Kevin Huang presents information on a robotic follower. The robot follows people using ultrasonic waves to detect their location from a tracking device. It then uses this location data and its microcontroller to control its motors and move toward the person. The document outlines the robot's usage, specifications including its size, weight, speed and range, major components, and its operating theory. Further development ideas to improve it are also provided.
Wall arm for UST( Ultra short throw) projector
I am an authorized Consultant for Dukane/Convey
Bill McIntosh
Authorized Dukane/Convey Consultant
Phone :843-442-8888
Email :WKMcIntosh@Comcast.net
3ª edição. Traz os seguintes artigos: O Desenvolvimento Territorial Sustentável a partir da Escala Local /A Experiência da Iniciativa art do PNUD na América Latina em Desenvolvimento Econômico Local / Desenvolvimento Territorial e Economia Solidária (ESOL): conexões com a geração local de trabalho e renda /Agente de Desenvolvimento – um elo entre a administração pública municipal e os pequenos negócios / A Contribuição do Instituto Sicoob PR com o Desenvolvimento Local e Sustentável por meio da Disseminação da Cultura Cooperativista / NIT (Núcleo de Inteligência Territorial) – A importância dos indicadores municipais
Wall arm for UST( Ultra short throw) projector
I am an authorized Consultant for Dukane/Convey
Bill McIntosh
Authorized Dukane/Convey Consultant
Phone :843-442-8888
Email :WKMcIntosh@Comcast.net
3ª edição. Traz os seguintes artigos: O Desenvolvimento Territorial Sustentável a partir da Escala Local /A Experiência da Iniciativa art do PNUD na América Latina em Desenvolvimento Econômico Local / Desenvolvimento Territorial e Economia Solidária (ESOL): conexões com a geração local de trabalho e renda /Agente de Desenvolvimento – um elo entre a administração pública municipal e os pequenos negócios / A Contribuição do Instituto Sicoob PR com o Desenvolvimento Local e Sustentável por meio da Disseminação da Cultura Cooperativista / NIT (Núcleo de Inteligência Territorial) – A importância dos indicadores municipais
Connected car solution and E-call system for OEM by SmartdrivingNikita Kasyanenko
We are combining technologies, data and vehicles into a unified ecosystem which benefits drivers, insurers, dealers, car makers and fleet operators. The Smart Driving Labs is founded by professionals in cross disciplinary fields of knowledge: developers, engineers, insurance specialists and data scientists.
SensMaster company profile.
SensMaster produces advanced RFID and wireless sensor technologies for demanding applications in the areas of security, safety, logistics, policy execution and environmental monitoring.
Developed hardware for warehouse management platform,RFID Forklift system, utilizes sensor, front installed antenna and IoT Gateway to identify an RFID pallet tag after it has been loaded onto the forklift. The System then identifies a pallet storage location, utilizing a bottom antenna, and IoT Gateway logic to confirm that the specific pallet has been picked up or dropped off at that location. These transactions are then sent to server.
One of the most pain points in a warehouse is finding the location finished goods that have been shelved. We can solve this problem by labeling each rack and shelf with an on-metal RFID tag/s. These tags can be read by RFID enabled forklifts during the loading and unloading stage and update the list of inventories and its respective locations in the warehouse. RFID enabled forklifts eliminate the need install RFID antennas throughout your warehouse to make tracking a possibility. This forklift solution has a mobile computer/tablet (rugged, for sustained use in a tough environment) integrated closely with RFID readers and antennae. This gives all the efficiency-boosting practices of a standard computerized RFID system that would be tracking the pallets and asset locations in real-time.
Autonomous Vehicles: the Intersection of Robotics and Artificial IntelligenceWiley Jones
Autonomous Vehicle Webinar. Crash course in AVs: high-level overview, technology deep-dives, and trends. Follow me on Twitter at https://twitter.com/wileycwj.
Link to YouTube Video: https://www.youtube.com/watch?v=CruCp6vqPQs
Google Slides: https://docs.google.com/presentation/d/1-ZWAXEH-5Xu7_zts-rGhNwan14VH841llZwrHGT_9dQ/edit?usp=sharing
Applications of Deep Learning in TelematicsDatabricks
Smart phones are equipped with many sensors which provide detailed and continuous information of the device's location and movement. The use of such signals for vehicle movement inference presents many challenges due to signal noise, unknown phone orientation, varying device sensor quality and so on. Signal processing and feature engineering are generally difficult and require deep domain knowledge and manual pattern recognition. We discuss how deep learning can be leveraged in this context for automatic signal processing and feature engineering. We present several applications of deep learning in vehicle telematics as well as the deep learning architecture designed for learning sensor embeddings for vehicle movement events. One challenge we face is that model training requires huge volumes of sensor data, which must be processed efficiently. We present a solution using Spark for model development and batch deployment.
