A review of ToF-based LiDAR

In recent years,propelled by the rapid iterative advancements in digital imaging technology and the semiconductor industry,encompassing microelectronic design,manufacturing,packaging,and testing,time-of-flight(ToF)-based imaging systems for acquiring depth information have garnered considerable attention from both academia and industry.This technology has emerged as a focal point of research within the realm of 3D imaging.Owing to its relatively straightforward principles and exceptional performance,ToF technology finds extensive applications across various domains including human−computer interaction,autonomous driving,industrial inspection,medical and healthcare,augmented reality,smart homes,and 3D reconstruction,among others.Notably,the increasing maturity of ToF-based LiDAR systems is evident in current developments.This paper comprehensively reviews the fundamental principles of ToF technology and LiDAR systems,alongside recent research advancements.It elucidates the innovative aspects and technical challenges encountered in both transmitter(TX)and receiver(RX),providing detailed discussions on corresponding solutions.Furthermore,the paper explores prospective avenues for future research,offering valuable insights for subsequent investigations.

On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault tolerance.However,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal mechanism.As a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain network.To address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional PoA.This paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided consensus.First,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart contracts.Second,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing throughput.In particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation variation.Moreover,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s liveness.Finally,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

Coordinated voltage regulation strategy of OLTC and BESS considering switching delay

When large-scale distributed renewable energy power generation systems are connected to the power grid,the risk of grid voltage fluctuations and exceeding the limit increases greatly.Fortunately,the on-load tap changer(OLTC)can adjust the transformer winding tap to maintain the secondary side voltage within the normal range.However,the inevitable delay in switching transformer taps makes it difficult to respond quickly to voltage fluctuations.Moreover,switching the transformer taps frequently will decrease the service life of OLTC.In order to solve this critical issue,a cooperative voltage regulation strategy applied between the battery energy storage systems(BESSs)and OLTSs.is proposed By adjusting the charge and discharge power of BESSs,the OLTC can frequently switch the transformer taps to achieve rapid voltage regulation.The effectiveness of the proposed coordinated regulation strategy is verified in the IEEE 33 node distribution systems.The simulation results show that the proposed coordinated regulation strategy can stabilize the voltage of the distribution network within a normal range and reduce the frequency of tap switching,as such elongating the service life of the equipment.

Electrical and nonlinear optical properties of TGZO transparent conducting films deposited by magnetron sputtering

Thin transparent oxide conducting films(TCOFs)of titanium and gallium substituted zinc oxide(TGZO)were fabricated via radio frequency(RF)magnetron sputtering technique.The effects of RF power on electrical,linear and nonlinear optical characteristics were investigated by Hall tester,Ultraviolet(UV)-visible spectrophotometer and optical characterization method.The results indicate that RF power significantly influences the electrical and optical properties of the deposited films.As RF power raises,the resistivity and Urbach energy fall initially and then rise,while the figure of merit,mean visible transmittance and optical bandgap show the reverse variation trend.At RF power of 190 W,the TGZO sample exhibits the highest electro-optical properties,with the maximum figure of merit(1.14×10^(4)Ω^(-1)∙cm^(-1)),mean visible transmittance(86.9%)and optical bandgap(3.50 eV),the minimum resistivity(6.26×10^(-4)Ω∙cm)and Urbach energy(174.23 meV).In addition,the optical constants of the deposited films were determined by the optical spectrum fitting method,and the RF power dependence of nonlinear optical properties was studied.It is observed that all the thin films exhibit normal dispersion characteristics in the visible region,and the nonlinear optical parameters are greatly affected by the RF power in the ultraviolet region.

