CS: CS Ground Penetrating Radar, History of Matching Pursuit, Reinforcement Learning Blog, Neuroscience and Dynamic Systems

Andriyan Suksmono at the Institut Technologi Bandug blogs on Compressed Sensing. The blog's name is Chaotic Pearls and most of it is in Indonesian. His latest entry on the use of Compressed Sensing in the context of a Ground Penetrating Radar application. This is new. The presentation (in english) is entitled: A Compressive SFCW-GPR System (also here) by Andriyan Suksmono, Endon Bharata, A. Andaya Lestari, A. Yarovoy, and L.P. Ligthart. The abstract of the paper reads:

Data acquisition speed is an inherent problem of the stepped-frequency continuous wave (SFCW) radars, which discouraging further usage and development of this technology. We propose an emerging paradigm called the compressed sensing (CS), to manage this problem. In the CS, a signal can be reconstructed exactly based on only a few samples below the Nyquist rate. Accordingly, the data acquisition speed can be increased significantly. A novel design of SFCW ground penetrating radar (GPR) with a high acquisition speed capability is proposed and evaluated. Simulation by a mono-cycle waveform and actual measurement by a Vector Network Analyzer in a GPR test-range confirm the implementability of the proposed system.

The architecture looks like this:

and some photos of the experiment are also shown below. The rest of the presentation show some of the reconstruction results using L1 magic.



Here is another blogger. Laurent Jacques, a contributor to this blog, has decided to start his own blog entitled: Le Petit Chercheur Illustré, Yet another signal processing (and applied math). His first technical entry is on an inspiring historical perspective on the Matching Pursuit technique.

Some of you know of my interest in Robotics and Artificial Intelligence. In particular, learning in low dimensional spaces. Two items appeared on my radar this week:

A blog: The Reinforcement Learning Blog

and a paper entitled:

Where neuroscience and dynamic system theory meet autonomous robotics: A contracting basal ganglia model for action selection. by B. Girard, Nicolas Tabareau, Quang Cuong Pham, Alain Berthoz, Jean-Jacques Slotine. The abstract reads:

Action selection, the problem of choosing what to do next, is central to any autonomous agent architecture. We use here a multi-disciplinary approach at the convergence of neuroscience, dynamical system theory and autonomous robotics, in order to propose an efficient action selection mechanism based on a new model of the basal ganglia. We first describe new developments of contraction theory regarding locally projected dynamical systems. We exploit these results to design a stable computational model of the cortico-baso-thalamo-cortical loops. Based on recent anatomical data, we include usually neglected neural projections, which participate in performing accurate selection. Finally, the efficiency of this model as an autonomous robot action selection mechanism is assessed in a standard survival task. The model exhibits valuable dithering avoidance and energy-saving properties, when compared with a simple if-then-else decision rule.