Department of Electrical and Electronics Engineering

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Browsing Department of Electrical and Electronics Engineering by Author "Abel, Kamagara"

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    Efficient recovery of linear predicted coefficients based on adaptive steepest descent algorithm in signal compression for end-to-end communications
    (Journal of Electrical and Computer Engineering, 2025-01) Abel, Kamagara; Abbas Kagudde; Baris Atakan
    The efficiency of recovery and signal decoding efficacy at the receiver in end-to-end communications using linearly predicted coefficients are susceptible to errors, especially for highly compressed signals. In this paper, we propose a method to efficiently recover linearly predicted coefficients for high signal compression for end-to-end communications. Herein, the steepest descent algorithm is applied at the receiver to decode the affected linear predicted coefficients. Tis algorithm is used to estimate the unknown frequency, time, and phase. Subsequently, the algorithm facilitates down-conversion, time and carrier recovery, equalization, and correlation processes. To evaluate the feasibility of the proposed method, parameters such as multipath interference, additive white Gaussian noise, timing, and phase noise are modeled as channel errors in signal compression using the software-defined receiver. Our results show substantial recovery efficiency with noise variance between 0 and y × 10E − 3, where y lies between 0 and 10 using the modeled performance metrics of bit error rate, symbol error rate, and mean square error. This is promising for modeling software-defined networks using highly compressed signals in end-to-end communications.
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    Optimized Multi-cloud Service Orchestration in Cloud Computing.
    (Springer Nature, 2025-02-20) Abel, Kamagara; Susan Babirye; Doniz Borsos
    This paper presents an optimization model for service orchestration in multi-cloud environments. The objective is to minimize the total cost of deploying a set of services over multiple clouds while ensuring that quality of service requirements are met. The proposed model considers the heterogeneity of cloud resources and the interdependence among the services. A case study is presented to demonstrate the effectiveness of the proposed approach. The results show that the proposed model can achieve significant cost of saving while satisfying QoS requirements. The execution time of the algorithm is also analyzed, and it is found that it increases with the number of network elements. The study provides a framework for efficient service orchestration in multi-cloud environments, which can be extended to include additional constraints and objectives. The findings of this study are promising for practical applications for cloud service providers and users, who can benefit from the proposed optimization algorithm to archive cost-effective service orchestration while meeting QoS requirements.