جستجوی استاد راهنمای دانشگاهی

• Drone Navigation
• Sensor Fusion
• Computer Vision
• +۳ مورد دیگر

Mohamed Alimoussa is a Postdoctoral Researcher at the National Institute of Applied Sciences of Toulouse since June 2025, working in the MICS (Metrology, Identification, Control and Surveillance) group at Espace Clément Ader. His research focuses on multi-instrumentation methods for drone pose estimation to characterize deformations of kite-sails in maritime propulsion systems using sensor fusion and computer vision. He holds a PhD from the University of the Littoral Opal Coast (2020-2024) where he developed compact hybrid descriptors for texture classification in color and hyperspectral imaging. His educational background includes advanced work in feature selection, dimensionality reduction, and GPU-accelerated image processing algorithms. Dr. Alimoussa's research spans drone navigation, sensor fusion (visual odometry, RTK GPS, laser rangefinders), SLAM algorithms, and Digital Image Correlation for mechanical deformation measurement. His work bridges computer vision with mechanical engineering, emphasizing robust real-world applications in non-structured outdoor environments and industrial metrology. His publication record shows consistent innovation in texture analysis and feature engineering, evolving from foundational work on color texture descriptors to current applications in drone-based metrology. Recent publications demonstrate increasing focus on multi-sensor fusion systems and robustness against environmental variables like lighting changes and rapid motion. He actively co-supervises Master's students and interns in texture classification projects while participating in the ANR-funded ESKIF project (JCJC 2024). His experimental work involves collaborations with LMGC (University of Montpellier) and Beyond the Sea for coastal validation trials. As a core member of the MICS research group at Espace Clément Ader, he contributes to metrology systems development and participates in workshops on drone applications for mechanical measurement, maintaining strong industry-academia partnerships for experimental validation.

• Food Science
• Food Analytics
• Biotechnology
• +۶ مورد دیگر

Birthe P Møller Jespersen is an Associate Professor and External Researcher in the Department of Food Science, specializing in Food Analytics and Biotechnology at the University of Copenhagen. Her research focuses on applying advanced analytical techniques to food science problems, particularly in cereal analysis and functional foods. Dr. Jespersen's research interests include: Food Analytics using hyperspectral imaging and near-infrared spectroscopy Barley and cereal analysis for food and brewing applications Functional foods development Application of machine learning and deep learning in food analysis Protein analysis in plant-based foods Food processing and quality assessment Her recent publications demonstrate a strong trend toward integrating advanced computational methods with traditional food analysis techniques. She has been actively working on applying hyperspectral imaging combined with machine learning algorithms to analyze grain quality, protein content, and food processing parameters. Her research bridges the gap between traditional food science and modern data science approaches. Dr. Jespersen has contributed to the scientific community through numerous publications and media appearances discussing food science topics. Her media contributions include discussions on converting animal feed to human food and the importance of grain fibers in healthy diets. Her professional activities include: Research in food analytics and biotechnology Development of analytical methods for food quality assessment Application of advanced imaging and spectroscopy techniques Integration of machine learning with food analysis

Tinghao Fengمشاهده پروفایل

استادیار

• Time Series Analysis
• Data Visualization
• Machine Learning
• +۳ مورد دیگر

Tinghao Feng serves as an Assistant Professor at Appalachian State University, specializing in time-oriented data analysis and visualization systems. His research develops comprehensive tools for exploring temporal patterns across environmental science, healthcare, and finance domains through innovative data mining algorithms and user-friendly software applications. His primary research interests focus on Time Series Analysis and Data Visualization, with significant contributions to Machine Learning applications. He investigates methods for visualizing complex temporal data, developing pattern recognition algorithms, and creating interactive analysis frameworks applicable to environmental monitoring, clinical informatics, and financial forecasting systems. Recent publications reveal a strong emphasis on visual analytics for temporal data, spanning environmental applications (honey bee monitoring, landslide prediction), healthcare informatics (patient messaging systems, pandemic modeling), and remote sensing. Key developments include the TimePool system for univariate time series querying and EVis for environmentally driven event analysis, demonstrating cross-disciplinary impact. Dr. Feng maintains active demonstration systems including TimePool for temporal query visualization and Polynomial Similarity Dependency Trees for linguistic structure analysis, reflecting his commitment to translating research into practical analytical tools for complex data exploration.

