- Friday, 25 October 2019
- room D@ta | EEMCS (EWI) building, ground floor
The quantum supremacy experimentDr. Rami Barends
Quantum supremacy, as originally defined by John Preskill, is "the day when well-controlled quantum systems can perform tasks surpassing what can be done in the classical world". I will discuss the challenges posed in performing a well-defined computational task on a programmable superconducting quantum processor, where the cost of doing the same task on a classical computer would be prohibitively higher.
- Wednesday, 16 October 2019
Ultra Wide Band Surveillance RadarDr. Mark E. Davis, IEEE Fellow, IEEE Distinguished Lecturer
Ultra Wide Band Surveillance Radar is an emerging technology for detecting and characterizing targets and cultural features for military and geosciences applications. It is essential to have fine range and cross-range resolution to characterize objects near and under severe clutter. This lecture will provide an in-depth look into:
- The early history of battlefield surveillance radar
- UWB phased array antenna
- UWB Synthetic aperture radar (SAR)
- UWB ground moving target indication
- New research in multi-node ultra wind band radar
Lecturer Biography: Dr Mark E Davis has over 50 years’ experience in Radar technology and systems development. He has held senior management positions in the Defense Advanced Research Projects Agency (DARPA), Air Force Research Laboratory, and General Electric Aerospace. At DARPA, he was the program manager on both the foliage penetration (FOPEN) radar advanced development program and the GeoSAR foliage penetration mapping radar.
His education includes a PhD in Physics from The Ohio State University, and Bachelor and Master’s Degrees in Electrical Engineering from Syracuse University. He is a Life Fellow of both the IEEE and Military Sensing Symposia, and a member of IEEE Aerospace Electronics Systems Society Board of Governors, VP Conferences, and past-Chair the Radar Systems Panel. He is the 2011 recipient of the AESS Warren D White Award for Excellence in Radar Engineering, and the 2018 IEEE Dennis J. Pickard Medal for Radar Technologies and Applications.
Signal Processing Seminar
- Thursday, 3 October 2019
- HB 17.150
A Data Scientific Approach to Efficient Submillimeter Astronomical SpectroscopyAkio Taniguchi
Nagoya University, Japan
Astronomical data have become huge, as a result of recent advances in wide-field and wide-band instruments. To efficiently extract astronomical signals from observations using these instruments, data scientific approaches are essential. In the (sub)millimeter waveband, spectroscopy with ground-based single-dish telescopes is the best method for surveying interstellar molecules and atoms. However, such observations are not efficient yet, because they always suffer from the intense and time-varying atmosphere of the Earth.
In this talk, I present a statistical method to remove the atmospheric emission from a large spectroscopic dataset by using its intrinsic frequency correlation or spectral shape. As an application, I introduce a recent development of frequency modulation (FM) spectroscopy, which is three times more efficient than a conventional method . As a collaboration with TU Delft, I introduce another application of spectral-cleaning for an ultra-wide-band (UWB) spectrometer DESHIMA . Grasping the UWB atmospheric characteristics by using our data analysis software , it removes atmospheric effects on an astronomical spectrum much better than a conventional method.
 Akio Taniguchi, Yoichi Tamura et al., "A new off-point-less observing method for millimeter and submillimeter spectroscopy with a frequency-modulating local oscillator (FMLO)", submitted to Publications of the Astronomical Society of Japan (2019)
 Akira Endo, Kenichi Karatsu, Yoichi Tamura, Tai Oshima, Akio Taniguchi, ..., Jochem J. A. Baselmans, "First light demonstration of the integrated superconducting spectrometer", Nature Astronomy (2019), Advanced Online Publication https://rdcu.be/bM2FN
 Akio Taniguchi, Tsuyoshi Ishida, "De:code - DESHIMA code for data analysis", DOI 10.5281/zenodo.3384216