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We propose an activity recognition system for the elderly using a wearable sensor module embedding a tri-axial accelerometer, considering maximization of battery life. The sensor module embedding both a tri-axial acceleration sensor and an RF transmission module is worn at the right side of one's waistband. It connects and transfers sensing data to subject's PDA phone. Then, an algorithm on the PDA phone accumulates the data and classifies them as an activity. We utilize 3 tilts in addition to 3 acceleration values, compared to previous works. However, we reduce the sampling rate of the sensing data for saving battery life. As an activity classifier, the SVM (Support Vector Machine) algorithm is used, and we have achieved 96% of accuracy in detecting an activity out of 9. It shows the proposed method can save the battery life without losing the recognition accuracy compared to related works.

The success of eScience research depends not only upon effective collaboration between scientists and technologists but also upon the active involvement of data archivists. Archivists rarely receive scientific data until findings are published, by which time important information about their origins, context, and provenance may be lost. Research reported here addresses the life cycle of data from collaborative ecological research with embedded networked sensing technologies. A better understanding of these processes will enable archivists to participate in earlier stages of the life cycle and to improve curation of these types of scientific data. Evidence from our interview study and field research yields a nine-stage life cycle. Among the findings are the cumulative effect of decisions made at each stage of the life cycle; the balance of decision-making between scientific and technology research partners; and the loss of certain types of data that may be essential to later interpretation.

In multihop wireless sensor networks, global clock error will accumulate as the hops grow. Sensors far away from the root are likely to suffer larger synchronization errors, which may deteriorate the accuracy of sensing data or even result in the failure of TDMA MAC protocols. A novel algorithm based on vector Kalman filter using multiple parents (KFMP) is proposed to address this problem. It's pointed out that receiving a synchronization message from one parent is equivalent to obtaining an observation of the global clock, thus more accurate result could be achieved by combining observations from multiple parents. In KFMP, vector Kalman filter is adopted to combine these observations. Both hardware implementation and system simulation are conducted to evaluate the performance of the proposed algorithm. The results show that global clock error is significantly reduced compared with FTSP, especially for sensors at the edge of the network.

Sensor node of RFID Middleware system can usually be reacted from six mouths to two years, they can be exhausted their power of storage battery only one day by communication rates and ranges. And 1:n sensor arrangement of n form command and data send-receive that is possible simulation do without interference other sensors and research to different sensor data analysis and conversion ways to convert Sensing data that accept from sensors to actuality information.

We present a semi-supervised multitask learning (MTL) framework, where we have multiple partially labeled data manifolds, each defining a classification task for which we wish to design a semi-supervised classifier. These different data sets may be observed simultaneously, or over the sensor "lifetime". We propose a soft sharing prior over the parameters of all classifiers and learn all tasks jointly. The soft-sharing prior enables any task to robustly borrow information from related tasks. The semi-supervised MTL combines the advantages of semi-supervised learning and multitask learning, thus further improving the generalization performance of each classifier. Our MTL (or life-long learning) framework is based on our previous semi-supervised learning formulation, termed neighborhood-based classifier (NeBC) [1]. The performance of the semi-supervised MTL is validated by experimental results on several sensing data sets.

This paper studies the stability robustness of a multirate system where sensing data are obtained n (n > 1 is an integer) times slower than the control variables are updated. The robust controller is designed for such systems by using the Hinfin technique. The procedure to check the robust stability of the controlled system is presented; and an illustrative example is given to show the effects of the value n on the robust stability of the system with the designed Hinfin controller.

We propose the simulation framework that is a software framework for minimizing the effort of accessing integrated information from sensing data within aquaculture monitoring system. Simulation framework is composed by filtering and data integration module, state modeling module, data analysis module, data maintenance module, and visualization module.

The detection of chemical warfare simulants (CWSs) in the presence of high concentrations of common interferences was demonstrated with solid-state chemical microsensor arrays. Conductometric SnO2 and TiO2 sensing films deposited on microhotplates were utilized as transducers. The embedded microheaters were used to modulate the temperature of the sensor-array elements, while measuring conductance at each temperature. The resulting temperature-modulated sensing data was normalized to the high-temperature conductance and an artificial neural network was used to perform pattern-recognition on the resultant conductivity data vectors

We present the results obtained by applying different feature selection (FS) methods to the analysis of chemical sensing data. Four datasets have been considered: a mass spectrometer (MS) dataset with 505 peaks (features) and three datasets from metal oxide sensor based E-noses with 30 features. For the MS data we first filter features individually and then apply optimal and suboptimal FS search strategies. The three E-nose datasets present different discrimination hardness and different number of data. For every classification problem three classifiers are tested (3NN, LDA, QDA). FS performance is calculated by two nested cross-validation cycles in order to prevent selection bias. FS always increases the correct test set classification ratio (sometimes the increase is substantial) and the discriminative features are recognized. To our knowledge, this is the first benchmarking study evaluating several FS options (classifiers and search strategies, including suboptimal ones) for the classification of different chemical sensing datasets

The new synergetic approach to sensing data for automative applications are presented. Software development to build networks of non-homogeneous technologies are discussed. Approach dealing with parts of the engine, detectors within the car and the car itself in connection to the outside world are also presented.

