Ibrahim Akinjobi Aromoye | Computer Vision | Best Researcher Awards

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Mr.Ibrahim Akinjobi Aromoye | Computer Vision | Best Researcher Awards

Aromoye Akinjobi Ibrahim is a dedicated researcher in Electrical and Electronic Engineering, currently pursuing an MSc (Research) at Universiti Teknologi PETRONAS, Malaysia. His research focuses on hybrid drones for pipeline inspection, integrating machine learning to enhance surveillance capabilities. With a B.Eng. in Computer Engineering from the University of Ilorin, Nigeria, he has excelled in robotics, artificial intelligence, and digital systems. Aromoye has extensive experience as a research assistant, STEM educator, and university teaching assistant, contributing to 5G technology, UAV development, and machine learning applications. He has authored multiple research papers in reputable journals and conferences. A proactive leader, he has held executive roles in student associations and led innovative projects. His expertise spans embedded systems, IoT, and cybersecurity, complemented by certifications in Python, OpenCV, and AI-driven vision systems. He actively contributes to academic peer review and professional development, demonstrating a commitment to technological advancements and education.

Profile

Education šŸŽ“

Aromoye Akinjobi Ibrahim is pursuing an MSc (Research) in Electrical and Electronic Engineering at Universiti Teknologi PETRONAS (2023-2025), focusing on hybrid drones for pipeline inspection under the supervision of Lo Hai Hiung and Patrick Sebastian. His research integrates machine learning with air buoyancy technology to enhance UAV flight time. He holds a B.Eng. in Computer Engineering from the University of Ilorin, Nigeria (2015-2021), graduating with a Second Class Honors (Upper) and a CGPA of 4.41/5.0. His undergraduate thesis involved developing a smart bidirectional digital counter with a light control system for energy-efficient automation. Excelling in digital signal processing, AI applications, robotics, and software engineering, he has consistently demonstrated technical excellence. His academic journey is enriched with top grades in core engineering courses and hands-on experience in embedded systems, IoT, and AI-driven automation, making him a skilled researcher and developer in advanced engineering technologies.

Experience šŸ‘Øā€šŸ«

Aromoye has diverse experience spanning research, teaching, and industry. As a Graduate Research Assistant at Universiti Teknologi PETRONAS (2023-present), he specializes in hybrid drone development, 5G technologies, and machine learning for UAVs. His contributions include designing autonomous systems and presenting research at international conferences. Previously, he was an Undergraduate Research Assistant at the University of Ilorin (2018-2021), where he worked on digital automation and AI-driven projects. In academia, he has been a Teaching Assistant at UTP, instructing courses in computer architecture, digital systems, and electronics. His industry roles include STEM Educator at STEMCafe (2022-2023), where he taught Python, robotics, and electronics, and a Mobile Games Development Instructor at Center4Tech (2019-2021), guiding students in game design. He also worked as a Network Support Engineer at the University of Ilorin (2018). His expertise spans AI, IoT, and automation, making him a versatile engineer and educator.

Awards & Recognitions šŸ…

Aromoye has received prestigious scholarships and leadership recognitions. He is a recipient of the Yayasan Universiti Teknologi PETRONAS (YUTP-FRG) Grant (2023-2025), a fully funded scholarship supporting his MSc research in hybrid drones. As an undergraduate, he demonstrated leadership by serving as President of the Oyun Studentsā€™ Association at the University of Ilorin (2019-2021) and previously as its Public Relations Officer (2018-2019). He led several undergraduate research projects, including developing a smart bidirectional digital counter with a light controller system, earning accolades for innovation in automation. His contributions extend to professional peer review for IEEE Access and Results in Engineering. Additionally, he has attained multiple certifications in cybersecurity (MITRE ATT&CK), IoT, and AI applications, reinforcing his technical expertise. His dedication to academic excellence, leadership, and research impact continues to shape his career in engineering and technology.

Research Interests šŸ”¬

Aromoyeā€™s research revolves around hybrid UAVs, AI-driven automation, and 5G-enabled surveillance systems. His MSc thesis at Universiti Teknologi PETRONAS explores the development of a Pipeline Inspection Air Buoyancy Hybrid Drone, enhancing flight efficiency through a combination of lighter-than-air and heavier-than-air technologies. His work integrates deep learning-based object detection algorithms for real-time pipeline monitoring. He has contributed to multiple research publications in IEEE Access, Neurocomputing, and Elsevier journals, covering UAV reconnaissance, transformer-based pipeline detection, and swarm intelligence. His research interests extend to AI-driven control systems, autonomous robotics, and IoT-based energy-efficient automation. Additionally, he investigates cybersecurity applications in UAVs and smart embedded systems. His interdisciplinary expertise enables him to develop innovative solutions for industrial surveillance, automation, and smart infrastructure, positioning him as a leading researcher in AI-integrated engineering technologies.

