Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Sensor networks software and architectures, wireless information networks, data, IoT, hardware, software manipulation, simulations, applications and uses.

Detectors --- Wireless sensor networks

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

NAECON (National Aerospace and Electronics Conference) is the oldest and premier IEEE Conference presenting research in all aspects of theory and applications of avionic systems.

Wireless sensor networks --- Nanoelectronics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Alternate physical layer (PHY) and necessary medium access control layer (MAC) enhancements to IEEE Std 802.22-2011 are specified in this amendment for operation in very high frequency/ultra-high frequency (VHF/UHF) television broadcast bands between 54 MHz and 862 MHz to support enhanced broadband services and monitoring applications. The amendment supports aggregate data rates greater than the maximum data rate supported by the IEEE Std 802.22-2011. This amendment defines new classes of IEEE 802.22(TM) devices to address these applications and supports more than 512 devices in a network. This amendment also specifies techniques to enhance communications among the devices and makes necessary amendments to the cognitive, security, and parameters and connection management clauses. This amendment supports mechanisms to enable coexistence with other IEEE 802(R) systems in the same band.

Wireless sensor networks.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This paper describes an unmanned aerial vehicle (UAV) ice tracking framework for use in sea ice management applications. The framework is intended to be used in an ice management scenario where the UAV should detect and track the movement of icebergs and ice floes in an Arctic environment, and seeks to enable the UAV to do so autonomously. This is achieved by using an occupancy grid map algorithm and a locations of interest generator coupled with a Model Predictive Control (MPC) UAV path planner. The main contribution of this paper is interfacing the occupancy grid map algorithm with a machine vision object detection module in order to enable the UAV to generate an occupancy grid map of a pre-defined search area in real-time using on-board processing of UAV sensor data. Further, the paper presents a locations of interest generator module which generates locations that the UAV should investigate based on the generated occupancy grid map. These locations of interest are then used by an MPC path planner in order to make the UAV autonomously investigate and track ice features at said locations. Furthermore, the paper verifies the use of the developed ice tracking framework for autonomously detecting and tracking ice features based on thermal images captured with a UAV, as well as verifying the usefulness and role of UAVs in ice management scenarios by conducting two flight experiments.

Wireless sensor networks

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Wireless sensor networks.