AI and Big Data For Smart City in Silicon Valley, USA - Issues, Solutions, and Challenges Presented by Jerry Gao, Ph.D., Professor, Director A Silicon Valley Excellence Research Center Smart Technology, Computing, and Complex Systems (STCCS) – SmartTech Center San Jose State University SJSU and City of San Jose are teamed up as a task force for Smart Cities
Building Smart City Complex Systems for San Jose - Current Project Activities Presented by Jerry Gao, Ph.D., Professor, Director A Silicon Valley Excellence Research Center Smart Technology, Computing, and Complex Systems (STCCS) – SmartTech Center San Jose State University SJSU and City of San Jose are teamed up as a task force for Smart Cities
The Research Center Mission and Capability SJSU University’s Mission: “ To enrich the lives of its students, to transmit knowledge to its students along with the necessary skills for applying it in the service of our society; and to expand the base of knowledge through research and scholarshi p.” SJSU STCCS’s Mission: “ To provide a multi-disciplinary research platform for SJSU faculty to create innovations and build practical and future solutions with cutting-edge technology to address the issues and challenges in building complex systems; and provide a live learning and research experience for SJSU students with rich hands-on experience and skills so that they are well-prepared to meet the future workforce needs in Silicon Valley. Focuses: - Research and develop sustainable technologies, intelligent solutions, and quality and safe systems that connect objects, people, and services-based on trustworthy data using - Validated intelligent techniques. • Provide a multi-disciplinary research platform • Use SJSU campus and local cities as living laboratories • Gain innovative research experience • Learn, use, and develop cutting-edge technologies • Solve complex issues in complex cyber systems SJSU and City of San Jose are teamed up as a task force for Smart Cities
Multidisciplinary Research Capabilities Smart City Complex Systems Big Data Services and Analytics Smart Learning & Campus SJSU IoT, Cloud and Mobile Clouds Smart Sensing and Platforms
Four Research Areas: Area #1: Smart Campus & Learning - Smart Campus Sensor Cloud & IoT - Smart Campus Management & Program - Smart Interactive Learning - Smart Campus as Lab. Area #2: Smart City - Smart Streets - Smart Community - Smart Transportation - Smart Government - Smart City as Lab - Smart City Safety Area #4: Smart Living - Smart Home + - Smart Food/Drink/Clothing - Smart Healthcare - Smart Living & Behaviors Area #3: Smart World - Smart Resource & Recycling Systems - Smart Green and Energy Systems - Smart Ecological Systems - Smart Earth Systems Engineering
Major Smart City Issues and Challenges in San Jose Where is the money? Graffiti in the City Illegal Dumping How to Provide Safe and Secure City? How to Build Clean and Green City? How to build connected communities? How to construct sustainable cities? City Big Data? Where we can find?
Smart Hot-Spot Illegal Dumping Monitor System - WiFi-based Monitor/Tracking City Service - Video object detection and learning Cloud - Communication with mobile APP - Communication with Mobile Station Mobile Station - Alerting & Reporting Mobile Services - Controlling service for wireless camera system - Hot-Spot station registration - Video object collection & detection - Data communication with the server - Data communication with mobile APP San Jose City Hot Spot – Illegal Dumping
Mobile-Edge Based Illegal Dumping Detecting & Service System - GPS-based Monitor/Tracking City Service - Video object detection and learning Mobile Cloud - Communication with mobile APP Station - Communication with Mobile Station - Alerting & Reporting Mobile Services - Controlling service for wireless camera system Mobile APP - Mobile station registration - Video object collection & detection Camera-Based Trash Truck - Data communication with the Server - Data communication with mobile APP One San Jose City Street
Smart Clean Street Assessment System Using Big Data Analytics - GPS-based Monitor/Tracking - Video object detection and learning City Service Mobile - Grid-based photo object detection Cloud Station - Communication with mobile APP - Communication with Mobile Station - Real-Time Static Assessment Mobile Services - Controlling service for wireless camera system - Mobile station registration - On-Land Trash Assessment on Mobile Station Camera-Based Trash Truck - Data communication with the Server Mobile APP - Data communication with mobile APP Level #3 Level #3 Level #1 Level #2 Level #1 Level #2