PublicationsĀ 

  • Significant Advancements in UAV Technology for Reliable Oil and Gas Pipeline Monitoring

    Computer Modeling in Engineering & Sciences
    2025-01-27 |Ā Journal article
    DOI:Ā 10.32604/cmes.2025.058598
    Part ofISSN:Ā 1526-1506
    CONTRIBUTORS:Ā Ibrahim Akinjobi Aromoye;Ā Hai Hiung Lo;Ā Patrick Sebastian;Ā Shehu Lukman Ayinla;Ā Ghulam E Mustafa Abro

  • Real-Time Pipeline Tracking System on a RISC-V Embedded System Platform

    14th IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE 2024
    2024 |Ā Conference paper
    DOI:Ā 10.1109/ISCAIE61308.2024.10576323
    EID:

    2-s2.0-85198901224

    Part ofĀ ISBN:Ā 9798350348798
    CONTRIBUTORS:Ā Wei, E.S.S.;Ā Aromoye, I.A.;Ā Hiung, L.H.

 

Alvaro Garcia | Computer vision | Best Researcher Award

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Dr. Alvaro Garcia | Computer vision | Best Researcher Award

Ɓlvaro GarcĆ­a MartĆ­n es Profesor Titular en la Universidad AutĆ³noma de Madrid, especializado en visiĆ³n por computadora y anĆ”lisis de video. šŸŽ“ Obtuvo su tĆ­tulo de Ingeniero de TelecomunicaciĆ³n en 2007, su MĆ”ster en IngenierĆ­a InformĆ”tica y Telecomunicaciones en 2009 y su Doctorado en 2013, todos en la Universidad AutĆ³noma de Madrid. šŸ« Ha trabajado en detecciĆ³n de personas, seguimiento de objetos y reconocimiento de eventos, con mĆ”s de 22 artĆ­culos en revistas indexadas y 28 en congresos. šŸ“ Ha realizado estancias en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. šŸŒ Su investigaciĆ³n ha contribuido al desarrollo de sistemas de videovigilancia inteligentes, anĆ”lisis de secuencias de video y procesamiento de seƱales multimedia. šŸ“¹ Ha sido reconocido con prestigiosos premios y ha participado en mĆŗltiples proyectos europeos de innovaciĆ³n tecnolĆ³gica. šŸš€

Profile

Education šŸŽ“

šŸŽ“ Ingeniero de TelecomunicaciĆ³n por la Universidad AutĆ³noma de Madrid (2007). šŸŽ“ MĆ”ster en IngenierĆ­a InformĆ”tica y Telecomunicaciones con especializaciĆ³n en Tratamiento de SeƱales Multimedia en la Universidad AutĆ³noma de Madrid (2009). šŸŽ“ Doctor en IngenierĆ­a InformĆ”tica y TelecomunicaciĆ³n por la Universidad AutĆ³noma de Madrid (2013). Su formaciĆ³n ha sido complementada con estancias en reconocidas universidades internacionales, incluyendo Carnegie Mellon University (EE.UU.), Queen Mary University (Reino Unido) y la Technical University of Berlin (Alemania). šŸŒ Durante su doctorado, recibiĆ³ la beca FPI-UAM para la realizaciĆ³n de su investigaciĆ³n. Su sĆ³lida formaciĆ³n acadĆ©mica le ha permitido contribuir significativamente al campo del anĆ”lisis de video y visiĆ³n por computadora, consolidĆ”ndose como un experto en la detecciĆ³n, seguimiento y reconocimiento de eventos en secuencias de video. šŸ“¹

Experience šŸ‘Øā€šŸ«

šŸ”¬ Se uniĆ³ al grupo VPU-Lab en la Universidad AutĆ³noma de Madrid en 2007. šŸ“” De 2008 a 2012, fue becario de investigaciĆ³n (FPI-UAM). šŸŽ“ Entre 2012 y 2014, trabajĆ³ como Profesor Ayudante. šŸ‘Øā€šŸ« De 2014 a 2019, fue Profesor Ayudante Doctor. šŸ“š De 2019 a 2023, ocupĆ³ el cargo de Profesor Contratado Doctor. šŸ›ļø Desde septiembre de 2023, es Profesor Titular en la Universidad AutĆ³noma de Madrid. šŸ† Ha participado en mĆŗltiples proyectos europeos sobre videovigilancia, transmisiĆ³n de contenido multimedia y reconocimiento de eventos, incluyendo PROMULTIDIS, ATI@SHIVA, EVENTVIDEO y MobiNetVideo. šŸš€ Ha realizado estancias de investigaciĆ³n en Carnegie Mellon University, Queen Mary University y Technical University of Berlin. šŸŒ Su experiencia docente abarca asignaturas en IngenierĆ­a de Telecomunicaciones, IngenierĆ­a InformĆ”tica e IngenierĆ­a BiomĆ©dica.

