Create a Thermal Camera With Python On a Raspberry Pi
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Guided by Mr Kenneth Pang, Senior Lecturer & Consultant of NUS-ISS' Software Systems Practice, learn how to use Python and MicroPython through this interesting workshop to build a thermal sensor that will be activated on pre-trained faces.
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This will be helpful for GTU IOT subject course understanding too!
If you like the video please subscribe to our channel and turn notifications on for future videos.
Follow us on:
Website: http://www.edtechnology.in/
Instagram: https://www.instagram.com/ed.tech/
Facebook: https://www.facebook.com/Edtech18/
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efficient. We implemented the MATLAB code, it perform slow when
neural network processing started. Implementing them in a parallel
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Accelerate performance on machine learning workloads with the Dell EMC PowerE...
Accelerate performance on machine learning workloads with the Dell EMC PowerE...Principled Technologies
This solution with NVIDIA Tesla V100 GPUs achieved more work than a Supermicro solution did and kept the GPUs coolerHand Finger Counting using Deep Convolutional Neural Network (CNN) on GPU
This poster represents active research topic in human
computer interaction (HCI) as automatic hand finger counting using deep Convolutional Neural Network (CNN). To accelerate projected algorithmic program, leverage CUDA 8.0 platform from the NVIDIA GPU. Hand finger Counting and recognition deals with real time application, that leads us optimize algorithm with maximum number of images for CNN training. Projected methodology implemented in C, CUDA. Algorithmic program is complicated a part of feature
extraction boosted up using multi-threaded CUDA calls. Application of this algorithm is proposed for autonomous fire-fighting robot which has on-board camera and embedded GPU processor. Testing accuracy is measured with known and unknown image dataset, typical testing accuracy achieved 98% for unknown finger counting. A CUDA GPU (GT820M) performance improvement of 40x over the single core Intel processor.
Processing large point clouds
More and more cities, regions and countries gather point cloud data through airborne Lidar sensors. We explain what is point cloud data, discuss Flanders' large point cloud and the challenges that pose the task of computing a 3D model for each building in Flanders.
Intro to GPGPU Programming with Cuda
1) The document provides an introduction to GPGPU programming with CUDA, outlining goals of providing an overview and vision for using GPUs to improve applications.
2) Key aspects of GPU programming are discussed, including the large number of cores devoted to data processing, example applications that are well-suited to parallelization, and the CUDA tooling in Visual Studio.
3) A hands-on example of matrix multiplication is presented to demonstrate basic CUDA programming concepts like memory management between host and device, kernel invocation across a grid of blocks, and using thread IDs to parallelize work.
Bare metal Javascript & GPIO programming in Linux
#IoTBE session about Javascript programming on Linux-based systems. Talking about Node.JS, Node-RED, CylonJS, Johnny-five.io
알리바바 클라우드 PAI (machine learning Platform for AI)
대규모 고성능 분산 컴퓨팅을 기반으로 구축된 알리바바 클라우드의 머신러닝 플랫폼 PAI에 대해 알아보세요. PAI는 고객이 대규모 데이터 마이닝 및 모델링을 쉽게 구현할 수 있도록 지원합니다.
중국 최초의 머신 러닝 플랫폼인 알리바바 클라우드 PAI는 AI 프로그램을 설계하기 위해 제작된 것으로, 여러 고객의 현실적인 문제를 해결하는 데 효과적인 도구입니다.
알리바바 클라우드 PAI의 주요 기능은 다음과 같습니다:
• 다양하고 혁신적인 알고리즘:PAI에는 데이터 전처리, 신경망, 회귀, 분류, 예측, 평가, 통계 분석, 기능 공학 및 딥러닝 아키텍처를 다루는 100가지 이상의 알고리즘이 설계되어 있습니다.
