Intelligent transportation services, such as smart driving, put forward high requirements for latency. When the vehicle itself has insufficient computing power, the vehicle needs the surrounding vehicles and roadside edge computing units to help it complete the task computation. In this paper, based on the existing vehicular edge computing (VEC) offloading strategy, considering the differences between the 5G-NR interface and PC-5 interface link of cellular-V2X (C-V2X) system, we propose a Q-Learning based joint PC-5/Uu interface edge computing offloading strategy. The successful transmission probability of PC-5 link in C-V2X system is modeled, and then the transmission rate characterization method of PC-5 link is deduced. We formulate a constrained Markov decision process (CMDP) to minimize the system latency, where the objective function is the task processing latency in C-V2X system, and constraints are transmission power at task vehicle and energy consumption of computation at vehicles with edge computing unit. By Lagrangian approach, the CMDP problem is transformed into an equivalent min-max non-constrained MDP problem, and Q-Learning is introduced to design the offloading strategy, and then the offloading strategy of C-V2X based VEC system based on Q-Learning is proposed. Simulation results show that compared with other baseline schemes, the proposed algorithm can significantly improve the system latency performance by at least 27.3%.
To solve the problem of multi-channel cross-talk in full-space metasurface, a strategy of tri-band transmission-reflection-transmission full-space wavefront control based on dual-geometric-phase metasurface is proposed. A frequency selective surface was cascaded with multilayer metasurface to obtain independent modulation of three frequency bands. The full-space independent amplitude and phase control combining transmission and reflection was realized based on dual geometric phase theory. For verification, a full-space multifunctional metadevice working at C, X and Ku bands was designed, fabricated and measured. Both numerical and experimental results show that transmissive and reflective dual-vortex beams along y-axis and x-axis are generated at 7 GHz and 10.2 GHz, respectively, meanwhile dual foci along x-axis is generated at 15.7 GHz under left-handed circularly-polarized wave excitation. The proposed strategy of tri-band transmission-reflection-transmission full-space wavefront control provides new approaches for new multifunctional devices and integrated electromagnetic wave control, and expands the application prospect of metasurface in large-capacity communication.
The effect of frame on the performance improvement of thin film bulk acoustic resonator(FBAR)is studied. Based on the basic theory of reflection and matching of two kinds of frame structures to keep acoustic energy, according to their functional characteristics, several groups of frame microstructure combinations of low frequency and high frequency are determined experimentally, and the two groups of FBAR are taped out. Finally, the resonator of all groups is obtained by on-wafer test. The selected frame structure group can improve the performance of both low-frequency and high-frequency FBAR. For low frequency (around 1.7 GHz) FBAR, the quality factor of parallel resonance can be increased by more than 1 000. For high frequency (around 5.5 GHz) FBAR, the quality factor of parallel resonance can be increased by more than 300. For the high frequency FBAR with strong transverse parasitic mode, the optimized frame structure can improve the transverse parasitic suppression and reduce the impedance phase fluctuation under the series resonant frequency by more than 5°.
The theoretical model of cyclotron electrons to radiate vortex microwave photons is crucial for the technology of quantum state vortex electromagnetic wave. This paper is the first part of “The Vortex Electron and Vortex Microwave Photon”, which establishes the theoretical model related to the “Vortex Electron Wave Packet”. To be specific, the radiation of energy level transition is one of the natural radiations of a vortex electron. Aiming to analyze the principle of OAM vortex microwave photons generated by single vortex electron cyclotron radiation, this paper derives the vortex electron wave function according to the conserved quantity of electrons in a constant magnetic field and analyzes the motion of the relativistic electron wave packet. This paper then explains the case where the spin angular momentum and orbital angular momentum are inseparable by solving the Dirac equation. In addition, the Landau levels of the transverse energy distribution of the cyclotron in the magnetic field are obtained in the solution process according to the relativistic energy eigenequation. Finally, This paper simulates the shape of the Landau energy level when the electron has the exact mode of the orbital angular momentum.