Research Interests šŸ”¬

šŸŽÆ Su investigaciĆ³n se centra en la visiĆ³n por computadora, el anĆ”lisis de secuencias de video y la inteligencia artificial aplicada a entornos de videovigilancia. šŸ“¹ Especialista en detecciĆ³n de personas, seguimiento de objetos y reconocimiento de eventos en video. šŸ§  Desarrolla algoritmos de aprendizaje profundo y visiĆ³n artificial para mejorar la seguridad y automatizaciĆ³n en ciudades inteligentes. šŸ™ļø Ha trabajado en proyectos sobre videovigilancia, transmisiĆ³n multimedia y detecciĆ³n de anomalĆ­as en video. šŸ”¬ Su investigaciĆ³n incluye procesamiento de imĆ”genes, anĆ”lisis semĆ”ntico y redes neuronales profundas. šŸš€ Participa activamente en proyectos internacionales y colabora con universidades como Carnegie Mellon, Queen Mary y TU Berlin. šŸŒ Ha publicado en IEEE Transactions on Intelligent Transportation Systems, Sensors y Pattern Recognition, consolidĆ”ndose como un referente en el campo de la visiĆ³n por computadora. šŸ“œ

Awards & Recognitions šŸ…

šŸ„‡ Medalla “Juan LĆ³pez de PeƱalver” 2017, otorgada por la Real Academia de IngenierĆ­a. šŸ“œ Reconocimiento por su contribuciĆ³n a la ingenierĆ­a espaƱola en el campo de la visiĆ³n por computadora y anĆ”lisis de video. šŸ›ļø Ha recibido financiaciĆ³n para mĆŗltiples proyectos de investigaciĆ³n europeos y nacionales. šŸ”¬ Ha participado en iniciativas de innovaciĆ³n en videovigilancia y anĆ”lisis de video para seguridad. šŸš€ Sus contribuciones han sido publicadas en las principales conferencias y revistas cientĆ­ficas del Ć”rea. šŸ“š Su trabajo ha sido citado mĆ”s de 4500 veces y cuenta con un Ć­ndice h de 16 en Google Scholar. šŸ“Š

PublicationsĀ 

1. Rafael Martƭn-Nieto, Ɓlvaro Garcƭa-Martƭn, Alexander G. Hauptmann, and Jose. M.
MartĆ­nez: ā€œAutomatic vacant parking places management system using multicamera
vehicle detectionā€. IEEE Transactions on Intelligent Transportation Systems, Volume 20,
Issue 3, pp. 1069-1080, ISSN 1524-9050, March 2019.

2. Rafael Martƭn-Nieto, Ɓlvaro Garcƭa-Martƭn, Jose. M. Martƭnez, and Juan C. SanMiguel:
ā€œEnhancing multi-camera people detection by online automatic parametrization using
detection transfer and self-correlation maximizationā€. Sensors, Volume 18, Issue 12, ISSN
1424-8220, December 2018.

3. Ɓlvaro GarcĆ­a-MartĆ­n, Juan C. SanMiguel and Jose. M. MartĆ­nez: ā€œCoarse-to-fine adaptive
people detection for video sequences by maximizing mutual informationā€. Sensors,
Volume 19, Issue 4, ISSN 1424-8220, January 2019.

4. Alejandro LĆ³pez-Cifuentes, Marcos Escudero-ViƱolo, JesĆŗs BescĆ³s and Ɓlvaro GarcĆ­aMartĆ­n: ā€œSemantic-Aware Scene Recognitionā€. Pattern Recognition. Accepted February
2020.

5. Paula Moral, Ɓlvaro Garcƭa-Martƭn, Marcos Escudero ViƱolo, Jose M. Martinez, Jesus
BescĆ³s, Jesus PeƱuela, Juan Carlos Martinez, Gonzalo Alvis: ā€œTowards automatic waste
containers management in cities via computer vision: containers localization and geopositioning in city mapsā€. Waste Management, June 2022.

6. Javier Montalvo, Ɓlvaro GarcĆ­a-MartĆ­n, Jesus BescĆ³s: ā€œExploiting Semantic Segmentation
to Boost Reinforcement Learning in Video Game Environmentsā€. Multimedia Tools and
Applications. September 2022.

