NAND flash chips have been innovated from two-dimension (2D) design which is based on planar NAND cells to three-dimension (3D) design which is based on vertical NAND cells. Two types of NAND flash technologies–charge-trap (CT) and floating-gate (FG) are presented in this paper to introduce NAND flash designs in detail. The physical characteristics of CT-based and FG-based 3D NAND flashes are analyzed. Moreover, the advantages and disadvantages of these two technologies in architecture, manufacture, interference and reliability are studied and compared.

Cloud download service, as a new application which downloads the requested content offline and reserves it in cloud storage until users retrieve it, has recently become a trend attracting millions of users in China. In the face of the dilemma between the growth of download requests and the limitation of storage resource, the cloud servers have to design an efficient resource allocation scheme to enhance the utilization of storage as well as to satisfy users’ needs like a short download time. When a user’s churn behavior is considered as a Markov chain process, it is found that a proper allocation of download speed can optimize the storage resource utilization. Accordingly, two dynamic resource allocation schemes including a speed switching (SS) scheme and a speed increasing (SI) scheme are proposed. Both theoretical analysis and simulation results prove that our schemes can effectively reduce the consumption of storage resource and keep the download time short enough for a good user experience.

Most existing handover decision system (HDS) designs are monolithic, resulting in high computational cost and unbalance of overall network. A novel modular handover algorithm with a comprehensive load index for the 5th generation (5G) heterogeneous networks (HetNets) is proposed. In this paper, the handover parameters, serving as the basis for handover, are classified into network’s quality of service (QoS) module, user preference (UP) module and degree of satisfaction (DS) module according to the new modular HDS design. To optimize switching process, the comprehensive network load index is deduced by using triangle module fusion operator. With respect to the existing handover algorithm, the simulation results indicate that the proposed algorithm can reduce the handover frequency and maintain user satisfaction at a higher level. Meanwhile, due to its block calculation, it can bring about 1.4 s execution time improvement.

In order to achieve the lateral control of the intelligent vehicle, use the bi-cognitive model based on cloud model and cloud reasoning, solve the decision problem of the qualitative and quantitative of the lateral control of the intelligent vehicle. Obtaining a number of experiment data by driving a vehicle, classify the data according to the concept of data and fix the input and output variables of the cloud controller, design the control rules of the cloud controller of intelligent vehicle, and clouded and fix the parameter of cloud controller: expectation, entropy and hyper entropy. In order to verify the effectiveness of the cloud controller, joint simulation platform based on Matlab/Simulink/CarSim is established. Experimental analysis shows that: driver’s lateral controller based on cloud model is able to achieve tracking of the desired angle, and achieve good control effect, it also verifies that a series of mental activities such as feeling, cognition, calculation, decision and so on are fuzzy and uncertain.

Cloud computing makes it possible for users to share computing power. The framework of multiple data centers gains a greater popularity in modern cloud computing. Due to the uncertainty of the requests from users, the loads of CPU(Center Processing Unit) of different data centers differ. High CPU utilization rate of a data center affects the service provided for users, while low CPU utilization rate of a data center causes high energy consumption. Therefore, it is important to balance the CPU resource across data centers in modern cloud computing framework. A virtual machine(VM) migration algorithm was proposed to balance the CPU resource across data centers. The simulation results suggest that the proposed algorithm has a good performance in the balance of CPU resource across data centers and reducing energy consumption.

The bionics-based swarm intelligence optimization algorithm is a typical natural heuristic algorithm whose goal is to find the global optimal solution of the optimization problem. It simulates the group behavior of various animals and uses the information exchange and cooperation between individuals to achieve optimal goals through simple and effective interaction with experienced and intelligent individuals. This paper first introduces the principles of various swarm intelligent optimization algorithms. Then, the typical application of these swarm intelligence optimization algorithms in various fields is listed. After that, the advantages and defects of all swarm intelligence optimization algorithms are summarized. Next, the improvement strategies of various swarm intelligence optimization algorithms are explained. Finally, the future development of various swarm intelligence optimization algorithms is prospected.

In order to meet various challenges in the Internet of things (IoT), such as identity authentication, privacy preservation of distributed data and network security, the integration of blockchain and IoT became a new trend in recent years. As the key supporting technology of blockchain, the consensus algorithm is a hotspot of distributed system research. At present, the research direction of the consensus algorithm is mainly focused on improving throughput and reducing delay. However, when blockchain is applied to IoT scenario, the storage capacity of lightweight IoT devices is limited, and the normal operations of blockchain system cannot be guaranteed. To solve this problem, an improved version of Raft (Imp Raft) based on Raft and the storage compression consensus (SCC) algorithm is proposed, where initialization process and compression process are added into the flow of Raft. Moreover, the data validation process aims to ensure that blockchain data cannot be tampered with. It is obtained from experiments and analysis that the new proposed algorithm can effectively reduce the size of the blockchain and the storage burden of lightweight IoT devices.