An ultra-wideband absorbing structure coving the whole ultra-high frequency (UHF) band is provided in this paper. The permeabilities of magnetic materials increase with the decrease of frequency. Therefore, the absorber is less susceptible to frequency. The performance of the absorber at low frequencies is further improved by introducing resistive loss. The permittivity and permeability of carbonyl iron powder are measured by vector network analyzer, the equivalent circuit parameters are optimized by genetic algorithm, and the structure parameters are optimized by full-wave EM simulator, all in whole UHF band. The absorber is measured at low frequencies. The measured, simulated, and equivalent circuit results show a good agreement. The absorber presents an absorption rate over 90% within a fractional absorption bandwidth over 164%, and it has a thickness of 0.029 at the lowest frequency.
The calculation principle of non-positional stochastic number (SN) is a promising technique for realizing high-performance computing owing to its extremely low hardware cost. This paper introduces detailly the origin, development process and the domestic and foreign development present situation. However, a disadvantage of stochastic bitstream is that the computing latency, and information-carrying efficiency and so on. We presented a hybrid stochastic computing (HSC) based on a hybrid bitstream to solve these problems, which achieves a lower hardware cost, better accuracy, and faster speed. The HSC neural networks is fabricated by 40 nm low-power CMOS process, with a core area of 0.73 mm × 0.73 mm, power of 102.3 mW and clock of 400 MHz, which has 4 544 multiply and accumulation (MAC).The proposed Hybrid stochastic computing is tested by FPGA and ASIC.Compared with other stochastic computing method, the method proposed gains 50×, 2.5×,and 3.26× energy efficiency than other methods of traditional stochastic computing.
Blockchain techniques have received extensive attention in recent years. The cryptocurrency market based on block-chain techniques is complex and unstable, vulnerable to political, economic and social factors. Existing studies focus on native cryptocurrencies, such as Bitcoin and Ethereum. However, a large number of ERC-20 tokens in the cryptocurrency market exist. ERC-20 tokens have a great market capitalization, attracting many investors’ attention. This paper proposes TokenVis, a visual analytics system, to help investors understand the evolutionary patterns of different ERC-20 standard tokens and provide explanations. The TokenVis prototype system integrates a visual analytics framework with different time granularities. We propose SegRank visualization for presenting evolutionary patterns of multiple time series and a time-based Constrained Optimization BY Linear Approximation (COBYLA) optimization algorithm to show the relationships between evolutionary patterns and news to provide explanations. We present two case studies involving the evolutionary patterns of different tokens to demonstrate the effectiveness and usability of the TokenVis prototype system.
Polynomial multiplication consumes a lot of time in hardware implementation in the underlying operations of Lattice-based post-quantum public-key cryptography algorithms. The paper analyzes the fast implementation of number theoretic transform algorithm in polynomial multiplication operations for CRYSTALS-Kyber and proposes a 2n-th unit root preprocessing fast number theoretic transform algorithm architecture that adapts to the hardware implementation. In order to reduce computing time, the architecture uses parallel processing of small bit-width number theoretic transformation and low-complexity computations. Taking into account the characteristics of the algorithm, the overall computing architecture adopts a 32-way parallel design model. Based on this, we design a unified computing unit that matches the architecture and a storage unit with non-conflicting mechanism while reading or writing data and optimal address assignment. Under the CMOS 65 nm process, a set of polynomial multiplication operations with term number 256 and modulus 3 329 can be completed in 108 cycles within 97 ns. The maximum operating frequency can reach 1.1 GHz, and the area time product is 20.7 .
Aiming at the system availability problem of free space optical (FSO) link and millimeter wave radio frequency (RF) link under various meteorological conditions, multiple ground station diversity and system outage probability performance were investigated by using Markov model steady-state probability equilibrium method. Based on the channel models of FSO link and RF link, hybrid link selection finite state Markov chain (FSMC) was established for single and dual station scheme respectively. Then their outage probability expressions were derived and calculated under different system parameters and weather conditions. Numerical results show that at outage probability 10-6, with link distance 1~7 km under rainy and foggy weather conditions, the dual ground station FSO/RF hybrid link can achieve 4~25 dB gain compared to single station case.
