Digital signal processing

The diagram depicts the forces of three types of bondage (fabric bondage, rigid bondage and variable stiffness bondage) in contact with human skin.

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Interference signals degrade the performance of a global navigation satellite system (GNSS) receiver. Detection and classification of these interference signals allow better situational awareness and facilitate appropriate countermeasures. However, classification is challenging and processing-intensive, especially in severe multipath environments. This dataset is the result of a proposal for a low-resource interference detection and classification approach that combines conventional statistical signal processing approaches with machine learning (ML).

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Motion analysis forms a very important research topic with a general mathematical background and applications in different areas including engineering, robotics, and neurology. This paper presents the use of the global navigation satellite system (GNSS) for detection and recording of the moving body position and the simultaneous acquisition of signals from further sensors. The application is related to monitoring of physical activity and the use of wearable sensors of the heart rate and acceleration during different motion patterns.

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176 Views

data

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We propose an algorithm based on linear prediction that can perform both the lossless and near-lossless compression of RF signals. The proposed algorithm is coupled with two signal detection methods to determine the presence of relevant signals and apply varying levels of loss as needed. The first method uses spectrum sensing techniques, while the second one takes advantage of the error computed in each iteration of the Levinson-Durbin algorithm. These algorithms have been integrated as a new pre-processing stage into FAPEC, a data compressor first designed for space missions.

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 The drawback of inter-subcarrier interference in OFDM systems makes the channel estimation and signal detection performance of OFDM systems with few pilots and short cyclic prefixes (CP) poor. Thus, we use deep learning to assist OFDM in recovering nonlinearly distorted transmission data. Specifically, we use a self-normalizing network (SNN) for channel estimation, combined with a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) for signal detection, thus proposing a novel SNN-CNN-BiGRU network structure (SCBiGNet). 

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This dataset provides data support for manuscripts that will be submitted soon.

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48 Views

In this dataset, based on a beam sweeping experiment in the 60 GHz band in an indoor environment, we provide the acquired IQ data samples (containing the announced TX antenna weighting vectors (AWV) index as information) for the given RX AWV index, location, and carrier frequency. We also include the information obtained after processing the PPDU in IQ data.

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702 Views

In this project, an LSTM-based Model Predictive Controller (LSTM-MPC), with 200 neurons of each layer, is designed to have a highly efficient control on the temperature. The resulted dataset is attached, for further considerations.

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557 Views

Simulation data for the following paper

DS2MA: A Deep Learning-Based Spectrum Sensing Scheme for a Multi-Antenna Receiver (K. Chae and Y. Kim)

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239 Views

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