This paper attempts to present an interactive color natural images segmentation method. This method extracts the feature of images by using the nonlinear compact structure tensor (NCST) and then uses GrabCut method to obtain the segmentation. This method not only realizes the non-parametric fusion of texture information and color information, but also improves the efficiency of the calculation. Then, the improved GrabCut algorithm is used to evaluate the foreground target segmentation. In order to calculate the simplicity and efficiency, this paper also extends the Gaussian mixture model (GMM) constructed base on the GrabCut to the tensor space, and uses the Kullback-Leibler (KL) divergence instead of the usual Riemannian geometry. Lastly, an iteration convergence criterion is proposed to reduce the time of the iteration of GrabCut algorithm dramatically with satisfied segmentation accuracy. After conducting a large number of experiments on synthetic texture images and natural images, the results demonstrate that this method has a more accurate segmentation effect.

Currentlyradio frequency identification (RFID) technology has been widely used in many kinds of applications. Store retailers use RFID readers with multiple antennas to monitor all tagged items. However, because of the interference from environment and limitations of the radio frequency technology, RFID tags are identified by more than one RFID antenna, leading to the false positive readings. To address this issue, we propose a RFID data stream cleaning method based on K-means to remove those false positive readings within sampling time. First, we formulate a new data stream model which adapts to our cleaning algorithm. Then we present the preprocessing method of the data stream model, including sliding window setting, feature extraction of data stream and normalization. Next, we introduce a novel way using K-means clustering algorithm to clean false positive readings. Last, the effectiveness and efficiency of the proposed method are verified by experiments. It achieves a good balance between performance and price.

In order to solve the hole-filling mismatch problem in virtual view synthesis, a three-step repairing (TSR) algorithm was proposed. Firstly, the image with marked holes is decomposed by the non-subsampled shear wave transform ( NSST), which will generate high-/ low-frequency sub-images with different resolutions. Then the improved Criminisi algorithm was used to repair the texture information in the high-frequency sub-images, while the improved curvature driven diffusion (CDD) algorithm was used to repair the low-frequency sub-images with the image structure information. Finally, the repaired parts of high-frequency and low-frequency sub-images are synthesized to obtain the final image through inverse NSST. Experiments show that the peak signal-to-noise ratio (PSNR) of the TSR algorithm is improved by an average of 2 - 3 dB and 1 - 2 dB compared with the Criminisi algorithm and the nearest neighbor interpolation (NNI) algorithm, respectively.

In the 6th generation mobile communication system (6G) era, a large number of delay-sensitive and computation-intensive applications impose great pressure on resource-constrained Internet of things (IoT) devices. Aerial edge computing is envisioned as a promising and cost-effective solution, especially in hostile environments without terrestrial infrastructures. Therefore, this paper focuses on integrating aerial edge computing into 6G for providing ubiquitous computing services for IoT devices. This paper first presents the layered network architecture of aerial edge computing for 6G. The benefits, potential applications, and design challenges are also discussed in detail. Next, several key techniques like unmanned aerial vehicle (UAV) deployment, operation mode, offloading mode, caching policy, and resource management are highlighted to present how to integrated aerial edge computing into 6G. Then, the joint UAV deployment optimization and computation offloading method is designed to minimize the computing delay for a typical aerial edge computing network. Numerical results reveal the significant delay reduction of the proposed method compared with the other benchmark methods. Finally, several open issues for aerial edge computing in 6G are elaborated to provide some guidance for future research.

This paper proposes a novel high-power supply rejection ratio (high-PSRR) high-order curvature-compensated CMOS bandgap voltage reference (BGR) in SMIC 0.18 μm CMOS process. Three kinds of current are added to a conventional BGR in order to improve the temperature drift within wider temperature range, which include a piecewise-curvature- corrected current in high temperature range, a piecewise-curvature-corrected current in low temperature range and a proportional-to-absolute-temperature current. The high-PSRR characteristic of the proposed BGR is achieved by adopting the technique of pre-regulator. Simulation results shows that the temperature coefficient of the proposed BGR with pre-regulator is /°C from 55 °C to 125 °C with a 1.8 V power supply voltage. The proposed BGR with pre-regulator achieves PSRR of 123.51 dB, 123.52 dB, 88.5 dB and 50.23 dB at 1 Hz, 100 Hz, 100 kHz and 1 MHz respectively.

