Acquisition of multi-modal expression of slip through pick-up experiences

To realize adaptive and robust manipulation, a robot should have several sensing modalities and coordinate their outputs to achieve the given task based on underlying constraints in the real environment. This paper discusses acquisition of multi-modal expression of slip consisting of vibration, pressure and vision sensations through pick-up experiences. A sensor network is proposed to acquire the expression, whose learning ability is demonstrated by a real experiment. The applicability of the learned network is also demonstrated by experiments to realize robust and adaptive picking.     

MEMS-based multi-modal vibration energy harvesters for ultra-low power autonomous remote and distributed sensing

Microsystem Technologies - In this contribution, we discuss the implementation of a novel microelectromechanical-systems (MEMS)-based energy harvester (EH) concept within the technology platform...     

基于SA-FQL算法的区域交通控制方法

将模拟退火算法的Metropolis准则用于平衡模糊Q学习中探索和扩张之间的关系,提出基于Metropolis准则的模糊Q学习算法Simulated Annealing Fuzzy Q-learning（SA-FQL）。利用SA-FQL算法优化区域的公共周期,在给定周期的基础上再用SA-FQL算法优化区域中各干线相邻两路口的相位差,最后根据交通流量确定各路口的绿信比。TSIS仿真结果表明,相比基于Q学习和模糊Q学习的控制方法,该方法能显著提高学习速度和交通效率。     

基于蓝牙技术的城市交通系统

提出了基于蓝牙技术的智能交通系统在城市中运用的系统方案。详细介绍了最新的蓝牙规范,结合现代数字无线通信技术,通过蓝牙通信收发模块,实现实时数据采集、监控查询、事故应急和交通控制以及与系统现有各种网络信息之间的数据融合。系统的蓝牙应用具有低成本、低功耗、高稳定性、高灵活性和可扩展性等特点,可极大地提高城市交通运行的效率,有效解决了城市交通系统信息化面临的众多难题。     

Urban traffic congestion propagation and bottleneck identification

Bottlenecks in urban traffic network are sticking points in restricting network collectivity traffic efficiency. To identify network bottlenecks effectively is a foundational work for improving network traffic condition and preventing traffic congestion. In this paper, a congestion propagation model of urban network traffic is proposed based on the cell transmission model （CTM）. The proposed model includes a link model, which describes flow propagation on links, and a node model, which represents link-to-link flow propagation. A new method of estimating average journey velocity （AJV） of both link and network is developed to identify network congestion bottlenecks. A numerical example is studied in Sioux Falls urban traffic network. The proposed model is employed in simulating network traffic propagation and congestion bottleneck identification under different traffic demands. The simulation results show that continual increase of traffic demand is an immediate factor in network congestion bottleneck emergence and increase as well as reducing network collectivity capability. Whether a particular link will become a bottleneck is mainly determined by its position in network, its traffic flow （attributed to different OD pairs） component, and network traffic demand.     

On the detection of card-sharing traffic through wavelet analysis and Support Vector Machines

In the last years, the interest in methods and techniques for circumventing the security of the available digital video broadcasting systems is continuously increasing. Digital TV providers are struggling to restrict access to their video contents only to authorized users, by deploying more and more sophisticated conditional access systems. At the state-of-the-art, the most significant menace is the card-sharing activity which exploits a known weakness allowing an authorized subscriber to provide access to digital contents to a potentially large group of unauthorized ones connected over a communication network. This is usually realized by using ad hoc customized devices. Detecting the presence of these illegal systems on a network, by recognizing their related traffic is an issue of primary importance. Unfortunately, to avoid the identification of such traffic, payload obfuscation strategies based on encryption are often used, hindering packet inspection techniques.This paper presents a strategy for the detection of card-sharing traffic, empowered by machine-learning-driven traffic classification techniques and based on the natural capability of wavelet analysis to decompose a traffic time series into several component series associated with particular time and frequency scales and hence allowing its observation at different frequency component levels and with different resolutions. These ideas have been used for the proof-of-concept implementation of an SVM-based binary classification scheme that relies only on time regularities of the traffic and not on the packet contents and hence is immune to payload obfuscation techniques.     

Interactive multi-modal suturing

We present a mechanics-based interactive multi-modal environment designed as part of a serious gaming platform. The specific objectives are to teach basic suturing and knotting techniques for simple skin or soft tissue wound closure. The pre-wound suturing target, skin, or deformable tissue is modeled as a modified mass-spring system. The suturing material is designed as a mechanics-based deformable linear object. Tools involved in a typical suturing procedures are also simulated. Collision management modules between the soft tissue and the needle, the soft tissue and the suture are analyzed. In addition to modeling the interactive environment of a typical suturing procedure, basics of the modeling approaches on the evaluation of a stitch formed by the user are also discussed. For example, if needle insertion points are too close from each other or to the edge of the wound, when the suture is pulled, the suture will tear the soft tissue instead of suturing the incision together. Experiment results show that our simulator can run on a standard personal computer and allow users to perform different suturing patterns with smooth graphics and haptic feedback.     

