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Analyzed about 8 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
(FEATURE) Merit of Audio/Music - Music Synthesizer from Random samples and State Machine
Srinivasan Kannan
More... over 7 years ago
------------------------------------------------------------------------------------------------------- 642. (FEATURE) Merit of Audio/Music - Music Synthesizer from Random samples and State Machine - 5 March 2019 ------------------------------------------------------------------------------------------------------- 1.New function notes_to_audio() has been implemented in music_pattern_mining/AudioToBitMatrix.py 2.It creates a .wav file from random Scipy array or traverses a finite state machine to create a stream of notes in CDEFGABC notation which are translated to integer array of frequencies in hertz 3.Scipy.io.wavefile.write() is invoked to write out this ndarray to a .wav file of sampling rate 44100 4.Finite state machine has been implemented simply in a dictionary and supports quasi-non-determinism (array of possible previous states) 5.Two .wav files automaton_synthesized_music.wav and notes_synthesized_music.wav are written for the two clauses: automaton or notes array 6.logs for synthesizer have been committed to testlogs/AudioToBitMatrix.log.MusicSynthesizer.5March2019 7.It is pertinent to mention here that Stanford Music Information Retrieval at https://musicinformationretrieval.com/genre_recognition.html does a statistical genre recognition of music as opposed to automata theoretic analysis in NeuronRain 8.State machine has been hardcoded presently which have to be read from file and literal_eval()-ed.
Srinivasan Kannan
More... over 7 years ago
Feature - Scheduler Analytics - /proc/sched_debug - Reference
Srinivasan Kannan
More... over 7 years ago
Theory - Word Diophantine Equations, Intrinsic Merit defined by Word Equations - Commit 2
Srinivasan Kannan
More... over 7 years ago
Theory - Word Diophantine Equations, Intrinsic Merit defined by Word Equations
Srinivasan Kannan
More... over 7 years ago
Theory - Complementary Sets and Functions - Beatty Sequences - Rayleigh Theorem - Wythoff Sequences from Golden Ratio - Partition of Natural Numbers - Reference
Srinivasan Kannan
More... over 7 years ago
(FEATUE) Software Analytics - Streaming Analytics + Scheduler Analytics - /proc/sched_debug
Srinivasan Kannan
More... over 7 years ago
-------------------------------------------------------------------------------------------------------- 640.(FEATUE) Software Analytics - Streaming Analytics + Scheduler Analytics - /proc/sched_debug - 28 February 2019 -------------------------------------------------------------------------------------------------------- 1.DeepLearning_SchedulerAnalytics.py - sched_debug_runqueue() has been changed to return a dictionary of process runqueue and loop has been removed 2.Streaming Analytics support has been provided for Operating System performance statistics (e.g runqueue) by adding new data_storage "OperatingSystem" and data_source "SchedulerRunQueue" to Streaming_AbstractGenerator.py 3.New clause for previous Scheduler Analytics streaming has been added in __iter__() which invokes sched_debug_runqueue() in a loop and yields the scheduler runqueue dictionaries. 4.This integrates Streaming and Software Analytics seamlessly and any Streaming or other machine learning algorithm can process Operating System Stats Dictionary Stream like a bigdata source. 5.DeepLearning_SchedulerAnalytics.py imports this Streaming Abstract Generator by "OperatingSystem" and "SchedulerRunQueue" parameters and reads the scheduler stats as input stream. 6.In future, any operating system data source other than scheduler runqueue can be plugged into Streaming Abstract Generator iterable and fulfils the foremost requirement for real time software streaming analytics.
Srinivasan Kannan
More... over 7 years ago
(FEATURE) Software Analytics - Scheduler Analytics - /proc/sched_debug support
Srinivasan Kannan
More... over 7 years ago
------------------------------------------------------------------------------------------------------- 639.(FEATURE) Software Analytics - Scheduler Analytics - /proc/sched_debug support - 27 February 2019 ------------------------------------------------------------------------------------------------------- 1.Missing kernel prefix for some of the /etc/sysctl.conf variables have been included in return array. 2./proc/sched_debug support has been included by defining a new function to read /proc/sched_debug and print the red-black tree of the Complete Fair Scheduler (CFS) [https://elixir.bootlin.com/linux/v3.2/source/kernel/sched_fair.c] runqueue for user defined iterations in a loop - reads the /proc/sched_debug periodically, reverses the list of lines to print the tail. 3./proc/sched_debug is a convenient alternative to BCC tools runqlat 4.Kernel CFS chooses the root of the red-black tree which has least virtual runtime to schedule it to run on CPU thereby prioritizing and fairly treating starved processes. 5.Tree Key field is the virtual runtime of the process id - Example below prints runqueue at any instant for a CPU:
Srinivasan Kannan
More... over 7 years ago
Theory - Program Analysis - Approximation of Non-trivial undecidable property - Worst Case Execution Time of a Program
Srinivasan Kannan
More... over 7 years ago
Updated Requirements.txt
Srinivasan Kannan
More... over 7 years ago
(FEATURE) Software Analytics - Scheduler Analytics - /etc/sysctl.conf kernel.sched_* optional clause
Srinivasan Kannan
More... over 7 years ago
1.DeepLearning_SchedulerAnalytics.py has been changed to include new clause for writing sysctl.conf kernel.sched_* variables instead of scheduler classes within learnt_scheduler_class() based on output layer of BackPropagation Neural Network(BPNN) 2.These variables are documented in: 2.1 https://www.kernel.org/doc/Documentation/scheduler/ 2.2 https://doc.opensuse.org/documentation/leap/tuning/html/book.sle.tuning/cha.tuning.taskscheduler.html 3.Different values of kernel.sched_* are returned based on numpy mean of output layer of BPNN. For example: ["kernel.sched_latency_ns=9000000", "kernel.sched_migration_cost_ns=100000", "kernel.sched_wakeup_granularity_ns=2000000", "kernel.rr_timeslice_ms=10", "sched_rt_runtime_us=990000", "sched_nr_migrate=12", "sched_time_avg_ms=100"] which increase CPU affinity of a process, its timeslice quantum and reduce cost of CPU loadbalancing and migration if it is CPU-intensive.
