A high-speed true random number generator based on Ag/SiNx/n-Si memristor

The intrinsic variability of memristor switching behavior can be used as a natural source of randomness,this variability is valuable for safe applications in hardware,such as the true random number generator(TRNG).However,the speed of TRNG is still be further improved.Here,we propose a reliable Ag/SiNx/n-Si volatile memristor,which exhibits a typical threshold switching device with stable repeat ability and fast switching speed.This volatile-memristor-based TRNG is combined with nonlinear feedback shift register(NFSR)to form a new type of high-speed dual output TRNG.Interestingly,the bit generation rate reaches a high speed of 112 kb/s.In addition,this new TRNG passed all 15 National Institute of Standards and Technology(NIST)randomness tests without post-processing steps,proving its performance as a hardware security application.This work shows that the SiNx-based volatile memristor can realize TRNG and has great potential in hardware network security.

Reflection behavior of insensitive explosive detonation propagating around a cylinder

Insensitive explosive detonation has wide applications in compressing and driving inert materials,and thereby the interaction between detonation and inert materials has received more attention.In this paper,a two-dimensional numerical simulation based on the Euler multiphase flow framework is used to investigate the reflection behavior of the insensitive explosive detonation propagating around a cylinder.The results show that there is a critical incident angle,defined as transition angle for detonation propagating around the cylinder,below which the regular reflection(RR)on the cylinder surface is observed.When the incident angle is greater than the transition angle,RR changes to Mach reflection.This transition angle is larger than that obtained by polar curve theory and the change of incident angle is used to interpret above phenomenon.In addition,the influence of cylindrical radius and detonation reaction zone width on the reflection behavior is examined.As the cylindrical radius increases,the height of Mach stem increases while the transition angle decreases and gradually approaches the value in pole curve theory.Von Neumann reflection is observed when the reaction zone width is relatively small.This is because the energy release rate in the reaction zone is high for small reaction zone width,resulting in the formation of a series of compression waves near the cylindrical interface.

Reconfigurable memristor based on SrTiO_(3) thin-film for neuromorphic computing

Neuromorphic computing aims to achieve artificial intelligence by mimicking the mechanisms of biological neurons and synapses that make up the human brain.However,the possibility of using one reconfigurable memristor as both artificial neuron and synapse still requires intensive research in detail.In this work,Ag/SrTiO_(3)(STO)/Pt memristor with low operating voltage is manufactured and reconfigurable as both neuron and synapse for neuromorphic computing chip.By modulating the compliance current,two types of resistance switching,volatile and nonvolatile,can be obtained in amorphous STO thin film.This is attributed to the manipulation of the Ag conductive filament.Furthermore,through regulating electrical pulses and designing bionic circuits,the neuronal functions of leaky integrate and fire,as well as synaptic biomimicry with spike-timing-dependent plasticity and paired-pulse facilitation neural regulation,are successfully realized.This study shows that the reconfigurable devices based on STO thin film are promising for the application of neuromorphic computing systems.

Enhancing train position perception through Al-driven multi-source information fusion

This paper addresses the challenge of accurately and timely determining the position of a train,with specific consideration given to the integration of the global navigation satellite system(GNSS)and inertial navigation system(INS).To overcome the increasing errors in the INS during interruptions in GNSS signals,as well as the uncertainty associated with process and measurement noise,a deep learning-based method for train positioning is proposed.This method combines convolutional neural networks(CNN),long short-term memory(LSTM),and the invariant extended Kalman filter(IEKF)to enhance the perception of train positions.It effectively handles GNSS signal interruptions and mitigates the impact of noise.Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.

A Wearable Multidimensional Motion Sensor for AI-Enhanced VR Sports

Regular exercise paves the way to a healthy life.However,conventional sports events are susceptible to weather conditions.Current motion sensors for home-based sports are mainly limited by operation power consumption,single-direction sensitivity,or inferior data analysis.Herein,by leveraging the 3-dimensional printing technique and triboelectric effect,a wearable self-powered multidimensional motion sensor has been developed to detect both the vertical and planar movement trajectory.By integrating with a belt,this sensor could be used to identify some low degree of freedom motions,e.g.,waist or gait motion,with a high accuracy of 93.8%.Furthermore,when wearing the sensor at the ankle position,signals generated from shank motions that contain more abundant information could also be effectively collected.By means of a deep learning algorithm,the kicking direction and force could be precisely differentiated with an accuracy of 97.5%.Toward practical application,a virtual reality-enabled fitness game and a shooting game were successfully demonstrated.This work is believed to open up new insights for the development of future household sports or rehabilitation.

