Dynamic sensor activation and decision-level fusion in wireless acoustic sensor networks for classification of domestic activities

For the past decades there has been a rising interest for wireless sensor networks to obtain information about an environment. One interesting modality is that of audio, as it is highly informative for numerous applications including speech recognition, urban scene classification, city monitoring, machine listening and classifying domestic activities. However, as they operate at prohibitively high energy consumption, commercialisation of battery-powered wireless acoustic sensor networks has been limited. To increase the network’s lifetime, this paper explores the joint use of decision-level fusion and dynamic sensor activation. Hereby adopting a topology where processing – including feature extraction and classification – is performed on a dynamic set of sensor nodes that communicate classification outputs which are fused centrally. The main contribution of this paper is the comparison of decision-level fusion with different dynamic sensor activation strategies on the use case of automatically classifying domestic activities. Results indicate that using vector quantisation to encode the classification output, computed at each sensor node, can reduce the communication per classification output to 8 bit without loss of significant performance. As the cost for communication is reduced, local processing tends to dominate the overall energy budget. It is indicated that dynamic sensor activation, using a centralised approach, can reduce the average time a sensor node is active up to 20% by leveraging redundant information in the network. In terms of energy consumption, this resulted in an energy reduction of up to 80% as the cost for computation dominates the overall energy budget.     

Demand-driven image transmission with levels of detail and regions of interest

In this paper, we introduce a formal model which describes consistently levels of detail and regions of interest for the progressive transmission of raster images. This model serves as the foundation of a wavelet-based image communication framework which supports the progressive, redundancy-free transmission of multiple, possibly overlapping regions of interest in an image. To support demand-driven transmission, we introduce a method for interleaving control commands with the embedded bit stream. Since the dyadic wavelet decomposition allows the image resolutions in x and y direction to differ at most by a factor of two, we propose a new wavelet decomposition scheme which raises this number to a power of two for increased flexibility. We discuss some applications including the rectangular fish eye view, a focus and context display technique for raster images which efficiently uses this transmission method and saves screen real-estate.     

多特征融合的兴趣点推荐算法

基于位置社交网络的兴趣点推荐越来越受到工业界和学术界的关注。由于用户签到数据集的稀疏性以及签到地理位置的聚集性，使得目前的推荐算法效率普遍不高，特别是当用户外出到新的地点时，推荐效果更是急剧下降。因此本文提出了一种基于用户-区域-内容主题的多特征联合推荐算法（UCRTM），以隐主题模型为基础，在统一的框架下利用隐含因子关联性融合了用户的偏好、兴趣点的内容以及兴趣点所属地理区域主题等信息来进行推荐，使得用户无论身处何地，都能获得理想的推荐服务。本文在两种真实的数据集上进行了实验，结果表明该方法不仅能够克服数据的稀疏性以及弱语义性等问题，而且与其他方法相比具有更高的推荐准确率。     

Metric sensing and control of a quadrotor using a homography-based visual inertial fusion method

The combination of a camera and an Inertial Measurement Unit (IMU) has received much attention for state estimation of Micro Aerial Vehicles (MAVs). In contrast to many map based solutions, this paper focuses on optic flow (OF) based approaches which are much more computationally efficient. The robustness of a popular OF algorithm is improved using a transformed binary image from the intensity image. Aided by the on-board IMU, a homography model is developed in which it is proposed to directly obtain the speed up to an unknown scale factor (the ratio of speed to distance) from the homography matrix without performing Singular Value Decomposition (SVD) afterwards. The RANSAC algorithm is employed for outlier detection. Real images and IMU data recorded from our quadrotor platform show the superiority of the proposed method over traditional approaches that decompose the homography matrix for motion estimation, especially over poorly-textured scenes. Visual outputs are then fused with the inertial measurements using an Extended Kalman Filter (EKF) to estimate metric speed, distance to the scene and also acceleration biases. Flight experiments prove the visual inertial fusion approach is adequate for the closed-loop control of a MAV.     

