Biblio

This paper builds an Augmented Reality Sandbox (AR Sandbox) system based on augmented reality technology, and performs a 3D reconstruction for the sandbox terrain using the depth sensor Microsoft Kinect in the AR Sandbox, as an entry point to pave the way for later development of related metaverse applications, such as the metaverse architecting and visual interactive modeling. The innovation of this paper is that for the AR Sandbox scene, a 3D reconstruction method based on depth sensor is proposed, which can automatically cut off the edge of the sandbox table in Kinect field of view, and accurately and completely reconstruct the sandbox terrain in Matlab.

Ubiquitous Electric Internet of Things (UEIoT) is the next generation electrical energy networks. The distributed and open structure of UEIoT is weak and vulnerable to security threats. To solve the security problem of UEIoT terminal, in this paper, the interaction between smart terminals and the malicious attackers in UEIoT as a differential game is investigated. A complex decision-making process and interactions between the smart terminal and attackers are analyzed. Through derivation and analysis of the model, an algorithm for the optimal defense strategy of UEIoT is designed. The results lay a theoretical foundation, which can support UEIoT make a dynamic strategy to improve the defensive ability.

Aiming at the poor stability of traditional communication data encryption methods, a point-to-point encryption method of power system communication data based on block chain technology is studied and designed. According to the principle of asymmetric key encryption, the design method makes use of the decentralization and consensus mechanism of block chain technology to develop the public key distribution scheme. After the public key distribution is completed, the sender and receiver of communication data generate the transfer key and pair the key with the public key to realize the pairing between data points. Xor and modular exponentiation are performed on the communication data content, and prime Numbers are used to fill the content data block. The receiver decrypts the data according to the encryption identifier of the data content, and completes the design of the encryption method of communication data point to ground. Through the comparison with the traditional encryption method, it is proved that the larger the amount of encrypted data is, the more secure the communication data can be, and the stability performance is better than the traditional encryption method.

In recent years, FPGAs have been deployed in data centres of major cloud service providers, such as Microsoft [1], Amazon [2], Alibaba [3], Tencent [4], Huawei [5], and Nimbix [6]. This marks the beginning of bringing FPGA computing to the masses, as being in the cloud, one can access an FPGA from anywhere. A wide range of applications are run in the cloud, including web servers and databases among many others. Memcached is a high-performance in-memory ob ject caching system, which acts as a caching layer between web servers and databases. It is used by many companies, including Flicker, Wikipedia, Wordpress, and Facebook [7, 8]. In this paper, we present a Memcached accelerator implemented on the AWS FPGA cloud (F1 instance). Compared to AWS ElastiCache, an AWS-managed CPU Memcached service, our Memcached accelerator provides up to 9 x better throughput and latency. A live demo of the Memcached accelerator running on F1 can be accessed on our website [9].

Affective1 engineering is a methodology of designing products by collecting customer affective needs and translating them into product designs. It usually begins with questionnaire surveys to collect customer affective demands and responses. However, this process is expensive, which can only be conducted periodically in a small scale. With the rapid development of e-commerce, a larger number of customer product reviews are available on the Internet. Many studies have been done using opinion mining and sentiment analysis. However, the existing studies focus on the polarity classification from a single perspective (such as positive and negative). The classification of multiple affective attributes receives less attention. In this paper, 3-class classifications of four different affective attributes (i.e. Soft-Hard, Appealing-Unappealing, Handy-Bulky, and Reliable-Shoddy) are performed by using two classical machine learning algorithms (i.e. Softmax regression and Support Vector Machine) and two deep learning methods (i.e. Restricted Boltzmann machines and Deep Belief Network) on an Amazon dataset. The results show that the accuracy of deep learning methods is above 90%, while the accuracy of classical machine learning methods is about 64%. This indicates that deep learning methods are significantly better than classical machine learning methods.