Numerical algorithms for dynamic traffic demand estimation between zones in a network

This paper presents numerical methods for dynamic traffic demand estimation between N zones in a network, where the zones are disjoint subsets of nodes of the network. Traffic is assumed to be generated or absorbed only in the zones and nowhere else in the network. Traffic volumes between zones over a fixed period of time are modeled as independent random variables with unknown means which it is desired to estimate. For each zone, the volume of all incoming and outgoing traffic is counted on a regular basis but no information about the origin or destination of the observed traffic is used. Procedures are suggested for a regular update of estimates of the N(N - 1) mean traffic demands between the zones on the basis of an incoming stream of the 2N traffic counts. The procedures are based on an exponential smoothing scheme and are reminiscent of the expectation maximization (EM) algorithm if smoothing is removed. Fast and reliable numerical algorithms, based on the conjugate gradient method, are presented for normal as well as for Poisson traffic demands. The Poisson case is linked with entropy maximization. Computational tests based on simulated data demonstrate both the numerical and statistical efficiency of the procedures.preprin

Automated robot-assisted assessment for wrist active ranges of motion

The measurement of wrist active range of motion (ROM) is essential for determining the progress of hand functional recovery, which can provide insight into quantitative improvements and enable effective monitoring during hand rehabilitation. Compared with manual methods, which depend on the experience of the therapist, the proposed robot-assisted assessment technique can measure active ROM of human wrists. The robot with a reconfigurable handle design allows for multiple wrist motions. Experiments were conducted with 11 human subjects to measure ROMs of human wrist flexion/extension and radial/ulnar deviation. Reliability analysis was conducted by calculating the intra-class correlation coefficients (ICC), standard error of measurement (SEM) and SEM%. Results showed high reliability (ICC2,1 ≥ 0.89, SEM ≤ 2.36°, SEM% ≤ 6.81%). Future will focus on adaptive joint self-alignment design between human users and robots to further improve its assessment accuracy

Automated objective robot-assisted assessment of wrist passive ranges of motion

The measurement of wrist passive ranges of motion (ROMs) can provide insight into improvements and allow for effective monitoring during a rehabilitation program. Compared with conventional methods, this study proposed a new robotic assessment technique for measuring passive ROMs of the wrist. The robotic system has a reconfigurable handle structure that allows for multi-dimensional applications of wrist motions. The assessment reliability of this robotic system was analysed on 11 subjects for measuring wrist extension/flexion and radial/ulnar deviation. Preliminary data demonstrated its potential with intraclass correlation coefficient (ICC2,1) all greater than 0.857 and standard error of measurement (SEM) less than 3.38°. Future work will focus on the standardization of the assessment protocol of this robotic system for assessment purposes, paving the way for its clinical application

Generating probabilistic Boolean networks from a prescribed stationary distribution

Modeling gene regulation is an important problem in genomic research. Boolean networks (BN) and its generalization probabilistic Boolean networks (PBNs) have been proposed to model genetic regulatory interactions. BN is a deterministic model while PBN is a stochastic model. In a PBN, on one hand, its stationary distribution gives important information about the long-run behavior of the network. On the other hand, one may be interested in system synthesis which requires the construction of networks from the observed stationary distribution. This results in an inverse problem which is ill-posed and challenging. Because there may be many networks or no network having the given properties and the size of the inverse problem is huge. In this paper, we consider the problem of constructing PBNs from a given stationary distribution and a set of given Boolean Networks (BNs). We first formulate the inverse problem as a constrained least squares problem. We then propose a heuristic method based on Conjugate Gradient (CG) algorithm, an iterative method, to solve the resulting least squares problem. We also introduce an estimation method for the parameters of the PBNs. Numerical examples are then given to demonstrate the effectiveness of the proposed methods. © 2010 Elsevier Inc. All rights reserved.postprin

Admissibility of a wide cluster solution in "anisotropic" higher-order traffic flow models

We analytically investigate a wide cluster solution and show that it is not admitted in some of the traffic flow models in the literature. For those traffic flow models that admit the wide cluster solution, the relationship between two important control parameters and the critical densities that divide an equilibrium solution into stable and unstable regions is thoroughly discussed in detail. We find that such wide clusters exist with a free traffic density in an unstable region, and with one or three critical densities. These results are different from the cases in the well-known higher-order traffic flow models of Payne and Whitham [H. J. Payne, "Models of freeway traffic and control," in Mathematical Models of Public Systems, A. G. Bekey, ed., Simulation Council Proc. Ser. 1, La Jolla, CA, 1971, pp. 51-61], [G. B. Whitham, Linear and Nonlinear Waves, John Wiley and Sons, New York, 1974], Kühne [R. D. Kühne, "Macroscopic freeway model for dense traffic-stop-start waves and incident detection," in Proceedings of the 9th International Symposium on Transportation and Traffic Theory, J. Volmuller and R. Hamerslag, eds., VNU Science Press, Utrecht, 1984, pp. 21-42], and Kerner and Konhäuser [B. S. Kerner and P. Konhäuser, Phys. Rev. E (3), 50 (1994), pp. 54-83]. © 2007 Society for Industrial and Applied Mathematics.published_or_final_versio

