Manager at MediaTek.
Ching-Yeh Chen received a B.S. degree in electrical engineering and a Ph.D. degree in electronics engineering from National Taiwan University (NTU), Taipei, Taiwan, in 2002 and 2006, respectively.
Chen is currently a Senior Department Manager with the Multimedia Technology Development Division, MediaTek Inc. He started attending international video coding standard meetings held by MPEG/VCEG in 2010.
His current research interests include image and video coding, image and video processing, video processing, and related hardware architectures.
Patent №: 11870991 (January 9, 2024) – Method and apparatus of encoding or decoding video blocks with constraints during block partitioning. This invention introduces innovative video processing methods that involve applying a mode constraint to a block, splitting it into multiple blocks, and ensuring that all the blocks within the original block are processed using the same prediction mode. This improved process contributes to enhanced video efficiency and compression.
Patent №: 11870978 (January 9, 2024) – Method and apparatus of matrix based intra prediction in image and video processing. This invention presents an intra prediction method that utilizes matrix-based techniques to generate reference and predicted samples for a given block. By applying matrix multiplication and up-sampling processes, the method produces highly accurate predictions while maintaining data integrity. This patent serves as a fundamental advancement in image and video processing, ensuring remarkable video quality and fidelity.
Data Scientist at Credera | Effective Altruist.
Data Scientist interested in fairness, accountability, and transparency in ML/AI, and leveraging ML to build engaging experiences.
Patent №: 11869161 (January 9, 2024) –Overlaying 3D augmented reality content on real-world objects using image segmentation. These techniques use a computer to place a 3D virtual object on a physical object in augmented reality (AR). It analyzes images of the physical object, adjusts a 3D model to match its position and scale, and overlays the virtual object accordingly.
Patent №: 11868393 (January 9, 2024) – Methods and systems for determining color labeling for a vehicle search service presents innovative techniques for determining color labeling in vehicle search services. By leveraging computer vision and image processing technologies, this invention allows for more accurate and standardized color representation in the search for vehicles.
Patent №: 11868388 (January 9, 2024) – Automatic annotation for vehicle damage focuses on developing an automated system for generating interactive multimedia content that highlights and annotates instances of vehicle damage. This invention, which involves trained classification models and image sensors, streamlines the process of identifying and documenting damage, benefiting insurance companies, vehicle owners, and repair service providers.
Mr. Pedersen received his M.Sc. degree (1996) and his Ph.D. (2000) from Aalborg University, Denmark.
He is a Nokia Bell Labs Fellow, leading the Radio Access Research Team in Aalborg, and an external professor at Aalborg University.
His research covers access protocols and radio resource management for 5G-Advanced.
Patent №: 11870565 (January 9, 2024) – Transport block size driven CQI feedback scheme introduces a method for optimizing radio link performance in user equipment. This involves receiving a channel quality report configuration from a network node, determining a channel quality indicator based on the transport block size, and transmitting this indicator back to the network node. This innovative approach enhances efficiency and reliability in wireless communication systems.
CTO and Co-Founder at Fyusion, Inc.
Before Fyusion, he was affiliated with the Computer Vision group at the Institute for Computer Aided Medical Procedures (CAMP), a Ph.D. student at Technische Universitaet Muenchen (TUM), and a maintainer with the Point Cloud Library (PCL) project.
He was also affiliated with the German Aerospace Center (DLR), the Airbus group, the group of Dr. Andrew Davison at the Department of Computing at Imperial College London (ICL), and Microsoft Research Cambridge.
Stefan gained experience in the industry while working with several companies, including Willow Garage (a pioneer in the field of personal robotics), Metaio (at the time the leading company in Augmented Reality, now acquired by Apple), and the European Aeronautic Defense and Space Company (EADS).
Patent №: 11869135 (January 9, 2024) – Creating action shot video from multi-view capture data introduces a novel approach to rendering three-dimensional representations of scenes captured in action shots. By identifying camera poses and objects in multi-view representations, Holzer’s technology generates captivating action shot videos with incredible realism and depth.
Tech Incubators Manager.
Program management for academic and research engagements, leading campus programs and early engagement opportunities for software and machine learning engineers.
Patent №: 11869063 (January 9, 2024) – Optimize shopping route using purchase embeddings.
This patent introduces new capabilities for recommending purchases to users based on their past purchasing history and the purchase history of others. Rafferty’s invention utilizes a novel descriptor called “purchase embeddings,” which represents data records in a multi-dimensional space to track and describe goods and services purchases. By leveraging this technology, Rafferty aims to enhance the shopping experience for users.
Senior Standards Engineer.
Mr. Jha received his B.E. in ECE from Tribhuvan University, Nepal, in 2004; M.E. in ICT from the Asian Institute of Technology (AIT), Thailand, in 2007; M.Sc. in communication networks and services from Telecom SudParis, France, in 2007; and Ph.D. in ECE from the University of British Columbia, Vancouver, Canada in 2013.
Patent №: 11868858 (January 9, 2024) –Distributed machine learning in an information centric network. This patent describes systems and techniques to efficiently implement finite message exchanges through the treatment of data packets as interest packets. By employing local coordinating nodes, Jha’s invention enhances network efficiency in distributed machine learning exercises. This breakthrough has the potential to revolutionize data processing and improve the overall performance of machine learning systems.
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