Artificial Intelligence
Abstract—This paper presents a novel approach to optimizing resource allocation in Internet of Things (IoT) networks, focusing on enhancing energy efficiency (EE) while maintaining age of information (AoI) awareness through device-to-device (D2D) communication. Our proposed solution integrates simultaneous wireless information and power transfer (SWIPT) with energy harvesting (EH) techniques. Specifically, D2D users employ time switching (TS) to harvest energy from the environment, while IoT users utilize power splitting (PS) to obtain energy from base stations (BS).
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A short time ago, the study of contactless fingerprint authentication gained appeal among biometric researchers. Contactless fingerprint systems offer various advantages, such as ease of capture and affordability, over conventional fingerprint identification systems, which demand that the user's finger make direct contact with the sensor.
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The dataset is named Chinese rose disease dataset, including healthy leaves, black spot leaves, powdery mildew leaves and downy mildew leaves. All images in this dataset were collected from Nanyang City, Henan Province, China. And all images were collected under natural conditions in order to ensure the true execution of the images. To improve the image variety, we randomly enhance the images in the dataset by flipped, changed the brightness, added Salt and pepper noise and added Gaussian noise.
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This dataset consists of “.csv” files of 4 different routing attacks (Blackhole Attack, Flooding Attack, DODAG Version Number Attack, and Decreased Rank Attack) targeting the RPL protocol, and these files are taken from Cooja (Contiki network simulator). It allows researchers to develop IDS for RPL-based IoT networks using Artificial Intelligence and Machine Learning methods without simulating attacks. Simulating these attacks by mimicking real-world attack scenarios is essential to developing and testing protection mechanisms against such attacks.
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Current radar fall detection techniques based on deep learning (DL) networks are often too complex for real-time detection. This paper proposes a real-time fall detection approach by reducing the complexity of the DL networks and the UWB radar hardware requirements. A multi-indoor scene behaviour dataset of 40 subjects is established using K-band UWB radar. A sliding window-based dataflow augmentation method is proposed to augment and balance the given datasets.
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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.
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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.
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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.
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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.
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