A new generalized stochastic Petri net modeling for energy-harvesting-wireless sensor network assessment

This paper proposes an energy-harvesting-aware model that aims to assess the performances of wireless sensor networks. Our model uses generalized stochastic Petri nets to define a sensor–neighbors relationship abstraction. The novelty of the proposed formulation is taking into account several real-life considerations such as battery-over breakdowns, unavailability of neighbors, retrial attempts, and sleeping mechanism in a single model. We use TimeNet tool to simulate the network behavior in order to evaluate its performance throughout different formulas after it had reached its steady state. Finally, we present a case study featuring the different solar energy recovery capabilities of the vast Algerian territory. The aim is to show with the presented model how to determine the kind of resources to be acquired in order to cope with the sensor deployment project requirements. The proposed model allows us to ensure that the battery energy level of sensors deployed in Algiers province for example is almost equal to 80% for 100 messages per day and (1 min/2 min) for (awakening time/sleeping time) ratio.     

Interactive design of 2D car profiles with aerodynamic feedback

The design of car shapes requires a delicate balance between aesthetic and performance. While fluid simulation provides the means to evaluate the aerodynamic performance of a given shape, its computational cost hinders its usage during the early explorative phases of design, when aesthetic is decided upon. We present an interactive system to assist designers in creating aerodynamic car profiles. Our system relies on a neural surrogate model to predict fluid flow around car shapes, providing fluid visualization and shape optimization feedback to designers as soon as they sketch a car profile. Compared to prior work that focused on time-averaged fluid flows, we describe how to train our model on instantaneous, synchronized observations extracted from multiple pre-computed simulations, such that we can visualize and optimize for dynamic flow features, such as vortices. Furthermore, we architectured our model to support gradient-based shape optimization within a learned latent space of car profiles. In addition to regularizing the optimization process, this latent space and an associated encoder-decoder allows us to input and output car profiles in a bitmap form, without any explicit parameterization of the car boundary. Finally, we designed our model to support pointwise queries of fluid properties around car shapes, allowing us to adapt computational cost to application needs. As an illustration, we only query our model along streamlines for flow visualization, we query it in the vicinity of the car for drag optimization, and we query it behind the car for vortex attenuation.     

Coupling Ferroelectric to colloidal Nanocrystals as a Generic Strategy to Engineer the Carrier Density Landscape

The design of infrared nanocrystals-based (NCs) photodiodes faces a major challenge related to the identification of barriers with a well-suited band alignment or strategy to finely control the carrier density. Here, this study explores a general complementary approach where the carrier density control is achieved by coupling an NC layer to a ferroelectric material. The up-and-down change in ferroelectric polarization directly impacts the NC electronic structure, resulting in the formation of a lateral pn junction. This effect is uncovered directly using nano X-ray photoemission spectroscopy, which shows a relative energy shift of 115 meV of the NC photoemission signal over the two different up- and down-polarized ferroelectric regions, a shift as large as the open circuit value obtained in the diode stack. The performance of this pn junction reveals enhanced responsivity and reduced noise that lead to a factor 40 increase in the detectivity value.