This document provides an overview of accelerometers and Freescale's low-g acceleration sensors. It discusses typical accelerometer applications, the six sensing functions of acceleration including gravity measurements, freefall, tilt, position/movement, shock, and vibration. It also provides details on Freescale's low-g acceleration sensor selector and contact information for ordering and support.
A presentation on Human Activity Recognition catered to the audience from an HCI or CS background. (Based on research by Bulling, A. et al. 2014. A tutorial on human activity recognition using body-worn inertial sensors. CSUR. 46, 3 (2014), 33.)
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sep-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Raghuraman Krishnamoorthi, Software Engineer at Facebook, delivers the presentation "Quantizing Deep Networks for Efficient Inference at the Edge" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Krishnamoorthi gives an overview of practical deep neural network quantization techniques and tools.
We realize that Garbage causes damage to local ecosystems, and it is a threat to plant and human life. To avoid all such situations we are going to implement a project called IoT Based Smart Garbage."When somebody dumps trash into a dustbin the bin ashes a unique code, which can be used to gain access to free Wi-Fi". Sensor check garbage lls in dustbin or not and Router pro- vides Wi-Fi to user. Major part of our project depends upon the working of the Wi-Fi module; essential for its implementation. The main aim of this project is to enhancement of a smart city vision.
A presentation on Human Activity Recognition catered to the audience from an HCI or CS background. (Based on research by Bulling, A. et al. 2014. A tutorial on human activity recognition using body-worn inertial sensors. CSUR. 46, 3 (2014), 33.)
For the full video of this presentation, please visit:
https://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/sep-2019-alliance-vitf-facebook
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Raghuraman Krishnamoorthi, Software Engineer at Facebook, delivers the presentation "Quantizing Deep Networks for Efficient Inference at the Edge" at the Embedded Vision Alliance's September 2019 Vision Industry and Technology Forum. Krishnamoorthi gives an overview of practical deep neural network quantization techniques and tools.
We realize that Garbage causes damage to local ecosystems, and it is a threat to plant and human life. To avoid all such situations we are going to implement a project called IoT Based Smart Garbage."When somebody dumps trash into a dustbin the bin ashes a unique code, which can be used to gain access to free Wi-Fi". Sensor check garbage lls in dustbin or not and Router pro- vides Wi-Fi to user. Major part of our project depends upon the working of the Wi-Fi module; essential for its implementation. The main aim of this project is to enhancement of a smart city vision.
Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
German Workshop from my buildingIoT 2016 MQTT Workshop "MQTT Deep Dive". It covers the creation of a Java MQTT Deathstar Simulator with Eclipse Paho and a Web Dashboard with to control the Deathstar with the Paho.js Javascript Library
Forecasted stock prices of Google using historical stock price data and sentiment scores using Sentiment Analyzer in Python from New York Times headlines, implemented different Time Series Models – ARIMA, Exponential Smoothing, Holtwinters, also used Sentiment Score regression models, Fb Prophet, also implemented Deep Learning Models
This Machine Learning presentation is ideal for beginners to learn Machine Learning from scratch. By the end of this presentation, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases).
This Machine Learning presentation will cover the following topics:
1. Life without Machine Learning
2. Life with Machine Learning
3. What is Machine Learning
4. Machine Learning Process
5. Types of Machine Learning
6. Supervised Vs Unsupervised
7. The right Machine Learning solutions
8. Machine Learning Algorithms
9. Use case - Predicting the price of a house using Linear Regression
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
- - - - - -
This presentation contains an introduction to reinforcement learning, comparison with others learning ways, introduction to Q-Learning and some applications of reinforcement learning in video games.
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) by Lex FridmanPeerasak C.
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) by Lex Fridman
Watch video: https://youtu.be/zR11FLZ-O9M
First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.
INFO:
Website: https://deeplearning.mit.edu
CONNECT:
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Talk on Optimization for Deep Learning, which gives an overview of gradient descent optimization algorithms and highlights some current research directions.
German Workshop from my buildingIoT 2016 MQTT Workshop "MQTT Deep Dive". It covers the creation of a Java MQTT Deathstar Simulator with Eclipse Paho and a Web Dashboard with to control the Deathstar with the Paho.js Javascript Library
Forecasted stock prices of Google using historical stock price data and sentiment scores using Sentiment Analyzer in Python from New York Times headlines, implemented different Time Series Models – ARIMA, Exponential Smoothing, Holtwinters, also used Sentiment Score regression models, Fb Prophet, also implemented Deep Learning Models
This Machine Learning presentation is ideal for beginners to learn Machine Learning from scratch. By the end of this presentation, you will learn why Machine Learning is so important in our lives, what is Machine Learning, the various types of Machine Learning (Supervised, Unsupervised and Reinforcement learning), how do we choose the right Machine Learning solution, what are the different Machine Learning algorithms and how do they work (with simple examples and use-cases).