Progress in Mechanical Modeling of Implantable Flexible Neural Probes

Implanted neural probes can detect weak discharges of neurons in the brain by piercing soft brain tissue,thus as important tools for brain science research,as well as diagnosis and treatment of brain diseases.However,the rigid neural probes,such as Utah arrays,Michigan probes,and metal microfilament electrodes,are mechanically unmatched with brain tissue and are prone to rejection and glial scarring after implantation,which leads to a significant degradation in the signal quality with the implantation time.In recent years,flexible neural electrodes are rapidly developed with less damage to biological tissues,excellent biocompatibility,and mechanical compliance to alleviate scarring.Among them,the mechanical modeling is important for the optimization of the structure and the implantation process.In this review,the theoretical calculation of the flexible neural probes is firstly summarized with the processes of buckling,insertion,and relative interaction with soft brain tissue for flexible probes from outside to inside.Then,the corresponding mechanical simulation methods are organized considering multiple impact factors to realize minimally invasive implantation.Finally,the technical difficulties and future trends of mechanical modeling are discussed for the next-generation flexible neural probes,which is critical to realize low-invasiveness and long-term coexistence in vivo.

Multiplexed stimulated emission depletion nanoscopy(mSTED)for 5-color live-cell long-term imaging of organelle interactome

Stimulated emission depletion microscopy(STED)holds great potential in biological science applications,especially in studying nanoscale subcellular structures.However,multi-color STED imaging in live-cell remains challenging due to the limited excitation wavelengths and large amount of laser radiation.Here,we develop a multiplexed live-cell STED method to observe more structures simultaneously with limited photo-bleaching and photo-cytotoxicity.By separating live-cell fluorescent probes with similar spectral properties using phasor analysis,our method enables five-color live-cell STED imaging and reveals long-term interactions between different subcellular structures.The results here provide an avenue for understanding the complex and delicate interactome of subcellular structures in live-cell.

Multi-Agent Deep Deterministic Policy Gradien-Based Task Offloading Resource Allocation Joint Offloading

With the advancement of technology and the continuous innovation of applications, low-latency applications such as drones, online games and virtual reality are gradually becoming popular demands in modern society. However, these applications pose a great challenge to the traditional centralized mobile cloud computing paradigm, and it is obvious that the traditional cloud computing model is already struggling to meet such demands. To address the shortcomings of cloud computing, mobile edge computing has emerged. Mobile edge computing provides users with computing and storage resources by offloading computing tasks to servers at the edge of the network. However, most existing work only considers single-objective performance optimization in terms of latency or energy consumption, but not balanced optimization in terms of latency and energy consumption. To reduce task latency and device energy consumption, the problem of joint optimization of computation offloading and resource allocation in multi-cell, multi-user, multi-server MEC environments is investigated. In this paper, a dynamic computation offloading algorithm based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed to obtain the optimal policy. The experimental results show that the algorithm proposed in this paper reduces the delay by 5 ms compared to PPO, 1.5 ms compared to DDPG and 10.7 ms compared to DQN, and reduces the energy consumption by 300 compared to PPO, 760 compared to DDPG and 380 compared to DQN. This fully proves that the algorithm proposed in this paper has excellent performance.

Curriculum Reform of Higher Vocational Mathematics Under the Background of Digital Transformation

Under the background of digital transformation,the reform of the higher vocational mathematics curriculum faces urgent challenges and opportunities.This article explores the impact of digital transformation on the reform of higher vocational mathematics curriculum and emphasizes the importance of improving teaching methods centered on learners.The article proposes specific reform methods and discusses the practical application of digital technology in the reform process.By combining digital technology with specific reform methods,further conducting innovative practice research,and continuously exploring the path of reform,we can effectively improve the quality of higher vocational mathematics classroom teaching and provide strong support for the cultivation of comprehensive qualities and employment abilities.

