Ning Zhang | Signal & Image Processing | Research Excellence Award

Published on by Electrical Engineering

Dr. Ning Zhang | Signal & Image Processing | Research Excellence Award

Postdoctor | Beijing institute of technology | China

Ning Zhang is a researcher in deep learning and remote sensing at the Beijing Institute of Technology, with expertise in computer vision, real-time processing, and onboard intelligent systems. He holds bachelor’s, master’s, and doctoral degrees in electronic information and information and communication engineering, with specialized training in lightweight neural networks and FPGA-based algorithm–hardware co-design. His professional experience includes leading and contributing to nationally funded and institutional projects focused on airborne and satellite AI deployment, where he has played key roles in algorithm development, system architecture design, and technical leadership. His research centers on remote sensing scene classification, object detection, model compression, and energy-efficient neural network accelerators, resulting in high-impact publications in leading IEEE journals, multiple authorized and accepted patents, and a citation record of 337 citations with an h-index of 9 and an i10-index of 8. He has received numerous prestigious scholarships, graduate honors, national-level competition awards, and maintains active engagement in academic and professional communities.

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Featured Publications


UniGeoSeg: Towards Unified Open-World Segmentation for Geospatial Scenes
S. Ni, D. Wang, H. Chen, H. Guo, N. Zhang, J. Zhang
arXiv Preprint · Open-World Geospatial Segmentation


S2Net: Spatial-aligned and Semantic-discriminative Network for Remote Sensing Object Detection
J. Yao, H. Chen, Y. Xie, N. Zhang, M. Yang, L. Chen
IEEE Transactions on Geoscience and Remote Sensing · Top-Tier Journal


High-throughput Energy-efficient Accelerator with Collaborative-Trainable Sparse-Quantization Method for On-Board Remote Sensing Processing
T. Wang, H. Chen, N. Zhang, S. Ni, X. Zhang, L. Chen, W. Li
IEEE Transactions on Geoscience and Remote Sensing · Energy-Efficient AI Hardware


High-Throughput and Energy-Efficient FPGA-Based Accelerator for All Adder Neural Networks
N. Zhang, S. Ni, L. Chen, T. Wang, H. Chen
IEEE Internet of Things Journal · FPGA Acceleration


Q-A2NN: Quantized All-Adder Neural Networks for Onboard Remote Sensing Scene Classification
N. Zhang, H. Chen, L. Chen, J. Wang, G. Wang, W. Liu
Remote Sensing · Lightweight Neural Networks

Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Qiaoning Yang | Signal & Image Processing | Best Researcher Award

Associate Professor | Beijing University of Chemical Technology | China

Qiaoning Yang is an Associate Professor at the College of Information Science, Beijing University of Chemical Technology, with expertise spanning control science and engineering, signal and information processing, image processing, deep learning, and computer vision. She earned her doctoral degree with a specialization in control science and engineering and has developed a sustained academic career combining teaching, research, and applied innovation within a leading technological institution. Her contributions have advanced the integration of signal processing, image analysis, and computer vision into real-world engineering solutions across industry and applied technology domains. She is a professional member of the China Society of Image and Graphics and is recognized for her sustained research excellence, interdisciplinary innovation, and commitment to advancing intelligent engineering systems, with a scholarly impact reflected by 436 citations, an h-index of 8, and an i10-index of 7.

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Featured Publications

Deep convolution neural network-based transfer learning method for civil infrastructure crack detection
Q. Yang, W. Shi, J. Chen, W. Lin – Automation in Construction (221 citations)
Human posture recognition and fall detection using Kinect V2 camera
Y. Xu, J. Chen, Q. Yang, Q. Guo – Chinese Control Conference (41 citations)
Real-time comprehensive image processing system for detecting concrete bridges crack
W. Lin, Y. Sun, Q. Yang, Y. Lin – Computers and Concrete (15 citations)

Mr. Nicholas Otumi | Biomedical Instrumentation | Research Excellence Award

Published on by Electrical Engineering

Mr. Nicholas Otumi | Biomedical Instrumentation | Research Excellence Award

Research Assistant | University of Rochester | United States

Nicholas Otumi is a biomedical imaging researcher and diagnostic imaging specialist affiliated with the University of Rochester, with expertise spanning diagnostic imaging, biomedical engineering, and artificial intelligence–driven medical image analysis. He holds a Master of Science in Diagnostic Imaging with specialization in bioengineering and biomedical engineering, and a Bachelor of Science in Diagnostic Imaging Technology, supported by advanced training in deep learning, signal processing, inverse problems, and clinical imaging. His professional experience includes research assistant roles across academic and clinical settings, where he has contributed to imaging model development, clinical data management, AI-based brain and chest image segmentation, and interdisciplinary healthcare research, alongside leadership and community engagement roles within university environments. His research focuses on medical image quality, user-centered imaging system evaluation, deep learning for disease classification, and the application of advanced MRI and CT techniques in both high- and low-resource settings, with multiple peer-reviewed journal publications and ongoing studies under review. He has received competitive research funding and fellowship recognition, institutional awards for leadership and versatility, and has participated in international scientific schools and specialized workshops in radiochemistry, medical physics, AI diagnostics, and imaging quality assurance. His academic service includes research collaboration, technical support, and scholarly dissemination, reflecting a strong commitment to advancing diagnostic imaging research and education.

Profiles:  Google Scholar | ORCID

Featured Publications

AD Piersson, G Nunoo, E Tettey, N Otumi, User-centered assessment of MRI equipment flexibility, workspace adequacy, user interface usability, and technical proficiency. Appl. Clin. Inform., 2025, 16(5), 1595–1605.

