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Multi-speed transmissions can enhance the comprehensive performance of electric vehicles (EVs), while exhibiting potential to further improve their shift quality. In this study, a gearshift control architecture combined with multi-objective trajectory planning was proposed for EVs with two-speed transmission. Three indices, namely shifting duration, jerk and friction work, were selected as optimization objectives. Numerous iterations of multi-objective optimization were conducted using the Legendre pseudospectral method, which is an optimization algorithm with high convergence speed and low sensitivity to initial values. Optimization results were presented through a Pareto solution set which was then used as a reference by the control architecture for optimal trajectory selection in the transmission control unit. In a hardware-in-the-loop experiment, the control architecture was applied in a passenger car to run the New European Driving Cycle, demonstrating the effectiveness and feasibility of the developed multi-objective trajectory planning and gearshift control architecture. [ABSTRACT FROM AUTHOR]

In the chain of stereoscopic video processing, stereoscopic video coding and viewing are usually two independent stages. Conventional stereoscopic video coding puts an emphasis on improving the coding efficiency by seeking the optimal tradeoff between the coding bit rate and the signal-based distortion, while neglecting the perceptual behaviors of binocular combination when stereoscopic video is viewed by human beings. In this paper, we propose to utilize binocular combination to optimize the stereoscopic video coding from the perspective of perceptual quality measurement. Specifically, we propose a novel binocular-combination-oriented measurement for visual distortion and then derive the Lagrange multiplier for the binocular-combination-oriented rate-distortion optimization (RDO). Via extensive subjective tests, the results show that the proposed perceptual RDO can save more than 5% BD rate over the traditional RDO in multiview extension of High Efficiency Video Coding for stereoscopic video coding. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a rate-distortion (RD) optimized interactive streaming method for multiview video pre-compressed by H.264 Joint Multiview Video Model (JMVM). In the proposed method, multiple encodings are first used to facilitate the flexible server–client interaction. Second, a RD-optimized scheduling strategy is provided to guarantee the optimal view-dependent delivery of multiview video. In the RD-optimized scheduling strategy, a distortion model is proposed to estimate the expected end-to-end distortion by accounting for both coding and packet-loss-induced distortions, as well as rendering-induced distortion. With the end-to-end distortion model, the server can select the optimal encoding combination for transmission. Experimental results demonstrate that the proposed method can achieve a significant end-to-end RD performance improvement over the selective streaming methods with simulcast coding or scalable multiview coding. In addition, it has better error-resilience performance to combat with packet-losses over the Internet protocol (IP) networks. [Copyright &y& Elsevier]

Abstract: Joint video/depth rate allocation is an important optimization problem in 3D video coding. To address this problem, this paper proposes a distortion model to evaluate the synthesized view without access to the captured original view. The proposed distortion model is an additive model that accounts for the video-coding-induced distortion and the depth-quantization-induced distortion, as well as the inherent geometry distortion. Depth-quantization-induced distortion not only considers the warping error distortion, which is described by a piecewise linear model with the video power spectral property, but also takes into account the warping error correlation distortion between two sources reference views. Geometry distortion is approximated from that of the adjacent view synthesis. Based on the proposed distortion model, a joint rate allocation method is proposed to seek the optimal trade-off between video bit-rate and depth bit-rate for maximizing the view synthesis quality. Experimental results show that the proposed distortion model is capable of approximately estimating the actual distortion for the synthesized view, and that the proposed rate allocation method can almost achieve the identical rate allocation performance as the full-search method at less computational cost. Moreover, the proposed rate allocation method consumes less computational cost than the hierarchical-search method at high bit-rates while providing almost the equivalent rate allocation performance. [Copyright &y& Elsevier]

The powertrain model of the series-parallel plug-in hybrid electric vehicles (PHEVs) is more complicated, compared with series PHEVs and parallel PHEVs. Using the traditional dynamic programming (DP) algorithm or Pontryagin minimum principle (PMP) algorithm to solve the global-optimization-based energy management strategies of the series-parallel PHEVs is not ideal, as the solution time is too long or even impossible to solve. Chief engineers of hybrid system urgently require a handy tool to quickly solve global-optimization-based energy management strategies. Therefore, this paper proposed to use the Radau pseudospectral knotting method (RPKM) to solve the global-optimization-based energy management strategy of the series-parallel PHEVs to improve computational efficiency. Simulation results showed that compared with the DP algorithm, the global-optimization-based energy management strategy based on the RPKM improves the computational efficiency by 1806 times with a relative error of only 0.12%. On this basis, a bi-level nested component-sizing method combining the genetic algorithm and RPKM was developed. By applying the global-optimization-based energy management strategy based on RPKM to the actual development, the feasibility and superiority of RPKM applied to the global-optimization-based energy management strategy of the series-parallel PHEVs were further verified. [ABSTRACT FROM AUTHOR]

The small-cell wireless network (SCN) paradigm is based on the deployment of low-power small cells close to the user so that spatial re-use is increased. Small cell base stations (SCBSs) can be connected either with a wired or a wireless backhaul link to the core network. However, since these SCBSs are expected to be deployed in significant numbers [1] , the increased density creates not only new problems but also optimization opportunities. In this paper we work towards the full exploitation of the broadcast advantage that dense SCNs offer in order to improve the video quality of unicast streaming applications. To accomplish our goal we design a protocol that exploits the broadcast channel in the neighborhood of each small cell by using the SCBSs opportunistically as a relay stations. Each SCBS can overhear packet transmissions both from the macro BS (MBS) and also from other SCBSs in its neighborhood. Furthermore, since a packet may be available at multiple SCBSs, we propose an algorithm for optimized relay selection that takes into account the content of each specific video packet. This algorithm selects jointly the optimal small cell relay and video packet for forwarding. Both the overhearing protocol and the relay selection algorithm use only passively collected information and require no explicit message passing. The evaluation of the two proposed sub-systems shows significant performance improvements under different key system configurations for the video content, the number of neighboring SCBS, and the type of the backhaul connection. [ABSTRACT FROM AUTHOR]