• Artificial Intelligence
• Machine Learning
• Data Science
• +۵ مورد دیگر

Professor Kristina Schädler is a faculty member at the West Coast University of Applied Sciences (FH Westküste), where she serves as Professor of Data Processing within the School of Technology. She has been with the university since 2005 and also served as Dean of the Department of Technology. Her academic background includes a PhD in machine learning from TU Berlin, where she was awarded the Chorafas Research Prize for young scientists. West Coast University of Applied Sciences (since 2005) TU Berlin, Institute of Computer Science (1994-1999) Martin Luther University Halle/Wittenberg (1990-1994) Professor Schädler's research focuses on artificial intelligence and machine learning applications, particularly in image processing and data analysis. Her work spans multiple domains including industrial automation, agricultural technology, renewable energy, and animal husbandry. She has led numerous research projects that bridge academic theory with practical industrial applications, with particular emphasis on developing robust image processing systems that can be deployed in real-world settings. Her research portfolio demonstrates a consistent pattern of applying advanced machine learning techniques to solve practical problems across diverse industries. The ANIMET project, which developed facial recognition for horses, and the MaviSeg system for multichannel image segmentation represent her innovative approach to adapting computer vision technologies for specialized applications. Her work often involves close collaboration with industry partners to ensure practical relevance and implementation. Chorafas Research Prize for young scientists Innovationspreis at Equitana (2013) for the ANIMET project Professor Schädler has supervised numerous student theses that have resulted in practical applications across various domains. Her research group has secured funding from multiple sources including the European Commission, BMBF, and regional development agencies. She has established the CICAD project as a sustainable competence center for industrial image processing, which has trained multiple doctoral students through cooperative programs with the University of Lübeck. Her work demonstrates strong industry connections with companies like HIT Hinrichs Innovation + Technik, MBJ Solutions, and Fischer und Tausche Kondensatoren. Her research laboratory focuses on industrial image processing applications, with specialized equipment for 2D/3D imaging, spectral analysis, and machine learning implementation. The CICAD project established a dedicated competence center that continues to develop new applications of image processing technology across multiple industries.

• Spiking Neural Networks
• Neuromorphic Information Processing
• Artificial Neural Networks
• +۷ مورد دیگر

Professor Martin Bogdan serves as a Professor for Neuromorphic Information Processing at the University of Leipzig within the Faculty of Mathematics and Computer Science. His research spans multiple interdisciplinary domains including spiking neural networks, neuromorphic computing, and medical signal processing applications. University: University of Leipzig School: Faculty of Mathematics and Computer Science Department: Department of Neuromorphic Information Processing Office: Augustusplatz 10, Room P 531, 04109 Leipzig Contact: +49 (341) 97-32208 | bogdan@informatik.uni-leipzig.de Professor Bogdan completed his doctorate at the University of Tübingen in 1998 with research focused on signal processing of biological nerve signals for controlling prostheses using artificial neural networks. His educational background includes communications engineering at Offenburg University of Applied Sciences and studies at Université Grenoble I Joseph Fourier. Professor Bogdan's research interests center on neuromorphic information processing, with particular emphasis on spiking neural networks (SNNs), neurological plausible learning algorithms, and the incorporation of dynamics within synaptic efficiency. His work bridges computational neuroscience with practical applications in medical diagnostics, embedded systems, and real-time signal processing. His team has made significant contributions to understanding consciousness in locked-in syndrome patients through EEG analysis and developing innovative approaches to agricultural quality control using hyperspectral imaging and neural networks. The research demonstrates a consistent trajectory from foundational neural network architectures toward increasingly sophisticated neuromorphic computing applications. Analysis of Professor Bogdan's recent publication record reveals a strong focus on advancing spiking neural network architectures, particularly liquid state machines and synaptic dynamics models. His work increasingly bridges theoretical neuroscience with practical applications in medical diagnostics (particularly consciousness assessment in locked-in syndrome patients) and agricultural technology (using hyperspectral imaging for seed purity analysis). A notable emerging theme is the exploration of cognitive concepts like boredom in artificial intelligence systems, suggesting expanding interest in higher-order cognitive modeling within neuromorphic computing frameworks. Professor Bogdan leads the Department of Neuromorphic Information Processing at the University of Leipzig, supervising numerous PhD students and postdoctoral researchers. His team includes researchers specializing in various aspects of neural networks, signal processing, and applications in medical and agricultural domains. The department maintains active collaborations with medical institutions for clinical applications of their research, particularly in the area of brain-computer interfaces and consciousness assessment.

