Machine Learning
As the field of human-computer interaction continues to evolve, there is a growing need for new methods of gesture recognition that can be used in a variety of applications, from gaming and entertainment to healthcare and robotics. While traditional methods of gesture recognition rely on cameras or other optical sensors, these systems can be limited by factors such as lighting conditions and occlusions.
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The study included 50 epilepsy patients undergoing long-term video-EEG monitoring at the Epilepsy Center of Guangdong 999 Brain Hospital. The inclusion criteria for patients were as follows: (1) VEEG reports confirming definite epileptic seizures, (2) complete video data containing both seizure and non-seizure periods, (3) no intentional interference during patient seizures, and no occlusion of the patient, such as patients were covered by quilts.
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This dataset is NOx concentration data used for training and testing support vector regression algorithms. There are two groups in total, one for offline algorithm and the other for online algorithm. This data comes from the measured data of a certain ultra supercritical coal-fired boiler under variable operating conditions. The offline model data sampling interval is 5 minutes, and the online model data sampling interval is 5 minutes.
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The dataset comprises image files of size 640 x 480 pixels for various grit sizes of Abrasive sheets. The data collected is raw. It can be used for analysis, which requires images for surface roughness. The dataset consists of a total of 8 different classes of surface coarseness. There are seven classes viz. P80, P120, P150, P220, P320, P400, P600 as per FEPA (Federation of European Producers of Abrasives) numbering system and one class viz. 60 as per ANSI (American National Standards Institute) standards numbering system for abrasive sheets.
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301 Views
The dataset encompasses a diverse array of electrical signals representing Power Quality Disturbances (PQD), both in single and combined forms, meticulously generated in adherence to the IEEE 1159 guideline. Crucially, the dataset includes both raw data and corresponding labels, facilitating supervised learning tasks and enabling the development and evaluation of classification algorithms.
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1148 Views
Our DeepCoAST dataset specifically explores the vulnerabilities of various traffic-splitting Website Fingerprinting (WF) Defenses, such as TrafficSliver, HyWF, and CoMPS. Our dataset comprises defended traces generated from the BigEnough dataset, which includes Tor cell trace instances of 95 websites, each represented by 200 instances collected under the standard browser security level. We simulated the traffic-splitting defenses assuming there are two split traces from the vanilla trace.
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We evaluate our approach on three popular domain adaptation benchmark datasets. The first one is Office-Caltech10 dataset, which contains images of 10 object categories from an office environment (e.g., keyboard, laptop) in 4 sources: Amazon, Caltech256, DSLR, and Webcam. We encode each source into 4096-dimensional feature vectors. Using each source as a domain, we get four domains leading to 12 domain adaptation tasks. The second one is Office-Home dataset, which contains images of 65 object categories found typically in Office and Home settings.
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This dataset contains both the artificial and real flower images of bramble flowers. The real images were taken with a realsense D435 camera inside the West Virginia University greenhouse. All the flowers are annotated in YOLO format with bounding box and class name. The trained weights after training also have been provided. They can be used with the python script provided to detect the bramble flowers. Also the classifier can classify whether the flowers center is visible or hidden which will be helpful in precision pollination projects.
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<p><span style="color: #3c4043; font-family: Inter, sans-serif; font-size: 14px;">The dataset is collected from 3 MPU9250 sensors connected simultaneously on different positions of the hand. One sensor was placed on the wrist, another between wrist and elbow and another between elbow and shoulder. The dataset contains a 3-axis accelerometer, 3-axis gyroscope and 3-axis magnetometer readings along with a result column in which '1' denoted shaking hand and '0' denoted stable hand.
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699 Views
This data set is a data set used for aircraft theoretical velocity ranking. Four sensors are randomly arranged in a 1*1 square map, and three aircraft will fly over the map coverage area at the same time. The velocity of the aircraft is simulated by a random process. The theoretical velocities of the three aircraft are similar, and the velocity of the aircraft will be disturbed during actual flight, causing large fluctuations, so that it is difficult to distinguish the theoretical velocity order of the aircraft flying into the map.
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