In foggy or hazy weather, the scattering of light by atmospheric particles weaken the details of optical images, which seriously affects the subsequent image analysis and processing tasks. The existing dehazing algorithms have problems such as loss of image information, blurring and excessive enhancement of sky area after dehazing. Starting from the perspective of polarization and the dark channel prior, this article proposes a target polarization degree estimation algorithm using the gradient feature of the direct transmission light as guidance for image dehazing. The polarization information of scene and atmosphere are obtained by polarized images. Then, guided by the gradient feature of the direct transmission light estimated by the dark channel prior algorithm, the target polarization estimation algorithm is proposed. Finally, the estimated target polarization degree is converted into atmospheric light intensity, and the optimized atmospheric light intensity is obtained through the principled constraint and guided filtering, and the optimized target polarization degree and image after dehazing is further solved. Qualitative experiments show that the proposed algorithm has good smoothness and overcomes the problems of low visibility, residual dehazing and excessive enhancement of sky area in the existing dehazing algorithms. Quantitative experiments show that the proposed algorithm neither causes loss of image information, nor generates excessive noise and blurs. The comparison results with five representative dehazing algorithms show that our proposed algorithm has good ability to restore details, improve image entropy, and enhance the degree of tonal restoration.

With the rapid development of aerospace technology, wireless power transmission(WPT) in the closed cavity has attracted extensive attention.WPT based on frequency control is proposed, which can realize controllable, and high-efficiency wireless charging of multi-directional sensors in electrically large closed cavities(10³×λ³).The electric field distribution in an electrically large cavity is very sensitive to the change of frequency, and the field distribution in the closed cavity can be controlled by changing frequency.The experimental results show that the highest WPT efficiency at S-band is 96.6%.The measured rectification efficiency of the designed broadband rectifier circuit is up to 80%, and the bandwidth with rectification efficiency higher than 50% is 1.65 GHz.The different working states of dual receivers can be controlled in the frequency band from 2.401 GHz to 2.495 GHz, which shows its application prospect in wireless power supply for sensors in closed spaces such as aerospace vehicles.

An all silicon carbide integrated process platform based on the wafer with N-substrate and P-epitaxy is proposed in this paper, which is compatible with CMOS devices, LDMOS and high-voltage diodes. A P-buffer layer is adopted to modulate the vertically distributed electric field and potential, which results in 212.4% improvement in vertical voltage withstanding. The LDMOS, high voltage diode and high side region can achieve more than 300V breakdown voltage in 2μm P-type epitaxial layer. Based on this platform, SiC CMOS inverter and inverter chain are constructed, all of which achieve voltage output ranging from 0 to 20V with rail-to-rail capability. A half-bridge driving circuit is designed with a four-stage inverter chain as the low-side driver circuit. The high-side driver circuit consists of level-shifting circuit and a high-side region inverter chain circuit, producing an output of 180V to 200V floating gate drive signal.

The performance of existing sampling rate offset (SRO) estimation algorithms can be degraded significantly in low signal-to-noise ratio (SNR) conditions. To address this problem, we propose the frequency-sliding double-cross correlation processing (FS-DXCP) algorithm based on the subband secondary generalized cross-correlation function to estimate SRO. The proposed algorithm adopts a frequency-domain sliding window to construct the subband SGCC function matrix of the sensor signals. Then, by utilizing the singular value decomposition (SVD), we adaptively mitigate the influence of low SNR frequency bins on estimating secondary generalized cross-correlation functions. Finally, a higher precision SRO estimation is achieved by tracking the maximum point of the estimated SGCC function. Computer simulations show that the root mean squared error of the proposed method for sampling rate offset is 4.21 ppm when the SNR is -5 dB, which is about 8.17 ppm lower than that of the double-cross correlation processing with phase transform (DXCP-PHAT) algorithm. The proposed algorithm effectively improves the estimation accuracy of the SRO in low SNR conditions.

Tile-based methods that use the divide-and-conquer and on-demand transmission techniques are promising to handle 3D holographic video streaming. However, the current solutions either lack an adaptive tiling scheme or cannot apply to mobile real-time scenarios. In this paper, we propose VVSTiler (Volumetric Video Streaming Tiling selector), an adaptive tiling selector for holographic video communications, which can adaptively maximize perceived video quality under dynamic and limited computing and bandwidth resources. To be specific, we first conduct a preliminary study on the impacts of different tiling schemes and find that fine-grained tiles improve the rational utilization of dynamic network resources and coarse tiles ensure coding efficiency and robustness, which stimulates us to construct an adaptive tiling optimization based on the predicted viewport, available computing resources, and network bandwidth; and then devise a fast algorithm to enable online tiling decisions. Rich experiments on the 8iVFB (8i Voxelized Full Bodies) datasets are conducted to compare VVSTiler with state-of-the-art tiling-based baselines. The results exhibit that VVSTiler can achieve up to 60.4% video quality improvements and save on average 27% bandwidth per frame against the closest competitor, in cases of terrible and accurate viewport predictions, respectively.

Element-level cooperation is the next stage of cooperative operation, and the dynamic reconstruction of kill chain is its typical feature. Aiming at the problem that platform-level cooperation is difficult to meet the demand for complex operational tasks, this paper proposes solution ideas and theoretical methods for heterogeneous platform element collaboration. Firstly analyzes the two main difficulties in realizing element-level cooperation, namely the complexity of element-level cooperation relationship and the uncertainty of cooperative ability; Secondly a based on feedback mechanism reconfigurable architecture, a dynamic reconstruction method for elements of the kill chain operator and a quantitative evaluation method for the effectiveness of collaborative order parameters were proposed through operational network modeling; Finally, The dynamic construction and reconstruction of the kill chain were achieved by simulation scenario, which can guide the algorithm design and engineering practice of element collaboration.

