Robust and Reliable Modulation Classification for MIMO Systems

Abstract: This paper develops a feature-based Automatic Modulation Classification (AMC) algorithm for spatially multiplexed Multiple-Input Multiple-Output (MIMO) systems. The proposed algorithm employs two Higher Order Cumulants (HOCs) of the estimated transmit signal streams as discriminating features, and a multiclass Support Vector Machine (SVM) as a classification system. A multi-classifier classification system is introduced to improve the robustness of the decision made by the classifier at each estimated transmit signal stream. Furthermore, an optimal decision fusion scheme using a Maximum-Likelihood (ML) criterion is also introduced to improve the accuracy and reliability of the final classification decision made in the fusion center. The proposed algorithm shows good performance under different operating conditions, over an acceptable range of SNR, without any prior information about the channel state

Study of the Acute Stress Effects on Decision Making Using Electroencephalography and Functional Near-Infrared Spectroscopy: A Systematic Review

This systematic review provides a comprehensive analysis of studies that use electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to investigate how acute stress affects decision-making processes. The primary goal of this systematic review was to examine the influence of acute stress on decision making in challenging or stressful situations. Furthermore, we aimed to identify the specific brain regions affected by acute stress and explore the feature extraction and classification methods employed to enhance the detection of decision making under pressure. Five academic databases were carefully searched and 27 papers that satisfied the inclusion criteria were found. Overall, the results indicate the potential utility of EEG and fNIRS as techniques for identifying acute stress during decision-making and for gaining knowledge about the brain mechanisms underlying stress reactions. However, the varied methods employed in these studies and the small sample sizes highlight the need for additional studies to develop more standardized approaches for acute stress effects in decision-making tasks. The implications of the findings for the development of stress induction and technology in the decision-making process are also explained

Effect of Interruptions and Cognitive Demand on Mental Workload: A Critical Review

Worker safety and productivity are crucial for effective job management. Interruptions to an individual’s work environment and their impact on mental health can have adverse effects. One prospective instrument for assessing and calculating an individual’s mental state in an interrupted scenario and cognitive demand levels is the use of physiological computing devices in conjunction with behavioral and subjective measurements. This study sought to address how to gather and compute data on individuals’ cognitive states in interrupted work settings through critical analysis. Thirty-three papers were considered after the literature search and selection procedure. This descriptive study is conducted from three perspectives: parameter measurement, research design, and data analysis. The variables evaluated were working memory, stress, emotional state, performance, and resumption lag. The subject recruitment, experimental task design, and measurement techniques were examined from the standpoint of the experimental design. Data analysis included computing and cognitive pre-processing. Four future research directions are suggested to address the shortcomings of the present studies. This study offers suggestions for researchers on experiment planning and using computing to analyze individuals’ cognitive states during interrupted work scenarios. Additionally, it offers helpful recommendations for organizing and conducting future research

Frontal Electroencephalogram Alpha Asymmetry during Mental Stress Related to Workplace Noise

This study aims to investigate the effects of workplace noise on neural activity and alpha asymmetries of the prefrontal cortex (PFC) during mental stress conditions. Workplace noise exposure is a pervasive environmental pollutant and is negatively linked to cognitive effects and selective attention. Generally, the stress theory is assumed to underlie the impact of noise on health. Evidence for the impacts of workplace noise on mental stress is lacking. Fifteen healthy volunteer subjects performed the Montreal imaging stress task in quiet and noisy workplaces while their brain activity was recorded using electroencephalography. The salivary alpha-amylase (sAA) was measured before and immediately after each tested workplace to evaluate the stress level. The results showed a decrease in alpha rhythms, or an increase in cortical activity, of the PFC for all participants at the noisy workplace. Further analysis of alpha asymmetry revealed a greater significant relative right frontal activation of the noisy workplace group at electrode pairs F4-F3 but not F8-F7. Furthermore, a significant increase in sAA activity was observed in all participants at the noisy workplace, demonstrating the presence of stress. The findings provide critical information on the effects of workplace noise-related stress that might be neglected during mental stress evaluations.Ministry of Higher Education Malaysia under the Higher Institutional Centre of Excellence (HICoE) Schem

Designing deep CNN models based on sparse coding for aerial imagery: a deep-features reduction approach

Traditional methods focus on low-level handcrafted features representations and it is difficult to design a comprehensive classification algorithm for remote sensing scene classification problems. Recently, convolutional neural networks (CNNs) have obtained remarkable performance outcomes, setting several remote sensing benchmarks. Furthermore, direct applications of UAV remote sensing images that use deep convolutional networks are extremely challenging given high input data dimensionality with relatively small amounts of available labelled data. We, therefore, propose a CNN approach to scene classification that architecturally incorporates sparse coding (SC) technique for dimension reduction to minimize overfitting. Outcomes were compared with principal component analysis (PCA) and global average pooling (GAP) alternatives that use fully connected layer(s) in pre-trained CNN architecture(s) to minimize overfitting. SC was used to encode deep features extracted from the last convolutional layer of pre-trained CNN models by using different features maps in which deep features had been converted into low-dimensional SC features. These same sparse-coded features were concatenated by means of different pooling techniques to obtain global image features for scene classification. The proposed algorithm outperformed current state-of-the-art algorithms based on handcrafted features. When using our own UAV-based dataset and existing datasets, it was also exceptionally efficient computationally when learning data representations, producing a 93.64% accuracy rate.

