NeuroBiometric:An Eye Blink Based Biometric Authentication System Using an Event-Based Neuromorphic Vision Sensor

The rise of the Internet and identity authentication systems has brought convenience to people's lives but has also introduced the potential risk of privacy leaks.Existing biometric authentication systems based on explicit and static features bear the risk of being attacked by mimicked data.This work proposes a highly efficient biometric authentication system based on transient eye blink signals that are precisely captured by a neuromorphic vision sensor with microsecond-level temporal resolution.The neuromorphic vision sensor only transmits the local pixel-level changes induced by the eye blinks when they occur,which leads to advantageous characteristics such as an ultra-low latency response.We first propose a set of effective biometric features describing the motion,speed,energy and frequency signal of eye blinks based on the microsecond temporal resolution of event densities.We then train the ensemble model and non-ensemble model with our Neuro Biometric dataset for biometrics authentication.The experiments show that our system is able to identify and verify the subjects with the ensemble model at an accuracy of 0.948 and with the non-ensemble model at an accuracy of 0.925.The low false positive rates(about 0.002)and the highly dynamic features are not only hard to reproduce but also avoid recording visible characteristics of a user's appearance.The proposed system sheds light on a new path towards safer authentication using neuromorphic vision sensors.

Time-Based Dynamic Trust Model Using Ant Colony Algorithm

The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts＇ change that is aroused by the time＇s lapse and the inter-operation through an instance.

Using Event-Based Method to Estimate Cybersecurity Equilibrium

Estimating the global state of a networked system is an important problem in many application domains.The classical approach to tackling this problem is the periodic(observation)method,which is inefficient because it often observes states at a very high frequency.This inefficiency has motivated the idea of event-based method,which leverages the evolution dynamics in question and makes observations only when some rules are triggered(i.e.,only when certain conditions hold).This paper initiates the investigation of using the event-based method to estimate the equilibrium in the new application domain of cybersecurity,where equilibrium is an important metric that has no closed-form solutions.More specifically,the paper presents an event-based method for estimating cybersecurity equilibrium in the preventive and reactive cyber defense dynamics,which has been proven globally convergent.The presented study proves that the estimated equilibrium from our trigger rule i)indeed converges to the equilibrium of the dynamics and ii)is Zeno-free,which assures the usefulness of the event-based method.Numerical examples show that the event-based method can reduce 98%of the observation cost incurred by the periodic method.In order to use the event-based method in practice,this paper investigates how to bridge the gap between i)the continuous state in the dynamics model,which is dubbed probability-state because it measures the probability that a node is in the secure or compromised state,and ii)the discrete state that is often encountered in practice,dubbed sample-state because it is sampled from some nodes.This bridge may be of independent value because probability-state models have been widely used to approximate exponentially-many discrete state systems.

Estimating time-based instantaneous total mortality rate based on the age-structured abundance index

The instantaneous total mortality rate(Z) of a fish population is one of the important parameters in fisheries stock assessment. The estimation of Z is crucial to fish population dynamics analysis,abundance and catch forecast,and fisheries management. A catch curve-based method for estimating time-based Z and its change trend from catch per unit effort(CPUE) data of multiple cohorts is developed. Unlike the traditional catch-curve method,the method developed here does not need the assumption of constant Z throughout the time,but the Z values in n continuous years are assumed constant,and then the Z values in different n continuous years are estimated using the age-based CPUE data within these years. The results of the simulation analyses show that the trends of the estimated time-based Z are consistent with the trends of the true Z,and the estimated rates of change from this approach are close to the true change rates(the relative differences between the change rates of the estimated Z and the true Z are smaller than 10%). Variations of both Z and recruitment can affect the estimates of Z value and the trend of Z. The most appropriate value of n can be different given the effects of different factors. Therefore,the appropriate value of n for different fisheries should be determined through a simulation analysis as we demonstrated in this study. Further analyses suggested that selectivity and age estimation are also two factors that can affect the estimated Z values if there is error in either of them,but the estimated change rates of Z are still close to the true change rates. We also applied this approach to the Atlantic cod(G adus morhua) fishery of eastern Newfoundland and Labrador from 1983 to 1997,and obtained reasonable estimates of time-based Z.