In today's rapidly evolving landscape of distributed machine learning, conventional data incentive solutions often fall short due to their reliance on simplistic single-server architectures, in addition, as computing environments become increasingly complex, particularly within the context of heterogeneous wireless networks, these traditional approaches struggle to meet the dynamic computational demands such as unbalanced resource allocation and exorbitant communication costs. In response to the above dilemma, we innovatively propose a hierarchical stackelberg game swarm learning incentive method for wireless edge network (HSISL). We innovatively introduce the Stackelberg game mechanism into the swarm learning. Based on the performance differences of each computing terminal, the cloud aggregation platform, edge cluster nodes, and edge computing nodes conduct dynamic games and jointly formulate personalized hierarchical resource allocation strategies through the fair incentive process of dual pricing, which can effectively guide the edge computing model to accelerate forward. Through theoretical and experimental analysis, the HSISL method can obtain the optimal incentive Nash equilibrium solution for model training. Compared with other incentive methods, the HSISL method can effectively improve the fairness of the model. With training efficiency, its accuracy on the MNIST data set can reach 96.06%.

Database parameter tuning is one of the crucial tasks in improving the performance of database systems. Database parameters can be classified based on their scopes and functionalities. It plays an essential role in investigating the mutual influence of parameters within a specific category or between different categories. But, the existing methods do not take into consideration this aspect. A collaborative multi-agent model called DBT-MADDPG (DataBase Tuning-Multi-Agent Deep Deterministic Policy Gradient) is proposed for database parameter tuning. A single-agent pre-training model called SA (Single Agent), a multi-agent joint training model called JAM (Joint Action Model), and a joint training model based on probabilistic selection called JAPM (Joint Action Probability Model) are designed for tuning the database parameters at different stages. The experimental results show that the DBT-MADDPG model is capable of tuning the database parameters at different functional and parameter levels, and can reach the performance of mainstream algorithms in the training stage of the SA model, and is 17.85% faster than the state-of-the-art algorithms to obtain the optimal configuration.

In view of the accuracy of existing 3D object detection algorithms based on Pseudo-LiDar is far lower than that based on real LiDAR (Light Detection and ranging), this paper studies the reconstruction of Pseudo-LiDar and proposes a 3D object detection algorithm suitable for Pseudo-LiDar. Considering that the Pseudo-LiDAR obtained by image depth is dense and gradually sparse along the increase of depth, a depth related Pseudo-LiDAR sparsification method is proposed to reduce the subsequent calculation amount while retaining more useful Pseudo-LiDAR in the middle and long distance, so as to realize the reconstruction of Pseudo-LiDAR. Furthermore, a 3D object detection algorithm based on object feature distribution convergence under the guidance of LiDar point cloud and semantic association is proposed. During network training, a laser point cloud branch is introduced to guide the generation of Pseudo-LiDAR object features, so that the generated Pseudo-LiDar object feature distribution converges to the feature distribution of laser point cloud object, thereby correcting the detection error caused by the difference between the two data sources. Aiming at the insufficient semantic association between Pseudo-LiDar in the 3D candidate bounding-box obtained by RPN (Region Proposal Network) network, an attention perception module is designed to embed the semantic association between points through the attention mechanism in the feature representation of Pseudo-LiDar, so as to improve the accuracy of 3D object detection. The experimental results on KITTI 3D object detection dataset show when the existing 3D object detection network adopts the reconstructed Pseudo-LiDar, the detection accuracy is improved by 2.61%. Furthermore, the proposed 3D object detection network with the feature distribution convergence and semantic association improves the accuracy by 0.57%. Compared with other excellent methods, it also improves the detection accuracy.

The well logging curve records the amplitude range of geophysical properties changing with depth and is the bond between well log and seismic data. It is also significant for reservoir lithology analysis and subsequent oil and gas exploration projects. However, instrument failure and other reasons will cause well-logging curves to be missing in the actual logging process. Re-logging is not only expensive but also difficult to achieve. Aiming at the problem that logging data is often missing during geological exploration, this paper proposes a logging curve reconstruction method based on the LSTM(Long Short-Term Memory)-attention model. At the same time, EMD-VMD (Empirical Mode Decomposition-Variational Mode Decomposition) decomposition is performed on the original logging signal and then the correlation between the components and the original curve is calculated. Some excrescent components are deletedto promote efficient and high-precision manual completion. This proposed method is applied to reconstruct missing logs acoustic (AC) and density (DEN), and the prediction results are compared with those predicted by LSTM and BP (Back Propagation) neural network. The results show that the LSTM-attention model has a better prediction performance, and the correlations between predictive and the original curves can reach 86.8% (AC) and 74.8% (DEN), higher than the traditional LSTM and BP neural network. After removing redundant signal components, the correlation coefficients increased by 1.4% (AC) and 4.0% (DEN). At the same time, the logging curve predicted by the proposed method has the lowest prediction error. Therefore, the representation learning based on LSTM with an attention mechanism has better prediction accuracy for well-logging curve reconstruction.

Von Neumann computer architecture faces the bottleneck of "storage wall", which hindering the performance improvement of AI (Artificial Intelligence) computing. Computing-In-Memory (CIM) breaks the limitation of "storage wall" and greatly improves the performance of AI computing. At present, CIM schemes have been implemented in a variety of storage media. According to the type of calculation signal, CIM scheme can be divided into digital CIM and analog CIM scheme. CIM has greatly improved the performance of AI computing, but the further development still faces major challenges. This article provides a detailed comparative analysis of CIM schemes in different signal domains, pointing out the main advantages and disadvantages of each scheme, and also pointing out the challenges faced by CIM. We believe that with the cross level collaborative research and development of process integration, devices, circuits, architecture, and software toolchains, CIM will provide more powerful and efficient computing power for AI computing at the edge and cloud ends.