• 딥러닝 아키텍처: PAI에는 전체 컴퓨팅 아키텍처가 다양한 딥러닝 프레임워크에 맞게 최적화되어 있습니다. 또한 이는 API(Application Program Interface)를 배포하는 원클릭 기능을 지원해, 모델링과 서비스 통합 문제를 해결합니다.
• 대규모 컴퓨팅 파워: 알리바바 클라우드의 대형 컴퓨팅 엔진인 PAI는 Apsara에 의해 구동되며, 페타바이트급 컴퓨팅 업무를 매일 처리할 수 있는 초대규모 분산 컴퓨팅 기능을 제공합니다.
• 사용자 친화적 인터페이스: PAI의 데이터 시각화 기능을 통해 개발자는 드래그 앤 드롭 기능으로 구성요소를 작업 흐름에 편리하고 신속하게 투입할 수 있습니다. 모델 구축 및 디버깅 효율성을 향상시키는데 도움을 드립니다.
Getting started with Intel IoT Developer Kit
This document provides an overview of setting up an Intel IoT Developer Kit including the hardware components, installing software, and running sample codes. It discusses the Galileo and Edison boards, microSD cards, IDEs, MRAA and UPM libraries, and connecting devices. It also demonstrates how to set up environments for C/C++ with Eclipse, JavaScript with XDK, and Arduino, and describes where to find documentation and sample codes for getting started with the kits and sensors.
Naive application development
The document provides lessons learned from developing the PlurQ Android application. It discusses challenges with naive assumptions around taking pictures, memory usage, networking, and layouts working across devices. Key lessons include testing on different devices, using the latest APIs, adding permissions only when needed, handling proxies, timeouts and secure connections for networking, and using density-independent units for robust layouts.
Raspberry Pi Based GPS Tracking System and Face Recognition System.
This document describes a Raspberry Pi based project involving GPS tracking and face recognition systems. It discusses interfacing various peripherals like a GPS module, compass, DC motors and Raspberry Pi camera to the Raspberry Pi. A GPS module is interfaced to implement a GPS tracking system. A compass and DC motors are interfaced to control a robot's movement. A Raspberry Pi camera is interfaced for facial recognition using eigenfaces algorithm. It provides details on installing OS on Raspberry Pi, testing and parsing data from GPS module, interfacing a compass and controlling motors. OpenCV is used for face recognition tasks.
Similar to Create a Thermal Camera With Python On a Raspberry Pi (20)
Enhanced Human Computer Interaction using hand gesture analysis on GPU
Enhanced Human Computer Interaction using hand gesture analysis on GPU
Accelerate performance on machine learning workloads with the Dell EMC PowerE...
Accelerate performance on machine learning workloads with the Dell EMC PowerE...
Accelerate performance on machine learning workloads with the Dell EMC PowerE...
Accelerate performance on machine learning workloads with the Dell EMC PowerE...
Hand Finger Counting using Deep Convolutional Neural Network (CNN) on GPU
Hand Finger Counting using Deep Convolutional Neural Network (CNN) on GPU
알리바바 클라우드 PAI (machine learning Platform for AI)
알리바바 클라우드 PAI (machine learning Platform for AI)
Raspberry Pi Based GPS Tracking System and Face Recognition System.
Raspberry Pi Based GPS Tracking System and Face Recognition System.