D-S证据融合的中医辨证模型

为提高传统不确定性推理在中医辨证中的准确率和灵活性,提出了基于D-S证据理论的中医辨证模型。模型采用群决策理论,以证素为识别框架,以不同专家的诊断结果为基本可信度分配,对专家的诊断结果进行证据融合,模型能够降低系统的不确定性,提高证素诊断的准确率和可信度。以冠心病的中医诊断为例,验证了模型的有效性。     

基于距离类别的多源兴趣点融合算法

为了更好地实现多源兴趣点（POI）数据的有效集成与精确融合,提出了一种结合空间与非空间属性的距离类别的兴趣点融合算法（MNMDC）。首先,对空间属性,通过标准化权重算法计算待融合对象的空间相似度得到融合集;其次,利用非空间Jaro-Winkle算法对融合集中类别一致的对象使用低阈值排除,对类别不一致的使用高阈值排除;最后,使用距离约束、类别一致约束和高阈值的非空间Jaro-Winkle算法找出空间算法遗漏的可融合对象。实验结果表明,该方法平均准确率达到93.3%,与空间和非空间算法（COM-NWT）及格网化纠正方法相比,在7组不同重合度的数据下MNMDC方法的平均准确率提高2.7和1.6个百分点、平均召回率提高2.3和1.4个百分点。MNMDC在实际融合过程中能更精确地融合POI数据。     

一种改进的证据算法及其在信息融合中的应用

阐述了基于证据理论的信息融合算法,提供了一种证据理论的改进方法以解决D-S算法在实现过程中存在的失效问题.并通过多传感器信息融合实验,对该方法的有效性进行了验证.     

Navigational issues in the design of online self-administered questionnaires

Answering questions on surveys involves the access of internal knowledge structures, the retrieval of records from external databases and the navigation of items on the interface. In this study a number of alternative designs for online questionnaire presentation were investigated. A long heterogeneous survey was partitioned in four ways: whole form, semantic sections, screen pages and single items. Questionnaires were presented with or without an index, resulting in eight versions. Neither initial completion times nor subjective assessments differed among the eight versions due to the highly linear navigation of the surveys. Respondents were asked to revisit 16 questions based on the topic of the question or on the question number and to change their answers. Revision times reflected ease of finding items in the structure of the survey and the use of an index to the sections of the questionnaire.     

Maximum acceptable weight of lift reflects peak lumbosacral extension moments in a functional capacity evaluation test using free style, stoop and squat lifting

It is unclear whether the maximum acceptable weight of lift (MAWL), a common psychophysical method, reflects joint kinetics when different lifting techniques are employed. In a within-participants study (n = 12), participants performed three lifting techniques--free style, stoop and squat lifting from knee to waist level--using the same dynamic functional capacity evaluation lifting test to assess MAWL and to calculate low back and knee kinetics. We assessed which knee and back kinetic parameters increased with the load mass lifted, and whether the magnitudes of the kinetic parameters were consistent across techniques when lifting MAWL. MAWL was significantly different between techniques (p = 0.03). The peak lumbosacral extension moment met both criteria: it had the highest association with the load masses lifted (r > 0.9) and was most consistent between the three techniques when lifting MAWL (ICC = 0.87). In conclusion, MAWL reflects the lumbosacral extension moment across free style, stoop and squat lifting in healthy young males, but the relation between the load mass lifted and lumbosacral extension moment is different between techniques. PRACTITIONER SUMMARY: Tests of maximum acceptable weight of lift (MAWL) from knee to waist height are used to assess work capacity of individuals with low-back disorders. This article shows that the MAWL reflects the lumbosacral extension moment across free style, stoop and squat lifting in healthy young males, but the relation between the load mass lifted and lumbosacral extension moment is different between techniques. This suggests that standardisation of lifting technique used in tests of the MAWL would be indicated if the aim is to assess the capacity of the low back.     

冲突证据融合方法的研究与改进

研究了D-S证据理论组合冲突证据时存在的缺陷,分析了国内外专家学者的研究成果,并在此基础上提出了一种改进的处理冲突证据的融合方法.该方法通过计算各证据到命题平均支持度的偏差来检测和融合冲突证据,并对最后结果进行了必要的修正,以解决来自不同识别框架的证据的合成问题.实验结果表明,该方法能有效处理冲突,得到符合实际的组合结果,相对于其它典型算法,该方法在收敛性和可靠性上有了明显改进.     

自适应视听信息融合用于抗噪语音识别

为了提高噪音环境中语音识别的准确性和鲁棒性,提出了基于自适应视听信息融合的抗噪语音识别方法,视听信息在识别过程中具有变化的权重,动态的自适应于环境输入的信噪比．根据信噪比和反馈的识别性能,通过学习自动机计算视觉信息的最优权重;根据视听信息的特征向量,利用隐马尔科夫模型进行视听信息的模式匹配,并根据最优权重组合视觉和声音隐马尔科夫模型的决策,获得最终的识别结果．实验结果表明,在各种噪音水平下,自适应权重比不变权重的视听信息融合的语音识别性能更优．     

Context-aware fusion: A case study on fusion of gait and face for human identification in video

Most work on multi-biometric fusion is based on static fusion rules. One prominent limitation of static fusion is that it cannot respond to the changes of the environment or the individual users. This paper proposes context-aware multi-biometric fusion, which can dynamically adapt the fusion rules to the real-time context. As a typical application, the context-aware fusion of gait and face for human identification in video is investigated. Two significant context factors that may affect the relationship between gait and face in the fusion are considered, i.e., view angle and subject-to-camera distance. Fusion methods adaptable to these two factors based on either prior knowledge or machine learning are proposed and tested. Experimental results show that the context-aware fusion methods perform significantly better than not only the individual biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX. Moreover, context-aware fusion based on machine learning shows superiority over that based on prior knowledge.     