The optimization problems in multiple areas can be modelled as many-objective optimization problems, which can be solved using many-objective evolutionary algorithms. However, it is difficult to balance convergence and diversity. To tackle this issue, this paper proposes a many-objective evolutionary algorithm based on dynamic decomposition and modified angle penalty distance referred to as duplication analysis based evolutionary algorithm (DAEA). DAEA decomposes the whole population into multiple clusters through dynamic decomposition, which is exempt from the predefined reference vectors and makes full use of the distribution information of the population itself to decompose. Then, DAEA selects solutions from each cluster based on modified angle penalty distance to balance convergence and diversity. Besides, DAEA operates mating selection according to Pareto dominance, knee points, and m-nearest angle binary tournament selection. Compared with nine many-objective evolutionary algorithms on 27 many-objective optimization problems, DAEA is effective on many-objective optimization problems with various shapes of Pareto front and stable on different numbers of objectives.

Since the traditional sentiment classification methods for text comments usually ignore the influence of user personality on sentiment classification results, a sentiment classification method (called BF_BiGAC) for text comments based on user personality and semantic-structural features is proposed. According to the advantage of Big Five personality model on effectively expressing the user personality, the user personality feature is obtained from the comment texts by calculating the personality scores from different dimensions. Moreover, the advantages of Bidirectional Gated Recurrent Unit (BiGRU) and Convolutional Neural Network (CNN) on effectively extracting the contextual semantic features and the local structural features are taken, and a new text semantic-structural feature acquisition method based on BiGRU, CNN and two-layer attention mechanism is proposed. Finally, in order to distinguish the influence of the features with different types, the hybrid attention layer is introduced to obtain the final text vector representation by integrating the user personality feature and the textural semantic-structural feature effectively. The experimental results on the datasets of IMDB, Yelp-2, Yelp-5 and Ekman show that BF_BiGAC achieves good performance when the measurements of Accuracy and weighted macro F₁ (F_w) are used. Specifically, it achieves the improvements of 0.020, 0.012, 0.017 and 0.011 compared to Sentiment Classification Method Concatenating BiGRU and CNN (BiGRU_CNN) on Accuracy, and achieves the improvements of 0.022, 0.013, 0.028 and 0.023 compared to Sentiment Classification Method Concatenating CNN and BiGRU (ConvBiLSTM) on F_w. Moreover, when comparing with the pre-trained models of BERT and RoBERTa, BF_BiGAC achieves higher executing efficiency while ensuring the classification accuracy.

Ramanujan Fourier mode decomposition uses scanning from low frequency to high frequency to obtain component signals, which is prone to excessive decomposition and information dispersion, resulting in decomposed components not having a single and complete mode information. To address the above issues, this paper proposes an sdaptive concise empirical Ramanujan decomposition (ACERD) method. In the ACERD method, the power spectral density is used to obtain the split frequency band for accurate frequency band division. Meanwhile, the Ramanujan Fourier transform is used to extract the mode components corresponding to each segmented frequency band, improve the recognition ability of periodic components, and obtain mode components with a single periodic feature information. The analysis results of composite fault simulation signals and measured signals indicate that the ACERD method has excellent capability of frequency band segmentation and periodic pulse feature extraction, which is suitable for compound fault diagnosis.

Based on the multiple spatial mode channels of conventional multimode optical fiber, the optical fiber transmission system capacity can be effectively improved by adopting multi-dimensional multiplexing technology and then meet the rapidly growing demand for data services. In this paper, we demonstrate the conventional OM2 fiber based large-capacity optical transmission with a combination of wavelength division multiplexing (WDM), polarization division multiplexing (PDM) and mode division multiplexing (MDM) technologies. Each of the total 80 channels with 40 wavelengths (1 535.04~1 566.31 nm) and 2 mode (LP01 and LP11b) channels is modulated by 60 Gbaud PDM16-ary quadrature amplitude modulation (PDM-16QAM) signal. The MDM link consists of a pair of mode multiplexer/de-multiplexer based on multi-plane light conversion (MPLC) and 20 m OM2 fiber. Thanks to the high mode isolation degree of two used mode channels ( dB), only 2×2 multiple input multiple output (MIMO) algorithm is applied for polarization de-multiplexing, and no need to do mode de-multiplexing. In this work, to improve the system capacity, the key system parameters have been optimized, including the roll-off factor of pulse shaping filter, clipping ratio and the received optical power (ROP). And the Volterra decision feedback equalization (VDFE) is also adopted not only to compensate for the nonlinear impairments introduced by the optical modulator, but also alleviate the high frequency noise enhancement caused by feed forward equalization (FFE). Then, a total capacity up to 38.4 Tbit/s has been realized with the bit error rate (BER) of all 80 channels lowering than the 20% soft decision forward error correction (SD-FEC) threshold of \mathrm{2.7} \times {\mathrm{10}}^{-\mathrm{2}}. The experimental results reveal that the MDM coherent optical transmission scheme based on multi-mode fiber has the potential in the future ultra-large capacity short-distance optical interconnection system.