Data Analytics in Steady-State Visual Evoked Potential-based Brain-Computer Interface: A Review

Electroencephalograph (EEG) has been widely applied for brain-computer interface (BCI) which enables paralyzed people to directly communicate with and control of external devices, due to its portability, high temporal resolution, ease of use and low cost. Of various EEG paradigms, steady-state visual evoked potential (SSVEP)-based BCI system which uses multiple visual stimuli (such as LEDs or boxes on a computer screen) flickering at different frequencies has been widely explored in the past decades due to its fast communication rate and high signal-to-noise ratio. In this paper, we review the current research in SSVEP-based BCI, focusing on the data analytics that enables continuous, accurate detection of SSVEPs and thus high information transfer rate. The main technical challenges, including signal pre-processing, spectrum analysis, signal decomposition, spatial filtering in particular canonical correlation analysis and its variations, and classification techniques are described in this paper. Research challenges and opportunities in spontaneous brain activities, mental fatigue, transfer learning as well as hybrid BCI are also discussed

Reviewing Clinical Effectiveness of Active Training Strategies of Platform-Based Ankle Rehabilitation Robots

Objective; This review aims to provide a systematical investigation of clinical effectiveness of active training strategies applied in platform-based ankle robots. Method. English-language studies published from Jan 1980 to Aug 2017 were searched from four databases using key words of “Ankle” AND “Robot” AND “Effect OR Improv OR Increas.” Following an initial screening, three rounds of discrimination were successively conducted based on the title, the abstract, and the full paper. Result. A total of 21 studies were selected with 311 patients involved; of them, 13 studies applied a single group while another eight studies used different groups for comparison to verify the therapeutic effect. Virtual-reality (VR) game training was applied in 19 studies, while two studies used proprioceptive neuromuscular facilitation (PNF) training. Conclusion. Active training techniques delivered by platform ankle rehabilitation robots have been demonstrated with great potential for clinical applications. Training strategies are mostly combined with one another by considering rehabilitation schemes and motion ability of ankle joints. VR game environment has been commonly used with active ankle training. Bioelectrical signals integrated with VR game training can implement intelligent identification of movement intention and assessment. These further provide the foundation for advanced interactive training strategies that can lead to enhanced training safety and confidence for patients and better treatment efficacy

Construction and Control of Genetic Regulatory Networks: A Multivariate Markov Chain Approach

In the post-genomic era, the construction and control of genetic regulatory networks using gene expression data is a hot research topic. Boolean networks (BNs) and its extension Probabilistic Boolean Networks (PBNs) have been served as an effective tool for this purpose. However, PBNs are difficult to be used in practice when the number of genes is large because of the huge computational cost. In this paper, we propose a simplified multivariate Markov model for approximating a PBN The new model can preserve the strength of PBNs, the ability to capture the inter-dependence of the genes in the network, qnd at the same time reduce the complexity of the network and therefore the computational cost. We then present an optimal control model with hard constraints for the purpose of control/intervention of a genetic regulatory network. Numerical experimental examples based on the yeast data are given to demonstrate the effectiveness of our proposed model and control policy.published_or_final_versio

Quadratic magnetic field dependence of magnetoelectric photocurrent

We experimentally study the spin and electric photocurrents excited by a linearly polarized light via direct interband transitions in an InGaAs/InAlAs quantum well. In the absence of a magnetic field, the linearly polarized light induces a pure spin current due to the spin-orbit coupling, which may be transformed into a measurable electric current by applying an in-plane magnetic field. The induced electric photocurrent is linear with the in-plane magnetic field. Here, we report a quadratic magnetic field dependence of the photocurrent in the presence of an additional perpendicular component of the magnetic field. We attribute the observation to the Hall effect of magnetoelectric photocurrent. © 2011 American Physical Society.published_or_final_versio

A new multiple regression approach for the construction of genetic regulatory networks

Objective: Re-construction of a genetic regulatory network from a given time-series gene expression data is an important research topic in systems biology. One of the main difficulties in building a genetic regulatory network lies in the fact that practical data set has a huge number of genes vs. a small number of sampling time points. In this paper, we propose a new linear regression model that may overcome this difficulty for uncovering the regulatory relationship in a genetic network. Methods: The proposed multiple regression model makes use of the scale-free property of a real biological network. In particular, a filter is constructed by using this scale-free property and some appropriate statistical tests to remove redundant interactions among the genes. A model is then constructed by minimizing the gap between the observed and the predicted data. Results: Numerical examples based on yeast gene expression data are given to demonstrate that the proposed model fits the practical data very well. Some interesting properties of the genes and the underlying network are also observed. Conclusions: In conclusion, we propose a new multiple regression model based on the scale-free property of real biological network for genetic regulatory network inference. Numerical results using yeast cell cycle gene expression dataset show the effectiveness of our method. We expect that the proposed method can be widely used for genetic network inference using high-throughput gene expression data from various species for systems biology discovery. © 2009 Elsevier B.V.postprin