Unattended Manner Research Articles

Abstract P311: Revisiting unattended vs attended automated office blood pressure measurements: A systematic review and meta-analysis

Background: Standardized automated office blood pressure (AOBP) measurement is now widely accepted as the preferred method for monitoring blood pressure (BP). However, disagreements persist amongst expert societies as to the need to perform AOBP in an unattended manner, where the patient is left alone in a quiet room. In 2019, two meta-analyses aiming to quantify the differences between unattended and attended BP showed opposite results. prompting the publication of several more studies on this subject. Our objective was thus to reassess the BP differences between unattended and attended AOBP when performed with the same device and an identical measurement protocol. Methods: This systematic review with meta-analysis was conducted according to the PRISMA guidelines and is registered on PROSPERO. Studies were included if both unattended and attended AOBP were measured on the same participants in a randomized or alternating sequence manner during a single visit with the same device and an identical measurement protocol apart from attendance status. Four databases were searched (Embase, Medline, Web of Science, Google Scholar). The primary outcome was the difference between unattended and attended systolic (SBP) and diastolic (DBP) BP, expressed as weighted mean differences (95% confidence interval) using random-effects models. Results: From 8,088 screened studies, data was extracted from 15 studies which met the inclusion criteria (n=1,747 participants). In the pooled analysis, the attended compared with unattended BP was higher for SBP by 2.7 mmHg (95%CI 0.6 to 4.7) and DBP by 0.9 mmHg (95%CI 0.1 to 1.8). In leave-one out analyses, results were similar except when excluding the study with the most extreme BP difference (Chotruangnapa 2023) where the difference became non-significant. Conclusion: Standardized attended AOBP measurements result in slightly higher readings than unattended AOBP. Whether such small differences translate into clinically significant changes in management of BP remains uncertain. Therefore, standardized attended AOBP could be viewed as a reasonable option for BP measurement in a real-life clinical setting.

A novel measurement system for unattended, in situ characterization of carbonaceous aerosols

Abstract. Carbonaceous aerosol is a relevant constituent of the atmosphere in terms of climate and health impacts. Nevertheless, measuring this component poses many challenges. There is currently no simple and sensitive commercial technique that can reliably capture its totality in an unattended manner, with minimal user intervention, for extended periods of time. To address this issue we have developed the fast thermal carbon totalizator (FATCAT). Our system captures an aerosol sample on a rigid metallic filter and subsequently analyses it by rapidly heating the filter directly, through induction, to a temperature around 800 ∘C. The carbon in the filter is oxidized and quantified as CO2 in order to establish the total carbon (TC) content of the sample. The metallic filter is robust, which solves filter displacement or leakage problems, and does not require a frequent replacement like other measurement techniques. The limit of detection of our system using the 3σ criterion is TC =0.19 µg-C (micrograms of carbon). This translates to an average ambient concentration of TC =0.32 µg-C m−3 and TC =0.16 µg-C m−3 for sampling interval of 1 or 2 h respectively using a sampling flow rate of 10 L min−1. We present a series of measurements using a controlled, well-defined propane flame aerosol as well as wood-burning emissions using two different wood-burning stoves. Furthermore, we complement these measurements by coating the particles with secondary organic matter by means of an oxidation flow reactor. Our device shows a good correlation (correlation coefficient, R2>0.99) with well-established techniques, like mass measurements by means of a tapered element oscillating microbalance and TC measurements by means of thermal–optical transmittance analysis. Furthermore, the homogeneous fast-heating of the filter produces fast thermograms. This is a new feature that, to our knowledge, is exclusive of our system. The fast thermograms contain information regarding the volatility and refractoriness of the sample without imposing an artificial fraction separation like other measurement methods. Different aerosol components, like wood-burning emissions, soot from the propane flame and secondary organic matter, create diverse identifiable patterns.

