SensiML Cloud

Current Release

2020.2.2 (09/08/2020)

Minor Features
  • Adds Additional Feature Extractors (linear regression stats, zero crossings, positive zero crossings, negative zero crossings, shape median difference, shape absolute median difference)

Bug Fixes
  • performance improvements and minor bug fixes

Past Releases

2020.2.1 (08/19/2020)

Major features
Minor Features
  • Adds support for multi-channel DTW

  • Adds additional feature generators

  • Performance improvements

Bug Fixes
  • Improved validation for custom sensors

2020.2.0 (07/17/2020)

What’s New

Major features

  • Adds ability to test multiple captures in a single calls.

  • Adds ability to generate confusion matrix for a target label when running test data.

  • Adds ability to download source file for enterprise level accounts.

  • Allow user to specify which classifier algorithms will be used as part of AutoML optimization.

Minor Features
  • Increased the number of decision trees available in random forest and boosted tree ensembles during inference.

Bug Fixes
  • Fixes issue where sensor columns could be generated in different order than sensor configuration specified

2020.2.0 (07/17/2020)

What’s New

Major features

  • Adds ability to test multiple captures in a single calls.

  • Adds ability to generate confusion matrix for a target label when running test data.

  • Adds ability to download source file for enterprise level accounts.

  • Allow user to specify which classifier algorithms will be used as part of AutoML optimization.

Minor Features
  • Increased the number of decision trees available in random forest and boosted tree ensembles during inference.

Bug Fixes
  • Fixes issue where sensor columns could be generated in different order than sensor configuration specified

2020.1.6 (05/04/2020)

What’s New

Major features

  • Adds ability to specify decision tree of strong classifiers to optimize against

Minor Features

  • Adds Interleave feature generator for combining sensors channels

  • Performance improvements and bug fixes

2020.1.5 (04/14/2020)

What’s New

Minor Features

  • Adds a threshold setting to tflite post processing to return unknown below threshold value

  • Improvements to database query performance

  • caching optimizations for increased performance

2020.1.4 (04/02/2020)

What’s New

Minor Features

  • Improvements to tensorflow-lite micro support

  • Improvements to database query performance

  • Adds API’s to Knowledge Pack for setting feature vector directly as well as recognizing feature vector

2020.1.3 (03/23/2020)

What’s New

Minor Features

  • Improvements to tensorflow-lite micro support

  • Additional Bulk API’s for faster egress

  • Minor bug fixes

2020.1.2 (03/03/2020)

What’s New

Minor Features

  • Adds new bulk API’s to improve performance of uploading/deleting/updating multiple segments at the same time

  • Improved performance for a number of feature transforms and extractors

  • adds beta support for tensorflow lite micro

  • Adds a more detailed query statistics endpoint for richer information

  • Adds ability to include the segment_uuid in the query

2020.1.1 (02/04/2020)

What’s New

Minor Features

  • Adds new segment filter threshold algorithm

2020.1.0 (01/20/2020)

What’s New

Major Features

  • Adds Profiling and better debug logs to Knowledge Packs

  • Increased Max Feature vector size to 2048

Minor Features

  • Adds option to use less than comparison as part of the windowing threshold algorithm

  • Adds DTW to Hierarchical Clustering Training Algorithm

Bug Fixes

  • Fixes issue where auto segmentation heuristics would generate invalid parameter settings

  • Fixes issue where DTW distances larger than uint16 were not being truncated

  • Fixes issue where Mayhew board could be configured incorrectly when generating a Knowledge Pack

  • Minor bug fixes and performance improvements

2019.3.6 (11/05/2020)

What’s New

Major Features

  • Support for sqlite optimized DCL

Minor Features

  • Computed distances for PME are stored as part of the knowledge pack

  • Model Size is stored as part of the Knowledge Pack

2019.3.5 (10/21/2020)

What’s New

Major Features

  • Support for AD7476 Sensor at up to 1Mhz

Minor Features

  • Additional API for flushing model ring buffer to clean state

  • Stability Improvements

2019.3.4 (10/08/2020)

What’s New

Major Features

  • Boosted tree classifiers now part of autosense optimization routine

Bug Fixes

  • Stability improvements to pipeline performance scheduling

2019.3.3 (09/23/2020)

Bug Fixes

  • Fixed issue where feature validation was to strict for some validation methods

  • Improved error message reporting for Knowledge Pack downloads

2019.3.2 (09/19/2020)

What’s New

Major Features

  • Implementation of bonsai decision tree classifier which combines dimensionality reduction with an efficient tree classifier structure

  • Store full results from train, validation and test in the Knowledge Pack

  • Performance and stability improvements

Bug Fixes

  • Fixed issue where some column name characters weren’t being correctly sanitized during firmware generation

  • Past Releases

2019.3.1 (08/22/2020)

What’s New

Major Features

  • Addition of Dynamic Time Warping as a distance metric for the PME classifier

  • Added two new model selection methods (metadata k-fold, and stratified metadata-kfold)

