Designing Precoding and Receive Matrices for Interference Alignment in MIMO Interference Channels

Interference is a key bottleneck in wireless communication systems. Interference alignment is a management technique that align interference from other transmitters in the least possibly dimension subspace at each receiver and provides the remaining dimensions for free interference signal. An uncoordinated interference is an example of interference which cannot be aligned coordinately with interference from coordinated part; consequently, the performance of interference alignment approaches are degraded. In this paper, we propose a rank minimization method to enhance the performance of interference alignment in the presence of uncoordinated interference sources. Firstly, to obtain higher multiplexing gain, a new rank minimization based optimization problem is proposed; then, a new class of convex relaxation is introduced which can reduce the optimal value of the problem and obtain lower rank solutions by expanding the feasibility set. Simulation results show that our proposed method can obtain considerably higher multiplexing gain and sum rate than other approaches in the interference alignment framework

Exploring undergraduates’ perceptions of and engagement in an AI-enhanced online course

In the age of globalization, an internet connection has become essential for enhancing various human activities across the economic, cultural, and defense sectors, among others. This is particularly true for online classrooms. Microsoft Teams, a widely used digital education platform, provides capabilities that allow online teachers to facilitate better interactions and create more effective learning environments in online settings. This study aimed to explore students’ perceptions of synchronous online learning that occurred in an AI-enhanced online course, delivered using MS Teams. As an explorative study that examines the educational intersection of engineering and artificial intelligence, it represents the convergence of these two branches of learning and thus enriches both fields. The research involved 35 online students at the Staffordshire University, with data collected via online questionnaires to gather information about students’ perceptions of online learning through Microsoft Teams. After completing the online course materials, the questionnaires were distributed to students via Google Forms. The data were then descriptively analyzed. The study’s findings revealed that although online learning through Microsoft Teams was a novel experience for the students, the platform’s interactive and engaging learning environment motivated them to participate more actively, ultimately leading to a better comprehension of the course materials. Incorporating AI-enhanced features within the Microsoft Teams platform further augmented the online learning experience, as students appreciated the personalized learning recommendations and real-time feedback, which showcases the synergistic potential of AI and education in the digital age

Interference alignment for one-hop and two-hops MIMO systems with uncoordinated interference

Providing higher data rate is a momentous goal for wireless communications systems, while interference is an important obstacle to reach this purpose. To cope with this problem, interference alignment (IA) has been proposed. In this paper, we propose two rank minimization methods to enhance the performance of IA in the presence of uncoordinated interference, i.e., interference that cannot be properly aligned with the rest of the network and thus is a crucial issue. In this scenario, perfect and imperfect channel state information (CSI) cases are considered. Our proposed approaches employ the l2 and the Schatten-p norms to approximate the rank function, due to its non-convexity. Also, we propose a new convex relaxation to expand the feasible set of our optimization problem, providing lower rank solutions compared to other IA methods from the literature. In addition, we propose a modified weighted-sum method to deal with interference in the relay-aided MIMO interference channel, which employs a set of weighting parameters in order to find more solutions

Test Procedures for Advanced Characterization of Bituminous Binders Employed for Pavement Construction in Public Works Authority Road Projects - State of Qatar

This paper illustrates the approach adopted by the Public Works Authority (Ashghal) of the State of Qatar for the advanced chemical and rheological characterization of bituminous binders (both neat and modified). The ultimate objective of testing is to create a database of employed binders, which may be meaningful for the optimization of the selection of materials and for the consequent enhancement of pavement performance. This paper provides an illustration of employed testing procedure and briefly discusses typical experimental results

An Approach toward Artificial Intelligence Alzheimer's Disease Diagnosis Using Brain Signals

Background: Electroencephalography (EEG) signal analysis is a rapid, low-cost, and practical method for diagnosing the early stages of dementia, including mild cognitive impairment (MCI) and Alzheimer’s disease (AD). The extraction of appropriate biomarkers to assess a subject’s cognitive impairment has attracted a lot of attention in recent years. The aberrant progression of AD leads to cortical detachment. Due to the interaction of several brain areas, these disconnections may show up as abnormalities in functional connectivity and complicated behaviors. Methods: This work suggests a novel method for differentiating between AD, MCI, and HC in two-class and three-class classifications based on EEG signals. To solve the class imbalance, we employ EEG data augmentation techniques, such as repeating minority classes using variational autoencoders (VAEs), as well as traditional noise-addition methods and hybrid approaches. The power spectrum density (PSD) and temporal data employed in this study’s feature extraction from EEG signals were combined, and a support vector machine (SVM) classifier was used to distinguish between three categories of problems. Results: Insufficient data and unbalanced datasets are two common problems in AD datasets. This study has shown that it is possible to generate comparable data using noise addition and VAE, train the model using these data, and, to some extent, overcome the aforementioned issues with an increase in classification accuracy of 2 to 7%. Conclusion: In this work, using EEG data, we were able to successfully detect three classes: AD, MCI, and HC. In comparison to the pre-augmentation stage, the accuracy gained in the classification of the three classes increased by 3% when the VAE model added additional data. As a result, it is clear how useful EEG data augmentation methods are for classes with smaller sample numbers

Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using image tags. Without additional information, this leads to poor localization accuracy. This problem, however, was alleviated by making use of objectness priors to generate foreground/background masks. Unfortunately these priors either require training pixel-level annotations/bounding boxes, or still yield inaccurate object boundaries. Here, we propose a novel method to extract markedly more accurate masks from the pre-trained network itself, forgoing external objectness modules. This is accomplished using the activations of the higher-level convolutional layers, smoothed by a dense CRF. We demonstrate that our method, based on these masks and a weakly-supervised loss, outperforms the state-of-the-art tag-based weakly-supervised semantic segmentation techniques. Furthermore, we introduce a new form of inexpensive weak supervision yielding an additional accuracy boost