A framework of enroute air traffic conflict detection and resolution through complex network analysis

The scalability of increasingly complex air transportation requires better automation support to prevent accidents and assure safety. Collaborative control mechanisms for error and conflict detection and prevention are an essential part of this support. Analysis of the network structure of proposed design for automated conflict detection and resolution for the next generation air transportation system was conducted. This analysis provides insight into the tradeoffs of alternative concepts of collaborative operations, including tradeoffs between expected conflicts, communications requirements, and vulnerability to targeted attack. The resulting design framework provides a structure for the application of network architecture methods to these, and other, techniques for air traffic control, enabling in-depth analysis and evaluation of the resulting system.     

基于深度强化学习的城市交通信号控制算法

针对城市交通信号控制中如何有效利用相关信息优化交通控制并保证控制算法的适应性和鲁棒性的问题，提出一种基于深度强化学习的交通信号控制算法，利用深度学习网络构造一个智能体来控制整个区域交通。首先通过连续感知交通环境的状态来选择当前状态下可能的最优控制策略，环境的状态由位置矩阵和速度矩阵抽象表示，矩阵表示法有效地抽象出环境中的主要信息并减少了冗余信息；然后智能体以在有限时间内最大化车辆通行全局速度为目标，根据所选策略对交通环境的影响，利用强化学习算法不断修正其内部参数；最后，通过多次迭代，智能体学会如何有效地控制交通。在微观交通仿真软件Vissim中进行的实验表明，对比其他基于深度强化学习的算法，所提算法在全局平均速度、平均等待队长以及算法稳定性方面展现出更好的结果。其中，与基线相比，平均速度提高9%，平均等待队长降低约13.4%。实验结果证明该方法能够适应动态变化的复杂的交通环境。     

Urban parking space reservation through bottom-up information provision: An agent-based analysis

The aim of this paper is to study the impacts of a reservation system for on-street parking. Such a system provides drivers looking for on-street parking with information on available parking spaces, thereby possibly reducing the need to cruise for parking and the accompanying negative externalities. The performance of the proposed system is studied using a highly detailed spatial agent-based simulation. The results of the simulations show that users of a reservation system benefit in terms of reduced search time and reduced walking distance under virtually all simulated circumstances. However, societal benefits are not as clear-cut. The benefit in search time for the users of the system comes at a cost to the regular drivers, which see a nearly identical increase in search time. In contrast, the positive impact on walking distance hardly influences walking distance for regular drivers. Hence, we conclude that the introduction of a reservation system for on-street parking results in a more efficient distribution of available parking spaces among drivers searching for parking.     

BNs-OLS-SARIMA对城市短时交通流的预测*

通过在目标路口构建贝叶斯交通网（BNs）,并对与此交通网相关的交通流建立非平稳季节（SARIMA）模型,采用最小二乘法（OLS）取得相应模型的最佳权重组合,对缺失数据下的城市道路短时交通流进行预测。使用重庆市某路口的交通流数据对模型进行检测,通过多种预测指标对结果进行对比分析,结果表明BNs-OLS-SARIMA把交通流的网络结构与其周期性结合在一起,对短时交通流有良好的预测效果。     

Migratory bird habitat monitoring through remote sensing

Abstract

Unsupervised classification of Landsat-TM data was employed to identify habitats important for migratory birds in Costa Rica. The overall habitat classification accuracy was 70 per cent (Kappa correction). Mature forest could be identified with high accuracy (93 per cent) but Landsat-TM classification accuracy for major successional stages was low. Habitat availability and conversion rates from 1976 to 1986 were derived from multidate Landsat imagery supplemented with interpretation of historical air photos to document the specific types of habitat change. The major trend in habitat conversion between 1976 and 1984 was forest clearing followed by establishment of permanent pasture. Some of the pasture land was converted to perennial tree crops by 1986. The implication of habitat modification on groups and species of migrant land birds are discussed.     

Decorrelating wireless sensor network traffic to inhibit traffic analysis attacks

Typical packet traffic in a sensor network reveals pronounced patterns that allow an adversary analyzing packet traffic to deduce the location of a base station. Once discovered, the base station can be destroyed, rendering the entire sensor network inoperative, since a base station is a central point of data collection and hence failure. This paper investigates a suite of decorrelation countermeasures aimed at disguising the location of a base station against traffic analysis attacks. A set of basic countermeasures is described, including hop-by-hop reencryption of the packet to change its appearance, imposition of a uniform packet sending rate, and removal of correlation between a packet’s receipt time and its forwarding time. More sophisticated countermeasures are described that introduce randomness into the path taken by a packet. Packets may also fork into multiple fake paths to further confuse an adversary. A technique is introduced to create multiple random areas of high communication activity called hot spots to deceive an adversary as to the true location of the base station. The effectiveness of these countermeasures against traffic analysis attacks is demonstrated analytically and via simulation using three evaluation criteria: total entropy of the network, total overhead/energy consumed, and the ability to frustrate heuristic-based search techniques to locate a base station.     

