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Sebastian Bader
Also published under:S. Bader
Affiliation
Department of Computer and Electrical Engineering, Mid Sweden University, Sundsvall, Sweden
Topic
Inference Time,Artificial Neural Network,Machine Learning,Machine Learning Models,Structural Health Monitoring,Acoustic Emission,Acoustic Emission Signals,Energy Consumption,Energy Harvesting,Learning Algorithms,Types Of Damage,Wrong Classification,Convolutional Neural Network,Long Short-term Memory,Output Power,Artificial Neural Network Model,Concrete Material,Convolutional Layers,Convolutional Neural Network Model,Deep Learning,Embedded System,Flash Memory,Hidden Layer,Magnetic Field,Model Size,Multilayer Perceptron,Neural Network,Support Vector Machine,Test Accuracy,Acoustic Signals,Activation Function,Anomaly Detection,Audio Recordings,Batch Normalization Layer,Classification Performance,Computational Requirements,Confusion Matrix,Copper Coil,Deep Learning Models,Deep Neural Network,Dropout Layer,Duty Cycle,Energy Harvesting System,Excitation Frequency,Exponential Linear Unit,Feeding Behavior,Instantaneous Amplitude,Internet Of Things,Jaw Movements,Lowest Energy Consumption,
Biography
Sebastian Bader (Senior Member, IEEE) received the Ph.D. degree in electronics from Mid Sweden University, Sundsvall, Sweden, in 2013, and the Dipl.-Ing. degree from the University of Applied Sciences, Wilhelmshaven, Germany, in 2008.
He is currently an Associate Professor of embedded systems with the Department of Electronics Design, Mid Sweden University. His research interests focus on energy aspects of embedded systems, including energy harvesting, low-power sensing systems, and machine learning on resource-constrained devices.
He is currently an Associate Professor of embedded systems with the Department of Electronics Design, Mid Sweden University. His research interests focus on energy aspects of embedded systems, including energy harvesting, low-power sensing systems, and machine learning on resource-constrained devices.