The flexible sensor based on photo plethysmo graphy (PPG) can detect heart rate (HR) and blood pressure (BP), but the calibration of their detection results is rarely reported. Therefore, this paper proposes a reflective PPG heart rate detection and blood pressure calibration system based on simulated blood circulation.The peristaltic pump is used to generate pulsating flow,and the frequency and pressure of simulated blood delivery are controlled by adjusting its rotational speed,thus causing the change of the volume of simulated blood in the elastic latex tube and changing the signal period and intensity of reflected light, which is closer to match the actual scenario of human pulse measurement process.The mean value of heart rate detection error of the system is 0.277 78, and the 95% consistency limit is (-2.595 62, 3.151 17). The goodness of fit of measured systolic blood pressure (SBP) and diastolic blood pressure (DBP) are 0.971 85 and 0.981 11, respectively. The mean value of MD±SD of SBP and DBP detected by the calibrated flexible PPG sensor on four volunteers is 1.21±2.16 mmHg and 0.76±2.02 mmHg, respectively, which are in line with and far less than the standard index of 5±8 mmHg for measuring the accuracy of blood pressure monitors set by the association for the advancement of medical instrumentation (AAMI). The results show that this system can calibrate the flexible PPG sensor accurately and efficiently, which provides the calibration basis for the accurate blood pressure detection of portable wearable devices.

In this paper, a novel high-bandwidth density and low-power 7-order correlated NRZ (Non-Return-to-Zero) coding interface circuit for D2D (Die to Die) interconnection is proposed. In order to further improve the SNR (signal-to-noise ratio) and bandwidth density of 5-order correlated NRZ coding, this paper designs encoding and decoding circuits based on transmission and reception matrices. Based on the transmission matrix, a voltage-mode encoding circuit is designed at the transmitting end to effectively reduce power consumption. Based on the reception matrix, a decoding equalization circuit based on active adjustable inductor is designed at the receiving end to improve communication speed. In order to solve the problem of clock skew at the receiving end, this paper also designs an error correction circuit. The interface circuit is designed using 28 nm CMOS (Complementary Metal Oxide Semiconductor) technology, with a core area of 3 mm², and can be applied to on-chip interconnects ranging from 10 to 50 mm. The backend simulation results indicate that, under the condition of a Nyquist frequency of 20 GHz and a channel loss of -8 dB, the receiver's narrowest eye width is 0.45 UI, with a bit error rate less than {\mathrm{10}}^{-\mathrm{15}}, energy efficiency of 1.2 pJ/b, and bandwidth density of 448 Gbps/mm.

At present, rumor detection methods on social platforms mainly focus on obtaining information from the propagation path, most of these methods only use text information as the initial propagation feature, which is difficult to capture the rich propagation structure representation. In this paper, according to the propagation path of rumors, text and user credibility features are extracted, and a multi-feature rumor detection model based on propagation tree is constructed. This model aggregates text propagation features through a graph convolutional network, and uses a multi-head attention module to mine the intra-layer dependencies of the text propagation tree. At the same time, a credibility sequence is constructed for each user in the user propagation tree, and the M-Attention module is used to capture effective user credibility features. The experimental results show that the experimental accuracy of Twitter15, Twitter16 and Weibo datasets reaches 89.3%, 91.7% and 96.4%, which are 4.8%, 4.2% and 3% higher than the current optimal propagation tree model Bi-GCN accuracy respectively.

This work presents a high-efficiency on-chip harmonic tuned power amplifier(PA) monolithic microwave integrated circuit(MMIC) for millimeter-wave applications. The MMIC PA efficiency at high frequency can be improved by accurate harmonic tuning method and proper harmonic terminations at both the input and output port. The output second and third harmonic impedance are controlled simultaneously by the proposed matching network. Besides, the input second harmonic impedance is tuned to the optimum region to achieve high-efficiency performance. The proposed PA topology and design method are verified by simulation and measurement based on 0.15μm GaN-on-SiC process. The PA features a measured bandwidth from 21.4 to 23 GHz with PAE larger than 39.2% and output power larger than 33 dBm. The maximum measured drain efficiency is 63.7% with an output power of 34.1 dBm at 22.2 GHz. The corresponding power added efficiency(PAE) is 50.2%. Close agreement between simulated and measured results is achieved for this PA. The total size of the PA is 1.87 mm², resulting in a power density of 1.31 W/mm². Meanwhile, the proposed PA has a high-efficiency and power density performance compared with other reported high-efficiency PAs.

The intricacy of wireless access networks continues to escalate, accompanied by a substantial proliferation in the number of wireless devices. In order to augment network capability and resource utilization, it is necessary to design effective access control and resource allocation schemes to balance traffic loads among subnetworks and promote cross-domain resource coordination and sharing. To address this, our paper builds upon the recently proposed Hash Access protocol and devises an optimization method, which dynamically adjusts access parameters based on the network load to alleviate network congestion. Furthermore, this paper presents traffic balancing and resource re-allocation schemes for multi-subnetwork scenarios, offering valuable approaches for integrating wireless resources and offloading traffics. Simulation results demonstrate that the proposed Hash Access optimization method maximizes network throughput while ensuring network stability, and the proposed resource allocation scheme effectively addresses load unbalancing issue, thereby enhancing the performance and fairness for complex integrated networks.

RISC-V instruction set architecture (ISA), as a new streamlined ISA, has developed rapidly due to its characteristics of free, open source, and freedom. Since the research on RISC-V at home and abroad mainly focuses on hardware development, the software ecosystem is still weak compared to mature ISAs. Implementing a set of high-performance basic math libraries for the RISC-V instruction set can further enrich the RISC-V software ecosystem. This paper realizes the transplantation of Sunway math library to RISC-V based on automatic transplantation technology, and provides the first basic math library system using vector instruction optimization for RISC-V instruction architecture. This paper proposes an automatic branch look-up table method and a path marker insertion method for vector registers, focusing on solving the problem of register multiplexing in the process of register mapping between different architectures, realizing the correct and efficient mapping of registers, and automatically transplanting 69 mathematical functions according to different instruction equivalence conversion strategies. The test results show that the RISC-V basic math library function can achieve correct calculation, the maximum error is 1.90ULP, and the average performance of functions is 157.03 beats.

