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Qeexo’s EarSense Tech Is An Alternative To Proximity Sensors
Qeexo has officially debuted its EarSense software, made to eliminate the proximity sensor by performing its primary function on the software side. Where a traditional proximity sensor turns off a phone’s display if it detects that something is near it or it’s being covered, Qeexo’s EarSense software uses the device’s touch screen to detect a touch from a face or an ear, effectively mimicking the function of a proximity sensor. EarSense is now available for device manufacturers to license and use, meaning it could be making its way into consumer devices as early as this year, though nobody has thus far announced their intention to use the software.
Qeexo Wants to Replace Your Proximity Sensor With EarSense
Compared to a traditional proximity sensor which is a piece of hardware that is generally packed in the top bezel of our smartphones, EarSense is a software alternative – so it doesn’t take up any space on the phone. The goal of this proprietary software solution is to allow OEMs to free up space from inside the phone (for other components) as EarSense will be able to recognize contact with someone’s face and ears so that it can turn off the screen during a phone call – a function normally accomplished through use of the proximity sensor.
Qeexo and STMicroelectronics Accelerate Adoption of Qeexo’s FingerSense by OEMs
Qeexo, a developer of machine learning and AI solutions for sensor data, and STMicroelectronics (NYSE: STM), a global semiconductor leader serving customers across a spectrum of electronics applications, today announced a collaboration to further accelerate integration of Qeexo’s FingerSense technology by OEMs.
Qeexo EarSense Arrives On OPPO’s Find X
Qeexo, developer of lightweight machine learning and AI solutions for sensor data, announces a partnership with smartphone and consumer electronics manufacturer, OPPO, to bring Qeexo’s EarSense technology to the OPPO Find X smartphone. EarSense is Qeexo’s state-of-the-art AI solution that allows for true bezel-less design. It replaces the traditional hardware proximity sensor on smartphones, allowing devices to turn off the screen during phone calls.
OPPO Find X has no proximity sensor due to Qeexo’s EarSense
OPPO has been absent in the flagship smartphone space for a very long time. The company’s last flagship smartphone was the Oppo Find 7, which was released back in 2014. After three years of releasing phones with mid-range chipsets, Oppo announced the flagship Oppo Find X last month. Now, the company has launched the Find X in India. The phone will be available for pre-order via Flipkart from July 25, with sales beginning from August 3.
Qeexo Launches Embedded Machine Learning Platform to Enable AI on Edge Devices
Qeexo, developer of machine learning and AI solutions for sensor data, today announced the launch of Qeexo Machine Learning Platform for embedded products and applications. The company also announced that its EarSense product has been named a CES® 2019 Innovation Awards Honoree.
Qeexo Launches AutoML, a Fully Automated Machine Learning Platform for Sensor Data at the Edge
Qeexo today announces the launch of its AutoML product, a one-click, fully automated platform that allows customers to rapidly build machine learning solutions for Edge devices using sensor data. Qeexo has selected the Arm® Cortex™-M0-M4 class MCUs as the first hardware targets to be supported by Qeexo AutoML. At launch, Qeexo AutoML will support STMicroelectronics’s SensorTile.box, a compact multi-sensor module which includes the Cortex-M4 MCU and will continue to augment support for other hardware platforms.
Exploring Embedded Machine Learning
In 1943 neurophysiologist Warren McCulloch and mathematician Walter Pitts wrote a paper on neurons and how they work. A model was created using an electrical circuit and the neural network came into being. Seventy years later these beginnings have evolved into a number of large-scale projects by some of the top technology companies and technology communities around the globe – GoogleBrain, AlexNet, OpenAI, Amazon Machine Learning Platform are examples of some of the most well-known initiatives relating to AI and machine learning.
Qeexo AutoML Demo: Automating Machine Learning for Embedded Devices
Qeexo spun out of Carnegie Mellon University, has for a long time developed multi-touch technology for handset manufacturers which does ML on the device level. It has applied this approach to a new AutoML technology that allows companies to embed ML into hardware and conduct learning on sensor data.