Exploiting behavioral user models for point of interest recommendation in smart museums

ABSTRACT

The Internet of Things (IoT) holds the promise to blend real-world and online behaviors in principled ways, yet we are only beginning to understand how to effectively exploit insights from the online realm into effective applications in smart environments. Such smart environments aim to provide an improved, personalized experience based on the trail of user interactions with smart devices, but how does recommendation in smart environments differ from the usual online recommender systems? And can we exploit similarities to truly blend behavior in both realms to address the fundamental cold-start problem? In this article, we experiment with behavioral user models based on interactions with smart devices in a museum, and investigate the personalized recommendation of what to see after visiting an initial set of Point of Interests (POIs), a key problem in personalizing museum visits or tour guides, and focus on a critical one-shot POI recommendation task—where to go next? We have logged users' onsite physical information interactions during visits in an IoT-augmented museum exhibition at scale. Furthermore, we have collected an even larger set of search logs of the online museum collection. Users in both sets are unconnected, for privacy reasons we do not have shared IDs. We study the similarities between users' online digital and onsite physical information interaction behaviors, and build new behavioral user models based on the information interaction behaviors in (i) the physical exhibition space, (ii) the online collection, or (iii) both. Specifically, we propose a deep neural multilayer perceptron (MLP) based on explicitly given users' contextual information, and set-based extracted features using users' physical information interaction behaviors and similar users' digital information interaction behaviors. Our experimental results indicate that the proposed behavioral user modeling approach, using both physical and online user information interaction behaviors, improves the onsite POI recommendation baselines' performances on all evaluation metrics. Our proposed MLP approach achieves 83% precision at rank 1 on the critical one-shot POI recommendation problem, realizing the high accuracy needed for fruitful deployment in practical situations. Furthermore, the MLP model is less sensitive to amount of real-world interactions in terms of the seen POIs set-size, by backing of to the online data, hence helps address the cold start problem in recommendation. Our general conclusion is that it is possible to fruitfully combine information interactions in the online and physical world for effective recommendation in smart environments.     

多源空—谱遥感图像融合方法进展与挑战

多源空—谱遥感图像融合方法作为两路不完全观测多通道数据的计算重构反问题,其挑战在于补充信息不足、模糊和噪声等引起的病态性,现有方法在互补特征保持的分辨率增强方面仍有很大的改进空间。为了推动遥感图像融合技术的发展,本文系统概述目前融合建模的代表性方法,包括成分替代、多分辨率分析、变量回归、贝叶斯、变分和模型与数据混合驱动等方法体系及其存在问题。从贝叶斯融合建模的角度,分析了互补特征保真和图像先验在优化融合中的关键作用和建模机理,并综述了目前若干图像先验建模的新趋势,包括：分数阶正则化、非局部正则化、结构化稀疏表示、矩阵低秩至张量低秩表示、解析先验与深度先验的复合等。本文对各领域面临的主要挑战和可能的研究方向进行了概述和讨论,指出解析模型和数据混合驱动将是图像融合的重要发展方向,并需要结合成像退化机理、数据紧致表示和高效计算等问题,突破现有模型优化融合的技术瓶颈,进一步发展更优良的光谱信息保真和更低算法复杂度的融合方法。同时,为了解决大数据问题,有必要在Hadoop和SPARK等大数据平台上进行高性能计算,以更有利于多源数据融合算法的加速实现。     

Multi-focal image fusion using degree of focus and fuzzy logic

The continuous advancement in the field of imaging sensor necessitates the development of an efficient image fusion technique. The multi-focal image fusion extracts the in-focus information from the source images to construct a single composite image with increased depth-of-field. Traditionally, the information in multi-focal images is divided into two categories: in-focus and out-of-focus data. Instead of using a binary focus map, in this work we calculate the degree of focus for each source image using fuzzy logic. The fused image is then generated based on the weighted sum of this information. An initial focus tri-state map is built for each input image by using spatial frequency and a proposed focus measure named as alternate sum modified Laplacian. For the cases where these measures indicate different source images to contain focused pixel or have equal strength, another focus measure based on sum of gradient is employed to calculate the degree of focus in a fuzzy inference system. Finally, the fused image is computed from the weights determined by the degree of focus map of each image. The proposed algorithm is designed to fuse two source images, whereas fusion of multiple input images can be performed by fusing a source image with the fusion output of the previous input group. The comparison of the proposed method with several transform and pixel domain based techniques are conducted in terms of both subjective visual assessment and objective quantitative evaluation. Experimental results demonstrate that our method can be competitive with or even outperforms the methods in comparison.     

