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The direct position determination (DPD) algorithm in wireless sensor array networks(WSANs) has high localization accuracy under low signal-to-noise ratios (SNRs). However, with the increasing of arrays, the DPD algorithm faces challenges in tracking of mobile sources in time-varying environments due to the growing communication pressure and computational complexity. In this brief, a novel low-complexity DPD algorithm is considered in WSANs. First, an adaptive-filtering-based DPD model is proposed to reduce computational complexity, and the position of the target is estimated using the gradient descent method. Second, the proposed DPD method is extended to the distributed framework based on the diffusion strategy to distribute the communication pressure and computational load across the WSANs. As indicated by the simulation results, the proposed model outperforms the existing model exploiting. Furthermore, a negligible difference exists in localization performance and tracking performance between the proposed DPD method based on the diffusion strategy and the corresponding DPD method.

In practical operation, the system parameters of a permanent magnet linear motor are affected by unknown factors, including nonlinear friction, sudden load changes, thrust fluctuations, and so on. Therefore, a controller designed for fixed parameters often cannot produce satisfactory results. To solve this problem, a novel design method for permanent magnet linear motor systems based on backstepping is proposed in this article. The proposed control scheme does not require the values or range of changes of the system parameters in advance but constructs an update law to perform online parameter estimation. A difficulty encountered in controller design and stability analysis is the estimation of the unknown coefficient of a single-variable state, which can be considered as a virtual input. In this article, the estimated virtual control coefficient is introduced into the coordinate transformation to construct a novel coordinate transformation. However, introducing the estimated coefficient into the virtual control makes the derivative of the Lyapunov function used in the stability analysis more complex. This novel nonlinear dynamic term can be precisely cancelled by changing the update law of the coefficient. Finally, simulations are performed to assess the performance of the proposed control scheme against that of a traditional proportional–integral–derivative controller, and the simulation results show that the proposed control law can effectively improve the system performance.

This article deals with the consensus problem of multi-agent systems by developing a fixed-time consensus control approach with a dynamic event-triggered rule. First, a new fixedtime stability condition is obtained where the less conservative settling time is given such that the theoretical settling time can well reflect the real consensus time. Second, a dynamic event-triggered rule is designed to decrease the use of chip and network resources where Zeno behaviors can be avoided after consensus is achieved, especially for finite/fixed-time consensus control approaches. Third, in terms of the developed dynamic event-triggered rule, a fixed-time consensus control approach by introducing a new item is proposed to coordinate the multi-agent system to reach consensus. The corresponding stability of the multi-agent system with the proposed control approach and dynamic event triggered rule is analyzed based on Lyapunov theory and the fixed-time stability theorem. At last, the effectiveness of the dynamic event-triggered fixed-time consensus control approach is verified by simulations and experiments for the problem of magnetic map construction based on multiple mobile robots.

ABSTRACTComputing trajectory similarity is a fundamental operation in movement analytics, required in search, clustering, and classification of trajectories, for example. Yet the range of different but interrelated trajectory similarity measures can be bewildering for researchers and practitioners alike. This paper describes a systematic comparison and methodical exploration of trajectory similarity measures. Specifically, this paper compares five of the most important and commonly used similarity measures: dynamic time warping (DTW), edit distance (EDR), longest common subsequence (LCSS), discrete Fréchet distance (DFD), and Fréchet distance (FD). The paper begins with a thorough conceptual and theoretical comparison. This comparison highlights the similarities and differences between measures in connection with six different characteristics, including their handling of a relative versus absolute time and space, tolerance to outliers, and computational efficiency. The paper further reports on an empirical evaluation of similarity in trajectories with contrasting properties: data about constrained bus movements in a transportation network, and the unconstrained movements of wading birds in a coastal environment. A set of four experiments: a. creates a measurement baseline by comparing similarity measures to a single trajectory subjected to various transformations; b. explores the behavior of similarity measures on network-constrained bus trajectories, grouped based on spatial and on temporal similarity; c. assesses similarity with respect to known behavioral annotations (flight and foraging of oystercatchers); and d. compares bird and bus activity to examine whether they are distinguishable based solely on their movement patterns. The results show that in all instances both the absolute value and the ordering of similarity may be sensitive to the choice of measure. In general, all measures were more able to distinguish spatial differences in trajectories than temporal differences. The paper concludes with a high-level summary of advice and recommendations for selecting and using trajectory similarity measures in practice, with conclusions spanning our three complementary perspectives: conceptual, theoretical, and empirical.

Model Set Adaptation (MSA) plays a key role in the Variable Structure Multi-Model tracking approach (VSMM). In this paper, the Error-Ambiguity Decomposition (EAD) principle is adopted to derive the EAD-MSA criterion that is optimal in the sense of minimizing the square error between the estimate and the truth. Consequently, the EAD Variable Structure first-order General Pseudo Bayesian (EAD-VSGPB1) algorithm and the EAD Variable Structure Interacting Multiple Model (EAD-VSIMM) algorithm are constructed. The proposed algorithms are tested in two groups of maneuvering target tracking scenarios under different modes and observation error conditions. The simulation results demonstrate the effectiveness of the EAD-VSMM approach and show that, compared to some existing multi-model algorithms, the proposed EAD-VSMM algorithms achieve more robust and accurate tracking results.