Charge state modulation on boron site by carbon and nitrogen localized bonding microenvironment for two-electron electrocatalytic H_(2)O_(2)production

Design of electrochemical active boron(B)site at solid materials to understand the relationships between the localized structure,charge state at the B site and electrocatalytic activity plays a crucial role in boosting the green electrochemical synthesis of hydrogen peroxide(H_(2)O_(2))via two-electron oxygen reduction(2eORR)pathway.Herein,we demonstrate a carbon(C)and nitrogen(N)localized bonding microenvironment to modulate the charge state of B site at the boron-carbon nitride solid(BCNs)to realize the efficient selective electrocatalytic H_(2)O_(2)production.The localized chemical structure of N-B-N,N-B-C and C-B-C bonds at B site can be regulated through solid-state reaction between boron nitride(BN)and porous carbon(C)at variable temperatures.The optimized BCN-1100 achieves an outstanding H_(2)O_(2)selectivity of 89%and electron transfer number of 2.2(at 0.55 V vs.RHE),with the production of 10.55mmol/L during 2.5 h and the catalytic stability duration for 15000 cycles.Further first-principles calculations identified the dependency of localized bonding microenvironment on the OOH~*adsorption energies and relevant charge states at the boron site.The localized structure of B site with BNC_(2)-Gr configuration is predicted to be the highest 2eORR activity.

Robust triboelectric information-mat enhanced by multi-modality deep learning for smart home

In metaverse,a digital-twin smart home is a vital platform for immersive communication between the physical and virtual world.Triboelectric nanogenerators(TENGs)sensors contribute substantially to providing smart-home monitoring.However,TENG deployment is hindered by its unstable out-put under environment changes.Herein,we develop a digital-twin smart home using a robust all-TENG based information mat(InfoMat),which consists of an in-home mat array and an entry mat.The interdigital electrodes design allows environment-insensitive ratiometric readout from the mat array to can-cel the commonly experienced environmental variations.Arbitrary position sensing is also achieved because of the interval arrangement of the mat pixels.Concurrently,the two-channel entry mat generates multi-modality informa-tion to aid the 10-user identification accuracy to increase from 93% to 99% compared to the one-channel case.Furthermore,a digital-twin smart home is visualized by real-time projecting the information in smart home to virtual reality,including access authorization,position,walking trajectory,dynamic activities/sports,and so on.

Real-time Synchrophasor Data Compression Technique with Phasor Interpolation and Extrapolation

To completely eliminate the time delays caused by phasor data compressions for real-time synchrophasor applications,a real-time synchrophasor data compression(RSDC)is proposed in this paper.The two-way rotation characteristic and elliptical trajectory of dynamic synchrophasors are introduced first to enhance the compressions along with a fast solving method for elliptical trajectory fitting equations.The RSDC for phasor data compression and reconstruction is then proposed by combining the interpolation and extrapolation compressions.The proposed RSDC is verified by both the actual phasor measurement data recorded in a two-phase short-circuit incident and a subsynchronous oscillation incident,and the synthetic dynamic synchrophasors.It is also compared with two previous real-time phasor data compression techniques,i.e.,phasor swing door trending(PSDT)and exception and swing door trending(SDT)data compression(ESDC).The verification results demonstrate that RSDC can achieve significantly higher compression ratios for offline applications with the interpolation and the zero-delay phasor data compression with the extrapolation for real-time applications simultaneously.

A Scale-Invariant Fifth Order WCNS Scheme for Hyperbolic Conservation Laws

In this article,a robust,effective,and scale-invariant weighted compact nonlinear scheme(WCNS)is proposed by introducing descaling techniques to the nonlinear weights of the WCNS-Z/D schemes.The new scheme achieves an essentially non-oscillatory approximation of a discontinuous function(ENO-property),a scaleinvariant property with an arbitrary scale of a function(Si-property),and an optimal order of accuracy with smooth function regardless of the critical point(Cp-property).The classical WCNS-Z/D schemes do not satisfy Si-property intrinsically,which is caused by a loss of sub-stencils’adaptivity in the nonlinear interpolation of a discontinuous function when scaled by a small scale factor.A new nonlinear weight is devised by using an average of the function values and the descaling function,providing the new WCNS schemes(WCNS-Zm/Dm)with many attractive properties.The ENO-property,Si-property and Cp-property of the new WCNS schemes are validated numerically.Results show that the WCNS-Zm/Dm schemes satisfy the ENO-property and Si-property,while only the WCNS-Dm scheme satisfies the Cp-property.In addition,the Gaussian wave problem is solved by using successively refined grids to verify that the optimal order of accuracy of the new schemes can be achieved.Several one-dimensional shock tube problems,and two-dimensional double Mach reflection(DMR)problem and the Riemann IVP problem are simulated to illustrate the ENOproperty and Si-property of the scale-invariant WCNS-Zm/Dm schemes.