Polynomial multiplication operations limit the practical applications of lattice-based post-quantum cryptography. In order to improve the performance and efficiency of post-quantum cryptography Crystal_Kyber algorithm, reduce the running time and reduce the influence of polynomial multiplication,this paper designs a new butterfly operation unit to optimize the Kyber scheme with prime modulus q = \mathrm{3329}. First of all,the algorithm is executed by 16-way parallel scheduling of the new butterfly operation unit, which shortens the calculation cycle. Secondly, using pipeline technology and improved K²RED algorithm, the design and implementation of a new butterfly operation unit for reducing resource consumption. Ultimately, the data is stored in the way of multi-RAM, and the multi-channel RAM is optimized to allow data to be stored alternately in RAM and improve the resource reuse rate. The experimental results show that the optimized number theory transform NTT (Number Theoretic Transform), inverse number theory transform INTT (Inverse NTT), point-wise multiplication PWM (Point-wise Multiplication) efficiency reaches 200MHz, and the combined execution Kyber efficiency reaches 175MHz, which is superior to other schemes and has good performance.

Frequency diversity array(FDA) radar was proposed by Antonik and Wicks in 2006. Since there is a frequency offset between each adjacent antenna of FDA radar, there exists two-dimensional dependence on range and angle in the transmitting array. For bistatic FDA-multiple input multiple output(MIMO) radar, direction of arrival(DOA)- direction of departure(DOD)-range information is coupled in the transmitting steering vector. How to decouple the three information has become the focus of research. In this paper, aiming at the problem of target parameter estimation of bistatic FDA-MIMO radar, a reduced-dimension multiple signal classification(RD-MUSIC) parameter estimation algorithm based on tensor framework is proposed. Firstly, in order to decouple the DOD and range information in the transmitting array, it is necessary to divide the transmitting array into subarrays. Then the signal subspace is obtained by high-order-singular value decomposition(HOSVD), and the two-dimensional spatial spectral function is constructed. Secondly, the dimension of spatial spectrum is reduced by Lagrange algorithm, so that it is only related to DOA, and the DOA estimation is obtained. Then the frequency increment between subarrays is used to decouple the DOD and range information, and eliminate the phase ambiguity at the same time. Finally, the DOD and range estimation automatically matched with DOA estimation are obtained. The proposed algorithm uses the multidimensional structure of high-dimensional data to improve the estimation accuracy. Meanwhile, the proposed RD-MUSIC algorithm can effectively reduce the computational complexity. Numerical experiments show the superiority of the proposed algorithm.

The existing side-channel protection schemes for the uBlock algorithm suffer from high latency, making them unsuitable for low-latency and high-throughput scenarios. Additionally, these schemes lack provable security under the glitch-extended probing model. To address these issues, this paper presents a low-latency Threshold Implementation of the uBlock algorithm with provable security under the glitch-extended probing model. Furthermore, we introduce the Changing of the guards technique to eliminate the need for additional random numbers during the execution of the protection scheme. To validate the security of our protection scheme, we employ the automated evaluation tool SILVER to assess the glitch-extended probing security of the S-box and utilize the leakage evaluation technology TVLA (Test Vector Leakage Assessment) to verify the security of the entire circuit. Finally, we evaluate the performance overhead of our protection scheme using the Design Compiler tool. The evaluation results demonstrate that our scheme achieves a significant reduction in latency, approximately 95% less compared to serialized implementations of uBlock protection schemes.

The mode division multiplexing (MDM) system based on the few mode fiber (FMF) can realize the parallel transmission of different independent channels by using the orthogonal modes in a single fiber core, which can effectively improve the system capacity and solve the “capacity crunch” of the single mode fiber (SMF) system. In this paper, we realize the transmission of the 32 Gbaud four-mode dual-polarization 16-ary quadrature amplitude modulation (16QAM) signals in 80 wavelength channels over 1000 km six-mode graded-index fiber by using jointly the MDM, wavelength division multiplexing (WDM) and polarization division multiplexing (PDM) techniques. In addition, we use the hybrid time-frequency domain data-assisted 8×8 multiple-input multiple-output (MIMO) least mean square (LMS) equalization at the receiver to compensate for the linear damages such as the mode coupling (MC) and differential mode group delay (DMGD) caused by the fiber transmission. Compared with the commonly used time-domain or frequency-domain MIMO-LMS equalization, the joint use of time and frequency domain can realize 57.1% improvement in convergence speed and 25.1% reduction in convergence error. After 1 000 km delivery of FMF, the bit error ratio (BER) of each mode and polarization state of all the 80 channels is lower than the 20% soft-decision forward error correction (SD-FEC) threshold of 2.4×10^-2, and the net transmission rate is up to 68.2 Tbit/s.