Manufacturing of graphene based synaptic devices for optoelectronic applications

Neuromorphic computing systems can perform memory and computing tasks in parallel on artificial synaptic devices through simulating synaptic functions,which is promising for breaking the conventional von Neumann bottlenecks at hardware level.Artificial optoelectronic synapses enable the synergistic coupling between optical and electrical signals in synaptic modulation,which opens up an innovative path for effective neuromorphic systems.With the advantages of high mobility,optical transparency,ultrawideband tunability,and environmental stability,graphene has attracted tremendous interest for electronic and optoelectronic applications.Recent progress highlights the significance of implementing graphene into artificial synaptic devices.Herein,to better understand the potential of graphene-based synaptic devices,the fabrication technologies of graphene are first presented.Then,the roles of graphene in various synaptic devices are demonstrated.Furthermore,their typical optoelectronic applications in neuromorphic systems are reviewed.Finally,outlooks for development of synaptic devices based on graphene are proposed.This review will provide a comprehensive understanding of graphene fabrication technologies and graphene-based synaptic device for optoelectronic applications,also present an outlook for development of graphene-based synaptic device in future neuromorphic systems.

A review of mobile robot motion planning methods:from classical motion planning workflows to reinforcement learning-based architectures

Motion planning is critical to realize the autonomous operation of mobile robots.As the complexity and randomness of robot application scenarios increase,the planning capability of the classical hierarchical motion planners is challenged.With the development of machine learning,the deep reinforcement learning(DRL)-based motion planner has gradually become a research hotspot due to its several advantageous feature.The DRL-based motion planner is model-free and does not rely on the prior structured map.Most importantly,the DRL-based motion planner achieves the unification of the global planner and the local planner.In this paper,we provide a systematic review of various motion planning methods.Firstly,we summarize the representative and state-of-the-art works for each submodule of the classical motion planning architecture and analyze their performance features.Then,we concentrate on summarizing reinforcement learning(RL)-based motion planning approaches,including motion planners combined with RL improvements,map-free RL-based motion planners,and multi-robot cooperative planning methods.Finally,we analyze the urgent challenges faced by these mainstream RLbased motion planners in detail,review some state-of-the-art works for these issues,and propose suggestions for future research.

Computation Rate Maximization in Multi-User Cooperation-Assisted Wireless-Powered Mobile Edge Computing with OFDMA

In the era of Internet of Things(Io T),mobile edge computing(MEC)and wireless power transfer(WPT)provide a prominent solution for computation-intensive applications to enhance computation capability and achieve sustainable energy supply.A wireless-powered mobile edge computing(WPMEC)system consisting of a hybrid access point(HAP)combined with MEC servers and many users is considered in this paper.In particular,a novel multiuser cooperation scheme based on orthogonal frequency division multiple access(OFDMA)is provided to improve the computation performance,where users can split the computation tasks into various parts for local computing,offloading to corresponding helper,and HAP for remote execution respectively with the aid of helper.Specifically,we aim at maximizing the weighted sum computation rate(WSCR)by optimizing time assignment,computation-task allocation,and transmission power at the same time while keeping energy neutrality in mind.We transform the original non-convex optimization problem to a convex optimization problem and then obtain a semi-closed form expression of the optimal solution by considering the convex optimization techniques.Simulation results demonstrate that the proposed multi-user cooperationassisted WPMEC scheme greatly improves the WSCR of all users than the existing schemes.In addition,OFDMA protocol increases the fairness and decreases delay among the users when compared to TDMA protocol.

Lyapunov-Based Output Containment Control of Heterogeneous Multi-Agent Systems With Markovian Switching Topologies and Distributed Delays

This paper considers the mean square output containment control problem for heterogeneous multi-agent systems(MASs)with randomly switching topologies and nonuniform distributed delays.By modeling the switching topologies as a continuous-time Markov process and taking the distributed delays into consideration,a novel distributed containment observer is proposed to estimate the convex hull spanned by the leaders'states.A novel distributed output feedback containment controller is then designed without using the prior knowledge of distributed delays.By constructing a novel switching Lyapunov functional,the output containment control problem is then solved in the sense of mean square under an easily-verifiable sufficient condition.Finally,two numerical examples are given to show the effectiveness of the proposed controller.

