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.

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.

To meet the demands of large-scale user access with computation-intensive and delay-sensitive applications,
combining ultra-dense networks (UDNs) and mobile edge computing (MEC)are considered as important solutions.
In the MEC enabled UDNs, one of the most important issues is computation offloading. Although a number of work
have been done toward this issue, the problem of dynamic computation offloading in time-varying environment,
especially the dynamic computation offloading problem for multi-user, has not been fully considered. Therefore, in
order to fill this gap, the dynamic computation offloading problem in time-varying environment for multi-user is
considered in this paper. By considering the dynamic changes of channel state and users queue state, the dynamic
computation offloading problem for multi-user is formulated as a stochastic game, which aims to optimize the delay
and packet loss rate of users. To find the optimal solution of the formulated optimization problem, Nash  Q-l earning
(NQLN) algorithm is proposed which can be quickly converged to a Nash equilibrium solution. Finally, extensive
simulation results are presented to demonstrate the superiority of NQLN algorithm. It is shown that NQLN algorithm
has better optimization performance than the benchmark schemes.  

Attacks on web servers are part of the most serious threats in network security fields. Analyzing logs of web attacks is an effective approach for malicious behavior identification. Traditionally, machine learning models based on labeled data are popular identification methods. Some deep learning models are also recently introduced for analyzing logs based on web logs classification. However, it is limited to the amount of labeled data in model training. Web logs with labels which mark specific categories of data are difficult to obtain. Consequently, it is necessary to follow the problem about data generation with a focus on learning similar feature representations from the original data and improve the accuracy of classification model. In this paper, a novel framework is proposed, which differs in two important aspects: one is that long short-term memory (LSTM) is incorporated into generative adversarial networks (GANs) to generate the logs of web attack. The other is that a data augment model is proposed by adding logs of web attack generated by GANs to the original dataset and improved the performance of the classification model. The results experimentally demonstrate the effectiveness of the proposed method. It improved the classification accuracy from 89.04% to 95.04%.

In this paper, a novel superjunction 4H-silicon carbide (4H-SiC) trench-gate insulated-gate bipolar transistor (IGBT) featuring an integrated clamping PN diode between the P-shield and emitter (TSD-IGBT) is designed and theoretically studied. The heavily doping superjunction layer contributes to a low specific on-resistance, excellent electric field distribution, and quasi-unipolar drift current. The anode of the clamping diode is in floating contact with the P-shield. In the on-state, the potential of the P-shield is raised to the turn-on voltage of the clamping diode, which prevents the hole extraction below the N-type carrier storage layer (NCSL). Additionally, during the turn-off transient, once the clamping diode is turned on, it also promotes an additional hole extraction path. Furthermore, the potential dropped at the semiconductor near the trench-gate oxide is effectively reduced in the off-state.

As a special type of distributed denial of service (DDoS) attacks, the low-rate DDoS (LDDoS) attacks have characteristics of low average rate and strong concealment, thus, it is hard to detect such attacks by traditional approaches. Through signal analysis, a new identification approach based on wavelet decomposition and sliding detecting window is proposed. Wavelet decomposition extracted from the traffic are used for multifractal analysis of traffic over different time scale. The sliding window from flow control technology is designed to identify the normal and abnormal traffic in real-time. Experiment results show that the proposed approach has advantages on detection accuracy and timeliness.

The traditional fault diagnosis method of industrial equipment has low accuracy and poor applicability. This paper proposes a equipment fault diagnosis method based on random stochastic adaptive particle swarm optimization (RSAPSO). The entire model is validated by using the data of healthy bearings collected by Case Western Reserve University. Different gradient descent algorithms and standard particle swarm optimization (PSO) algorithms in a back propagation (BP) network are compared experimentally. The results show that the RSAPSO algorithm has a higher accuracy of weight threshold updating than the gradient descent algorithm and does not easily fall into a local optimum. Compared with PSO, it has a faster optimization speed and higher accuracy. Finally, the RSAPSO algorithm is validated with the data of bearings collected from the laboratory rotating machinery test bench and motor data collected from the tower reflux pump. The average recognition rate of the four kinds of bearing data constructed is 97.5% , and the average recognition rate of the two kinds of motor data reaches 100% , which prove the universality of the method.

Facial recognition has become the most common identity authentication technologies. However, problems such as  uneven light and occluded faces have increased the hardness of liveness detection. Nevertheless, there are a few  pieces of research on face liveness detection under occlusion conditions. This paper designs a face recognition  technique suitable for different degrees of facial occlusion, which employs the facial datasets of near-infrared (NIR)  images and visible (VIS) light images to examine the single-modality detection accuracy rate (experimental control  group) and the corresponding high-dimensional features through the residual network (ResNet). Based on the idea  of data fusion, we propose two feature fusion methods. The two methods extract and fuse the data of one and two  convolutional layers from two single-modality detectors respectively. The fusion of high-dimensional features apply a  new ResNet to get the dual-modality detection accuracy. And then, a new ResNet is applied to test the accuracy of  dual-modality detection. The experimental results show that the dual-modality face liveness detection model  improves face live detection accuracy and robustness compared with the single-modality. The fusion of two-layer  features from the single-modality detector can also improve face detection accuracy by utilizing the above-mentioned  dual-modality detector, and it doesn't increase the algorithm's complexity.

