Laboratories and monitors provide medical data for processing, and depending on the priority, the results are sent to the patients�� bedside terminal, the physicians�� tablet or e-mail address. The UCHS (Ubiquitous Context-Aware Healthcare Service System) [11] context-aware decision support system uses RFID sensors to sense users life vital signal, such as electrocardiogram (ECG/EKG), heart rate (HR), respiratory rate (RR), blood pressure (BP), blood sugar (BS), temperature and light. Medical expert suggestions are translated into a medical ontology, which provides users requirements inference and a relative services search in a UDDIrepository by a semantic inference engine.

Similarly, CAMPH (Context-Aware Middleware for Pervasive Home Care) [12] offers several key-enabling system services that consist of P2P-based context query processing, context reasoning for activity recognition and context-aware service management. It can be used to support the development and deployment of various home care services for the elderly, such as patient monitoring, location-based emergency response, anomalous daily activity detection, pervasive access to medical data and social networking. CAMPH is aimed to support autonomous physical spaces owned by different organizations, which enables the elderly to
Technological and methodological improvements allow for the study of increasingly complex processes and systems, not least for studying the inner workings of living cells [1,2]. Various detection modalities are used to this end, providing complementary advantages and information for probing and labeling cellular metabolites.

For example, several small-molecule and genetically encoded fluorescent probes are under examination for their potential to measure steady-state concentrations, enzyme activities and resulting intracellular reaction kinetics [1,3]. Other methods include IR [4], UV-Vis, luminescence, Raman [5] and NMR spectroscopy as well as destructive detection by mass spectrometry [2]. The choice of appropriate methods GSK-3 requires consideration of the ease of use, commercial availability, sensitivity, biocompatibility, selectivity, spatiotemporal resolution, general applicability, non-invasiveness and quantifiability [1].NMR spectroscopy is a robust, generally applicable and noninvasive method yielding quantifiable and high-resolution spectroscopic data that can distinguish analytes by resolving individual atomic sites. On the other hand, NMR spectroscopy has shortcomings in terms of sensitivity.

In particular, the energy consumption model is given for the target tracking application. In Section 4, the energy-efficient target tracking method is described in detail. The future target position forecasted by ARMA-RBF is adopted in the sleep mode scheduling and committee decision. Beside, the sensor-to-observer routing is presented for target position reporting. The experiments results are presented in Section 5, where the energy-efficient target tracking method with robust target forecasting is applied in WMSN. We conclude the paper in Section 6.2. Related WorkEnergy efficiency has drawn a lot of attention from various aspects of WSN research, such as hardware layer, media access control (MAC) layer, network layer, application layer, and so on [13].

Here, the target tracking application is discussed and we focus on the energy optimization on the network and application layers. Still, the multiple operation modes of sensor node are considered for power management. That is because the modules of sensor node can be well controlled by their operation system now [14].First of all, the deployment of WSN is discussed. The regular deployment is considered in this paper. To deploy the sensors based on a regular geometric topology, a precision weapon can be used to place the sensor nodes [15]. Although it is costly to deploy a regular structure of WSN, simpler and more efficient methods are readily available and a regular structure may benefit the specified application.Furthermore, the WSN we discuss can capture and process multimedia data, which is so-called WMSN.

Video or audio sensors can be used to enhance and complement existing surveillance systems against crime and terrorist attacks [1]. Here, the acoustic sensors are adopted to localize the target. In [16], an environmental monitoring system is provided to record animal behaviors for a long period of time. The shooter localization system collects the time stamps of the acoustic detection from different nodes within the network to localize the positions of the snipers [17]. The Line-in-the-Sand project focus
Ammonia (NH3) concentration measurement has a great importance in many scientific and technological areas. In environmental monitoring, automotive and chemical industry, electronic and optical ammonia sensors are widely used [1].

Recently the possibility to diagnose by ammonia sensing certain diseases, as ulcer or kidney disorder, has been proved. For example, NH3 concentration level measurement in exhaled air is a fast and non-invasive method to detect the presence of Helicobacter AV-951 pylori bacterial stomach infection [2].The most frequently used technique in commercial ammonia detectors is based on SnO2[3] and MoO3[4] semiconductor thin films. These sensors are mainly used in combustion gas detectors or gas alarm systems, but they show some limitations in reproducibility, stability, sensitivity and selectivity.

The measurement noise and sensor faults are likely to be stochastically unrelated, while event measurements are likely to be spatially correlated. The Bayesian detection scheme in [14] selects the minimum neighbors for a given detection error boundary such that the communication volume is minimized during the fault correction. Luo et al. in [14] did not explicitly attempt to detect faulty sensors, instead the schemes they proposed improve the event detection accuracy in the presence of faulty sensors.Article [15] presents a distributed fault detection algorithm for wireless sensor networks. Each sensor node identifies its own status based on
The increasing availability of commercial high-resolution satellite imaging sensors such as SPOT5, IKONOS, QuickBird and TerraSAR, requires the availability of suitable automatic interpretation tools to extract and identify cartographic features, especially in rapidly changing urban areas.

Roads are one of the most important linear cartographic features. Particularly, extraction of road networks from remotely sensed imagery is not only meaningful for cartography and topography [1], but also significant for various applications of geodata such as automatically aligning two spatial datasets [2] or automated vehicle navigation [3]. Therefore, research on the automatic extraction of road networks from remotely sensed imagery has been a topical research theme in the various fields of photogrammetry, remote sensing, geographic information systems, pattern recognition, and computer vision.

As a result, many strategies, methodologies and algorithms for road network extraction have been presented since the 1970s, which have achieved varying degrees of success [4]. According to the level of automation, Cilengitide the techniques for road extraction with the aid of computer vision can be coarsely classified into automatic and semi-automatic approaches.The automatic methods attempt to seek an analysis and interpretation of the image similar to that of a human operator. Nevatia and Babu [5] utilized an edge detection method to identify ribbon roads with lateral and parallel anti-edges. Radon transform was employed to locate the roadsides and to measure the width of a road [6]. Due to the variation of the complexity of image contents, the above low level edge detection methods are insufficient to extract the road features with high completeness, correctness, quality and accuracy. Therefore, more high level techniques have been developed. For example, angular texture signatures can make use of the characteristics of road’s texture, and this is utilized to find candidate road centerline points [7] or to discriminate road surfaces from the parking lots [8].