Leader-Following Consensus of Multi-Agent Systems via Fully Distributed Event-Based Control

This paper aims to study the leader-following consensus of linear multi-agent systems on undirected graphs.Specifically,we construct an adaptive event-based protocol that can be implemented in a fully distributed way by using only local relative information.This protocol is also resource-friendly as it will be updated only when the agent violates the designed event-triggering function.A sufficient condition is proposed for the leader-following consensus of linear multi-agent systems based on the Lyapunov approach,and the Zeno-behavior is excluded.Finally,two numerical examples are provided to illustrate the effectiveness of the theoretical results.

Teacher-student learning of generative adversarial network-guided diffractive neural networks for visual tracking and imaging

Efficiently tracking and imaging interested moving targets is crucial across various applications,from autonomous systems to surveillance.However,persistent challenges remain in various fields,including environmental intricacies,limitations in perceptual technologies,and privacy considerations.We present a teacher-student learning model,the generative adversarial network(GAN)-guided diffractive neural network(DNN),which performs visual tracking and imaging of the interested moving target.The GAN,as a teacher model,empowers efficient acquisition of the skill to differentiate the specific target of interest in the domains of visual tracking and imaging.The DNN-based student model learns to master the skill to differentiate the interested target from the GAN.The process of obtaining a GAN-guided DNN starts with capturing moving objects effectively using an event camera with high temporal resolution and low latency.Then,the generative power of GAN is utilized to generate data with position-tracking capability for the interested moving target,subsequently serving as labels to the training of the DNN.The DNN learns to image the target during training while retaining the target’s positional information.Our experimental demonstration highlights the efficacy of the GAN-guided DNN in visual tracking and imaging of the interested moving target.We expect the GAN-guided DNN can significantly enhance autonomous systems and surveillance.

Leader-Following Consensus of Multi-Agent Systems via Adaptive Event-Based Control

In this paper, the adaptive event-based control approach is applied to study leader-following consensus of multi-agent systems with linear dynamic models. Adaptive event-based controller and triggering function for each agent are designed, where the adaptive function is only dependent on its own event time instants. A sufficient condition on consensus is proposed, which shows that the adaptive event-based method presented in this paper not only can reduce the communication among neighboring agents, but also can determine the event time instants for each agent without using the global information. Furthermore, the Zeno-behavior for the concerned closed-loop system is excluded. Finally, an example is presented to ilhistratc the effectiveness of the obtained theoretical results.

A review on reflection-waveform inversion

Full-waveform inversion(FWI)utilizes optimization methods to recover an optimal Earth model to best fit the observed seismic record in a sense of a predefined norm.Since FWI combines mathematic inversion and full-wave equations,it has been recognized as one of the key methods for seismic data imaging and Earth model building in the fields of global/regional and exploration seismology.Unfortunately,conventional FWI fixes background velocity mainly relying on refraction and turning waves that are commonly rich in large offsets.By contrast,reflections in the short offsets mainly contribute to the reconstruction of the high-resolution interfaces.Restricted by acquisition geometries,refractions and turning waves in the record usually have limited penetration depth,which may not reach oil/gas reservoirs.Thus,reflections in the record are the only source that carries the information of these reservoirs.Consequently,it is meaningful to develop reflection-waveform inversion(RWI)that utilizes reflections to recover background velocity including the deep part of the model.This review paper includes:analyzing the weaknesses of FWI when inverting reflections;overviewing the principles of RWI,including separation of the tomography and migration components,the objective functions,constraints;summarizing the current status of the technique of RWI;outlooking the future of RWI.

Event-based Control and Filtering of Networked Systems:A Survey

In recent years, theoretical and practical research on event-based communication strategies has gained considerable research attention due primarily to their irreplaceable superiority in resource-constrained systems（especially networked systems）. For networked systems, event-based transmission scheme is capable of improving the efficiency in resource utilization and prolonging the lifetime of the network components compared with the widely adopted periodic transmission scheme. As such, it would be interesting to 1） examining how the event-triggering mechanisms affect the control or filtering performance for networked systems, and 2） developing some suitable approaches for the controller and filter design problems. In this paper, a bibliographical review is presented on event-based control and filtering problems for various networked systems. First, the event-driven communication scheme is introduced in detail according to its engineering background, characteristic, and representative research frameworks. Then, different event-based control and filtering（or state estimation） problems are categorized and then discussed. Finally, we conclude the paper by outlining future research challenges for event-based networked systems.

