18,724 research outputs found
Signal representation for compression and noise reduction through frame-based wavelets
No full textPublished versio
Signal Processing for Radio Astronomy
No full textRadio astronomy is known for its very large telescope dishes but is currently making a transition towards the use of a large number of small antennas. For example, the Low Frequency Array, commissioned in 2010, uses about 50 stations each consisting of 96 low band antennas and 768 or 1536 high band antennas. The low-frequency receiving system for the future Square Kilometre Array is envisaged to initially consist of over 131,000 receiving elements and to be expanded later. These instruments pose interesting array signal processing challenges. To present some aspects, we start by describing how the measured correlation data is traditionally converted into an image, and translate this into an array signal processing framework. This paves the way to describe self-calibration and image reconstruction as estimation problems. Self-calibration of the instrument is required to handle instrumental effects such as the unknown, possibly direction dependent, response of the receiving elements, as well a unknown propagation conditions through the Earth’s troposphere and ionosphere. Array signal processing techniques seem well suited to handle these challenges. Interestingly, image reconstruction, calibration and interference mitigation are often intertwined in radio astronomy, turning this into an area with very challenging signal processing problems.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
IEEE Signal Processing Society: Celebrating 75 Years of Remarkable Achievements (Part 2) [From the Guest Editors]
No full textSignal Processing System
Adaptive digital signal processing Java teaching tool
No full textThis publication presents a JAVA program for teaching the rudiments of adaptive digital signal processing (DSP) algorithms and techniques. Adaptive DSP is on of the most important areas of signal processsing, and provides the core algorithmic means to implement applications ranging from mobile telephone speech coding, to noise cancellation, to communication channel equalization. Over the last 30 years adaptive digital signal processing has progressed from being a strictly graduate level advanced class in signal processing theory to a topic that is part of the core curriculum for many undergraduate signal processing classes. The JAVA applet presented in this publication has been devised for students to use in combination with lecture notes and/or one of the recognised textbooks such that they can quickly and conveniently simulate algorithms such as the LMS (least mean squares), RLS (recursive least squares) and so on in a variety of applications without requiring to write programs or scripts or using any special purpose software. By the very nature of the JAVA code therefore, the applet can be run from any browser, even over a low bandwidth modem connection
Learning Expanding Graphs for Signal Interpolation
No full textPerforming signal processing over graphs requires knowledge of the underlying fixed topology. However, graphs often grow in size with new nodes appearing over time, whose connectivity is typically unknown; hence, making more challenging the downstream tasks in applications like cold start recommendation. We address such a challenge for signal interpolation at the incoming nodes blind to the topological connectivity of the specific node. Specifically, we propose a stochastic attachment model for incoming nodes parameterized by the attachment probabilities and edge weights. We estimate these parameters in a data-driven fashion by relying only on the attachment behaviour of earlier incoming nodes with the goal of interpolating the signal value. We study the non-convexity of the problem at hand, derive conditions when it can be marginally convexified, and propose an alternating projected descent approach between estimating the attachment probabilities and the edge weights. Numerical experiments with synthetic and real data dealing in cold start collaborative filtering corroborate our findings.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Multimedia Computin
Subset Selection for Kernel-Based Signal Reconstruction
No full textIn this work, we introduce subset selection strategies for signal reconstruction based on kernel methods, particularly for the case of kernel-ridge regression. Typically, these methods are employed for exploiting known prior information about the structure of the signal of interest. We use the mean squared error and a scalar function of the covariance matrix of the kernel regressors to establish metrics for the subset selection problem. Despite the NP-hard nature of the problem, we introduce efficient algorithms for finding approximate solutions for the proposed metrics. Finally, numerical experiments demonstrate the applicability of the proposed strategies.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
When the client is not the abuser, but one of the abused: Commentary on Lachance on breaking silence
No full textSignal, TD ORCiD: 0000-0001-5677-9496The question of client confidentiality and reporting animal abuse is complicated when the client is not the abuser, and when the abuse (of both people and animals) may escalate precisely because it has been (or may be) reported
Highlights from the Signal Processing Theory and Methods Technical Committee [In the Spotlight]
No full textReports on the activities of the Signal Processing Theory and Methods Technical Committee.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
Teaching Signal Processing to the Medical Profession
No full textKnowledge of signal processing is very important for medical students. A medical signal may be used for monitoring, constructing an image, or for extracting the numerical quantity of a parameter. This information forms a basis for medical decisions. However, the processing of the signal may lead to distortion and an incorrect interpretation. The present article describes an educational practical for first year medical students. It uses the electrocardiogram, which can be obtained easily, as a convenient example of a medical signal. The practical was developed at the VU University Amsterdam and summarizes the elementary concepts of signal processing
Computational Array Signal Processing via Modulo Non-Linearities
No full textConventional literature on array signal processing (ASP) is based on the "capture first, process" later philosophy and to this end, signal processing algorithms are typically decoupled from the hardware. This poses fundamental limitations because if the sensors result in information loss, the algorithms may no longer be able to achieve their guaranteed performance. In this paper, our goal is to overcome the barrier of information loss via sensor saturation and clipping. This is a significant problem in application areas including physiological monitoring and extra-terrestrial exploration where the amplitudes may be unknown or larger than the dynamic range of the sensor. To overcome this fundamental bottleneck, we propose "computational arrays" which are based on a co-design approach so that a collaboration between the sensor array hardware and algorithms can be harnessed. Our work is inspired by the recently introduced unlimited sensing framework. In this context, our computational arrays encode the high-dynamic-range information by folding the signal amplitudes, thus introducing a new form of information loss in terms of the modulo measurements. On the decoding front, we develop mathematically guaranteed recovery algorithms for spatio-temporal array signal processing tasks that include DoA estimation, beamforming and signal reconstruction. Numerical examples corroborate the applicability of our approach and pave a path for the development of novel computational arrays for ASP.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Signal Processing System
- …
