Seamless Infrastructure independent Multi Homed NEMO Handoff Using Effective and Timely IEEE 802.21 MIH triggers

Handoff performance of NEMO BS protocol with existent improvement proposals is still not sufficient for real time and QoS-sensitive applications and further optimizations are needed. When dealing with single homed NEMO, handoff latency and packet loss become irreducible all optimizations included, so that it is impossible to meet requirements of the above applications. Then, How to combine the different Fast handoff approaches remains an open research issue and needs more investigation. In this paper, we propose a new Infrastructure independent handoff approach combining multihoming and intelligent Make-Before-Break Handoff. Based on required Handoff time estimation, L2 and L3 handoffs are initiated using effective and timely MIH triggers, reducing so the anticipation time and increasing the probability of prediction. We extend MIH services to provide tunnel establishment and switching before link break. Thus, the handoff is performed in background with no latency and no packet loss while pingpong scenario is almost avoided. In addition, our proposal saves cost and power consumption by optimizing the time of simultaneous use of multiple interfaces. We provide also NS2 simulation experiments identifying suitable parameter values used for estimation and validating the proposed mode

Unobtrusive hand gesture recognition using ultra-wide band radar and deep learning

Hand function after stroke injuries is not regained rapidly and requires physical rehabilitation for at least 6 months. Due to the heavy burden on the healthcare system, assisted rehabilitation is prescribed for a limited time, whereas so-called home rehabilitation is offered. It is therefore essential to develop robust solutions that facilitate monitoring while preserving the privacy of patients in a home-based setting. To meet these expectations, an unobtrusive solution based on radar sensing and deep learning is proposed. The multi-input multi-output convolutional eXtra trees (MIMO-CxT) is a new deep hybrid model used for hand gesture recognition (HGR) with impulse-radio ultra-wide band (IR-UWB) radars. It consists of a lightweight architecture based on a multi-input convolutional neural network (CNN) used in a hybrid configuration with extremely randomized trees (ETs). The model takes data from multiple sensors as input and processes them separately. The outputs of the CNN branches are concatenated before the prediction is made by the ETs. Moreover, the model uses depthwise separable convolution layers, which reduce computational cost and learning time while maintaining high performance. The model is evaluated on a publicly available dataset of gestures collected by three IR-UWB radars and achieved an average accuracy of 98.86%

Advanced Human Activity Recognition through Data Augmentation and Feature Concatenation of Micro-Doppler Signatures

Developing accurate classification models for radar-based Human Activity Recognition (HAR), capable of solving real-world problems, depends heavily on the amount of available data. In this paper, we propose a simple, effective, and generalizable data augmentation strategy along with preprocessing for micro-Doppler signatures to enhance recognition performance. By leveraging the decomposition properties of the Discrete Wavelet Transform (DWT), new samples are generated with distinct characteristics that do not overlap with those of the original samples. The micro-Doppler signatures are projected onto the DWT space for the decomposition process using the Haar wavelet. The returned decomposition components are used in different configurations to generate new data. Three new samples are obtained from a single spectrogram, which increases the amount of training data without creating duplicates. Next, the augmented samples are processed using the Sobel filter. This step allows each sample to be expanded into three representations, including the gradient in the x-direction (Dx), y-direction (Dy), and both x- and y-directions (Dxy). These representations are used as input for training a three-input convolutional neural network-long short-term memory support vector machine (CNN-LSTM-SVM) model. We have assessed the feasibility of our solution by evaluating it on three datasets containing micro-Doppler signatures of human activities, including Frequency Modulated Continuous Wave (FMCW) 77 GHz, FMCW 24 GHz, and Impulse Radio Ultra-Wide Band (IR-UWB) 10 GHz datasets. Several experiments have been carried out to evaluate the model\u27s performance with the inclusion of additional samples. The model was trained from scratch only on the augmented samples and tested on the original samples. Our augmentation approach has been thoroughly evaluated using various metrics, including accuracy, precision, recall, and F1-score. The results demonstrate a substantial improvement in the recognition rate and effectively alleviate the overfitting effect. Accuracies of 96.47%, 94.27%, and 98.18% are obtained for the FMCW 77 GHz, FMCW 24 GHz, and IR- UWB 10 GHz datasets, respectively. The findings of the study demonstrate the utility of DWT to enrich micro-Doppler training samples to improve HAR performance. Furthermore, the processing step was found to be efficient in enhancing the classification accuracy, achieving 96.78%, 96.32%, and 100% for the FMCW 77 GHz, FMCW 24 GHz, and IR-UWB 10 GHz datasets, respectively

