Qeexo

Introduction Sound Recognition is a technology based on traditional pattern recognition theories and signal analysis methods which is widely used in speech recognition, music recognition and many other research areas such as acoustical oceanography...

Zhongyu Ouyang and Dr. Geoffrey Newman 21 September 2020

Qeexo AutoML enables machine learning application developers to customize inference settings based on their use-case. These parameters are critical for achieving the best live performance of models on the embedded target. In this article, we will...

Xun (Jared) Liu, Dr. Rajen Bhatt, and Dr. Geoffrey Newman 09 September 2020

Inference settings contain two important parameters; Instance length and Classification interval. In this blog, we will explain the Classification Interval and in conjunction with raw sensor signals, ODR, Instance length, latency, and performance of...

Sidharth Gulati and Dr. William Levine 12 August 2020

Qeexo AutoML supports three one-class classification algorithms widely used for anomaly/outlier detection; Isolation Forest, Local Outlier Factor, and One-class Support Vector Machine. These algorithms build models by learning from only one class of...

Dr. Karanpreet Singh and Dr. Rajen Bhatt 15 July 2020

In industrial environments, it is often important to be able to recognize when a machine needs to be serviced before the machine experiences a critical failure. This type of problem is often called predictive maintenance. One approach to solving...

Josh Stone 07 June 2020

Qeexo’s AutoML enables Machine Learning and AI applications development for a range of sensors. A comprehensive list of sensors includes Accelerometer, Gyroscope, Magnetometer, Temperature, Pressure, Humidity, Microphone, Doppler Radar, Geophone,...

Dr. Rajen Bhatt and Josh Stone 28 May 2020