Artificial Intelligence

The dataset contains a collection of V2X (Vehicle-to-Everything) messages for classification, prioritization, and spam message detection. It comprises 1,000 messages with varying message types, content, priorities, and spam labels. The messages are sourced from different vehicles with specific destination vehicles or broadcast to all vehicles. They cover various message types, including traffic updates, emergency alerts, weather notifications, hazard warnings, roadwork information, and spam messages. The priority of the messages is categorized as either high, medium, or low.

This dataset contain the pulse responses of the Sallen-Key bandpass filter circuit and the amplifier board circuit. The test excitation is a 10 us pulse signal with an amplitude of 5 V and a frequency of 5 kHZ that exhibits abundant frequency components. By observing the pulse response, the sampling frequency is set to 5 MHz and the number of sampling points for each sample is fixed at 1000 in Case 1. PSPICE is applied for circuit simulation to set up the circuit fault according to the range of fault component parameter values.

At present, various technologies have been developed for indoor positioning, such as Bluetooth, infrared, wireless local area network, radio frequency identification (RFID), and ultra-wide band (UWB) , but most of these technologies require hardware, are costly, and are susceptible to electromagnetic interference.


Translator   Translator   Translator   


SYPHAX dataset was collected from Tunisia in “Sfax” city, the second largest Tunisian city after the capital. A total of 2008 images were gathered through manual collection one by one, with each image energizing text detection challenges in nature according to real existing complexity of 15 different routes (downtown, Nasryia, Sidi Mansour, Sakiet Ezziet, Sakiet Eddayer, Mahdia road, Tunis main road, Chehia, Taniour, Lafran, Elayn, Gremda, Manzel Chaker, Matar route, Gabes road) along with ring roads, intersections and roundabouts.


The e-nose device used in this study was constructed using a gas sensor array, LCD display, micro air pumps for inhalation and exhalation, a microcontroller, and a mini-PC. Gas samples from the sample chamber were periodically drawn into the device through a hose. Each sample underwent a 30-hour sampling process at room temperature (25°C). The sampling frequency was 15 times per hour, resulting in 60 records per sample.


SCD Dataset: This new dataset has been specifically created for the development and education of children with down syndrome. The dataset, containing a total of 13,500 Turkish question-answer pairs, has "positive" and "negative" emotion labels. In the context of human-robot interaction, accurately identifying and addressing positive and negative emotions has a significant impact on user experience and satisfaction. Neutral questions and answers provide less information in terms of sentiment analysis and are less relevant to the purpose of this study.


A hybrid-field STAR-RIS dataset. we have provided the paired samples for hybrid-field cascaded channel estimation in STAR-RIS systems, in which the data preprocessing and normalization operations have been completed.

The simulation parameters of this dataset have been elaborated in our submitted paper. For instance, M_1 x M_2 = 4 x 8, N_1 x N_2 = 4 x 32, f_c = 73GHz, and Q=N/4.  The  description of each data file is listed as follows.

inHmix_28_32_128_K2_32pilot.mat: the training dataset and validation dataset in the ES protocol.<br/>


Dataset consisting of 17 images of Nile Tilapia bred in a circular pond (diameter=10m, deep = 1.5m ) located at the tronconal in Hermosillo, Sonora, México and an aquarium.

From the total of fish, 17 were selected. Each fish was measured with a ruler; the longitude was measured from the beginning of tail to tip and the height was measured from the beginning of the dorsal fin to the bottom, and its weight was measured with a standard balance.






This dataset was initially collected by Mrs Athira P K  with the help of  teachers and students of Rahmania school for handicapped, Kozhikode, Kerala, India. Later the dataset was extended by many other BTech and MTech students with the help of their friends.

MUDRA NITC dataset consists of videos of static and dynamic gestures of Indian sign language. In static gestures mainly static alphabets videos and  preprocessed image frames are included.