An Autonomous Vehicle for Video Surveillance of Indoor Environments

In this paper, the problem of the surveillance and the security of indoor environments is addressed through the development of an autonomous surveillance vehicle (ASV). The ASV has been designed to perform, in addition to the classical robotic tasks (e.g., navigation and obstacle avoiding), the tracking of objects (e.g., persons) moving in indoor environments. The selection of the target object to be tracked can be decided by a remote operator or autonomously by the ASV itself in the case that a suspicious behavior has been detected (e.g., a person entering a forbidden area, etc.). The tracking procedure allows the ASV to maintain the interesting objects in the center of the image and in specific cases to localize particular parts of the object (e.g., face of a person, etc.) in order to recognize it. Experimental results have been performed on different real scenarios where no objects move inside the monitored scene and where a group of people move in a hallway     

Estimation of orientation tensors for simple signals by means of second-order filters

Tensors have become a popular tool for representation of local orientation and can be used also for estimation of velocity. A number of computational approaches have been presented for tensor estimation which, however, are difficult to analyze or compare since there has been no common framework in which analysis or comparisons can be made. In this article, we propose such a framework based on second-order filters and show how it applies to three different methods for tensor estimation. The framework contains a few conditions on the filters which are sufficient to obtain correctly oriented rank one tensors for the case of simple signals. It also allows the derivation of explicit expressions for the variation of the tensor across oriented structures which, e.g., can be used to formulate conditions for phase invariance.     

Effectiveness-analysis of real-time data acquisition and processingmultichannel systems

Consider a real-time data acquisition and processing multiserver (e.g., unmanned air vehicles and machine controllers) and multichannel (e.g., surveillance regions, communication channels, and assembly lines) system involving maintenance. In this kind of system, jobs are executed immediately upon arrival, conditional on system availability. That part of the job which is not served immediately is lost forever and cannot be processed later. Thus, queuing of jobs in such systems is impossible. The effectiveness analysis of real-time systems in a multichannel environment is important. Several definitions of performance effectiveness index for the real-time system under consideration are suggested. The real-time system is treated with exponentially distributed time-to-failure, maintenance, interarrival and duration times as a Markov chain in order to compute its steady-state probabilities and performance effectiveness index via analytic and numerical methods. Some interesting analytic results are presented concerning a worst-case analysis, which is most typical in high-performance data acquisition and control real-time systems     

Coverage and Lifetime Optimization of Wireless Sensor Networks with Gaussian Distribution

A wireless sensor network (WSN) has to maintain a desirable sensing coverage and periodically report sensed data to the administrative center (i.e., base station) and the reporting period may range from months to years. Coverage and lifetime are two paramount problems in a WSN due to constraint of associated battery power. All previous theoretical analysis on the coverage and lifetime is primarily focused on the random uniform distribution of sensors or some specific network scenarios (e.g., a controllable WSN). In this paper, we provide an analytical framework for the coverage and lifetime of a WSN that follows a two-dimensional Gaussian distribution. We also study the coverage and lifetime when the dimensions of Gaussian dispersion (i.e., x, y) admit different Gaussian parameters (i.e., standard deviation, $sigma_x neqsigma_y$). We identify intrinsic properties of coverage/lifetime in terms of Gaussian distribution parameters, which is a fundamental issue in designing a WSN. Following the results obtained, we further determine the sensor deployment strategies for a WSN that could satisfy a predefined coverage and lifetime. Two deployment algorithms are developed based on using our analytical models and are shown to effectively increase the WSN lifetime.     

流量工程在IP组播中的实现

随着IP网络的进一步发展,用户不仅要求Web等一般的数据传输,而且对实时数据流的需求也急剧上升.这些实时业务往往是以组播方式向用户提供服务.而传统的IP组播已经很难满足用户对业务的QoS的需求.对流量工程技术在IP组播中的实现进行讨论,并提出了一个基于QoS的IP组播流量工程模型.最后总结了将来的研究方向.     

