtle variance data that allows unique renders... Such as a chaos key or Entropy / RNG Random data...

Potentially allocating a static key in the form of AES Output from base pair F16c Value...

The additional data make potentially each game player render unique!

Fast Conversion Proposals include:

Unique per player additional data (AES Key conversion for example, Or DES; DES Produces smaller faster values)

Static key, Sorted by data type (Base on player profile or Game map)

Dynamic Key

0000 or empty buffer hash

Side by Side : Wide format texture = 2xF16 Value on same 32Bit Value
Top & Bottom : F16 Double layered format texture = 2xF16 Value on same 32Bit Value

Yes transparency for alien skin can use : Top & Bottom F16 layered texture
Machines also; Or even 4 layers for a truly special effect.

Combine both methodology and crypto hash with one or more layer of BumpMap RayTracing SiMD

SiMD is also 16Bit compatible so no conversion required.

Weather & clouds are examples perfect for light fast loads over massive GPU Arrays.

F16 are also theoretically ideal for 16Bit audio if using SiMD..

In the case of AVX probably worth using dynamic key conversion..
A Dynamic Remainder key that allows lower bits to interpolate Sound data.

Other object sources such as render can potentially use the F16 system to..
Interpolate or Tessellate bits on shift from F16 to F32 on final plane write to frame buffer..
The memory required would be the buffer & not the source process..

An example is to replace the bits missing from F16 in F32/F64 with tessellation shaping and sharpening code; Dynamically relative to performance of the GPU/CPU...
F16 values obviously transfer from GPU to CPU fast & CPU to GPU..

Image enhancement is also possible with a bitshift stack buffer that passes additional data to the missing bits..
For example pre processed micro BumpMapping or Compute shading process; That will pull the bits in.. Under the F16 data  453000.172000 > 453545.172711 bit swap.. could be complex!
Done with a cache? Entirely possible with united cache L3

DLSS & Dynamic sharpen & Smooth/Filter enhanced virtual resolution .. Can significantly enhance the process..
Of dynamic buffer pipelining to render path. (on requirement benefit)

(c)Rupert S https://science.n-helix.com/2019/06/vulkan-stack.html

https://gpuopen.com/learn/first-steps-implementing-fp16/