Agenda

Signal Processing Seminar

• Thursday, 14 November 2019
• 13:30-14:30
• HB 17.150

MSc SS Thesis Presentation

• Thursday, 17 October 2019
• 10:00-10:45
• HB 21.120

Indoor localization using narrowband radios and switched antennas in indoor environment

MSc SS Thesis Presentation

• Tuesday, 15 October 2019
• 13:30-14:15
• HB 17.150

Radio astronomy image formation using Bayesian learning techniques

CAS MSc Midterm Presentations

• Tuesday, 8 October 2019
• 10:30-11:00
• HB 17.150

CAS MSc Midterm Presentations

Signal Processing Seminar

• Thursday, 3 October 2019
• 13:30-14:30
• HB 17.150

A Data Scientific Approach to Efficient Submillimeter Astronomical Spectroscopy

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 [1]. As a collaboration with TU Delft, I introduce another application of spectral-cleaning for an ultra-wide-band (UWB) spectrometer DESHIMA [2]. Grasping the UWB atmospheric characteristics by using our data analysis software [3], it removes atmospheric effects on an astronomical spectrum much better than a conventional method.

[1] 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)
[2] 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
[3] Akio Taniguchi, Tsuyoshi Ishida, "De:code - DESHIMA code for data analysis", DOI 10.5281/zenodo.3384216

Signal Processing Seminar

• Thursday, 26 September 2019
• 13:30-14:30
• HB 17.150

Biomedical Signal Processing

MSc SS Thesis Presentation

• Friday, 20 September 2019
• 10:00-10:45
• HB 17.150

A Generative Neural Network Model for Speech Enhancement

Listening in noise is a challenging problem that affects the hearing capability of not only normal hearing but especially hearing impaired people. Since the last four decades, enhancing the quality and intelligibility of noise corrupted speech by reducing the effect of noise has been addressed using statistical signal processing techniques as well as neural networks. However, the fundamental idea behind implementing these methods is the same, i.e., to achieve the best possible estimate of a single target speech waveform. This thesis explores a different route using generative modeling with deep neural networks where speech is artificially generated by conditioning the model on previously predicted samples and features extracted from noisy speech. The proposed system consists of the U-Net model for enhancing the noisy features and the WaveRNN synthesizer (originally proposed for text-to-speech synthesis) re-designed for synthesizing clean sounding speech from noisy features. Subjective results indicate that speech generated by the proposed system is preferred over listening to noisy speech, however, the improvement in intelligibility is limited. 

Signal Processing Seminar

• Thursday, 12 September 2019
• 13:30-14:30
• HB 17.150

Signal processing for communication

MSc ME Thesis Presentation

• Friday, 30 August 2019
• 10:00-10:30
• Lecture Hall 10 in PULSE, building 33a

A low-noise amplifier for ultrasound imaging with continuous time-gain compensation

This work presents a low-noise amplifier (LNA) for ultrasound imaging with built-in continuous time-gain compensation (TGC), which compensates for the time-dependent attenuation of the received echo signal and thus significantly reduces its dynamic range (DR).

The proposed design combines the LNA and TGC functions in a single variable-gain current-to-current amplifier. Compared to conventional ultrasound front-ends, which implement the TGC function after an LNA that needs to handle the full DR of the echo signal, this approach can highly reduce the power consumption and the size. Compared to earlier programmable gain LNAs with discrete gain steps, the continuous gain control avoids switching transients that may lead to imaging artefacts.

The TGC function is realized by a novel feedback network consisting of a double differential pair that feeds a fraction of the output current back to the input. This fraction can be changed continuously using a control voltage that is applied to the gates of the differential pairs, to realize a gain range from -20 dB to +20 dB. To achieve an approximately constant closed-loop bandwidth in the presence of the changing feedback factor, a loop amplifier has been implemented whose gain is changed along with the feedback factor by dynamically changing its bias currents. This loop amplifier employs a current-reuse architecture to achieve high power-efficiency. In addition, a variable bias current source has been designed to appropriately bias the TGC feedback network. By employing a similar double differential pair topology as in the feedback network, this current source provides the required low noise at the highest gain setting and high current at the lowest gain setting within the available headroom.

The LNA with built-in TGC function has been realized in 180nm CMOS technology. It has been optimized to interface with a 7.5 MHz capacitive micro-machined ultrasonic transducer (CMUT). Simulation results show that it achieves a 3dB bandwidth higher than 40 MHz across the full gain range. At the highest gain setting, its input current noise is 0.96 pA/rt-Hz at 7.5 MHz. This leads to an input dynamic range of 93 dB, which is compressed into an output dynamic range of 53 dB by means of the 40 dB variable gain. The amplifier consumes 10.8 mW from a 1.8V supply, and occupies an estimated 320 x 320 um2 die area.

MSc SS Thesis Presentation

• Thursday, 29 August 2019
• 10:00-10:45
• EWI HB 17.150

Spoofing detection in a loosely coupled GNSS and INS system via Synthetic Arrays

Master thesis defence

• Monday, 26 August 2019
• 11:00

Multiple-input Multiple-output Grating Lobe Selection Scheme for Radar Applications

MSc SS Thesis Presentation

• Thursday, 22 August 2019
• 14:00-14:45
• EWI HB 17.150

Calibration of single element 3D ultrasound with a aberration mask

Previous work has demonstrated the possibility of high-resolution 3D ultrasound imaging through the use of a single element and an aberration mask. This thesis will expand on the previous work by examining the proposed method for errors in the creation of the model.

The analysis is performed by examining the various aspects of the measurements setup and underlying theoretical model, after which measurements are performed to determine their contribution and correctness with regard to the model. Results demonstrated a systematic error of a non-linear frequency scaling and semi-linear phase shift. The origin of the error lies in the unwanted addition of transfer functions of some of the components. A Tikhonov regularized least squares method is proposed to estimate this transfer function and supply compensation based on all the measurements.

The results of the application of this method on the uncalibrated model are demonstrated through 1D imaging experiments. The result of which shows a significant improvement over the previous uncalibrated results. After which the possibility of calibration due to a singular measurement is explored and an adaptation of the Tikhonov regularized least squares method is proposed for a close approximation of the previously found transfer function. Further to obtain an indication of possible remaining hurdles and successes with this method, extensive simulations are performed to examine the individual impact of various sources of noise and interference.

MSc TC Thesis Presentation