Magnetoresistive sensors based on the elasticity of domain walls

Magnetic sensors based on the magnetoresistance effects have a promising application prospect due to their excellent sensitivity and advantages in terms of the integration. However, competition between higher sensitivity and larger measuring range remains a problem. Here, we propose a novel mechanism for the design of magnetoresistive sensors: probing the perpendicular field by detecting the expansion of the elastic magnetic Domain Wall (DW) in the free layer of a spin valve or a magnetic tunnel junction. Performances of devices based on this mechanism, such as the sensitivity and the measuring range can be tuned by manipulating the geometry of the device, without changing the intrinsic properties of the material, thus promising a higher integration level and a better performance. The mechanism is theoretically explained based on the experimental results. Two examples are proposed and their functionality and performances are verified via micromagnetic simulation.Comment: 4 figures, 13 page

Well-posedness and regularity of the Darcy-Boussinesq system in layered porous media

We investigate the Darcy-Boussinesq model for convection in layered porous media. In particular, we establish the well-posedness of the model in two and three spatial dimension, and derive the regularity of the solutions in a novel piecewise H2 space

Event- and time-triggered dynamic task assignments for multiple vehicles:Special Issue on Multi-Robot and Multi-Agent Systems

We study the dynamic task assignment problem in which multiple dispersed vehicles are employed to visit a set of targets. Some targets’ locations are initially known and the others are dynamically randomly generated during a finite time horizon. The objective is to visit all the target locations while trying to minimize the vehicles’ total travel time. Based on existing algorithms used to deal with static multi-vehicle task assignment, two types of dynamic task assignments, namely event-triggered and time-triggered, are studied to investigate what the appropriate time instants should be to change in real time the assignment of the target locations in response to the newly generated target locations. Furthermore, for both the event- and time-triggered assignments, we propose several algorithms to investigate how to distribute the newly generated target locations to the vehicles. Extensive numerical simulations are carried out which show better performance of the event-triggered task assignment algorithms over the time-triggered algorithms under different arrival rates of the newly generated target locations

All-optical switching of magnetic domains in Co/Gd heterostructures with Dzyaloshinskii-Moriya Interaction

Given the development of hybrid spintronic-photonic devices and chiral magnetic structures, a combined interest in all-optical switching (AOS) of magnetization and current-induced domain wall motion in synthetic ferrimagnetic structures with strong Dzyaloshinskii-Moriya Interaction (DMI) is emerging. In this study, we report a study on single-pulse all-optical toggle switching and asymmetric bubble expansion in specially engineered Co/Gd-based multilayer structures. In the absence of any external magnetic fields, we look into the AOS properties and the potential role of the DMI on the AOS process as well as the stability of optically written micro-magnetic domains. Particularly, interesting dynamics are observed in moon-shaped structures written by two successive laser pulses. The stability of domains resulting from an interplay of the dipolar interaction and domain-wall energy are compared to simple analytical models and micromagnetic simulations

Distributed Model Predictive Control and Optimization for Linear Systems With Global Constraints and Time-Varying Communication

In the article, we study the distributed model predictive control (DMPC) problem for a network of linear discrete-time systems, where the system dynamics are decoupled, the system constraints are coupled, and the communication networks are described by time-varying directed graphs. A novel distributed optimization algorithm called the push-sum dual gradient (PSDG) algorithm is proposed to solve the dual problem of the DMPC optimization problem in a fully distributed way. We prove that the sequences of the primal, and dual variables converge to their optimal values. Furthermore, to solve the implementation issues, stopping criteria are designed to allow early termination of the PSDG Algorithm, and the gossip-based push-sum algorithm is proposed to check the stopping criteria in a distributed manner. It is shown that the optimization problem is iteratively feasible, and the closed-loop system is exponentially stable. Finally, the effectiveness of the proposed DMPC approach is verified via an example

Distributed multi-vehicle task assignment in a time-invariant drift field with obstacles

This study investigates the task assignment problem where a fleet of dispersed vehicles needs to visit multiple target locations in a time-invariant drift field with obstacles while trying to minimise the vehicles' total travel time. The vehicles have different capabilities, and each kind of vehicles can visit a certain type of the target locations; each target location might require to be visited more than once by different kinds of vehicles. The task assignment problem has been proven to be NP-hard. A path planning algorithm is first designed to minimise the time for a vehicle to travel between two given locations through the drift field while avoiding any obstacle. The path planning algorithm provides the travel cost matrix for the target assignment, and generates routes once the target locations are assigned to the vehicles. Then, a distributed algorithm is proposed to assign the target locations to the vehicles using only local communication. The algorithm guarantees that all the visiting demands of every target will be satisfied within a total travel time that is at worst twice of the optimal when the travel cost matrix is symmetric. Numerical simulations show that the algorithm can lead to solutions close to the optimal