Endoscopic video manifolds for targeted optical biopsy

Gastro-intestinal (GI) endoscopy is a widely used clinical procedure for screening and surveillance of digestive tract diseases ranging from Barrett's Oesophagus to oesophageal cancer. Current surveillance protocol consists of periodic endoscopic examinations performed in 3-4 month intervals including expert's visual assessment and biopsies taken from suspicious tissue regions. Recent development of a new imaging technology, called probe-based confocal laser endomicroscopy (pCLE), enabled the acquisition of in vivo optical biopsies without removing any tissue sample. Besides its several advantages, i.e., noninvasiveness, real-time and in vivo feedback, optical biopsies involve a new challenge for the endoscopic expert. Due to their noninvasive nature, optical biopsies do not leave any scar on the tissue and therefore recognition of the previous optical biopsy sites in surveillance endoscopy becomes very challenging. In this work, we introduce a clustering and classification framework to facilitate retargeting previous optical biopsy sites in surveillance upper GI-endoscopies. A new representation of endoscopic videos based on manifold learning, "endoscopic video manifolds" (EVMs), is proposed. The low dimensional EVM representation is adapted to facilitate two different clustering tasks; i.e., clustering of informative frames and patient specific endoscopic segments, only by changing the similarity measure. Each step of the proposed framework is validated on three in vivo patient datasets containing 1834, 3445, and 1546 frames, corresponding to endoscopic videos of 73.36, 137.80, and 61.84 s, respectively. Improvements achieved by the introduced EVM representation are demonstrated by quantitative analysis in comparison to the original image representation and principal component analysis. Final experiments evaluating the complete framework demonstrate the feasibility of the proposed method as a promising step for assisting the endoscopic expert in retargeting the optical biopsy sites.     

A programmable MAC framework for utility-based adaptive quality ofservice support

We describe the design and evaluation of a programmable medium access control framework which is based on a hybrid centralized/distributed data link controller. The programmable framework and its associated algorithms are capable of supporting adaptive real-time applications over time-varying and bandwidth limited networks (e.g., wireless networks) in a fair and efficient manner taking into account application-specific adaptation needs. The framework is flexible, extensible and supports the dynamic introduction of new adaptive services on-demand. As part of the service creation process, applications interact with a set of distributed adaptation handlers to program services without the need to upgrade the centralized adaptation controller. This approach is in contrast to existing techniques that offer a fixed set of “hard-wired” services at the data link from which applications select. We present a framework where a centralized adaptation controller responsible for the fair allocation of available bandwidth among adaptive applications is driven by application specific bandwidth utility curves. A set of distributed adaptation handlers execute at edge devices interacting with a central controller allowing applications to program their adaptation needs in terms of utility curves, adaptation time scales and adaptation policy. The central controller offers a set of simple meta-services called “profiles” that distributed handlers use to build sophisticated adaptive real-time services     

A Modeling Framework for Jamming Structures

Jamming is a structural phenomenon that provides tunable mechanical behavior. A jamming structure typically consists of a collection of elements with low effective stiffness and damping. When a pressure gradient, such as vacuum, is applied, kinematic and frictional coupling increase, resulting in dramatically altered mechanical properties. Engineers have used jamming to build devices from tunable-stiffness grippers to tunable-damping landing gear. This study presents a rigorous framework that systematically guides the design of jamming structures for target applications. The force-deflection behavior of major types of jamming structures (i.e., grain, fiber, and layer) in fundamental loading conditions (e.g., tension, shear, and bending) is compared. High-performing pairs (e.g., grains in compression, layers in shear, and bending) are identified. Parameters that go into designing, fabricating, and actuating a jamming structure (e.g., scale, material, geometry, and actuator) are described, along with their effects on functional metrics. Two key methods to expand on the design space of jamming structures are introduced: using structural design to achieve effective tunable-impedance behavior in specific loading directions, and creating hybrid jamming structures to utilize the advantages of different types of jamming. Collectively, this study elaborates and extends the jamming design space, providing a conceptual modeling framework for jamming-based structures.     

Self-reconfiguration of real-time communication in cyber-physical systems

Today, in domains like automation and robotics systems consist of various sensors and computation nodes. Due to the temporal dependency in quality of measured data, such Cyber-Physical Systems (CPS) commonly have real-time requirements on communication. In addition, these systems shall become more flexible and scalable, e.g., by adding new components to the CPS. This would be most suitable if a CPS could react to the presence of a new component and reconfigure itself to run afterward with the new component integrated to the CPS. This capability is covered by the term Plug-and-Produce. In this paper, we propose a concept to enable Plug-and-Produce within a CPS whose network uses different communication media, e.g., Ethernet and CAN. To enable real-time communication provided by different communication protocols, their different synchronization mechanisms have to be combined to get a common time base within the entire system. For this purpose, we consider Ethernet as well as CAN-based real-time communication protocols and their synchronization mechanisms. The proposed concept for self-reconfiguration aims to be integrated into our three layered software architecture that is presented as well.     

