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Section: Publications & Resources
This article presents an adaptive multi-sensing (MS) framework for a network of densely deployed solar energy harvesting wireless nodes. Each node is mounted with heterogeneous sensors to sense multiple cross-correlated slowly-varying parameters/signals.
Section: Publications & Resources
This article presents limited feedback-based precoder quantization schemes for Interference Alignment (IA) with bounded channel state information (CSI) uncertainty. Initially, this work generalizes the min-max mean squared error (MSE) framework, followed by the development of robust precoder and decoder designs based on worst case MSE minimization.
Section: Publications & Resources
Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Distributed data clustering in sensor networks is receiving increasing attention with the development of network technology. A variety of algorithms for distributed data clustering have been proposed recently. However, most of these algorithms have trouble with either non-Gaussian shaped data clustering or model order selection problem.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
This paper proposes a novel algorithm to determine the optimal orientation of sensing axes of redundant inertial sensors such as accelerometers and gyroscopes (gyros) for increasing the sensing accuracy. In this paper, we have proposed a novel iterative algorithm to find the optimal sensor configuration.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
In this paper, we study the problem of compressed sensing using binary measurement matrices and ℓ1 -norm minimization (basis pursuit) as the recovery algorithm. We derive new upper and lower bounds on the number of measurements to achieve robust sparse recovery with binary matrices.
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
DUAL-energy computed tomography (DECT) differentiates materials by exploiting the varying material linear attenuation coefficients (LACs) for different x-ray energy spectra. Multi-material decomposition (MMD) is a particularly attractive DECT clinical application to distinguish the complicated material components within the human body.
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
The intrinsically limited spatial resolution of positron emission tomography (PET) confounds image quantitation. This paper presents an image deblurring and super-resolution framework for PET using anatomical guidance provided by high-resolution magnetic resonance (MR) images. The framework relies on image-domain postprocessing of already-reconstructed PET images by means of spatially variant deconvolution stabilized by an MR-based joint entropy penalty function.
Section: Publications & Resources
Publication: IEEE Transactions on Computational Imaging
The image blurring that results from moving a camera with the shutter open is normally regarded as undesirable. However, the blurring of the images encapsulates information that can be extracted to recover the light rays present within the scene. Given the correct recovery of the light rays that resulted in a blurred image, it is possible to reconstruct images...
Section: Publications & Resources
Publication: IEEE Signal Processing Letters
We consider the problem of detecting an unknown signal that lies in a union of subspaces (UoS) and that is observed in additive white Gaussian noise with unknown variance. The main contribution of this letter is the derivation of a detector that can accommodate a union made of nested subspaces. This detector includes the generalized likelihood ratio test (GLRT) as a special case when the subspace dimensions are all identical. It relies on the framework of multifamily likelihood ratio tests (MFLRT) and is shown by numerical examples to achieve better performance than existing detectors.
