Original Research Paper
Artificial Intelligence
S. H. Zahiri; R. Iranpoor; N. Mehrshad
Abstract
Background and Objectives: Person re-identification is an important application in computer vision, enabling the recognition of individuals across non-overlapping camera views. However, the large number of pedestrians with varying appearances, poses, and environmental conditions makes this task particularly ...
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Background and Objectives: Person re-identification is an important application in computer vision, enabling the recognition of individuals across non-overlapping camera views. However, the large number of pedestrians with varying appearances, poses, and environmental conditions makes this task particularly challenging. To address these challenges, various learning approaches have been employed. Achieving a balance between speed and accuracy is a key focus of this research. Recently introduced transformer-based models have made significant strides in machine vision, though they have limitations in terms of time and input data. This research aims to balance these models by reducing the input information, focusing attention solely on features extracted from a convolutional neural network model. Methods: This research integrates convolutional neural network (CNN) and Transformer architectures. A CNN extracts important features of a person in an image, and these features are then processed by the attention mechanism in a Transformer model. The primary objective of this work is to enhance computational speed and accuracy in Transformer architectures. Results: The results obtained demonstrate an improvement in the performance of the architectures under consistent conditions. In summary, for the Market-1501 dataset, the mAP metric increased from approximately 30% in the downsized Transformer model to around 74% after applying the desired modifications. Similarly, the Rank-1 metric improved from 48% to approximately 89%.Conclusion: Indeed, although it still has limitations compared to larger Transformer models, the downsized Transformer architecture has proven to be much more computationally efficient. Applying similar modifications to larger models could also yield positive effects. Balancing computational costs while improving detection accuracy remains a relative goal, dependent on specific domains and priorities. Choosing the appropriate method may emphasize one aspect over another.
Original Research Paper
Artificial Intelligence
S. Kabiri Rad; V. Afshin; S. H. Zahiri
Abstract
Background and Objectives: When dealing with high-volume and high-dimensional datasets, the distribution of samples becomes sparse, and issues such as feature redundancy or irrelevance arise. Dimensionality reduction techniques aim to incorporate correlation between features and map the original features ...
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Background and Objectives: When dealing with high-volume and high-dimensional datasets, the distribution of samples becomes sparse, and issues such as feature redundancy or irrelevance arise. Dimensionality reduction techniques aim to incorporate correlation between features and map the original features into a lower dimensional space. This usually reduces the computational burden and increases performance. In this paper, we study the problem of predicting heart disease in a situation where the dataset is large and (or) the proportion of instances belonging to one class compared to others is significantly low.Methods: We investigated three of the prominent dimensionality reduction techniques, including Principal Component Analysis (PCA), Information Bottleneck (IB) and Variational Autoencoder (VAE) on popular classification algorithms. To have adequate samples in all classes to properly feed the classifier, an efficient data balancing technique is used to compensate for fewer positives than negatives. Among all data balancing methods, a SMOTE-based method is selected, which generates new samples at the boundary of the samples distribution and avoids the synthesis of noise and redundant data. Results: The experimental results show that VAE-based method outperforms other dimensionality reduction algorithms in the performance measures. The proposed hybrid method improves accuracy to 97.1% and sensitivity to 99.2%.Conclusion: Finally, it can be concluded that the combination of VAE with oversampling algorithms can significantly enhance system performance as well as computational time.
Original Research Paper
Wireless Networks
F. Rahdari; M. Sheikh-Hosseini; Mina Jamshidi
Abstract
Background and Objectives: This research addresses the performance drop of edge users in downlink non-orthogonal multiple access (NOMA) systems. The challenging issue is paring the users, which becomes more critical in the case of edge users due to poor signal quality as well as the similarity of users' ...
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Background and Objectives: This research addresses the performance drop of edge users in downlink non-orthogonal multiple access (NOMA) systems. The challenging issue is paring the users, which becomes more critical in the case of edge users due to poor signal quality as well as the similarity of users' channel gains.Methods: To study this issue, the capabilities of intelligent reflecting surface (IRS) technology are investigated to enhance system performance by modifying the propagation environment through intelligent adjusting of the IRS components. In doing so, an optimization problem is formulated to determine the optimal user powers and phase shifts of IRS elements. The objective is to maximize the system sum rate by considering the channel gain difference constraint. Additionally, the study addresses the effect of the IRS location in the cell on system performance.Results: The proposed approach is evaluated for various scenarios and compared with benchmarks in terms of average bit error rate (BER) and sum rate. The numerical results show that IRS-assisted NOMA improves the performance of edge users and distributes resources more fairly compared to conventional NOMA.Conclusion: Simulation results demonstrate that using IRS-assisted NOMA can effectively address the issue of edge users. By modifying the channel between the BS and the edge users using IRS, the channel gain difference of the users is increased, thereby enhancing the overall system performance. Particularly, the proposed IRS-NOMA system offers a gain of about 4 dB at a BER of 0.01 and 3 dB at the sum rate of 0.1 bps/Hz compared to conventional NOMA. In addition, it was observed that the location of the IRS in the cell affects the system's performance.
