Adding intelligence to your LoRaWAN deployment - The Things Virtual ConferenceJan Jongboom
LoRaWAN devices are typically simple, they grab some sensor data and deliver it back to the network. By adding some embedded machine learning we can make them a lot more intelligent!
Teaching your sensors new tricks with Machine Learning - CENSIS Tech Summit 2019Jan Jongboom
We collect more sensor data than ever, but throw most of it away due to cost, bandwidth or power constraints. In this presentation we'll look at embedded machine learning, pushing intelligence directly to the sensor edge. Given during the CENSIS Tech Summit 2019 in Glasgow, Scotland.
Adding intelligence to your LoRaWAN devices - The Things Conference on tourJan Jongboom
Want to get started? Check the tutorial here: https://www.edgeimpulse.com/blog/adding-machine-learning-to-your-lorawan-device/
Talk about machine learning for IoT devices (TinyML), and everything that it entails. From signal processing to neural networks to classic ML algorithms. Presented in Reading, UK and Hyderabad, India during The Things Conference on Tour.
SPICE MODEL of ZR6_RL=8.2(Ohm) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
Adding intelligence to your LoRaWAN deployment - The Things Virtual ConferenceJan Jongboom
LoRaWAN devices are typically simple, they grab some sensor data and deliver it back to the network. By adding some embedded machine learning we can make them a lot more intelligent!
Teaching your sensors new tricks with Machine Learning - CENSIS Tech Summit 2019Jan Jongboom
We collect more sensor data than ever, but throw most of it away due to cost, bandwidth or power constraints. In this presentation we'll look at embedded machine learning, pushing intelligence directly to the sensor edge. Given during the CENSIS Tech Summit 2019 in Glasgow, Scotland.
Adding intelligence to your LoRaWAN devices - The Things Conference on tourJan Jongboom
Want to get started? Check the tutorial here: https://www.edgeimpulse.com/blog/adding-machine-learning-to-your-lorawan-device/
Talk about machine learning for IoT devices (TinyML), and everything that it entails. From signal processing to neural networks to classic ML algorithms. Presented in Reading, UK and Hyderabad, India during The Things Conference on Tour.
SPICE MODEL of ZR6_RL=8.2(Ohm) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
SPICE MODEL of R6NT_RL=22(Ohm) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
The Report contains detailed information about the current level of stocks and the changes that took place throughout the day.In order to provide a better understanding it provides information about the Index future and Stock future.The report also contains information about the Future level of stocks and it acts as a guide and teacher for its readers so that they can properly plan out their stocks and investment.
SPICE MODEL of R6NT_RL=22(Ohm) in SPICE PARK. English Version is http://www.spicepark.net. Japanese Version is http://www.spicepark.com by Bee Technologies.
The Report contains detailed information about the current level of stocks and the changes that took place throughout the day.In order to provide a better understanding it provides information about the Index future and Stock future.The report also contains information about the Future level of stocks and it acts as a guide and teacher for its readers so that they can properly plan out their stocks and investment.
Market Review for STI: STI gave a 2.12 points down opening @3295.18 ,it formed a red candle for the day and gave a closing @3280.17.The high for the day is marked @3298.07 and low for the day is @3273.58.
Presentation on customer access and channel shift presented to annual conference of public sector IT management organisation, Socitm, on 12 October 2010.
Machine learning on 1 square centimeter - Emerce Next 2019Jan Jongboom
Machine Learning is widely applied, but the models operate on digital data and run in big data centers. But there's more to the world. This is my presentation from Emerce Next 2019 about pushing ML to the smallest of devices.
Fundamentals of IoT - Data Science Africa 2019Jan Jongboom
As data scientists your job is to create order in the data chaos. But where does this data come from? Real-world data does not magically appear cleanly in your Matlab scripts. This is a talk about the fundamentals of IoT, and how to retrieve data from the real world using sensors and devices. Given during Data Science Africa 2019 in Addis Ababa.
Recording: https://www.youtube.com/watch?v=DxTetwYsXvo&index=1&list=PLiVCejcvpsevQ_I9oDIK6eIgau45fWje2
The Mbed Simulator allows you to cross-compile Mbed OS 5 applications and run them on your computer.
LoRaWAN is great, but it requires so much hardware. As I live on a plane I want something better. Presentation about simulating LoRaWAN devices. Here's a video of the simulator: https://www.youtube.com/watch?v=C1S8knMlX7w
Firmware Updates over LoRaWAN - The Things Conference 2019Jan Jongboom
IoT deployments last for ten years, but that's a long time. Requirements change, vulnerabilities are found, and standards evolve. You'll need a firmware update solution.
Talk during The Things Conference 2019.