2019.3.0 (07/30/2020)

What’s New

Major Features

  • Support for SensorTile 1.0 Knowledge Pack Binary and Library Builds

2019.2.0 (06/27/2020)

What’s New

Major Features

  • Adding for under sampling the majority class in order to balance a data set

  • Adding support as part of auto sense pipeline for balancing data sets

  • Adding support for supplying a user specified validation method to the auto sense pipeline

  • Adding support for specifying capture uuid as part of the metadata in a query

Bug Fixes

  • Fixes issue with some queries failing due to the names of the metadata

2019.1.4 (06/11/2020)

What’s New

Major Features

  • Adding support for Chilkat Hardware Knowledge Pack creation

Minor Features

  • Improvements to accuracy calculations of AutoSense pipeline

2019.1.3 (06/04/2020)

What’s New

Minor Features

  • Speed optimizations for recognize signal

  • Project statistics now returns information about all captures and segments

Bug Fixes

  • Fixed issue where having a decision tree ensemble and gradient boost classifier in the same model would fail to compile

  • Fixed issue where terminating a pipeline wasn’t always removing it from the active pipeline queue

2019.1.2 (05/22/2020)

What’s New

Minor Features

  • Speed improvements to AutoSense pipeline and underlying training algorithms

  • QuickAI SDK 1.2.1 release.

Bug Fixes

  • Fixed issue in QuickAI 1.2 SDK when recording using ADC with 3 channels

2019.1.1 (05/14/2020)

What’s New

Minor Features

  • Improved server performance to increase number of batch jobs executed in parallel during pipeline execution

Bug Fixes

  • Fixed issue where uploading a large feature vector file could being split up before being sent to the TVO or selector set steps

2019.1.0 (05/05/2020)

What’s New

QuickLogic S3 AI Recognition updates

  • Support for recognition from 1-4 Channel ADC Mayhew at 16khz

  • Support for recognition for 1 Channel ADC Mayhew at 100khz

  • Support for recognition of Audio at 16khz

  • Support for IMU recognition from 25-1600Hz

AutoSense Pipeline

  • Includes random forest algorithm as part of the search over the classifier space

  • Now allows users to select whether or not to use a classifier that will return unknown when it is unsure of the result

  • Allows users to build a submodel using autogrouping of classes

Other Major Features

  • Added a boosted tree ensemble classifier that performs binary classification

  • Captures can now be associated with the capture configuration that created them

  • Improvements to upload speed of metadata labels

  • Status messages now return more information about running pipelines

Notes: The minimum SensiML client version is 2019.1.0

Bug Fixes

  • Fixed issue where pipeline would appear to be in the queue but actually be running

2.5.1 (02/28/2020)

What’s New

  • General performance improvements

  • General Security improvements

Notes: The minimum SensiML client version is 2.5.3

2.5.0 (01/15/2020)

What’s New

  • Additional Board Support for ChilKat platform

  • Feature generators automatically iterate through input columns and specify their correct input

  • Major Server Stability Improvements for handling larger data sizes

  • AutoGenerated Knowledge Packs now support knowledge rehydration, previously only pipeline rehydration was supported

  • New segmentation, feature generation and sampling algorithms added

  • Any segmenter can be used as input to cascade feature

  • Ability to specify multiple datafiles as input to a pipeline

  • Better error messages returned for many endpoints

  • Naming convention for classifier “PVP” has been deprecated, all pipelines are required to use the name “PME” for this classifier

Notes: The minimum SensiML client version is 2.5

Bug Fixes

  • Knowledge Pack rehydration now accounts for feature family generators

2.4.0 (11/09/2018)

What’s New

  • Additional Feature generator

    • Convolution Max

  • Additional Streaming Filter

    • Downsample

    • High Pass

  • High Frequency Data Collection using the Quick AI Module

  • HW acceleration support for QuickAI Hard Neurons

  • DSP optimizations for Knowledge Packs built targeting arm m3/m4 processors

2.3.3 (10/31/2018)

Bug Fixes

  • Support for segments up to length 8192

  • Server Stability Improvements

  • Improvements to error messages

  • Improvements to QuickAI FFE data capture

2.3.2 (10/24/2018)

What’s New

  • Adding Support for QuickAI low power ffe for pre-processing sensor data

  • Increase number of classes supported by PME reinforcement learning

  • Adding model.json to Knowledge Pack download that has information about the contained model

  • Stability and speed improvements to Auto Sense pipelines

Bug Fixes

  • Fix Hierarchical Clustering bug where Nan was being returned and causing a crash

2.3.1

What’s New

  • Custom validation method can be used by the automation engine

  • Additional API’s for Knowledge Pack to enable loading/saving models to/from flash

