Deep Learning
This dataset comprises 2,052 .jpeg image samples from 74 students, offering a comprehensive portrayal of student life. Capturing academic, extracurricular, and social dimensions, it provides insights into diverse learning environments, activities, and interactions. From classrooms to sports fields, cultural events to social gatherings, the dataset encapsulates the multifaceted nature of student experiences. Researchers can utilize these images to explore educational dynamics, analyze social behaviors, and develop algorithms for image recognition and analysis.
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This dataset comprises 33,800 images of underwater signals captured in aquatic environments. Each signal is presented against three types of backgrounds: pool, marine, and plain white. Additionally, the dataset includes three water tones: clear, blue, and green. A total of 12 different signals are included, each available in all six possible background-tone combinations.
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318 Views
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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265 Views
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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66 Views
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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132 Views
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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128 Views
MobRFFI is a WiFi device fingerprinting and re-identification dataset collected in the Orbit testbed facility in July and April 2024. The dataset contains raw IQ samples of WiFi transmissions captured at 25 Msps on channel 11 (2462 MHz) in the 2.4 GHz band, using Ettus Research N210r4 USRPs as receivers and a set of WiFi nodes equipped with Atheros AR5212 chipsets as transmitters. The data collection spans two days (July 19 and August 8, 2024) and includes 12,068 capture files totaling 5.7 TB of data.
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34 Views
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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641 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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108 Views