Systematic measuring cortical thickness in tibiae for bio-mechanical analysis

Background and Objective:Measuring the thickness of cortical bone tissue helps diagnose bone diseases or monitor the progress of different treatments. This type of measurement can be performed visually from CAT images by a radiologist or by semi-automatic algorithms from Hounsfield values. This article proposes a mechanism capable of measuring thickness over the entire bone surface, aligning and orienting all the images in the same direction to have comparable references and reduce human intervention to a minimum. The objective is to batch process large numbers of patients’ CAT images obtaining thicknesses profiles of their cortical tissue to be used in many applications. Methods:Classical morphological and Deep Learning segmentation is used to extract the area of interest, filtering and interpolation to clean the bones and contour detection and Signed Distance Functions to measure the cortical Thickness. The alignment of the set of bones is achieved by detecting their longitudinal direction, and the orientation is performed by computing their principal component of the center of mass slice. Results:The method processed in an unattended manner 67% of the patients in the first run and 100% in the second run. The difference in the thickness values between the values provided by the algorithm and the measures done by a radiologist was, on average, 0.25 millimetres with a standard deviation of 0.2. Conclusion:Measuring the cortical thickness of a bone would allow us to prepare accurate traumatological surgeries or study their structural properties. Obtaining thickness profiles of an extensive set of patients opens the way for numerous studies to be carried out to find patterns between bone thickness and the patients’ medical, social or demographic variables.

A Four-Channel Surface Plasmon Resonance Sensor Functionalized Online for Simultaneous Detections of Anti-SARS-CoV-2 Antibody, Free Viral Particles, and Neutralized Viral Particles.

Current tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detect either the constituent nucleic acids/proteins of the viral particles or antibodies specific to the virus, but cannot provide information about viral neutralization by an antibody and the efficacy of an antibody. Such information is important about individuals' vulnerability to severe symptoms or their likelihood of showing no symptoms. We immobilized online SARS-CoV-2 spike (S1) protein and angiotensin-converting enzyme 2 (ACE2) into separate surface plasmon resonance (SPR) channels of a tris-nitrilotriacetic acid (tris-NTA) chip to simultaneously detect the anti-S1 antibody and viral particles in serum samples. In addition, with a high-molecular-weight-cutoff filter, we separated the neutralized viral particles from the free antibody molecules and used a sensing channel immobilized with Protein G to determine antibody-neutralized viral particles. The optimal density of probe molecules in each fluidic channel can be precisely controlled through the closure and opening of the specific ports. By utilizing the high surface density of ACE2, multiple assays can be carried out without regenerations. These three species can be determined with a short analysis time (<12 min per assay) and excellent sensor-to-sensor/cycle-to-cycle reproducibility (RSD < 5%). When coupled with an autosampler, continuous assays can be performed in an unattended manner at a single chip for up to 6 days. Such a sensor capable of assaying serum samples containing the three species at different levels provides additional insights into the disease status and immunity of persons being tested, which should be helpful for containing the SARS-CoV-2 spread during the era of incessant viral mutations.

SINK MOBILITY MODEL FOR WIRELESS SENSOR NETWORKS USING KOHONEN SELF-ORGANIZING MAP

Wireless sensor networks are expected to operate in an unattended manner for long periods of time. As a result, different mobility models were proposed by many research papers in order to improve the performance and extend the lifetime of the network. In this paper a new sink mobility model that is based on kohonen self-organizing maps is proposed in order to provide a mobile sink node with the ability to collect data from static sensor nodes. Moreover, the performance of the proposed mobility model was studied using NS-2 simulator under different network sizes and movement speeds of the mobile sink. Finally, the performance of the proposed model was evaluated based on different performance metrics namely, end-to-end delay, packet delivery ratio and throughput.