Adaptive Event-Based Consensus of Multi-Agent Systems with General Linear Dynamics

In this paper, adaptive event-based consensus of multi-agent systems with general linear dynamics is considered. A novel adaptive event-based controller and a state-dependent triggering function are proposed for each agent. The consensus can be achieved without the assumption that（A, B） is stabilizable. Furthermore, the Zeno-behavior of the concerned closed-loop system is also excluded under certain conditions. Finally, a numerical simulation example is presented to show the effectiveness of the theoretical results.

Astrometric calibration and source characterisation of the latest generation neuromorphic event-based cameras for space imaging

As an emerging approach to space situational awareness and space imaging,the practical use of an event-based camera(EBC)in space imaging for precise source analysis is still in its infancy.The nature of event-based space imaging and data collection needs to be further explored to develop more effective event-based space imaging systems and advance the capabilities of event-based tracking systems with improved target measurement models.Moreover,for event measurements to be meaningful,a framework must be investigated for EBC calibration to project events from pixel array coordinates in the image plane to coordinates in a target resident space object’s reference frame.In this paper,the traditional techniques of conventional astronomy are reconsidered to properly utilise the EBC for space imaging and space situational awareness.This paper presents the techniques and systems used for calibrating an EBC for reliable and accurate measurement acquisition.These techniques are vital in building event-based space imaging systems capable of real-world space situational awareness tasks.By calibrating sources detected using the EBC,the spatiotemporal characteristics of detected sources or“event sources”can be related to the photometric characteristics of the underlying astrophysical objects.Finally,these characteristics are analysed to establish a foundation for principled processing and observing techniques which appropriately exploit the capabilities of the EBC.

Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks

The increasing use of the Internet with vehicles has made travel more convenient.However,hackers can attack intelligent vehicles through various technical loopholes,resulting in a range of security issues.Due to these security issues,the safety protection technology of the in-vehicle system has become a focus of research.Using the advanced autoencoder network and recurrent neural network in deep learning,we investigated the intrusion detection system based on the in-vehicle system.We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior.In order to verify the accuracy and efficiency of the proposed model,it was evaluated using real vehicle data.The experimental results show that the combination of the two technologies can effectively and accurately identify abnormal boundary behavior.The parameters of the model are self-iteratively updated using the time-based back propagation algorithm.We verified that the model proposed in this study can reach a nearly 96%accurate detection rate.

VPN and Non-VPN Network Traffic Classification Using Time-Related Features

The continual growth of the use of technological appliances during the COVID-19 pandemic has resulted in a massive volume of data flow on the Internet,as many employees have transitioned to working from home.Furthermore,with the increase in the adoption of encrypted data transmission by many people who tend to use a Virtual Private Network(VPN)or Tor Browser(dark web)to keep their data privacy and hidden,network traffic encryption is rapidly becoming a universal approach.This affects and complicates the quality of service(QoS),traffic monitoring,and network security provided by Internet Service Providers(ISPs),particularly for analysis and anomaly detection approaches based on the network traffic’s nature.The method of categorizing encrypted traffic is one of the most challenging issues introduced by a VPN as a way to bypass censorship as well as gain access to geo-locked services.Therefore,an efficient approach is especially needed that enables the identification of encrypted network traffic data to extract and select valuable features which improve the quality of service and network management as well as to oversee the overall performance.In this paper,the classification of network traffic data in terms of VPN and non-VPN traffic is studied based on the efficiency of time-based features extracted from network packets.Therefore,this paper suggests two machine learning models that categorize network traffic into encrypted and non-encrypted traffic.The proposed models utilize statistical features(SF),Pearson Correlation(PC),and a Genetic Algorithm(GA),preprocessing the traffic samples into net flow traffic to accomplish the experiment’s objectives.The GA-based method utilizes a stochastic method based on natural genetics and biological evolution to extract essential features.The PC-based method performs well in removing different features of network traffic.With a microsecond perpacket prediction time,the best model achieved an accuracy of more than 95.02 percent in the most demanding traffic classification task,a drop in accuracy of only 2.37 percent in comparison to the entire statistical-based machine learning approach.This is extremely promising for the development of real-time traffic analyzers.

