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High-precision mapping and localization contribute to mobile robot navigation. It also provides convenience, robustness and safety for navigation. But for the single feature and large-scale scene, to build high-precision mapping and localization has great challenges and difficulties. Meanwhile, high precise is unnecessary for simple task. Therefore, this paper proposes a new methods of environment...
In this paper, an agent framework for solving multidisciplinary decisions is proposed, including a conceptual decision model coupled with an agent scheme, and a set of functional signatures that drive the inference. The framework is specially designed to support decision-making in organisational structuring among care specialists towards complex problems, individual planning and argumentation, and...
In this paper, a novel spectral-spatial low-rank subspace clustering (SS-LRSC) algorithm is presented for clustering of hyperspectral images (HSI). Generally, employing the traditional LRSC framework directly cannot fully exploit the sample correlations in original spatial domain. Therefore, the proposed method utilizes a novel modulation strategy to modify the low rank representation matrix, which...
One significant advantage of the deep convolutional neural networks (DCNN) is their representational ability for local complex structures. Inspired by this observation, a DCNN based residual learning model is proposed to learn a nonlinear mapping function between the high-resolution (HR) and low-resolution (LR) image patches. The DCNN is trained based on image patches, which are only sampled from...
In this paper, a new heterogeneous neural networks based deep learning method, named HNNDL, is presented for supervised classification of hyperspectral image (HSI) with a small number of labeled samples. Specifically, a deep neural Network (DNN) and a convolutional neural network (CNN) are combined to build a HNNDL architecture. The proposed architecture contains three modules: 1) dimension reduction...
Image classification mainly uses the classifier to classify the extracted image features. In the traditional image feature extraction, it is difficult to set the appropriate feature patterns for the complex images. Simultaneously, the training algorithm of the classifier also affects the accuracy of image classification. In order to solve these problems, the combination of deep belief networks and...
Cloud storage is vulnerable to advanced persistent threats (APTs), in which an attacker launches stealthy, continuous, well-funded and targeted attacks on storage devices. In this paper, cumulative prospect theory (CPT) is applied to study the interactions between a defender of cloud storage and an APT attacker when each of them makes subjective decisions to choose the scan interval and attack interval,...
Privacy issues are confronting rapidly increasing challenges in this big data era. There is an increasing trend that more adversaries and hackers are aiming at individual privacy with updated technology, which leads to financial loss and safety issues. We have an observation that existing models, for example, classic differential privacy, provide certain privacy protection to statistical databases...
An Advanced Persistent Threat (APT) attacker applies multiple sophisticated methods to continuously and stealthily attack targeted cyber systems. In this paper, the interactions between an APT attacker and a cloud system defender in their allocation of the Central Processing Units (CPUs) over multiple devices are formulated as a Colonel Blotto game (CBG), which models the competition of two players...
In this paper, we propose a physical (PHY)-layer authentication system that exploits the channel state information of radio transmitters to detect spoofing attacks in wireless networks. By using multiple landmarks and multiple antennas in the channel estimation, this authentication system enhances the spatial resolution of the channel information and thus improves the spoofing detection accuracy....
Multiple-input multiple-output (MIMO) systems are threatened by smart attackers, who apply programmable radio devices such as software defined radios to perform multiple types of attacks such as eavesdropping, jamming and spoofing. In this paper, MIMO transmission in the presence of smart attacks is formulated as a noncooperative game, in which a MIMO transmitter chooses its transmit power level and...
In this paper, a two-dimensional anti-jamming communication scheme for cognitive radio networks is developed, in which a secondary user (SU) exploits both spread spectrum and user mobility to address jamming attacks, while not interfering with primary users. By applying a deep Q-network algorithm, this scheme determines whether to recommend that the SU leave an area of heavy jamming and chooses a...
A single sensor camera can capture scenes by means of color filter array. Each pixel samples only one of the three primary colors. Color demosaicking (CDM) is a process of reconstruction a full color image from this sensor data. In this paper, we propose a novel CDM scheme based on learned simultaneous sparse coding over nonlocal tensor representation. First, similar 2D patches are grouped to form...
In this paper, we investigate the PHY-layer authentication that exploits radio channel information to detect spoofing attacks in multiple- input multiple-output (MIMO) systems. We formulate the interactions between a receiver and a spoofing node in the spoofing detection as a zero-sum game. In this game, the receiver chooses the test threshold of the hypothesis test in the PHY-layer authentication...
Cloud storage is vulnerable to Advanced Persistent Threats (APTs), which are stealthy, continuous, well funded and targeted. In this paper, prospect theory is applied to study the interactions between a subjective cloud storage defender and a subjective APT attacker. Two subjective APT games are formulated, in which the defender chooses its interval to scan the storage device and the attacker decides...
Recently, it has been an increasing interest in modeling abnormal temporal dynamics of functional interactions in psychiatric disorders. However, the accuracy of differentiating attention-deficit/hyperactivity disorder (ADHD) children form normal children has still much space for improvement. To further improve the accuracy, the key issue is to extract more effective features from original fMRI data...
By applying smart and programmable radio devices, selfish end-users can launch smart attacks and choose multiple types of attacks such as jamming and eavesdropping according to the ongoing transmission of wireless networks. In this paper, we apply prospect theory (PT) to formulate the interaction between a smart attacker as an end-user who makes subjective decision regarding his or her attack mode...
In this paper, we presented a hybrid method to distinguish normal brain tissue from lesion regions based on the T1-weighted and T2-weighted MR images of the same anatomic structure. Regions of interest were extracted using an iterative Otsu's thresholding method with normal brain tissue extracted from T1-weighted MR images, and lesion regions extracted from T2-weighted MR images. Markov random field...
The non-local means algorithm has been proven to allow a good image restoration performance of MR images. However, this method utilizes all pixels of the images, some of which might be useless for noise removal. In this paper, an improved pixel-selection-based non-local means algorithm which avoids the usage of useless pixels is supplied. The principle of pixel selection is based on the similarity...
In this paper, a goal-driven and agent-oriented modelling approach is proposed for clinical decision support. Goal-decomposition Structures and Goal-directed Clinical Decision Pathways guide requirements modelling. Goal-driven Agent Interaction Models, composable by Interaction Patterns reusable across different scenarios, guide agent-oriented design and even later in implementation. Additional measures...
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