Sort by
A New Era of Automated Market-Makers (AMM) powered by Non-Fungible Tokens- A Review

The most well-known cryptocurrency, Bitcoin, is one of many that are sweeping the globe. Along with them, fungible tokens traded on numerous centralised or decentralised exchanges are different from non-fungible tokens traded on NFT marketplaces. The majority of NFTs are currently digital, and in the future, producers may innovate in this area to allow for more innovative user experiences. The blockchain and its NFTs can provide fantastic chances for artists and content producers to profit from their work. The NFT Marketplace intends to be at the centre of all these fantastic use cases for NFTs by giving users a platform to produce and exchange non-fungible tokens. NFTs offer a wide range of application cases. Automated Market Makers, which are essentially decentralised markets for crypto-tokens and offer users three core operations—depositing crypto tokens in exchange for AMM shares, performing a dual operation in which shares are obtained in exchange for base tokens, and exchanging two tokens for one another—are some of the main applications of DeFi. This conceptual research discusses AMMs that are already in existence on the Ethereum blockchain and their developments, including the AMM that is now being created on the Tezos blockchain. The goal of this study is to present a thorough understanding of blockchain technology and all of its practical uses, including voting, trading NFTs, etc. It then focuses on how NFTs are traded on various platforms before aiming for improved NFT trading marketplaces, namely Automated Market Makers on various blockchains like Ethereum and Tezos.

Open Access
Relevant
Design of Road surveillance system for low visibility, bad weather and emergency situations

Traffic accidents are major causes of death and disability worldwide. Lack of visibility due to Fog and darkness are main causes of accidents. These accidents may turn into fatalities and long traffic jams if proper actions are not taken. In this paper we initially review the accident data and scenarios and then propose a road surveillance system in low visibility and emergency. This proposed system that mainly performs three tasks as turning ON road-alert lights, if a vehicle unexpectedly stops on road-side due to any technical fault or accidents. This will help upcoming vehicles to have prior information of unexpectedly stopped vehicle that will reduce accident due to vehicles collision, other side turning ON road-street lights automatically with darkness detection. This will reduce accident by improving visibility in night or fog and Send Emergency Notification to the control room if vehicle remain still for more than 20 seconds. This will help in taking proper and timely action in case of emergencies avoiding fatalities and traffic jams for hours. The system is based on Arduino and GSM module and tested for each of the three scenarios (fog, darkness, emergency). The result shows that the proposed system worked efficiently in each condition and can be used to improve traffic safety.

Open Access
Relevant
U-Net for Medical Imaging: A Novel Approach for Brain Tumor Segmentation

In medical imaging, brain tumor segmentation is critical. The segmentation of a brain tumor might be done manually or automatically. Finding anomalies in magnetic resonance imaging (MRI) images manually is time-consuming and complex. Automatic segmentation, on the other hand, is incredibly accurate and time-saving. Any technique that can detect a brain tumor early would improve the diagnosis method. As a result, the number of cases of death will decrease. MRI (Magnetic Resonance Imaging) scans have proven to be quite helpful in the detection and segmentation of brain tumors in recent years. MRI images can aid in the detection of a brain tumor. MRI scans can detect abnormal tissue growth and blood blockages in the neurological system. The U-Net model is being used to segment the brain tumor region. The U-Net model is simply a more advanced version of CNN's algorithm. The U-Net model was created to segment biological pictures. We create a 3D U-Net design to segment the brain tumor infection zone in this paper. We combine clinical data with novel radiometric parameters based on the geometry, position, and shape of the segmented tumor to estimate each patient's survival length. The loss graph and accuracy graph are given together with the scores. Finally, we run the tests on various original photographs using the masks that correspond to them.

Open Access
Relevant
Compact HMSIW based Centre-Fed Series Antenna Array for ISM Band Energy Harvesting

The global move towards wireless access point densification has alluded towards the possibility of harvesting the unused ambient RF energy, especially in the 2.4GHz and 5.8GHz unlicensed ISM bands, in order to power useful electronic devices. This is done by collecting the ambient RF energy present in the environment growing more and more as a result of the rapid growth in the wireless communication business and transforming that collected energy into electrical power. This paper focus on realization of a compact, dual band, linearly polarized HMSIW antenna and two- and four- element centre-fed series array antenna designed based on HMSIW technique used as a receiving antenna in the RF energy harvesting system. The HMSIW is formed by bisecting the SIW along the quasi-magnetic wall when operating at TE101 and TE201 modes with the similar magnetic field strength observed at both the resonance modes. The feeding position and edge to edge spacing between the elements of the array antenna for HMSIW is chosen such that the proper impedance matching is achieved. Moreover a truncation is made in HMSIW to suppress the unwanted bands at the TE201 mode. The antenna’s performance is analysed based on comparing the simulated and measured return loss, VSWR, gain, axial ratio and radiation pattern which matches well for both the frequencies of interest (2.45GHz and 5.8GHz) can be used in a RF energy harvesting (RF-EH) system.

Open Access
Relevant
Study of AI and ML Based Technologies used in International Space Station

There are billions of galaxies, stars, solar systems, planets, and other undiscovered mysterious objects in expanding space. Humans took a giant step forward in exploring such a three-dimensional dark box when the International Space Station was deployed into Earth's lower orbit for study and to better understand the environment of space. Previous publications have discussed how artificial intelligence is being utilized to explore space and identify habitable worlds. This paper describes some of the AI-based technologies that are employed in the International Space Station, which is a key component of future space exploration. The study also looks at AI technologies that might be deployed on the International Space Station to increase its efficiency and provide security to the crew. The study includes a full explanation of the requirement, operation, and construction of NASA's "Robonauts" designed for the International Space Station. The paper also mentions ATLAS, an asteroid detecting system. The methods for providing medical aid to the crew, debris and its influence, analyzing data and extracting insight from space research data using machine learning are also highlighted. This study investigates how technology used in space exploration could be used to the ISS to improve its performance and provides an overview of some of the existing AI-based technologies utilized in the International Space Station (ISS).

Open Access
Relevant