It is of great value and significance to model the interests of microblog user in terms of business and sociology. This paper presents a framework for mining and analyzing personal interests from microblog text with a new algorithm which integrates term frequency-inverse document frequency (TF-IDF) with TextRank. Firstly, we build a three-tier category system of user interest based on Wikipedia. In order to obtain the keywords of interest, we preprocess the posts, comments and reposts in different categories to select the keywords which appear both in the category system and microblogs. We then assign weight to each category and calculate the weight of keyword to get TF-IDF factors. Finally we score the ranking of each keyword by the TextRank algorithm with TF-IDF factors. Experiments on real Sina microblog data demonstrate that the precision of our approach significantly outperforms other existing methods.

A new procedure of distributed self-control coverage for monitoring the dynamic targets with mobile sensor network is proposed. A special model is given for maintaining the nodes bi-connectivity and optimizing the coverage of the moving targets. The model consists of two parts, the virtual force model is proposed for motion control and the whale optimization algorithm is improved to further optimize the node positions and to reach the steady state quickly. The virtual resultant force stretches the network toward the uncovered targets by its multi-target attractive force, and maintains the network connectivity by its attractive force while network stretching, and avoids node collisions by its repulsive force while nodes moving. The operating mechanism of multi-target attractive force and other forces is also profoundly anatomized. The adjustment criteria for the model in different application scenarios are also given. Finally, the comparisons are shown to be significant advantages over other similar kinds.

For the task of content retrieval, analysis and generation of film and television scene images in the field of
intelligent editing, fine-grained emotion recognition and prediction of images is of great significance. In this paper,
the fusion of traditional perceptual features, art features and multi-channel deep learning features are used to reflect
the emotion expression of different levels of the image. In addition, the integrated learning model with stacking
architecture based on linear regression coefficient and sentiment correlations, which is called the LS-stacking
model, is proposed according to the factor association between multi-dimensional emotions. The experimental
results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-
built image dataset. This study improves the fine-grained recognition ability of image emotion by computers, which
helps to increase the intelligence and automation degree of visual retrieval and post-production system.

Mobile data traffic is going through an explosive growth recently as mobile smart devices become more and more ubiquitous, causing huge pressure on cellular network. Taking advantage of its low cost and easy-to-deploy feature, wireless local-area networks (WLAN) becomes increasingly popular to offload data streams from cellular network, followed by higher and higher density of its deployment. However, the high density of WLAN will cause more interference, which results in degradation of its performance. Therefore, in order to enhance the performance of the network, we aim to minimize the interference caused by high density of WLAN. In this paper, we propose a novel power control scheme to achieve the above aim. We use the quality of experience (QoE) evaluation to coordinate the power of each access point (AP) and finally realize the optimization of the entire network. According to the simulation results, our scheme improves the performance of the network significantly in many aspects, including throughput and QoE.

For the interference hidden in the expected multi-carrier signal, this paper proposes a novel detection and recognition algorithm. The algorithm cannot only detect the single-carrier interference (SCI) by the high-order cumulant, but also finds the multi-carrier signal based on spectrum character. Besides, the algorithm can distinguish the modulation types of the SCI. The algorithm does not depend on any prior knowledge and data-aided, which is propitious to practical applications. The analysis and simulation results demonstrate that the proposed algorithm is effective.

Proactive dialogue generates dialogue utterance based on a conversation goal and a given knowledge graph (KG). Existing methods combine knowledge of each turn of dialogue with knowledge triples by hidden variables, resulting in the interpretability of generation results is relatively poor. An interpretable knowledge-aware path (KAP) model was proposed for knowledge reasoning in proactive dialogue generation. KAP model can transform explicit and implicit knowledge of each turn of dialogue into corresponding dialogue state matrix, thus forming the KAP for dialogue history. Based on KAP, the next turn of dialogue state vector can be infered from both the topology and semantic of KG. This vector can indicate knowledge distribution of next sentence, so it enhances the accuracy and interpretability of dialogue generation. Experiments show that KAP  model’s dialogue generation is closer to actual conversation than other state-of-the-art proactive dialogue models.

Membership inference attacks on machine learning models have drawn significant attention. While current  research primarily utilizes shadow modeling techniques, which require knowledge of the target model and training  data, practical scenarios involve black-box access to the target model with no available information. Limited  training data further complicate the implementation of these attacks. In this paper, we experimentally compare  common data enhancement schemes and propose a data synthesis framework based on the variational autoencoder  generative adversarial network (VAE-GAN) to extend the training data for shadow models. Meanwhile, this paper  proposes a shadow model training algorithm based on adversarial training to improve the shadow model's ability to  mimic the predicted behavior of the target model when the target model's information is unknown. By conducting  attack experiments on different models under the black-box access setting, this paper verifies the effectiveness of the  VAE-GAN-based data synthesis framework for improving the accuracy of membership inference attack.  Furthermore, we verify that the shadow model, trained by using the adversarial training approach, effectively  improves the degree of mimicking the predicted behavior of the target model. Compared with existing research  methods, the method proposed in this paper achieves a 2% improvement in attack accuracy and delivers better  attack performance.