Real-time Detection of Vehicles Using the Haar-like Features and Artificial Neuron Networks

AbstractIn this document, a vehicle detection system is presented. This system is based on two algorithms, a descriptor of the image type haar-like, and a classifier type artificial neuron networks. In order to ensure rapidity in the calculation extracts features by the descriptor the concept of the integral image is used for the representation of the image. The learning of the system is performed on a set of positive images (vehicles) and negative images (non-vehicle), and the test is done on another set of scenes (positive or negative). To address the performance of the proposed system by varying one element among the determining parameters which is the number of neurons in the hidden layer; the results obtained have shown that the proposed system is a fast and robust vehicle detector

Ultra-Reliable Low-Latency Vehicular Networks: Taming the Age of Information Tail

While the notion of age of information (AoI) has recently emerged as an important concept for analyzing ultra-reliable low-latency communications (URLLC), the majority of the existing works have focused on the average AoI measure. However, an average AoI based design falls short in properly characterizing the performance of URLLC systems as it cannot account for extreme events that occur with very low probabilities. In contrast, in this paper, the main objective is to go beyond the traditional notion of average AoI by characterizing and optimizing a URLLC system while capturing the AoI tail distribution. In particular, the problem of vehicles' power minimization while ensuring stringent latency and reliability constraints in terms of probabilistic AoI is studied. To this end, a novel and efficient mapping between both AoI and queue length distributions is proposed. Subsequently, extreme value theory (EVT) and Lyapunov optimization techniques are adopted to formulate and solve the problem. Simulation results shows a nearly two-fold improvement in terms of shortening the tail of the AoI distribution compared to a baseline whose design is based on the maximum queue length among vehicles, when the number of vehicular user equipment (VUE) pairs is 80. The results also show that this performance gain increases significantly as the number of VUE pairs increases.Comment: Accepted in IEEE GLOBECOM 2018 with 7 pages, 6 figure

A Miniature L-slot Microstrip Printed Antenna for RFID

This work presents a miniature microstrip antenna at 2.45 GHz by using the slots technique. This microstrip antenna is fed by a CPW technique and designed for RFID reader system on FR4 substrate. A size reduction equal to 66.6% has been obtained compared to the conventional rectangular microstrip antenna. The total area of the final circuit is 19x31 mm2. The validated antenna has good matching input impedance with a stable radiation pattern, a loss return of -40 dB, and a gain of 1.78 dBi, a prototype of the proposed antenna has been fabricated and measured

Intent Profiling and Translation Through Emergent Communication

To effectively express and satisfy network application requirements, intent-based network management has emerged as a promising solution. In intent-based methods, users and applications express their intent in a high-level abstract language to the network. Although this abstraction simplifies network operation, it induces many challenges to efficiently express applications' intents and map them to different network capabilities. Therefore, in this work, we propose an AI-based framework for intent profiling and translation. We consider a scenario where applications interacting with the network express their needs for network services in their domain language. The machine-to-machine communication (i.e., between applications and the network) is complex since it requires networks to learn how to understand the domain languages of each application, which is neither practical nor scalable. Instead, a framework based on emergent communication is proposed for intent profiling, in which applications express their abstract quality-of-experience (QoE) intents to the network through emergent communication messages. Subsequently, the network learns how to interpret these communication messages and map them to network capabilities (i.e., slices) to guarantee the requested Quality-of-Service (QoS). Simulation results show that the proposed method outperforms self-learning slicing and other baselines, and achieves a performance close to the perfect knowledge baseline

Trans-Inpainter: Wireless Channel Information- Guided Image Restoration via Multimodal Transformer

Image inpainting is a critical computer vision task to restore missing or damaged image regions. In this paper, we propose Trans-Inpainter, a novel multimodal image inpainting method guided by Channel State Information (CSI) data. Leveraging the power of transformer architectures, Trans-Inpainter effectively extracts visual information from CSI time sequences, enabling high-quality and realistic image inpainting. To evaluate its performance, we compare Trans-Inpainter with RF-Inpainter, the state-of-the-art radio frequency (RF) signal-based image inpainting technique. Through comprehensive experiments, Trans-Inpainter consistently demonstrates superior performance in various scenarios. Additionally, we investigate the impact of CSI data variations on Trans-Inpainter's imaging ability, analyzing individual sensor data, fused data from multiple sensors, and altered CSI matrix dimensions. These insights provide valuable references for future wireless sensing and computer vision studies

A New Design of a CPW-Fed Dual-Band Monopole Antenna for RFID Readers

This paper comes with a new dual-band planar monopole antenna fed by Coplanar Waveguide (CPW) line designed for RFID readers and it operates at 2.45 GHz, 5.80 GHz. This antenna is designed with reasonable gain, low profile and low cost production. The designed antenna based on theoretical equations is simulated and validated by using ADS from Agilent technologies and CST Microwave Studio electromagnetic solvers. A parametric study of the proposed antenna has been carried out by optimizing some critical parameters. The antenna has a total area of 35×38 mm2 and mounted on an FR4 substrate with dielectric permittivity constant 4.4 and thickness of 1.6 mm and loss tangent 0.025. The comparison between simulation and measurement results permits to validate the final achieved antenna structure in the desired RFID frequencies bands. Details of the proposed antenna design and both simulated and experimental results are described and discusse

Neurons Expressing Pathological Tau Protein Trigger Dramatic Changes in Microglial Morphology and Dynamics

International audienceMicroglial cells, the resident macrophages of the brain, are important players in the pathological process of numerous neurodegenerative disorders, including tauopathies, a heterogeneous class of diseases characterized by intraneuronal Tau aggregates. However, microglia response in Tau pathologies remains poorly understood. Here, we exploit a genetic zebrafish model of tauopathy, combined with live microglia imaging, to investigate the behavior of microglia in vivo in the disease context. Results show that while microglia were almost immobile and displayed long and highly dynamic branches in a wild-type context, in presence of diseased neurons, cells became highly mobile and displayed morphological changes, with highly mobile cell bodies together with fewer and shorter processes. We also imaged, for the first time to our knowledge, the phagocytosis of apoptotic tauopathic neurons by microglia in vivo and observed that microglia engulfed about as twice materials as in controls. Finally, genetic ablation of microglia in zebrafish tauopathy model significantly increased Tau hyperphosphorylation, suggesting that microglia provide neuroprotection to diseased neurons. Our findings demonstrate for the first time the dynamics of microglia in contact with tauopathic neurons in vivo and open perspectives for the real-time study of microglia in many neuronal diseases