Double sliding window variance detection-based time-of-arrival estimation in ultra-wideband ranging systems

Ultra-wideband (UWB) ranging via time-of-arrival (TOA) estimation method has gained a lot of research interests because it can take full advantage of UWB capabilities. Energy detection (ED) based TOA estimation technique is widely used in the area due to its low cost, low complexity and ease of implementation. However, many factors affect the ranging performance of the ED-based methods, especially, non-line-of-sight (NLOS) condition and the integration interval. In this context, a new TOA estimation method is developed in this paper. Firstly, the received signal is denoised using a five-level wavelet decomposition, next, a double sliding window algorithm is applied to detect the change in the variance information of the received signal, the first path (FP) TOA is then calculated according to the first variance sharp increase. The simulation results using the CM1 and CM2 IEEE 802.15.4a channel models, prove that our proposed approach works effectively compared with the conventional ED-based methods

Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model

Radar-based hand gesture recognition is an important research area that provides suitable support for various applications, such as human-computer interaction and healthcare monitoring. Several deep learning algorithms for gesture recognition using Impulse Radio Ultra-Wide Band (IR-UWB) have been proposed. Most of them focus on achieving high performance, which requires a huge amount of data. The procedure of acquiring and annotating data remains a complex, costly, and time-consuming task. Moreover, processing a large volume of data usually requires a complex model with very large training parameters, high computation, and memory consumption. To overcome these shortcomings, we propose a simple data processing approach along with a lightweight multi-input hybrid model structure to enhance performance. We aim to improve the existing state-of-the-art results obtained using an available IR-UWB gesture dataset consisting of range-time images of dynamic hand gestures. First, these images are extended using the Sobel filter, which generates low-level feature representations for each sample. These represent the gradient images in the x-direction, the y-direction, and both the x- and y-directions. Next, we apply these representations as inputs to a three-input Convolutional Neural Network- Long Short-Term Memory- Support Vector Machine (CNN-LSTM-SVM) model. Each one is provided to a separate CNN branch and then concatenated for further processing by the LSTM. This combination allows for the automatic extraction of richer spatiotemporal features of the target with no manual engineering approach or prior domain knowledge. To select the optimal classifier for our model and achieve a high recognition rate, the SVM hyperparameters are tuned using the Optuna framework. Our proposed multi-input hybrid model achieved high performance on several parameters, including 98.27% accuracy, 98.30% precision, 98.29% recall, and 98.27% F1-score while ensuring low complexity. Experimental results indicate that the proposed approach improves accuracy and prevents the model from overfitting

L'impact onomastique dans la genèse de sens dans L'olympe des infortunes de Yasmina Khadra

Résumé : Dans cet article, nous explorons les dimensions de l'onomastique et de l'anthroponymie à travers l'analyse littéraire de l'œuvre "L'Olympe des Infortunes". Le titre lui-même, "L'Olympe des Infortunes", révèle une tension entre le divin et le tragique, ouvrant ainsi la voie à une exploration subtile des thèmes, notamment celui du matérialisme. En scrutant les protagonistes, Ach et Benadam, nous découvrons une dualité existentielle chez Ach, illustrant les épreuves de la vie matérialiste, tandis que Benadam incarne une transcendance mythique qui remet en question les normes établies. Cette analyse approfondie offre une perspective nuancée sur la manière dont le matérialisme est tissé dans la trame même de l'œuvre, enrichissant ainsi notre compréhension des messages sous-jacents. Mots-clés : Onomastique, Anthroponymie, Analyse littéraire, Tension existentielle, Matérialisme, Transcendance mythique