An active detection of compromised nodes based on en-route trap in wireless sensor network

With the development and wide use of wireless sensor network, security arises as an essential issue since sensors with restrict resources are deployed in wild areas in an unattended manner. Most of current en-route filtering schemes could filter false data effectively; however, the compromised nodes could take use of the filtering scheme to launch Fictitious False data Dropping attack, the detection of this attack is extremely difficult since the previous hop node is unable to distinguish whether the forwarding node dropt a false data report with incorrect Message Authentication Codes or a legitimate report. This is the first attempt to address the Fictitious False data Dropping attack; in this article, we propose an Active Detection of compromised nodes based on En-route Trap to trap compromised nodes in the scenario of a false data dropping. A trust model is used to evaluate trust level of neighbor nodes with respect to their authentication behaviors. A detecting algorithm of compromised node is used to detect compromised nodes. Simulation results showed that our scheme can address the Fictitious False data Dropping attack and detect 60% of compromised nodes with a low false positive rate; consequently, the packet accuracy of an Active Detection of compromised nodes based on En-route Trap increases rapidly and reaches to 86%.

SINK MOBILITY MODEL FOR WIRELESS SENSOR NETWORKS USING KOHONEN SELF-ORGANIZING MAP

Wireless sensor networks are expected to operate in an unattended manner for long periods of time. As a result, different mobility models were proposed by many research papers in order to improve the performance and extend the lifetime of the network. In this paper a new sink mobility model that is based on kohonen self-organizing maps is proposed in order to provide a mobile sink node with the ability to collect data from static sensor nodes. Moreover, the performance of the proposed mobility model was studied using NS-2 simulator under different network sizes and movement speeds of the mobile sink. Finally, the performance of the proposed model was evaluated based on different performance metrics namely, end-to-end delay, packet delivery ratio and throughput.

Automated High-Throughput Capillary Circular Dichroism and Intrinsic Fluorescence Spectroscopy for Rapid Determination of Protein Structure.

Assessing the physical stability of proteins is one of the most important challenges in the development, manufacture, and formulation of biotherapeutics. Here, we describe a method for combining and automating circular dichroism and intrinsic protein fluorescence spectroscopy. By robotically injecting samples from a 96-well plate into an optically compliant capillary flow cell, complementary information about the secondary and tertiary structural state of a protein can be collected in an unattended manner from considerably reduced volumes of sample compared to conventional techniques. We demonstrate the accuracy and reproducibility of this method. Furthermore, we show how structural screening can be used to monitor unfolding of proteins in two case studies using (i) a chaotropic denaturant (urea) and (ii) low-pH buffers used for monoclonal antibody (mAb) purification during Protein A chromatography.

A Light-Weight White-Box Encryption Scheme for Securing Distributed Embedded Devices

Distributed embedded devices are widely used in sensor networks and the Internet of Things for gathering and sending data. Many of them are deployed in an unattended manner (e.g., sensor nodes and tag readers), while others may be easily lost (e.g., smart wristbands and watches). These distributed embedded devices could be potentially captured and accessed in an unauthorized manner due to their physical natures. From a security perspective, they are typically working in the white-box attack context, where adversaries have total visibility on the implementations of built-in cryptosystems and full control over their execution processes. It is undoubtedly a significant challenge to deal with white-box attacks on these devices. Existing encryption algorithms for white-box attack contexts require large memory footprint and thus are not suitable for resource- constrained embedded devices. To address this challenge, we propose a novel light-weight encryption scheme for protecting data confidentiality. The encryption is conducted with specialized secret components, and the encryption algorithm requires a small volume of static data for storing critical information. In addition, this scheme uniquely supports efficient key-updating at very small cost. The security and the cost of the proposed scheme have been theoretically analyzed with positive results, and the extensive experimental evaluations indicate that the new scheme satisfies the requirements of distributed embedded devices in terms of limited memory usage and low computational cost.

