Different types of cloth show distinctive appearances owing to their unique yarn-level geometrical details. Despite its importance in applications such as cloth rendering and simulation, capturing yarn-level geometry is nontrivial and requires special hardware, e.g., computed tomography scanners, for conventional methods. In this paper, we propose a novel method that can produce the yarn-level geometry of real cloth using a single micro-image, captured by a consumer digital camera with a macro lens. Given a single input image, our method estimates the large-scale yarn geometry by image shading, and the fine-scale fiber details can be recovered via the proposed fiber tracing and generation algorithms. Experimental results indicate that our method can capture the detailed yarn-level geometry of a wide range of cloth and reproduce plausible cloth appearances.
Visual inspection of wheat growth has been a useful tool for understanding and implementing agricultural techniques and a way to accurately predict the growth status of wheat yields for economists and policy decision makers. In this paper, we present a polygonal approach for modeling the growth process of wheat ears. The grain, lemma, and palea of wheat ears are represented as editable polygonal models, which can be re-polygonized to detect collision during the growth process. We then rotate and move the colliding grain to resolve the collision problem. A linear interpolation and a spherical interpolation are developed to simulate the growth of wheat grain, performed in the process of heading and growth of wheat grain. Experimental results show that the method has a good modeling effect and can realize the modeling of wheat ears at different growth stages.
Traditional networks face many challenges due to the diversity of applications, such as cloud computing, Internet of Things, and the industrial Internet. Future Internet needs to address these challenges to improve network scalability, security, mobility, and quality of service. In this work, we survey the recently proposed architectures and the emerging technologies that meet these new demands. Some cases for these architectures and technologies are also presented. We propose an integrated framework called the service customized network which combines the strength of current architectures, and discuss some of the open challenges and opportunities for future Internet. We hope that this work can help readers quickly understand the problems and challenges in the current research and serves as a guide and motivation for future network research.
The security threats to software-defined networks (SDNs) have become a significant problem, generally because of the open framework of SDNs. Among all the threats, distributed denial-of-service (DDoS) attacks can have a devastating impact on the network. We propose a method to discover DDoS attack behaviors in SDNs using a feature-pattern graph model. The feature-pattern graph model presented employs network patterns as nodes and similarity as weighted links; it can demonstrate not only the traffic header information but also the relationships among all the network patterns. The similarity between nodes is modeled by metric learning and the Mahalanobis distance. The proposed method can discover DDoS attacks using a graph-based neighborhood classification method; it is capable of automatically finding unknown attacks and is scalable by inserting new nodes to the graph model via local or global updates. Experiments on two datasets prove the feasibility of the proposed method for attack behavior discovery and graph update tasks, and demonstrate that the graph-based method to discover DDoS attack behaviors substantially outperforms the methods compared herein.
The fifth-generation (5G) network cloudification enables third parties to deploy their applications (e.g., edge caching and edge computing) at the network edge. Many previous works have focused on specific service strategies (e.g., cache placement strategy and vCPU provision strategy) for edge applications from the perspective of a certain third party by maximizing its benefit. However, there is no literature that focuses on how to efficiently allocate resources from the perspective of a mobile network operator, taking the different deployment requirements of all third parties into consideration. In this paper, we address the problem by formulating an optimization problem, which minimizes the total deployment cost of all third parties. To capture the deployment requirements of the third parties, the applications that they want to deploy are classified into two types, namely, computation-intensive ones and storage-intensive ones, whose requirements are considered as input parameters or constraints in the optimization. Due to the NP-hardness and non-convexity of the formulated problem, we have designed an elitist genetic algorithm that converges to the global optimum to solve it. Extensive simulations have been conducted to illustrate the feasibility and effectiveness of the proposed algorithm.
In this paper, we deal with both velocity control and force control of a single-rod electro-hydraulic actuator subject to external disturbances and parameter uncertainties. In some implementations, both velocity control and force control are required. Impedance control and an extended disturbance observer are combined to solve this issue. Impedance control is applied to regulate the dynamic relationship between the velocity and output force of the actuator, which can help avoid impact and keep a proper contact force on the environment or workpieces. Parameters of impedance rules are regulated by a fuzzy algorithm. An extended disturbance observer is employed to account for external disturbances and parameter uncertainties to achieve an accurate velocity tracking. A detailed model of load force dynamics is presented for the development of the extended disturbance observer. The stability of the whole system is analyzed. Experimental results demonstrate that the proposed control strategy has not only a high velocity tracking performance, but also a good force adjustment performance, and that it should be widely applied in construction and assembly.
Frequent itemset mining serves as the main method of association rule mining. With the limitations in computing space and performance, the association of frequent items in large data mining requires both extensive time and effort, particularly when the datasets become increasingly larger. In the process of associated data mining in a big data environment, the MapReduce programming model is typically used to perform task partitioning and parallel processing, which could improve the execution efficiency of the algorithm. However, to ensure that the associated rule is not destroyed during task partitioning and parallel processing, the inner-relationship data must be stored in the computer space. Because inner-relationship data are redundant, storage of these data will significantly increase the space usage in comparison with the original dataset. In this study, we find that the formation of the frequent pattern (FP) mining algorithm depends mainly on the conditional pattern bases. Based on the parallel frequent pattern (PFP) algorithm theory, the grouping model divides frequent items into several groups according to their frequencies. We propose a non-group PFP (NG-PFP) mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks. Moreover, we present the NG-PFP algorithm for task partition and parallel processing, and its performance in the Hadoop cluster environment is analyzed and discussed. Experimental results indicate that the non-group model shows obvious improvement in terms of computational efficiency and the space utilization rate.