Matching Uncertain Renewable Supply with Electric Vehicle Charging Demand-A Bi-Level Event-Based Optimization Method

The matching between dynamic supply of renewable power generation and flexible charging demand ofthe Electric Vehicles (EVs) can not only increase the penetration of renewables but also reduce the load to the stateelectric power grid. The challenges herein are the curse of dimensionality, the multiple decision making stagesinvolved, and the uncertainty of both the supply and demand sides. Event-Based Optimization (EBO) provides a newway to solve large-scale Markov decision process. Considering different spatial scales, we develop a bi-level EBOmodel in this paper which can both catch the changes on the macro and micro levels. By proper definition, the sizeof event space stays fixed with the scale of the problem, which shows good scalability in online optimization. Then abi-level Q-learning method is developed to solve the problem iteratively. We demonstrate the performance of themethod by numerical examples. Our method outperforms other methods both in performance and scalability.

Temporal and spatial variation characteristics of extreme precipitation on the Loess Plateau of China facing the precipitation process

The preceding and succeeding precipitation(PSP)often act together with extreme precipitation(EP),in turn,causing floods,which is an objective component that is often overlooked with regard to summer flood hazards in the arid region of Northwest China.In this study,event-based extreme precipitation(EEP)was defined as continuous precipitation that includes at least one day of EP.We analyzed the spatiotemporal variation characteristics of four EEP types(front EEP,late EEP,balanced EEP,and single day EEP)across the Loess Plateau(LP)based on data acquired from 87 meteorological stations from 1960 to 2019.Precipitation on the LP basically maintained a spatial pattern of"low in the northwest region and high in the southeast region",and EP over the last 10 a increased significantly.The cumulative precipitation percentage of single day EEP was 34%and was dominant for 60 a,while the cumulative precipitation percentage of front,late,and balanced EEP types associated with PSP accounted for 66%,which confirms to the connotation of EEP.The cumulative frequencies of front and late EEP types were 23%and 21%,respectively,while the cumulative frequency of balanced EEP had the lowest value at only 13%.Moreover,global warming could lead to more single day EEP across the LP,and continuous EEP could increase in the northwestern region and decrease in the eastern region in the future.The concept of process-oriented EP could facilitate further exploration of disaster-causing processes associated with different types of EP,and provide a theoretical basis for deriving precipitation disaster chains and construction of disaster cluster characteristics.

Development of Power Consumption Models for ESP8266-Enabled Low-Cost IoT Monitoring Nodes

The development of energy and cost efficient IoT nodes is very important for the successful deployment of IoT solutions across various application domains. This paper presents energy models, which will enable the estimation of battery life, for both time-based and event-based low-cost IoT monitoring nodes. These nodes are based on the low-cost ESP8266 (ESP) modules which integrate both transceiver and microcontroller on a single small-size chip and only cost about $2. The active/sleep energy saving approach was used in the design of the IoT monitoring nodes because the power consumption of ESP modules is relatively high and often impacts negatively on the cost of operating the nodes. A low energy application layer protocol, that is, Message Queue Telemetry Transport (MQTT) was also employed for energy efficient wireless data transport. The finite automata theory was used to model the various states and behavior of the ESP modules used in IoT monitoring applications. The applicability of the models presented was tested in real life application scenarios and results are presented. In a temperature and humidity monitoring node, for example, the model shows a significant reduction in average current consumption from 70.89 mA to 0.58 mA for sleep durations of 0 and 30 minutes, respectively. The battery life of batteries rated in mAh can therefore be easily calculated from the current consumption figures.