Collaboration based multi-modal multi-label learning

Applied Intelligence - Complex objects can be represented as multiple modal features and associated with multiple labels. The major challenge of complex object classification is how to jointly...     

Personalized retrieval of sports video based on multi-modal analysis and user preference acquisition

In this paper, we present a novel framework on personalized retrieval of sports video, which includes two research tasks: semantic annotation and user preference acquisition. For semantic annotation, web-casting texts which are corresponding to sports videos are firstly captured from the webpages using data region segmentation and labeling. Incorporating the text, we detect events in the sports video and generate video event clips. These video clips are annotated by the semantics extracted from web-casting texts and indexed in a sports video database. Based on the annotation, these video clips can be retrieved from different semantic attributes according to the user preference. For user preference acquisition, we utilize click-through data as a feedback from the user. Relevance feedback is applied on text annotation and visual features to infer the intention and interested points of the user. A user preference model is learned to re-rank the initial results. Experiments are conducted on broadcast soccer and basketball videos and show an encouraging performance of the proposed method.

Loosely-coupled approach towards multi-modal browsing

Contemplating the concept of universal-access multi-modal browsing comes as one of the emerging killer technologies that promises broader and more flexible access to information, faster task completion, and advanced user experience. Inheriting the best from GUI and speech, based on the circumstances, hardware capabilities, and environment, multi-modalitys great advantage is to provide application developers with a scalable blend of input and output channels that may accommodate any user, device, and platform. This article describes a flexible multi-modal browser architecture, named Ferda the Ant, which reuses uni-modal browser technologies available for VoiceXML, WML, and HTML browsing. A central component, the Virtual Proxy, acts as a synchronization coordinator. This browser architecture can be implemented in either a single client configuration, or by distributing the browser components across the network. We have defined and implemented a synchronization protocol to communicate the changes occurring in the context of a component browser to the other browsers participating in the multi-modal browser framework. Browser wrappers implement the required synchronization protocol functionality at each of the component browsers. The component browsers comply with existing content authoring standards, and we have designed a set of markup-level authoring conventions that facilitate maintaining the browser synchronization .     

Robust multi-modal medical image fusion via anisotropic heat diffusion guided low-rank structural analysis

This paper proposes a novel and robust multi-modal medical image fusion method, which is built upon a novel framework comprising multi-scale image decomposition based on anisotropic heat kernel design, scale-aware salient information extraction based on low-rank analysis, and scale-specific fusion rules. Our framework respects multi-scale structure features, while being robust to complex noise perturbation. First, anisotropic heat kernel is computed by constructing an image pyramid and embedding multi-level image properties into 2D manifolds in a divide-and-conquer way, consequently, multi-scale structure-preserving image decomposition can be accommodated. Second, to extract meaningfully scale-aware salient information, we conduct low-rank analysis over the image layer groups obtained in the first step, and employ the low-rank components to form the scale space of the salient features, wherein the underlying noise can be synchronously decoupled in a natural way. Third, to better fuse the complementary salient information extracted from multi-modal images, we design an S-shaped weighting function to fuse the large-scale layers, and employ the maximum selection principle to handle the small-scale layers. Moreover, we have conducted extensive experiments on MRI and PET/SPECT images. The comprehensive and quantitative comparisons with state-of-the-art methods demonstrate the informativeness, accuracy, robustness, and versatility of our novel approach.     

网络信息流的血检分析模型

血液成份的变化反映了人体健康状态的改变,借鉴医学中的血液检查机制诊断网络的健康状态,提出了基于血检机制和网络信息流分析的网络健康状态分析框架BETA。以Hurst指数和典型网络数据包信息为数据源建立了网络信息流的指标体系,设计了流量分析的知识库并给出知识的表达形式。在此基础之上,详细阐述了流量分析引擎的实现方法,提出了推理前件生成算法以及诊断分析算法,实现了网络健康状态的诊断。最后描述了BETA的体系结构以及系统实现细节。     

自相似业务网络性能分析

最近10多年的大量研究结果表明,网络业务具有普遍的自相似特性.自相似特性对网络的性能分析和设计具有广泛的影响.自相似业务的性能分析问题是一个没有很好解决的问题.提出了自相似业务环境下的分形界到达过程,研究了基于分形界到达过程的自相似业务漏桶参数的优化方法,给出了时延约束条件下漏桶速率和桶深的计算公式.最后研究了网络演算在确保服务性能分析中的建模方法,推导出了基于网络演算的端到端时延界、队列长度界和有效带宽的计算公式.通过具体的应用实例,验证了分析结果的正确性和有效性.