Differential spatial modulation (DSM) is a newly proposed differential modulation technique tailored to spatial modulation (SM), which requires no channel state information (CSI) at the receiver. DSM can offer flexibl...
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ISBN:
(纸本)9781467364300
Differential spatial modulation (DSM) is a newly proposed differential modulation technique tailored to spatial modulation (SM), which requires no channel state information (CSI) at the receiver. DSM can offer flexible tradeoff between the reception reliability and the system complexity. In this paper, we are the first to study the adoption of DSM in a dual-hop amplify-and-forward (AF) relaying system, which consists of a two-antenna source, a single-antenna relay, and a single-antenna destination, so as to reduce the burden of channel tracking on both the relay and the destination. We derive a general upper bound on the average bit error probability (ABEP) achieved by the system. Moreover, an exact closed-form ABEP expression and the asymptotic result are provided for BPSK signaling in Rayleigh fading environment. The same system setup with the adoption of SM at the source is chosen as a benchmark for performance comparisons. Simulation results validate the analysis and reveal a 3dB signal-to-noise power ratio (SNR) penalty of the considered system compared with the benchmark.
It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time ...
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It has been recognized by many researchers that accurate bus travel time prediction is critical for successful deployment of traffic signal priority (TSP) systems. Although there exist a lot of studies on travel time prediction for Advanced Traveler Information systems (ATIS), this problem for TSP purpose is a little different and the amount of literature is limited. This paper proposes a deep learning based approach for continuous travel time prediction problem. Parameters of the deep network are fine-tuned following a layer-by-layer pre-training procedure on a dataset generated by traffic simulations. Variables that may affect continuous travel time are selected carefully. Experiments are conducted to validate the performance of the proposed model. The results indicate that the proposed model produces prediction with mean absolute error less than 4 seconds, which is accurate enough for TSP operations. This paper also reveals that, except for obvious factors like speed, travel distance and traffic density, the signal time when the prediction is made is also an important factor affecting travel time.
Pose variation is a major challenge in face recognition. In this paper, we propose a novel cross-pose face recognition method by learning associate appearance manifolds to model the connection of faces under different...
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Robust scene-text-extraction system can be used in lots of areas. In this work, we propose to learn co-occurrence of local strokes for robust character recognition by using a spatiality embedded dictionary (SED). Diff...
In the Still-to-Video (S2V) face recognition, each subject is enrolled with only few high resolution images, while the probe is video clips of complex variations. As faces present distinct characteristics under differ...
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Safe moving is a basic ability for a mobile robot, and it is beneficial for the robot to avoid the collisions with the environment if it knows the boundaries between the obstacles and free space. In this paper, a cont...
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The problem of tracking control for stochastic nonlinear systems is investigated in this paper. Because of the randomness and nonlinearity of stochastic nonlinear systems, the existing methods are sometimes difficult ...
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ISBN:
(纸本)9781479999767
The problem of tracking control for stochastic nonlinear systems is investigated in this paper. Because of the randomness and nonlinearity of stochastic nonlinear systems, the existing methods are sometimes difficult to achieve the desired tracking performance. In this paper, a new network controller (multi-dimensional Taylor network) is proposed, which only relies on the output of system. Firstly we give the structure of multi-dimensional Taylor network (MTN), and then prove the MTN has a good approximation performance. Secondly, Design a MTN control strategy relying on the system output, which will guarantee the tracking of system output to desired output. An example is given to illustrate the effectiveness of the proposed design approach.
With the increasing resolution and availability of digital cameras, text detection in natural scene images receives a growing attention. When taking pictures using a mobile device, people generally only concerned with...
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The control input item is added to constitute the nonlinear dynamic model, on the basis of the original multi-dimensional Taylor network in this paper. And this nonlinear dynamic model is used to optimally control MIS...
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ISBN:
(纸本)9781479999767
The control input item is added to constitute the nonlinear dynamic model, on the basis of the original multi-dimensional Taylor network in this paper. And this nonlinear dynamic model is used to optimally control MISO nonlinear system only by output feedback without the disturbance estimation of the system or needing the state observer. The back-propagation algorithm is used to train the parameters of the multi-dimensional network with the control input item. Through the simulation, it is demonstrated the multi-dimensional Taylor network used as the optimal MISO nonlinear system tracking controller is effective.
A new type of Biomimetic Underwater Vehicle (RobCutt-I) inspired by cuttlefish was designed and fabricated in this paper. The RobCutt-I has a good maneuverability and can perform multiple motion modes especially can d...
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