Real-time beacon identification using linear and kernel (non-linear) Support Vector Machine, Multiple Kernel Learning (MKL), and Light Detection and Ranging (LIDAR) 3D data

Reza, Tasmia; Cagle, Lucas; Pan, Wei; Ball, John

doi:10.1117/12.2518714

Cited by 1 publication

(1 citation statement)

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“…They used equally spaced centreline points to calculate lane widths with an accuracy of 3 cm for lane width estimation [ 8 ]. Reza et al achieved beacon recognition on roads based on linear and kernel (non-linear) support vector machines, multi-core learning, light detection and radar ranging 3D data [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

IoT Wheelchair Control System Based on Multi-Mode Sensing and Human-Machine Interaction

Cui

Huang

et al. 2022

Micromachines

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Traditional wheelchairs are unable to actively sense the external environment during use and have a single control method. Therefore, this paper develops an intelligent IoT wheelchair with the three functions, as follows. (1) Occupant-wheelchair-environment multimode sensing: the PAJ7620 sensor is used to recognize gesture information, while GPS (Global Positioning System) and IMU (Inertial Measurement Unit) sensors are used to sense positioning, speed and postural information. In addition, Lidar, DHT11, and BH1750 sensors obtain environmental information such as road information, temperature and humidity and light intensity. (2) Fusion control scheme: a mobile control scheme based on rocker and gesture recognition, as well as a backrest and footrest lifting, lowering and movement control scheme based on Tencent Cloud and mobile APP (Application). (3) Human-machine interaction: the wheelchair is docked to Tencent IoT Explorer through ESP8266 WiFi module, using MQTT (Message Queuing Telemetry Transport) protocol is used to upload sensory data, while the wheelchair status can be viewed and controlled on the APP. The wheelchair designed in this paper can sense and report the status of the occupant, environment and wheelchair in real time, while the user can view the sensory data on the mobile APP and control the wheelchair using the rocker, gestures and APP.

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Section: Introductionmentioning

confidence: 99%