Metaphor and Manifestation Cross-Reality with Ubiquitous Sensor/Actuator Networks

A series of projects by the MIT Media Lab's Responsive Environments Group explore ways of bridging the rapidly expanding reach of networked electronic sensors with the limited realm of human perception. These include various implementations of cross-reality which render and manifest phenomena between the real world and shared online virtual environments via densely embedded sensor and actuator networks. We visualize information from ubiquitously deployed real-world smart power strips and sensor-rich media portals at different levels of abstraction through analogous Second Life constructs. Conversely, we manifest virtual world events into physical space using on-platform actuators and displays. We also show a set of simpler 2D visualizations that enable mobile devices to efficiently browse and interact with sensor network data. We touch on a recently developed system that uses a small badge to passively manage dynamic privacy in environments such as these that stream potentially revealing information across the real/virtual divide. These technologies' application areas involve fluid browsing of and interaction with the geographically dispersed real world in an unconstrained virtual environment and ubiquitous multiscale telepresence.     

Query processing in sensor networks

Smart sensors are small wireless computing devices that sense information such as light and humidity at extremely high resolutions. A smart sensor query-processing architecture using database technology can facilitate deployment of sensor networks. Smart-sensor technology enables a broad range of ubiquitous computing applications. Their low cost, small size, and untethered nature lets them sense information at previously unobtainable resolutions. We discuss about query processing in sensor networks.     

Interactive relational reinforcement learning of concept semantics

We present a framework for the machine learning of denotational concept semantics using a simple form of symbolic interaction of machines with human users. The capability of software agents and robots to learn how to communicate verbally with human users would obviously be highly useful in several real-world applications, and our framework is meant to provide a further step towards this goal. Whereas the large majority of existing approaches to the machine learning of word sense and other language aspects focuses on learning using text corpora, our framework allows for the interactive learning of concepts in a dialog of human and agent, using an approach in the area of Relational Reinforcement Learning. Such an approach has a wide range of possible applications, e.g., the interactive acquisition of semantic categories for the Semantic Web, Human-Computer Interaction, (interactive) Information Retrieval, and Natural Language Processing.     

Human interaction discovery in smartphone proximity networks

Since humans are fundamentally social beings and interact frequently with others in their daily life, understanding social context is of primary importance in building context-aware applications. In this paper, using smartphone Bluetooth as a proximity sensor to create social networks, we present a probabilistic approach to mine human interaction types in real life. Our analysis is conducted on Bluetooth data continuously sensed with smartphones for over one year from 40 individuals who are professionally or personally related. The results show that the model can automatically discover a variety of social contexts. We objectively validated our model by studying its predictive and retrieval performance.     

A methodology for learning across application domains for databasedesign systems

Although database design tools have been developed that attempt to automate (or semiautomate) the design process, these tools do not have the capability to capture common sense knowledge about business applications and store it in a context-specific manner. As a result, they rely on the user to provide a great deal of "trivial" details and do not function as well as a human designer who usually has some general knowledge of how an application might work based on his or her common sense knowledge of the real world. Common sense knowledge could be used by a database design system to validate and improve the quality of an existing design or even generate new designs. This requires that context-specific information about different database design applications be stored and generalized into information about specific application domains (e.g., pharmacy, daycare, hospital, university, manufacturing). Such information should be stored at the appropriate level of generality in a hierarchically structured knowledge base so that it can be inherited by the subdomains below. For this to occur, two types of learning must take place. First, knowledge about a particular application domain that is acquired from specific applications within that domain are generalized into a domain node (e.g., entities, relationships, and attributes from various hospital applications are generalized to a hospital node). This is referred to as within domain learning. Second, the information common to two (or more) related application domain nodes is generalized to a higher-level node; for example, knowledge from the car rental and video rental domains may be generalized to a rental node. This is called across domain learning. This paper presents a methodology for learning across different application domains based on a distance measure. The parameters used in this methodology were refined by testing on a set of representative cases; empirical testing provided further validation     

