Embedded Vision Machine Learning on Embedded Devices for Image Classification in Industrial Internet of Things
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Getting Started with Machine Learning
Getting Started with Machine Learning CSC131 The Beauty & Joy of Computing Cornell College 600 First Street SW Mount Vernon, Iowa 52314 September 2018 ii Contents 1 Applications: where machine learning is helping1 1.1 Sheldon Branch............................1 1.2 Bram Dedrick.............................4 1.3 Tony Ferenzi.............................5 1.3.1 Benefits of Machine Learning................5 1.4 William Golden............................7 1.4.1 Humans: The Teachers of Technology...........7 1.5 Yuan Hong..............................9 1.6 Easton Jensen............................. 11 1.7 Rodrigo Martinez........................... 13 1.7.1 Machine Learning in Medicine............... 13 1.8 Matt Morrical............................. 15 1.9 Ella Nelson.............................. 16 1.10 Koichi Okazaki............................ 17 1.11 Jakob Orel.............................. 19 1.12 Marcellus Parks............................ 20 1.13 Lydia Sanchez............................. 22 1.14 Tiff Serra-Pichardo.......................... 24 1.15 Austin Stala.............................. 25 1.16 Nicole Trenholm........................... 26 1.17 Maddy Weaver............................ 28 1.18 Peter Weber.............................. 29 iii iv CONTENTS 2 Recommendations: How to learn more about machine learning 31 2.1 Sheldon Branch............................ 31 2.1.1 Course 1: Machine Learning................. 31 2.1.2 Course 2: Robotics: Vision Intelligence and Machine Learn- ing............................... 33 2.1.3 Course -
EECS 498/598: Brain-Inspired Computing: Models, Architectures, and Programming
NEW COURSE ANNOUNCEMENT FOR FALL 2019 EECS 498/598: Brain-Inspired Computing: Models, Architectures, and Programming Time: Tuesday and Thursday 1:30 to 3:00 pm Instructor: Prof. Pinaki Mazumder Phone: 734-763-2107; e-mail: [email protected] Brain-inspired computing is a subset of AI-based machine learning and is generally referred to both deep and shallow artificial neural networks (ANN) and spiking neural networks (SNN). Deep convolutional neural networks (CNN) have made pervasive market inroads in numerous commercial applications and their software implementations are widely studied in computer vision, speech processing and other courses. The purpose of this course will be to study the wide gamut of shallow and deep neural network models, the methodologies for specialized hardware design of popular learning algorithms, as well as adapting hardware architectures on crossbar fabrics of emerging technologies such as memristors and spin torque nanomagnetic devices. Existing software development tools such as TensorFlow, Caffe, and PyTorch will be leveraged to teach various aspects of neuromorphic designs. Prerequisites: Senior undergrad and grad student standing in Electrical Engineering, Computer Engineering, Computer Science or Applied Physics program. Outline: i) Fundamentals of brain-inspired computing and history of neural computing, ii) Basics of linear algebra and probability theory needed for modeling of neural networks, iii) Deep learning by convolutional neural networks such as AlexNet, VGG, GoogLeNet, and ResNet, iv) Deep Neural -
Edge Computing in the Context of Open Manufacturing
Edge Computing in the Context of Open Manufacturing 01.07.2021 LEGAL DISCLAIMERS © 2021 Joint Development Foundation Projects, LLC, OMP Series and its contributors. All rights reserved. THESE MATERIALS ARE PROVIDED ”AS IS.” The parties expressly disclaim any warranties (express, implied, or otherwise), including implied warranties of merchantability, non- infringement, fitness for a particular purpose, or title, related tothe materials. The entire risk as to implementing or otherwise using the materials is assumed by the implementer and user. IN NO EVENT WILL THE PARTIES BE LIABLE TO ANY OTHER PARTY FOR LOST PROFITS OR ANY FORM OF INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES OF ANY CHARACTER FROM ANY CAUSES OF ACTION OF ANY KIND WITH RESPECT TO THIS DELIVERABLE OR ITS GOVERNING AGREEMENT, WHETHER BASED