In the field of identity authentication, existing researches on user keystroke authentication only focus on the situation in which a single user uses a single account. When multiple users share one account, there emer...
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This paper proposes an enhanced scheduling solution to support different quality of service (QoS) for IEEE 802.16e broadband wireless access network. Based on the existing scheduling algorithm M-LWDF, we do deeper res...
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This paper proposes an enhanced scheduling solution to support different quality of service (QoS) for IEEE 802.16e broadband wireless access network. Based on the existing scheduling algorithm M-LWDF, we do deeper research into how to integrate it into IEEE 802.16e. The proposed scheduling solution combines the keyservice flow parameters defined by IEEE 802.16e standard with M-LWDF scheme. Simulation results show that the delay character is improved for real-time services using the proposed method.
One of the main destinations of image classification methods is to screen out the images belonging to the target class (positive) and identify the images of other classes (negative). Although most classifiers are trai...
One of the main destinations of image classification methods is to screen out the images belonging to the target class (positive) and identify the images of other classes (negative). Although most classifiers are trained on both positive samples and negative samples, in reality, negative samples are often unavailable. One-class classifiers trained only on positive samples, are proposed to solve this problem. However, how to train effectively a classifier remains a daunting challenge. Considering the success of deep learning in the field of computer vision in recent years, we propose a one-class classification model for images based on convolutional neural network (CNN). The model consists of two parts of networks with different responsibilities. The network of the first part works as the discriminator, used to identify whether the images are positive. The networks of the second part, each of which consists of an encoder-decoder pair work as the guiders of the discriminator. They guide the discriminator to learn what images it should identify to be negative. Different encoder-decoder pairs can restore images to different degrees. Images restored to different degrees can be used to train the discriminator. The discriminator learns that images under a specific restored degree don't belong to the target class. The experiment results on CIFAR-10 show that our method can achieve good performance, with less difficulty in training than the GAN-based counterparts'.
Traffic flow forecasting is indispensable in modern urban life. Considering the complexity, variability and strong timeliness of traffic flow, traffic flow forecasting is a worth exploring but challenging research fie...
Traffic flow forecasting is indispensable in modern urban life. Considering the complexity, variability and strong timeliness of traffic flow, traffic flow forecasting is a worth exploring but challenging research field. To achieve better traffic flow forecasting effect, we focus on two critical aspects that assume noteworthy importance: i) the features inside the traffic outflows and inflows. ii) the supplementary information regarding exterior region which is the area outside the grid division regions. To address these challenges, we propose a novel deep learning model Spatial-Temporal Flow Holistic Interaction Graph Convolution Network (STHGCN). In STHGCN, graph convolution based modules are applied through multi-step simulation. An exterior region feature estimation module is designed to estimate the influence of the special exterior region through the characteristics of complete trajectories, which enables a more comprehensive reasoning for traffic flow forecasting in grid division regions. Furthermore, a flow feature fusion integrator and stackable convolution modules are proposed to aggregate the intermediate features extracted from various perspectives, which simulate the constantly-updating and interlinked states of traffic flows through the process of multi-layer feature separation and fusion. We conduct extensive experiments on real-world traffic datasets and our proposed model outperforms all baselines.
In this work, for a wireless sensor network (WSN) of n randomly placed sensors with node density λ ∈ [1, n], we study the tradeoffs between the aggregation throughput and gathering efficiency. The gathering efficien...
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In orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, the subcarriers used by the licensed users (LUs) have to be deactivated to avoid interference. Thus only part of subcarriers can...
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In orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, the subcarriers used by the licensed users (LUs) have to be deactivated to avoid interference. Thus only part of subcarriers can be used for transmission and the positions of the activated subcarriers may be non-contiguous. The conventional pilot design methods are no longer effective for such systems. In this paper, we propose a new practical pilot design method for OFDM-based CR systems. We first formulate the pilot design as a new optimization problem, where a simple objective function related to the mean-square error (MSE) of the least-squares (LS) channel estimation method is minimized. We then propose an efficient scheme to solve the optimization problem. Specifically, the pilot tones are obtained sequentially by solving some one-dimensional optimization problems. Only real additions are needed in the proposed scheme. The simulation results show that the pilot sequence obtained by the proposed method exhibits better performance than those obtained by existing pilot design methods.
Booming development in brain science injects new vitality into the reform of teaching methods. Based on the perspective of brain cognition, this article addresses the optimum state for classroom learning- the relaxed ...
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Booming development in brain science injects new vitality into the reform of teaching methods. Based on the perspective of brain cognition, this article addresses the optimum state for classroom learning- the relaxed alertness. This is a good state of mind combined low threats with high challenges. Specially, the vigilance is estimated by extracting useful electroencephalogram (EEG) information in time-domain, frequency-domain and spatial distribution. It is used as an indicator to judge whether a student is in the optimum state. Focusing on how to achieve that ideal state, this article discusses some new teaching methods, including the use of Baroque music to stimulate alpha brainwave, the design of effective pattern of guiding class to arouse positive emotions of students and the conversion of teaching forms as novel stimuli to heip them improve vigilance and tide over thinking trough. The effectiveness of these methods is verified by the relevant experimental results.
Rising global energy demand, increasing electricity prices, and the limitation of natural resources have led to increased interest in energy monitoring of residential buildings. Much research has been done to examine ...
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The personalized recommendation systems could better improve the personalized service for network user and alleviate the problem of information overload in the Internet. As we all know, the key point of being a succes...
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This paper proposes a generalized model by extending Markov chain with spatial resources labels, which can describe the functional and performance properties and some basic characteristics such as nondeterminacy and r...
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