Energy saving potential diagnosis for Moroccan university campuses

Public buildings are energy-intensive users, especially when energy management is lacking. More than ever, the use of energy efficiency strategies and renewable energy sources (RES) in buildings are a national priority for Morocco in order to improve energy self-sufficiency, replace fossil fuel use and lower energy bills and greenhouse gas emissions. Relating to the exemplarity of the Moroccan government in terms of energy efficiency and sustainable development, the study support that aim and presents results of a deep energy performance analysis of more than 20 university campuses across Morocco, which has concluded that around 80% of the energy consumed in the university campuses is designated for lightning and hot water for sanitary use. Later, this study examined the potential for energy saving and the environmental benefits of implementing actions to reduce energy demand from the grid, considering the use of on-site solar energy. Thereafter, the study aimed to analyze the impact of RES integration in public university campuses, namely the photovoltaic (ESM1) for electricity output and solar thermal system for hot water use (ESM2), to assess the techno-economic-environmental performance on building energy consumption reduction. Hence, the paper reported a detailed energetic-economic and environmental (3E) analysis simulation for campuses by integration of the two Energy Saving Measurements (ESM). The results showed that the integration of ESM1 system can reduce the annual energy demand by 22% and the energy bill by 34%, whereas the integration of ESM2 achieved 67% in energy saving. According to the analysis of the results, the integration of ESM1 is expected to save 6044 MWh of electrical energy annually on the 30222 MWh for all campuses and 2559 MWh for ESM2 which is equivalent to 284 m3/yr of diesel. With the reduced energy consumption, it is possible to cut down fossil fuels for electricity production and offset greenhouse gas emissions by 672 tons of carbon dioxide annually. Besides, the evaluation of results showed that the energy performance indicator was reduced from 530 kWh/bed /yr to 248 kWh/bed/yr, which represents 56% of energy saving

Moustiques (Diptera : Culicidae) de la région du M’Zab-Ghardaïa, Algérie : biodiversité et importance médico-vétérinaire

De nombreuses espèces de moustiques sont susceptibles de jouer un rôle dans la transmission de divers agents pathogènes responsables des maladies infectieuses humaines et animales. D'autres, outre leur rôle vecteur, sont un véritable fléau par les piqûres douloureuses qu’ils occasionnent et, constituent de ce fait un grand problème de nuisance. L'identification précise et la connaissance de la biodiversité fonctionnelle des vecteurs est un pas essentiel pour la compréhension du risque de (ré)-émergence et la dynamiques des maladies vectorielles. Le présent travail est une étude rétrospective des inventaires des Culicidae réalisés sur le terrain dans la région du M’Zab (Ghardaïa, Algérie) durant les années allant de 2008 à 2012. Les résultats de l’étude morphotaxonomique des Culicidae inventoriés ont montré la présence de dix espèces réparties en cinq genres (Culex, Culiseta, Ochlerotatus, Anopheles et Uranotaenia). Un aperçu sur le rôle vectoriel des espèces signalées dans la région et leur importance au niveau de la santé médicale et vétérinaire sont présentés à partir des données bibliographiques. Les espèces potentiellement vectrices d’arbovirus (Virus de Nile occidentale et Virus de la Vallée du Rift) et de protozoaires (Plasmodium spp) qui peuvent causer des problèmes épidémiologique au niveau de la région du M’Zab sont Cx. pipiens sl L., 1758, Cx. theileri Theobald, 1903, Ochlerotatus caspius Pallas, 1771, Anopheles sergentii Theobald, 1907, An. dthali Patton, 1905 et An. multicolor Cambouliu, 1902

Surveillance des populations de phlébotomes (Diptera: Psychodidae), vecteurs des agents responsables des leishmanioses dans la région du M'Zab-Ghardaïa (Algérie)

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