80 research outputs found
L'impact onomastique dans la genèse de sens dans L'olympe des infortunes de Yasmina Khadra
Get PDFRésumé : 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
Author response to: Cardiovascular risk factors in offspring exposed to gestational diabetes mellitus in utero: systematic review and meta-analysis
No full textLetter to the EditorThis commentary is an author response to Yu and colleagues regarding the manuscript entitled ‘Cardiovascular risk factors in offspring exposed to gestational diabetes mellitus in utero: Systematic review and meta-analysis’. We address their concern regarding minor errors in our manuscript, our search strategy and assessment of heterogeneity.Maleesa M. Pathirana, Zohra S. Lassi, Claire T. Roberts, and Prabha H. Andraweer
L’ALTERNANCE CODIQUE DANS LE TEXTE MAGHRÉBIN D’EXPRESSION FRANÇAISE CHEZ NINA BOURAOUI ET ASSIA DJEBAR
Get PDFThe French-language Maghreb text is sprinkled with foreign words belonging to Algerian
Arabic. The Maghreb author often returns to draw on his mother's speech to express himself in a
language he borrows. Wanting to vacillate between two totally opposite grammatical systems, the
Maghreb authors, through their linguistic choice to highlight the Algerian dialect, adorn their creations
with snippets exotic to French to embellish it. Based on this observation, we ask ourselves why such a
linguistic summons, and we propose to lift the veil on such discursive use
Unobtrusive hand gesture recognition using ultra-wide band radar and deep learning
Get PDFHand 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
Get PDFDeveloping 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
Get PDFUltra-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
یونس جاوید کے ناول ”کنجری کا پل“ کا فکری و فنی جائزہ: An intellectual and technical review of Yunus Javed's novel "Kinjri Ka Pul"
No full textYunis Javed has got an emmanent position in his contemporary Urdu writers. His work for Urdu Literature both in prose and poetry spreads over dramas, novels, mystics, criticism and biological sketches. “Kanjri Ka Pull” is one of his three novels. The author has used a symbolic name of “Kanjri Ka Pull” for the boutique center run by Zohra Mushtaque. The main character of the novel “Kanjri Ka Pull” means in English as bridge for prostitute. The author by sheer dint of his artistic talent skill and temperament has unveiled the sexual abuse as game changer between sex sellers and money holders purchasing sex commodities in the form cat walker girls, call girls. Zohra Mushtaque the main character having been fully acquired with skill to attract the sex commodities in the shape of girl beauty and exchange it with mony holders who can pay its price. The author has successfully achieved his aim as social reformer by unveiling the ill of sex monger rich men, politicians and fraudy religious men who all take part in this game changer business. Though his living characters and versatility of skill of expression the author attracts readers for meaningful awareness and ground realities facts
Locally Distributed Handover Decision Making for Seamless Connectivity in Multihomed Moving Networks
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Enhancing Dynamic Hand Gesture Recognition using Feature Concatenation via Multi-Input Hybrid Model
Get PDFRadar-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
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