A flexible network architecture for data multicasting in“multiservice networks”

The paper describes a canonical model of data-transport architecture that offers a flexible framework for implementations of data multicasting on backbone networks to support multiservice applications (e.g., videoconferencing, digital TV broadcast). The architecture is based on acyclic graph structured communication channels that provide connectivity among data sources and destinations through switches and links in a backbone network. The paper adopts a network-wide logical addressing of communication channels, which allows data multicasting to be realized on specific backbone networks by establishing local bindings between a logical address and the information on network-specific routing of data over switches and links. The approach allows various sources to share the switches and links in a multicast path connecting to destinations. This is a desirable feature in view of the significant reduction in network routing control costs and data transfer costs when dealing with high-volume multisource data (say, in videoconferencing). In addition, logical addressing allows grouping of selected destinations to overlay different “virtual networks” on a base-level multicast channel (e.g., private discussion groups in a conference). As a demonstration of architectural flexibility, the paper describes the embedding of our multicast model on sample backbone networks capable of supporting multiservice applications: interconnected LANs, ATM networks, and high-speed public data networks (viz., SMDS networks)     

Occlusion detection and drift-avoidance framework for 2D visual object tracking

This paper presents a long-term 2D tracking framework for the coverage of live outdoor (e.g., sports) events that is suitable for embedded system application (e.g. Unmanned Aerial Vehicles). This application scenario requires 2D target (e.g., athlete, ball, bicycle, boat) tracking for visually assisting the UAV pilot (or cameraman) to maintain proper target framing, or even for actual 3D target following/localization when the drone flies autonomously. In these cases, it should be expected that the target to be tracked/followed, may disappear from the UAV camera field of view, due to fast 3D target motion, illumination changes, or due to visual target occlusions by obstacles, even if the actual UAV continues following it (either autonomously, by exploiting alternative target localization sensors, or by pilot maneuvering). Therefore, the 2D tracker should be able to recover from such situations. The proposed framework solves exactly this problem. Target occlusions are detected from the 2D tracker responses. Depending on the occlusion immensity, the proposed framework decides whether to not update the tracking model, or to employ target re-detection in a broader window. As a result, the proposed framework allows continued target tracking once the target re-appears in the video stream, without tracker re-initialization.     

A Framework for Engineering Pervasive Applications Applied to Intra-vehicular Sensor Network Applications

The vehicle sector is one of the most exciting application areas for wireless ad hoc networks and pervasive computing technologies. Vehicles are constantly being equipped with more sensors and devices able to collect real-time data on traffic, vehicle condition, passenger health, and so on. Being a relatively new field of application, this area needs tools and methodologies in order to specify requirements and prototype applications. This paper presents a Framework for Pervasive Applications and describes how it can be customized in the case of Vehicular Applications. The framework consists of a set of software requirements, some metrics, and some middleware services for rapid prototyping pervasive applications. In addition, the paper introduces an ongoing project that aims at granting high-quality on-road transportation services for fragile food (e.g. wine). In particular, its objective is to provide a system able to monitor several conditions, like temperature, humidity, light, shocks, etc., which carried products are subject to during transportation.     

Real-time video super resolution network using recurrent multi-branch dilated convolutions

Recent developments of video super-resolution reconstruction often exploit spatial and temporal contexts from input frame sequence by making use of explicit motion estimation, e.g., optical flow, which may introduce accumulated errors and requires huge computations to obtain an accurate estimation. In this paper, we propose a novel multi-branch dilated convolution module for real-time frame alignment without explicit motion estimation, which is incorporated with the depthwise separable up-sampling module to formulate a sophisticated real-time video super-resolution network. Specifically, the proposed video super-resolution framework can efficiently acquire a larger receptive field and learn spatial–temporal features of multiple scales with minimal computational operations and memory requirements. Extensive experiments show that the proposed super-resolution network outperforms current state-of-the-art real-time video super-resolution networks, e.g., VESPCN and 3DVSRnet, in terms of PSNR values (0.49 dB and 0.17 dB) on average in various datasets, but requires less multiplication operations.     