Cellular automata are widely considered as a fundamental architecture for nano-computers and quantum computers based on molecular self-assembly technology. In such a situation, the complexity of cellular automata will directly affect their efficiency of parallel distributed computing, together with the feasibility of physical implementation. The simplest model of all asynchronous cellular automata in the literatures employs three cellular states and three transition rules, which can construct all logic circuits and thus hold the computational universality equivalent to Turing machine (Turing universality). In order to further reduce the complexity of universal asynchronous cellular automata, this paper proposes a new model, which requires only three cellular states and two transition rules. The smaller number of transition rules is mainly attributed to the new circuit elements and the design of large-scale distributed circuits. Different from the logic gates of synchronous circuits, the novel circuit elements can effectively utilize the random fluctuations of signals, whereby they may promise potential applications via nano-technologies such as single-electron tunnel transistors. In addition to Turing universality, this paper explicitly provides a scalable and distributed scheme to construct parallel logic circuits, which enables our proposed asynchronous cellular automaton to realize the same parallel computing capability as synchronous cellular automata.

Association analysis between genes and phenotypes is crucial to reveal the inherent genetic association of organisms. Random walk-based algorithms can fuse multiple omics data, aggregate the label information of first-order or higher-order neighbors, complete the association information between different nodes in the network, improve the accuracy of association prediction and further discover the potential genetic associations between genes and phenotypes. However, existing random walk algorithms usually treat each node equally and ignore the varying importance of different nodes, as such non-important nodes can be excessively propagated and the model performance is compromised. To this end, an individual Multiple Random Walks (iMRW) algorithm based on multi-omics data fusion is proposed. On the heterogeneous genetic network composed with genes, miRNAs and phenotype nodes, we design the individual multiple random walks strategy based on the network topology, assign nodes of different importance with different walking lengths. We then complete the genetic information of different nodes by fusing multi-source association matrix, Gaussian interaction profile kernel similarity and random walk, and accurately obtain the gene-phenotype association prediction matrix. Under different experimental settings, iMRW can achieve the best prediction performance compared with the state-of-the-art algorithms. The case study with respect to maize photosynthetic ability and starch content further confirm the usefulness and effectiveness of iMRW in identifying potential gene-phenotype associations.

A high-concentration doped density of states model (HCD-DOS model) was established for amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors (TFTs) with back-channel etch (BCE) technology. The effect of the key parameters of the density of states on the device performance was also investigated by numerical simulation to reveal the physical mechanism of the preparation process to repair the conductive channel in a-IGZO TFTs. Firstly, the molybdenum/copper interconnect structure with high bonding strength was used as gate/source/drain electrodes, and the bottom-gate top-contact (BG-TC) TFTs was prepared by introducing the BCE method. Secondly, the HCD-DOS model of a-IGZO TFTs suitable for BCE technology was developed. Subsequently, the key parameters of the density of states were investigated numerically based on the TCAD (Technology Computer Aided Design) simulator. The results demonstrated that different density of states parameters had different effects on the transfer characteristic curves, electrical characteristics, and electron concentration distribution inside the channel of the a-IGZO TFTs device. At last, the influence of SiO_x passivation-layer deposition and N₂O plasma treatment on the internal mechanism of the device was explored based on the HCD-DOS model. It was found that N₂O plasma treatment had a significant effect on the density of states distribution and channel carrier concentration, which in turn caused the threshold voltage to drift.

In order to meet the market demand of medium and low voltage consumer electronics, the small size and high density Bipolar-CMOS-DMOS technology has been vigorously developed. Low loss and high reliability have become the focus and difficulty in the design of lateral double-diffused metal-oxide-semiconductor field effect transistors in Bipolar-CMOS-DMOS technology. This paper introduces a lateral double-diffused metal-oxide-semiconductor field effect transistor based on the high temperature oxidation layer structure, and studies and analyzes the degradation mechanism of its hot carrier injection. The high temperature oxidation layer structure is used to improve the traditional shallow trench isolation structure, in which the oxide steps embedded in the semiconductor have adverse effects on the hot carrier injection of the device. Thus improve the reliability of the device. Proposed structure shortens the current path length in the on state of the device and reduce the loss. In addition, this paper also proposes a self-aligned implantation process optimization method for the P-type body region. By increasing the implantation process of the high-energy body region, the depletion region in the accumulation area is expanded, the surface electric field of the drift region is reduced, and the breakdown voltage is improved. The proposed HTO-LDMOS has a breakdown voltage of 43 V, a specific on-resistance of 9.5 mΩ·mm², a linear region current degradation of 0.87% after 10 000 s.

Based on super pixel technology, a digital driven strategy for color silicon OLED (Organic Light Emitting Diode) micro-display is proposed. By reusing adjacent pixel information, a single pixel can be used for imaging multiple adjacent pixels to greatly improve the display resolution. A digital driving circuit for color OLEDoS (Organic Light Emitting Diode on Silicon) micro-display is designed. Under the condition of 120 Hz frame rate, 256 grey levels and 4K display resolution can be achieved while the circuit area and data transmission per second are only 50% of the traditional driving mode. The test results show that the average current range of OLED pixel realized by the driving circuit is 13.1 pA~3.74 nA, which can meet the demand of near-eye display of micro display.

As an emerging distributed machine learning architecture, federated learning (FL) allows multiple users to train local models and achieve global aggregation of models with data privacy protection, thus providing reliable Internet of Vehicle (IoV) services. However, in the training process of FL, many training terminals may switch among domains due to the high mobility, resulting in low accuracy of the global model. Besides, malicious terminals may frequently upload invalid or incorrect model data which leads to low service reliability. Therefore, we build the dual-layer FL based edge collaborative computing mechanism for high dynamic IoV businesses. First, we comprehensively consider the mobility, computing ability and reliability to construct the service capability model for the terminal, and then propose the edge collaborative computing domain (ECCD) construction algorithm based on Deep Reinforcement Learning. By clustering the vehicle terminals covered by multiple edge nodes, the switching probability of the terminal local model will be reduced, and the sustainability of the FL model training can be guaranteed. Furthermore, we design a dual-layer FL framework including the inter-ECCD aggregation layer and cross-ECCD aggregation layer, respectively. It adopts the semi-asynchronous aggregation mechanism for local models based on the adaptive aggregation factor in the inter-ECCD aggregation layer, and the asynchronous aggregation mechanism for domain's regional model based on data volume in the cross-ECCD aggregation layer, which jointly improve the aggregation efficiency of the FL system. In particular, considering that the high speed terminals may cause the cross-domain problem, we introduce the partial conditional update mechanism for the local model to avoid the situation that the high-quality models are covered by the low-quality models, which further improves the accuracy of the global model and the utilization of FL system resources. The simulation results verify that the proposed framework outperforms the local computing and asynchronous/synchronous FL algorithms in terms of model accuracy and service reliability.