The reliability of CoSi2/p-poly Si electrical fuse (eFUSE) programmed by electromigration for 90nm technology will be presented. Both programmed and unprogrammed fuse elements were shown to be stable through extensive reliability evaluations. A qualification methodology is demonstrated to define an optimized reliable electrical fuse programming window by combining fuse resistance measurements, physical analysis, and functional sensing data. This methodology addresses the impact on electrical fuse reliability caused by process variation and device degradation (e.g., NBTI) in the sensing circuit and allows an adequate margin to ensure electrical fuse reliability over the chip lifetime.

In this paper, we consider the problem of positioning mobile cluster heads and balancing traffic load in a hybrid sensor network, which consists of two types of nodes: basic static sensor nodes and mobile cluster heads. In such a network, sensor nodes are organized into clusters and form the lower layer of the network. At the higher layer, cluster heads collect sensing data from sensors and forward data to outside observers. Such two-layer hybrid networks are more scalable and energy-efficient than homogeneous sensor networks. We show that the locations of cluster head-s can affect network lifetime significantly. The problem of maximizing network lifetime through dynamically positioning cluster heads in the network (referred to as the CHL problem in this paper) turns out to be NP-hard. We present a heuristic algorithm for positioning cluster heads and balancing traffic load in the network. We show that by moving the cluster head to a better location, the traffic load can be balanced and network lifetime can be prolonged. We conducted simulations on the NS-2 simulator, and the result-s show that our clustering algorithm can increase network lifetime by up to 35% after only three rounds of adjustments, compared to the optimal lifetime of the initial network layout.

Hithin the batholith of Nont LozPre (Civennes, Southern France) a special f acies corresponding to the uppermost part of the granite hathol it6 was distinguished by petrographs (granite of "Soruet de Finiels"). This facies actually gives a particular spectral response on the high spatial resolution satellite imagery of SPOT, notably in the region of the Tarn springs. an interdisciplinar y approach including geororphological, botanical and geological studies, based on field and rerote sensing data, shows that this area precisely corresponds to the part of bathol ith which was submitted to the strongest Cenozoic glacial events, Other evidences of glaciogenic features in aubrac and Yelay region, farther to the Worth, are also discussed.

'Intellectual property' is a developjng category of Law not wholly recognised or assimilated into all legal systems. Many contracts for the supply of remote sensing data are not contracts of sale, but a licensing of rights ti, use the data subject to conditions. The suppliers of data are in a strong position. The formation of 'user's associations' may help to offset that strength, and allow the negotiation of better terms and condjtions. Obviously when there is a matter of commercial gain involved the argument is less strong, but for academic use the experience of other areas of knowledge indicates that some flexibility can be attained.

It is important to detect the position of contact points between the grasped object and the fingertips of dexterous hand. The method using force/torque sensor mounted at fingertip is simpler and easier than the tactile sensor to get the contact position. However, there are two problems held back the application of the former when the sensing data is contaminated with disturbance. First, when the hard finger contact model is used to calculate the points, the solution may not exist. This paper investigates the problem under the contact models of hard finger and soft finger respectively. Mathematical verification is developed to find the existence condition of solutions. A simple criterion is provided to determine which contact model must be selected for calculating contact points. The next problem is accuracy. The error of soft finger contact model is analyzed. Some strategies are given to improve the accuracy of the contact localization. Simulations and experiments show that the reliable and precise measurement of contact points can be achieved by the proposed approaches.

The paper is devoted to analysis of experimental sensing data of clouds and precipitation by microwave radars and their comparison with theoretical models. As a result, a features set of polarimetric variables, which are necessary as a priori data for synthesis of processing algorithms of radar signals, is formed with the purpose to solve various applied problems, in particular, hail recognition for flight safety and air traffic control.

Sensor fusion method is more robust than the method using a single sensor, so sensor fusion is effective for vehicle recognition in a complex scene. However, if each sensing data is processed individually for most of the stages, recognition performance is not always good. In this paper, we propose the extensible and generalized fusion method. First, fusion vector which is combined with image sensor data and laser radar data at a primitive level is prepared. We regard fusion vector as sensing data by one robust sensor. Next, fusion vector is compared with a discriminated dictionary. We report the efficiency of our method at a complex scene in which recognition error tends to occur by using a single sensor.