Christoph Krafftمشاهده پروفایل

پژوهشگر

• Raman Spectroscopy
• Infrared Spectroscopy
• Biomedical Imaging
• +۵ مورد دیگر

Christoph Krafft serves as Group Leader ( Arbeitsgruppenleiter ) at the Leibniz Institute of Photonic Technology (Leibniz-IPHT) in Jena, Germany, where he leads the Raman and IR Spectroscopic Analytics research group within the Spectroscopy/Imaging Research Department. His research program bridges physics, chemistry, and biomedical applications through advanced optical technologies. Dr. Krafft's expertise centers on vibrational spectroscopy techniques, particularly Raman and infrared methods. His research spans biomedical diagnostics (cancer detection, cellular analysis, infection mechanisms) and environmental applications (microplastic detection). He has developed innovative instrumentation including specialized Raman imaging systems, 1064 nm fiber probe spectrometers, and high-throughput screening platforms that address fluorescence interference challenges in biological samples. Analysis of his publication record reveals a strategic focus on clinical translation of spectroscopic technologies. His recent work demonstrates progression from fundamental methodology development toward practical diagnostic applications, particularly in intraoperative settings. The consistent collaboration with Jürgen Popp and international research teams across multiple disciplines highlights his position within a robust scientific network focused on advancing optical technologies for real-world challenges. Dr. Krafft actively participates in large-scale collaborative efforts, including international interlaboratory comparisons for microplastic detection standardization. His laboratory at Leibniz-IPHT maintains a dual focus on pushing technical boundaries in spectroscopic instrumentation while ensuring practical applicability in medical and environmental contexts.

• Weed Science
• Precision Agriculture
• Crop-Weed Competition
• +۹ مورد دیگر

Svend Christensen is a Professor at the Department of Plant and Environmental Sciences, Faculty of Science, University of Copenhagen. He has held prominent positions at the Danish Institute of Agricultural Sciences (now Aarhus University), the University of Southern Denmark, and the University of Copenhagen. For 16 years, he served as a professor and head of the Department of Plant and Environmental Sciences with more than 500 employees. Currently, he is a full professor in Crop Science and Technologies. Education: Ph.D., The Royal Veterinary and Agricultural University, 1993 M.Sc. Agr., The Royal Veterinary and Agricultural University, 1988 Svend Christensen's research focuses on establishing the scientific foundation needed to transform agricultural systems to address the dual challenges of feeding a growing global population and preserving the planet's ecological balance. His work explores the interactions between crops and their environment, utilizing advanced technologies such as biologicals, breeding, remote sensing, AI, and robotics to promote sustainable farming practices. He is particularly known for his significant contributions to understanding crop-weed competition and developing weed sensing and site-specific weed management technologies that have advanced precision agriculture globally. His recent publications demonstrate a strong trend toward integrating cutting-edge technologies with traditional agricultural science. The research shows increasing sophistication in plant phenotyping, with a particular emphasis on high-throughput systems, hyperspectral imaging, and AI-driven analysis. There's a clear interdisciplinary approach connecting plant science, data science, and engineering to develop innovative solutions for crop management, disease resistance, and resource efficiency in agricultural systems. Notable Projects: MATRIX project funded by the Novo Nordisk Foundation Nordic Public Private Partnership Plant Phenotyping Project Professor Christensen actively leads several high-impact collaboration platforms including Crop Innovation Denmark (involving four breeding companies and two universities), the Nordic-Baltic Plant Phenotyping Network (with more than 15 breeding companies, universities, and research organizations), and the Plant Biologicals Network (comprising more than 20 members from industries and universities in Denmark and Sweden). His leadership extends to numerous professional activities including service on the Danish Research Council for Technology and Production (2007-2013), coordination of the Collaborative Working Group on 'ICT and Robotics in Agriculture' under the European Commission, and participation in various expert groups related to agricultural technology and policy development.