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World Endurance Championship (WEC) racing events are characterised by a relevant performance gap among competitors. The fastest vehicles category, consisting in hybrid vehicles, has to respect energy usage constraints set by the technical…
Co-optimization of both vehicle speed and gear position via model predictive control (MPC) has been shown to offer benefits for fuel-efficient autonomous driving. However, optimizing both the vehicle's continuous dynamics and discrete gear…
A framework for energy-efficient resource allocation in a single-user, amplify-and-forward relay-assisted MIMO system is devised in this paper. Previous results in this area have focused on rate maximization or sum power minimization…
Skeleton-based multi-entity action recognition is a challenging task aiming to identify interactive actions or group activities involving multiple diverse entities. Existing models for individuals often fall short in this task due to the…
Piezoelectric vibration energy harvesters have attracted much attention due to its potential to replace currently popular batteries and to provide an sustainable power sources. Many researchers have proposed ways to increase the performance…
This paper introduces a framework to systematically optimize the control and design of an electric vehicle transmission, connecting powertrain sizing studies to detailed gearbox design methods. To this end, we first create analytical models…
In this paper, we study the resource allocation algorithm design for energy-efficient simultaneous wireless information and power transfer (SWIPT) systems. The considered system comprises a transmitter, an information receiver, and multiple…
The performance of many machine learning models depends on their hyper-parameter settings. Bayesian Optimization has become a successful tool for hyper-parameter optimization of machine learning algorithms, which aims to identify optimal…
Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems,…
Robust estimators for linear regression require non-convex objective functions to shield against adverse affects of outliers. This non-convexity brings challenges, particularly when combined with penalization in high-dimensional settings.…
Semiconductor device models are essential to understand the charge transport in thin film transistors (TFTs). Using these TFT models to draw inference involves estimating parameters used to fit to the experimental data. These experimental…
This paper studies an online algorithm for an energy harvesting transmitter, where the transmission (completion) time is considered as the system performance. Unlike the existing online algorithms which more or less require the knowledge on…
Parallel applications often rely on work stealing schedulers in combination with fine-grained tasking to achieve high performance and scalability. However, reducing the total energy consumption in the context of work stealing runtimes is…
In this paper, we examine the internet of things system which is dedicated for smart cities, smart factory, and connected cars, etc. To support such systems in wide area with low power consumption, energy harvesting technology without wired…
In this paper, we develop optimal energy-bandwidth allocation algorithms in fading channels for multiple energy harvesting transmitters, each may communicate with multiple receivers via orthogonal channels. We first assume that the side…
This paper considers a multi-user simultaneous wireless information and power transfer (SWIPT) system with a non-linear energy harvesting model, in which a multi-antenna base station (BS) estimates the downlink channel state information…
We report in this paper the design, the optimization and the fabrication of an electret-based cantilever energy harvester. We develop the mechanical and the electrostatic equations of such a device and its implementation using Finite…
In the robotics literature, different knowledge transfer approaches have been proposed to leverage the experience from a source task or robot -- real or virtual -- to accelerate the learning process on a new task or robot. A commonly made…
Sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) and its variants, have been used extensively for motion planning. Control barrier functions (CBFs) have been recently proposed to synthesize controllers for…
Many existing covariate shift adaptation methods estimate sample weights given to loss values to mitigate the gap between the source and the target distribution. However, estimating the optimal weights typically involves computationally…