In this paper, a broadband low-profile dual-polarized cross-dipole antenna loaded with a metasurface (MS) is proposed. The antenna is consisted of three parts: a pair of crossed dipoles, four parasitic patches, and a metasurface structure. The crossed dipoles are used to achieve dual-polarization characteristics of the antenna. By loading parasitic patches above the dipoles and slotting the dipole arms, the impedance bandwidth of the antenna is extended, and the low profile of the antenna is achieved by replacing the metal reflector below the dipole with a metasurface. In order to improve the isolation between the input ports, four metal shorting columns are introduced. The simulation and measured results show that the impedance bandwidth of dB is 42.5% (2.26~3.48 GHz), and the port isolation and cross-polarization within the bandwidth range are greater than 21 dB and below -31 dB, respectively. The size is only ( 0 is the wavelength of free space corresponding to the operating frequency of 2.9 GHz).
Aiming at the speed bottleneck of traditional single-slope analog-to-digital converters (ADC) and serial two-step ADC in the readout process for large area array CMOS (Complementary Metal Oxide Semiconductor) image sensors, this paper proposes a fully parallel ADC design method for high-speed CMOS image sensors. Based on the idea of time sharing and time compression, the ADC design method advances the fine quantization time to the coarse quantization time period, which solves the time redundancy problem of the traditional method; at the same time, the interpolated time difference TDC (Time-to-Digital Converter) is used to realize the global Fast transition mechanism at low frequency clocks. Based on the 55-nm 1P4M CMOS process, this paper completes the detailed circuit design and comprehensive testing and verification of the proposed method. Under the analog voltage of 3.3 V, the digital voltage of 1.2 V, the clock frequency of 250 MHz, and the input voltage range of 1.2~2.7 V, the line time is compressed to 825 ns, the differential nonlinearity and integral nonlinearity of the ADC are LSB and LSB, respectively, the signal-to-noise-distortion ratio (SNDR) is 68.271 dB, the effective number of bits (ENOB) reaches 11.049 bit, column The inconsistency is less than 0.05%. Compared with the existing advanced ADC, the method proposed in this paper can ensure the low power consumption and high precision, while the ADC conversion rate is increased by more than 87.1%. Quantification provides some theoretical support.
To overcome the drawbacks of current traffic network flow prediction methods, such as the low capability of capturing highly dynamic spatio-temporal correlation and long-term spatial dependence, this paper constructs a novel traffic flow prediction model based on multi-head self-attention network. The model takes the data tensor at daily period and weekly period scales as model inputs to express the temporal similarity of traffic flow data, and obtains its static spatio-temporal characteristics by encoding the spatio-temporal position embedding of the input data. The main model designs temporal multi-head attention module and spatial multi-head attention module respectively based on multi-head self-attention mechanism for considering the dynamic spatio-temporal characteristics of traffic flow and the long-range spatial dependences. The temporal multi-head attention module obtains the local attention using a graph masking matrix and fuses it into a multi-head self-attention to extract the dynamic temporal characteristics of traffic flow. The spatial multi-head attention module obtains the local attention and global attention using two graph masking matrices and fuses them into a multi-head self-attention to extract the dynamic spatial characteristics and long-range spatial dependences of road network nodes. Finally, a gated fusion module is designed to adaptively fuse the spatio-temporal correlation characteristics of traffic flow data. The effectiveness of the proposed model is verified on three real traffic flow benchmark datasets PEMS04, PEMS07 and PEMS08, and the results show that the three prediction accuracy metrics of the proposed model on the three data sets improved by 4.437%, 2.930%, and 4.275% on average compared with the other models with the highest accuracy.
In order to handle the realistic issue of mutual interference in vehicle-mounted frequency-modulated continuous wave radars, this paper proposes a super-resolution direction of arrival estimation approach based on the third-order cross cumulant. According to the property of non-correlation between the echo signals and the interference signals, the method uses the synchronously sampled array signals to build a third-order cross cumulant matrix, which is then used to obtain a two-dimensional spatial spectrum using the subspace method for joint range and bearing estimation. Simulation results demonstrate that the proposed method can suppress the effect of strong interference and identify multiple targets within 3 degrees. Compared with the existing super-resolution and reconstruction methods, the method has higher angular resolution and estimation accuracy.
