Physics and application of photon number resolving detectors based on superconducting parallel nanowires

The Parallel Nanowire Detector (PND) is a photon number resolving (PNR) detector which uses spatial multiplexing on a subwavelength scale to provide a single electrical output proportional to the photon number. The basic structure of the PND is the parallel connection of several NbN superconducting nanowires (100 nm-wide, few nm-thick), folded in a meander pattern. PNDs were fabricated on 3-4 nm thick NbN films grown on MgO (TS=400C) substrates by reactive magnetron sputtering in an Ar/N2 gas mixture. The device performance was characterized in terms of speed and sensitivity. PNDs showed a counting rate of 80 MHz and a pulse duration as low as 660ps full width at half maximum (FWHM). Building the histograms of the photoresponse peak, no multiplication noise buildup is observable. Electrical and optical equivalent models of the device were developed in order to study its working principle, define design guidelines, and develop an algorithm to estimate the photon number statistics of an unknown light. In particular, the modeling provides novel insight of the physical limit to the detection efficiency and to the reset time of these detectors. The PND significantly outperforms existing PNR detectors in terms of simplicity, sensitivity, speed, and multiplication noise

A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles

Vehicle to Vehicle (V2V) communication has a great potential to improve reaction accuracy of different driver assistance systems in critical driving situations. Cooperative Adaptive Cruise Control (CACC), which is an automated application, provides drivers with extra benefits such as traffic throughput maximization and collision avoidance. CACC systems must be designed in a way that are sufficiently robust against all special maneuvers such as cutting-into the CACC platoons by interfering vehicles or hard braking by leading cars. To address this problem, a Neural- Network (NN)-based cut-in detection and trajectory prediction scheme is proposed in the first part of this paper. Next, a probabilistic framework is developed in which the cut-in probability is calculated based on the output of the mentioned cut-in prediction block. Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed which incorporates this cut-in probability to enhance its reaction against the detected dangerous cut-in maneuver. The overall system is implemented and its performance is evaluated using realistic driving scenarios from Safety Pilot Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I

Airborne Advanced Reconfigurable Computer System (ARCS)

A digital computer subsystem fault-tolerant concept was defined, and the potential benefits and costs of such a subsystem were assessed when used as the central element of a new transport's flight control system. The derived advanced reconfigurable computer system (ARCS) is a triple-redundant computer subsystem that automatically reconfigures, under multiple fault conditions, from triplex to duplex to simplex operation, with redundancy recovery if the fault condition is transient. The study included criteria development covering factors at the aircraft's operation level that would influence the design of a fault-tolerant system for commercial airline use. A new reliability analysis tool was developed for evaluating redundant, fault-tolerant system availability and survivability; and a stringent digital system software design methodology was used to achieve design/implementation visibility

Experimental analysis of computer system dependability

This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance

Holistic Temporal Situation Interpretation for Traffic Participant Prediction

For a profound understanding of traffic situations including a prediction of traf- fic participants’ future motion, behaviors and routes it is crucial to incorporate all available environmental observations. The presence of sensor noise and depen- dency uncertainties, the variety of available sensor data, the complexity of large traffic scenes and the large number of different estimation tasks with diverging requirements require a general method that gives a robust foundation for the de- velopment of estimation applications. In this work, a general description language, called Object-Oriented Factor Graph Modeling Language (OOFGML), is proposed, that unifies formulation of esti- mation tasks from the application-oriented problem description via the choice of variable and probability distribution representation through to the inference method definition in implementation. The different language properties are dis- cussed theoretically using abstract examples. The derivation of explicit application examples is shown for the automated driv- ing domain. A domain-specific ontology is defined which forms the basis for four exemplary applications covering the broad spectrum of estimation tasks in this domain: Basic temporal filtering, ego vehicle localization using advanced interpretations of perceived objects, road layout perception utilizing inter-object dependencies and finally highly integrated route, behavior and motion estima- tion to predict traffic participant’s future actions. All applications are evaluated as proof of concept and provide an example of how their class of estimation tasks can be represented using the proposed language. The language serves as a com- mon basis and opens a new field for further research towards holistic solutions for automated driving

Integrated assurance assessment of a reconfigurable digital flight control system

The integrated application of reliability, failure effects and system simulator methods in establishing the airworthiness of a flight critical digital flight control system (DFCS) is demonstrated. The emphasis was on the mutual reinforcement of the methods in demonstrating the system safety

Lower limb locomotion activity recognition of healthy individuals using semi-Markov model and single wearable inertial sensor

International audienceLower limb locomotion activity is of great interest in the field of human activity recognition. In this work, a triplet semi-Markov model-based method is proposed to recognize the locomotion activities of healthy individuals when lower limbs move periodically. In the proposed algorithm, the gait phases (or leg phases) are introduced into the hidden states, and Gaussian mixture density is introduced to represent the complex conditioned observation density. The introduced sojourn state forms the semi-Markov structure, which naturally replicates the real transition of activity and gait during motion. Then, batch mode and on-line Expectation-Maximization (EM) algorithms are proposed, respectively, for model training and adaptive on-line recognition. The algorithm is tested on two datasets collected from wearable inertial sensors. The batch mode recognition accuracy reaches up to 95.16%, whereas the adaptive on-line recognition gradually obtains high accuracy after the time required for model updating. Experimental results show an improvement in performance compared to the other competitive algorithm

Intelligent manipulation technique for multi-branch robotic systems

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system