Sparse representation-based human detection: a scale-embedded dictionary approach

Human detection is a complex problem owing to the variable pose that they can adopt. Here, we address this problem in sparse representation framework with an overcomplete scale-embedded dictionary. Histogram of oriented gradient features extracted from the candidate image patches are sparsely represented by the dictionary that contain positive bases along with negative and trivial bases. The object is detected based on the proposed likelihood measure obtained from the distribution of these sparse coefficients. The likelihood is obtained as the ratio of contribution of positive bases to negative and trivial bases. The positive bases of the dictionary represent the object (human) at various scales. This enables us to detect the object at any scale in one shot and avoids multiple scanning at different scales. This significantly reduces the computational complexity of detection task. In addition to human detection, it also finds the scale at which the human is detected due to the scale-embedded structure of the dictionary.

On the Geometrically Dependent Quasi-Saturation and g(m) Reduction in Advanced DeMOS Transistors

This paper reveals an early quasi-saturation (QS) effect attributed to the geometrical parameters in shallow trench isolation-type drain-extended MOS (STI-DeMOS) transistors in advanced CMOS technologies. The quasi-saturation effect leads to serious g(m) reduction in STI-DeMOS. This paper investigates the nonlinear resistive behavior of the drain-extended region and its impact on the particular behavior of the STI-DeMOS transistor. In difference to vertical DMOS or lateral DMOS structures, STI-DeMOS exhibits three distinct regions of the drain extension. A complete understanding of the physics in these regions and their impact on the QS behavior are developed in this paper. An optimization strategy is shown for an improved g(m) device in a state-of-the-art 28-nm CMOS technology node.