An all-fiber few-mode erbium-doped amplifier was built to compare the effects of different pump modes and different pump directions on the gain characteristics of the three signal modes, LP₀₁, LP_11a, and LP_11b. The experimental results show that the amplifier has the best performance under forward LP₁₁ pumping. The signal gain is more than 20 dB, the differential modal gain (DMG) is less than 0.9 dB and the noise figure is less than 9.6 dB in the whole C band. At a signal input power of -10 dBm/mode, the gain of all three signal modes at 1 550 nm exceeds 20.8 dB, the DMG is as low as 0.3 dB, the noise figure of LP₀₁ signaling light is lower than 6.2 dB, and the noise figure of LP₁₁ signaling light is lower than 9.6 dB. Comparing the different pumping directions under the four pumping schemes, it can be found that the noise figure of the forward-pumped amplifier is the smallest, but the gains of the three signal modes are also smaller, while the gain of the higher-order signal modes is increased by using the backward-pumped one, but the noise figure will also become larger. Comparing the pumping modes, it can be found that compared with the LP₀₁ pumping, the LP₁₁ pumping can significantly increase the gain of the LP₁₁ signaling light, and has less effect on the gain of the LP₀₁ signaling light, which can reduce the DMG value.

In this paper, single-layer Graphene, single-layer VS₂ and single-layer BN were synthesized into Graphene/VS₂/BN van der Waals heterostructure by van der Waals interaction, and the feasibility of using it as anode electrode material in li-ion batterys (LIBs) was studied by combining it with different amounts of lithium. Graphene/VS₂/BN van der Waals three-layer heterostructure has a formation energy of - \mathrm{0.33} ev/Ų, which has strong stability and can be synthesized theoretically. At the same time, the in-plane stiffness of Graphene/VS₂/BN van der waals heterostructure is also calculated, and the Young’s modulus (Y) is 886.88 N/m, which is higher than that of single-layer VS₂ (82.5 N/m), and it has good mechanical properties. The adsorption energy (2~5 eV) of Li adsorbed on the surface and interface of Graphene/VS₂/BN van der Waals three-layer heterostructure is much larger than that of the corresponding monolayer, which indicates that it has good adsorption performance for Li. When Li migrates at different surfaces and interfaces of Graphene/VS₂/BN van der Waals three-layer heterostructure, the diffusion barrier is very small (0.3~0.6 eV), which is extremely beneficial to the battery rate performance. Our research shows that the Graphene/VS₂/BN van der Waals three-layer heterostructure has a broad prospect in anode electrode materials of LIBs.

As the most commonly used sensor type in the field of magnetic sensing, Hall sensors have been widely used in industrial production, automotive electronics, aerospace, biomedicine and many other fields. In this work, the AlGaN/GaN heterojunction Hall sensor was developed by using the standard semiconductor chip manufacturing process, taking advantage of the high temperature tolerance and high mobility of wide-bandgap GaN semiconductor. The sensor in high temperature application environment requires the packaging material to resist high temperature, produce low stress and have little impact on the key indicators of the device. In this work, the influence mechanism of three different packaging materials on the performance of the sensor and the bonding wire is simulated, the changes of sensor sensitivity, offset voltage, temperature drift coefficient and other physical parameters before and after packaging with different packaging materials are verified by experiments. Finally, the packaging material with the best comprehensive performance and can be used in 550 K high temperature environment is selected. The offset voltage of the final packaged Hall sensor is less than 115 μV when the excitation current is kept at 1.0 mA, and the signal linearity is better than 0.3% in the range of 0~3.0 mA input current and 0~1.0 T magnetic field. The temperature drift coefficient is only -120.9 ppm/K in the temperature range of 300~550 K, which is better than the results reported in the literatures. The fabricated Hall sensor is expected to be applied in the magnetic field detection in extreme environments.

In this paper, a new type of gallium nitride (GaN) /Ti₃C₂T_x composite material was synthesized by solvothermal method and nitriding method, and the sensing characteristics of trimethylamine (TMA) gas sensor based on GaN/Ti₃C₂T_x composite were studied. Through a series of characterization methods, the morphology and elemental composition of the composites were analyzed, and the successful composite of GaN and Ti₃C₂T_x was confirmed. The results show that GaN/Ti₃C₂T_x sensor can efficiently detect 1~200 ppm TMA gas at room temperature, and the detection limit of TMA is reduced from 10 ppm to 1 ppm compared with pure GaN sensor. In addition, the gas sensing results of the composite sensor further confirm its good anti-interference characteristics and long-term stability. The gas-sensitive mechanism of GaN/Ti₃C₂T_x composite sensor and the reason of its improved gas-sensitive performance compared with pure GaN sensor are explained by using the surface depletion layer model. Finally, a contactless TMA gas detection device was developed in this paper, and the test results show that the device is expected to achieve efficient real-time detection of exhaled air in patients with liver and kidney disease, and has great application potential in early screening of liver and kidney diseases.