Contribution à l'étude de l'application de la technique CDMA aux systèmes de transmission optique

L'accès multiple par répartition de codes, appelé CDMA, est une technique de partage des ressources utilisée dans les systèmes radiofréquences. Basée sur la technique de l'étalement de spectre, elle permet à plusieurs utilisateurs d'accéder de manière asynchrone à un même canal de transmission par l'attribution à chacun d'une séquence de code spécifique. Depuis une vingtaine d'années, cette technique est envisagée comme solution pour l'accès multiple dans les systèmes de transmission optiques notamment dans le contexte des réseaux d'accès devant fournir à l'horizon 2010~2015 des services à très haut débit de l'ordre de 155 Mbit/s jusqu'au Gbit/s à l'abonné. Ce mémoire de thèse présente l'étude théorique des sous-ensembles " émission réception " d'une solution CDMA Optique (OCDMA) avec l'étalement des données dans le domaine temporel, dans le cas d'une transmission optique incohérente. L'accent est mis sur les différentes structures de récepteurs permettant de réduire une des principales limitations de ce type de réseaux, l'Interférence d'Accès Multiple (IAM), liée à l'utilisation de codes d'étalement unipolaires en lumière incohérente et donc quasi-orthogonaux. Les probabilités d'erreur théoriques sont déterminées dans chaque cas et en particulier pour une structure d'annulation d'interférence série dérivée des systèmes hertziens. Les études théoriques sont validées par la simulation des différents types de récepteurs. Une des originalités de ce travail réside dans la mise au point de la simulation de la chaîne étudiée par une double approche " signal composant ". Un logiciel de simulation système (COMSIS) est utilisé pour tenir compte à la fois du traitement du signal optique et des limitations introduit par les composants électroniques, optoélectroniques et optiques. Une chaîne idéale OCDMA à étalement par séquence directe en temps a été définie comme chaîne de référence. Ce travail exploratoire va permettre lors d'études ultérieures de tenir compte de l'ensemble des paramètres d'une liaison et d'étudier la possibilité d'implantation réaliste du CDMA sur l'optiqueLIMOGES-BU Sciences (870852109) / SudocSudocFranceF

Robust and Reliable Modulation Classification for MIMO Systems

This paper develops a feature-based Automatic Modulation Classification (AMC) algorithm for spatially multiplexed Multiple-Input Multiple-Output (MIMO) systems. The proposed algorithm employs two Higher Order Cumulants (HOCs) of the estimated transmit signal streams as discriminating features, and a multiclass Support VectorMachine (SVM) as a classification system. A multi-classifier classification system is introduced to improve the robustness of the decision made by the classifier at each estimated transmit signal stream. Furthermore, an optimal decision fusion scheme using aMaximum-Likelihood (ML) criterion is also introduced to improve the accuracy and reliability of the final classification decision made in the fusion center. The proposed algorithm shows good performance under different operating conditions, over an acceptable range of SNR, without any prior information about the channel state

Hemodynamic Response Asymmetry of the Prefrontal Cortex During a Cognitive Load Task

Investigations of the prefrontal cortex (PFC) asymmetry have been conducted in neuroscience research during cognitive load using +electroencephalography (EEG) and other neuroimaging techniques. A few studies used functional near-infrared signals (fNIRS) to analyze asymmetry during cognitive load. This study examined the hemodynamic response asymmetry in the PFC area during N-back load memory tasks, including ive, 2-, and 3-back electroencephalography (EEG) and other neuroimaging techniques. A few studies used functional near-infrared signals (fNIRS) to analyze asymmetry during cognitive load. This study examined the hemodynamic response asymmetry in the PFC area during N-back load memory tasks, including 1-, 2-, and 3-back. The investigation results show that the asymmetry index value fluctuates as the level of memory load rises. In particular, the 1-back task's positive asymmetry index value (M = 0.2761,SD = 0.4139) suggested that left-hemisphere activity was more remarkable than right-hemisphere activation. The asymmetry index, on the other hand, revealed a negative value of (M = - 0.2105,SD= 0.4252) and (M = - 0.3665,SD = 1.2472) for the 2-back and 3-back memory tasks, respectively, indicating that the right hemisphere experienced a more significant increase in Hbo activation than the left.YUTP-FRG through PRF under grant number 015LC0-35