École Polytechnique de Montréal

About: École Polytechnique de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Finite element method & Computer science. The organization has 8015 authors who have published 18390 publications receiving 494372 citations.

DeMIMA: A Multilayered Approach for Design Pattern Identification

Abstract: Design patterns are important in object-oriented programming because they offer design motifs, elegant solutions to recurrent design problems, which improve the quality of software systems. Design motifs facilitate system maintenance by helping to understand design and implementation. However, after implementation, design motifs are spread throughout the source code and are thus not directly available to maintainers. We present DeMIMA, an approach to identify semi-automatically micro-architectures that are similar to design motifs in source code and to ensure the traceability of these micro-architectures between implementation and design. DeMIMA consists of three layers: two layers to recover an abstract model of the source code, including binary class relationships, and a third layer to identify design patterns in the abstract model. We apply DeMIMA to five open-source systems and, on average, we observe 34% precision for the considered 12 design motifs. Through the use of explanation-based constraint programming, DeMIMA ensures 100% recall on the five systems. We also apply DeMIMA on 33 industrial components.

Analysis of squat and stoop dynamic liftings: muscle forces and internal spinal loads

Abstract: Despite the well-recognized role of lifting in back injuries, the relative biomechanical merits of squat versus stoop lifting remain controversial. In vivo kinematics measurements and model studies are combined to estimate trunk muscle forces and internal spinal loads under dynamic squat and stoop lifts with and without load in hands. Measurements were performed on healthy subjects to collect segmental rotations during lifts needed as input data in subsequent model studies. The model accounted for nonlinear properties of the ligamentous spine, wrapping of thoracic extensor muscles to take curved paths in flexion and trunk dynamic characteristics (inertia and damping) while subject to measured kinematics and gravity/external loads. A dynamic kinematics-driven approach was employed accounting for the spinal synergy by simultaneous consideration of passive structures and muscle forces under given posture and loads. Results satisfied kinematics and dynamic equilibrium conditions at all levels and directions. Net moments, muscle forces at different levels, passive (muscle or ligamentous) forces and internal compression/shear forces were larger in stoop lifts than in squat ones. These were due to significantly larger thorax, lumbar and pelvis rotations in stoop lifts. For the relatively slow lifting tasks performed in this study with the lowering and lifting phases each lasting ∼2 s, the effect of inertia and damping was not, in general, important. Moreover, posterior shift in the position of the external load in stoop lift reaching the same lever arm with respect to the S1 as that in squat lift did not influence the conclusion of this study on the merits of squat lifts over stoop ones. Results, for the tasks considered, advocate squat lifting over stoop lifting as the technique of choice in reducing net moments, muscle forces and internal spinal loads (i.e., moment, compression and shear force).

Improving background subtraction using Local Binary Similarity Patterns

Abstract: Most of the recently published background subtraction methods can still be classified as pixel-based, as most of their analysis is still only done using pixel-by-pixel comparisons. Few others might be regarded as spatial-based (or even spatiotemporal-based) methods, as they take into account the neighborhood of each analyzed pixel. Although the latter types can be viewed as improvements in many cases, most of the methods that have been proposed so far suffer in complexity, processing speed, and/or versatility when compared to their simpler pixel-based counterparts. In this paper, we present an adaptive background subtraction method, derived from the low-cost and highly efficient ViBe method, which uses a spatiotemporal binary similarity descriptor instead of simply relying on pixel intensities as its core component. We then test this method on multiple video sequences and show that by only replacing the core component of a pixel-based method it is possible to dramatically improve its overall performance while keeping memory usage, complexity and speed at acceptable levels for online applications.