The aim of this Ph.D. project is to investigate the impact of selected RF impairments on the radar measurement and further develop monitoring functions for safety relevant parameters using sophisticated signal processing algorithms.
Phase Noise Monitoring in Automotive Radar Sensors
ECG Signal Analysis
Current investigations focus on the electrocardiogram (ECG) which is a well-established and easy to obtain physiological signal of remarkable diagnostic power. It provides a wide spectrum of information regarding a patient‘s condition. However, the clinically relevant information is often transient or masked by noise and therefore hard – if not even impossible – for the human observer to detect and interpret. In general, consistent interpretation of ECG phenomena is a difficult task due to inter-patient and inter-observer variability. This research aims to develop analysis tools that provide reliable parameters and predictors for distinct diseases, thereby supporting practicing clinicians in their daily business.
Medical Ultrasound Imaging
Ultrasound has the ability to penetrate deep into soft tissues maintaining a good spatial resolution. Despite the great advances in ultrasound imaging technology, there is still space for improvement in terms of image resolution enhancement. This research focuses on the algorithmic development of signal processing and system identification techniques that tackle the unwanted effects of noise originating either from the body itself or the ultrasound system, providing higher quality images with enhanced diagnostic value.
Ultrasound Gesture Recognition
With the advance of ultrasound transducer technology reducing sensor size, cost and power consumption while increasing bandwidth ultrasound based designs become a viable alternative in a range of new application areas. To promote research and explore different use cases of ultrasound technology is one of the main aspirations of the European Horizon 2020 project “Silense”. In this context, applications for the automotive market, indoor navigation and smart home environment, to name a few, are investigated. As part of the Silense consortium our research focus lies on the realization of a wearable gesture recognition system.
Airborne Ultrasound in Home Environments
As a result of a significant decrease in manufacturing costs during the last few years, the application of capacitive ultrasound technology is facilitated in smart home applications, where low cost and lean design are crucial. In this joint project with Infineon Technologies AG, we implement systems composed of small arrays of capacitive micromachined ultrasound transducers and investigate them in terms of spatial and temporal resolution, range, required hardware and energy consumption. A second focus in this project is the development of signal and image processing algorithms with respect to dedicated tasks in smart home environments. This includes in particular the development and improvement of object and person detection algorithms, people counting or gesture recognition. In addition to classical signal and image processing approaches, segmentation and pattern recognition techniques and also machine learning approaches are investigated.
Acoustic Beamforming and Event Detection
Microphone arrays can be used to enhance the speech quality and suppress ambient noise. Microphone arrays utilize the spatial information of the sound sources and focus on the direction of the desired source while suppressing sources from other directions. This is called beamforming. In this Ph.D. project we develop model based as well as deep learning based beamforming algorithms for voice assistance applications and investigate the influence of microphone and array imperfections, such as microphone self noise, complex frequency response mismatch between microphones in the array, and microphone displacement, on the performance of these algorithms.
Acoustic based Predictive Maintenance
The detection of faulty conditions in steel plants by characterization of the emitted sound is a versatile tool with usually small installation effort. The advantages of acoustic condition monitoring with localization features range from condition-based maintenance to early diagnosis of unexpected behavior of the plant and thus to an improvement of the economic efficiency. This Ph.D. project focuses on acoustic condition monitoring by analyzing patterns of sound signals in the time-frequency domain in combination with machine and deep learning based classification or regression methods.
Sparsity Aware Signal Processing
Sparse signal processing is a rapidly growing field that has attracted considerable attention in the scientific community in the last years, especially in the field of compressed sensing. Compressed Sensing (CS) is a signal processing technique for efficiently acquiring and reconstructing sparse signals. The main goal of this Ph.D. project is to design and implement efficient novel reconstruction algorithms allowing for a reasonable performance/complexity trade-off.
Threshold Based Sampling
This project investigates the theory and applications of threshold based sampling in signal processing.
Unique Word OFDM
Orthogonal Frequency Division Multiplexing (OFDM) is currently the dominating digital transmission technique in most of the modern broadband wireless and wired communication systems. One of the drawbacks of classical OFDM is that up to 20% of the transmit energy is wasted for so called guard intervals (GIs), which are placed between successive OFDM symbols to cope with the effects of multipath propagation. Unique Word OFDM is a novel signaling scheme that promises to overcome this flaw.