Self-powered intelligent buoy system by water wave energy for sustainable and autonomous wireless sensing and data transmission

Harvesting ocean wave energy is a killer application of triboelectric nanogenerator (TENG) for advantages in simple mechanism, high power density and high efficiency at low frequency. Here we report a self-powered intelligent buoy system (SIBS), in which a high-output multilayered TENG is used for water wave energy harvesting. With a power management module, the output voltage can be converted and regulated as a steady DC voltage of 2.5 V for the operations of a microprogrammed control unit (MCU), several micro sensors and a transmitter. With the intelligent monitoring mechanism of the MCU, the harvested energy can be deployed for each sensor with different priority and data transmission cycle, and the SIBS can be at standby and active status by itself at different energy levels. At a wave frequency of 2 Hz, the SIBS can provide an average output power density of 13.2 mW/m2 and realize sustainable and autonomous wireless sensing for acceleration, magnetic intensity and temperature in range of 15 m, with amount of 19 bytes every 30 s. The SIBS has first demonstrated a complete TENG-based micro-energy solution for self-powered intelligent system, including energy harvesting, management, deployment and utilization, with an unattended manner and infinite lifetime. As a significant milestone for the TENG, this work has provided a universal platform for self-powered wireless sensor network nodes and exhibited broad prospects in internet of things, big data, artificial intelligence and blue energy.

A Comparative Study of Mobility Models for Wireless Sensor Networks

Wireless sensor networks consist of hundreds or thousands of small, lightweight and low-powered sensor nodes that are deployed in the area of interest to collect information in an unattended manner. Since sensor nodes have limited battery, many research papers proposed techniques to enhance the performance and lifetime of wireless sensor networks. Using energy rich mobile sink to collect data from static sensor nodes is one of the techniques that can be used to improve the performance of wireless sensor networks. As a result, several mobility models were proposed to achieve this goal. In this study, we aim to study the performance of wireless sensor network under three mobility models for the mobile sink namely, depth first based mobility model, random waypoint mobility model and Gauss Markov mobility model. Consequently, ns-2 simulator is used to study the performance of the network under different scenarios and speeds of the mobile sink. Additionally, end-to-end delay, throughput and packet delivery ratio are the performance metrics considered in this study to measure the performance. Finally, AODV routing protocol is used to route messages from their sources to the mobile sink.

