Fixed Resource Constraints Research Articles

Keeping Score: Predictive Analytics in Policing

Predictive analytics in policing is a data-driven approach to ( a) characterizing crime patterns across time and space and ( b) leveraging this knowledge for the prevention of crime and disorder. This article outlines the current state of the field, providing a review of forecasting tools that have been successfully applied by police to the task of crime prediction. We then discuss options for structured design and evaluation of a predictive policing program so that the benefits of proactive intervention efforts are maximized given fixed resource constraints. We highlight examples of predictive policing programs that have been implemented and evaluated by police agencies in the field. Finally, we discuss ethical issues related to predictive analytics in policing and suggest approaches for minimizing potential harm to vulnerable communities while providing an equitable distribution of the benefits of crime prevention across populations within police jurisdiction.

Monte Carlo analysis of voxel resolution of off-axially distributed image sensing system

In this paper, we present a generalization framework to analyze the effect of voxel resolution on Monte Carlo simulation for off-axially distributed image sensing (ODIS) system under fixed resource constraints. Our framework can evaluate the performance of ODIS systems based on various sensing parameters such as the slanted angle between the optical axis and the moving direction, the number of cameras, the pixel size, the distance between the optical axis and the point source plane and so on. We carry out Monte Carlo simulations based on this framework to evaluate ODIS system performance as a function of sensing parameters. To the best of our knowledge, this is the first report on quantitative analysis of ODIS systems under fixed resource constraints.

Depth Resolution Analysis of Axially Distributed Stereo Camera Systems under Fixed Constrained Resources

In this paper, we propose a novel framework to evaluate the depth resolution of axially distributed stereo sensing (ADSS) under fixed resource constraints. The proposed framework can evaluate the performance of ADSS systems based on various sensing parameters such as the number of cameras, the number of total pixels, pixel size and so on. The Monte Carlo simulations for the proposed framework are performed and the evaluation results are presented.

Resolution Analysis of Axially Distributed Image Sensing Systems under Equally Constrained Resources

In this paper, a unifying framework to evaluate the depth resolution of axially distributed image sensing (ADS) systems under fixed resource constraints is proposed. The proposed framework enables one to evaluate the system performance as a function of system parameters such as the number of cameras, the number of pixels, pixel size, and so on. The Monte Carlo simulations are carried out to evaluate ADS system performance as a function of system parameters. To the best of our knowledge, this is the first report on quantitative analysis of ADS systems under fixed resource constraints.

Optimization of 3D Integral Imaging System Parameters

Integral imaging is a promising technology for 3D imaging and display. A number of parameters affect the performance of 3D integral imaging systems. Improved system performance is obtained by optimization of these parameters with respect to defined performance metrics. In this paper, we present an approach to optimize the performance of 3D integral imaging system in terms of performance metrics under fixed resource constraints. In this analysis, system parameters such as lens numerical aperture, pitch between image sensors, the number of image sensors, the pixel size, and the number of pixels are selected to optimize performance metrics. Wave optics is utilized to describe the imaging process.

Generalization of three-dimensional N-ocular imaging systems under fixed resource constraints

The performance of multiview three-dimensional imaging systems depends on several factors, including the number of sensors, sensor pixel size, relative sensor configuration, imaging optics, and computational reconstruction algorithm. Therefore, it is important to compare the performance of such systems under equally constrained resources. In this Letter, we develop a unifying framework to evaluate the lateral and axial resolution of N-ocular imaging systems ranging from stereo (two cameras) to multiple sensors (integral imaging) under fixed resource constraints. The proposed framework enables one to evaluate the system performance as a function of sensing parameters such as the number of cameras, the number of pixels, parallax, pixel size, lens aperture, and focal length. We carry out Monte Carlo simulations based on this framework to evaluate system performance as a function of sensing parameters. To the best of our knowledge, this is the first report on quantitative analysis of N-ocular imaging systems under common resource constraints.

Heuristics for the minimum project-duration problem with minimal and maximal time lags under fixed resource constraints

The authors consider the problem of minimizing the duration of a project under fixed resource constraints. For the case that there are only minimal time lags between the start of successive activities and that therefore the project can be described by an acyclic network, a large number of exact and heuristic algorithms can be found in the literature. In this paper, the authors permit both minimal and maximal time lags between activities. The project can then be modelled by an activityon-node network containing cycles. Efficient priority-rule heuristics are presented for solving the resource-constrained project-scheduling problem. Computational results are discussed for projects containing up to 1000 activities and several resources.

Information Decision Systems in Higher Education

The challenge facing higher education is to maintain and strengthen the quality of its programs and faculty while meeting anticipated educational needs and requirements of students, society, and scholarship within relatively fixed resource constraints. Effective information decision systems are increasingly crucial in enabling higher educational institutions to meet this challenge successfully. The purpose of this paper is to provide a conceptual framework for describing and understanding the nature and role of information systems in higher educational decision-making.