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Robust optimization (RO) can be applied to location problems under uncertainty. In this paper, we present a robust optimization model for stochastic multi-objective operation of capacitated P-hub location problems (MCpHLP-s). Most existing approaches to p-hub location problems are restricted to deterministic environments. However, the volume of demand in(More)
Recently, sparse representation based methods have proven to be successful towards solving image restoration problems. The objective of these methods is to use sparsity prior of the underlying signal in terms of some dictionary and achieve optimal performance in terms of mean-squared error, a metric that has been widely criticized in the literature due to(More)
The problem of reconstruction of digital images from their blurred and noisy measurements is unarguably one of the central problems in imaging sciences. Despite its ill-posed nature, this problem can often be solved in a unique and stable manner, provided appropriate assumptions on the nature of the images to be recovered. In this paper, however, a more(More)
There has been an increasing number of image super-resolution (SR) algorithms proposed recently to create images with higher spatial resolution from low-resolution (LR) images. Nevertheless, how to evaluate the performance of such SR and interpolation algorithms remains an open problem. Subjective assessment methods are useful and reliable, but are(More)
In this paper, we look at making backscatter practical for ultra-low power on-body sensors by leveraging radios on existing smartphones and wearables (e.g. WiFi and Bluetooth). The difficulty lies in the fact that in order to extract the weak backscattered signal, the system needs to deal with self-interference from the wireless carrier (WiFi or Bluetooth)(More)
Reconstructing a diffusion field from spatiotemporal measurements is an important problem in engineering and physics with applications in temperature flow, pollution dispersion, and disease epidemic dynamics. In such applications, sensor networks are used as spatiotemporal sampling devices and a relatively large number of spatiotemporal measurements may be(More)
—In this paper, the Two-Way Channel (TWC) with Cannel State Information (CSI) is investigated. First, an achievable rate region is derived for the discrete memoryless channel. Then by extending the result to the Gaussian TWC with additive interference noise, it is shown that the capacity region of the later channel is the same as the capacity when there is(More)
Mixture of Experts (ME) is a modular neural network architecture for supervised learning. In this paper, we propose an evidence-based ME to deal with the classification problem. In the basic form of ME the problem space is automatically divided into several subspaces for the experts and the outputs of experts are combined by a gating network. Satisfactory(More)
Surface reconstruction from measurements of spatial gradient is an important computer vision problem with applications in photometric stereo and shape-from-shading. In the case of morphologically complex surfaces observed in the presence of shadowing and transparency artifacts, a relatively large dense gradient measurements may be required for accurate(More)
Image interpolation techniques that create high-resolution images from low-resolution (LR) images are widely used in real world applications, but how to evaluate the quality of interpolated images is not a well-resolved issue. Subjective assessment methods are useful and reliable, but are also slow and expensive. Here, we propose an objective method to(More)