Clinical Implications

HomeHypertensionVol. 71, No. 2Clinical Implications Free AccessIn BriefPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessIn BriefPDF/EPUBClinical Implications Originally published1 Feb 2018https://doi.org/10.1161/HYPERTENSIONAHA.117.10771Hypertension. 2018;71:217Metabolic Predictors of Vascular Change (p 237)Download figureDownload PowerPointAortic stiffness increases with age and contributes to the pathogenesis of hypertension and cardiovascular disease. In previous cross-sectional studies, metabolic traits were associated with aortic stiffness and mean arterial pressure. We examined whether metabolic traits at baseline and their changes over time were associated with changes in vascular function longitudinally. Vascular and metabolic traits were examined in 5779 middle-aged participants attending sequential examinations of the Framingham Heart Study cohorts (mean follow-up, 5.9±0.6 years). Multivariable regression was used to relate changes in vascular measures to changes in body mass index, serum lipids, and blood glucose. Aortic stiffness measures and pressure pulsatility increased, whereas mean arterial pressure decreased, suggesting that factors other than distending pressure may have contributed to the increase in aortic stiffness. Interindividual variation in the longitudinal changes of arterial stiffness (carotid-femoral pulse wave velocity) and mean arterial pressure were explained more by changes in metabolic traits than by baseline metabolic trait levels, consistent with the concept that modification of metabolic factors may mitigate arterial stiffness over time.Unattended Blood Pressure Measurement (p 243)Download figureDownload PowerPointDo you remember your reaction when looking at your angry math teacher? Or your reaction meeting the most beautiful girl in class on the schoolyard? In both cases, your blood pressure (BP) probably increased. The person that measures someone’s BP—either frightening or attractive—affects BP as well. The technique of unattended BP measurement is intended to minimize this alert reaction. Although the protocol did not really call for it, several study centers in the SPRINT study (Systolic Blood Pressure Intervention Trial) measured BP in an unattended manner, leading to a controversial discussion on the generalizability of the trial’s findings. The discussion was heated by an article from Filipovský et al reporting a 16 mm Hg difference in measurement techniques in a hypertension center. It is noteworthy that there is convincing evidence from the 80s that BP substantially decreases from the first visit of a new doctor to the following ones. So, what about the disparity of unattended versus attended BP in a familiar medical environment? Our study shows that the difference between measurements is low in a general practitioner setting. These findings make it improbable that the BP values in SPRINT substantially underestimate office BP values in primary care. It is currently being discussed whether unattended BP measurement should serve as the new gold standard for the assessment of office BP. The differences of the present data and those of Filipovský et al suggest that it may indeed be wise to implement this approach in secondary or tertiary care institutions, whereas it is dispensible in a general practitioner setting.sFlt-1/PlGF Ratio in Prediction of Preeclampsia (p 306)Download figureDownload PowerPointA substantial body of evidence suggests that angiogenic factors (sFlt-1 [soluble FMS-like tyrosine kinase-1] and PlGF [placenta growth factor]) could be useful biomarkers for prediction, diagnosis, and prognosis of preeclampsia from 24 to 37 weeks gestation. In this group, the sFlt-1/PlGF ratio has a very high negative predictive value for ruling out preeclampsia and its positive predictive value, whereas lower than the negative predictive value, outperforms current clinical tools. We conducted a meta-analysis of studies using sFlt-1/PlGF ratio to predict development of preeclampsia. The pooled sensitivity was 80% (95% confidence interval [CI], 0.68–0.88); pooled specificity 92% (95% CI, 0.87–0.96); and area under the curve 0.94 (95% CI, 0.91–0.95). With positive and negative likelihood ratios of 10.5 (95% CI, 6.2–18.0) and 0.22 (95% CI, 0.13–0.35), respectively, the probability of developing preeclampsia increased to 78% with a positive test compared with 7% with a negative test. Importantly, all included studies showed a high negative predictive value (10/15 studies reporting negative predictive value >98.5%). These results have important clinical implications. A low sFlt-1/PlGF ratio is a strong negative predictor of disease <37 weeks, allowing safe discharge or decreased surveillance. Concomitantly, a high sFlt-1/PlGF ratio could help prioritize patient care (transfer to tertiary centers or increased surveillance). The use of angiogenic factors for ruling out preeclampsia is used throughout Europe and was recently approved for pregnancies <35 weeks in the United Kingdom. The effective segregation of patients in a disease with an unpredictable course, which has substantial maternal and fetal morbidity and mortality, could have important clinical benefits. Previous Back to top Next FiguresReferencesRelatedDetails February 2018Vol 71, Issue 2 Advertisement Article InformationMetrics © 2017 American Heart Association, Inc.https://doi.org/10.1161/HYPERTENSIONAHA.117.10771 Originally publishedFebruary 1, 2018 PDF download Advertisement

Robust detection of false data injection attacks for data aggregation in an Internet of Things-based environmental surveillance

Data aggregation is an important technology for environmental surveillance based on the Internet of Things (IoT) in the process of compressing redundant data collected from small devices that are distributed over a wide area in a network. However, since most IoT devices work in an unattended manner with limited security guarantees, they are extremely vulnerable to node compromise. Once adversaries take charge of compromised nodes, they can launch false data injection (FDI) attacks, which are known to be destructive for data aggregation. To minimize the damage caused by an FDI attack, we adopt a Hierarchical Bayesian Spatial-Temporal (HBST) model to describe the statistical characteristics of sensory data in an aggregation-based communication mode and propose an anomaly detection-based scheme to detect compromised nodes in the early stage. The basic idea behind our scheme is using state estimation techniques based on divided difference filtering (DDF) to detect false aggregated data and determining the nodes that are suspected of injecting false data using sequential hypothesis testing (SHT). Additionally, we model the problem of FDI attack detection using a quantitative two-player game theory analysis, derive the optimal strategies for both the adversaries and defenders, and demonstrate that the adversaries’ gain from the attack is greatly limited by the defenders, even in the worst case when both players follow their respective optimal strategies. Moreover, we present theoretical and simulation analyses to evaluate the performance of the proposed scheme in terms of the effectiveness, efficiency and overhead. The analytical results show that the proposed scheme achieves a high detection rate and low false positive rate with a small amount of detection samples.

