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We introduce an online-offline Iraquian hand-drawing dataset for early Parkinson’s disease detection, exclusively collected using smartphones, thus eliminating the need for specialized equipment like digitizing tablets and pens. Our dataset comprises data from 30 healthy individuals (17 men, 13 women) with an average age of 56 years (SD = 6.12) and 30 PD patients (23 men, 7 women) with an average age of 60 years (SD = 4.91), gathered at Marjan Hospital in Hilla, Babil Governorate, Iraq.
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These datasets are gathered from an array of six gas sensors to be used for the odor recognition system. The sensors those used to create the data set are; Df-NH3, MQ-136, MQ-135, MQ-8, MQ-4, and MQ-2.
odors of different 10 samples are taken from these six sensors
1- Natural Air
2- Fresh Onion
3- Fresh Garlic
4- Fresh Lemon
5- Tomato
6- Petrol
7- Gasoline
8- Coffee 1,2
9- Orange
10- Colonia Perfume
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Regular and rigorous inspection of outdoor insulators is essential for uninterrupted power grid operation. Recent advances in computer vision enabled replacing conventional subjective, costly, and inefficient visual insulator inspection with automated diagnosis from unmanned aerial vehicle (UAV) taken images. In this study, advanced computer vision algorithms, namely, family of YOLOv3 and YOLOv5 architectures, are trained and compared for classification of frequently encountered insulator mechanical faults from UAV images.
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In order to obtain the ex-ante least-cost schedule of energy generation and reserves for online generating units, the system operator addresses a dynamic decision-making process known as the economic dispatch (ED) problem. Current industry practice involves adopting a deterministic two-stage optimization framework that relies on a one-day-ahead horizon and a forecast of uncertain parameters. The optimal solution to the resulting problem thus yields a generation schedule for the entire day ahead.
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The presented dataset comprises the electrical conductivity and relative permittivity data derived from the lower-scale model simulations as a part of multiscale computational modelling of electrical properties of thyroid and parathyroid tissues, which relates to the paper 'Multiscale Model Development for Electrical Properties of Thyroid and Parathyroid Tissues' submitted to IEEE Open Journal of Engineering in Medicine and Biology.
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Automatic Modulation Classification (AMC) is a technique used to identify signal modulations in applications like cognitive radar, software-defined radio, and electronic warfare. With future communication systems like 6G operating at higher transmission frequencies than 5G, AMC algorithms need to be more complex yet suitable for embedded devices with limited resources. Although current AMC algorithms deliver high accuracy, they require substantial computing power, making them unsuitable for such devices.
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Deep learning has revolutionized the field of robotics. To deal with the lack of annotated training samples for learning deep models in robotics, Sim-to-Real transfer has been invented and widely used. However, such deep models trained in simulation environment typically do not transfer very well to the real world due to the challenging problem of “reality gap”. In response, this letter presents a conceptually new Digital Twin (DT)-CycleGAN framework by integrating the advantages of both DT methodology and the CycleGAN model so that the reality gap can be effectively bridged.
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This article presents the details of the Cardinal RF (CardRF) dataset. CardRF is acquired to foster research in RF- based UAV detection and identification or RF fingerprinting. RF signals were collected from UAV controllers, UAV, Bluetooth, and Wi-Fi devices. Signals are collected at both visual line-of-sight and beyond-line-of-sight. The assumptions and procedure for the data acquisition are presented. A detailed explanation of how the data can be utilized is discussed. CardRF is over 65 GB in storage memory.
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