ACCM - Wireless Transceiver Technology
The demand for rapidly growing mobile data rates has posed unprecedented challenges to the wireless industry. Carrier Aggregation (CA) is one of the key enablers to offer peak data rates that is required for 4G LTE-A RF transceivers. However, implementing this feature will give raise to several new issues. The self-interference problem due to modulated spurs is the significant obstacle to enable a fully functional CA. This project aims to model and investigate the modulated spur problem, and to develop an all digital cancellation mechanisms using adaptive signal processing algorithms.
Spurs Mitigation in Digital-to-Time Converters
A Digital-to-Time Converter (DTC) converts a digital code into a time shift of a clock edge, which can be used in transceivers for frequency synthesis or phase modulation of a carrier. DTC-based transceiver architectures offer many advantages such as wide frequency coverage or high frequency resolution, but are also prone to generate spurious tones (spurs). Therefore, the goal of this project is to develop sophisticated spurs cancellation techniques.
Radar Signal Processing
Short-Range Leakage Cancellation in Automotive Radar Systems
Today‘s cars are equipped with radar sensors, which provide precise information about the distance, speed and angle to surrounding objects on the road. For reasons of car appearance as well as protection of the device itself, the radar sensors are often mounted right behind the bumper. This, however, causes unwanted signal reflections from such. Particularly, the reflections yield so-called short-range (SR) leakage, which superimposes reflections of true objects that have to be detected most precisely. In this Ph.D. project, novel concepts that aim to cancel the SR leakage in the automotive application have been proposed.
Ultrasound and Acoustic Signal Processing
Photoacoustic and thermographic non-destructive testing
Photoacoustic imaging, also called optoacoustic or thermoacoustic imaging, is based on the generation of ultrasound following a temperature rise after illumination of light absorbing structures with-in a (semi)transparent and turbid material, such as a biological tissue. In photoacoustic tomography the temporal evolution of the acoustic pressure field is sampled using an array of ultrasound detectors. Images of the optical absorption within the tissue are then reconstructed by solving an inverse source problem. The main goal of this research is to improve the reconstruction accuracy by using state space models and optimization of the excitation laser signals.
Battery and Power Management
Highly Efficient Wireless Power Transfer
The research focus of this Ph.D. project is the modelling, control and optimization of wireless power transfer systems in the low to medium power range up to 15W, which is perfectly suited for charging devices like smartphones and tablet computers.
Advanced Control and System Identification Techniques for Multi-Phase DC-DC Converters
The increasing amount of required functionality in today’s electronic devices demands for more advanced power supply concepts. A possible solution to increase the performance and efficiency of DC-DC converters is to use multi-phase topologies. In this project, a novel digital constant frequency sliding mode control (SMC) law for interleaved multi-phase DC-DC converters is developed.
Digital Compensation of Supply Voltage Variations for Switched-Capacitor Power Amplifiers (SCPA)
This project focuses on digital compensation techniques of power supply distortions for a Switched-Capacitor Power Amplifier (SCPA) in mobile transmitters. The SCPA is a configurable capacitive voltage divider, combining the functionality of a digital-to-analog converter (DAC), a mixer, and a power amplifier (PA). Variations on the supply of the SCPA cause additional mixing products with the input signal and create undesired harmonics in the output signal, increasing the adjacent channel leakage ratio (ACLR). This work investigates fully digital compensation techniques to maintain the system’s linearity while at the same time increase the efficiency by omitting additional required circuitry to suppress supply voltage variations.
Low Complexity Iterative Signal Processing Methods
The goal of this project is to develop low complexity iterative signal processing methods, mainly for estimation. The focus lies on developing low complexity algorithms for hardware implementation, as well as formulating the theoretical framework behind such algorithms.
Knowledge-Aided Methods in Estimation Theory and Adaptive Filtering
In this project, novel classical estimation concepts as well as interesting advances in Bayesian estimation were analyzed in-depth. Finally, novel adaptive filters were developed, also allowing the incorporation of additional model knowledge.