Impact of user influence on information multi-step communication in a micro-blog

User influence is generally considered as one of the most critical factors that affect information cascading spreading. Based on this common assumption, this paper proposes a theoretical model to examine user influence on the information multi-step communication in a micro-biog. The multi-steps of information communication are divided into first-step and non-first-step, and user influence is classified into five dimensions. Actual data from the Sina micro-blog is collected to construct the model by means of an approach based on structural equations that uses the Partial Least Squares （PLS） technique. Our experimental results indicate that the dimensions of the number of fans and their authority significantly impact the information of first-step conxrnunication. Leader rank has a positive impact on both first-step and non-first-step communication. Moreover, global centrality and weight of friends are positively related to the information non-first-step communication, but authority is found to have much less relation to it.

Coastal erosion risk assessment of Hainan Island, China

Coastal erosion on islands is increasing due to sea level rise,frequent extreme events,and anthropogenic activities.However,studies on the multifactorial coastal erosion risk and the vulnerability of islands are limited.In this study,the Coastal Erosion Risk Assessment(CERA)method was applied for the first time to the study area in China to assess the erosion risk on the coast of Hainan Island;to explore the effects of coastal ocean dynamics,sediment movement characteristics,and anthropogenic construction;and to discuss the suitability of the method and countermeasures for coastal protection.The results show that the coast of Hainan Island shows high sensitivity,high value,low exposure,and moderate erosion.The whole island showed high vulnerability but low erosion risk,with the eastern region being more affected by erosion,particularly the eastern side of Wulong Port and Yalin Bay in Wenchang,and the shore section of Yalong Bay in Sanya,having a very high risk of coastal erosion.In addition,Monte Carlo simulation was used to check the applicability of the CERA method,and it was found that the rate of shoreline change,population density,and number of storms significantly contributed to coastal erosion,but only the short-term effects of sea level rise were considered.The effects of sea level rise and sediment grain size were primarily analyzed as influencing factors.The effects of sea level rise continue to strengthen,with coastal retreat expected to be greater than 2 m by the mid-21st century.Moreover,Hainan Island is primarily composed of the fine and medium sand types,which have little resistance to coastal erosion.Currently,the impact of sediment grain size is rarely considered in coastal erosion risk assessment studies.However,it can be incorporated into the indicator system in the future,and the spatial variation of indicators can be fully considered to strengthen the refinement study.

Novel layout design of 4H-SiC merged PiN Schottky diodes leading to improved surge robustness

A method to improve the surge current capability of silicon carbide(SiC)merged PiN Schottky(MPS)diodes is presented and investigated via three-dimensional electro-thermal simulations.When compared with a conventional MPS diode,the proposed structure has a more uniform current distribution during bipolar conduction due to the help of the continuous P+surface,which can avoid the formation of local hotspots during the surge process.The Silvaco simulation results show that the proposed structure has a 20.29%higher surge capability and a 15.06%higher surge energy compared with a conventional MPS diode.The bipolar on-state voltage of the proposed structure is 4.69 V,which is 56.29%lower than that of a conventional MPS diode,enabling the device to enter the bipolar mode earlier during the surge process.Furthermore,the proposed structure can suppress the occurrence of‘snapback'phenomena when switching from the unipolar to the bipolar operation mode.In addition,an analysis of the surge process of MPS diodes is carried out in detail.

Enhancement of holding voltage by a modified low-voltage trigger silicon-controlled rectifier structure for electrostatic discharge protection

A novel structure of low-voltage trigger silicon-controlled rectifiers(LVTSCRs) with low trigger voltage and high holding voltage is proposed for electrostatic discharge(ESD) protection. The proposed ESD protection device possesses an ESD implant and a floating structure. This improvement enhances the current discharge capability of the gate-grounded NMOS and weakens the current gain of the silicon-controlled rectifier current path. According to the simulation results, the proposed device retains a low trigger voltage characteristic of LVTSCRs and simultaneously increases the holding voltage to 5.53 V, providing an effective way to meet the ESD protection requirement of the 5 V CMOS process.

