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Found 10 papers, 1 papers with code
Log-FSK: A Frequency Modulation for Over-the-Air Computing
In this study we introduce Logarithmic Frequency Shift Keying (Log-FSK), a novel frequency modulation for over-the-air computing (AirComp).
Adaptive function approximation based on the Discrete Cosine Transform (DCT)
This paper studies the cosine as basis function for the approximation of univariate and continuous functions without memory.
LoRa Modulation for Split Learning
In this paper we introduce a task-oriented communication design for split learning (SL) over a communication channel.
ENN: A Neural Network with DCT Adaptive Activation Functions
Under a signal processing perspective, in this paper we present Expressive Neural Network (ENN), a novel model in which the non-linear activation functions are modeled using the Discrete Cosine Transform (DCT) and adapted using backpropagation during training.
Frequency Modulation Aggregation for Federated Learning
In FEEL, edge devices periodically transmit model parameters to the server, which aggregates them to generate a global model.
LoRa-based Over-the-Air Computing for Sat-IoT
In this paper we propose to use the Long Range (LoRa) modulation for the purpose of estimation in a Sat-IoT scenario.
DCT-based Air Interface Design for Function Computation
With the integration of communication and computing, it is expected that part of the computing is transferred to the transmitter side.
Over the Air Computing for Satellite Networks in 6G
We outline existing gaps in the literature, evincing that current state of the art requires a theoretical framework based on analog and hybrid digital-analog schemes that will boost the evolution of OTA computing.
Edge Computing and Communication for Energy-Efficient Earth Surveillance with LEO Satellites
In this paper, we propose an algorithm that allows the satellites to select between computing the tasks at the edge or at a cloud server and to allocate an adequate power for communication.
Mapping Motor Cortex Stimulation to Muscle Responses: A Deep Neural Network Modeling Approach
In this work, potential DNN models are explored and the one with the minimum squared errors is recommended for the optimal performance of the M2M-Net, a network that maps transcranial magnetic stimulation of the motor cortex to corresponding muscle responses, using: a finite element simulation, an empirical neural response profile, a convolutional autoencoder, a separate deep network mapper, and recordings of multi-muscle activation.