    • flush_model

    • get_model_header

    • get_model_pattern

  • Additional API’s for Knowledge Pack to support cascade windowing with reset

2.3.0

What’s New

Major Features

  • QuickAI board now supports capture and SensiML recognition without re-flashing

  • Adding support for reinforcement learning to PME algorithm on the device

  • Adding API’s to the c Knowledge Pack to retrieve information about the model such as the class map, model patterns, model map etc. (see kb.h for full list of API’s)

Minor Features

  • Pipeline status is returned during pipeline execution

  • General stability improvements and bug fixes

  • Return a model.json file with all Knowledge Packs that describes the model

  • Bug Fix where terminating a pipeline didn’t terminate correctly all the time

2.2.2

What’s New

Major Features

  • Adding software support for QuickAI board (hardware accelerated classification and FPGA feature generation acceleration support will be added in future releases)

  • Adding ability to use the emulator for hierarchical models via recognize_signal

  • Added Knowledge Pack support for decision tree ensemble trained via random forest training algorithm

  • Adding new class of feature generators (cross column) for use in comparing features across sensor columns

Minor Features

  • Hierarchical models now generate their calls to arbitrary depth

  • Added two tail t-test based feature selector

  • Min max scale now accepts partial parameters and will scale the rest

  • General stability improvements and bug fixes

2.2.1

What’s New

Major Features

  • Library Code generation for RPI, ARM and Ubuntu with gcc version 7.2

  • Support for constructing a feature vector from multiple sliding windows

Minor Features

  • Added Moving Average Sensor Transform

  • SensiML Labs (Experimental Features)

  • Random Forest Classifier (Important: This feature is in an early concept stage, it cannot be used on a device)

  • Adding an API to add new patterns to the device while it is running

Bug Fixes

  • Schema error on upload now returns message for which fields are incorrect

  • Minor bug fixes and stability improvements

2.2.0

What’s New

New Platforms

  • FreeRTOS

Major Features

  • Automation now has a cross validation option to prevent under/overfitting

  • Adding double peak segmentation algorithm (a key based segmentation algorithm)

Minor Features

  • KP download now includes option to explicitly define source (Audio, Motion, Custom)

  • Combine labels allows renaming labels and creating new groups

  • Auto Combine label, automatically splits many events into two groups

SensiML Labs (Experimental Features)

  • Adding Cascade Windowing Segmenter (Important: This feature is in an early concept stage, it cannot be used on a device)

  • Adding Bonsai Decision Tree Classifier (Important: This feature is in an early concept stage, it cannot be used on a device)

Bug Fixes

  • Fixes to pipeline seeds for automation

  • Fix overflow bug for raw data

2.1.3

What’s New

Major Features

  • Metadata Separator for choosing the best class split in Hierarchical models

2.1.2

What’s New

Major Features

  • Adds outlier removal samplers for improving model accuracy

Bug Fixes

  • Fixes bug with Hierarchical Models not returning correct results

2.1.1

What’s New

Major Features

  • Support for building Knowledge Packs using audio data for Nordic Thingy

Minor Features

  • Added ability to specify a capture file as the test data in recognize signal

  • Add new transform for grouping labels into subgroups. See Combine Labels in docs

  • Added ability to use entire segment from parent model for submodels

  • Adds a padding option to min max scale which can improve classification accuracy in some cases

Bug Fixes

  • Fixed issues with some feature generators (conv avg, min percentile, sum)

  • Fix some issues with correlation feature selectors

2.1.0

What’s New

Major Features

  • Knowledge Pack compatibility with Nordic Thingy

Minor Features

  • Server side optimizations for faster query performance

Bug Fixes

  • Queries not correctly selecting segments when labels have been created by more than one autosegmenter

  • Fix integer overflow in magnitude sensor transform when more than 2 axis are used

2.0.0

What’s New

Major Features

  • Pipeline Automation - Automated pipelines reduce the amount of code you have to write to find good features and pipeline parameters. Use pre-defined Pipeline Seeds (“Basic Features”, “Advanced Features”, “Downsample Features”, “Histogram Features”) - or define your own pipeline and let the automation API fine-tune the parameters with its genetic algorithm.

  • Convolution/Submodels - A segment captured and classified by one model, can be fed and used by other models which can use the entire segment or perform their own segmentation

  • Segmenter Discovery/Optimization - Given a labeled data set, the server will optimize the parameters for detecting those segments from the signal

Minor Features

  • Optimization for core function for latency and memory usage

  • KB Client list functionality - for most types of objects on the server is now supported. dsk.list_* to allow easy information discovery

  • Knowledge Packs can now be saved and retrieved by name. models from multiple pipelines/projects can now be combined into a single binary file

  • Grid Search can now be performed over the validation, classifier and training algorithm of the tvo step. The option to replace Hierarchical Clustering with Neuron Optimization in the grid search has been removed. To use Neuron Optimization, use it in the pipeline tvo step like all other training algorithms

  • Addition of new feature generators and transforms

    • Transform: Pre-emphasis Filter

    • Feature Generator: MFCC

    • Feature Generator: FFT

    • Feature Selector: Custom Feature Selection

    • Validation Method: Set Sample Validation