Srinivasan Kannan
More... over 7 years ago
Feature - NeuronRain Usecases - UAV Online Shopping Delivery
Srinivasan Kannan
More... over 7 years ago
1.This commit creates a new NeuronRainApps/ directory in python-src/ for illustrating usecases defined in asfer-docs/NeuronRainUsecases.txt 2.An unmanned aerial vehicle usecase is implemented by a pseudocode in NeuronRainApps/Drones/OnlineShoppingDelivery.py 3.Following static mission plan example from DronecodeSDK has been changed to OnlineShoppingDelivery.py: 3.1 Example Drone Mission - https://github.com/Dronecode/DronecodeSDK-Python/blob/master/examples/mission.py 3.2 Drone MissionItem Proto - https://github.com/Dronecode/DronecodeSDK-Proto/blob/a54b353d73ff8d6e36c716b5278990e0f8cb770c/protos/mission/mission.proto 4.Dynamic Mission Plan: Autopilots the UAV drone based on GIS analytics navigation variables - e.g. longitude, latitude, altitude, speed, camera action etc., - read by Streaming Abstract Generator socket streaming and appends MissionItems to flight plan dynamically restricted by ordinates convex hull. Convex Hull for a terrain image is obtained from ImageNet/ImageGraph_Keras_Theano.py. When drone is within convex hull airspace, its altitude is set to 0 (or minimal value) and landed 5.Dynamic flight trajectory remote-controlled by analytics are applicable to online shopping deliveries when multiple courier items have to be delivered en route which is NP-Hard Hamiltonian problem - Finding most optimal shortest tour from origin, through intermediate delivery point vertices and back to origin 6.This is not compileable, executable code but only a pseudocode and has not been tested on a drone because of lack of it and aviation licensing requirements
Srinivasan Kannan
More... over 7 years ago
New file created for NeuronRain Usecases
Srinivasan Kannan
More... over 7 years ago
Theory - EventNet, Video Merit, Causality, Linear Decomposition of EventNet Time Tensor
Srinivasan Kannan
More... over 7 years ago
Theory - Parallel Planar Point Location - NC^1 and Logspace - reference
Srinivasan Kannan
More... over 7 years ago
Theory - Parallel Planar Point Location - Limitations of NC - reference
Srinivasan Kannan
More... over 7 years ago
Theory - Factorization of Multiple Integers
Srinivasan Kannan
More... over 7 years ago
Theory - Computational Geometric Factorization and Van Der Waerden/Ramsey Coloring
Srinivasan Kannan
More... over 7 years ago
Theory - Parallel Planar Point Location by Parallel Plane Sweep - Reference
Srinivasan Kannan
More... over 7 years ago
Theory - Parallel Planar Point Location by Parallel Construction of Subdivision Hierarchies - Reference
Srinivasan Kannan
More... over 7 years ago
Theory - Parallel Kirkpatrick Planar Point Location for locating factor point - Reference
Srinivasan Kannan
More... over 7 years ago
Theory - Shell Turing Machines and Functions on Hilbert Space - Reference
Srinivasan Kannan
More... over 7 years ago
Theory - Successive Convex Hulls Point Location - P-Complete PRAM algorithms - reference
Srinivasan Kannan
More... over 7 years ago
Theory - SAT Solver for Van Der Waerden Number - Reference
Srinivasan Kannan
More... over 7 years ago
(THEORY) Computational Geometric Factorization - Parallel Planar Point Location of factors in Monotone Subdivision formed by Hyperbolic Arc Pixel Polygons with no necessity for Ray Shooting Queries
Srinivasan Kannan
More... over 7 years ago
(THEORY and FEATURE) Intrinsic Merit of Texts, Ramsey Theory, Complement Diophantines - Ramsey number of text graph
Srinivasan Kannan
More... over 7 years ago