Mobile station movement direction prediction (MMDP) based handover scanning for mobile WiMAX system

Mobile WiMAX is a broadband technology that is capable of delivering triple play services (voice, data, and video). However, mobility in mobile WiMAX system is still an issue when the mobile station (MS) moves and its connection is handed over between base stations (BSs). In the handover process, scanning is one of the required phases to find the target BS. During the handover scanning process, the MS must synchronize with all the advertised neighbour BSs (nBSs) to select the best BS candidate for the incoming handover action. Without terminating the connection between the SBS and MS, the SBS will schedule the scanning intervals and sleep-intervals (also called interleaving interval) to MS for the handover scanning. However, during the scanning interval period, all the coming transmissions will be paused. Therefore, the redundant or unnecessary scanning of neighbouring BS cause delay and MAC overhead which may affect real-time applications. In this paper, the MS movement direction prediction (MMDP) based handover scanning scheme is introduced to overcome the mobile WiMAX handover scanning issue. It based on dividing the BS coverage area is into zones and sectors. According to the signal quality; there are three zones, no handover (No-HO), low handover (Low-HO) and high handover (High-HO) zones respectively and six sectors. In this scheme, only two BSs can become candidates; the two that the MS moves toward them will be chosen as the candidate for the handover scanning purpose. Hence, the handover scanning process repetition will be reduced with these two shortlisted BS candidates instead of scanning all nBSs. Thus, MMDP will reduce scanning delay and the number of exchange messages during the handover scanning comparing to the conventional scanning scheme. Although, the MMDP may need an extra computational time, the prediction and scanning process will be finished before the MS reach the High-HO zone, which mean the end-user’s running application will be affected. Simulation results show that the proposed MMDP scheme reduces the total handover scanning delay and scanning interval duration by 25 and 50 % respectively. Also, the size of scanning message is reduced, which leads to reduced signalling overhead

Traitement du bruit impulsionnel dans des postes électriques pour les systèmes de communication sans fil

Afin de déployer un réseau sans fil dans les postes électriques à haute tension, l’environnement électromagnétique doit être convenablement caractérisé. L’environnement des postes électriques est sujet à divers types de distorsions. Une partie significative de ces distorsions apparait sous forme de bruit impulsionnel. Ce bruit particulier n’a pas des caractéristiques Gaussiennes. Habituellement, les récepteurs conventionnels sont conçus pour fonctionner dans des environnements typiques où le bruit est supposé être un bruit blanc Gaussien. Appliquer ces récepteurs à l’environnement des postes électriques aboutit à des performances médiocres. Pour concevoir un système de communication fiable, des récepteurs plus adaptés à ces environnements particuliers doivent être considérés. De ce fait, des méthodes de traitement de bruit impulsionnel plus sophistiquées doivent être développées. Dans ce mémoire, nous étudions et développons des méthodes de lutte contre le bruit impulsionnel. Cette étude est élaborée dans le contexte de systèmes de communication sans fil dans les postes électriques. Elle traite les systèmes à une antenne ainsi que les systèmes à antennes multiples à la réception. Les performances des différents récepteurs existants dans le contexte du bruit impulsionnel, ainsi que ceux que nous avons développés, sont évaluées. L’évaluation est élaborée en termes de taux d’erreurs binaires à travers des simulations numériques

Towards an approach based on particle swarm optimization for Arabic named entity recognition on social media

Named entity recognition is an essential task for various applications related to natural language processing (NLP). It aims to retrieve a variety of named entities (NEs) from text and categorize them according to predetermined target categories. In many cases, using the entire feature set can be time-consuming and negatively impact the performance. Moreover, it is challenging to find the relevant subsets of features for a particular task due to the high number. The feature selection technique is an unsupervised process for selecting informative features by creating a new subset of informative features. This technique is used to enhance the underlying algorithm's performance. This article implements an effective feature selection algorithm using particle swarm optimization (PSO) to identify and classify the Arabic NEs in the text from social media. PSO is a search algorithm that utilizes a population of particles in a multidimensional space. The proposed method is evaluated using two publicly available Arabic Dialect social media datasets. It is demonstrated through comparisons with both baselines and previous models that the new approach achieves significant accuracy with considerably reduced feature sets in all parameters

Fuzzy Logic Based Self-Adaptive Handover Algorithm for MobileWiMAX.