In order to solve the problem of filter performance degradation caused by finite word length (FWL) effect when traditional digital filter is implemented in finite precision, a sparse algorithm of digital filter state space realization based on L 2 -sensitivity minimization is proposed. A transmission function of a forward difference operator digital filter structure and an equivalent state space realization of that forward difference operator digital filter structure are deduce, an L 2-sensitivity expression based on a similar transformation matrix is obtained according to a controllable and observable Gram matrix, sparse calibration is carry out, an L 2-sensitivity minimization problem is converted into a convex function optimization problem, and derivation is carried out to obtain an L 2-sensitivity minimization expression. The sparse state space realization of the forward difference operator digital filter is obtained by substitution.The simulation results show that the digital filter designed by the proposed method has better resistance to the FWL effect.
Three-dimensional (3D) multi-object tracking is a key module in the autonomous driving system, and the quality of the tracking results mainly depends on the accuracy of the data association process in the tracking module. Existing tracking methods mostly calculate the similarity of objects between two frames from appearance characteristics or motion characteristics, while methods based on motion characteristics usually associate the current frame with the historical frame by using the intersection over union (IoU) of three-dimensional bounding box. However, this method has serious drawbacks when the observation point is moving. When the observation point is moving, the data observed in two frames would lie in different local coordinate systems, making it impossible to use the motion model to accurately predict the position of the tracked objects in the next frame. To solve the above problems, this paper introduces the inertial measurement unit (IMU) or the global positioning system (GPS) data of the observation point itself, and caculates the relationship of rotation and translation between local coordinate systems of the current and the previous frames after each frame data arrives then the state of the tracked object is compensated according to the obtained coordinate transformation relationship, making it counteract the offset caused by the movement of the observation point itself. This motion compensation enhances the data association process in the tracking module, improving the correlation success rate of the 3D bounding boxes, reducing the number of false correlations, and improving the accuracy of 3D multi-object tracking. The prototype verification on related tracking frameworks and the KITTI dataset shows the proposed motion compensation optimization method achieves an accuracy improvement of about 1%.
Since it was first proposed, the SM2 signature algorithm has become increasingly popular. A typical application scenario is the electronic contract service. Due to the inadequate anti-attack capability of a single user and the high risk of private key leakage, users who use electronic contract services to sign contracts frequently host the private key on the service provider’s cloud server. However, this calls for consumers to have faith in service providers, and it will even impact the contract’s legitimacy. We suggest a two-party SM2 signing protocol based on the concept of homomorphic encryption to address this conundrum. In order to simultaneously address the issues of security and trust, users and service providers work together to create and save their own private key fragments as well as generate signatures through online interaction. We discover that the two-party SM2 signing protocols currently in use have flaws or security mistakes. This protocol is the first strictly proven secure two-party SM2 signature protocol that we are aware of.
In the wireless positioning system using time-of-arrivals (TOAs) and frequency-of-arrivals (FOAs) measurements, in addition to TOAs/FOAs estimation errors and sensor position/velocity prior observation errors, sensor clock synchronization error between receivers and transmitters is a key factor affecting positioning accuracy. In order to restrain the effects of clock synchronization errors and various types of observation errors, a novel multi-source cooperative positioning method using TOAs/FOAs based on weighted multidimensional scaling analysis is proposed for the localization of multiple disjoint moving emitters. Firstly, this paper deduces the positioning relationship by constructing two sets of scalar product matrices. Then, the asymptotically statistical characteristics of the errors in the positioning relationship are obtained using a first-order error analysis approach. Subsequently, an optimization criterion for joint synchronization and localization is established. In order to obtain the global optimal solution, a parameter decoupling optimization algorithm is presented using the mathematical properties of orthogonal projection matrix. The algorithm can sequentially estimate the position/velocity parameters of the multiple moving emitters and the parameters of synchronization errors, thus significantly reducing the number of variables involved in the optimization iteration. Furthermore, the Cramér-Rao bound (CRB) for the multi-source cooperative localization model using TOAs/FOAs in the presence of imperfect synchronization between receivers and transmitters is derived. We also formally prove that the performance gain can be obtained from multi-source cooperative positioning. Additionally, the new estimator is proved to be asymptotically statistically efficient using the first-order error analysis method and the mathematical properties of orthogonal projection matrix. Simulation results demonstrate the superiority of the proposed cooperative localization method.
