Yulin Wang Technology Analyst

Mr Yulin Wang is a Technology Analyst at IDTechEx specialising in robotics.
Prior to joining IDTechEx, Yulin received his master's degree in medical robotics from Imperial College London, specialising in artificial intelligence, robotics and mechanical engineering. His research involved using computer vision and artificial intelligence to detect the intention of users. Prior to his master's degree, Yulin completed his undergraduate degree from both the University of Edinburgh and Dalian University of Technology, undertaking research projects ranging from using machine learning for carbon reabsorption and robotic 3D printing.
Before joining IDTechEx, Yulin worked as a part-time software engineer and machine learning consultant in London, where he collaborated with a range of universities to collect data and develop the machine learning model to develop a 'smart' rehabilitation robotic device.

Speaking at Forthcoming Events

EE Times' Green Engineering Summit
Virtual Event - 13 - 15 Sep 2022
The EE Times' Green Engineering Summit is running virtually from September 13-15. IDTechEx are participating in Day 2: Data Monitoring and Modeling, Computing and Automation The agricultural sector is undergoing a data-driven transformation, and farmers are adopting data analytics and smart farming technologies to maximize crop yields, profitability, and a sustainable business model. A shift from preventive to predictive agriculture is underway. Recent advances in remote sensing, hyperspectral imaging cameras, connectivity, robotics, IoT, and AI have enabled new approaches to precision agriculture. Farmers can now monitor, control, and visualize various agricultural operations in real time to make timely decisions. Improvements in environmental sensing and embedded systems to process varied urban and rural settings would also improve people's quality of life while reducing pollution, promoting sustainability, and protecting human health. Various challenges must be addressed in this context, ranging from designing and developing more ecological, and efficient smart sensing objects that must be energy-efficient and low-power, as well as able to communicate via wide-area networks with the support of high-performance computing.

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