Progress in wearable electronics/photonics—Moving toward the era of artificial intelligence and internet of things

The past few years have witnessed the significant impacts of wearable electronics/photonics on various aspects of our daily life,for example,healthcare monitoring and treatment,ambient monitoring,soft robotics,prosthetics,flexible display,communication,human-machine interactions,and so on.According to the development in recent years,the next-generation wearable electronics and photonics are advancing rapidly toward the era of artificial intelligence(AI)and internet of things(IoT),to achieve a higher level of comfort,convenience,connection,and intelligence.Herein,this review provides an opportune overview of the recent progress in wearable electronics,photonics,and systems,in terms of emerging materials,transducing mechanisms,structural configurations,applications,and their further integration with other technologies.First,development of general wearable electronics and photonics is summarized for the applications of physical sensing,chemical sensing,humanmachine interaction,display,communication,and so on.Then self-sustainable wearable electronics/photonics and systems are discussed based on system integration with energy harvesting and storage technologies.Next,technology fusion of wearable systems and AI is reviewed,showing the emergence and rapid development of intelligent/smart systems.In the last section of this review,perspectives about the future development trends of the next-generation wearable electronics/photonics are provided,that is,toward multifunctional,self-sustainable,and intelligent wearable systems in the AI/IoT era.

Observation of miniaturized bound states in the continuum with ultra-high quality factors

Light trapping is a constant pursuit in photonics because of its importance in science and technology.Many mechanisms have been explored,including the use of mirrors made of materials or structures that forbid outgoing waves,and bound states in the continuum that are mirror-less but based on topology.Here we report a compound method,combining lateral mirrors and bound states in the continuum in a cooperative way,to achieve a class of on-chip optical cavities that have high quality factors and small modal volumes.Specifically,light is trapped in the transverse direction by the photonic band gap of the lateral hetero-structure and confined in the vertical direction by the constellation of multiple bound states in the continuum.As a result,unlike most bound states in the continuum found in photonic crystal slabs that are de-localized Bloch modes,we achieve light-trapping in all three dimensions and experimentally demonstrate quality factors as high as Q=1.09×10^(6)and modal volumes as low as V=17.74(λ_(0)/n)^(3)in the telecommunication regime.We further prove the robustness of our method through the statistical study of multiple fabricated devices.Our work provides a new method of light trapping,which can find potential applications in photonic integration,nonlinear optics and quantum computing.

Triboelectric Nanogenerators and Hybridized Systems for Enabling Next-Generation IoT Applications

In the past few years,triboelectric nanogenerator-based(TENG-based)hybrid generators and systems have experienced a widespread and flourishing development,ranging among almost every aspect of our lives,e.g.,from industry to consumer,outdoor to indoor,and wearable to implantable applications.Although TENG technology has been extensively investigated for mechanical energy harvesting,most developed TENGs still have limitations of small output current,unstable power generation,and low energy utilization rate of multisource energies.To harvest the ubiquitous/coexisted energy forms including mechanical,thermal,and solar energy simultaneously,a promising direction is to integrate TENG with other transducing mechanisms,e.g.,electromagnetic generator,piezoelectric nanogenerator,pyroelectric nanogenerator,thermoelectric generator,and solar cell,forming the hybrid generator for synergetic single-source and multisource energy harvesting.The resultant TENG-based hybrid generators utilizing integrated transducing mechanisms are able to compensate for the shortcomings of each mechanism and overcome the above limitations,toward achieving a maximum,reliable,and stable output generation.Hence,in this review,we systematically introduce the key technologies of the TENG-based hybrid generators and hybridized systems,in the aspects of operation principles,structure designs,optimization strategies,power management,and system integration.The recent progress of TENG-based hybrid generators and hybridized systems for the outdoor,indoor,wearable,and implantable applications is also provided.Lastly,we discuss our perspectives on the future development trend of hybrid generators and hybridized systems in environmental monitoring,human activity sensation,human-machine interaction,smart home,healthcare,wearables,implants,robotics,Internet of things(IoT),and many other fields.

Deep learning-enabled triboelectric smart socks for IoT-based gait analysis and VR applications

The era of artificial intelligence and internet of things is rapidly developed by recent advances in wearable electronics.Gait reveals sensory information in daily life containing personal information,regarding identification and healthcare.Current wearable electronics of gait analysis are mainly limited by high fabrication cost,operation energy consumption,or inferior analysis methods,which barely involve machine learning or implement nonoptimal models that require massive datasets for training.Herein,we developed low-cost triboelectric intelligent socks for harvesting waste energy from low-frequency body motions to transmit wireless sensory data.The sock equipped with self-powered functionality also can be used as wearable sensors to deliver information,regarding the identity,health status,and activity of the users.To further address the issue of ineffective analysis methods,an optimized deep learning model with an end-to-end structure on the socks signals for the gait analysis is proposed,which produces a 93.54%identification accuracy of 13 participants and detects five different human activities with 96.67%accuracy.Toward practical application,we map the physical signals collected through the socks in the virtual space to establish a digital human system for sports monitoring,healthcare,identification,and future smart home applications.