Adversarial patch attacks in the physical world have gained a lot of attention in recent years due to their safety implications. Existing work has mostly focused on generating adversarial patches that can attack certain models in the physical world, but the resulting patterns are often unnatural and easy to identify. To tackle this problem, we propose a guided diffusion-based approach to natural adversarial patch generation. Specifically, we construct a predictor for attack success rate (ASR) prediction by parsing the output of the target detector, such that the reverse process of a pre-trained diffusion model can be guided by the gradient of the classifier to generate adversarial patches with improved naturalness and high ASR. We conduct extensive experiments in both the digital and the physical worlds to evaluate the attack effectiveness against various object detection models, as well as the naturalness of generated patches. The experimental results show that by combining the ASR predictor with a pre-trained diffusion model, our method is able to produce more natural adversarial patches than the state-of-art approaches while remaining highly effective.
Spatiotemporal action detection requires incorporation of video spatial and temporal information. Current state-of-the-art approaches usually use a 2D CNN (Convolutionsl Neural Networks) or a 3D CNN architecture. However, due to the complexity of network structure and spatiotemporal information extraction, these methods are usually non-real-time and offline. To solve this problem, this paper proposes a real-time action detection method based on spatiotemporal interaction perception. First of all, the input video is rearranged out of order to enhance the temporal information. As 2D or 3D backbone networks cannot be used to model spatiotemporal features effectively, a multi-branch feature extraction network is proposed to extract features from different sources. And a multi-scale attention network is proposed to extract long-term time-dependent and spatial context information. Then, for the fusion of temporal and spatial features from two different sources, a new motion saliency enhancement fusion strategy is proposed, which guides the fusion between features by encoding temporal and spatial features to highlight more discriminative spatiotemporal features. Finally, action tube links are generated online based on the frame-level detector results. The proposed method achieves an accuracy of 84.71% and 78.4% on two spatiotemporal motion datasets UCF101-24 and JHMDB-21. And it provides a speed of 73 frames per second, which is superior to the state-of-the-art methods. In addition, for the problems of high inter-class similarity and easy confusion of difficult sample data in the JHMDB-21 dataset, this paper proposes an action detection method of key frame optical flow based on action representation, which avoids the generation of redundant optical flow and further improves the accuracy of action detection.
COVID-19 has seriously affected human life and health since its outbreak. In recent years, residual neural network has been widely used in COVID-19 recognition task to assist doctors to quickly diagnose COVID-19 patients. However, the shape of COVID-19 image lesion regions is complex, the size is different, and the boundary with surrounding tissues is blurred, which make it difficult for the network to extract effective features. Aiming at the above problems, a M3 Res-Transformer model for COVID-19 Chest X-ray image recognition is proposed. Res-Transformer is used as the backbone network of the model, combining ResNet and ViT to effectively integrate local lesion features and global features; A mixed residual attention module (mraM) is designed to enhance the feature expression ability of the network by considering the interdependence of channels and spatial locations; In order to increase the receptive field and extract multi-scale features, the multi-scale dilated residual module (mdrM) is constructed by superimposing dilated convolution with different dilation rates, and three mdrM with gradually shrinking scales are used for multi-scale feature extraction according to the difference of feature scales at different layers; The contextual cross-awareness module (ccaM) is proposed, which uses the semantic information of deep features to guide shallow features, then embeds the spatial information of shallow features into deep features, and uses the cross-weighted attention mechanism to efficiently aggregate deep and shallow features to obtain richer contextual information. In order to verify the effectiveness of the model in this paper, experiments were conducted on the Chest X-ray image dataset of COVID-19, and through comparison with advanced CNN classification models, comparison with ResNet50 models fusing different attention mechanisms, comparison with Transformer-based classification models and ablation experiment, the results showed that the Acc, Pre, Rec, F 1-Score and Spe indexes of the proposed model are 96.33%, 96.36%, 96.33%, 96.35% and 96.26% respectively, which effectively improves the recognition accuracy in COVID-19 Chest X-ray image recognition task, then it is further verified by visualization method, which provides important reference value for COVID-19 aided diagnosis.
