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Range-based localisation and tracking methods use the time-of-arrival (TOA) between the mobile station and several base stations, but the multipath propagation of non-line-of-sight channels complicates the estimation and processing. For channel modelling, the Gaussian scatterer distribution model has been reported to have a reasonable match between its TOA probability density distribution (PDF) and measured TOA data. In this study, this TOA PDF is adapted, along with selection from multiple motion models of the mobile station, for a new location and tracking algorithm. Since the TOA PDF is non-Gaussian and is a non-linear function of the position of the mobile, particle filtering is used which increases the complexity of the algorithm. The focus is on the tracking performance, and this is evaluated by simulation using idealised statistical channels, allowing direct comparison between different location algorithms. In this context, the presented algorithm is more accurate than the benchmarks of extended Kalman filter tracking, and positioning using least squares.

With the wide application of ultra-wideband (UWB) ranging technology in industry, production and aerospace, how to improve the accuracy of UWB ranging has become a research hotspot. If the UWB ranging data is treated as sequence data, it is possible to improve the ranging performance by sequence analysis, which has low computation complexity compared to direct UWB signal processing. However, as the UWB ranging data has its inherent properties, existing sequence analysis methods may not achieve good performance on UWB ranging data. In this paper, a two-path deep-learning framework to process, namely the Multiple Network model with Second order motion (MNS), is proposed the improve UWB ranging performance via ranging sequence analysis. The proposed method fuses the target motion prediction via Newton’s laws of motion and error estimation via GRU, LSTM and Bayes. To evaluate our algorithm, we also proposed a method to collect both UWB ranging data and the accurate answer via laser ranging. The collected dataset D A T A _ T S , the proposed MNS algorithm, and the trained model are all open sourced to the community to help researchers for further research,please visit( https://github.com/xiaojiuwotongxue/data-store.git ). Experiments on D A T A _ T S shows our proposed method outperforms traditional regresion methods significantly.

Human motion analysis often relies on skin markers to define local reference frames for tracking the movement of body segments. For the humerus, defining its local reference frame requires estimating the glenohumeral joint rotation center (GH-r), which is not directly palpable. Multiple linear regression models have been developed to estimate the GH-r from palpable landmarks, but they present limitations that affect their performance. The objective of this study was to develop a linear regression model that improves GH-r estimation from palpable landmarks and addresses key shortcomings of existing approaches. A dataset of 73 CT scans was divided into training, validation, and test sets using a 60:20:20 ratio. Several linear regression models were constructed using different algorithms, with 4 scapular skin landmarks digitized from the CT scans and subject characteristics as predictors, and the GH-r coordinates as dependent variables. The ground-truth GH-r was estimated through spherical fitting of the humeral head. The final regression model, selected for its favorable balance between accuracy and simplicity, achieved a mean Euclidean distance error (EDE) of 6.81 mm on the test set, representing a reduction of at least 10.73 mm compared to established predictive models of the GH-r applied to the same dataset, a difference that was statistically significant (p < 0.001). Sensitivity analyses to marker placement variability showed an increase in mean EDE up to 8.46 mm, still well below the errors obtained for the other literature models. Overall, the model’s performance was not markedly affected by the observed inter-observer variability, further supporting its advantages.

In the real world, complex dynamic scenes often arise from the composition of simpler parts. The visual system exploits this structure by hierarchically decomposing dynamic scenes: When we see a person walking on a train or an animal running in a herd, we recognize the individual’s movement as nested within a reference frame that is, itself, moving. Despite its ubiquity, surprisingly little is understood about the computations underlying hierarchical motion perception. To address this gap, we developed a class of stimuli that grant tight control over statistical relations among object velocities in dynamic scenes. We first demonstrate that structured motion stimuli benefit human multiple object tracking performance. Computational analysis revealed that the performance gain is best explained by human participants making use of motion relations during tracking. A second experiment, using a motion prediction task, reinforced this conclusion and provided fine-grained information about how the visual system flexibly exploits motion structure.

Femoral deformities, e.g. increased or decreased femoral anteversion (AVA) and neck-shaft angle (NSA), can lead to pathological gait patterns, altered joint loads, and degenerative joint diseases. The mechanism how femoral geometry influences muscle forces and joint load during walking is still not fully understood. The objective of our study was to investigate the influence of femoral AVA and NSA on muscle forces and joint loads during walking. We conducted a comprehensive musculoskeletal modelling study based on three-dimensional motion capture data of a healthy person with a typical gait pattern. We created 25 musculoskeletal models with a variety of NSA (93°-153°) and AVA (-12°-48°). For each model we calculated moment arms, muscle forces, muscle moments, co-contraction indices and joint loads using OpenSim. Multiple regression analyses were used to predict muscle activations, muscle moments, co-contraction indices, and joint contact forces based on the femoral geometry. We found a significant increase in co-contraction of hip and knee joint spanning muscles in models with increasing AVA and NSA, which led to a substantial increase in hip and knee joint contact forces. Decreased AVA and NSA had a minor impact on muscle and joint contact forces. Large AVA lead to increases in both knee and hip contact forces. Large NSA (153°) combined with large AVA (48°) led to increases in hip joint contact forces by five times body weight. Low NSA (108° and 93°) combined with large AVA (48°) led to two-fold increases in the second peak of the knee contact forces. Increased joint contact forces in models with increased AVA and NSA were linked to changes in hip muscle moment arms and compensatory increases in hip and knee muscle forces. Knowing the influence of femoral geometry on muscle forces and joint loads can help clinicians to improve treatment strategies in patients with femoral deformities.

