1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.
The school of Electrical, Information and Media Engineering,
Institute for High Frequency & Communication Technology (Head: Prof. Dr. Ullrich Pfeiffer), invites applications for a position as:
Research Assistant in the Field of 1-bit 3D Imaging
This position is to be filled as soon as possible for 3 years.
The school of Electrical, Information and Media Engineering,
Institute for High Frequency & Communication Technology (Head: Prof. Dr. Ullrich Pfeiffer), invites applications for
a position as
Research Assistant in the Field of Computational Time-of-Flight 3D Imaging
This position is to be filled as soon as possible for 3 years.
Date: 11 April 2024
Chapter: Oregon Chapter
Chapter Chair: Jinsub Kim
Title: Some Reflections on Distributed Optimization for Machine Learning: Beyond the Common Wisdom
Date: 22 February 2024
Time: 9:00 AM ET (New York Time)
Presenter(s): Dr. Abderrahim Halimi, Dr. Sandor Plosz,
Dr. Aurora Maccarone, Dr. Stephen McLaughlin,
Dr. Gerald S. Buller
Date: 5 March 2024
Chapter: Twin Cities Chapter
Chapter Chair: Tao Zhang
Title: Signal Processing and Deep Learning for Practical Active Noise Control
Date: 13 February 2024
Chapter: Switzerland Chapter
Chapter Chair: Thomas Mittelholzer
Topic: Digital Twins for Communications: How to create and use them
Date: 18-20 June 2024
Location: Karlshamn, Sweden
Date: 15-16 July 2024
Location: Niagara Falls, Canada
The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants. In this study, we propose an algorithm for computing graph autoregressive moving average (graph ARMA) processes based on learning the joint time-vertex power spectral density of the process from its incomplete realizations for the task of signal interpolation.
Conventional beamforming methods for intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs) typically entail the full channel state information (CSI). However, the computational cost of channel acquisition soars exponentially with the number of IRSs. To bypass this difficulty, we propose a novel strategy called blind beamforming that coordinates multiple IRSs by means of statistics without knowing CSI.
Algorithmic solutions for multi-object tracking (MOT) are a key enabler for applications in autonomous navigation and applied ocean sciences. State-of-the-art MOT methods fully rely on a statistical model and typically use preprocessed sensor data as measurements. In particular, measurements are produced by a detector that extracts potential object locations from the raw sensor data collected at discrete time steps. This preparatory processing step reduces data flow and computational complexity but may result in a loss of information.
This paper proposes an interpretable ensembled seizure detection procedure using electroencephalography (EEG) data, which integrates data driven features and clinical knowledge while being robust against artifacts interference.
This work explores the challenging problems of nonlinear dynamics, nonaffine structures, heterogeneous properties, and deception attack together and proposes a novel distributed model-free adaptive predictive control (DMFAPC) for multiple-input-multiple-output (MIMO) multi-agent systems (MASs). A dynamic linearization method is introduced to address the nonlinear heterogeneous dynamics which is transformed as the unknown parameters in the obtained linear data model.
Representation learning considering high-order relationships in data has recently shown to be advantageous in many applications. The construction of a meaningful hypergraph plays a crucial role in the success of hypergraph-based representation learning methods, which is particularly useful in hypergraph neural networks and hypergraph signal processing.