RGB-D图像中的分步超像素聚合和多模态融合目标检测

目的 受光照变化、拍摄角度、物体数量和物体尺寸等因素的影响,室内场景下多目标检测容易出现准确性和实时性较低的问题。为解决此类问题,本文基于物体的彩色和深度图像组,提出了分步超像素聚合和多模态信息融合的目标识别检测方法。方法 在似物性采样（object proposal）阶段,依据人眼对显著性物体观察时先注意其色彩后判断其空间深度信息的理论,首先对图像进行超像素分割,然后结合颜色信息和深度信息对分割后的像素块分步进行多阈值尺度自适应超像素聚合,得到具有颜色和空间一致性的似物性区域;在物体识别阶段,为实现物体不同信息的充分表达,利用多核学习方法融合所提取的物体颜色、纹理、轮廓、深度多模态特征,将特征融合核输入支持向量机多分类机制中进行学习和分类检测。结果 实验在基于华盛顿大学标准RGB-D数据集和真实场景集上将本文方法与当前主流算法进行对比,得出本文方法整体的检测精度较当前主流算法提升4.7%,运行时间有了大幅度提升。其中分步超像素聚合方法在物体定位性能上优于当前主流似物性采样方法,并且在相同召回率下采样窗口数量约为其他算法的1/4;多信息融合在目标识别阶段优于单个特征和简单的颜色、深度特征融合方法。结论 结果表明在基于多特征的目标检测过程中本文方法能够有效利用物体彩色和深度信息进行目标定位和识别,对提高物体检测精度和检测效率具有重要作用。     

基于一维卷积神经网络与改进D-S证据理论的警务云安全数据融合技术

针对系统内评估信息来源单一、准确度偏差过大与异构数据提取融合不充分的问题,提出了一种可扩展攻击行为的多源异构网络安全数据融合框架。首先,建立以攻击模式为核心的安全事件分析模型,进一步精简安全数据;其次,针对决策层数据特征提取不足的问题,建立了基于攻击行为的1D-CNN （1D convolutional neural network,1D-CNN）模型,对警务安全数据进行特征学习和重构;为了进一步提高警务云安全数据的分类能力,模型改进了D-S证据理论并结合多源安全数据的可信度进行数据融合。实验分析表明,基于1D-CNN的改进D-S证据理论模型进一步提高了警务云中安全事件的报警识别率,与其他相关技术相比,该模型具有较好的分析能力,对警务云的安全入侵检测和漏洞分析具有重要意义。     

一种基于超声和射频融合的无线传感器网络网格定位方法

提出了一种基于超声和射频融合的网格定位方法.首先对射频的无线信号强度进行筛选,采用压缩感知方法进行粗定位.然后基于粗定位结果对超声测量的计时结果进行评价和加权,以抵抗局部非视距(NLoS)环境影响.之后再迭代时进行进一步精细定位与超声计时结果的评价加权,直至达到终止条件.测试结果显示:所提出的融合定位方法的定位精度优于经典的CHAN算法,在实际测试中取得较好的定位性能.     

河南省信息化发展水平评估及发展对策研究

信息化是衡量一个地区综合实力和现代化程度的重要标志。对河南省信息化发展水平进行了评估,分析了存在的主要问题,提出了加快河南省信息化建设的对策建议。     

Modeling and simulation in support of the design of a data fusion system

This paper uses a modeling and simulation approach in the engineering process of designing a data fusion system for military applications. Fundamental issues in the design of a data fusion system are the selection of appropriate architecture and efficient and dedicated real-time algorithms for the application of interest. Selection of data fusion algorithms is performed using a simulation test bed where realistic modeling of sensor performance is being performed. The test bed also allows the use of multiple algorithms in a dynamic sense based on the particular regime in which the fusion system is operating. This paper first presents the data fusion system engineering guidelines that are being used. Then the paper addresses the complex problem of the tactical picture compilation. Finally the paper shows a case study where the selection of data fusion algorithms is performed for an airborne platform.