It is well known that WiMAX is a broadband technology that is capable of delivering triple play (voice, data, and video) services. However, mobility in WiMAX system is still a main issue when the mobile station (MS) moves across the base station (BS) coverage and be handed over between BSs. Among the challenging issues in mobile WiMAX handover are unnecessary handover, handover failure and handover delay, which may affect real-time applications. The conventional handover decision algorithm in mobile WiMAX is based on a single criterion, which usually uses the received signal strength indicator (RSSI) as an indicator, with the other fixed handover parameters such as handover threshold and handover margin. In this paper, a fuzzy logic based self-adaptive handover (FuzSAHO) algorithm is introduced. The proposed algorithm is derived from the self-adaptive handover parameters to overcome the mobile WiMAX ping-pong handover and handover delay issues. Hence, the proposed FuzSAHO is initiated to check whether a handover is necessary or not which depends on its fuzzy logic stage. The proposed FuzSAHO algorithm will first self-adapt the handover parameters based on a set of multiple criteria, which includes the RSSI and MS velocity. Then the handover decision will be executed according to the handover parameter values. Simulation results show that the proposed FuzSAHO algorithm reduces the number of ping-pong handover and its delay. When compared with RSSI based handover algorithm and mobility improved handover (MIHO) algorithm, respectively, FuzSAHO reduces the number of handovers by 12.5 and 7.5 %, respectively, when the MS velocity is <17 m/s. In term of handover delay, the proposed FuzSAHO algorithm shows an improvement of 27.8 and 8 % as compared to both conventional and MIHO algorithms, respectively. Thus, the proposed multi-criteria with fuzzy logic based self-adaptive handover algorithm called FuzSAHO, outperforms both conventional and MIHO handover algorithms

Hybrid AHP and TOPSIS Methods Based Cell Selection (HATCS) Scheme for Mobile WiMAX

Hybrid AHP and TOPSIS Methods Based Cell Selection (HATCS) Scheme for Mobile WiMA

TFS-ViT: Token-Level Feature Stylization for Domain Generalization

Standard deep learning models such as convolutional neural networks (CNNs) lack the ability of generalizing to domains which have not been seen during training. This problem is mainly due to the common but often wrong assumption of such models that the source and target data come from the same i.i.d. distribution. Recently, Vision Transformers (ViTs) have shown outstanding performance for a broad range of computer vision tasks. However, very few studies have investigated their ability to generalize to new domains. This paper presents a first Token-level Feature Stylization (TFS-ViT) approach for domain generalization, which improves the performance of ViTs to unseen data by synthesizing new domains. Our approach transforms token features by mixing the normalization statistics of images from different domains. We further improve this approach with a novel strategy for attention-aware stylization, which uses the attention maps of class (CLS) tokens to compute and mix normalization statistics of tokens corresponding to different image regions. The proposed method is flexible to the choice of backbone model and can be easily applied to any ViT-based architecture with a negligible increase in computational complexity. Comprehensive experiments show that our approach is able to achieve state-of-the-art performance on five challenging benchmarks for domain generalization, and demonstrate its ability to deal with different types of domain shifts. The implementation is available at: https://github.com/Mehrdad-Noori/TFS-ViT_Token-level_Feature_Stylization

A Systematic Design of a Compact Wideband Hybrid Directional Coupler Based on Printed RGW Technology