This Machine Learning presentation will cover the following topics:
1. Life without Machine Learning
2. Life with Machine Learning
3. What is Machine Learning
4. Machine Learning Process
5. Types of Machine Learning
6. Supervised Vs Unsupervised
7. The right Machine Learning solutions
8. Machine Learning Algorithms
9. Use case - Predicting the price of a house using Linear Regression
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - - -
Who should take this Machine Learning Training Course?
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
- - - - - -
This presentation contains an introduction to reinforcement learning, comparison with others learning ways, introduction to Q-Learning and some applications of reinforcement learning in video games.
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) by Lex FridmanPeerasak C.
MIT 6.S091: Introduction to Deep Reinforcement Learning (Deep RL) by Lex Fridman
Watch video: https://youtu.be/zR11FLZ-O9M
First lecture of MIT course 6.S091: Deep Reinforcement Learning, introducing the fascinating field of Deep RL. For more lecture videos on deep learning, reinforcement learning (RL), artificial intelligence (AI & AGI), and podcast conversations, visit our website or follow TensorFlow code tutorials on our GitHub repo.
INFO:
Website: https://deeplearning.mit.edu
CONNECT:
- If you enjoyed this video, please subscribe to this channel.
- Twitter: https://twitter.com/lexfridman
- LinkedIn: https://www.linkedin.com/in/lexfridman
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- Instagram: https://www.instagram.com/lexfridman
The ADXL345 is a small, thin, low power, 3-axis accelerometer with high resolution (13-bit)measurement at up to ±16 g. Digital output data is formatted as 16-bit twos complement and is accessible through either a SPI (3- or 4-wire) or I2C digital interface. The ADXL345 is well suited for mobile device applications. It measures the static acceleration of gravity in tilt-sensing applications, as well as dynamic acceleration resulting from motion or shock. Its high resolution (4 mg/LSB) enables measurement of inclination changes less than 1.0°. Several special sensing functions are provided. Activity and inactivity sensing detect the presence or lack of motion and if the acceleration on any axis exceeds a user-set level. Tap sensing detects single and double taps. Free-fall sensing detects if the device is falling. These functions can be mapped to one of two interrupt output pins. An integrated, patent pending 32-level first in, first out (FIFO) buffer can be used to store data to minimize host processor intervention. Low power modes enable intelligent motion based power management with threshold sensing and active acceleration measurement at extremely low power dissipation.
This slide contains information about two type of accelerometer :- 1. Seismic Accelerometer 2 :- Displacement type accelerometer.
it contains working and construction.
This presentation gives the information about 'vibration measuring instruments' covering syllabus of Unit-5 of Theory of vibrations or mechanical vibrations for BE course under VTU, Belgaum. This presentation is prepared by Hareesha N G, Asst. Prof, Dept of Aerospace, DSCE, B'Lore-78.
Electrical Engineer Job Duties: Evaluates electrical systems, products, components, and applications by designing and conducting research programs; applying knowledge of electricity and materials. Confirms system's and components' capabilities by designing testing methods; testing properties.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online
Developing a New Auto-Loading Analytical ProberPhillip Corson
A methodology for selecting a vendor to collaborate with for the development of a new analytical probing system used for on-wafer device characterization in the hardware verification of compact device models in process design kits (PDK).
IMU (inertial measurement unit) has already played significant roles in the control system of aerospace and other vehicle platforms. Due to the maturity and low cost of MEMS technology, IMU starts to penetrate consumer products such as smartphone, wearables and VR/AR devices.
This sharing will focus on the general introduction of IMU components, signal characteristics and application concepts, with an attempt to guide those who is interested in the IMU-based system integration and algorithm development.
This prototype aims to measure the angle at which light intensity is at its maximum using an Arduino Uno, an LDR (Light Dependent Resistor), and an MPU6050 module. The project leverages the Arduino Uno's capabilities to interface with these components and gather the required data. The LDR is utilized to sense the ambient light intensity, employing a voltage divider circuit to translate it into an analog voltage. The MPU6050 module, equipped with a gyroscope and accelerometer, provides orientation information such as roll, pitch, and yaw angles. The project's main focus lies in determining the angle at which the light intensity reaches its peak.
