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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) International Journal of Engineering, Science and Information Technology
Vian S. Al-Doori
Al-Rafidain University College
Astronomy Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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A simplified spatial Modulation MISO-OFDM scheme Vian S. Al-Doori; Emad H. Al-Hemiary
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 4: August 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i4.13873

Abstract

Index modulation is one of the promising techniques for future communications systems due to many improvement over the classical Orthogonal frequency division multiplexing systems such as single RF chain, increased throughput for the same modulation order, achieved tradeoff between the efficiencies of the power and the spectral, and elimination of inter-channel interference. Many forms of index modulation researches exist where symbols are conveyed in antennas, subcarriers, time slots, and the space-time matrix.  Spatial modulation is one member of index modulation family where symbols are conveyed in activating transmit/receive antennas. In this paper, a modification to a standard multiple input single output scheme by integrating spatial modulation using simplified mathematical procedure is achieved. In the transmitter side, data and activation symbols are distributed simultaneously using mathematical module and floor functions. At the receiver, a simplified maximum likelihood detector is used to obtain transmitted pair of symbols. To verify this, MATLAB SIMULINK is used to simulate a downlink system where spatial modulation is applied to a base station. Results for different transmit antenna number and modulation order are obtained in the form of bit error rate versus signal to noise ratio.
Neuromorphic Hardware Design for Energy-Aware Artificial Intelligence Computation Yaser Issam Hamodi Aljanabi; Salah Yehia Hussain; Darin Shafiq Salim; Vian S. Al-Doori; Jassim Mohamed Brieg; M. Batumalay
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.1279

Abstract

Rapid growth of the energy-efficient artificial intelligence (AI) systems has attracted substantial interest in neuromorphic computing that emulates organization and actions of a biological neural?system to support low-power, event-driven information processing. In this work, we propose a neuromorphic hardware architecture for energy-efficient AI computing that utilizes spiking neural networks and monolithic?vertical integration to improve the performance of a variety of vision tasks. The architecture is tested against three benchmark datasets— MNIST, N-MNIST, and DVS128,?representing static, spiking and dynamic input modalities, respectively. The performance metrics, such as energy efficiency, inference latency,?throughput, classification accuracy, and unified Energy Efficiency Index (EEI) are compared to characterize the generalization power of the system in different processing environments. Experimental results show that the proposed chip provides a sharply lower energy per inference with a competitively performing accuracy over conventional AI?accelerators, including GPU-based and microcontroller platforms. Additionally, the hardware achieves sub-2 ms inference latency and high throughput, indicating suitability for real-time, embedded AI applications. Comparative analysis with existing neuromorphic platforms highlights the advantage of architectural co-design in balancing energy and performance constraints. While the absence of on-chip learning presents a limitation, the system offers a scalable foundation for edge AI systems requiring efficient, continuous inference. Future directions include integrating adaptive learning mechanisms and extending evaluation to broader AI domains as a process innovation.
Data-Driven Cloud Systems for Renewable Energy Optimization Hayder Abdulameer Yousif; Salah Yehia Hussain; Taif Sami Hassan Ali; Vian S. Al-Doori; Ahmed Sabah; M. Batumalay
International Journal of Engineering, Science and Information Technology Vol 5, No 2 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i2.1729

Abstract

The growing share of renewable generation in global power systems creates operational instability due to the volatile nature of solar, wind, and hydropower. This study presents a novel cloud-edge integrated model designed to enhance the performance and efficiency of these renewable sources through a data-centric approach. The proposed architecture relies on an IoT-enabled sensor network for real-time data gathering, processed through a hybrid infrastructure combining edge-level filtration with cloud-based analytics. For energy output prediction, we compared Linear Regression, Random Forest, and Long Short-Term Memory (LSTM) models, with LSTM demonstrating superior performance. To optimize operations, a multi-objective Genetic Algorithm was implemented to simultaneously minimize energy losses and costs while improving grid utilization balance. Furthermore, exergy-based modeling was employed to evaluate the thermodynamic quality of energy transformations. The results confirmed that the system significantly improved predictive accuracy, responsiveness, and energy savings. Under varying loads, the system maintained low latency and high energy allocation efficiency, validating its real-time adaptability. In summary, this research delivers a modular and scalable solution for intelligent energy management, highlighting the power of predictive analytics and adaptive control in creating data-driven, next-generation sustainable energy efficiency systems.
Co-Authors Ahmed Sabah Darin Shafiq Salim Emad H. Al-Hemiary Hayder Abdulameer Yousif Jassim Mohamed Brieg M. Batumalay Salah Yehia Hussain Taif Sami Hassan Ali Yaser Issam Hamodi Aljanabi