ON BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE), OR OTHERWISE, AND WHETHER OR NOT THE OTHER MEMBER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 2 ACKNOWLEDGMENTS This document is a work product of the Open Manufacturing Platform – IoT Connectivity Working Group, chaired by Sebastian Buckel (BMW Group) and co-chaired by Dr. Veit Hammerstingl (BMW Group). AUTHORS Anhalt, Christopher, Dr. (Softing) Buckel, Sebastian (BMW Group) Hammerstingl, Veit, Dr. (BMW Group) Köpke, Alexander (Microsoft) Müller, Michael (Capgemini) Muth, Manfred (Red Hat) Ridl, Jethro (Reply) Rummel, Thomas (Softing) Weber Martins, Thiago, Dr. (SAP) FURTHER CONTRIBUTION BY Attrey, Kapil (Cognizant) Kramer, Michael (ZF) Krapp, Chiara (BMW Group) Krebs, Jeremy (Microsoft) McGrath, Daniel (Cognizant) Title Image by Possessed Photography from unsplash.com 3 Contents Contents 1 Introduction: The Importance of Edge Computing 5 2 Definition of Edge Computing 6 3 Reference Use Case 6 4 Views on Edge Computing 8 4.1 Infrastructural View . -
CV, Dlib, Flask, Opencl, CUDA, Matlab/Octave, Assembly/Intrinsics, Cmake, Make, Git, Linux CLI
Arun Arun Ponnusamy Visakhapatnam, India. Ponnusamy Computer Vision [email protected] Research Engineer www.arunponnusamy.com github.com/arunponnusamy ㅡ Skills Passionate about Image Processing, Computer Vision and Machine Learning. Key skills - C/C++, Python, Keras,TensorFlow, PyTorch, OpenCV, dlib, Flask, OpenCL, CUDA, Matlab/Octave, Assembly/Intrinsics, CMake, Make, Git, Linux CLI. ㅡ Experience care.ai (5 years) Computer Vision Research Engineer JAN 2019 - PRESENT, VISAKHAPATNAM Key areas worked on - image classification, object detection, action recognition, face detection and face recognition for edge devices. Tools and technologies - TensorFlow/Keras, TensorFlow Lite, TensorRT, OpenCV, dlib, Python, Google Cloud. Devices - Nvidia Jetson Nano / TX2, Google Coral Edge TPU. 1000Lookz Senior Computer Vision Engineer JAN 2018 - JAN 2019, CHENNAI Key areas worked on - head pose estimation, face analysis, image classification and object detection. Tools and technologies - TensorFlow/Keras, OpenCV, dlib, Flask, Python, AWS, Google Cloud. MulticoreWare Inc. Software Engineer JULY 2014 - DEC 2017, CHENNAI Key areas worked on - image processing, computer vision, parallel processing, software optimization and porting. Tools and technologies - OpenCV, dlib, C/C++, OpenCL, CUDA. ㅡ PSG College of Technology / Bachelor of Engineering Education JULY 2010 - MAY 2014, COIMBATORE Bachelor’s degree in Electronics and Communication Engineering with CGPA of 8.42 (out of 10). Favourite subjects - Digital Electronics, Embedded Systems and C Programming. ㅡ Notable Served as one of the Joint Secretaries of Institution of Engineers(I) (students’ chapter) of PSG College of Technology. Efforts Secured first place in Line Tracer Event in Kriya ‘13, a Techno management fest conducted by Students Union of PSG College of Technology. Actively participated in FIRST Tech Challenge, a robotics competition, conducted by Caterpillar India. -
A Deep Neural Network for On-Board Cloud Detection on Hyperspectral Images
remote sensing Article CloudScout: A Deep Neural Network for On-Board Cloud Detection on Hyperspectral Images Gianluca Giuffrida 1,* , Lorenzo Diana 1 , Francesco de Gioia 1 , Gionata Benelli 2 , Gabriele Meoni 1 , Massimiliano Donati 1 and Luca Fanucci 1 1 Department of Information Engineering, University of Pisa, Via Girolamo Caruso 16, 56122 Pisa PI, Italy; [email protected] (L.D.); [email protected] (F.d.G.); [email protected] (G.M.); [email protected] (M.D.); [email protected] (L.F.) 2 IngeniArs S.r.l., Via Ponte a Piglieri 8, 56121 Pisa PI, Italy; [email protected] * Correspondence: [email protected] Received: 31 May 2020; Accepted: 5 July 2020; Published: 10 July 2020 Abstract: The increasing demand for high-resolution hyperspectral images from nano and microsatellites conflicts with the strict bandwidth constraints for downlink transmission. A possible approach to mitigate this problem consists in reducing the amount of data to transmit to ground through on-board processing of hyperspectral images. In this paper, we propose a custom Convolutional Neural Network (CNN) deployed for a nanosatellite payload to select images eligible for transmission to ground, called CloudScout. The latter is installed on the Hyperscout-2, in the frame of the Phisat-1 ESA mission, which exploits a hyperspectral camera to classify cloud-covered images and clear ones. The images transmitted to ground are those that present less than 70% of cloudiness in a frame. We train and test the network against an extracted dataset from the Sentinel-2 mission, which was appropriately pre-processed to emulate the Hyperscout-2 hyperspectral sensor. -
