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Autonomous and Connected Vehicles

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The WAVES Lab is currently leading a major MSU initiative in the area of autonomous and connected vehicles. This initiative involves faculty members across multiple departments within the College of Engineering and other colleges at MSU. In addition to leading the overall effort for the CANVAS (Connected and Autonomous Networked-Vehicles Active Safety) initiative, the WAVES Lab is heavily involved in developing key technologies that will enable self-driving cars' occupants and users achieve: (a) active safety through advanced sensing, learning and fusion solutions; and (b) optimal productivity through novel mobility and transportation services. Research problems pursued by the WAVES Lab and in close collaborations with other faculty members of the CANVAS initiative includes:

  • ▪ Autonomous and connected vehicles architecture
  • ▪ Joint deep learning that integrates sensing data from multiple modalities (RADARs, cameras, and LIDARs) with localization and mapping data
  • ▪ Joint deep learning and optimal fusion
  • ▪ Integrated recognition and situational awareness
  • ▪ Precise tracking of objects moving at different speeds
  • ▪ Seeing behind moving objects
  • ▪ Multi-vehicle sensor data sharing and fusion
  • ▪ Future location sensing
  • ▪ Mobility and transportation services

Compressed sensing & rank minimization

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The WAVES Lab is developing new approaches for compressed sensing and rank minimization for sparse signals, in general, and for digital imaging in particular. This research, partly funded by NSF, is enabling novel algorithms for demosaicking, deblurring, denoising, super-resolution, and visual coding. It also includes a major collaboration with Kodak Research Labs. The utility of hybrid (sparse and dense) projections that lead to low-complexity decoding algorithms are being explored. This framework also leads to channel-coding like compressive sampling. Apllications include video, speech, networking, and pattern recognition.

Social network analysis

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In collaboration with other research groups, the WAVES Lab is pursuing an intriguing technology direction for the analysis of on-line social networks and their underlying infrastructure and services. We are developing new graph transforms, signal processing, information-theoretic, and machine learning tools for the analysis and understanding of massiv social network graphs and services. A core component of this NSF funded multidisciplinary research is close interaction with social scientists and other experts in related fields.

Visual retargeting & summarization

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New approaches for content-aware visual "retargeting" are being developed. A variety of display devices that prohibit maintaining the original aspect ratio of the captured visual content are considered. Multiresolution approaches that are robust to a variety of visual content distortion are being designed and analyzed. Extensions of these approaches to 3D visuals and displays are being investigated. Novel video summarization frameworks are being explored.

Reliable & stable wireless multimedia

Reliable, efficient and stable wireless link-layer protocols are being designed, analyzed and implemented for high-end emerging multimedia applications over wireless networks. These protocols, designed by support in part from NSF and industry, provide joint reliability and stability for both delay constrained real-time video applications and traditional applications that are built on TCP/IP. Target applications include multimedia streaming, telemedicine/health monitoring, surveillance, and gaming.


The WAVES Lab ACE Protocol

network coding & network channel coding

Migrating coding functions from end nodes into intermediate nodes within the network represent a major paradigm shift with improvements in throughput, delay, and/or reliability. Based on support from two NSF grants, the WAVES Lab has developed advanced network-embedded source and channel coding approaches over network topologies that are representatives of next generation Internet and wireless networks, sensor networks, peer-to-peer networks, and ad-hoc networks.


Optimal Distribution of Source and Channel Coding
Functions over Random Networks using Network
Coding and Network Channel Coding

practical distributed video coding

 

Practical Distributed Video Coding (PDVC) includes designing and analyzing new practical approaches for the mapping of Distributed Video Coding (DVC) algorithms into low-complexity low-power visual sensors and devices. Applications of PDVC onto emerging multi-view/3D video are being explored. The WAVES Lab also developed novel approaches based on multi-hypothesis distributed visual coding. Partly supported by NSF, this effort includes the redesign of state-of-the-art coding schemes, such as Low-Density-Parity-Check (LDPC) and Polar codes for DVC.

 
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WAVES Lab, Electrical and Computer Engineering Department, Michigan State University