AD Piersson, G Nunoo, K Dzefi-Tettey, N Otumi, Utility of advanced brain MRI techniques for clinical and research purposes in a low-resource setting: A multicentre survey. Next Res., 2025, 100638.

A Piersson, E Frost, K Dzefi-Tettey, N Otumi, H Mumuni, S Kafwimbi, et al., Public attitudes and preferences towards artificial intelligence in diagnostic imaging for clinical diagnosis in a low-resource setting (n = 1,032). Eur. Soc. Radiol. (ECR), 2025.

E Fiagbedzi, J Arkorful, IF Brobbey, E Appiah, S Nyarko, N Otumi, et al., A case of ruptured infrapatellar bursa sac with Baker’s cyst. Radiol. Case Rep., 2025, 20(1), 700–704.

E Fiagbedzi, J Arkorful, E Appiah, N Otumi, I Ofori, PN Gorleku, A rare case of intussusception in a 6-month-old baby. Radiol. Case Rep., 2025, 19(10), 4451–4456.

Arturo Sánchez Pérez | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Prof. Dr. Arturo Sánchez Pérez | Signal & Image Processing | Best Researcher Award

Porfesor Contratado Doctor | Universidad de Murcia | Spain

Arturo J. Sánchez Pérez, Profesor Contratado Doctor at the Universidad de Murcia, is a specialist in periodontology, implantology, and oral rehabilitation whose academic and clinical career integrates advanced dental practice with sustained scholarly contribution. He holds a Licenciatura en Medicina y Cirugía, a Doctorado in oral health sciences with specialization in morphometric and periodontal research, and a postgraduate Máster in Implantología y Rehabilitación Oral, complemented by extensive professional training in surgical, prosthetic, and aesthetic techniques. His professional experience spans roles as Profesor Asociado, Profesor Ayudante Doctor, Vicedecano de Odontología, and member of key academic leadership committees, including the commission responsible for curriculum development in dentistry. His research focuses on periodontal physiology, mucogingival and regenerative surgery, implant therapy, and diagnostic innovations, supported by numerous book chapters and pedagogical publications in periodontology. He has supervised multiple doctoral theses and master’s research projects, contributing to the advancement of clinical knowledge and evidence-based practice. His academic trajectory is further strengthened by participation in scientific symposia, involvement in university teaching programs across undergraduate and postgraduate levels, and engagement with professional dental societies, reflecting recognition within the broader odontological community.

Profile:  ORCID

Featured Publications

Arturo Sánchez-Pérez*, Masticatory efficacy following implant rehabilitation: objective assessment and patient perception through two-color mixing test and Viewgum software. Prosthesis, Accepted.

Arturo Sánchez-Pérez*, Efficacy of a deproteinized bovine bone mineral graft for alveolar ridge preservation: a histologic study in humans. Biomedicines, Accepted.

Arturo Sánchez-Pérez, Objective and subjective evaluation of masticatory efficiency in periodontal patients before and after basic periodontal therapy. Applied Sciences, Accepted.

Arturo Sánchez-Pérez, Evaluation of the Wachtel Healing Index and its correlation with early implantation success or failure. Applied Sciences, Accepted.

Assoc. Prof. Dr. Krzysztof Stepien | Signal & Image Processing | Best Researcher Award

Published on by Electrical Engineering

Assoc. Prof. Dr. Krzysztof Stepien | Signal & Image Processing | Best Researcher Award

Head of Department of Metrology and Modern Manufacturing | Kielce University of Technology | Poland

Assoc. Prof. Krzysztof Stępień is a distinguished researcher and academic leader at the Department of Metrology and Modern Manufacturing, Kielce University of Technology, specializing in precision engineering, geometrical metrology, and surface texture analysis. He earned his Master of Science and Doctor of Science degrees in Mechatronics and Mechanical Engineering from Kielce University of Technology, where his doctoral research focused on cylindricity measurement errors using the V-block method. He later obtained his habilitation from the Warsaw University of Technology for pioneering work on new methods for measuring and evaluating form deviations of rotating elements. Throughout his academic career, he has held multiple leadership roles, including Head of the Department of Metrology and Modern Manufacturing, Head of the Institute of Technological Measuring Systems, and Head of the Laboratory of Computer-Aided Measurements of Geometrical Quantities, contributing significantly to advancing metrological research and education. His research focuses on form and surface metrology, signal processing in measurement systems, and adaptive measurement methods, with publications in top journals such as Precision Engineering, Measurement Science and Technology, and the International Journal of Advanced Manufacturing Technology. Prof. Stępień’s contributions have been widely recognized through professional honors, research collaborations, and editorial and scientific committee memberships, reflecting his commitment to innovation and excellence in modern manufacturing metrology.

Profile: ORCID

Featured Publications

Stępień, K.*, Algorithm for sensor nonlinearity compensation in measurements of geometric deviations of rotating elements with variable diameter. Precision Engineering, Accepted.

Janecki, D., Stępień, K.*, & Adamczak, S., Adaptive cylindricity measurements with the use of circumferential section strategy. Int. J. Adv. Manuf. Technol., 2024, 132, 585–600.

Stępień, K., In situ measurement of cylindricity—Problems and solutions. Precision Engineering, 2014, 38(3), 697–701.

Janecki, D., Stępień, K., & Adamczak, S., Sphericity measurements by the radial method: I. Mathematical fundamentals. Meas. Sci. Technol., 2016, 27(1), 015005.