Query auto-completion (QAC) facilitates query formulation by predicting completions for given query prefix inputs. Most web search engines use behavioral signals to customize query completion lists for users. To be effective, such personalized QAC models rely on the access to sufficient context about each user’s interest and intentions. Hence, they often suffer from data sparseness problems. For this reason, we propose the construction and application of cohorts to address context sparsity and to enhance QAC personalization. We build an individual’s interest profile by learning his/her topic preferences through topic models and then aggregate users who share similar profiles. As conventional topic models are unable to automatically learn cohorts, we propose two cohort topic models that handle topic modeling and cohort discovery in the same framework. We present four cohortbased personalized QAC models that employ four different cohort discovery strategies. Our proposals use cohorts’ contextual information together with query frequency to rank completions. We perform extensive experiments on the publicly available AOL query log and compare the ranking effectiveness with that of models that discard cohort contexts. Experimental results suggest that our cohort-based personalized QAC models can solve the sparseness problem and yield significant relevance improvement over competitive baselines.
A high-performance noncoherent transmission scheme is proposed in the broadcasting phase of a twoway relay transmission (TWRT), where multiple-symbol differential detection (MSDD) is performed because of its excellent detection performance with no channel estimation. Specifically, the generalized likelihood ratio test aided MSDD (GLRT-MSDD) is developed for the down-link. Furthermore, GLRT-MSDD is reformulated and a semidefinite relaxation aided MSDD (SDR-MSDD) is proposed. The reformulation of GLRT-MSDD to SDR-MSDD is desirable owing to its reduced complexity. Performance analysis and the simulations validate that the proposed SDR-MSDD provides the bit-error-rate performance close to that of GLRT-MSDD with reasonable complexity in TWRT.
Rapid developments in information and communication technology in recent years have posed a significant challenge in wireless multimedia sensor networks (WMSNs). End-to-end delay and reliability are the critical issues in multimedia applications of sensor networks. In this paper we provide a new cross-layer approach for provisioning the end-to-end delay of the network at a desirable level of the packet delivery ratio (PDR), used here as a measure of network reliability. In the proposed multi-level cross-layer (MLCL) protocol, the number of hops away from the sink is used to set a level for each node. A packet is routed through the path with the minimum hop count to the sink using this level setting. The proposed protocol uses cross-layer properties between the network and medium access control (MAC) layers to estimate the minimum delay, with which a node can deliver a packet to the sink. When a node wants to send a packet, the MLCL protocol compares this minimum delay with the time to live (TTL) of a packet. If the TTL of the packet is higher than the minimum delay, the node sends the packet through the path with the minimum delay; otherwise, the node drops the packet as the node cannot deliver it to the sink within the TTL duration. This packet dropping improves network performance because the node can send a useful packet instead of an unusable packet. The results show a superior performance in terms of end-to-end delay and reliability for the proposed protocol compared to state-of-the-art protocols.
Penetration testing offers strong advantages in the discovery of hidden vulnerabilities in a network and assessing network security. However, it can be carried out by only security analysts, which costs considerable time and money. The natural way to deal with the above problem is automated penetration testing, the essential part of which is automated attack planning. Although previous studies have explored various ways to discover attack paths, all of them require perfect network information beforehand, which is contradictory to realistic penetration testing scenarios. To vividly mimic intruders to find all possible attack paths hidden in a network from the perspective of hackers, we propose a network information gain based automated attack planning (NIG-AP) algorithm to achieve autonomous attack path discovery. The algorithm formalizes penetration testing as a Markov decision process and uses network information to obtain the reward, which guides an agent to choose the best response actions to discover hidden attack paths from the intruder’s perspective. Experimental results reveal that the proposed algorithm demonstrates substantial improvement in training time and effectiveness when mining attack paths.
Bistable electrowetting display (EWD) is a promising low-power electronic paper technology, where power is consumed only during the switching between two stable states; however, it is not required for state maintenance once switched. In this paper, a bistable electrowetting device with non-planar designed controlling electrodes is fabricated by a fully conventional photolithography process. The device has potential for video display applications with a controllable gray scale. The novel electrode design realizes a lower driving voltage and a higher contrast between two stable states than the EWDs with planar electrodes reported previously.
The performance of an integrated packet voice/data multiplexer using a stop-and-wait (SW) automatic repeat request (ARQ) protocol is discussed. We assume that the input for the data traffic is exponentially distributed in increments via the Poisson process, with each data packet transmitted within an individual slot time. Another assumption is that there is only a single voice signal, which has a higher priority over the data packet, and whose traffic is given via an on-off Markov process. Whenever the voice signal is active, the output link is used and will be blocked for the data packet. We introduce the concept of buffer occupancy to simplify the analysis, and discover that data multiplexers using the SW ARQ protocol exhibit a behavior of queueing delay and buffering when the interruption signal is given via a Markov process. Simulation results verify the validity of the analytical results.