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Section: Publications & Resources

5G technology, with its promises of self-driving vehicles and immersive virtual reality, will be a data-hungry generation of wireless communications.

Section: Publications & Resources

The IEEE Signal Processing Society Malaysia Chapter has been selected as the recipient of the 2018 Chapter of the Year Award!

Section: Publications & Resources

Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things (IoT). As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by system architects and design engineers, and present a new set of design trade-offs.

Section: Publications & Resources

Improving the modeling and processing of nonstationary signals remains an important yet challenging problem. In the past, the most effective approach for processing these signals has been statistical modeling.

Section: Publications & Resources

This study investigates various aspects of multi-speaker interference and its impact on speaker recognition. Single-channel multi-speaker speech signals (aka co-channel speech) comprise a significant portion of speech processing data. Examples of co-channel signals are recordings from multiple speakers in meetings, conversations, debates, etc.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Extracting information from a signal exhibiting damped resonances is a challenging task in many practical cases due to the presence of noise and high attenuation. The interpretation of the signal relies on a model whose order (i.e., the number of resonances) is in general unknown.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset of features. A central machine is then responsible for learning the structure based on received messages from the other nodes. We present a set of communication-efficient strategies, which are theoretically proved to convey sufficient information for reliable learning of the structure.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

We generalize the 1-bit matrix completion problem to higher order tensors. Consider a rank- r order- d tensorT in RN ×⋯×RN  with bounded entries. We show that when r=O(1) , such a tensor can be estimated efficiently from only m=Or (Nd)  binary measurements. This shows that the sample complexity of recovering a low-rank tensor from 1-bit measurements of a subset of its entries is roughly the same as recovering it from unquantized measurements—a result that had been known only in the matrix case, i.e., when d=2.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

In this paper, four iterative algorithms for learning analysis operators are presented. They are built upon the same optimization principle underlying both Analysis K-SVD and Analysis SimCO. The forward and sequential analysis operator learning (AOL) algorithms are based on projected gradient descent with optimally chosen step size. The implicit AOL algorithm is inspired by the implicit Euler scheme for solving ordinary differential equations and does not require to choose a step size.

Advisor: Muhammad Ali Qureshi Email: ali.qureshi@iub.edu.pk Chair: Muhammad Usman Zafar Email: uzc007@gmail.com