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HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
GNSS spoofing via SDR (Criptored Talks 2024)
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-Efficiency
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
"Choosing proper type of scaling", Olena Syrota
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Create a Thermal Camera With Python On a Raspberry Pi
- 1. Create a thermal camera solution using Python on a Raspberry Pi Pandemic moves Edge Computing Kenneth Phang Tak Yan #ISSLearningFest
- 2. Objectives ● Upon completion of this workshop ○ Knowledge on how to build a DIY thermal camera ○ Hardware required to make this prototype ○ Understand how to retrieve temperature values ○ Generate and stream the thermal image as MJPEG ○ Implement face/mask detection and stream the person’s face ■ Edge computing ○ Configurable temperature threshold ○ Build a Simple Web App (Replace the LCD screen) ○ Save sensor data to the cloud server (Firebase) 2
- 3. Why build a DIY thermal camera solution? ● Customization ● Low cost ● Automation ● Integrate with the third-party visitor management system ● Detect temperature reading from 1m to 7M range ● Strictly for human (No object and animals) ● Educational 3
- 4. What is required to build this prototype? - Raspberry Pi 3 or 4 (microSD) - Adafruit AMG 8833 Thermal Camera - Google Coral TPU Edge Processor - Pi Camera 8MP - RGB Addressable LED Stick - Jumper cables - 3D print enclosure (Casing) 4 SGD 270
- 5. Wiring Diagram - Thermal Camera 5 - VIN - 5V - GND - SDA - SCL Electromagnetic spectrum I2C must be enable (Use sudo raspi-config) The Inter-integrated Circuit (I2C) Protocol is a protocol intended to allow multiple "slave" digital integrated circuits ("chips") to communicate with one or more "master" chips. https://github.com/adafruit/Adafruit_ CircuitPython_AMG88xx
- 6. Wiring Diagram - Pi Camera 6 Raspberry Pi Camera Connector Stream person’s image standing in front of the pi camera Enable camera interface via raspi- config
- 7. Wiring Diagram - RGB led stick 7 - VCC - 5V - GND - GPIO 18 Detect a person having fever flash RED Flash green if the person is OK
- 8. Edge computing vs Cloud Computing (iot) Face detection & mask wearing detection is on the device itself ! What is the pros doing edge computing over cloud for iot?
- 9. Connect Coral TPU via Raspberry Pi USB port 9 Face detection and mask-wearing detection The classification model is generated by Tensorflow full quantization optimization.
- 10. Sensor Data structure 10 An array of 64 individual infrared temperature values General tolerance : ±0.2 ±0.08 https://cdn.sparkfun.com/assets/4/1/c/0/1/Grid-EYE_Datasheet.pdf
- 11. Enclosure - 3D print prototype 11
- 12. Overall Software & Hardware Architecture 12 Web App (LCD substitute)
- 13. Software Design and Engineering - Python 13 Thermal-camera.py (Process) - Read temperature from sensor - Tune up the temperature reading - Build up the heatmap data structure - Generate thermal image - Save person’s image above threshold to the cloud database Server.py (Flask / Restful API) - Render front end page - Provide save POST endpoint of temperature configuration - Provide GET endpoint of the temp config - Retrieve thermal and person image (Motion Image Streaming) Face-detection.py (Process) - Capture face from Pi Camera - Load face detection engine using the Edge TPU python library for Google Coral - Detect human wearing a face mask using a classification - A bounding box on human face - Determine human presence blinkfever.py (Process) - Light up red when a person in front having temp above threshold
- 14. Software Itself - Da Product 14 V1.0 V1.1 V1.2
- 15. Pros and Cons - Current Prototype 15 Pros - Extensibility - Modular design - Do not need to download app from mobile app store - Its open source and diy possible instructions - Customizable with third party app via Rest API - Over the air update Cons - Heavy & Bulky enclosure - Only allow a person at a time - Heat issue - Isn’t waterproof - Lacking of power backup (UPS) - No monitoring of multiple devices - Lack of security - Camera Lighting issue
- 19. Give Us Your Feedback #ISSLearningFest Day 4 Programme
- 20. References 20 • https://gitlab.com/kenken64/tf-face-mask-detection • https://gitlab.com/kenken64/pi-thermal-camera • https://bit.ly/3ialJBJ • https://github.com/adafruit/Adafruit_AMG88xx • https://github.com/adafruit/Adafruit_CircuitPython_AMG88xx • https://coral.ai/docs/accelerator/get-started/ • https://github.com/thisbejim/Pyrebase • https://pypi.org/project/colour/ • https://www.raspberrypi.org/documentation/usage/camera/python/README.md • https://flask.palletsprojects.com/en/1.1.x/