Nowadays high speed signal processing has become the only alternative in modern communication system, given the rapidly growing microelectronics technology. This high speed, real time signal processing depends critically both on the parallel algorithms and on parallel processor technology. Special purpose array processor structures will have become the real possibility for high speed signal pro...

Electrostatic sensor arrays (ESAs) are promising in industrial applications related to charged particle monitoring. Sensitivity is a fundamental and commonly-used sensing characteristic of an ESA. However, the usually used spatial sensitivity, which is called static sensitivity here, is not proper for moving particles or capable of reflecting array signal processing algorithms integrated in an ...

One of the research areas of the Signal Processing group at the Eindhoven University of Technology is in adaptive array signal processing. It is concerned with the application of adaptive signal processing techniques to the outputs of a spatially distributed array of sensors. The power of these techniques lies in the possibility of achieving improvements in directivity and measured Signalto-Noi...

The compressive sensing method is an effective way of under-sampling array signal processing, and the measurement matrix a key technology which decisive significance to improve performance processing. In this paper, processing based on improved Hadamard was proposed. This construction by compressing zero capable reducing redundant data, enhancing non-correlation between columns, improving RIP c...

Tuberculosis (TB) has long been recognized as a significant health concern worldwide. Recent advancements in noninvasive wearable devices and machine learning (ML) techniques have enabled rapid cost-effective testing for the real-time detection of TB. However, small datasets are often encountered biomedical chemical engineering domains, which can hinder success ML models result overfitting issu...

The multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. Even though MMV problems have been traditionally addressed within the context of sensor array signal processing, the recent trend is to apply compressive sensing (CS) due to its capability to estimate sparse support even with an insufficient number of snapshots, ...