The rapid development of vehicle-to-vehicle and vehicle-to-infrastructure (V2X) communications calls for highly spectral efficient communication techniques under time-selective channels. Spatial modulation (SM) facili...
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The rapid development of vehicle-to-vehicle and vehicle-to-infrastructure (V2X) communications calls for highly spectral efficient communication techniques under time-selective channels. Spatial modulation (SM) facilitates flexible trade-off between spectral and energy efficiency. In this paper, we propose a novel modulation technique based on SM for V2X, which discards the requirement of channel state information (CSI) at the receiver and exhibits enhanced robustness against time-selective fading and Doppler effects. Our proposed scheme tailors differential modulation to SM and is named differential spatial modulation (DSM). Monte Carlo simulations are carried out to demonstrate the advantage of the new scheme in terms of bit error rate (BER) performance for both point-to-point and dual-hop amplify-and-forward (AF) relaying systems in V2X channels.
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.
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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In search auctions, when the total budget for an advertising campaign during a certain promotion period is determined, advertisers have to distribute their budgets over a series of sequential temporal slots (e.g., dai...
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In search auctions, when the total budget for an advertising campaign during a certain promotion period is determined, advertisers have to distribute their budgets over a series of sequential temporal slots (e.g., daily budgets). However, due to the uncertainties existed in search markets, advertisers can only obtain the value range of budget demand for each temporal slot based on promotion logs. In this paper, we present a stochastic model for budget distribution over a series of sequential temporal slots during a promotion period, considering the budget demand for each temporal slot as a random variable. We study some properties and present feasible solution algorithms for our budget model, in the case that the budget demand is characterized either by uniform random variable or normal random variable. We also conduct some experiments to evaluate our model with the empirical data. Experimental results show that the budget demand is more likely to be normal distributed than uniform distributed, and our strategy can outperform the baseline strategy commonly used in practice.
A new approach is proposed in this paper to recover the original image of an underwater scene from a given sequence distorted by water fluctuation. The contribution of our method is to use a motion field-based kernel ...
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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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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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This paper proposes to obtain high-level, domain-robust representations for cross-view face recognition. Specially, we introduce Convolutional Deep Belief Networks (CDBN) as the feature learning model, and an CDBN bas...
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A novel adaptive cruise control (ACC) system is proposed in this paper. A hierarchical control framework is adopted for the adaptive cruise control problem. For the upper level, a supervised actor-critic (SAC) reinfor...
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A novel adaptive cruise control (ACC) system is proposed in this paper. A hierarchical control framework is adopted for the adaptive cruise control problem. For the upper level, a supervised actor-critic (SAC) reinforcement learning approach is presented to obtain the desired acceleration controller. In the lower level, throttle and brake controllers calculate the required throttle and/or brake signals based on the desired longitudinal acceleration. Feed-forward neural networks are used to implement the actor and critic components of the SAC learning algorithm. An online learning mechanism is introduced to implement the SAC training process. dSPACE simulator is used to verify the effectiveness of the ACC system. Typical emergency braking scenario is simulated to test the adaptability of the ACC system. Road condition change (e.g. wintry or wet conditions) simulation is first investigated to evaluate the robustness of the ACC system. Performance of the proposed ACC system is proved to be very practical.
In this paper, we develop data-based methods to analyze the characteristics of linear discrete-time systems, which have unknown parameter matrices. These characteristics include output controllability, asymptotic stab...
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