The Arduino code facilitates the coordination of these components. It initializes both the LDR and MPU6050 in the setup function, capturing analog data from the LDR and orientation data from the MPU6050. By continuously integrating the gyroscope readings, the Arduino calculates and updates the orientation angles over time. Mapping the LDR's analog values to representative light intensity levels enhances the correlation between the orientation angles and the corresponding light intensities.
As a result, this project underscores the connection between physical orientation and environmental conditions. Through the data collected, users can identify the angle at which light intensity reaches its maximum, potentially offering insights into optimal lighting conditions for specific applications. The project's output, displayed via the Arduino's serial monitor, provides a real-time view of the relationship between orientation angles and light intensity, contributing to a more nuanced understanding of environmental interactions.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
14. Low-g Acceleration Sensors Selector Device Analog / Digital Output Acceleration (g) Sensing Axis Sensitivity (mV/g) MMA1220 Analog 8 Z 250 MMA1250EG Analog 5 Z 400 MMA1260EG Analog 1.5 Z 1200 MMA1270EG Analog 2.5 Z 750 MMA2260 Analog 1.5 X 1200 MMA6270QT Analog 1.5 X,Y 800,800 MMA6271QT Analog 2.5 X,Y 480,480 MMA6280QT Analog 1.5 X,Z 800,800 MMA6281QT Analog 2.5 X,Z 480,480 MMA7260QT Analog 1.5 / 2 / 4 / 6 X,Y,Z - MMA7261QT Analog 2.5 X,Y,Z 480,480,480 MMA7330L Analog 3 / 12 X,Y,Z - MMA7331L Analog 4 / 12 X,Y,Z - MMA7340L Analog 3 / 11 X,Y,Z - MMA7360L Analog 1.5, 6 X,Y,Z - MMA7361L Analog 1.5 / 6 X,Y,Z - MMA7455L Digital 2 / 4 / 8 X,Y,Z - MMA7456L Digital 3 / 4 / 8 X,Y,Z -
15.
Editor's Notes
Welcome to the training module on Freescale Low-g accelerometers. The intent of this module is to provide you with an overview of acceleration sensors and basic knowledge in accelerometer technology.
MEMS (Micro-ElectroMechanical Systems) Technology is inherent in micron-sized mechanical devices that can sense, process and/or control the surrounding environment. Sensing capabilities derive from mechanical features measured in microns. Freescale’s MEMS-based sensors are a class of devices that builds very small electrical and mechanical components on a single chip.
Here are some of the typical applications for acceleration sensors, which cover a wide range of products in multiple industries. Target markets include consumer, instrumentations, industrial, health care, automotive applications.
There are several sensing functions that accelerometers are capable of detecting. These are movement, position, fall, shock, vibration, and tilt. Fall is a sensing function that can be used to identify that a large impact is highly probable, which can be integrated into HDDs. It is also used for fall log and motion control and awareness. Tilt can be applied to an e-compass, inclinometer, gaming devices, text scrolling and user interfacing, image rotating, LCD projection, physical therapy, and camera stability. Movement covers motion control, pedometers, and general movement detection. Positioning applications require more complex algorithms for double integrating the acceleration to determine position. Position applications include personal navigation, car navigation, back-up GPS, etc. Shock applications include fall logs, black box event recorders, HDD protection, and shipping and handling monitoring. Vibration applications include high sensitivity and high frequency accelerometers for seismic activity monitors, smart motor maintenance, appliance balance and monitoring, and acoustics.
Understanding the range of acceleration for an application enables a product to be designed with the optimal accelerometer. This graph shows applications and their respective acceleration ranges. As you can see, every acceleration range has different applications. For example, fall detection and tilt control is in the 1g to 2g range. Shock detection is in the 2g to 8g range. Vibration is in the 8g to 10g range, and a pedometer is in the 20g to 30g range. Freescale has acceleration sensors with detection ranges from 1.5g to 10g in the low-g portfolio of accelerometers, 40g to 100g in the medium-g portfolio, and 150g up to 250g in the high-g portfolio.
