University of Baltimore

About: University of Baltimore is a education organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Population & Supreme court. The organization has 2339 authors who have published 4197 publications receiving 105944 citations. The organization is also known as: UB.

Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery

Abstract: Linear spectral mixture analysis (LSMA) is a widely used technique in remote sensing to estimate abundance fractions of materials present in an image pixel. In order for an LSMA-based estimator to produce accurate amounts of material abundance, it generally requires two constraints imposed on the linear mixture model used in LSMA, which are the abundance sum-to-one constraint and the abundance nonnegativity constraint. The first constraint requires the sum of the abundance fractions of materials present in an image pixel to be one and the second imposes a constraint that these abundance fractions be nonnegative. While the first constraint is easy to deal with, the second constraint is difficult to implement since it results in a set of inequalities and can only be solved by numerical methods. Consequently, most LSMA-based methods are unconstrained and produce solutions that do not necessarily reflect the true abundance fractions of materials. In this case, they can only be used for the purposes of material detection, discrimination, and classification, but not for material quantification. The authors present a fully constrained least squares (FCLS) linear spectral mixture analysis method for material quantification. Since no closed form can be derived for this method, an efficient algorithm is developed to yield optimal solutions. In order to further apply the designed algorithm to unknown image scenes, an unsupervised least squares error (LSE)-based method is also proposed to extend the FCLS method in an unsupervised manner.

Sustainable Operations Management

Abstract: Operations management researchers and practitioners face new challenges in integrating issues of sustainability with their traditional areas of interest. During the past 20 years, there has been growing pressure on businesses to pay more attention to the environmental and resource consequences of the products and services they offer and the processes they deploy. One symptom of this pressure is the movement towards triple bottom line reporting (3BL) concerning the relationship of profit, people and the planet. The resulting challenges include integrating environmental, health, and safety concerns with green product design, lean and green operations, and closed-loop supply chains. We review these and other 'sustainability' themes covered in the first 50 issues of Production and Operations Management and conclude with some thoughts on future research challenges in sustainable operations management.

Qualitative methods in empirical studies of software engineering

Abstract: While empirical studies in software engineering are beginning to gain recognition in the research community, this subarea is also entering a new level of maturity by beginning to address the human aspects of software development. This added focus has added a new layer of complexity to an already challenging area of research. Along with new research questions, new research methods are needed to study nontechnical aspects of software engineering. In many other disciplines, qualitative research methods have been developed and are commonly used to handle the complexity of issues involving human behaviour. The paper presents several qualitative methods for data collection and analysis and describes them in terms of how they might be incorporated into empirical studies of software engineering, in particular how they might be combined with quantitative methods. To illustrate this use of qualitative methods, examples from real software engineering studies are used throughout.