Determination of Genotypes Using a Fully Automated Molecular Detection System

Although the value of pharmacogenomics to improve patient outcomes has become increasingly clear, adoption in medical practice has been slow, which can be attributed to several factors, including complicated and expensive testing procedures and required equipment, lack of training by private practice physicians, and reluctance of both private and commercial payers to reimburse for such testing. To evaluate a fully automated molecular detection system for human genotyping assays, starting with anticoagulated whole blood samples, and to perform all sample preparation, assay, and analysis steps automatically with actionable results reported by the system's software. The genotypes of 254 random individuals were determined by performing bidirectional DNA sequencing, and that information was used to statistically train the imaging software of the automated molecular detection system to distinguish the 3 possible genotypes (ie, homozygous wild type, heterozygous, and homozygous mutant) at each of 3 different loci (CYP2C9*2, CYP2C9*3, and VKORC1). The resulting software algorithm was able to correctly identify the genotypes of all 254 individuals (100%) evaluated without any further user analysis. The EncompassMDx workstation (Rheonix, Inc, Ithaca, New York) is a molecular detection system that can automatically determine the genotypes of individuals in an unattended manner. Considerably less technical expertise was required to achieve results identical to those obtained using more complex, time-consuming, and expensive bidirectional DNA sequencing. This optimized system may dramatically simplify and reduce the costs of pharmacogenomics testing, thus leading to more-widespread use.

An innate immune system-mimicking, real-time biosensing of infectious bacteria.

An animal cell-based biosensor was investigated to monitor bacterial contamination in an unattended manner by mimicking the innate immune response. The cells (RAW 264.7 cell line) were first attached onto the solid surfaces of a 96-well microtiter plate and co-incubated in the culture medium with a sample that might contain bacterial contaminants. As Toll-like receptors were present on the cell membrane surfaces, they acted as a sentinel by binding to pathogen-associated molecular patterns (PAMPs) of any contaminant. Such biological recognition initiates signal transmission along various pathways to produce different proinflammatory mediators, one of which, tumor necrosis factor-α (TNF-α) was measured using an immunosensor. To demonstrate automated bacterium monitoring, a capture antibody specific for TNF-α was immobilized on an optical fiber sensor tip and then used to measure complex formation in a label-free sensor system (e.g., Octet Red). The sensor response time depended significantly on the degree of agitation of the culture medium, controlling the biological recognition and further autocrine/paracrine signaling by cytokines. The response, particularly under non-agitated conditions, was also influenced by the medium volume, revealing a local gradient change of the cytokine concentration and also acidity, caused by bacterial growth near the bottom surfaces. A biosensor system retaining 50 μL medium and not employing agitation could be used for the early detection of bacterial contamination. This novel biosensing model was applied to the real-time monitoring of different bacteria, Shigella sonnei, Staphylococcus aureus, and Listeria monocytogenes. They (<100 CFU mL(-1)) could be detected automatically within the working time. Such analysis was carried out without any manual handling regardless of the bacterial species, suggesting the concept of non-targeted bacterial real-time monitoring. This technique was further applied to real sample testing (e.g., with milk) to exemplify, for example, the food quality control process without using any additional sample pretreatment such as magnetic concentration.

Potential-based routing for supporting robust any-to-any communication in wireless sensor networks

In most applications, wireless sensor networks are supposed to operate in an unattended manner for a long period after sensor nodes’ deployment. However, in such networks, sensor nodes frequently become faulty and unreliable because of the harsh environment of the observed area. Therefore, protocols used in wireless sensor networks must be designed to be robust. Moreover, because the battery capacity of a node is limited, energy savings are crucial in wireless sensor networks. To meet the requirements of future diverse wireless sensor networks, a sophisticated any-to-any routing protocol is thus required. As well as meeting the typical demands of wireless sensor networks, an any-to-any routing protocol needs to achieve low energy consumption, high scalability, robustness, and reliability. In this paper, we realize a potential-based any-to-any routing protocol (PBAR) by merging potential-based upstream and downstream routing. In PBAR, sensor nodes can send data to a certain sensor node by routing the data via a sink node. In simulation experiments, we show that, given a suitable node density, PBAR attains a data delivery ratio greater than 99.7%. We also show that the data delivery ratio recovers immediately after failure of 30% of sensor nodes or failure of a sink node.

