Quantum identification system

A secure quantum identification system combining a classical identification procedure and quantum key distribution is proposed. Each identification sequence is always used just once and new sequences are ``refuelled'' from a shared provably secret key transferred through the quantum channel. Two identification protocols are devised. The first protocol can be applied when legitimate users have an unjammable public channel at their disposal. The deception probability is derived for the case of a noisy quantum channel. The second protocol employs unconditionally secure authentication of information sent over the public channel, and thus it can be applied even in the case when an adversary is allowed to modify public communications. An experimental realization of a quantum identification system is described.Comment: RevTeX, 4 postscript figures, 9 pages, submitted to Physical Review

Lightning discharge identification system

A system for differentiating between cloud to cloud and cloud to ground lightning discharges is described which includes an electric field antenna that senses the rate of charge of an electric field produced by a lightning discharge. When the signal produced by the electric field exceeds a predetermined threshold, it is fed to a coincidence detector. A VHF antenna is also provided and generates a video signal responsive to a cloud to cloud lightning discharge, and this signal is fed through a level sensor, an inverter, to the coincidence detector simultaneously with the signal from the field detector. When signals from the electric field antenna and the VHF antenna appear at the coincidence detector simultaneously, such indicates that there is a cloud to cloud lightning discharge; whereas, when there is not a signal produced on the VHF antenna simultaneously with a signal produced by the field sensor, then a strike indicator connected to the coincidence detector indicates a cloud to ground lightning discharge

Automated drug identification system

System speeds up analysis of blood and urine and is capable of identifying 100 commonly abused drugs. System includes computer that controls entire analytical process by ordering various steps in specific sequences. Computer processes data output and has readout of identified drugs

Robot training using system identification

This paper focuses on developing a formal, theory-based design methodology to generate transparent robot control programs using mathematical functions. The research finds its theoretical roots in robot training and system identification techniques such as Armax (Auto-Regressive Moving Average models with eXogenous inputs) and Narmax (Non-linear Armax). These techniques produce linear and non-linear polynomial functions that model the relationship between a robot’s sensor perception and motor response. The main benefits of the proposed design methodology, compared to the traditional robot programming techniques are: (i) It is a fast and efficient way of generating robot control code, (ii) The generated robot control programs are transparent mathematical functions that can be used to form hypotheses and theoretical analyses of robot behaviour, and (iii) It requires very little explicit knowledge of robot programming where end-users/programmers who do not have any specialised robot programming skills can nevertheless generate task-achieving sensor-motor couplings. The nature of this research is concerned with obtaining sensor-motor couplings, be it through human demonstration via the robot, direct human demonstration, or other means. The viability of our methodology has been demonstrated by teaching various mobile robots different sensor-motor tasks such as wall following, corridor passing, door traversal and route learning

Learning deep dynamical models from image pixels

Modeling dynamical systems is important in many disciplines, e.g., control, robotics, or neurotechnology. Commonly the state of these systems is not directly observed, but only available through noisy and potentially high-dimensional observations. In these cases, system identification, i.e., finding the measurement mapping and the transition mapping (system dynamics) in latent space can be challenging. For linear system dynamics and measurement mappings efficient solutions for system identification are available. However, in practical applications, the linearity assumptions does not hold, requiring non-linear system identification techniques. If additionally the observations are high-dimensional (e.g., images), non-linear system identification is inherently hard. To address the problem of non-linear system identification from high-dimensional observations, we combine recent advances in deep learning and system identification. In particular, we jointly learn a low-dimensional embedding of the observation by means of deep auto-encoders and a predictive transition model in this low-dimensional space. We demonstrate that our model enables learning good predictive models of dynamical systems from pixel information only.Comment: 10 pages, 11 figure

System identification and pid control of toothbrush simulator system

Toothbrush simulator was invented for industry and dentist researchers to do research related to plaque removal. The toothbrush simulator system repeatedly has a problem in achieving the desired speed control. The brushing movement is inconsistence and stops eventually if there is a force exerted on the toothbrush holder. Further research is required to increase the reliability and controllability of the speed response achievable from the toothbrush simulator system. In this study, a PID controller is designed and embedded in the system. A real-time experiment has been conducted on the real system via the Matlab Simulink environment to construct the model. The model parameters are optimized with model order 2, 3 and 4 where each model order has been analyzed for ten (10) times iteration by the genetic algorithm in obtaining the accurate transfer function. The model has been validated through correlation analysis. The PID controller was tuned through the PID tuner and Ziegler-Nichols method. Simulated and real-time system response from both tuning methods was compared. The simulated response with the selected PID controller is then compared with the response from the real-time experiment. The closed-loop system without controller was compared with the response with the PID controller. The PID controller was then deployed into the real system by uploaded into the microcontroller. The brushing simulator remote control was created to control the desired speed through a smartphone. Genetic algorithm model based on model order 4 has been selected as the best model as it able to achieve the minimum MSE value of 0.0176 and past all the validation tests. The selected PID parameters was from PID tuner tuning method with gain values of; Kp= 17.9287, Ki= 40.751 and Kd= -0.52705. Both results of simulation and real-time tests were compared, and they show about similar performances. The controlled system response had achieved all five desired speed of 175, 195, 215, 235 and 255 rpm with the percentage of improvement 67%, 65%, 65%, 65%, and 68%. Throughout this study, a genetic algorithm model based and tuned PID controller parameters has been applied to the real system improvised in better system response