Let’s take a closer look at the sensing functions that are achievable through using accelerometers. Let’s start with the things to consider when measuring freefall. The g-range will typically be +/- 1g. What is the cross-axis acceleration? Is it in freefall and being moved, or is it just in freefall? And also, what is the height requirement for detection? Some people will require a height of one meter, while others will require a height of a couple inches. Detection in both instances is achieved by the sampling rate of the microprocessor, and how involved the algorithm is with the micro-controller. Three types of freefall can be determined: linear, rotational, and projectile. With linear, the accelerometer will be dropped in one translation down from the height to the earth. For rotational, the accelerometer will drop, but it will also have a spin to it, and a rotation. Third, the projectile fall is when you throw the device, so not only will it have a horizontal movement as well as a vertical movement, but it also will have a slight rotation in it as well.
To achieve the highest resolution of tilt, the angles of operation are needed to determine the sensing axis that would be optimal for the application. The second thing to consider is how the accelerometer is mounted. How is the accelerometer going to be mounted on the PCB and how is the PCB going to be mounted on the equipment? Here shows the typical tilt equation. Vout equals the sensitivity of the accelerometer multiplied by 1g times the sine of the tilt angle, all added to the offset voltage of the accelerometer. The accelerometer output will vary from –1.0g to +1.0g when it is tilted from -90° to +90°. It is important to note that since the output is not linear, the mounting orientation that achieves the most sensitivity is when the sensing access is parallel to the earth's surface.
Here are some considerations for measuring position and movement. First is the displacement: how far will the accelerometer be moving to detect the change in movement? What is the g-range of the device? If it's going to be on a person, the levels are at a higher g-force and require a higher g-range accelerometer. If it's going to be a very small change, such as in a g-mouse, it requires an accelerometer that's even more sensitive. In this case, a lower g-range accelerometer is needed. After this, the sensing axis has to be determined. Where is it going to be moving: in the X plane, the Y plane, or Z plane, or all three? This answer will determine how many sensing axes are required in the accelerometer. For determining velocity, integration is used. To determine position, a double integration is performed on the acceleration data.
The biggest thing to consider for shock measurements is the g-range. The accelerometer uses the deceleration of the object being measured to determine the shock. For example, a force of +/-1g is measured for shock detection during tapping or measured up to +/-250g during a car crash. The algorithm for each design also varies with the type of shock or fall that its receiving. The algorithm entails setting the threshold at a predetermined shock level.
For measuring vibration, the first thing to consider is the frequency of the vibration. This will determine the type of roll-off frequency for the different devices that Freescale offers. The second thing to consider is the g-range, depending on the vibration measured or the strength of the motor, there will be a different type of g-range. This means you might have to go with a small +/- 2.5g accelerometer for a pager vibration, all the way up to a 10g accelerometer for a washing machine out-of-balance detection. The third thing to consider is the accelerometer and where it's mounted. Depending on the mounting, there will be a different acceleration, which also depends on the cross-axis sensitivity of the accelerometer.
Freescale accelerometers are two-chip solutions. There is a control IC on one die, and a sensing cell, also called the “g-cell”. The sensing element is sealed hermetically at the wafer level using a bulk micromachined cap wafer. The g-cell is a mechanical structure formed from semiconductor materials (polysilicon) using semiconductor processes (masking and etching). It can be modeled as a set of beams attached to a movable central mass that move between fixed beams. The movable beams can be deflected from their rest position by subjecting the system to an acceleration. The control IC measures g-cell capacitance and extracts acceleration data, provides amplification, signal conditioning, low pass filtering and temperature compensation. Freescale has X-axis, Z-axis, XY-axis, and now XYZ-axis solutions in one package. These sensing axes options fulfill the designers requirements for single-, dual-, or triple-axis sensing. Note that orientation is not a problem because for each solution, the accelerometer can be mounted flat on the PCB.
Freescale’s g-Select low g acceleration sensors are designed to detect on one, two or three axes, allowing the end application the freedom of movement detection it needs. In addition, for multifunctional applications, these devices allow to select between 1.5g to 12g levels of acceleration. The product portfolio includes both analog and digital (I²C/SPI) products. These devices have a fast response time, low current consumption, low voltage operation and a standby mode all in a small profile package to detect fall, tilt, motion, positioning, shock or vibration.
Freescale’s g-Select low g acceleration sensors are designed to detect on one, two or three axes, and devices allow you to select between 1.5g to 12g levels of acceleration. The product portfolio includes both analog and digital (I²C/SPI) products. The table here lists all low-g acceleration sensors.
Thank you for taking the time to view this presentation on Freescale low-g acceleration sensors. If you would like to learn more or go on to purchase some of these devices, you may either click on the part list link, or simple call our sales hotline. For more technical information you may either visit the Freescale site – link shown – or if you would prefer to speak to someone live, please call our hotline number, or even use our ‘live chat’ online facility.