ML Cheatsheet Documentation
ML Cheatsheet Documentation Team Sep 02, 2021 Basics 1 Linear Regression 3 2 Gradient Descent 21 3 Logistic Regression 25 4 Glossary 39 5 Calculus 45 6 Linear Algebra 57 7 Probability 67 8 Statistics 69 9 Notation 71 10 Concepts 75 11 Forwardpropagation 81 12 Backpropagation 91 13 Activation Functions 97 14 Layers 105 15 Loss Functions 117 16 Optimizers 121 17 Regularization 127 18 Architectures 137 19 Classification Algorithms 151 20 Clustering Algorithms 157 i 21 Regression Algorithms 159 22 Reinforcement Learning 161 23 Datasets 165 24 Libraries 181 25 Papers 211 26 Other Content 217 27 Contribute 223 ii ML Cheatsheet Documentation Brief visual explanations of machine learning concepts with diagrams, code examples and links to resources for learning more. Warning: This document is under early stage development. If you find errors, please raise an issue or contribute a better definition! Basics 1 ML Cheatsheet Documentation 2 Basics CHAPTER 1 Linear Regression • Introduction • Simple regression – Making predictions – Cost function – Gradient descent – Training – Model evaluation – Summary • Multivariable regression – Growing complexity – Normalization – Making predictions – Initialize weights – Cost function – Gradient descent – Simplifying with matrices – Bias term – Model evaluation 3 ML Cheatsheet Documentation 1.1 Introduction Linear Regression is a supervised machine learning algorithm where the predicted output is continuous and has a constant slope. It’s used to predict values within a continuous range, (e.g. sales, price) rather than trying to classify them into categories (e.g. cat, dog). There are two main types: Simple regression Simple linear regression uses traditional slope-intercept form, where m and b are the variables our algorithm will try to “learn” to produce the most accurate predictions. -
IOT-EDGE-SERVER BASED EMBEDDED SYSTEM for WIDE-RANGE HABITATS by Archit Gajjar, B.Tech. THESIS Presented to the Faculty of the U
IOT-EDGE-SERVER BASED EMBEDDED SYSTEM FOR WIDE-RANGE HABITATS by Archit Gajjar, B.Tech. THESIS Presented to the Faculty of The University of Houston-Clear Lake In Partial Fulfillment Of the Requirements For the Degree MASTER OF SCIENCE in Computer Engineering THE UNIVERSITY OF HOUSTON-CLEAR LAKE MAY, 2019 IOT-EDGE-SERVER BASED EMBEDDED SYSTEM FOR WIDE-RANGE HABITATS by Archit Gajjar APPROVED BY Xiaokun Yang, PhD, Chair Hakduran Koc, PhD, Committee Member Ishaq Unwala, PhD, Committee Member APPROVED/RECEIVED BY THE COLLEGE OF SCIENCE AND ENGINEERING: Said Bettayeb, PhD, Associate Dean Ju H. Kim, PhD, Dean Dedication I would like to dedicate this thesis to my parents, family members, roommates, and professors. I would never have made it here without all of you. Acknowledgments First and foremost, I am grateful to my advisor, Dr. Xiaokun Yang, for being friendly, caring, supportive, and help in numerous ways. Without his support, I could not have done what I was able to do. He was very generous in sharing his experiences on electrical and computer engineering, academic life and beyond. He is not only my adviser but also, a friend inspiring me for the rest of my life. Next, I would like to thank the members of my committee, Dr. Hakduran Koc and Dr. Ishaq Unwala for their support and suggestions in improving the quality of this dissertation. It is truly honored to have such great fantastic and knowledgeable professors serving as my committee members. I would also like to thank all the lab mates and members at the Integrated Cir- cut (IC) and Internet-of-Things (IoT) - IICOT Laboratory for creating an amazing working environment, and thank my friend, Yunxiang Zhang, for his assistance on work related to my research. -
The Drivers and Benefits of Edge Computing
