Few-photon routing via chiral light-matter couplings

Quantum router is one of the essential elements in the quantum network. Conventional routers only direct a single photon from one quantum channel into another. Here, we proposed a few-photon router. The active element of the router is a single qubit chirally coupled to two independent waveguides simultaneously, where each waveguide mode provides a quantum channel. By introducing the operators of the scatter-free space and the controllable space, the output state of the one-photon and two-photon scattering are derived analytically. It is found that the qubit can direct one and two photons from one port of the incident waveguide to an arbitrarily selected port of the other waveguide with unity, respectively. However, two photons cannot be simultaneously routed to the same port due to the anti-bunch effect.

Quantum Optical Experiments Modeled by Long Short-Term Memory

We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies, such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search, but is also an essential step towards the automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.

Inverse optimal adaptive output feedback control of a class of Euler–Lagrange systems: A nonlinear filter based approach

In this paper, we present an inverse optimal tracking controller for a class of Euler–-Lagrange systems having uncertainties in their dynamical terms under the restriction that only the output state ( i.e. position for robotic systems) is available for measurement. Specifically, a nonlinear filter is used to generate a velocity substitute, then a controller formulation ensuring a globally asymptotically stable closed-loop system while minimizing a performance index despite the presence of parametric uncertainty, is proposed. The stability proof is established using a Lyapunov analysis of the system with proposed optimal output feedback controller. Inverse optimality is derived via designing a meaningful cost function utilizing the control Lyapunov function. Numerical simulations are presented to illustrate the viability and performance of the derived controller.

The Impact of the Dynamic Model in Feedback Linearization Trajectory Tracking of a Mobile Robot

This paper proposes the impact of the Dynamic model in Input-Output State Feedback Linearization (IO-SFL) technique for trajectory tracking of differential drive mobile robots, which has been restricted to using just the kinematics in most of the previous approaches. To simplify the control problem, this paper develops a novel control approach based on the velocity and position control strategy. To improve the results, the dynamics are taken into account. The objective of this paper is to illustrate the flaws unseen when adopting the kinematics-only controllers because the nonlinear kinematic model will suffice for control design only when the inner velocity (dynamic) loop is faster than the slower outer control loop. This is a big concern when using kinematic controllers to robots that don’t have a low-level controller, Arduino robots for example. The control approach is verified using the Lyapunov stability analysis. MATLAB/SIMULINK is carried out to determine the impact of the proposed controller for the trajectory tracking problem, from the simulation, it was discovered that the proposed controller has an excellent dynamic characteristic, simple, rapid response, stable capability for trajectory-tracking, and ignorable tracking error. A comparison between the presence and absence of the dynamic model shows the error in tracking due to dynamic system that must be taken into account if our system doesn’t come with a built-in one, thus, confirming the superiority of the proposed approach in terms of precision, with a neglectable difference in computations.

LSTM-based Electroencephalogram Classification on Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is categorized as a neurodevelopmental disability. Having an automated technology system to classify the ASD trait would have a huge influence on paediatricians, which can aid them in diagnosing ASD in children using a quantifiable method. A novel autism diagnosis method based on a bidirectional long-short-term-memory (LSTM) network's deep learning algorithm is proposed. This multi-layered architecture merges two LSTM blocks with the other direction of propagation to classify the output state on the brain signal data from an electroencephalogram (EEG) on individuals; normal and autism obtained from the Simon Foundation Autism Research Initiative (SFARI) database. The accuracy of 99.6% obtained for 90:10 train:test data distribution, while the accuracy of 97.3% was achieved for 70:30 distribution. The result shows that the proposed approach had better autism classification with upgraded efficiency compared to single LSTM network method and potentially giving a significant contribution in neuroscience research.

A Simple Soft Computing Structure for Modeling and Control

Using the interpolation/extrapolation skills of the core function of an iterative adaptive controller, a structurally simple single essential layer neural network-based topological structure is suggested with fast and explicit single-step teaching and data-retrieving abilities. Its operation does not assume massive parallelism, therefore it easily can be simulated by simple sequential program codes not needing sophisticated data synchronization mechanisms. It seems to be advantageous in approximate model-based common, robust, or adaptive controllers that can compensate for the effects of minor modeling imprecisions. In this structure a neuron can be in either a firing or a passive (i.e., producing zero output) state. In firing state its activation function realizes an abstract rotation that maps the desired kinematic data into the space of the necessary control forces. The activation function allows the use of a simple and fast incremental model modification for slowly varying dynamic models. Its operation is exemplified by numerical simulations for a van der Pol oscillator in free motion, and within a Computed Torque type control. To reveal the possibility for efficient model correction, a robust Variable Structure/Sliding Mode Controller is applied, too. The novel structure can be obtained by approximate experimental observations as e.g., the fuzzy models.

Optimization Control of Cooperative Trajectory towards Dual Arms Based on Time-Varying Constrained Output State

Aiming at the impact and disturbance of dual-arm robots in the process of coordinated transportation, a dual-arm cooperative trajectory optimization control based on time-varying constrained output state is proposed. According to the constraint relationship of the end-effector trajectory of the dual-arm coordinated transportation, the joint space trajectory mathematical model of the dual-arm coordinated transportation was established by using the master-slave construction method. Based on the time impact optimization index of joint trajectory, a multiobjective nonlinear equation is established. Using random probability distribution to extract the interpolation features of nonuniform quintic B-spline trajectory, the feature optimization target is selected, and the Newton numerical algorithm is used for iterative optimization. At the same time, it is combined with an elite retention genetic algorithm to further optimize the target. Based on the disturbance and tracking problem, a PD control method based on time-varying constrained output state is proposed, and the control law is designed. Its convergence is verified by establishing the Lyapunov function equation and asymmetric term. The trajectory optimization results show that the proposed trajectory optimization method can increase the individual diversity and enhance the individual local optimization, thus avoiding the premature impact of the elite retention genetic algorithm. Finally, the proposed control method is simulated on the platform of Gazebo; compared with the traditional PD control method, the results show that the proposed control algorithm has high robustness, and the rationality of the coordinated trajectory control method is verified by the double-arm handling experiment.

A case report: percutaneous management of high-output heart failure from iatrogenic aortocoronary venous grafting to the coronary sinus

Background Aortocoronary arteriovenous fistula (ACAVF) due to iatrogenic bypass grafting to a cardiac vein is an exceedingly rare complication resulting from coronary artery bypass grafting (CABG) surgery. If not identified in a timely fashion, ACAVF has known significant clinical consequences related to left to right shunting and possible residual myocardial ischemia. Case presentation An 82-year-old male with a history of CABG, presented with dyspnea. Over the span of 2 years following CABG, the patient experienced progressive exertional dyspnea and peripheral edema. The patient was found to have a new cardiomyopathy with a severely reduced ejection fraction at 30–35%. The patient underwent diagnostic left heart catheterization, and an ACAVF was discovered between a saphenous vein graft and the coronary sinus. The patient underwent successful percutaneous coiling of the ACAVF with no residual flow. Follow-up echocardiography at 3 months revealed restoration of left ventricular systolic function to 50% and significant improvement in heart failure symptoms. Conclusions ACAVF is an exceedingly rare iatrogenic complication of CABG that may result in residual ischemia from the non-grafted myocardial territory and other sequelae relating to left to right shunting and a high-output state. Management for this pathology includes but is not limited to the use of percutaneous coiling, implantation of covered stents, graft removal and regrafting, and ligation.