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Vehicle speed is one of the important quantities in vehicle dynamics control. Estimation of the slope angle is in turn a necessity for correct dead reckoning from vehicle acceleration. In the present work, estimation of vehicle speed is applied to a hybrid vehicle with an electric motor on the rear axle and a combustion engine on the front axle. The wheel torque information, provided by electric motor, is used to early detect excessive wheel slip and improve the accuracy of the estimate. A best-wheel selection approach is applied as the observation variable of a Kalman filter which reduces the influence of slipping wheels as well as reducing the computational effort. The performance of the proposed algorithm is illustrated on a test data recorded at a winter test ground with excellent results, even for extreme conditions such as when all four wheels are spinning.

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In this paper, we describe a general methodology for enhancing measurement accuracy in cyber-physical systems that involve structured human interactions with a noisy physical environment. We define structured human interactions as those that follow a domain-specific workflow. The idea of the paper is simple: we exploit knowledge of the workflow to correct unreliable sensor data. The intellectual contribution lies in an algorithm for joint estimation of the current state of the workflow together with correction of noisy sensor measurements, given only the noisy measurements and an overall workflow description. We demonstrate through simulations and a physical implementation the degree to which knowledge of workflow can increase sensing accuracy. As a specific instantiation of this idea, we present a novel situation-awareness tool called the Emergency Transcriber designed to automatically document operational procedures followed by teams of first responders in emergency-response scenarios. Evaluation shows that our system provides a significant fidelity enhancement over the state of the art, effectively coping with the noisy environment of emergency teams.

In this article, we describe a general methodology for enhancing sensing accuracy in cyber-physical systems that involve structured human interactions in noisy physical environment. We define structured human interactions as domain-specific workflow. A novel workflow-aware sensing model is proposed to jointly correct unreliable sensor data and keep track of states in a workflow. We also propose a new inference algorithm to handle cases with partially known states and objects as supervision. Our model is evaluated with extensive simulations. As a concrete application, we develop a novel log service called Emergency Transcriber , which can automatically document operational procedures followed by teams of first responders in emergency response scenarios. Evaluation shows that our system has significant improvement over commercial off-the-shelf (COTS) sensors and keeps track of workflow states with high accuracy in noisy physical environment.

Computer-generated hologram (CGH) makes possible the three-dimensional (3D) display of true stereo. It has characteristics of strong flexibility, small noise, easy replication, and computable virtual object. However, there are still some difficulties with the CGH 3D display presently, such as slow computation speed of complex object hologram, small size and small field angle of 3D scene, much noise of reconstruction image, and true color display. In this paper, the problem of reconstruction image noise and true color display of the CGH are studied, and the hologram of true color 3D object with complex morphologies is calculated. First of all, the angular-spectrum layer-oriented method can avoid error caused by the paraxial approximation and be used to accurately generate and calculate 3D object hologram. And it also has advantages of efficient computation, reduced complexity, and less storage memory. We achieve the true color display of a 3D object by using the angular-spectrum method based on intensity and depth maps. We also analyze the problem of multi-wavelength sampling, and mitigate the phenomenon of frequency mixing effectively. Then, we propose to use the Gerchberg-Saxton (GS) algorithm along with the angular-spectrum layer oriented method to reduce the speckle noise in the reconstruction image. The root mean-square error (RMSE) and peak signal-to-noise ratio (PSNR) of the reconstruction image by angular-spectrum layer-oriented method with the GS algorithm are compared with those obtained in the case without using the GS algorithm. The RMSE and PSNR are the main methods of evaluating the image quality. Smaller RMSE and bigger PSNR correspond to higher quality of the image. The hologram and reconstruction image of the true color locomotive with complex morphologies are calculated using the method proposed in this paper and the locomotive is divided into three parts:head, middle and tail. The RMSE and the PSNR of reconstruction image of the head are approximately 0.77 and 65.7, respectively. The RMSE and the PSNR of reconstruction image of the middle are approximately 0.68 and 70.0, respectively, and so are those of the tail. Comparing with the traditional angular-spectrum layer-oriented method, the RMSE of the reconstruction images of the head, middle and tail are reduced approximately by 0.11, 0.40, 0.41, and the PSNR are increased approximately by 1.15, 5.70, 4.13, respectively. The simulation results show that the speckle noise is suppressed effectively and the quality of the reconstruction image is improved when the GS algorithm along with the angular-spectrum layer oriented method is used. The proposed method is more suitable for the calculation of complex 3D objects with true color.

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