Uncovering offline event similarity of online friends by constructing null models

The emergence of Event-based Social Network(EBSN) data that contain both social and event information has cleared the way to study the social interactive relationship between the virtual interactions and physical interactions. In existing studies, it is not really clear which factors affect event similarity between online friends and the influence degree of each factor. In this study, a multi-layer network based on the Plancast service data is constructed. The the user’s events belongingness is shuffled by constructing two null models to detect offline event similarity between online friends. The results indicate that there is a strong correlation between online social proximity and offline event similarity. The micro-scale structures at multi-levels of the Plancast online social network are also maintained by constructing 0 k–3 k null models to study how the micro-scale characteristics of online networks affect the similarity of offline events. It is found that the assortativity pattern is a significant micro-scale characteristic to maintain offline event similarity. Finally, we study how structural diversity of online friends affects the offline event similarity. We find that the subgraph structure of common friends has no positive impact on event similarity while the number of common friends plays a key role, which is different from other studies. In addition, we discuss the randomness of different null models, which can measure the degree of information availability in privacy protection. Our study not only uncovers the factors that affect offline event similarity between friends but also presents a framework for understanding the pattern of human mobility.

Optimizing air purification for household particulate matters using sensor-based and time-based intervention strategies

Filtration efficiency of portable air cleaner(PAC)is affected by resident perceptions and adherences to when and how to operate the PAC.Incorporating PAC with smart control and sensor technology holds the promise to effectively reduce indoor air pollutants.This study aims to evaluate the efficiency of a PAC at removing indoor fine particulate matters(PM_(2.5))exposure under two automated operation settings:(1)a time-based mode in which the operation time is determined based on perceived time periods of indoor pollution by residents;(2)a sensor-based mode in which an air sensor monitor is used to determine the PAC based on the actual PM_(2.5) level against the indoor air quality guideline.The study was conducted in a residential room for 55 days with a rolling setting on PAC(no filtration,sensor-based,time-based fil-trations)and a continuous measurement of PM_(2.5).We found that the PAC operated with sensor-based mode removed PM_(2.5) concentrations by 47%and prolonged clean air(<35 μg/m^(3))period by 23%compared to the purifications with time-based mode which reduced PM_(2.5) by 29%and increased clean air period by 13%.The sensor-based filtration identified indoor pollution episodes that are hardly detected by personal perceptions.Our study findings support an automated sensor-based approach to optimize the use of PAC for effectively reducing indoor PM_(2.5) exposure.

M-LSM:An Improved Multi-Liquid State Machine for Event-Based Vision Recognition

Event-based computation has recently gained increasing research interest for applications of vision recogni-tion due to its intrinsic advantages on efficiency and speed.However,the existing event-based models for vision recogni-tion are faced with several issues,such as large network complexity and expensive training cost.In this paper,we propose an improved multi-liquid state machine(M-LSM)method for high-performance vision recognition.Specifically,we intro-duce two methods,namely multi-state fusion and multi-liquid search,to optimize the liquid state machine(LSM).Multi-state fusion by sampling the liquid state at multiple timesteps could reserve richer spatiotemporal information.We adapt network architecture search(NAS)to find the potential optimal architecture of the multi-liquid state machine.We also train the M-LSM through an unsupervised learning rule spike-timing dependent plasticity(STDP).Our M-LSM is evalu-ated on two event-based datasets and demonstrates state-of-the-art recognition performance with superior advantages on network complexity and training cost.

Toward time-based design： Creating an applied time evaluation checklist for urban design research

The perception of a 3D space, in which movement takes place, is subjectively based on experience. The pedestrians＇ perception of subjective duration is one of the related issues that receive tittle attention in urban design Literature. Pedestrians often misperceive the required time to pass a certain distance. A wide range of factors affects one＇s perception of time in urban environments. These factors include individua（ factors （e.g., gender, age, and psychoLogicaL state）, social and cu（tural contexts, purpose and motivation for being in the space, and knowledge of the given area. This study aims to create an applied checklist that can be used by urban designers in analyzing the effects of individual experience on subjective duration. This checklist wilt enable urban designers to perform a phenomenotogicat assessment of time perception and compare this perception in different urban spaces, thereby improving pedestrians＇ experiences of time through a purposeful design. A combination of exploratory and descriptive anaLyticaL research is used as methodology due to the complexity of time perception.