The integration of the Internet and the traditional manufacturing industry makes the industrial Internet of things (IIoT) as a popular research topic. However, traditional industrial networks continue to face challenges of resource management and limited raw data storage and computation capacity. A novel software defined industrial network (SDIN) architecture was proposed to address the existing drawbacks in IIoT such as resource utilization, data processing and storage, and system compatibility. The architecture is developed based on the software defined network (SDN) architecture, combining hierarchical cloud and fog computing and content-aware caching technologies. Based on the SDIN architecture, two types of edge computing strategies in industrial applications are discussed. Different scenarios and service requirements are considered. The simulation results confirm that the SDIN architecture is feasible and effective in the application of edge computing offloading

In this paper, a novel trench gate gallium nitride insulated gate bipolar transistor (GaN IGBT), in which the collector is divided into multiple regions to control the hole injection efficiency, is designed and theoretically studied. The incorporation of a P+/P- multi-region alternating structure in the collector region mitigates hole injection within the collector region. When the device is in forward conduction, the conductivity modulation effect results in a reduced storage of carriers in the drift region. As a result, the number of carriers requiring extraction during device turn-off is minimized, leading to faster turn-off speed. The results illustrate that the GaN IGBT with controlled hole injection efficiency (CEH GaN IGBT) exhibits markedly enhanced performance compared to conventional GaN IGBT, showing a remarkable 42.2% reduction in turn-off time and a notable 28.5% decrease in turn-off loss.

In order to solve the energy crisis and pollution problems, smart grid is widely used. However, there are many
challenges such as the management of distributed energy during the construction. Blockchain, as an emerging
technology, can provide a secure and transparent solution to the decentralized network. Meanwhile, fog computing
network is considered to avoid the high deployment cost. The edge servers have abundant computing and storage
resources to perform as nodes in grid blockchain. In this paper, an innovative structure of smart grid blockchain
integrated with fog computing are proposed. And a new consensus mechanism called scalable proof of cryptographic
selection (SPoCS) is designed to adapt the hybrid networks. The mechanism not only includes a special index,
contribution degree, to measure the loyalty of fog nodes and the probability of being a function node, but also has
flexible block interval adjustment method. Meanwhile, the number of function nodes (validating nodes and ordering
nodes) can also be adjusted. And a deep reinforcement learning (DRL) method is used to select the appropriate
quantity to improve the performance under the strict constraints of security and decentralization. The simulation
shows the scheme performs well in the throughput, cost and latency.  

Through-silicon via (TSV) is a key enabling technology for the emerging 3-dimension (3D) integrated circuits
(ICs). However, the crosstalk between the neighboring TSVs is one of the important sources of the soft faults. To
suppress the crosstalk, the Fibonacci-numeral-system-based crosstalk avoidance code ( FNS-CAC) is an effective
scheme. Meanwhile, the self-repair schemes are often used to deal with the hard faults, but the repaired results
may change the mapping between signals to TSVs, thus may reduce the crosstalk suppression ability of FNS-CAC.
A TSV self-repair technique with an improved FNS-CAC codec is proposed in this work. The codec is designed
based on the improved Fibonacci numeral system (FNS) adders, which are adaptive to the health states of TSVs.
The proposed self-repair technique is able to suppress the crosstalk and repair the faulty TSVs simultaneously. The
simulation and analysis results show that the proposed scheme keeps the crosstalk suppression ability of the original
FNS-CAC, and it has higher reparability than the local self-repair schemes, such as the signal-switching-based and
the signal-shifting-based counterparts.  

This paper proposes a multi-access and multi-user semantic communication scheme based on semantic matching and intent deviation to address the increasing demand for wireless users and data. The scheme enables flexible management of long frames, allowing each unit of bandwidth to support a higher number of users. By leveraging semantic classification, different users can independently access the network through the transmission of long concatenated sequences without modifying the existing wireless communication architecture. To overcome the potential disadvantage of incomplete semantic database matching leading to semantic intent misunderstanding, the scheme proposes using intent deviation as an advantage. This allows different receivers to interpret the same semantic information differently, enabling multiplexing where one piece of information can serve multiple users with distinct purposes. Simulation results show that at a bit error rate (BER) of 0.1, it is possible to reduce the transmission by approximately 20 semantic basic units.

