Décomposition en sous bandes pour la compression d'images sans perte par extension de la transformation par arrondi

Dans cet article, nous proposons une nouvelle approche utilisant la transformation par arrondi (rounding transform : RT) pour la compression sans perte par décomposition en sous-bandes. Dans ce but, nous définissons une extension de la RT, nommée ORT (overlapping RT). Celle-ci est définie dans le domaine de la transformée en Z, et est utilisée pour développer un système de codage en sous-bandes sans perte. L'idée principale de cette approche est de décomposer la matrice polyphasé du banc de filtres d'analyse en plusieurs matrices de l'ORT. La méthode proposée est plus générale que les propositions antérieures car elle offre de multiples possibilités de mise en oeuvre. Cinq bancs de filtres sont considérés à titre d'exemples et leurs performances sont comparées en terme d'entropie totale dans la compression d'images variées

An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT.

Social Edge Service (SES) is an emerging mechanism in the Social Internet of Things (SIoT) orchestration for effective user-centric reliable communication and computation. The services are affected by active and/or passive attacks such as replay attacks, message tampering because of sharing the same spectrum, as well as inadequate trust measurement methods among intelligent devices (roadside units, mobile edge devices, servers) during computing and content-sharing. These issues lead to computation and communication overhead of servers and computation nodes. To address this issue, we propose the HybridgrAph-Deep-learning (HAD) approach in two stages for secure communication and computation. First, the Adaptive Trust Weight (ATW) model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead. Second, a Quotient User-centric Coeval-Learning (QUCL) mechanism to formulate secure channel selection, and Nash equilibrium method for optimizing the communication to share data over edge devices. The simulation results confirm that our proposed approach has achieved effective communication and computation performance, and enhanced Social Edge Services (SES) reliability than state-of-the-art approaches

ASXC2 approach: a service-X cost optimization strategy based on edge orchestration for IIoT.

Most computation-intensive industry applications and servers encounter service-reliability challenges due to the limited resource capability of the edge. Achieving quality data fusion and accurate service reliability with optimized service-x execution cost is challenging. While existing systems have taken into account factors such as device service execution, residual resource ratio, and channel condition; the service execution time, cost, and utility ratios of requested services from devices and servers also have a significant impact on service execution cost. To enhance service quality and reliability, we design a 2-step adaptive service-X cost consolidation (ASXC 2) approach. This approach is based on the node-centric Lyapunov method and distributed Markov mechanism, aiming to optimize the service execution error rate during offloading. The node-centric Lyapunov method incorporates cost and utility functions and node-centric features to estimate the service cost before offloading. Additionally, the Markov mechanism-inspired service latency prediction model design assists in mitigating the ratio of offload-service execution errors by establishing a mobility-correlation matrix between devices and servers. In addition, the non-linear programming multi-tenancy heuristic method design help to predict the service preferences for improving the resource utilisation ratio. The simulations show the effectiveness of our approach. The model performance is enhanced with 0.13% service offloading efficiency, 0.82% rate of service completion when transmitting data size is 400 kb, and 0.058% average service offloading efficiency with 40 CPU Megacycles when the vehicle moves 60 Km/h speed around the server communication range. Our model simulations indicate that our approach is highly effective and suitable for lightweight, complex environments

Efficient LiDAR-trajectory affinity model for autonomous vehicle orchestration.

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target's object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

A quantum-inspired sensor consolidation measurement approach for cyber-physical systems.

Cyber-Physical System (CPS) devices interconnect to grab data over a common platform from industrial applications. Maintaining immense data and making instant decision analysis by selecting a feasible node to meet latency constraints is challenging. To address this issue, we design a quantum-inspired online node consolidation (QONC) algorithm based on a time-sensitive measurement reinforcement system for making decisions to evaluate the feasible node, ensuring reliable service and deploying the node at the appropriate position for accurate data computation and communication. We design the Angular-based node position analysis method to localize the node through rotation and t-gate to mitigate latency and enhance system performance. We formalize the estimation and selection of the feasible node based on quantum formalization node parameters (node contiguity, node optimal knack rate, node heterogeneity, probability of fusion variance error ratio). We design a fitness function to assess the probability of node fitness before selection. The simulation results convince us that our approach achieves an effective measurement rate of performance index by reducing the average error ratio from 0.17-0.22, increasing the average coverage ratio from 29% to 42%, and the qualitative execution frequency of services. Moreover, the proposed model achieves a 74.3% offloading reduction accuracy and a 70.2% service reliability rate compared to state-of-the-art approaches. Our system is scalable and efficient under numerous simulation frameworks

3D harmonic loss: towards task-consistent and time-friendly 3D object detection on edge for V2X orchestration.