7. Paula Moral, Ɓlvaro GarcĆ­a-MartĆ­n, Jose M. Martinez, Jesus BescĆ³s: ā€œEnhancing Vehicle
Re-Identification Via Synthetic Training Datasets and Re-ranking Based on Video-Clips
Informationā€. Multimedia Tools and Applications. February 2023.

8. Roberto Alcover-Couso, Juan C. SanMiguel, Marcos Escudero-ViƱolo and Alvaro GarciaMartin: ā€œOn exploring weakly supervised domain adaptation strategies for semantic
segmentation using synthetic dataā€. Multimedia Tools and Applications. February 2023.

9. Juan Ignacio Bravo PĆ©rez-Villar, Ɓlvaro GarcĆ­a-MartĆ­n, JesĆŗs BescĆ³s, Marcos EscuderoViƱolo: ā€œSpacecraft Pose Estimation: Robust 2D and 3D-Structural Losses and
Unsupervised Domain Adaptation by Inter-Model Consensusā€. IEEE Transactions on
Aerospace and Electronic Systems. August 2023.

10. Javier Montalvo, Ɓlvaro GarcĆ­a-MartĆ­n, JosĆ© M. Martinez. “An Image-Processing Toolkit
for Remote Photoplethysmography”, Multimedia Tools and Applications. July 2024.

11. Juan Ignacio Bravo PĆ©rez-Villar, Ɓlvaro GarcĆ­a-MartĆ­n, JesĆŗs BescĆ³s, Juan C. SanMiguel:
ā€œTest-Time Adaptation for Keypoint-Based Spacecraft Pose Estimation Based on
Predicted-View Synthesisā€. IEEE Transactions on Aerospace and Electronic Systems.
May 2024.

12. Kirill Sirotkin, Marcos Escudero-ViƱolo, Pablo Carballeira, Ɓlvaro Garcƭa-Martƭn:
ā€œImproved Transferability of Self-Supervised Learning Models Through Batch
Normalization Finetuningā€. Applied Intelligence. Aug 2024.

13. Javier GalƔn, Miguel GonzƔlez, Paula Moral, Ɓlvaro Garcƭa-Martƭn, Jose M. Martinez:
ā€œTransforming Urban Waste Collection Inventory: AI-Based Container Classification and
Re-Identificationā€. Waste Management, Feb 2025.

Raveendra Pilli | Image Processing | Best Researcher Award

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Mr. Raveendra Pilli | Image Processing | Best Researcher Award

He mentored B.Tech. projects focused on the early detection of Alzheimerā€™s Disease. One project involved utilizing multi-modality neuroimaging techniques, where MRI and PET images were collected from the OASIS database, preprocessed, and robust features were extracted for classification. MATLAB and the SPM-12 toolbox were used for this task. Another project focused on the early detection of Alzheimerā€™s Disease using deep learning networks, where an MRI dataset from the ADNI database was collected, preprocessed, and the performance was compared with baseline algorithms. For this project, he used MATLAB and Python.

NIT-Silchar, India

Profile

Education

A dedicated research scholar with a Ph.D. in Electronics and Communication Engineering from the National Institute of Technology Silchar (Thesis Submitted, CGPA 9.0), specializing in brain age prediction and early detection of neurological disorders using neuroimaging modalities. With extensive teaching experience, a strong passion for research, and a proven ability to develop engaging curricula, deliver effective lectures, and guide students toward academic success, I am committed to contributing to the field through research, publications, and presentations. My academic journey includes an M.Tech. from JNTU Kakinada (76.00%, 2011) and a B.Tech. from JNTU Hyderabad (65.00%, 2007), along with a strong foundational background in science, having completed 10+2 (MPC) with 89.00% in 2003 and SSC with 78.00% in 2001.

Work experience

He worked as a Junior Research Fellow at the National Institute of Technology, Silchar, Assam, from July 2021 to June 2023, where he assisted professors with course delivery for Basic Electronics, conducted laboratory sessions, graded assignments, and provided office hours for student support. From July 2023 to December 2024, he served as a Senior Research Fellow at the same institute, taking on additional responsibilities, including mentoring B.Tech. projects and assisting with Digital Signal Processing laboratory duties. Prior to his research roles, he was an Assistant Professor at SRK College of Engineering and Technology, Vijayawada, Andhra Pradesh, where he taught courses such as Networks Theory, Digital Signal Processing, RVSP, SS, and LICA. He utilized innovative teaching methods, including active learning techniques, to enhance student engagement and learning outcomes. He also mentored undergraduate research projects in image processing and received positive student evaluations for his teaching effectiveness.

Publication