LINEAR DISCRIMINANT RULES for HIGH-DIMENSIONAL CORRELATED DATA: ASYMPTOTIC and FINITE SAMPLE RESULTS

A new class of linear discrimination rules, designed for problems with many correlated variables, is proposed. This proposal tries to incorporate the most important patterns revealed by the empirical correlations and accurately approximate the optimal Bayes rule as the number of variables increases. In order to achieve this goal, the new rules rely on covariance matrix estimates derived from Gaussian factor models with small intrinsic dimensionality. Asymptotic results, based on a analysis that allows the number of variables to grow faster than the number of observations, show that the worst possible expected error rate of the proposed rules converges to the error of the optimal Bayes rule when the postulated model is true, and to a slightly larger constant when this model is a close approximation to the data generating process. Simulation results suggest that, in the data conditions they were designed for, the new rules can clearly outperform both Fisher's and naive linear discriminant rules.Discriminant Analysis, High Dimensionality, Expected Misclassification Rate, Min-Max Regret

Exact and heuristic algorithms for variable selection: Extended Leaps and Bounds

An implementation of enhanced versions of the classical Leaps and Bounds algorithm for variable selection is provided. Features of this implementation include: (i) The availability of general routines capable of handling many different statistical methodologies and comparison criteria. (ii) Routines designed for exact and heuristic searches. (iii) The possibility of dealing with problems with more variables than observations. The implementation is supplied in two different ways: i) as a C++ library with abstract classes that can be specialized to different problems and criteria. ii) as a console application ready to be applied to searches according to some of the most important comparison criteria proposed to date. The code of the C++ library and console application described here, can be freely obtained by sending an email to the author.Variable Selection Algorithms; All-Subsets; Heuristics

Wireless sensors and IoT platform for intelligent HVAC control

Energy consumption of buildings (residential and non-residential) represents approximately 40% of total world electricity consumption, with half of this energy consumed by HVAC systems. Model-Based Predictive Control (MBPC) is perhaps the technique most often proposed for HVAC control, since it offers an enormous potential for energy savings. Despite the large number of papers on this topic during the last few years, there are only a few reported applications of the use of MBPC for existing buildings, under normal occupancy conditions and, to the best of our knowledge, no commercial solution yet. A marketable solution has been recently presented by the authors, coined the IMBPC HVAC system. This paper describes the design, prototyping and validation of two components of this integrated system, the Self-Powered Wireless Sensors and the IOT platform developed. Results for the use of IMBPC in a real building under normal occupation demonstrate savings in the electricity bill while maintaining thermal comfort during the whole occupation schedule.QREN SIDT [38798]; Portuguese Foundation for Science & Technology, through IDMEC, under LAETA [ID/EMS/50022/2013

Diagnóstico histopatológico em estomatologia pediátrica: estudo retrospectivo de 43 anos de 1.480 casos de uma instituição brasileira

Trabalho de Conclusão de Curso (Graduação)An analytical, cross-sectional retrospective study was performed with biopsy files of patients ≤ 14 years of age from a Brazilian oral pathology laboratory over a 43-year period. Data included sex, age, location, and diagnoses. The prevalence was calculated by means of relative frequency. Associations between sex, age groups and diagnoses were verified with Pearson’s chi-square test

Efficient screening of variable subsets in multivariate statistical models

info:eu-repo/semantics/publishedVersio

Exact and heuristic algorithms for variable selection: Extended Leaps and Bounds

An implementation of enhanced versions of the classical Leaps and Bounds algorithm for variable selection is provided. Features of this implementation include: (i) The availability of general routines capable of handling many different statistical methodologies and comparison criteria. (ii) Routines designed for exact and heuristic searches. (iii) The possibility of dealing with problems with more variables than observations. The implementation is supplied in two different ways: i) as a C++ library with abstract classes that can be specialized to different problems and criteria. ii) as a console application ready to be applied to searches according to some of the most important comparison criteria proposed to date. The code of the C++ library and console application described here, can be freely obtained by sending an email to the authorinfo:eu-repo/semantics/publishedVersio

Efficient hardware design and implementation of the voting scheme-based convolution

Due to a point cloud’s sparse nature, a sparse convolution block design is necessary to deal with its particularities. Mechanisms adopted in computer vision have recently explored the advantages of data processing in more energy-efficient hardware, such as the FPGA, as a response to the need to run these algorithms on resource-constrained edge devices. However, implementing it in hardware has not been properly explored, resulting in a small number of studies aimed at analyzing the potential of sparse convolutions and their efficiency on resource-constrained hardware platforms. This article presents the design of a customizable hardware block for the voting convolution. We carried out an in-depth analysis to determine under which conditions the use of the voting scheme is justified instead of dense convolutions. The proposed hardware design achieves an energy consumption about 8.7 times lower than similar works in the literature by ignoring unnecessary arithmetic operations with null weights and leveraging data dependency. Access to data memory was also reduced to the minimum necessary, leading to improvements of around 55% in processing time. To evaluate both the performance and applicability of the proposed solution, the voting convolution was integrated into the well-known PointPillars model, where it achieves improvements between 23.05% and 80.44% without a significant effect on detection performance.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) (Project no. 037902; Funding Reference: POCI-01-0247-FEDER-037902)

DISCARDING VARIABLES in PRINCIPAL COMPONENT ANALYSIS : ALGORITHMS for ALL-SUBSETS COMPARISONS

The traditional approach to the interpretation of the results from a Principal Component Analysis implicitly discards variables that are weakly correlated with the most important and/or most interesting Principal Components. Some authors argue that this practice is potentially misleading and that it would be preferable to take a variable selection approach comparing variable subsets according to appropriate approximation criteria. In this paper, we propose algorithms for the comparison of all possible subsets according to some of the most important criteria proposed to date. The computational effort of the proposed algorithms is studied and it is shown that, given current computer technology, they are feasible for problems involving up to 30 variables. A software implementation is freely available on the internet.Principal Component Analysis, Principal Variables, Variable Selection, All-Subsets Algorithms.