An isotropic electromagnetic (EM) lens based on Huygens’ metasurface is proposed for 28.0 GHz lens antenna design. The lens consists of a series of non-resonant and subwavelength metallic patterns etched on both sides of an ultrathin dielectric substrate. Both electric and magnetic responses are introduced to realize desired abrupt phase change and high-efficiency transmission for the secondary wavelets in the incident wavefront. Then, a substrate-integrated waveguide (SIW) fed patch antenna is combined with the lens as the primary feed to form a low-profile lens antenna system. The simulated and measured results coincide with each other, and demonstrate that the prototype realizes 8.8 dB~12.6 dB gain increment and low side-lobe levels over the bandwidth of 26.7 GHz~30.0 GHz. The novel design leads to a low-profile, light weight, and low-cost antenna solution in a wireless communication system.

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.

Unmanned aerial vehicle base stations ( UAV-BSs) can provide a fast network deployment scheme for heterogeneous networks. However, unmanned aerial vehicle (UAV) has limited capability and cannot assist the base station (BS) well. The ability of a UAV to assist the BSs is limited, and the cluster deployment relies on the leading UAV. The dispersive deployment of multiple UAVs (multi-UAVs) need a macro base station (MBS) to determine their positions to prevent collisions or interference. Therefore, a distributed cooperative deployment scheme is proposed for UAVs to solve this problem. The scheme can increase the ability of UAVs to assist users and reduce the pressure on BSs to deploy UAVs. Firstly, the randomly distributed users are pre-clustered. Then the placement problem was modeled as a circle expansion problem and a pre-clustering radius expansion algorithm was proposed. Under the constraint of users-data rates, it provides services for more users. Finally, the proposed algorithm was compared with the density-aware placement algorithm. The simulation results show that the proposed algorithm can provide services for more users and improve the coverage rate of users while ensuring the data rates.

At present, there is an urgent need for blockchain interoperability technology to realize interconnection between various blockchains, data communication and value transfer between blockchains, so as to break the ‘ value silo’ phenomenon of each blockchain. Firstly, it lists what people understand about the concept of interoperability. Secondly, it gives the key technical issues of cross-chain, including cross-chain mechanism, interoperability, eventual consistency, and universality. Then, the implementation of each cross-chain key technology is analyzed, including Hash-locking, two-way peg, notary schemes, relay chain scheme, cross-chain protocol, and global identity system. Immediately after that, five typical cross-chain systems are introduced and comparative analysis is made. In addition, two examples of cross-chain programmability and their analysis are given. Finally, the current state of cross-chain technology is summarized from two aspects: key technology implementation and cross-chain application enforcement. The cross-chain technology as a whole has formed a centralized fixed mechanism, as well as a trend of modular design, and some of the solutions to mature applications were established in the relevant standards organizations, and the cross-chain technology architecture tends to be unified, which is expected to accelerate the evolution of the open cross-chain network that supports the real needs of the interconnection of all chains.

Internet of things ( IoT) can provide the function of product traceability for industrial systems. Emerging  blockchain technology can solve the problem that the current industrial Internet of things ( IIoT) system lacks  unified product data sharing services. Blockchain technology based on the directed acyclic graph (DAG) structure  is more suitable for high concurrency environments. But due to its distributed architecture foundation, direct storage  of product data will cause authentication problems in data management. In response, IIoT based on DAG  blockchain is proposed in this paper, which can provide efficient data management for product data stored on DAG  blockchain, and an authentication scheme suitable for this structure is given. The security of the scheme is based  on a discrete-logarithm-based assumption put forth by Lysyanskaya, Rivest, Sahai and Wolf(LRSW) who also show  that it holds for generic groups. The sequential aggregation signature scheme is more secure and efficient, and the  new scheme is safe in theory and it is more efficient in engineering.

Unmanned aerial vehicles (UAVs) are increasingly applied in various mission scenarios for their versatility, scalability and cost-effectiveness. In UAV mission planning systems (UMPSs), an efficient mission planning strategy is essential to meet the requirements of UAV missions. However, rapidly changing environments and unforeseen threats pose challenges to UMPSs, making efficient mission planning difficult. To address these challenges, knowledge graph technology can be utilized to manage the complex relations and constraints among UAVs, missions, and environments. This paper investigates knowledge graph application in UMPSs, exploring its modeling, representation, and storage concepts and methodologies. Subsequently, the construction of a specialized knowledge graph for UMPS is detailed. Furthermore, the paper delves into knowledge reasoning within UMPSs, emphasizing its significance in timely updates in the dynamic environment. A graph neural network (GNN)-based approach is proposed for knowledge reasoning, leveraging GNNs to capture structural information and accurately predict missing entities or relations in the knowledge graph. For relation reasoning, path information is also incorporated to improve the accuracy of inference. To account for the temporal dynamics of the environment in UMPS, the influence of timestamps is captured through the attention mechanism. The effectiveness and applicability of the proposed knowledge reasoning method are verified via simulations.

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.