Connected car solution and E-call system for OEM by SmartdrivingNikita Kasyanenko
We are combining technologies, data and vehicles into a unified ecosystem which benefits drivers, insurers, dealers, car makers and fleet operators. The Smart Driving Labs is founded by professionals in cross disciplinary fields of knowledge: developers, engineers, insurance specialists and data scientists.
SensMaster company profile.
SensMaster produces advanced RFID and wireless sensor technologies for demanding applications in the areas of security, safety, logistics, policy execution and environmental monitoring.
Developed hardware for warehouse management platform,RFID Forklift system, utilizes sensor, front installed antenna and IoT Gateway to identify an RFID pallet tag after it has been loaded onto the forklift. The System then identifies a pallet storage location, utilizing a bottom antenna, and IoT Gateway logic to confirm that the specific pallet has been picked up or dropped off at that location. These transactions are then sent to server.
One of the most pain points in a warehouse is finding the location finished goods that have been shelved. We can solve this problem by labeling each rack and shelf with an on-metal RFID tag/s. These tags can be read by RFID enabled forklifts during the loading and unloading stage and update the list of inventories and its respective locations in the warehouse. RFID enabled forklifts eliminate the need install RFID antennas throughout your warehouse to make tracking a possibility. This forklift solution has a mobile computer/tablet (rugged, for sustained use in a tough environment) integrated closely with RFID readers and antennae. This gives all the efficiency-boosting practices of a standard computerized RFID system that would be tracking the pallets and asset locations in real-time.
Autonomous Vehicles: the Intersection of Robotics and Artificial IntelligenceWiley Jones
Autonomous Vehicle Webinar. Crash course in AVs: high-level overview, technology deep-dives, and trends. Follow me on Twitter at https://twitter.com/wileycwj.
Link to YouTube Video: https://www.youtube.com/watch?v=CruCp6vqPQs
Google Slides: https://docs.google.com/presentation/d/1-ZWAXEH-5Xu7_zts-rGhNwan14VH841llZwrHGT_9dQ/edit?usp=sharing
Applications of Deep Learning in TelematicsDatabricks
Smart phones are equipped with many sensors which provide detailed and continuous information of the device's location and movement. The use of such signals for vehicle movement inference presents many challenges due to signal noise, unknown phone orientation, varying device sensor quality and so on. Signal processing and feature engineering are generally difficult and require deep domain knowledge and manual pattern recognition. We discuss how deep learning can be leveraged in this context for automatic signal processing and feature engineering. We present several applications of deep learning in vehicle telematics as well as the deep learning architecture designed for learning sensor embeddings for vehicle movement events. One challenge we face is that model training requires huge volumes of sensor data, which must be processed efficiently. We present a solution using Spark for model development and batch deployment.
1. Team member: Kevin Huang
Phone number: (647) 989-9134
E-Mail: khuang12@myseneca.ca
Seneca College of Applied Arts and Technology
2. Agenda
Introduction of the Robotic Follower
Usage of the product
Specification
Demonstration
Major parts and Components
Operation theory
Question
Further development
3. Introduction
The Robotic Follower is a robot that
follows people, or whoever holds the
tracking device.
It uses ultrasonic waves to detect your
location.
According your location, MCU controls
the motors to move towards you.
4. Usage of the Product
Shopping Malls
Supermarkets
Walk
Moving
5. Specification
Size (car): 180mm x 230mm x 137mm
(width x length x height)
Size (tracking device): 80mm x 90mm x
37mm
Weight (car): 60 g
Weight (tracking device): 10 g
Maximum working time: 2 hours (when fully
charged)
Maximum speed: 61 cm/s
Maximum carrying capacity: 4 Kg
Maximum tracking distance: 5 meters
8. Operation Theory
Car transmits RF message
Pack receives it and transmits burst
Sensors on the car starts ranging
MCU reads data ,converts to distance
Calculation
Controls the motors by adjusting PWM
10. Thank You
Team member: Kevin Huang
Phone number: (647) 989-9134
E-Mail: khuang12@myseneca.ca
linightz@hotmail.com
Seneca College of Applied Arts and
Technology