Aiming at the problem of low efficiency of electromagnetic scattering characteristics calculation in synthetic aperture radar (SAR) ship imaging simulation applications, two points of improvement based on the existing shooting and bouncing rays (SBR) technique are proposed in this work. Firstly, a ray tube intersecting surface panel detection algorithm with leaf node spatial neighborhood coding search is constructed, which effectively improves the intersection detection speed while avoiding missing intersecting panels by tracing only the center ray of the ray tube and searching for potential intersecting panels around the leaf node space. Secondly, a fast splitting technique for ray tube triangulation is introduced to project the ray tube and the intersecting panels to the ray tube’s virtual aperture surface, and adaptively split the ray tube into consecutive sub-ray tubes by using the Delaunay triangulation algorithm. Finally, the radar cross section (RCS) calculation and SAR imaging simulation experiments are carried out on typical ship targets. The experimental results show that under the premise of ensuring the accuracy, the efficiency of the proposed method is improved by more than 14 times than that of the Kd (K-dimension) tree accelerated SBR and by more than 3 times compared with the classical adaptive ray tube splitting SBR, showing that the computational efficiency is significantly promoted.
According to variation laws of channel state information (CSI) power spectral density (PSD) in the timing series caused by different target states in indoor through-the-wall scenarios, this paper proposes a passive target detection algorithm based on graph convolutional neural (GCN). Different from the traditional correlation system for target detection based on CSI statistical features, this algorithm starts from the graph domain of CSI, constructs the GCN graph structure based on CSI time-frequency diagram, and uses the GCN that can classify the nodes in the complex graph as the classifier, which improves the performance of target detection in the indoor complex environment. Based on outlier removal and wavelet threshold denoising for original CSI information, it uses the short-time Fourier transform to obtain the time-frequency diagram of the CSI amplitude on each subcarrier. Then, according to the characteristics of each subcarrier’s CSI time-frequency diagram, the total spectrum is divided into five frequency bands on average, and the average PSD of each frequency band is calculated and sorted at every sample time. Finally, a GCN graph is constructed based on the variation law of the index of each frequency band after sorting the average PSD, and then its adjacency matrix and feature matrix are input into the GCN network for training, which can finally realize the one-to-one mapping between graph node features and target states. Experimental results show that under the scenarios of glass wall and brick wall, the proposed algorithm can essentially characterize the difference of CSI PSD change regularity caused by different target states; and its average detection accuracy is higher than that of the existing R-TTWD (Robust device-free Through-The-Wall Detection) and TWMD (The-Wall Moving Detection) target detection algorithms.
Quantum-dot cellular automata (QCA) is considered as an emerging technology, because of its unique characteristics such as low power consumption, nanoscale design, and high computing speed, which can be used as an alternative for CMOS technology in circuit design for quantum computers in the near future. In recent years, many FAs (Full Adder) are designed using three-input majority gate (M 3) and three-input XOR gate (XOR3) in QCA circuits. Three new types of n-bit full adders (FA1, FA2 and FA3) are designed based on these two logic gates and the unique clock characteristics of QCA circuits in this paper. FA1 is implemented using only a 1-bit FA, and its cell number and circuit area are reduced by at least 78% and 90% by comparing with the published 8-bit FA. But FA1 can only calculate one bit in one clock cycle, so it has a large delay. The number of cells and circuit area of FA2 are reduced by at least 47% and 63% by comparing with the published 8-bit full adder. And FA2 can calculate two bits in one clock cycle. FA3 can perform four-bit calculations in one clock cycle with minimum delay. As n-bit full adders, the number of cells and circuit area of FA1, FA2 and FA3 will not change with the increase of the number n, which can’t be realized by the previous design.