Printed ridge gap waveguide (PRGW) is considered among the state of art guiding technologies due to its low signal distortion and low loss at Millimeter Wave (mmWave) spectrum, which motivates the research community to use this guiding structure as a host technology for various passive microwave and mmWave components. One of the most important passive components used in antenna beam-switching networks is the quadrature hybrid directional coupler providing signal power division with 90° phase shift. A featured design of a broadband and compact PRGW hybrid coupler is propose in this paper. A novel design methodology, based on mode analysis, is introduced to design the objective coupler. The proposed design is suitable for mmWave applications with small electrical dimensions ( 1.2λo×1.2λo ), low loss, and wide bandwidth. The proposed hybrid coupler is fabricated on Roger/RT 6002 substrate material of thickness 0.762 mm. The measured results highlight that the coupler can provide a good return loss with a bandwidth of 26.5% at 30 GHz and isolation beyond 15 dB. The measured phase difference between the coupler output ports is equal 90∘± 5∘ through the interested operating bandwidth. A clear agreement between the simulated and the measured results over the assigned operating bandwidth has been illustrated

CLIPArTT: Light-weight Adaptation of CLIP to New Domains at Test Time

Pre-trained vision-language models (VLMs), exemplified by CLIP, demonstrate remarkable adaptability across zero-shot classification tasks without additional training. However, their performance diminishes in the presence of domain shifts. In this study, we introduce CLIP Adaptation duRing Test-Time (CLIPArTT), a fully test-time adaptation (TTA) approach for CLIP, which involves automatic text prompts construction during inference for their use as text supervision. Our method employs a unique, minimally invasive text prompt tuning process, wherein multiple predicted classes are aggregated into a single new text prompt, used as pseudo label to re-classify inputs in a transductive manner. Additionally, we pioneer the standardization of TTA benchmarks (e.g., TENT) in the realm of VLMs. Our findings demonstrate that, without requiring additional transformations nor new trainable modules, CLIPArTT enhances performance dynamically across non-corrupted datasets such as CIFAR-10, corrupted datasets like CIFAR-10-C and CIFAR-10.1, alongside synthetic datasets such as VisDA-C. This research underscores the potential for improving VLMs' adaptability through novel test-time strategies, offering insights for robust performance across varied datasets and environments. The code can be found at: https://github.com/dosowiechi/CLIPArTT.gi

Fuzzy logic-based vehicle safety estimation using V2V communications and on-board embedded ROS-based architecture for safe traffic management system in hail city

Estimating the state of surrounding vehicles is crucial to either prevent or avoid collisions with other road users. However, due to insufficient historical data and the unpredictability of future driving tactics, estimating the safety status is a difficult undertaking. To address this problem, an intelligent and autonomous traffic management system based on V2V technology is proposed. The main contribution of this work is to design a new system that uses a real-time control system and a fuzzy logic algorithm to estimate safety. The robot operating system (ROS) is the foundation of the control architechture, which connects all the various system nodes and generates the decision in the form of a speech and graphical message. The safe path is determined by a safety evaluation system that combines sensor data with a fuzzy classifier. Moreover, the suitable information processed by each vehicle unit is shared in the group to avoid unexpected problems related to speed, sudden braking, unplanned deviation, street holes, road bumps, and any kind of street issues. The connection is provided through a network based on the ZigBee protocol. The results of vehicle tests show that the proposed method provides a more reliable estimate of safety as compared to other methods

GeoMask3D: Geometrically Informed Mask Selection for Self-Supervised Point Cloud Learning in 3D

We introduce a pioneering approach to self-supervised learning for point clouds, employing a geometrically informed mask selection strategy called GeoMask3D (GM3D) to boost the efficiency of Masked Auto Encoders (MAE). Unlike the conventional method of random masking, our technique utilizes a teacher-student model to focus on intricate areas within the data, guiding the model's focus toward regions with higher geometric complexity. This strategy is grounded in the hypothesis that concentrating on harder patches yields a more robust feature representation, as evidenced by the improved performance on downstream tasks. Our method also presents a complete-to-partial feature-level knowledge distillation technique designed to guide the prediction of geometric complexity utilizing a comprehensive context from feature-level information. Extensive experiments confirm our method's superiority over State-Of-The-Art (SOTA) baselines, demonstrating marked improvements in classification, and few-shot tasks