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Topic Review
Topology Designs for Data Centers
The adoption of simple network topologies allows for an easier way to forward packets. On the other hand, more complex topologies may achieve greater performance, although network maintenance may become harder. Hence, a balance between performance and simplicity is a convenient point when choosing a data center design. Therefore, some topology designs for data centers are going to be proposed; these are classified into tree-like and graph-like architectures. With respect to the former, a hierarchical switching layout interconnects all nodes, thus showing the form of an inverted tree within multiple roots, where nodes are the leaves of such a tree. Regarding the latter, nodes are directly interconnected to each other, hence no switch is involved in the design.
  • 424
  • 07 Jul 2023
Topic Review
Trust Management Model for Secure Internet of Vehicles
The Internet of Vehicles (IoV) enables vehicles to share data that help vehicles perceive the surrounding environment. However, vehicles can spread false information to other IoV nodes; this incorrect information misleads vehicles and causes confusion in traffic, therefore, a vehicular trust model is needed to check the trustworthiness of the message. 
  • 424
  • 28 Jul 2023
Topic Review
Convolution Neural Network  and Transformer-Based Human Pose Estimation
Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images.
  • 424
  • 03 Aug 2023
Topic Review
Federated Learning and Blockchain Applications in Vehicular Networks
The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI.  While transmitting data, valuable information can be disclosed through the model parameters by reverse engineering. The disclosure of valuable data motivated researchers and developers to adopt known security and privacy defense methods, e.g., functional encryption and differential privacy, to FL.
  • 423
  • 17 Jun 2022
Topic Review
Blockchain-Based Approaches for User Reputation on E-Commerce
User trust is a fundamental issue in e-commerce. To address this problem, recommendation systems have been widely used in different application domains including social media healthcare, e-commerce, and others. In the literature, on the one hand, blockchain-based reputation systems have been highlighted as possible solutions to effectively provide the necessary transparency, as well as effective identity management. On the other hand, new challenges are posed in terms of user privacy and performance, due to the specific characteristics of the blockchain. According to the literature, two major approaches have been proposed based on public and permissioned blockchains. Each approach applies adjusted models for calculating reputation scores.
  • 423
  • 18 Jan 2023
Topic Review
PCB Defect Based on Improved YOLOv7
The printed circuit board (PCB) holds immense importance in the electronic industry as a crucial component for the development of electronic products. PCBs are becoming increasingly integrated and smaller due to the excellent craftsmanship, precise wiring, and rapid development of integrated circuits.
  • 423
  • 11 May 2023
Topic Review
Single-Image Super-Resolution Neural Network
Single-image super-resolution (SISR) seeks to reconstruct a high-resolution image with the high-frequency information (meaning the details) restored from its low-resolution counterpart.
  • 422
  • 01 Mar 2022
Topic Review
Compati Hero Series
The Compati Hero Series (コンパチヒーローシリーズ, Konpachi Hīrō Shirīzu) is a video game series published exclusively in Japan by Banpresto and Namco Bandai Games (formerly Bandai) that began in Template:Vgy, that serves as 16 crossover teams between Ultraman, Kamen Rider (also known as Masked Rider) and Gundam. It was the first video game series to involve a crossover between animated giant robots and live action tokusatsu heroes from different established franchises. The series makes this possible by using caricaturized versions of the characters (officially referred as "SD" or "super deformed" characters), which allowed the different heroes and villains to co-exist and interact with each other without the need to reconcile their contrasting styles, settings, or sizes. The first game in the series, SD Battle Ōzumō: Heisei Hero Basho for the Famicom, which mixed franchises that were originally licensed to Popy, was developed as a congratulatory present to Yukimasa Sugiura when he was promoted to president of Banpresto at the time, which was soon followed by series of spin-offs and related games featuring the same cast of characters that developed into the Compati Hero Series. The series was successful with children thanks to the SD Gundam craze, but after the release of Charinko Hero for the GameCube, there were no new games afterward for nearly eight years. Banpresto released a new game in the series titled Lost Heroes, for the Nintendo 3DS and the PlayStation Portable on September 2012.
  • 422
  • 30 Oct 2022
Topic Review
Deep Reinforcement Learning and Games
Deep learning (DL) algorithms were established in 2006 and have been extensively utilized by many researchers and industries in subsequent years. Ever since the impressive breakthrough on the ImageNet classification challenge in 2012, the successes of supervised deep learning have continued to pile up. Many researchers have started utilizing this new and capable family of algorithms to solve a wide range of new tasks, including ways to learn intelligent behaviors in reward–driven complex dynamic problems successfully. The agent––environment interaction expressed through observation, action, and reward channels is the necessary and capable condition of characterizing a problem as an object of reinforcement learning (RL). Learning environments can be characterized as Markov decision problems, as they satisfy the Markov property, allowing RL algorithms to be applied. From this family of environments, games could not be absent. In a game–based environment, inputs (the game world), actions (game controls), and the evaluation criteria (game score) are usually known and simulated. With the rise of DL and extended computational capability, classic RL algorithms from the 1990s could now solve exponentially more complex tasks such as games over time, traversing through huge decision spaces.
  • 422
  • 22 Feb 2023
Topic Review
3D Estimation Using an Omni-Camera and a Spherical-Mirror
There is a novel approach for estimating the 3D information of an observed scene utilizing a monocular image based on a catadioptric imaging system employing an omnidirectional camera and a spherical mirror. Researchers aim to develop a method that is independent of learning and makes it possible to capture a wide range of 3D information using a compact device.
  • 422
  • 08 Aug 2023
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