All hail the future of tech-enabled dumb devices
As technology pervades more devices, the assumption is that these gadgets should have some form of internet connection. But some of my favorite gadgets this year have been devices or services that don’t require a connection back to the web to work. Instead, they apply sensors and other technology in novel ways to deliver a tech-enabled product that I view as smart, but not connected.
Dr. Rajen Bhatt Presents Qeexo Tech at ARM AIOT Summit
Qeexo's Director of Engineer, Dr. Rajen Bhatt, gave an excellent presentation of Qeexo AutoML at this year's ARM AIOT Conference
On the Radar: Cortex Labs, RadicalBit, Qeexo
Qeexo is a machine learning startup that develops AutoML software specifically tuned to automatically detect patterns in sensor data. Models developed by Qeexo AutoML (which it touts as a “one-click, fully automated platform”) are designed to run on low-power edge devices that are sensitive to network latency, such as mobile apps, IoT devices, wearables, and automobiles.
New AI technology from Arm delivers unprecedented on-device intelligence for IoT
“Our Qeexo AutoML platform can automatically build ‘TinyML’ solutions, the lightweight machine learning that brings the benefit of intelligence to all sorts of small devices. Arm’s new Cortex-M55 and Ethos-U55 processors allow Qeexo to further innovate embedded ML solutions for endpoint devices with the combination of unprecedented high performance, low latency, and ease of developing machine learning for microcontrollers.”
Machine Learning on the Edge, Hold the Code
Many companies are scrambling to find machine learning engineers who can build smart applications that run on edge devices, like mobile phones. One company that’s attacking the problem in a broad way is Qeexo, which sells an AutoML platform for building and deploying ML applications to microcontrollers without writing a line of code.
Qeexo AutoML is Now Live on Amazon Web Services
Qeexo today announces several new features for its AutoML product, including support for Amazon Web Services (AWS) and tools designed to give Qeexo AutoML users greater flexibility and performance in the collection and analysis of sensor data:
Qeexo is making machine learning accessible to all
Every now and then I see technology that’s so impressive, I can’t wait to write about it, even if no one else finds it cool. I had that experience last week while watching a demonstration of a machine learning platform built by Qeexo.
Qeexo named in insideBIGDATA IMPACT 50 List for Q2 2020
The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!
Deep Learning in Qeexo AutoML Platform
Deep learning (DL) has gradually become one of the most popular areas in artificial intelligence after the 1990s. Deep learning is a branch of machine learning and uses neural layers to build models. It combines low-level features and gradually forms abstract representation features to model the input data. Deep learning builds neural networks that simulates the human brain for analysis and learning. It mimics the mechanism of the human brain to interpret data, such as images, sounds, texts, and sensor readings.
Feature Selection Approaches: Part – I
In machine learning, the quality of feature selection strongly affects the quality of the trained model. Feature selections approaches differ depending on the type of machine learning problem, e.g., supervised learning or unsupervised learning. For supervised learning algorithms two most popular feature selection techniques are Wrapper-based and Model meta-transformer approach. For unsupervised learning algorithms, filter-based approaches are widely used. In the part-I of this article, we will look into the wrapper-based feature selection approaches used for supervised learning problems.
Detecting Air Gestures with Qeexo AutoML
We would like to build a machine learning model to distinguish between the following three classes: "X", "O", "No Gesture". This blog describes building the Air Gesture with Arduino Nano 33 BLE Sense. You can also build the same using any of the boards available on your Qeexo AutoML.
ODR and FSR of Sensors
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, Colorimeter, Ambient light, and Proximity. In this article, we will discuss two very important configurable parameters that apply to many of these sensors, Output Data Rate (ODR) and Full-Scale Range (FSR).
Detecting Anomalies in Machine Data with Qeexo AutoML
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 predictive maintenance problems is the use of a one-class classification model for anomaly detection, where the model can make a monitoring system aware that a machine is running in a manner that is different than its standard operating behavior.
Qeexo Announces General Availability of the Qeexo AutoML Platform to Enable TinyML for Edge Devices
Qeexo, developer of an automated machine learning (ML) platform that accelerates the deployment of TinyML at the edge/endpoint, announced today the general availability of its Qeexo AutoML platform on Amazon Web Services (AWS).