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7 results on '"Chikwendu, Ijeoma Amuche"'

1. A Distributed Diffusion Kalman Filter In Multitask Networks

Author

Chikwendu, Ijeoma Amuche, Bensah, Kulevome Delanyo Kwame, Ukwuoma, Chiagoziem Chima, and Ejiyi, Chukwuebuka Joseph

Subjects
Computer Science - Multiagent Systems
Abstract

The Distributed Diffusion Kalman Filter (DDKF) algorithm in all its magnitude has earned great attention lately and has shown an elaborate way to address the issue of distributed optimization over networks. Estimation and tracking of a single state vector collectively by nodes have been the point of focus. In reality, however, there are several multi-task-oriented issues where the optimal state vector for each node may not be the same. Its objective is to know many related tasks simultaneously, rather than the typical single-task problems. This work considers sensor networks for distributed multi-task tracking in which individual nodes communicate with its immediate nodes. A diffusion-based distributed multi-task tracking algorithm is developed. This is done by implementing an unsupervised adaptive clustering process, which aids nodes in forming clusters and collaborating on tasks. For distributed target tracking, an adaptive clustering approach, which gives agents the ability to identify and select through adaptive adjustments of combination weights nodes who to collaborate with and who not to in order to estimate the common state vector. This gave rise to an effective level of cooperation for improving state vector estimation accuracy, especially in cases where a cluster's background experience is unknown. To demonstrate the efficiency of our algorithm, computer simulations were conducted. Comparison has been carried out for the Diffusion Kalman Filter multitask with respect to the Adapt then combine (ATC) diffusion schemes utilizing both static and adaptive combination weights. Results showed that the ATC diffusion schemes algorithm has great performance with the adaptive combiners as compared to static combiners., Comment: 8 pages, 8 figures

Published
2022

3. A Comprehensive Survey on Deep Graph Representation Learning Methods.

Author

Chikwendu, Ijeoma Amuche, Xiaoling Zhang, Agyemang, Isaac Osei, Adjei-Mensah, Isaac, Chima, Ukwuoma Chiagoziem, and Ejiyi, Chukwuebuka Joseph

Subjects
DEEP learning, REPRESENTATIONS of graphs, NEURAL computers, GRAPH theory, EMPIRICAL research
Abstract

There has been a lot of activity in graph representation learning in recent years. Graph representation learning aims to produce graph representation vectors to represent the structure and characteristics of huge graphs precisely. This is crucial since the effectiveness of the graph representation vectors will influence how well they perform in subsequent tasks like anomaly detection, connection prediction, and node classification. Recently, there has been an increase in the use of other deep-learning breakthroughs for data-based graph problems. Graph-based learning environments have a taxonomy of approaches, and this study reviews all their learning settings. The learning problem is theoretically and empirically explored. This study briefly introduces and summarizes the Graph Neural Architecture Search (G-NAS), outlines several Graph Neural Networks' drawbacks, and suggests some strategies to mitigate these challenges. Lastly, the study discusses several potential future study avenues yet to be explored. [ABSTRACT FROM AUTHOR]

Published
2023
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5. Synthetic Aperture Radar Automatic Target Recognition Based on a Simple Attention Mechanism

Author

Ukwuoma, Chiagoziem Chima, Zhiguang, Qin, Tienin, Bole W., Yussif, Sophyani B., Ejiyi, Chukwuebuka Joseph, Urama, Gilbert C., Ukwuoma, Chibueze D., and Chikwendu, Ijeoma Amuche

Subjects
Statistics and Probability, convolutional neural network (CNN), target recognition, Artificial Intelligence, Computer Networks and Communications, Signal Processing, rynthetic aperture radar, IJIMAI, attention model, Computer Vision and Pattern Recognition, Computer Science Applications
Abstract

A simple but effective channel attention module is proposed for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). The channel attention technique has shown recent success in improving Deep Convolutional Neural Networks (CNN). The resolution of SAR images does not surpass optical images thus information flow of SAR images becomes relatively poor when the network depth is raised blindly leading to a serious gradients explosion/vanishing. To resolve the issue of SAR image recognition efficiency and ambiguity trade-off, we proposed a simple Channel Attention module into the ResNet Architecture as our network backbone, which utilizes few parameters yet results in a performance gain. Our simple attention module, which follows the implementation of Efficient Channel Attention, shows that avoiding dimensionality reduction is essential for learning as well as an appropriate cross-channel interaction can preserve performance and decrease model complexity. We also explored the One Policy Learning Rate on the ResNet-50 architecture and compared it with the proposed attention based ResNet-50 architecture. A thorough analysis of the MSTAR Dataset demonstrates the efficacy of the suggested strategy over the most recent findings. With the Attention-based model and the One Policy Learning Rate-based architecture, we were able to obtain recognition rate of 100% and 99.8%, respectively.

Published
2023
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