Faster Device Development - GSMA @ CES 2019Jan Jongboom
Presentation about interesting open source developments that can be used in conjunction with LTE Cat-M1 and NB-IoT. Presentation from the GSMA IoT workshop at CES 2019.
Introduction to Mbed - Etteplan seminar - August 2018Jan Jongboom
What is Arm Mbed, what is Arm Pelion, and how can it help me create IoT devices faster? Introductionary talk during the Etteplan seminars in Oulu and Espoo 21-22 August 2018 about LoRa in Mbed.
Machine Learning on 1 cm2 - Daho.am 2018Jan Jongboom
Putting machine learning on edge nodes adds local control, reduces privacy concerns, and doesn't require a fast internet connection. uTensor and CMSIS-NN bring ML to the cheapest of computers: microcontrollers. $2 cost and 1 cm2 in size they're found everywhere.
What if we can push machine learning to edge nodes? Presentation about uTensor, Mbed OS and ML in general. https://gbgtechweek.com/program/iot-bootcamp/
Embedded Development: meet the web browser - TEQNation 2018Jan Jongboom
Embedded development is stuck in the 90s, building and flashing takes just as long as 20 years ago and development tools are horrendous. Browser development goes way faster, new tools and languages are being launched every other day. What if we can bring these two together? Meet the Mbed Simulator. It allows you to cross-compile embedded code to the browser for a faster development cycle and much better debugging tools.
Machine learning on microcontrollers - Tech Power Summit 2018Jan Jongboom
When we think about machine learning we think about data centers full of GPUs and TPUs, but many interesting usecases lay on the edge: local control, no latency and no privacy issues. Presentation during STX Next's Tech Power Summit in Poznan.
Deep learning on microcontrollers - IETF 101 - T2TRG Jan Jongboom
Machine learning is cool, but requires clusters of GPUs. But what if we can put machine learning on the edge? It would enable new use cases such as sensor fusion, federated learning, or super-advanced compression through auto-encoders. uTensor and CMSIS-NN make it possible for microcontrollers that cost <1$.
Videos in this presentation:
Slide 12: https://www.youtube.com/watch?v=FhbCAd0sO1c
Slide 23: https://twitter.com/janjongboom/status/953014129580748800
Slide 25: https://www.youtube.com/watch?v=PdWi_fvY9Og
Presentation for Thing-to-Thing Research Group during IETF 101 in London.
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
3. 3
Typical industrial sensor in 2020
Vibration sensor (up to 1,000 times per second)
Temperature sensor
Water & explosion proof
Can send data >10km using 25 mW power
Processor capable of running >20 million
instructions per second
4. 4
But... what does it actually do?
Once an hour:
• Average motion (RMS)
• Peak motion
• Current temperature
5. 5
99% of sensor data is discarded due to
cost, bandwidth or power constraints.
https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/
The%20Internet%20of%20Things%20The%20value%20of%20digitizing%20the%20physical%20world/The-
Internet-of-things-Mapping-the-value-beyond-the-hype.ashx
8. 8
On-device intelligence is the only solution
Vibra&on pa+ern
heard that lead to fault
state in a weekTemperature
varies in a way that
I've never seen
before
Machine
oscillates different
than all other
machines in the
factory
12. 12
TinyML
Inspired by "OK Google"
Focus on inferencing, not training
Machine learning model is just a mathematical
function with lots of parameters
Accuracy vs. speed, reducing parameters, hardware-
optimized paths
Targeting battery-powered microcontrollers
Pete Warden
Neil Tan
13. https://www.flickr.com/photos/oceanyamaha/7091324605
13
What is it good for?
Recognizing sounds Detecting abnormal vibration
https://pixabay.com/photos/washing-machine-wash-cat-4120449/
Biosignal analysis
https://www.flickr.com/photos/sheishine/16696564563
Anything with messy, high-resolu3on sensor data
14. 14
Enabling new use cases
Sensor fusion
http://www.gierad.com/projects/supersensor/
20. Classification
What's happening right now?
Anomaly detection
Is this behavior out of the ordinary?
Forecasting
What will happen in the future?
20
3. Letting the computers figure it out
21. 21
Picking the right algorithm
Classification
Neural network
Anomaly detection
K-means clustering
Forecasting
Regression
24. 24
Get some hardware
ST B-L475E-IOT01A
80MHz, 128K RAM, $50
Any smartphone Any dev board w/
the ingestion service
25. 25
Edge Impulse - TinyML as a service
Embedded or edge
compute deployment
options
Test
Edge Device Impulse
Dataset
Acquire valuable
training data securely
Test impulse with
real-time device
data flows
Enrich data and
generate ML process
Real sensors in real time
Open source SDK
Free for developers: edgeimpulse.com