Deep Learning
Two publicly available datasets, the PASS and EmpaticaE4Stress databases, were utilised in this study. They were chosen because they both used the same Empatica E4 device, which allowed the acquisition of a variety of signals, including PPG and EDA. The dataset consists of in 1587 30-second PPG segments. Each segment has been filtered and normalized using a 0.9–5 Hz band-pass and min-max normalization scheme.
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The 'AirScript' dataset consists of surface electromyography (sEMG) signals obtained while writing the uppercase English alphabets (A–Z) in free space. The Delsys Trigno device was used to record forearm muscle activity from 16 subjects. Every subject performs two trials for each letter, thus resulting in 52 samples per subject. sEMG signals obtained from all subjects were stored at a 2000 Hz sampling rate for high temporal resolution. The dataset consists of raw sEMG signals that are stored in subject-specific folders and saved as `.npy` files.
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The increasing number of wildfires damages nature and human life, making the early detection of wildfires in complex outdoor environments critical. With the advancement of drones and remote sensing technology, infrared cameras have become essential for wildfire detection. However, as the demand for higher accuracy in detection algorithms grows, the detection model's size and computational costs increase, making it challenging to deploy high-precision detection algorithms on edge computing devices onboard drones for real-time fire detection.
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The growing demand to address environmental sustainability and climate change has emphasized the need for innovative solutions in supply chain and energy management. This study investigates the transformative role of the Internet of Things (IoT) in reducing carbon footprints and optimizing energy utilization within supply chains. A well-structured methodology was employed including regression modeling, cluster analysis, IoT simulation frameworks and optimization techniques. The data was collected from diverse energy and emission databases.
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With the accelerating pace of population aging, the urgency and necessity for elderly individuals to control smart home systems have become increasingly evident. Smart homes not only enhance the independence of older adults, enabling them to complete daily activities more conveniently, but also ensure safety through health monitoring and emergency alert systems, thereby reducing the caregiving burden on families and society.
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When training supervised deep learning models for despeckling SAR images, it is necessary to have a labeled dataset with pairs of images to be able to assess the quality of the filtering process. These pairs of images must be noisy and ground truth. The noisy images contain the speckle generated during the backscatter of the microwave signal, while the ground truth is generated through multitemporal fusion operations. In this paper, two operations are performed: mean and median.
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604 Views
A deep learning (DL)--based detector is proposed for underwater acoustic (UWA) communication systems using orthogonal chirp division multiplexing with index modulation (OCDM-IM). The proposed high-performance and lightweight network integrates the detection of the index bits and the carrier bits as a whole, employing a squeeze-and-excitation (SE) mechanism enhanced residual neural network (ResNet) cascaded with a bidirectional gated recurrent unit (BiGRU) to detect OCDM-IM signals.
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This dataset contains LoRa physical layer signals collected from 60 LoRa devices and six SDRs (PLUTO-SDR, USRP B200 mini, USRP B210, USRP N210, RTL-SDR). It is intended for use by researchers in the development of a federated RFFI system, whereby the signals collected from different receivers and locations can be employed for evaluation purposes.
More details can be found at https://github.com/gxhen/federatedRFFI
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580 Views
In this letter, a deep learning (DL)--based detector is proposed for underwater acoustic (UWA) communication systems using orthogonal chirp division multiplexing with index modulation (OCDM-IM). The proposed high-performance and lightweight network integrates the detection of the index bits and the carrier bits as a whole, employing a squeeze-and-excitation (SE) mechanism enhanced residual neural network (ResNet) cascaded with a bidirectional gated recurrent unit (BiGRU) to detect OCDM-IM signals.
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155 Views
The advancement and ubiquity of digital networks have fundamentally transformed numerous spheres of human activity. At the heart of this phenomenon lies the Transmission Control Protocol (TCP) model, whose influence is particularly notable in the exponential growth of the Internet due to its potential ability to transmit flexibly through an advanced Congestion Control (CC). Seeking an even more efficient CC mechanism, this work proposes the construction of Deep Learning Neural Networks (MLP, LSTM, and CNN) for classifying network congestion levels.
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