The dynamics of the shoreline in French Guiana are characterized mainly by the northwestward migration of nearshore mudbanks. The passage of these features is responsible for significant geomorphic changes at the shoreline, such as the creation and erosion of extensive mudflats and the deviation of estuarine river mouths. Indeed, the hydrodynamic perturbation of the mean alongshore coastal currents generated by river discharge and tidal outflow from these estuaries can result in the immobilization of parts of a migrating mudbank, leading to the creation of a mudflat. Due to the general northwestward direction of coastal oceanic dynamics in this area, such mudflat development always occurs southeast (updrift) of the river mouths and consequently contributes to their northwestward diversion, forming a mudcape. Erosional processes on the marine side of mudcapes often result in a secondary outlet channel that gradually replaces the former main channel. In order to quantify the rate of these geomorphic changes, monitoring of an estuarine area was carried out at different time scales: on a decadal basis, from 1951 to 2001, with airborne sensing data, and over a shorter period covering a complete interbank situation (i.e., between the passage of two successive mudbanks), from 1999 to 2002, by means of field surveys and airborne digital videography. This short-term morphological monitoring was conducted from the early stage of mudflat consolidation and mangrove colonization to the arrival of the following mudbank at the edge of the study area. Various modes of erosion and the diversity of the eroded topography were highlighted by the study. Other aspects brought out by the work are the rate of mangrove colonization and its influence on the erosion processes, the influence of the tide on geomorphic processes, and the role of pioneer fluid mud deposits, related to the proximity of the next mudbank, on the dynamics of the mudflat system.

Li-, Znand Au-doped F@O3 thin films have been investigated as humidity sensing materials. Depending on the nature and loading of the dopant, microstructural modifications on the host metal oxide have been observed. The humidity sensing properties of these films were studied by means of Electrochemical Impedance Spectroscopy (EIS). Humidity sensing data have been then analysed by an electrical modeling and correlated to the microstructural characteristics.

While sensor-enabled devices have greatly enriched human interactions in our daily life, discovering the essential knowledge behind sensing data is a critical issue to connect the cyber world and the physical world. This motivates us to design an innovative sensor-aided social network system, termed cyber-physical handshake. It allows two users to naturally exchange personal information with each other after detecting and authenticating the handshaking patterns between them. This work describes our design of detection and authentication mechanisms to achieve this purpose and our prototype system to facilitate handshake social behavior.

Remote sensing by satellites is providing some valuable insights—or are they outsights?—into large-scale microbiological activities in the oceans, according to two recent reports in Geophysical Research Letters (GRL). In one case, satellite sensing data suggest that the 2010 oil spill in the Gulf of Mexico triggered a massive algae bloom. In the other, data gathered above the Southern Ocean suggest that regional marine microbial responses are affecting global weather patterns.

Wavefront sensing using a Shack-Hartrnann sensor relies on the accuracy of the centroid estimation. The traditional process however suffers from several problems. A modelfitting procedure is shown to have superior performance.

A polarization backscattering phase matrix for oriented semitransparent plates is given in the present paper. The matrix elements have been numerically investigated. Regular dependences have been established of the matrix elements on the orientation angle of particles with respect to the sensing direction, the orientation angle of the polarization plane, and the refractive index of particles. An algorithm has been developed for determining the refractive index and the orientation angle of ice plates from the data of polarization lidar sensing for two-angle sensing geometry. A formula has been derived that relates the main parameters of the medium with the backscattering coefficient that can be used for interpretation of highly intense lidar return signal.

The objectives of this study were to compare and characterize the soil water measurement responses using the EnviroSCAN semi permanent multisensor capacitance probes (MCP) and the Diviner 2000 (D2k) portable capacitance probe. Both probes are manufactured by Sentek Pty. Ltd., Stepney, South Australia. Comparative soil water measurements were made using the portable D2k and the MCP probes at three field sites where 48 polyvinylchloride (PVC) access tubes were previously installed and being used to near-continuously monitor soil water content at four or more depths. The D2k and MCP soil water sensing data were highly correlated values (R 2 > 0.98), but the D2k water contents averaged about 5% more than the corresponding MCP values. As a result of this high correlation we were able to transform the D2k readings so that the two probes gave essentially the same water content values when measured as the same time and position.