The Drivers and Benefits of Edge Computing White Paper 226 Revision 0 by Steven Carlini Executive summary Internet use is trending towards bandwidth-intensive content and an increasing number of attached “things”. At the same time, mobile telecom networks and data networks are converging into a cloud computing architecture. To support needs today and tomorrow, computing power and storage is being inserted out on the network edge in order to lower data transport time and increase availability. Edge computing brings bandwidth-intensive content and latency-sensitive applications closer to the user or data source. This white paper explains the drivers of edge computing and explores the various types of edge computing available. RATE THIS PAPER by Schneider Electric White Papers are now part of the Schneider Electric white paper library produced by Schneider Electric’s Data Center Science Center [email protected] The Drivers and Benefits of Edge Computing Edge computing places data acquisition and control functions, storage of high bandwidth Edge content, and applications closer to the end user. It is inserted into a logical end point of a computing network (Internet or private network), as part of a larger cloud computing architecture. defined On-premise application Applications Service Edge Computing Figure 1 Applications Database Service IoT Basic diagram of cloud Applications Service computing with edge aggregation and control Edge Computing devices Cloud Applications Service Edge Computing High bandwidth content There are three primary applications of Edge Computing we will discuss in this white paper. 1. A tool to gather massive information from local “things” as an aggregation and control point. -
AI EDGE up Series
AI EDGE UP Series Jason Lu Director, PSM AAEON Centralized AI = Real Time Decision an the Edge AI at the Edge with CPUs + GPUs = Not as Efficient (Gflops/W) AI On The Edge for Robots, Drones, Portable/Mobile Devices Outdoor Devices = Efficient (Gflops/W) Reasonable Cost Industrial Grade Archit. CREDIT SIZED STACKABLE ARTIFICIAL INTELLIGENCE PLATFORM FULLY POWERED by Intel® TECHNOLOGY UP CORE PLUS X86 AI PLUS FPGA AI CORE VPU Intel® Intel® Intel® Cyclone® MOVIDIUS™ Atom x5/x7 + 10 GX + Celeron/Pentium Myriad 2 Low Power Consumption Programmable Versatile Architecture Video Processing Unit Quad Core 64 bit x86 Architecture (Hardware Neural Network) Super Rich I/O GPU integrated Optimized for Machine Learning Good for CPU Offload, and Real Time Rich I/O Real Time High Speed Signal Analysis Pattern Recognition UP CORE PLUS Credit Card Form Factor Low Power Consumption 6th Generation Atom, Celeron Stackable & Expandable Pentium Quad Core 64 bit x86 Architecture GPU integrated Low Power Consumption Intel Sensor HUB Cost Effective Solution Support of Windows 10, Linux Ubuntu, Yocto, Debian UP CORE PLUS Intel® Atom™ x5 / x7 / Celeron ®/ Pentium ® 2/4/8 GB DDRL 4 Dual Channel 2.400 MHz 32/64/128 GB eMMC DP 4K@60 Hz eDP CSI 2 Lane + CSI 4 Lane USB 3.0 Type A USB 3.0 Type B WiFi 802.11 AC 2T2R 2 x 100 pin expansion connector Compatible with UP Core expansion board 12V DC In AI PLUS Programmable Versatile Architecture Rich high speed I/O Credit Card Expansion Board Hardened Floating Point Capabilities Good for CPU Off-loading Reach I/O Real Time -
Artificial Intelligence Computing for Automotive Webcast for Xilinx Adapt: Automotive: Anywhere January 12Th, 2021
From Technologies to Markets Artificial Intelligence Computing for Automotive Webcast for Xilinx Adapt: Automotive: Anywhere January 12th, 2021 © 2020 AGENDA • Scope • From CPU to accelerators to platforms • Levels of autonomy • Forecasts • Trends • Ecosystem • Conclusions Artificial Intelligence Computing for Automotive | Webcast for Xilinx Adapt: Automotive: Anywhere | www.yole.fr | ©2020 2 SCOPE Not included in the report Robotic cars Autonomous driving Data center computing Cloud computing Performance Understand the impact of Artificial Centralized computing Intelligence on the computing Level 5 Level 4 hardware for automotive Advanced Driver- Assistance Systems Driver environment Infotainment Level 3 (ADAS) Multimedia computing Level 2 Edge computing Computing close to Gesture Speech sensor recognition recognition Artificial Intelligence Computing for