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All Journal Buletin Ilmiah Sarjana Teknik Elektro
Abdullah, Hikmat N.
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Electrical & Electronics Engineering

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Implementation of Filter Bank Multicarrier Transmitter Using Universal Software Radio Peripheral Abdulhussein, Ali A.; Abdullah, Hikmat N.; Marhoon, Hamzah M.
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13876

Abstract

Particularly in 5G and beyond, Filter Bank Multicarrier (FBMC) modulation is becoming more widely acknowledged as a potent substitute for traditional Orthogonal Frequency Division Multiplexing (OFDM) in upcoming wireless communication systems. FBMC is robust in situations impacted by multipath fading, synchronisation errors, and spectral leakage because of its improved spectral efficiency, superior time-frequency localisation, and removal of cyclic prefix. The Universal Software Radio Peripheral (USRP) N210, combined with GNU Radio version 3.,7 is used in this paper to design and implement a working FBMC transmitter. The system architecture supports 32 and 64 subcarrier allocations that can be changed to accommodate different communication scenarios, allowing for real-time signal generation and transmission. While SDR hardware was used for transmission and reception, software was used to develop the entire signal processing chain, including modulation, prototype filtering, and transmission. To evaluate performance metrics like constellation accuracy, spectrum containment, and signal quality, a number of experiments were carried out. These tests validate the viability of the suggested SDR-based architecture in real-world settings by confirming the successful generation and over-the-air transmission of FBMC signals. Notably, the work tackles important real-time implementation issues like subcarrier reconfigurability, synchronisation overhead, and hardware limitations. Along with its usefulness, this implementation lays the groundwork for future improvements by incorporating clever optimization algorithms like Harris Hawks Optimization, Particle Swarm Optimization, and Genetic Algorithms. Aspects like filter design, subcarrier spacing, and power efficiency can all be enhanced by utilising these algorithms. According to the results, the suggested system is in a good position to be implemented in adaptive and cognitive radio applications, where resilience to changing channel conditions and effective spectrum utilization is essential.
Artificial Intelligence and IoT for Riverine Oil Spill Detection: A Focused Review and Proposed Adaptive Edge Framework AL-Khafahji, Shireen M.; Abdullah, Hikmat N.
Buletin Ilmiah Sarjana Teknik Elektro Vol. 8 No. 1 (2026): February
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v8i1.14677

Abstract

Riverine oil spills are more challenging to detect than marine spills due to shallow depths, high turbidity, and rapidly changing hydrodynamics, which degrade the performance of satellite- and SAR-based detection methods. This review examines how artificial intelligence and the Internet of Things can deliver accurate, low-latency detection in freshwater and defines an AI-IoT system as distributed river-edge nodes with RGB/NIR cameras, thermal infrared sensors, UV/fluorometric probes, and turbidity/multispectral units running lightweight deep models on low-power hardware and networked via LPWAN or NB-IoT with optional federated coordination. The novel contribution is a hydrodynamics-aware, adaptive framework that embeds river flow and turbidity, couples explicit constraints on edge compute, energy, and inference latency, and derives multi-sensor fusion logic from comparative synthesis. Performance is organized along accuracy, decision latency, deployment cost, and environmental adaptability. Using a structured narrative review with scoping elements, the research screened 145 records from major databases. It synthesized 47 peer-reviewed studies (2020-2025), harmonized definitions, and applied descriptive synthesis to manage heterogeneous metrics and protocols. Results show that SAR and hyperspectral methods that excel in marine or controlled settings often degrade in narrow, turbid rivers because of clutter and revisit latency. In contrast, hybrid AI-IoT architectures employing compact CNN/Transformer variants at the edge report high accuracy with millisecond-scale inference and moderate power budgets. Limitations include heterogeneous reporting, non-standard datasets, and limited multi-site validation. The framework and synthesis motivate open benchmarks and coordinated river trials to standardize evaluation and accelerate translation.
Co-Authors Abdulhussein, Ali A. AL-Khafahji, Shireen M. Marhoon, Hamzah M.