Smart Illegal Dumping Service System - Infrastructure Illegal Dump App San Jose City Smart Illegal Dumping Service System City Wireless Network Edge-Based Trash Truck And Mobile Station Street Clean Monitor Car Edge-Based Hot-Spot Mobile Street Mobile Station Smart City App Sweeper truck
Smart City – Smart Emergency Alerting System Major Project Objectives: Objective #1 : To find out the data-driven emergency alerting coverages for six different nature disaster scenarios, such as earthquakes, floods, fire accidents, and so on. Objective #2: To find the system performance, Problems: limitations, and research ability problems in Alert System Coverage?? underlying emergency system infrastructures. Alert System Performance?? Limitations of Current Systems Objective #3 : To propose the ideas, enhancement Improvements for Future Systems approaches, and even new alerting system infrastructures and solutions for the near future. Major Challenges: Challenge #1: Lack of big data and lack of useful big data integration Challenge #2: L ack of built-in testing and simulation services Challenge #3: Lack of big data Legacy System with slow performance Challenge #4: Lack of effective and easy way to get big data
How to Provide Smart & Safe Living Environment? California Drought Earthquake in California Forest Fire in California Homes and cars are swamped on Last Wednesday in San Jose
Real-Time Forest Fire Monitor, Analysis, and Alerting System camera Forest on Fire Satellite Based Forest FireDetection Sensor Based Forest Fire Detection
A Smart Graffiti Clean-up System Based on An Autonomous Drone • Project Goal: Focused Issue: - Building a smart graffiti clean-up (a) Graffiti Detection and Reporting system using autonomous drone (b) Graffiti Cleaning up using smart solution and machine learning. Major Reasons: - High-Cost and Labor Intensive in Clean-Up • Challenges: - Impact the City Image and Environment - Automatic graffiti detection and alerts - Affect City Traffic and Transportation - Automatic graffiti clean-up Safety - Auto Pilot for Drone in City Street
A Smart Graffiti Clean-up System Based on An Autonomous Drone
City AI and Big Data Analysis for Smart Cities - Part I - City Illegal Dumping Object Detection Paper and Report from: Akshay Dabholkar, Bhushan Muthiyan, Shilpa Srinivasan, Swetha Ravi, Hyeran Jeon, Jerry Gao Paper and Master Project Report from: Wei-Chung Chen, Xiaoming Chuang, Wendy Hu, Luwen Miao, and Jerry Gao - Part II - Street Litter Object Detection and Framework Paper and Master Project Report from: Chandni Balchandani, Rakshith Koravadi Hatwar, Parteek Makkar, Yanki Shah, Pooja Yelure, Magdalini Eirinaki Paper and Master Project Report from: Bharat Bhushan Kavin Pradeep Sriram Kumar Mithra Desinguraj Sonal Gupta, and Jerry Gao
Edge-Based Mobile Smart Service System for Illegal Dumping Illegal Dump App San Jose City Il legal dumping Mobile Station Smart Illegal Dumping C ity Wireless Service System Network Edge-Based Trash Truck And Mobile Station Street Clean Monitor Car Hot Spot Mobile Street Edge-Based Hot-Spot Sweeper truck Mobile Station Smart City App
Smart Mobile Illegal Dumping Service System (SMIDS) Illegal Dumping App Illegal Dumping Edge-Based Service Server Mobile Station Illegal Dumping Illegal Dumping Server UI Dashboard Illegal Dumping Mobile Client Illegal Dumping Illegal Dumping Reporter Analytics Illegal Dumping Computing Vision & Controller Object Detection Illegal Dumping Illegal Dumping Service Manager Detection Engine Edge-Based Data IoT Mobile Platform Repository & Sensors Illegal Dumping Illegal Dumping Service Connector Service DB Program Mobile Edge QoS & Security Computing Platform Crime/Safty QoS & Security Illegal Dumping Service Protocol Illegal Dumping Service Protocol
Edge-Based Automatic City Illegal Dumping Object Detection Group #1
AI Model and Technology • Convolutional Neural Network (CNN) is a practically useful algorithm especially in computer vision tasks by reduced complexity with resolving overfitting problems from deep learning. • CNN inside is made of simple repeated matrix multiplications without branch operations - Create illegally dumped material labeled dataset. - Create lmdb database from the dataset. - Tweak the existing model as per need and train the model using - Caffe interface with the generated lmdb files. - Deploy the model file on the embedded platform Jetson TX1 - Test the model for prediction accuracy.
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