PVDF-Based Microfabricated Tactile Sensor for Minimally Invasive Surgery

This paper aimed to develop a miniaturized tactile sensor capable of measuring force and force position in minimally invasive surgery. The in situ measurement of tactile information is a step forward toward restoring the loss of the sense of touch that has occurred due to shift from traditional to minimally invasive surgeries. The sensor was designed such that it can sense low forces which could be comparable to those produced by pulsating delicate arteries, yet can withstand high forces comparable to grasping forces. The influence of some hidden anatomical features, such as lumps, voids, and arteries, on the stress distribution at the grasping surface was studied. In this paper, the capability of the sensor to determine and locate any point load was also investigated. The proposed sensor was designed and manufactured to be highly sensitive, using polyvinylidene fluoride (PVDF). The microfabrication procedure of the sensor, including corner compensation for toothlike projections and patterning of PVDF film, was discussed. The micromachined sensor was tested, and the experimental results were compared with the results of 3-D finite element modeling. $hfill$[2007-0286]      

DMSP/OLS夜间灯光数据应用研究综述

DMSP/OLS以其独特的光电放大特性与对夜间灯光的获取能力,成为人类活动监测的良好数据源。回顾了DMSP/OLS夜间灯光数据在城镇信息提取、社会经济因子估计及光污染、火灾、渔火、天然气燃烧监测等方面的应用,其中重点介绍了利用灯光数据估算城市化水平和人口的方法和步骤,并总结了DMSP/OLS夜间灯光数据在应用中的优越性与局限性,最后对其未来应用方向进行了展望。      

Taking clues from the world outside: navigating interactive panoramas

Physical and physiological properties play an important role in how we perceive and interact with the world around us. The subtle perceptual effects are often overlooked when mapping the environment on screens such as computer monitors or PDA displays. This article shows some ways to improve interactive exploration of virtual scenes through panoramic photography, by the effect of adding the subtleties of physical aspects of the human body and interaction with the environment. The same principle of taking clues from the physicality of the world around us to enhance interaction can be extended to other applications as well. This paper is part of the 3AD design colloquium creative connections     

MidFusion: An adaptive middleware for information fusion in sensor network applications

Applications and services are increasingly dependent on networks of smart sensors embedded in the environment to constantly sense and react to events. In a typical sensor network application, information is collected from a large number of distributed and heterogeneous sensor nodes. Information fusion in such applications is a challenging research issue due to the dynamicity, heterogeneity, and resource limitations of sensor networks. We present MidFusion, an adaptive middleware architecture to facilitate information fusion in sensor network applications. MidFusion discovers and selects the best set of sensors or sensor agents on behalf of applications (transparently), depending on the quality of service (QoS) guarantees and the cost of information acquisition. We also provide the theoretical foundation for MidFusion to select the best set of sensors using the principles of Bayesian and Decision theories. A sensor selection algorithm (SSA) for selecting the best set of sensors is presented in this paper. Our theoretical findings are validated through simulation of the SSA algorithm on an example scenario.     

面向ERT大面积触觉传感的自适应优化成像方法

在动态的非结构化环境中,有效地感知物理接触对于智能机器人安全交互至关重要。为了能够检测各种潜在的物理交互,需要在机器人表面部署大面积触觉传感器。目前,现有的大面积触觉传感器主要是通过传感阵列方式实现的,但是大规模部署传感元件在实际应用中存在巨大挑战。电阻层析成像(Electrical Resistance Tomography, ERT)技术作为一种连续传感方式,有望克服传统触觉传感阵列的一些限制。为此,利用ERT设计了一款新型的大面积触觉传感器。在此基础上,提出了一种基于自适应感兴趣区(Region of Interest, ROI)的图像重构算法,将图像重构限制在交互区域内,以提高传感器的空间分辨率。为了验证提出成像算法的有效性,通过仿真与物理实验对其进行了全面评估。实验验证了该算法可以有效提高触觉传感器在交互区域的空间分辨率,使其具有较高的测量精度。实验结果表明,该传感器的平均定位误差为0.823 cm,能够准确地识别8种不同交互模式,其精度高达98.6%。这一研究工作表明,该传感器为机器人具身触觉传感的实现提供了一个新的解决方案。     