A touch screen panel (TSP) embedded 12.1-inch LCD employing a standard a-Si:H TFT-LCD process has been successfully developed. Compared with conventional touch screen panels, which use additional components to detect touch events, the new TSP exhibits a clearer image and improved touch feeling, as well as enabling the LCD module to provide the sensing data through the driver ICs with an integrated readout function. Our new internal digital switching TSP can be fabricated with low cost because it does not require any additional components compared to a standard a-S:H TFT LCD.

Collection, Processing, and Documentation of Reliable Environmental Informations - Airborne close-range sensing data volumes require new data management concepts Computer-aided methods provide today the possibility to handle, process, analyse, and retrieve large information volumes. Both, remote and close-range sensing, collect extreme quantities of data, but with different focus. Therefore, different processing strategies need to be developed to combine and handle 2-dimensional spectral data covering wide areas or analog stereo models of small regions. TOPOGRAMM uses both, digital and analog acquisition and, merges local and global scopes combining conventional aerial photogrammetry with high precision measurements.

While the characterization of food consumption level has been extensively studied in nutrition and psychology research, advancements in passive smartphone sensing have not been fully utilized to complement mobile food diaries in characterizing food consumption levels. In this study, a new dataset regarding the holistic food consumption behavior of 84 college students in Mexico was collected using a mobile application combining passive smartphone sensing and self-reports. We show that factors such as sociability and activity types and levels have an association to food consumption levels. Finally, we define and assess a novel ubicomp task, by using machine learning techniques to infer self-perceived food consumption level (eating as usual, overeating, undereating) with an accuracy of 87.81% in a 3-class classification task by using passive smartphone sensing and self-report data. Furthermore, we show that an accuracy of 83.49% can be achieved for the same classification task by using only smartphone sensing data and time of eating, which is an encouraging step towards building context-aware mobile food diaries and making food diary based apps less tedious for users.

Wireless communications have a rapid growth that leads to huge demand on the deployment of new wireless services in both licensed and unlicensed frequency spectrum. The performance of Cooperative Spectrum Sensing (CSS) in cognitive radio is based on two factors: cooperative gain and cooperative overhead. There are many other dominating factors that affect the performance of CSS like the presence of attacks, energy efficiency, sensing time and delay, spectrum efficiency, etc. The major threat to CSS is Spectrum Sensing Data Falsification (SSDF) attack. Existing methods to mitigate the SSDF attack doesn't focus on energy efficiency which is performed here. To deal with these challenges, we propose an energy efficient mechanism against SSDF attack for healthcare application in Cognitive Radio Networks (CRNs). We propose a Malicious Node Identification Algorithm (MIA) to mitigate the problem of SSDF attack in the network and to eliminate the redundant data at the fusion centre, Balanced Cluster-based Redundancy Check Aggregation (BCRCA) mechanism is proposed. Finally, the decision taken by the fusion centre is based on Weighted Selection Combining (WSC) technique that improves spectrum decision accuracy.

Currently, there are several ongoing efforts for the definition of new regulation policies, paradigms, and technologies aiming a more efficient usage of the radio spectrum. In this context, cognitive radio (CR) emerges as one of the most promising players by enabling the dynamic access to vacant frequency bands on a non-interference basis. However, the intrinsic characteristic of CR opens new ways for attackers, namely in the context of the effective detection of incumbent or primary users (PUs), the most fundamental and challenging requirement for the successful operation of CR networks. In this article, we provide a global and integrated vision of the main threats affecting CR environments in the context of the detection of primary users, with a particular focus on spectrum sensing data falsification and primary user emulation attacks. We also address solutions and research challenges still required to address such threats. Our discussion aims at being complete and self-contained, while also targeting readers with no specific background on this important topic of CR environments. It is, as far as our knowledge goes, the first work providing a global and clear vision of security threats and countermeasures in the context of primary user detection in CR.

Against the background of a worldwide decrease in the number of gauging stations, the estimation of river discharge using spaceborne data is crucial for hydrological research, river monitoring, and water resource management. Based on the at-many-stations hydraulic geometry (AMHG) concept, a novel approach is introduced for estimating river discharge using Sentinel-1 time series within an automated workflow. By using a novel decile thresholding method, no a priori knowledge of the AMHG function or proxy is used, as proposed in previous literature. With a relative root mean square error (RRMSE) of 19.5% for the whole period and a RRMSE of 15.8% considering only dry seasons, our method is a significant improvement relative to the optimized AMHG method, achieving 38.5% and 34.5%, respectively. As the novel approach is embedded into an automated workflow, it enables a global application for river discharge estimation using solely remote sensing data. Starting with the mapping of river reaches, which have large differences in river width over the year, continuous river width time series are created using high-resolution and weather-independent SAR imaging. It is applied on a 28 km long section of the Mekong River near Vientiane, Laos, for the period from 2015 to 2018.