Recently, the outstanding text generation language models represented by ChatGPT, which can adapt to complex scenes and meet various application demands of human beings, has become the focuses of both the academic and industrial circles. However, the advantage of large language models (LLM) such as ChatGPT that are highly faithful to user intent implies some factual errors, and it is also necessary to rely on prompt content to control the detailed generation quality and domain adaptability, so it is still of great significance to study text generation with intrinsic quality constraints as the core. Based on the comparative study of key content generation models and technologies in recent years, this paper defined the basic form of text generation with intrinsic quality constraints, and six quality features based on “credibility, expressiveness and elegance”. In view of these 6 quality features, we provided analysis and comparison of generator model design and related algorithms. Besides, various automatic and human evaluation methods for different intrinsic quality features are summarized. Finally, this paper looks forward to the future research directions of intrinsic quality constraint technology.
Gas and humidity sensors are widely applied in various fields such as environmental detection, industrial and agricultural production, and medical health. However, the mainstream reported resistive, capacitive, and optoelectric gas/humidity sensors require external energy supply, not only causing the environment pollution from the battery leakage after frequently changed and maintained, but also restricting the sensor’s operation under the energy shortage circumstance. As one of the novel energy harvesting devices, triboelectric nanogenerators (TENG) have been widely applied in mechanical energy harvesting and self-powered sensors owing to the merits of low-cost, designable structure, and high energy conversion efficiency. Furthermore, researchers have endowed TENG with the ability of acquiring information from the outside, which is expected to integrate the energy collection and sensing unit into one device. The above-mentioned integrated triboelectric self-powered sensor is one of the hottest topics in the sensing technique. This article provides a review of the current research status and latest progress of integrated triboelectric self-powered gas /humidity sensors, which can be summarized from the following three aspects. (1) The working principle and gas sensing mechanism of integrated triboelectric gas/humidity sensors. Meanwhile, parameters (triboelectric charges density and dielectric constant of dielectric layer, conductivity of electrode layer) that affect the sensing performances are discussed based on TENG’s equivalent circuit model. Triboelectric charges density can be effectively influenced based on the screening effect of the condensed thin film on the friction surface or the electrons transfer between the friction surface and the gas/water molecules, resulting in the increased/decreased equivalent triboelectric charges; Output changes based on dielectric constant of the dielectric layer (also the triboelectric layer) usually occur in the situation where there is a significant difference in the dielectric constant between the test sample and the dielectric layer. However, the limited gas/water adsorption causes little changes in dielectric constant, resulting in the poor sensing response; Variations of the output changes of TENG based on the resistances change of the sensing electrode can be attributed to the Kirchhoff voltage divider law, where the voltage received by each load is directly proportional to its impedance in a series circuit. However, owing to the high impedance of the air layer and dielectric layers, significant resistance/impedance changes of sensing electrodes during sensing period are demanded, restricting the material selection. (2) The research progresses of integrated triboelectric gas sensors (TGS) are mainly divided into ammonia (NH3), ethanol, and other volatile organic compound (VOC) gas sensors according to the detection subjects. Furthermore, the applications of TGS in detecting exhaled gas, food spoilage, and exhaust emissions are introduced. (3) Based on the different effects of humidity on the amplitude of output electrical signals, the research progresses of integrated triboelectric humidity sensors (THS) are mainly divided into the sensors with the performances of humidity induced signal drops and rises. Furthermore, the applications of THS in non-contact switches, skin humidity and diaper detection are introduced. Finally, the research status and challenges of TGS and THS are summarized. Meanwhile, the prospects of the future development are illustrated, providing references for the future research of integrated triboelectric self-powered gas/humidity sensors.
As the core of information technology, integrated circuit technology is not only the cornerstone of economic development, but also related to national security and strategic competitive position. The National Natural Science Foundation of China has supported China’s foundation research in the field of integrated circuits, gradually forming a multi-level science foundation funding system. This article analyzes the research directions, funding project quantities, and funding conditions of various types of projects under the codes of “integrated circuit design” (F0402) and “integrated circuit devices, manufacturing and packaging” (F0406) in the past decade (2014—2023). Through the analysis of the research directions of funded projects over the years, we can understand the changes in research hot topics in China’s integrated circuit field. The purpose of this article is to explore the funding characteristics of the integrated circuit field in recent years, and to provide a reference for researchers in domestic research institutes, enterprises and institutions to understand the research hot topics, future development directions and paths in this field.