The use of edge computing for 3D perception has garnered interest in intelligent transportation systems (ITS) due to its potential to enhance Vehicle-to-Everything (V2X) orchestration through real-time traffic monitoring. The ability to accurately measure depth information in the environment using LiDAR has led to a growing emphasis on 3D detection based on this technology, which has significantly advanced the field of 3D perception. However, the computationally-intensive nature of these operations has made it challenging to meet the real-time deployment requirements using existing methods. The object detection task in the pointcloud domain is hindered by a substantial inconsistency problem caused by its high sparsity, which remains unaddressed. This paper conducts an in-depth analysis of the issue, which has been brought to light by recent research on detecting inconsistency problems in image specialization. To address this problem, we propose a solution in the form of a 3D harmonic loss function, which aims to alleviate the inconsistent predictions based on pointcloud data. In addition, we showcase the viability of optimizing 3D harmonic loss mathematically. Our simulations employ the KITTI dataset and DAIR-V2X-I dataset, and our proposed approach significantly surpasses the performance of benchmark models. Additionally, we validate the efficiency of our proposed model through its deployment on an edge device (Jetson Xavier TX) in a simulated environment

Leptomeningeal Dissemination of a Low-Grade Brainstem Glioma without Local Recurrence

It is rare for low-grade gliomas to disseminate to the leptomeninges. However, low-grade gliomas with dissemination to the leptomeninges have been occasionally reported in children, and have generally been associated with local recurrence. A 16-year-old boy sought evaluation for diplopia and gait disturbance. A brain magnetic resonance imaging (MRI) revealed pontine mass, which was proved to be fibrillary astrocytoma on biopsy, later. Radiation therapy (5400 cGy) was given and the patient's symptoms were improved. He was followed-up radiologically for brain lesion. Seven months after diagnosis he complained of back pain and gait disturbance. A brain MRI showed a newly-developed lesion at the left cerebellopontine angle without an interval change in the primary lesion. A spinal MRI demonstrated leptomeningeal dissemination of the entire spine. Radiation therapy (3750 cGy) to the spine, and adjuvant chemotherapy with a carboplatin plus vincristine regimen were administered. However, he had a progressive course with tumoral hemorrhage and expired 13 months after diagnosis. We report an unusual case of a low-grade brainstem glioma with spinal dissemination, but without local recurrence, and a progressive course associated with hemorrhage

Décomposition en ondelettes de maillages triangulaires 3D irrégulièrement subdivisés. Application à la compression

- Nous proposons un nouvel algorithme de subdivision qui permet la simplification des maillages triangulaires quelconques à l'aide de la transformée en ondelettes. Cet algorithme est appliqué à la compression sans pertes des maillages. Des résultats expérimentaux montrent l'efficacité de cette approche dans des représentations multirésolutions

KINEMATIC ANALYSIS OF THE WOMEN’S JAVELIN THROW AT THE IAAF WORLD CHAMPIONSHIPS, DAEGU 2011

The purpose of this study was to analyze the kinematic variables for the women's javelin throw at the IAAF World Championships, Daegu 2011. Three-dimensional motion analyses of the eight players who qualified for the final round were carried out to obtain the data. The results showed that average release, attitude, and attack angles were 38.0±2.0°, 40.4±4.3°, and 3.7±1.1°, respectively. At the release, the average inclination angle of the trunk, upper arm, forearm were 60.8±8.3°, 47.3±10.1°, and 62.6±10.6°, respectively. Moreover, the release velocity and the release height results averaged 25.60±1.16 m/s and 1.86±0.05 m. The crossover phase and delivery phase had average distances of 1.88±0.31 m and 1.53±0.21 m. After release, the average distance between the landing foot and the foul line was 1.72±0.63 m