An adaptive approach for designing phase compensation filters

The objective of this communication is to introduce an adaptive method for designing filters to compensate for nonlinear phase shift introduced into a data stream by fixed (i.e., nonadaptive) recursive digital filters, e.g., those obtained via the bilinear transform.     

Big data-enabled multiscale serviceability analysis for aging bridges☆

This work is dedicated to constructing a multi-scale structural health monitoring system to monitor and evaluate the serviceability of bridges based on the Hadoop Ecosystem (MS-SHM-Hadoop). By taking the advantages of the fault-tolerant distributed file system called the Hadoop Distributed File System (HDFS) and high-performance parallel data processing engine called MapReduce programming paradigm, MS-SHM-Hadoop features include high scalability and robustness in data ingestion, fusion, processing, retrieval, and analytics. MS-SHM-Hadoop is a multi-scale reliability analysis framework, which ranges from nationwide bridge-surveys, global structural integrity analysis, and structural component reliability analysis. This Nationwide bridge survey uses deep-learning techniques to evaluate the bridge serviceability according to real-time sensory data or archived bridge-related data such as traffic status, weather conditions and bridge structural configuration. The global structural integrity analysis of a targeted bridge is made by processing and analyzing the measured vibration signals incurred by external loads such as wind and traffic flow. Component-wise reliability analysis is also enabled by the deep learning technique, where the input data is derived from the measured structural load effects, hyper-spectral images, and moisture measurement of the structural components. As one of its major contributions, this work employs a Bayesian network to formulate the integral serviceability of a bridge according to its components serviceability and inter-component correlations. Here the inter-component correlations are jointly specified using a statistics-oriented machine learning method (e.g., association rule learning) or structural mechanics modeling and simulation.     

可移动系统安全模型统一框架

本文的主要工作是利用可移动进程的形式化模型π演算为工具,考虑系统的移动性(Mobility),将系统安全属性的刻画归结为特定系统进程等价的验证,提出一种新的安全模型框架.在此框架下,可以方便表示不同的不干涉安全属性,并对其进行强弱对比;针对不同安全需求,可定义新的安全属性.并且,该框架建立一个新的安全属性研究的平台,可广泛地适用于具有移动进程的分布式系统的安全分析.     

Markov analysis of the PRMA protocol for local wireless networks

PRMA (packet reservation multiple access) is a reservation-ALOHA access protocol specifically designed for wireless microcellular networks that handle both real-time and non-real-time traffic. We present a thorough analysis of this protocol, considering real-time traffic only, based on a suitable Markov model. The size of the model is such that it can be directly used for an exact quantitative analysis of the system. In particular, we are able to analyze the packet dropping process, by evaluating both average and distribution measures. The latter are particularly useful to characterize the degradation caused to real-time traffic (e.g., voice) by the loss of consecutive packets. Besides, we also derive from the Markov model a qualitative analysis of the system stability, based on the equilibrium point analysis (EPA) technique. By this technique, we characterize the system stability and analyze the effect on it of several system parameters (e.g., load, permission probability). This revised version was published online in June 2006 with corrections to the Cover Date.     

Wireless,Flexible, Ionic,Perspiration-Rate Sensor System with Long-Time and High Sweat Volume Functions Toward Early-Stage,Real-Time Detection of Dehydration

Flexible sensors that can be attached to the body to collect vital data wirelessly enable real-time, early-stage diagnosis for human health management. Wearable sweat sensors have received considerable attention for real-time physiological monitoring. Unlike conventional methods that require blood-drawing in a clinic, sweat analyses may enable noninvasive tracking of health conditions for early-stage diagnosis. Even though a variety of studies to monitor metabolites and other substances have been conducted, automatic, continuous, long-term, simultaneous monitoring of perspiration rate and electrolytes, which are important parameters in dehydration, has yet to be achieved because of challenges related to sensor design. Here a wireless, wearable, integrated, microfluidic sensor system that can continuously measure these parameters in real-time for prolonged periods are presented. The proposed sensors are systematically characterized, and machine learning is used to predict device tilt angle to calibrate sensor output signals. Using the sensor design to form a water droplet in a fluidic channel, high-volume perspiration rate is continuously monitored for more than 7000 s (total sweat volume >170 µL). By testing 10 subjects, physiological responses to ingestion of a sports drink are confirmed by measuring perspiration rhythm changes extracted from real-time, continuous sweat impedance and rate.