Internet is a crucial information infrastructure that promotes economic development and technology innovation. However, TCP/IP (Transport Control Protocol/Internet Protocol) architecture, i.e., the core technology that supports and regulates the operation of Internet, has remained almost unchanged for decades. This paper introduces the necessity of innovating Internet architecture from four aspects: the drawbacks of technology itself, the development of social economy, the transformation cycle of technology, and the revolution law of science and technology. From the perspective of the systems thinking, this paper elaborates the underlying causes of the disadvantages in TCP/IP architecture. Moreover, this paper leverages the systems thinking and reveals the nature of network functionality and the nature of network interconnectivity, and then clarifies the four properties of delivering information (i.e., object property, identity property, location property, and means property). Then this paper briefly introduces how CoLoR (Coupling service Location and inter-domain Routing) leverages the two natures and four properties to innovate the Internet architecture. Finally, this paper takes CoLoR as the example, and presents the basic idea of deploying novel Internet architectures.

Cryptographic ransomware extorts a ransom by encrypting user files. Existing early detection methods based on the first encryption-related application programming interface cannot detect ransomware before it executes encryption behavior. Because the point at which different ransomware families begin executing their encryption behavior varies, existing early detection methods based on fixed time thresholds can only accurately detect a small fraction of ransomware before it executes encryption behavior. To further improve the timeliness of ransomware detection, this article proposes a concept that characterizes the time period from the start of software operation to the first call of encryption-related dynamic-link library (DLL), namely theInitial phase of operation (IPO). Based on the analysis of DLL and API call behavior in the early operational phase of several ransomwares, this article presents a method based on the API sequences generated by the software within the IPO as the detection object, namely the ransomware early detection method based on API latent semantics (REDMALS). REDMALS captures the API sequences within the IPO, uses the term frequency-inverse document frequency algorithm and the latent semantic analysis algorithm to generate feature vectors on the captured API sequences and to extract potential semantic structures, respectively, and then uses a machine learning algorithm to construct a detection model for ransomware detection. The experimental results show that REDMALS using the random forest algorithm achieves 97.7% and 96.0% accuracy on the constructed variant test set and unknown test set, respectively, and 83% and 76% of the ransomware samples in both test sets, respectively, can be detected before they perform any encryption behavior.

The electric power infrastructure of China has developed into a highly informationized, automated, and intelligent cyber physical integration system. The interaction of cyber and physical not only significantly improves the efficiency and performance of power supply, but also introduces new network security threat. Cross-domain attacks that occurring in the cyber domain and acting on the physical domain can cause the systematic breakdown of power infrastructure and then lead to large-scale power outages. However, the current isolated cyber side or physical side defense system is difficult to effectively deal with these cross-domain attack threats.This paper introduces the current situation of information and physical cross domain attack threats faced by the power system, elaborates on the shortcomings of traditional defense methods in facing cross domain attacks, proposes a cross domain attack defense architecture based on information and physical collaboration, and designs defense methods from the perspectives of perception, identification, and blocking on the attack time scale. Through example design, it is proven that the proposed information and physical collaboration defense architecture can ensure the safe and stable operation of the power system.

Microdroplet injection has a promising application in the field of printed electronics, which can complete the integrated molding of antenna dielectric substrates and conductive patterns in a single device. The surface topography quality of printed functional devices has a significant impact on their electrical properties. In this paper, for the problem of difficult to control the surface topographic quality in microdroplet jet 3D printing, a predictive control-based topographic compensation method for printed parts is proposed. Firstly, based on the layer-by-layer stacking behavior of the droplets, a topography prediction model of the printed part is established, which uses the matrix element update to describe the layer-by-layer evolution of the part topography, so as to accurately predict the existence of topographic defects in the multilayer printed part, such as the edge collapse and the large surface roughness. Then, a prediction controller is built based on this model to realize efficient compensation of topographic defects of the printed part by adjusting the print pattern of subsequent layers. Comparison experiments between open-loop printing and compensated printing are used to verify the effectiveness of the method. The experimental results show that the compensated printing method reduces the surface roughness of the printed part by 66.80% and the edge collapse by 43.22%, which effectively compensates for the surface morphology defects of the printed part. Finally, the microstrip patch antenna was fabricated using the microdroplet jet 3D printing process. The surface roughness of the dielectric substrate fabricated with the compensated printing method proposed in this paper is lower than that of the open-loop printed samples, which ensures the high-quality connection of the RF layer of the antenna. The S11 (return loss) parameters were tested to be closer to the simulation results, thus demonstrating the significance of this study in printed electronics.

Program analysis plays a critical role in software development and maintenance. However, traditional logic-based program analysis methods exhibit significant limitations when dealing with modern, complex, large-scale, and dynamically rich software systems. The root cause of these limitations lies in the uncertainty present in software systems. To address this issue, researchers have proposed a series of new techniques for specific program analysis problems. These techniques combine probability information with traditional logic analysis to capture the uncertainty inherent in software systems. By summarizing and abstracting existing work in this area, this paper introduces the Bayesian Program Analysis framework. The core idea of this framework is to integrate program analysis with Bayesian statistical inference. It does so by modeling and updating probability distributions about the program to infer information about program behavior. Bayesian Program Analysis employs probabilistic logic programming to simultaneously handle both probability and logic information, providing a unified approach that encompasses various existing works. It can also be generalized to non-traditional static program analysis tasks, such as program fault localization and delta debugging. This paper provides a definition of the Bayesian Program Analysis framework, demonstrates its applications in program analysis and related fields, and outlines future directions for development.