EasiTest:多Radio异构无线传感器网络测试平台

设计并实现了一种多Radio的异构无线传感器网络综合测试平台系统EasiTest,该平台采用了两种不同性能、功能的测试节点,可以支持大规模、异构传感器网络综合性能测试以及应用性研究.平台内采用了结构复杂、功能完备的多Radio的EZ271作为中高速测试节点,以及以 WiFi作为测试通道的EZ521作为中低速测试节点;系统提供传感/测试数据收集、数据管理、资源分配、服务器状态检查、节点状态检查与参数配置以及资源注册共享等功能模块,保障用户能够方便快捷地实现功能实验;通过友好的 Web访问页面,用户能够模拟真实的网络环境对多种类,如802.15.4,802.11协议等,以及多层次,如 MAC层、路由层、应用层等的网络协议研究成果进行灵活的综合实验验证,从而能够有效地提高无线传感器网络技术的研究效率.     

Sensor planning for mobile robot localization using Bayesian network inference

We propose a new method of sensor planning for mobile robot localization using Bayesian network inference. Since we can model causal relations between situations of the robot's behavior and sensing events as nodes of a Bayesian network, we can use the inference of the network for dealing with uncertainty in sensor planning and thus derive appropriate sensing actions. In this system we employ a multi-layered-behavior architecture for navigation and localization. This architecture effectively combines mapping of local sensor information and the inference via a Bayesian network for sensor planning. The mobile robot recognizes the local sensor patterns for localization and navigation using a learned regression function. Since the environment may change during the navigation and the sensor capability has limitations in the real world, the mobile robot actively gathers sensor information to construct and reconstruct a Bayesian network, and then derives an appropriate sensing action which maximizes a utility function based on inference of the reconstructed network. The utility function takes into account belief of the localization and the sensing cost. We have conducted some simulation and real robot experiments to validate the sensor planning system.     

Applying video sensor networks to nearshore environment monitoring

Environmental monitoring is an important emerging application area for pervasive computing. We describe shore-based sensing using standard video cameras and measurement techniques for important variables such as wave and ocean current conditions. We apply networked sensors for monitoring environmentally sensitive beaches and nearshore coastal oceans. We give some steps to improve the Argus video sensor network's functionality to quantify the time-space characteristics of the imaged world. We also discuss future system architectures, on the basis of the experience with a sensor network that have already deployed.     

Pattern-based event detection in sensor networks

Many applications of wireless sensor networks monitor the physical world and report events of interest. To facilitate event detection in these applications, in this paper we propose a pattern-based event detection approach and integrate the approach into an in-network sensor query processing framework. Different from existing threshold-based event detection, we abstract events into patterns in sensory data and convert the problem of event detection into a pattern matching problem. We focus on applying single-node temporal patterns, and define the general patterns as well as five types of basic patterns for event specification. Considering the limited storage on sensor nodes, we design an on-node cache manager to maintain the historical data required for pattern matching and develop event-driven processing techniques for queries in our framework. We have conducted experiments using patterns for events that are extracted from real-world datasets. The results demonstrate the effectiveness and efficiency of our approach.     

FlexRFID: A design,development and deployment framework for RFID-based business applications

The surge of RFID technology and ensuing competition in the RFID vendor market has increased the complexity of integration of RFID into current business processes. To address this problem, we present FlexRFID, a modeling template composed of state indicators, conditions, a simulation engine, and a device-independent deployment architecture for rapid prototyping of control applications using RFID and sensor hardware. The modeling technique is based on Moore Machines, a variant of finite state automata that allow states to be associated with outputs. With FlexRFID, users design applications by modeling business logic via transition of states based on sensor events. Outputs associated with individual states can handle operational characteristics of the application. In this article, we present the modeling methodology and demonstrate how organizations can develop complex applications easily using FlexRFID. We believe that the simplicity and flexibility of FlexRFID will lead to the development of RFID applications quickly even with limited resources. In addition, the parking entry application presented in our paper demonstrates FlexRFID’s capability of being used in business application context, as well as in research, training, and teaching.     

