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.

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.

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.

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.

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.

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.

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.

Gone full circle:A radial approach to visualize event-based networks in digital humanities

In the application domain of digital humanities network visualization is increasingly being used to conduct research as the main interests of the domain experts lie in exploring and analyzing relationships between entities and their changes over time.Visualizing the dynamics and different perspectives of such data is a non-trivial task but it enables researchers to explore connections between disparate entities and investigate historical narratives that emerge.In this paper we present Circular,an interactive exploration environment to visualize event-based networks and support research in digital humanities through visualization of historical subjects in space and time.Our radial design is the result of iterative collaboration with domain experts,and we discuss the process of collaborative development and exploration of public music festivities in Vienna as an example of immersive development methodology.We validate our approach by means of both domain and visualization expert interviews and show the potential of this approach in supporting the visual exploration of historical subjects.We discuss our design rationales,visual encodings,and interactions as to allow the reproducibility of this approach within a framework of transdisciplinary collaboration with digital humanities.

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.

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.

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.

Control strategy for gait transition of an underactuated 3D bipedal robot

Significant research interest has recently been attracted to the study of bipedal robots due to the wide variety of their potential applications.In reality,bipedal robots are often required to perform gait transitions to achieve flexible walking.In this paper,we consider the gait transition of a five-link underactuated three-dimensional(3 D)bipedal robot,and propose a two-layer control strategy.The strategy consists of a unique,event-based,feedback controller whose feedback gain in each step is updated by an adaptive control law,and a transition controller that guides the robot from the current gait to a neighboring point of the target gait so that the state trajectory can smoothly converge to the target gait.Compared with previous works,the transition controller is parameterized and its control parameters are obtained by solving an optimization problem to guarantee the physical constraints in the transition process.Finally,the effectiveness of the control strategy is illustrated on the underactuated 3 D bipedal robot.

BEAP： An End-User Agile Programming Paradigm for Business Applications

Business applications are subject to changes with technology trends or market demands. However, quick response to these changes is still a challenging issue. Most of the existing architectures （e.g., CORBA, Web Services） still expose the developers to excessive low-level details and force a tight coupling between program modules. For end users, developing, customizing, and reengineering applications remain difficult and time-consuming tasks. A high-level programming model is presented, together with a descriptive programming paradigm called BEAP, to facilitate end-user programming. In this approach, applications could be visually composed from well-defined software components called ＂funnels＂ in an event-driven fashion. Application examples have shown that, by raising the level of abstraction as well as simplifying the programming model, BEAP could empower end users to build business applications on demand with improved productivity.

Distributed Algorithms for Event Reporting in Mobile-Sink WSNs for Internet of Things

Wireless Sensor Networks（WSNs） have many applications, such as climate monitoring systems, fire detection, smart homes, and smart cities. It is expected that WSNs will be integrated into the Internet of Things（IoT）and participate in various tasks. WSNs play an important role monitoring and reporting environment information and collecting surrounding context. In this paper we consider a WSN deployed for an application such as environment monitoring, and a mobile sink which acts as the gateway between the Internet and the WSN. Data gathering is a challenging problem in WSNs and in the IoT because the information has to be available quickly and effectively without delays and redundancies. In this paper we propose several distributed algorithms for composite event detection and reporting to a mobile sink. Once data is collected by the sink, it can be shared using the IoT infrastructure. We analyze the performance of our algorithms using WSNet simulator, which is specially designed for event-based WSNs. We measure various metrics such as average residual energy, percentage of composite events processed successfully at the sink, and the average number of hops to reach the sink.

Online calibration of combustion phase in a diesel engine

In this work, an online calibration mechanism is proposed for the combustion phase in a diesel engine. In particular, a simplified event-based engine model, of which the output predicts the optimum combustion phase, is used to aid the calibration, and the model is updated online along with the engine operation to keep the integrity high so as to improve the quality of optimum combustion phase prediction. It is found this mechanism can be applied to develop an online automated calibration process when the engine system shifts to a new operating point. of the proposed mechanism. Engine test results are included to demonstrate the effectiveness

Event-driven flood management:design and computational modules

Flood management is a set of activities that have to be carried out in collaboration with multiple agencies.Advanced flood information with early warning generated using remote sensing satellite technologies can help the agencies to effectively manage the situation on ground.Various environmental parameters and forecasts provided by different agencies can be analyzed and compared with historical flood events for generating probable flood event alerts.The information(environmental parameters)provided by the agencies are heterogeneous and noncompliant to standards and distributed in nature.Synchronization of data from distributed resources and automation of data analysis process for flood management is a primary prerequisite for faster and efficient decision-making.Web 2.0-based web services enable data creation,sharing,communication,and collaboration on web.Spatial data sharing on web 2.0 for making quality of service using open-source software for efficient flood management is a challenge.Available software architectures proposed for risk and environmental crisis management are too generic in nature and needs lot of modification for flood management.An event-driven model coupled with data standardization procedures using service-oriented architecture provides an effective framework for flood management.In this paper,a framework capable of collecting heterogeneous distributed flood-related information for analyzing and alerting probable flood events is proposed.The framework has been implemented to generate automatic flood extent maps,by analyzing the distributed satellite data(as service).The automation of flood delineation process reduces the overall flood product generation time.Open-source web tools have been utilized in development of spatial information system to visualize and analyze the actual situation on ground facilitating overall decision-making process.

Passive non-line-of-sight imaging for moving targets with an event camera

Non-line-of-sight[NLOS]imaging is an emerging technique for detecting objects behind obstacles or around corners.Recent studies on passive NLOS mainly focus on steady-state measurement and reconstruction methods,which show limitations in recognition of moving targets.To the best of our knowledge,we propose a novel event-based passive NLOS imaging method.We acquire asynchronous event-based data of the diffusion spot on the relay surface,which contains detailed dynamic information of the NLOS target,and efficiently ease the degradation caused by target movement.In addition,we demonstrate the event-based cues based on the derivation of an event-NLOS forward model.Furthermore,we propose the first event-based NLOS imaging data set,EM-NLOS,and the movement feature is extracted by time-surface representation.We compare the reconstructions through event-based data with frame-based data.The event-based method performs well on peak signal-to-noise ratio and learned perceptual image patch similarity,which is 20%and 10%better than the frame-based method.