An Ocean CAL-VAL Site at Kavaratti in Lakshadweep for Vicarious Calibration of OCM-2 and Validation of Geophysical Products—Development and Operationalization

A pair of buoys (system), MET and OPTICAL, consisting of fully automated hyperspectral radiometers, fluorometer, and meteorological sensors, has been realized and deployed in deep ocean case-I site at Kavaratti in Lakshadweep, Arabian Sea, for preprogrammed in situ data collection and transmission via INSAT-3C satellite. The buoy of described configuration is capable of measuring in-water optical and biological parameters in an unattended manner for long-term time series with less vertical tilt. A robotic sun/sky photometer installed on Kavaratti Island simultaneously provides information on aerosols over the site. A combination of these parameters available hourly in real time throughout the day from unattended systems in the ocean as well as on island provides an ideal reference site. The paper reports recent collection of bio-optical marine observations over the site and use of the data for OCM-2 vicarious calibration and validation of geophysical products.

H/L transition time estimation in JET using conformal predictors

Recent advances in data mining allow the automatic recognition of physical phenomena in the databases of fusion devices without human intervention. This is important to create large databases of physical events (thereby increasing the statistical relevance) in an unattended manner. Important examples are the L/H and H/L transitions. In this contribution, a novel technique is introduced to automatically locate H/L transitions in JET by using conformal predictors. The focus is on H/L transitions because typically there is not a clear signature in the time series of the most widely available signals to recognize the change of confinement. Conformal predictors hedge their prediction by means of two parameters: confidence and credibility. The technique has been based on binary supervised classifiers to separate the samples of the respective confinement modes. Results with several underlying classifiers are presented.

Managing context data for diverse operating spaces

Context-aware computing is an exciting paradigm in which applications perceive and react to changing environments in an unattended manner. To enable behavioral adaptation, a context-aware application must dynamically acquire context data from different operating spaces in the real world, such as homes, shops and persons. Motivated by the sheer number and diversity of operating spaces, we propose a scalable context data management system in this paper to facilitate data acquisition from these spaces. In our system, we design a gateway framework for all operating spaces and develop matching algorithms to integrate the local context schemas of operating spaces into a global set of domain schemas upon which SQL-based context queries can be issued from applications. The system organizes the operating space gateways as peers in semantic overlay networks and employs distributed query processing techniques over these overlays. Evaluation results on a prototype implementation demonstrate the effectiveness of our system design.

The Upcoming and Proliferation of Ubiquitous Technologies in Products and Processes

Ubiquitous computing is a research field that started in the late 1980s, and is now believed to be at the brink of a steep acceleration in terms of technology development and applications. Ubiquitous computing is often regarded as the third wave of computing, after a first wave of mainframe computing and a second wave of PC computing. It aims at supporting humans in their daily life activities in a personal, unattended and remote manner. Towards this end, it scatters computing capacity across the environment, and takes out the oblique PC man-machine interface. Instead it employs networked sensors and devices surrounding us. There is no dedication in the sense that many devices in an environment collectively serve multiple humans around. Both humans and devices are assumed to be nomadic, and possibly enter or leave the environment. In addition, to materialize a personal and context-dependent interaction, identification and context awareness are also key factors. Although the vision itself has become fairly well-conceived, several technological and non-technological problems are yet to be overcome. This paper provides a comprehensive overview and a critical survey of the current and future state of ubiquitous technologies. © 2010 Journal of Mechanical Engineering.