3.7k citations
- University of California San DiegoUS25 papers
- The University of Texas at AustinUS14 papers
- Qualcomm (United Kingdom)GB11 papers
- University of California, IrvineUS10 papers
- University of California, Los AngelesUS10 papers
- École Polytechnique Fédérale de LausanneCH9 papers
- Market MattersUS9 papers
- Princeton UniversityUS9 papers
- University of California, BerkeleyUS9 papers
- University of TorontoCA9 papers
- Texas A&M UniversityUS8 papers
- University of Illinois Urbana-ChampaignUS8 papers
15 papers · 1 filter
Magnetless Circulators Based on Synthetic Angular-Momentum Bias: Recent Advances and Applications
Ahmed Kord, Andrea Alu
In this paper, we discuss recent progress in magnet-free non-reciprocal structures based on a synthetic form of angular momentum bias imparted via spatiotemporal modulation. We dis…
Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks
Saurav Prakash, Sagar Dhakal, Mustafa Akdeniz +4
Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federa…
The Stability of Low-Density Parity-Check Codes and Some of Its Consequences
Wei Liu, Rüdiger Urbanke
We study the stability of low-density parity-check (LDPC) codes under blockwise or bitwise maximum (MAP) decoding, where transmission takes place over a bin…
Adaptive and Fast Combined Waveform-Beamforming Design for mmWave Automotive Joint Communication-Radar
Preeti Kumari, Nitin Jonathan Myers, Robert W. Heath
Millimeter-wave (mmWave) joint communication-radar (JCR) will enable high data rate communication and high-resolution radar sensing for applications such as autonomous driving. Pri…
Feedback Enhanced Motion Planning for Autonomous Vehicles
Ke Sun, Brent Schlotfeldt, Stephen Chaves +3
In this work, we address the motion planning problem for autonomous vehicles through a new lattice planning approach, called Feedback Enhanced Lattice Planner (FELP). Existing latt…
Differentiable Joint Pruning and Quantization for Hardware Efficiency
Ying Wang, Yadong Lu, Tijmen Blankevoort
We present a differentiable joint pruning and quantization (DJPQ) scheme. We frame neural network compression as a joint gradient-based optimization problem, trading off between mo…