Artificial Intelligence
The proposed SDTB dataset is collected from microscopic testicular tissue sections of 15 patients diagnosed with azoospermia. It simulates the process of selecting high-quality sperm in a testicular puncture scenario for further infertility diagnosis and treatment. Specifically, a testicular puncture is performed on these patients, followed by cleansing and shredding of the tubule tissue. The samples are then examined under a Nikon ECLIPSS Ti microscope at 200× magnification.
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155 Views
The dataset has 99 rows, corresponding to 99 odor samples, their labels are shown in label.xlsx
The dataset has 450 columns, corresponding to the responses of 30 odor sensors under 15 heating voltages (2.6V-5.4V).
Taking the first row as an example, the first to 15th elements correspond to the response values of sensor No.1 at heating voltage range from 2.6 V to 5.4 V in 0.2 V increments; The 16th to 30th elements correspond to the response values of sensor No.2 at heating voltage range from 2.6 V to 5.4 V in 0.2 V increments; And so on.
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172 Views
The decoupling method provides three key advantages compared to existing MRI reconstruction frameworks: it effectively incorporates prior knowledge of MRI structure using information from various angular perspectives, merges 4D data into multiple subspaces to streamline feature processing within each subspace, and showcases a flexible nature suitable for various tasks related to MR image processing.
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23 Views
We evaluated the strategy performance on three different datasets (MNIST, FMNIST, and CIFAR10), which is simulated heterogeneity by assigning different data volume labels to these datasets. These datasets all consist of image data for vehicle perception tasks. The MNIST dataset contains 70,000 images from 10 different classes, including 60,000 train and 10,000 test samples . FMNIST and MNIST have similar data structures, both are grayscale images. In contrast, FMNIST focuses on more complex target recognition tasks, which contains 10 categories of everyday items .
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73 Views
Six publicly available datasets are applied to Speech/Music Classification (SMC), Music Genre Classification (MGC), and Environmental Sound Classification (ESC), respectively. The utilized datasets include: 1) For SMC tasks, we employed the GTZAN-SMC and MUSAN datasets. 2) In ESC, the commonly employed ESC-10 and US8K datasets were included. 3) Classic GTZAN and Homburg datasets for MGC.
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207 Views
The \textit{Plectropomus leopardus (P. leopardus)}, a species found in underwater environments, possesses substantial strategic importance due to its rich underwater resources. However, the natural habitat and industrial breeding environment of \textit{P. leopardus} is generally dark and complex, which presents notable challenges to object detection and recognition. In this research, we propose Plectropomus Leopardus recognition using Global Attention mechanism and Transfer learning(PLGAT), integrating a Global Attention Mechanism (GAM) with Transfer Learning to recognize \textit{P.
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22 Views
Moroccan Dialect Emotion Recognition Dataset is a collection of voice records of people speaking Moroccan dialect in 5 states of emotion: Neutral, Happy, Sad, Angry and Fearful. The dataset has been collected in different Moroccan cities in 2024. Each recorder has 5 records per emotion class. The dataset contains 2000 record. The records are saved in .wav format, which is useful for signal processing with python libraries. The dataset is used for signal processing and emotion recognition using deep Learning models.
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340 Views
Anomaly detection in Phasor Measurement Unit (PMU) data requires high-quality, realistic labeled datasets for algorithm training and validation. Obtaining real field labelled data is challenging due to privacy, security concerns, and the rarity of certain anomalies, making a robust testbed indispensable. This paper presents the development and implementation of a Hardware-in-the-Loop (HIL) Synchrophasor Testbed designed for realistic data generation for testing and validating PMU anomaly detection algorithms.
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1129 Views
Seismocardiography (SCG) Signal Processing Dataset is a comprehensive collection of data samples to simulate the real-world application of the advanced technique in cardiac health monitoring. The dataset has been collected in different medical conditions of the patient in a real-time medical environment at varying timestamps. This dataset contains 1,000 samples collected over a period from 10 November 2023 to 10 January 2024, providing a robust timeframe in various conditions.
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418 Views
This dataset consists of radiation pattern images of three distinct antennas: Patch, Monopole, and Dipole, sourced from existing literature. The database was developed using pixel sampling techniques to generate a large and diverse set of images. These images were further processed to include various geometric shapes, such as symmetric and asymmetric forms, as well as triangle and square shapes, with window sizes ranging from 2 × 2 to 100 × 100 pixels.
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69 Views