An integrated multi-population genetic algorithm for multi-vehicle task assignment in a drift field

This paper investigates the task assignment problem for a team of autonomous aerial/marine vehicles driven by constant thrust and maneuvering in a planar lateral drift field. The aim is to minimize the total traveling time in order to guide the vehicles to deliver a number of customized sensors to a set of target points with different sensor demands in the drift field. To solve the problem, we consider together navigation strategies and target assignment algorithms; the former minimizes the traveling time between two given locations in the drift field and the latter allocates a sequence of target locations to each vehicle. We first consider the effect of the weight of the carried sensors on the speed of each vehicle, and construct a sufficient condition to guarantee that the whole operation environment is reachable for the vehicles. Then from optimal control principles, time-optimal path planning is carried out to navigate each vehicle from an initial position to its given target location. Most importantly, to assign the targets to the vehicles, we combine the virtual coding strategy, multiple offspring method, intermarriage crossover strategy, and the tabu search mechanism to obtain a co-evolutionary multi-population genetic algorithm, short-named CMGA. Simulations on sensor delivery scenarios in both fixed and time-varying drift fields are shown to highlight the satisfying performances of the proposed approach against popular greedy algorithms

Efficient Routing for Precedence-Constrained Package Delivery for Heterogeneous Vehicles

This paper studies the precedence-constrained task assignment problem for a team of heterogeneous vehicles to deliver packages to a set of dispersed customers subject to precedence constraints that specify which customers need to be visited before which other customers. A truck and a micro drone with complementary capabilities are employed where the truck is restricted to travel in a street network and the micro drone, restricted by its loading capacity and operation range, can fly from the truck to perform the last-mile package deliveries. The objective is to minimize the time to serve all the customers respecting every precedence constraint. The problem is shown to be NP-hard, and a lower bound on the optimal time to serve all the customers is constructed by using tools from graph theory. Then, integrating with a topological sorting technique, several heuristic task assignment algorithms are proposed to solve the task assignment problem. Numerical simulations show the superior performances of the proposed algorithms compared with popular genetic algorithms. Note to Practitioners - This paper presents several task assignment algorithms for the precedence-constrained package delivery for the team of a truck and a micro drone. The truck can carry the drone moving in a street network, while the drone completes the last-mile package deliveries. The practical contributions of this paper are fourfold. First, the precedence constraints on the ordering of the customers to be served are considered, which enables complex logistic scheduling for customers prioritized according to their urgency or importance. Second, the package delivery optimization problem is shown to be NP-hard, which clearly shows the need for creative approximation algorithms to solve the problem. Third, the constructed lower bound on the optimal time to serve all the customers helps to clarify for practitioners the limiting performance of a feasible solution. Fourth, the proposed task assignment algorithms are efficient and can be adapted for real scenarios

Efficient Heuristic Algorithms for Single-Vehicle Task Planning With Precedence Constraints

This article investigates the task planning problem where one vehicle needs to visit a set of target locations while respecting the precedence constraints that specify the sequence orders to visit the targets. The objective is to minimize the vehicle’s total travel distance to visit all the targets while satisfying all the precedence constraints. We show that the optimization problem is NP-hard, and consequently, to measure the proximity of a suboptimal solution from the optimal, a lower bound on the optimal solution is constructed based on the graph theory. Then, inspired by the existing topological sorting techniques, a new topological sorting strategy is proposed; in addition, facilitated by the sorting, we propose several heuristic algorithms to solve the task planning problem. The numerical experiments show that the designed algorithms can quickly lead to satisfying solutions and have better performance in comparison with popular genetic algorithms

Tuning the Dzyaloshinskii-Moriya Interaction in Pt/Co/MgO heterostructures through MgO thickness

The interfacial Dzyaloshinskii-Moriya interaction (DMI) in the ferromagnetic/heavy metal ultra-thin film structures , has attracted a lot of attention thanks to its capability to stabilize Neel-type domain walls (DWs) and magnetic skyrmions for the realization of non-volatile memory and logic devices. In this study, we demonstrate that magnetic properties in perpendicularly magnetized Ta/Pt/Co/MgO/Pt heterostructures, such as magnetization and DMI, can be significantly influenced through both the MgO and the Co ultrathin film thickness. By using a field-driven creep regime domain expansion technique, we find that non-monotonic tendencies of DMI field appear when changing the thickness of MgO and the MgO thickness corresponding to the largest DMI field varies as a function of the Co thicknesses. We interpret this efficient control of DMI as subtle changes of both Pt/Co and Co/MgO interfaces, which provide a method to investigate ultra-thin structures design to achieve skyrmion electronics.Comment: 18 pages, 11 figure