What is she best known for?
The fields of study she is best known for:
- Electrical engineering
- Computer network
- Artificial intelligence
Michele Magno mainly focuses on Wireless sensor network, Energy harvesting, Real-time computing, Electrical engineering and Key distribution in wireless sensor networks.
Her Wireless sensor network study incorporates themes from Energy consumption, Electronic engineering and Efficient energy use.
Her Energy harvesting research focuses on Wearable computer and how it relates to Fluidics and Nanotechnology.
Her Real-time computing research incorporates elements of Wireless, Power management and Embedded system.
The various areas that Michele Magno examines in her Electrical engineering study include Wearable technology and Simulation.
Her work in Key distribution in wireless sensor networks covers topics such as Computer network which are related to areas like Distributed computing.
Her most cited work include:
- Context-Adaptive Multimodal Wireless Sensor Network for Energy-Efficient Gas Monitoring (140 citations)
- Human body heat for powering wearable devices: From thermal energy to application (107 citations)
- Human body heat for powering wearable devices: From thermal energy to application (107 citations)
What are the main themes of her work throughout her whole career to date?
Her primary areas of investigation include Wireless sensor network, Efficient energy use, Real-time computing, Wireless and Energy harvesting.
Her research integrates issues of Sensor node, Key distribution in wireless sensor networks, Power management, Embedded system and Node in her study of Wireless sensor network.
The concepts of her Efficient energy use study are interwoven with issues in Energy consumption, Computer network, Asynchronous communication, Base station and Edge computing.
Her Real-time computing research is multidisciplinary, incorporating perspectives in Image sensor, Convolutional neural network and Video processing.
The study incorporates disciplines such as Transmitter and Transceiver in addition to Wireless.
Her Energy harvesting research is multidisciplinary, relying on both Battery, Photovoltaic system, Electrical engineering and Wearable computer.
She most often published in these fields:
- Wireless sensor network (37.69%)
- Efficient energy use (34.17%)
- Real-time computing (31.66%)
What were the highlights of her more recent work (between 2019-2021)?
- Efficient energy use (34.17%)
- Real-time computing (31.66%)
- Energy harvesting (33.17%)
In recent papers she was focusing on the following fields of study:
Efficient energy use, Real-time computing, Energy harvesting, Electrical engineering and Microcontroller are her primary areas of study.
She combines subjects such as Wireless, Energy consumption, Quality of service, Base station and Edge computing with her study of Efficient energy use.
Her Real-time computing study typically links adjacent topics like Node.
Her studies deal with areas such as Wireless sensor network and Quantization as well as Node.
Her research in Energy harvesting intersects with topics in Photovoltaic system, Computer hardware, Power management and Wearable computer.
Michele Magno studies Electrical engineering, focusing on Small form factor in particular.
Between 2019 and 2021, her most popular works were:
- FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things (18 citations)
- An Accurate EEGNet-based Motor-Imagery Brain-Computer Interface for Low-Power Edge Computing (6 citations)
- An Accurate EEGNet-based Motor-Imagery Brain–Computer Interface for Low-Power Edge Computing (5 citations)
In her most recent research, the most cited papers focused on:
- Electrical engineering
- Computer network
- Artificial intelligence
Her scientific interests lie mostly in Microcontroller, Convolutional neural network, Real-time computing, Efficient energy use and Reduction.
Her work carried out in the field of Microcontroller brings together such families of science as Energy consumption, Edge computing, Perceptron and Firmware.
Her Convolutional neural network study combines topics from a wide range of disciplines, such as Gesture, Gesture recognition, Classifier, Wearable technology and Radar.
Her Real-time computing study integrates concerns from other disciplines, such as Preprocessor and Quantization.
Michele Magno interconnects Wireless, Distributed computing, Quality of service, Wireless sensor network and Electric power transmission in the investigation of issues within Efficient energy use.
Her Reduction study integrates concerns from other disciplines, such as Upsampling, ARM architecture, Brain–computer interface and Communication channel.
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