August 14, 2017

Compressed Sensing of Compressible Signals

  • Beygi S.
  • Jalali S.
  • Maleki A.
  • Mitra U.

We present a novel and general approach that elevates the scope of compressed sensing recovery algorithms far beyond simple structures such as sparsity. The proposed method, referred to as compressionbased gradient descent (C-GD), is capable of employing state-of-the-art compression algorithms to solve structured signal recovery problems. This enables CGD to take advantage of complex structures that are used by the state-of-the-art compression codes, such as JPEG2000 and MPEG4. Our simulation results show that, in many instances, C-GD achieves state-of-the-art signal recovery performance. Furthermore, our theoretical results justify the performance of C-GD observed in our simulations.

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Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...