background and objectives: hospitals usually generate large amounts of hazardous waste. if not managed properly, hospital waste can pose threats to public safety and damage the environment. waste minimization is a waste management approach that focuses on reducing the amount and toxicity of hazardous wastes. this study pursues a two-fold purpose: 1) to gain insight into hospital waste minimizat...

We introduce a novel approach for online facial components tracking based on energy minimization criterion. The tracker, known as EMoTracker, employs template matching as the principal technique. As feature appearance changes during tracking, template matching suffers in providing good detection results. Therefore, instead of utilizing only the similarity (correlation values) independently, we ...

A new set of algorithms for transform adaptation in adaptive transform coding is presented. These algorithms are inspired by standard techniques in adaptive nite impulse response (FIR) Wiener ltering and demonstrate that similar algorithms with simple updates exist for tracking principal components (eigenvectors of a correlation matrix). For coding an N-dimensional source, the transform adaptat...

– The design process for analog network design is formulated as a dynamic controllable system. A special control vector is defined to redistribute the compute expensive between a network analysis and a parametric optimization. This redistribution permits the minimization of a computer time. The problem of the minimal-time network design can be formulated in this case as a classical problem of t...

One of the most important problems in dynamic systems theory is to approximate a higher-order system model with a low-order, relatively simpler model. However, the nominal high-order model is never an exact representation of the true physical system. In this paper the problem of approximating an uncertain high-order system with constant real parameter uncertainty by a robust reduced-order model...

Tensor robust principal component analysis (TRPCA) has received a substantial amount of attention in various fields. Most existing methods, normally relying on tensor nuclear norm minimization, need to pay an expensive computational cost due to multiple singular value decompositions (SVDs) at each iteration. To overcome the drawback, we propose a scalable and efficient method, named Parallel Ac...

There is a growing need in computer vision applications for stereopsis, requiring not only accurate distance but also fast and compact physical implementation. Global energy minimization techniques provide remarkably precise results. But they suffer from huge computational complexity. One of the main challenges is to parallelize the iterative computation, solving the memory access problem betwe...

This paper is concerned with an indefinite weight linear eigenvalue problem in cylindrical domains. We investigate the minimization of the positive principal eigenvalue under the constraint that the weight is bounded by a positive and a negative constant and the total weight is a fixed negative constant. Biologically, this minimization problem is motivated by the question of determining the opt...

In this work, we have explored several subspace reconstruction methods for facial ethnic appearance synthesis (FEAS). In our experiments, our proposed dual subspace modeling using the Fukunaga Koontz transform (FKT) yields much better facial ethnic synthesis results than the `1 minimization, the `2 minimization and the principal component analysis (PCA) reconstruction method. With that, we are ...

In this article, we explore the concept of minimization of information loss (MIL) as a a target for neural network learning. We relate MIL to supervised and unsupervised learning procedures such as the Bayesian maximum a-posteriori (MAP) discriminator, minimization of distortion measures such as mean squared error (MSE) and cross-entropy (CE), and principal component analysis (PCA). To deal wit...