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This paper is concerned with the estimation of correlated noise and packet dropout for information fusion in distributed sensing networks. By studying the problem of the correlation of correlated noise in sensor network information fusion, a matrix weight fusion method with a feedback structure is proposed to deal with the interrelationship between multi-sensor measurement noise and estimation noise, and the method can achieve optimal estimation in the sense of linear minimum variance. Based on this, a method is proposed using a predictor with a feedback structure to compensate for the current state quantity to deal with packet dropout that occurs during multi-sensor information fusion, which can reduce the covariance of the fusion results. Simulation results show that the algorithm can solve the problem of information fusion noise correlation and packet dropout in sensor networks, and effectively reduce the fusion covariance with feedback.

In this paper a Bayesian method is proposed to estimate dynamic origin-destination (O-D) demand. The proposed method can synthesize multiple sources of data collected by various sensors, including link counts, turning movements at intersections, flows, and travel times on partial paths. Time-dependent demand for each O-D pair at each departure time is assumed to satisfy the normal distribution. The connections among multiple sources of field data and O-D demands for all departure times are established by their variance-covariance matrices. Given the prior distribution of dynamic O-D demands, the posterior distribution is developed by updating the traffic count information. Then, based on the posterior distribution, both point estimation and the corresponding confidence intervals of O-D demand variables are estimated. Further, a stepwise algorithm that can avoid matrix inversion, in which traffic counts are updated one by one, is proposed. Finally, a numerical example is conducted on Nguyen-Dupuis network to demonstrate the effectiveness of the proposed Bayesian method and solution algorithm. Results show that the total O-D variance is decreasing with each added traffic count, implying that updating traffic counts reduces O-D demand uncertainty. Using the proposed method, both total error and source-specific errors between estimated and observed traffic counts decrease by iteration. Specifically, using 52 multiple sources of traffic counts, the relative errors of almost 50% traffic counts are less than 5%, the relative errors of 85% traffic counts are less than 10%, the total error between the estimated and "true" O-D demands is relatively small, and the O-D demand estimation accuracy can be improved by using more traffic counts. It concludes that the proposed Bayesian method can effectively synthesize multiple sources of data and estimate dynamic O-D demands with fine accuracy.

In the 802.11 protocol, the fundamental medium access mechanism is called Distributed Coordination Function (DCF). In DCF, before making any transmission attempt, the nodes count down a timer with a value randomly selected from the Contention Window (CW) size. If the transmitted packet is involved in a collision, the node increases the CW size in an attempt to reduce the collision rate. Conversely, if the packet is transmitted successfully, the node reduces the CW size in order to increase the frequency of the transmission attempts. The growth or reduction in the CW size has a critical effect on the network performance. Several backoff algorithms have been proposed to improve the system throughput. However, none of these methods enable the system to approach the theoretical maximum throughput possible under DCF. Accordingly, this study proposes the Rapidly Adaptive Collision Backoff (RACB) algorithm, in which the CW size is adjusted dynamically based on the collision rate, as analyzed by a mathematical model. Notably, RACB requires no knowledge of the number of nodes in the wireless network and is applicable to both lightly loaded and heavily loaded networks. The numerical results show that, by adjusting the CW size such that the collision rate is maintained at a value close to 0.1, RACB enables the system throughput to approach the maximum DCF throughput in wireless environments containing any number of nodes. [ABSTRACT FROM AUTHOR]

Human lying is influenced by cognitive neural mechanisms in the brain, and conducting research on lie detection in speech can help to reveal the cognitive mechanisms of the human brain. Inappropriate deception detection features can easily lead to dimension disaster and make the generalization ability of the widely used semi-supervised speech deception detection model worse. Because of this, this paper proposes a semi-supervised speech deception detection algorithm combining acoustic statistical features and time-frequency two-dimensional features. Firstly, a hybrid semi-supervised neural network based on a semi-supervised autoencoder network (AE) and a mean-teacher network is established. Secondly, the static artificial statistical features are input into the semi-supervised AE to extract more robust advanced features, and the three-dimensional (3D) mel-spectrum features are input into the mean-teacher network to obtain features rich in time-frequency two-dimensional information. Finally, a consistency regularization method is introduced after feature fusion, effectively reducing the occurrence of over-fitting and improving the generalization ability of the model. This paper carries out experiments on the self-built corpus for deception detection. The experimental results show that the highest recognition accuracy of the algorithm proposed in this paper is 68.62% which is 1.2% higher than the baseline system and effectively improves the detection accuracy. [ABSTRACT FROM AUTHOR]