On Energy Efficient Hierarchical Cross-Layer Design: Joint Power Control and Routing for Ad Hoc Networks

In this paper, a hierarchical cross-layer design approach is proposed to increase energy efficiency in ad hoc networks through joint adaptation of nodes' transmitting powers and route selection. The design maintains the advantages of the classic OSI model, while accounting for the cross-coupling between layers, through information sharing. The proposed joint power control and routing algorithm is shown to increase significantly the overall energy efficiency of the network, at the expense of a moderate increase in complexity. Performance enhancement of the joint design using multiuser detection is also investigated, and it is shown that the use of multiuser detection can increase the capacity of the ad hoc network significantly for a given level of energy consumption.Comment: To appear in the EURASIP Journal on Wireless Communications and Networking, Special Issue on Wireless Mobile Ad Hoc Network

Proper Reuse of Image Classification Features Improves Object Detection

A common practice in transfer learning is to initialize the downstream model weights by pre-training on a data-abundant upstream task. In object detection specifically, the feature backbone is typically initialized with Imagenet classifier weights and fine-tuned on the object detection task. Recent works show this is not strictly necessary under longer training regimes and provide recipes for training the backbone from scratch. We investigate the opposite direction of this end-to-end training trend: we show that an extreme form of knowledge preservation -- freezing the classifier-initialized backbone -- consistently improves many different detection models, and leads to considerable resource savings. We hypothesize and corroborate experimentally that the remaining detector components capacity and structure is a crucial factor in leveraging the frozen backbone. Immediate applications of our findings include performance improvements on hard cases like detection of long-tail object classes and computational and memory resource savings that contribute to making the field more accessible to researchers with access to fewer computational resources

A micromechanical damage model for initially anisotropic materials

We propose a new model of brittle damage for initially orthotropic materials. The proposed strain energy-based formulation allows to account for the interaction between initial and induced anisotropies and to address the very challenging issue of opening-closure effects (unilateral behaviour). In order to derive the complete model including the damage growth, we take advantage of micromechanical developments suitably combined with the thermodynamics framework of the standard generalized materials. The model has been implemented within the finite-element code ABAQUS and various numerical simulations have been carried out to illustrate its predictive capabilities. In particular, emphasis is put on the evolution of the material symmetry and the influence of microcracks opening-closure states on the damage process

Probabilistic Ray-Tracing Aided Positioning at mmWave frequencies

We consider the following positioning problem where several base stations (BS) try to locate a user equipment (UE): The UE sends a positioning signal to several BS. Each BS performs Angle of Arrival (AoA) measurements on the received signal. These AoA measurements as well as a 3D model of the environment are then used to locate the UE. We propose a method to exploit not only the geometrical characteristics of the environment by a ray-tracing simulation, but also the statistical characteristics of the measurements to enhance the positioning accuracy.Comment: Accepted at the conference Indoor Positioning and Indoor Navigation (IPIN) 202

Acoustic traffic monitor for a smart city concept

Treball desenvolupat en el marc del programa "European Project Semester".Acoustic Traffic Monitor for a Smart City Concept aims to create a high-performance sensor that can accurately detect and analyse sound waves. The sensor is designed to have a compact form factor, low cost, and energy-efficient operation, making it a suitable solution for a broad range of applications such as security surveillance, environmental monitoring, and industrial automation. The sensor uses advanced signal processing algorithms, including machine learning, to classify and identify different types of sounds, making it versatile in its applications. The project team will use a combination of experimental and parnumerical techniques to optimise the sensor's performance and explore different ways of integrating the sensor into existing systems. In addition to the technical aspects of the project, the team will also focus on creating a viable business and marketing plan. The project will involve market research to identify potential customers and competitors, as well as determining the best pricing strategy for the sensor. The team will also explore different distribution channels and work on creating a strong brand identity for the sensor. By creating a high-performance, cost-effective sensor and implementing an effective business and marketing plan, the project aims to bring a new product to the market that can have a significant impact on various industries. The successful development of this acoustic sensor concept has the potential to revolutionise sound wave detection and analysis, making it an exciting project with significant commercial potential.Incomin

Influence of Dataset Parameters on the Performance of Direct UE Positioning via Deep Learning

User equipment (UE) positioning accuracy is of paramount importance in current and future communications standard. However, traditional methods tend to perform poorly in non line of sight (NLoS) scenarios. As a result, deep learning is a candidate to enhance the UE positioning accuracy in NLoS environments. In this paper, we study the efficiency of deep learning on the 3GPP indoor factory (InF) statistical channel. More specifically, we analyse the impacts of several key elements on the positioning accuracy: the type of radio data used, the number of base stations (BS), the size of the training dataset, and the generalization ability of a trained model.Comment: Accepted for publication at the European Conference on Networks and Communications (EuCNC) 202