A Flexible Architecture for Virtual Humans in Networked Collaborative Virtual Environments

Complex virtual human representation provides more natural interaction and communication among participants in networked virtual environments, hence it is expected to increase the sense of being together within the same virtual world. We present a flexible framework for the integration of virtual humans in networked collaborative virtual environments. A modular architecture allows flexible representation and control of the virtual humans, whether they are controlled by a physical user using all sorts of tracking and other devices, or by an intelligent control program turning them into autonomous actors. The modularity of the system allows for fairly easy extensions and integration with new techniques making it interesting also as a testbed for various domains from “classic” VR to psychological experiments. We present results in terms of functionalities, example applications and measurements of performance and network traffic with an increasing number of participants in the simulation.     

Highly sensitive mass sensor using film bulk acoustic resonator

Film bulk acoustic resonators (FBAR) have recently been adopted as alternatives to surface acoustic wave (SAW) in high frequency devices, due to their inherent advantages, such as low insertion loss, high power handling capability and small size. FBAR device can also be one of the standard components as mass sensor applications. FBAR sensors have high sensitivity, good linearity, low hysteresis and wide adaptability. In this study, a highly sensitive mass sensor using film bulk acoustic resonator was developed. The device structure of FBAR is simulated and designed by the Mason model, and fabricated using micro electromechanical systems (MEMS) processes. The fabricated FBAR sensor exhibits a resonant frequency of 2442.188 MHz, measured using an HP8720 network analyzer and a CASCADE probe station. Experimental results indicate that the mass loading effects agree with the simulated ones. Results of this study demonstrate that the sensitivity of the device can be achieved as high as 3654 Hz cm²/ng.     

Human tracking from single RGB-D camera using online learning

Human detection and tracking is an essential component in several robotics applications, especially in indoor built environments where humans and robots are expected to coexist or collaborate. Due to their low cost and capability to capture both color and depth data, RGB-D cameras have shown significant promise in human detection and tracking for robotic applications. In this paper, a new human tracking method is proposed to detect and track a specific individual from a single RGB-D sensor using online learning classifiers with no ground plane assumption. Given a previous target human position, a candidate sampling method is designed to find potential positive samples while negative samples are obtained by a random sampling process. The kernelized Support Vector Machine (SVM) is employed as the online classifier to recognize the target human and updated using both the positive and negative examples. The experimental results on six RGB-D videos of a public dataset demonstrate that the proposed method achieves higher success rates compared to a 2D tracker and a 3D human detection method at a frame rate of 3.8 fps, and is capable of efficiently retrieving the target human following intermittent occlusion.     

Situated Computing: A Paradigm for the Mobile User-Interaction with Multimedia Sources

Today, users interact with computers in an explicit manner and the system's response is independent from their situations. Hence, it is difficult to integrate computers with working life as embedded tools, which can facilitate users to accomplish real world objectives easily. Situated computing is a new paradigm for mobile computer users based on their physical context and activities carried out as a part of their working business. It provides the mechanism to have a mobile computer as a utility to satisfy the user's real world requirements as well as an infrastructure for the situated interaction using applications. In this paper, we are presenting a metaphor called situation metaphor to model interaction between the user and mobile computers in order to achieve expectations of situated computing. A three-layered schema is followed in developing situation metaphor. We discuss extensively the theoretical foundation and framework for the situation metaphor, and followed by applications developed based on the framework.     

A survey of energy-efficient context recognition systems using wearable sensors for healthcare applications

Human context recognition (HCR) from on-body sensor networks is an important and challenging task for many healthcare applications because it offers continuous monitoring capability of both personal and environmental parameters. However, these systems still face a major energy issue that prevent their wide adoption. Indeed, in healthcare applications, sensors are used to capture data during daily life or extended stays in hospital. Thus, continuous sampling and communication tasks quickly deplete sensors’ battery reserves, and frequent battery replacement is not convenient. Therefore, there is a need to develop energy-efficient solutions for long-term monitoring applications in order to foster the acceptance of these technologies by the patients. In this paper, we survey existing energy-efficient approaches designed for HCR based on wearable sensor networks. We propose a new classification of the energy-efficient mechanisms for health-related human context recognition applications and we review the related works in detail. Moreover, we provide a qualitative comparison of these solutions in terms of energy-consumption, recognition accuracy and latency. Finally, we discuss open research issue and give directions for future works.