Ambipolar performance improvement of the C-shaped pocket TFET with dual metal gate and gate–drain underlap

Dual-metal gate and gate–drain underlap designs are introduced to reduce the ambipolar current of the device based on the C-shaped pocket TFET(CSP-TFET).The effects of gate work function and gate–drain underlap length on the DC characteristics and analog/RF performance of CSP-TFET devices,such as the on-state current(I_(on)),ambipolar current(I_(amb)),transconductance(g_(m)),cut-off frequency(f_(T))and gain–bandwidth product(GBP),are analyzed and compared in this work.Also,a combination of both the dual-metal gate and gate–drain underlap designs has been proposed for the C-shaped pocket dual metal underlap TFET(CSP-DMUN-TFET),which contains a C-shaped pocket area that significantly increases the on-state current of the device;this combination design substantially reduces the ambipolar current.The results show that the CSP-DMUN-TFET demonstrates an excellent performance,including high I_(on)(9.03×10^(-4)A/μm),high I_(on)/I_(off)(~10^(11)),low SS_(avg)(~13 mV/dec),and low I_(amb)(2.15×10^(-17)A/μm).The CSP-DMUN-TFET has the capability to fully suppress ambipolar currents while maintaining high on-state currents,making it a potential replacement in the next generation of semiconductor devices.

Optimization of Animal-Glue Binders for Casting Applications

In typical metal foundry applications,sand casting is still the most used technology.The related binder plays a very important role as its performances can directly influence the quality of castings.Among many binders,glues of animal origin have attracted much attention in recent years due to their reduced environmental impact.How-ever,they display some drawbacks such as the tendency to coagulate easily at room temperature and a relatively low strength.In this study,a novel gas-hardening casting binder was prepared using an animal glue and anhy-drous potassium carbonate as a hydrolyzing agent to avoid undesired agglomeration.Moreover,sodium pyropho-sphate and furfuryl alcohol were exploited as modifiers to obtain a binder with a high compressive strength.The best modification conditions,determined by means of an orthogonal design matrix approach,were 4 g of Na2CO3,sodium pyrophosphate,furfuryl alcohol and animal glue with a ratio of 4:12:100,at 85°C and with a duration of 115 min,respectively.The viscosity of the mixture obtained under these optimized conditions was 1250 mPa⋅s.The compressive strength of the binder,hardened by CO_(2) gas,was 4.00 MPa.Its gas evolution at 850°C was 15 ml⋅g-1,and its residual strength after 10 min calculation at 800°C was 0.01 MPa,which is high enough to meet the requirement of core-making in foundry.Moreover,after hydrolysis and further modification,animal glue and modifiers displayed a grafting reaction and an esterification reaction,respectively,which made the adhesive network denser and improved its thermal stability.

Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

A network lightweighting method for difficult segmentation of 3D medical images

Currently,deep learning is widely used in medical image segmentation and has achieved good results.However,3D medical image segmentation tasks with diverse lesion characters,blurred edges,and unstable positions require complex networks with a large number of parameters.It is computationally expensive and results in high requirements on equipment,making it hard to deploy the network in hospitals.In this work,we propose a method for network lightweighting and applied it to a 3D CNN based network.We experimented on a COVID-19 lesion segmentation dataset.Specifically,we use three cascaded one-dimensional convolutions to replace a 3D convolution,and integrate instance normalization with the previous layer of one-dimensional convolutions to accelerate network inference.In addition,we simplify test-time augmentation and deep supervision of the network.Experiments show that the lightweight network can reduce the prediction time of each sample and the memory usage by 50%and reduce the number of parameters by 60%compared with the original network.The training time of one epoch is also reduced by 50%with the segmentation accuracy dropped within the acceptable range.