Motivated by the concept of spatial modulation (SM), a differential scheme has been recently proposed. This scheme, termed as differential (D-) SM, dispenses with channel estimation while maintains a similar bit error...
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ISBN:
(纸本)9781479959525
Motivated by the concept of spatial modulation (SM), a differential scheme has been recently proposed. This scheme, termed as differential (D-) SM, dispenses with channel estimation while maintains a similar bit error rate (BER) performance to SM. The conventional optimal DSM detector based on a maximum likelihood (ML) criterion, however, gives rise to prohibitive computational complexity when either the space-domain or signal-domain constellation size is large. In this paper, we propose a low-complexity yet optimal detection algorithm for DSM by utilizing sphere decoding (SD). The complexity analysis concerning Euclidian distance equation for DSM-SD is derived. Simulation results show that the proposed DSM-SD algorithm maintains an identical BER performance and achieves a significant reduction of computational complexity compared with the DSM-ML algorithm. The DSM-SD algorithm is especially efficient when the number of receive antennas is large. Moreover, compared with SD applied to SM, its application to DSM is verified to be more useful and attractive.
The paper presents a new approach on haptic interface control for NAO robotic hand. The haptic teleoperation of NAO robot hand raises some issues, mapping the haptic device dynamic being the most important. The mappin...
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The paper presents a new approach on haptic interface control for NAO robotic hand. The haptic teleoperation of NAO robot hand raises some issues, mapping the haptic device dynamic being the most important. The mapping was achieved through Neural Network by determining a dynamic gain of robot haptic feedback. The results lead to the achievement of the haptic intelligent interfaces for the NAO robot hand control which can be integrated into an innovative haptic robot control system.
For e-commerce websites collective actions have significant influence on the behaviors and decisions of individual customers. In this work, we propose a dynamic utility model for customers in e-commerce by considering...
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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.
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.
In this paper, we address the second-order consensus problem for networked mechanical systems on a directed topology in the case of existence of nonuniform communication delays. To realize the goal of second-order con...
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ISBN:
(纸本)9781479958252
In this paper, we address the second-order consensus problem for networked mechanical systems on a directed topology in the case of existence of nonuniform communication delays. To realize the goal of second-order consensus, we propose an adaptive consensus scheme that consists of an adaptive controller and a distributed velocity observer. It is demonstrated that the position and velocity consensus errors between the mechanical systems converge to zero, and that the velocities of the mechanical systems converge to the scaled weighted average of their initial values. We further demonstrate that the proposed consensus scheme can be used to solve the second-order consensus problem for multiple mechanical systems with a constant-velocity leader. The performance of the proposed consensus scheme is shown by a numerical simulation.
Optical inspection techniques have been widely used in industry as they are non-destructive. Since defect patterns are rooted from the manufacturing processes in semiconductor industry, efficient and effective defect ...
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Optical inspection techniques have been widely used in industry as they are non-destructive. Since defect patterns are rooted from the manufacturing processes in semiconductor industry, efficient and effective defect detection and pattern recognition algorithms are in great demand to find out closely related causes. Modifying the manufacturing processes can eliminate defects, and thus to improve the yield. Defect patterns such as rings, semicircles, scratches, and clusters are the most common defects in the semiconductor industry. Conventional methods cannot identify two scale-variant or shift-variant or rotation-variant defect patterns, which in fact belong to the same failure causes. To address these problems, a new approach is proposed in this paper to detect these defect patterns in noisy images. First, a novel scheme is developed to simulate datasets of these 4 patterns for classifiers' training and testing. Second, for real optical images, a series of image processing operations have been applied in the detection stage of our method. In the identification stage, defects are resized and then identified by the trained support vector machine. Adaptive resonance theory network 1 is also implemented for comparisons. Classification results of both simulated data and real noisy raw data show the effectiveness of our method.
In this paper, an effective approach to vehicle license plate recognition based on Extremal Regions (ERs) and Self-adaptive Evolutionary Extreme Learning Machine (SaE-ELM) is proposed. In the license plate detection s...
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ISBN:
(纸本)9781479960781
In this paper, an effective approach to vehicle license plate recognition based on Extremal Regions (ERs) and Self-adaptive Evolutionary Extreme Learning Machine (SaE-ELM) is proposed. In the license plate detection step, some computations including morphological operations, various filters, different contours and validations are sequentially performed to extract some image regions as candidate license plates. Then, accurate character segmentation is achieved through a proper selection of ERs. In the character recognition step, the HOG (histogram of oriented gradients) feature vector in each character region is extracted, and then the characters are recognized using an offline trained pattern classifier of SaE-ELM. Experimental results show that our approach works quite well in complex traffic environments.
This paper proposes an approach to moving vehicle tracking in surveillance videos based on conditional random fields (CRF). The key idea is to integrate a variety of relevant knowledge about vehicle tracking into a un...
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ISBN:
(纸本)9781479960781
This paper proposes an approach to moving vehicle tracking in surveillance videos based on conditional random fields (CRF). The key idea is to integrate a variety of relevant knowledge about vehicle tracking into a uniform probabilistic framework by using the CRF model. In this work, the CRF model integrates spatial and temporal contextual information of vehicle motion, and the appearance information of the vehicle. An approximate inference algorithm, loopy belief propagation, is used to recursively estimate the vehicle region from the history of observed images. Moreover, the background model is updated adaptively to cope with non-stationary background processes. Experimental results show that the proposed approach is able to accurately track moving vehicles in monocular image sequences. Besides, region-level tracking realizes precise localization of vehicles.
In this paper, surface electromyography (sEMG) from muscles of the lower limb is acquired and processed to estimate the singlejoint voluntary motion intention, based on which, two single-joint active training strategi...
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