Ship design involves optimizing the hull in order to enhance safety, economic efficiency, and technical efficiency. Despite the long-term research on this problem and a number of significant conclusions, some of its content still needs to be improved. In this study, block and midship coefficients are incorporated to optimize the ship’s hull. The considered ship was a patrol vessel. The seakeeping analysis was performed employing strip theory. The hull form was generated using a fuzzy model. Though the body lines generated by the midship coefficient ( C M ) and block coefficient ( C B ) varied indecently, the other geometric parameters remained the same. Multi-objective optimization was used to optimize C B and C M . According to the results of this study, these coefficients have a significant impact on the pitch motion of the patrol vessel as well as the motion sickness index. Heave and roll motions, as well as the added resistance, were not significantly influenced by the coefficients of C M and C B . However, increasing the hull form parameters increases the maximum Response Amplitude Operator (RAO) of heave and roll motions. The frequency of occurrence of the maximum roll RAO was in direct relation with C B and C M . These coefficients, however, had no meaningful impact on the occurrence frequency of other motion indices. In the end, the C B and C M coefficients were selected based on the vessel’s seakeeping performance. These findings might be used by shipbuilders to construct the vessel with more efficient seakeeping performance.

In dynamic environments, a central task of the attentional system is to keep track of objects changing their spatial location over time. In some instances, it is sufficient to track only the spatial locations of moving objects (i.e., multiple object tracking; MOT). In other instances, however, it is also important to maintain distinct identities of moving objects (i.e., multiple identity tracking; MIT). Despite previous research, it is not clear whether MOT and MIT performance emerge from the same tracking mechanism. In the present report, we study gaze coherence (i.e., the extent to which participants repeat their gaze behaviour when tracking the same object locations twice) across repeated MOT and MIT trials. We observed more substantial gaze coherence in repeated MOT trials compared to the repeated MIT trials or mixed MOT-MIT trial pairs. A subsequent simulation study suggests that MOT is based more on a grouping mechanism than MIT, whereas MIT is based more on a target-jumping mechanism than MOT. It thus appears unlikely that MOT and MIT emerge from the same basic tracking mechanism.

We examine properties of residual-based tests for the null of no cointegration for dynamic panels in which both the short-run dynamics and the long-run slope coefficients are permitted to be heterogeneous across individual members of the panel. The tests also allow for individual heterogeneous fixed effects and trend terms, and we consider both pooled within dimension tests and group mean between dimension tests. We derive limiting distributions for these and show that they are normal and free of nuisance parameters. We also provide Monte Carlo evidence to demonstrate their small sample size and power performance, and we illustrate their use in testing purchasing power parity for the post–Bretton Woods period.I thank Rich Clarida, Bob Cumby, Mahmoud El-Gamal, Heejoon Kang, Chiwha Kao, Andy Levin, Klaus Neusser, Masao Ogaki, David Papell, Pierre Perron, Abdel Senhadji, Jean-Pierre Urbain, Alan Taylor, and three anonymous referees for helpful comments on various earlier versions of this paper. The paper has also benefited from presentations at the 1994 North American Econometric Society Summer Meetings in Quebec City, the 1994 European Econometric Society Summer Meetings in Maastricht, and workshop seminars at the Board of Governors of the Federal Reserve, INSEE-CREST Paris, IUPUI, Ohio State, Purdue, Queens University Belfast, Rice University–University of Houston, and Southern Methodist University. Finally, I thank the following students who provided assistance in the earlier stages of the project: Younghan Kim, Rasmus Ruffer, and Lining Wan.

We extend the notion of cointegration for time series taking values in a potentially infinite dimensional Banach space. Examples of such time series include stochastic processes in \\(C[0,1]\\) equipped with the supremum distance and those in a finite dimensional vector space equipped with a non-Euclidean distance. We then develop versions of the Granger–Johansen representation theorems for I(1) and I(2) autoregressive (AR) processes taking values in such a space. To achieve this goal, we first note that an AR(p) law of motion can be characterized by a linear operator pencil (an operator-valued map with certain properties) via the companion form representation, and then study the spectral properties of a linear operator pencil to obtain a necessary and sufficient condition for a given AR(p) law of motion to admit I(1) or I(2) solutions. These operator-theoretic results form a fundamental basis for our representation theorems. Furthermore, it is shown that our operator-theoretic approach is in fact a closely related extension of the conventional approach taken in a Euclidean space setting. Our theoretical results may be especially relevant in a recently growing literature on functional time series analysis in Banach spaces.

It is well known that, under suitable regularity conditions, the normalized fractional process with fractional parameter d converges weakly to fractional Brownian motion (fBm) for$d>\\frac {1}{2}$. We show that, for any nonnegative integer M, derivatives of order$m=0,1,\\dots ,M$of the normalized fractional process with respect to the fractional parameter d jointly converge weakly to the corresponding derivatives of fBm. As an illustration, we apply the results to the asymptotic distribution of the score vectors in the multifractional vector autoregressive model.