Automotive | Webcast for Xilinx Adapt: Automotive: Anywhere | www.yole.fr | ©2020 3 FROM GENERAL APPLICATIONS TO NEURAL NETWORKS The focus for the semiconductor industry is shifting from to General Applications + Neural Networks General workloads + Deep learning workloads • Integer operations + Floating operations • Tend to be sequential in nature + Tend to be parallel in nature Parallelization is key Scalar Engines Platforms and Accelerators explaining why Few powerful cores that tackle computing Hundred of specialized cores working in this is so tasks sequentially parallel popular Only allocate a portion of transistors for Most transistors are devoted to floating floating point operations -
The Edge Computing Advantage
The Edge Computing Advantage An Industrial Internet Consortium White Paper Version 1.0 2019-10-24 - 1 - The Edge Computing Advantage Computing at the edge has grown steadily over the past decade, driven by the need to extend the technologies used in data centers to support cloud computing closer to things in the physical world. This is a key factor in accelerating the development of the internet of things (IoT). But, as is often the case with new technology, there is an abundance of overlapping terminology that obfuscates what the fundamentals are and how they relate to one another. We aim here to demystify edge computing, discuss its benefits, explain how it works, how it is realized and the future opportunities and challenges it presents. BUSINESS BENEFITS OF EDGE COMPUTING Cloud computing in data centers offers flexibility and scale to enterprises. We can extend those benefits towards “things” in the real world, towards the edge. We accrue business benefits by connecting things and isolated systems to the internet, but there are risks that should be balanced against them. Disciplined security of the whole system is needed to make it trustworthy. The flexibility to decide where to perform computation on data improves performance and reduces costs. Sensors are generally limited in terms of what they can do, while computing in data centers induces bandwidth costs as data is transmitted to them. Computing in the edge enables collecting data from multiple sources, fusing and abstracting it as needed, and computing on it right there. For example, the data from a surveillance camera can be abstracted into geometric features and eventually a face. -
Why Organizations Are Betting on Edge Computing
Research Insights Why organizations are betting on edge computing Insights from the edge How IBM can help Clients need strategies and operating plans to harness the game-changing potential of real-time actionable insights, apply predictive and learning analytics to workflows and assets, and enable their digital transformations. Edge computing with AI is creating the potential to enable new markets and grow new revenue streams. We see this rapidly evolving competence pushing the boundaries of what is possible for in-the-moment human and machine collaboration—a new workflow partnership. IBM’s Intelligent Connected Operations offer integrated services, software, and edge computing solutions. Connect with us to navigate this dynamic, rapidly changing landscape and apply the computing power of AI on the edge to integrate end-to-end processes. For more information visit ibm.com/ services/process/iot-consulting By Skip Snyder, Rob High, Karen Butner, and Anthony Marshall Key takeaways Living on the edge Power of edge While edge computing has been on the IT and operations radar for some time, it has now moved into the corporate Edge computing brings computation, data mainstream. According to Network World, edge will be storage, and power closer to the point of part of almost every industry.1 Rollout of 5G will only action or event, reducing response time increase demand and need. In the different normal of a COVID-19 world, edge computing’s significance and and saving bandwidth. Its revolutionary potential are more important than ever. capabilities coupled with artificial A combination of edge computing and industrial Internet intelligence (AI) enable interpretation of Things (IoT) devices enables smarter supply chains, of data patterns, learning, and decisions better equipping them to handle disruption of all kinds.