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The paper reports the design and tests of the planar autopilot navigation system in the three-degree-of-freedom (3-DOF) plane (surge, sway and yaw) for a ship. The aim of the tests was to check the improved maneuverability of the ship in open waters using the improved nonlinear control algorithm, developed based on the sliding mode control theory for the ship-trajectory tracking problem of under-actuated ships with static constraints, actuator saturation, and parametric uncertainties. With the integration of the simple increment feedback control law, the dynamic control strategy was developed to fulfill the under-actuated tracking and stabilization objectives. In addition, the LOS (line of sight) guidance system was applied to control the motion path, whereas the sliding mode controller was used to emulate the rudder angle and propeller rotational speed control. Firstly, simulation tests were performed to verify the validity of the basic model and the tracking control algorithm. Subsequently, full scale maneuverability tests were done with a novel container ship, equipped with trajectory tracking control and sliding mode controller algorithm, to check the dynamic stability performance of the ship. The results of the theoretical and numerical simulation on a training ship verify the invariability and excellent robustness of the proposed controller, which: effectively eliminates system chattering, solves the problem of lateral drift of the ship, and maintains the following of the trajectory while simultaneously achieving global stability and robustness. [ABSTRACT FROM AUTHOR]

A spatial odor distribution in an environment can be used for navigation, goal search, localization and mapping, like by video, ultrasonic, temperature and other sensors. Modern e-noses can perform the selective detection of different gases with an extremely low concentration but the source localization algorithms of a selected gas against the background of other odors are still underinvestigated. This paper studies an odor field representation in terms of an e-nose based on an array of low-selective sensors. Using a simulation model, we show how the vector measurements of a field of several odor sources can be processed to navigate for reaching a selected odor source. In addition, we demonstrate that the source having a high level of odor intensity can interfere with the search of another odor source of a low intensity. The well-known class of matching receivers does not solve this problem. However, a solution can be obtained by distributed measurements. As shown below, the spatial structure of an odor field allows to implement vector selection. Using deep learning machines, we may reach a high resolution of odor sources in the space. Our future research will be focused on augmented odor reality and autonomous mobile e-nose (e-dog) design. [ABSTRACT FROM AUTHOR]

Reconfigurable models were shown to be very powerful in solving many problems faster than non-reconfigurable models. WECPAR $$W(M,N,k)$$ is an $$M\times N$$ reconfigurable model that has point-to-point reconfigurable interconnection with $$k$$ wires between neighboring processors. This paper studies several aspects of WECPAR. We first consider solving the list ranking problem on WECPAR. Some of the results obtained show that, ranking one element in a list of $$N$$ elements can be solved on $$W(N,N,N)$$ WECPAR in $$O(1)$$ time. Also, on $$W(N,N,k)$$ , ranking a list $$L(N)$$ of $$N$$ elements can be done in $$O((\log N)(\lceil {\log _{k+1} N} \rceil ))$$ time. Then, we assess the relative computational power of WECPAR and transfer a large body of algorithms to work directly on WECPAR. We introduce several simulation algorithms between WECPAR and well-known models such as PRAM and RMBM. Simulation algorithms show that a PRIORITY CRCW PRAM $$P(N,S)$$ of $$N$$ processors and $$S$$ shared memory locations can be simulated on $$W(S,N,k)$$ WECPAR in $$O(\lceil {\log _{k+1} N}\rceil +\lceil {\log _{k+1} S}\rceil )$$ time. Also, we show that a PRIORITY CRCW basic RMBM( $$P,B)$$ , of $$P$$ processors and $$B$$ buses can be simulated on $$W(B, P+B, k)$$ WECPAR in $$O(\lceil {\log _{k+1} (P+B)}\rceil )$$ time. This directly migrate a large number of algorithms to work on WECPAR with the simulation overhead. [ABSTRACT FROM AUTHOR]

The control of nonlinear process is the main problem in process industries which repeatedly show its nonlinear activity. In this paper the control action of nonlinear framework using Digital controller with Deadbeat Algorithm was presented. The Nonlinear process which has been considered for its application is conical tank system. System identification of this nonlinear method is made using mathematical modeling and it is approximated to a first order system. Simulation is carried out by the MATLAB software and its servo and regulatory operation is resolved. [ABSTRACT FROM AUTHOR]

The ever-growing mobile station (MS) localization technologies provide an increasingly important role in all aspects of the wireless cellular systems and Internet of Things (IoT). The accurate MS location information is the basis in connection of different devices in IoT. The MS localization techniques based on time of arrival (TOA) trilateration algorithm, which determines the location of MS using an intersection point of three circles based on distances between MS and base stations (BS) and coordinates of BSs, have been actively studied. In general, the distance between the MS and BS is calculated by counting the number of delay samples or measuring the power of the received signal. Since the estimated distance (radius of a circle) between MS and BS is commonly increased, three circles may not meet at a single point, resulting in the estimation error of MS localization. In order to improve this problem, in this paper, we propose the hybrid TOA trilateration algorithm based on the line intersection algorithm for the general case for intersection of three circles and the comparison approach of intersection distances for the specific case where a small circle is located inside the area of two large circles. The line intersection algorithm has an excellent location estimation performance in the general case, but it does not work in the specific case. The comparison approach of intersection distances has good performance only for the specific case. In addition, we propose the mode selection algorithm to efficiently select a proper mode between the general and specific cases. The representative computer simulation examples are provided to verify the localization performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]