When users zoom in or out on a digital map, the map should change correspondingly to present geographical information at proper levels. A way to help map users better keep track of their interested objects is to change the map smoothly instead of discretely switching between several levels of detail. This paper focuses on the problem of providing smooth merging of area objects. We propose to merge multiple areas simultaneously to share their animation durations. In this way, each merging operation can be prolonged, and it is visually smoother. We present a greedy algorithm to decide which areas should be merged at each step. The merging process is pre-computed and is recorded into a space-scale cube (SSC). When a user accesses our web map, the SSC is sent to the client side so that the map can be generated by slicing the SSC in the graphics processing unit (GPU). We also explain how to snap the zooming to valid states so that the zooming will not stop halfway of the merging operations. Our case study shows that it is visually smoother to merge simultaneously than to sequentially merge each pair of areas.

The variable structure multiple-model (VSMM) estimation approach, one of the multiple-model (MM) estimation approaches, is popular in handling state estimation problems with mode uncertainties. In the VSMM algorithms, the model sequence set adaptation (MSA) plays a key role. The MSA methods are challenged in both theory and practice for the target modes and the real observation error distributions are usually uncertain in practice. In this paper, a geometrical entropy (GE) measure is proposed so that the MSA is achieved on the minimum geometrical entropy (MGE) principle. Consequently, the minimum geometrical entropy multiple-model (MGEMM) framework is proposed, and two suboptimal algorithms, the particle filter k-means minimum geometrical entropy multiple-model algorithm (PF-KMGEMM) as well as the particle filter adaptive minimum geometrical entropy multiple-model algorithm (PF-AMGEMM), are established for practical applications. The proposed algorithms are tested in three groups of maneuvering target tracking scenarios with mode and observation error distribution uncertainties. Numerical simulations have demonstrated that compared to several existing algorithms, the MGE-based algorithms can achieve more robust and accurate estimation results when the real observation error is inconsistent with a priori.

This paper proposes a three degrees-of-freedom tensegrity structure with a mechanism inspired by the ligamentous structure of the shoulder. The proposed mechanism simulates the wide motion ranges of the human shoulder joint and is composed of 3 rigid bodies and 16 steel wires with 3 mutually perpendicular rotating axes. Since it belongs to the class 1 tensegrity structure that the rigid bodies do not make any contact with each other, the joint has a certain amount of flexibility, which not only can help protect its mechanism from external impacts but also can prevent human injury that might happen when the mechanism and humans interact each other. Moreover, the proposed mechanism can be manufactured using fewer materials than a fully rigid mechanism, and thus, it can be made in a lightweight fashion and reduce the inertial effects as well. Finally, to actuate the robotic shoulder, the cables connected to each motor are able to drive the rotating shafts of the joint mechanism.

In this paper, we present a review of electron transport properties of magnetic granular films. Magnetic granular films are nanocomposite materials which consist of magnetic nanoparticles embedded in a nonmagnetic matrix or assembling of magnetic nanoparticles. According to the style of the nonmagnetic matrix, microstructure and the electron transport mechanism of the films, the magnetic granular films were divided into three groups: (1) magnetic metal-metal granular films, (2) magnetic metal-insulator granular films and (3) magnetic nanocluster-assembled granular films. Moreover, we also systematically review the magnetic properties, transport properties and magnetoresistance effect of size-monodispersed Co and Fe nanocluster-assembled films.

We investigate how to better use mutual information (MI) to select bands for hyperspectral image classification with less human intervention. Mutual information effectively measures the statistical dependence between two random variables. By modeling ground truth (e.g., a reference map) as one of the two random variables, MI can be used to find the spectral bands that contribute most to image classification. Extending our earlier work, we propose a sliding window model and apply mutual information to construct the estimated reference map, which need less human intervention. Experiments on the AVIRIS 92AV3C data set show that the proposed approach outperformed the benchmark methods, removing up to 55% of bands without significant loss of classification accuracy, compared to the 40% from that using the reference map accompanied with the data set. Meanwhile, its performance is found to be much robust to accuracy degradation when bands are cut off beyond 60%, revealing a better agreement in the mutual information estimation.

Abstract: Magnetically soft Fe-Co-based nanocrystalline alloy films were produced by two preparation methods: One using a new energetic cluster deposition technique and another using a conventional magnetron sputtering technique. Their structural, static magnetic properties and high-frequency magnetic characteristics were investigated. In the energetic cluster deposition method, by applying a high-bias voltage to a substrate, positively charged clusters in a cluster beam were accelerated electrically and deposited onto a negatively biased substrate together with neutral clusters from the same cluster source, to form a high-density Fe-Co alloy cluster-assembled film with good high-frequency magnetic characteristics. In the conventional magnetron sputtering method, only by rotating substrate holder and without applying a static inducing magnetic field on the substrates, we produced Fe-Co-based nanocrystalline alloy films with a remarkable in-plane uniaxial magnetic anisotropy and a good soft magnetic property. The obtained Fe-Co-O, Fe-Co-Ti-N, and Fe-Co-Cr-N films all revealed a high real permeability exceeding 500 at a frequency up to 1.2 GHz. This makes Fe-Co-based nanocrystalline alloy films potential candidates as soft magnetic thin film materials for the high-frequency applications.

In this study, we report the significant enhancement of high-frequency performance of solenoid inductors incorporated [Fe80Ni20-O/SiO2] n magnetic multilayer film prepared by magnetron sputtering as magnetic core over their air-core counterparts. The integrated solenoid inductor with multilayered magnetic core revealed excellent high-frequency performance in a wide operation frequency range of 0-4 GHz, and acquired a peak inductance density of 72 nH/mm2. Compared to their air-core solenoid inductors with equivalent geometry, there were more than 35-fold enhancement for inductance and 24-fold enhancement for peak quality factor of the 21-turn integrated solenoid inductor with multilayered magnetic core operating at 1.5 GHz. The results indicated that the multilayer magnetic core had promising prospect for applications in GHz frequency electromagnetic devices.