Deep matrix factorization models for estimation of missing data in a low-cost sensor network to measure air quality

According to the WHO, pollution is a worldwide public health problem. In Colombia, low-cost strategies for air quality monitoring have been implemented using wireless sensor networks (WSNs), which achieve a better spatial resolution than traditional sensor networks for a lower operating cost. Nevertheless, one of the recurrent issues of WSNs is the missing data due to environmental and location conditions, hindering data collection. Consequently, WSNs should have effective mechanisms to recover missing data, and matrix factorization (MF) has shown to be a solid alternative to solve this problem. This study proposes a novel MF technique with a neural network architecture (i.e., deep matrix factorization or DMF) to estimate missing particulate matter (PM) data in a WSN in Aburrá Valley, Colombia. We found that the model that included spatial-temporal features (using embedding layers) captured the behavior of the pollution measured at each node more efficiently, thus producing better estimations than standard matrix factorization and other variations of the model proposed here.     

Research on contaminant sources identification of uncertainty water demand using genetic algorithm

Urban water supply network is easily affected by intentional or occasional chemical and biological pollution, which threatens the health of consumers. In recent years, drinking water contamination happens occasionally, which seriously harms social stabilization and safety. Placing sensors in water supply pipes can monitor water quality in real time, which may prevent contamination accidents. However, how to reversely locate pollution sources through the detecting information from water quality sensors is a challengeable issue. Its difficulties lie in that limited sensors, massive pipe network nodes and dynamic water demand of users lead to the uncertainty, large-scale and dynamism of this optimization problem. This paper mainly studies the uncertainty problem in contaminant sources identification (CSI). The previous study of CSI supposes that hydraulic output (e.g., water demand) is known. Whereas, the inherent variability of urban water consumption brings an uncertain problem that water demand presents volatility. In this paper, the water demand of water supply network nodes simulated by Gaussian model is stochastic, and then being used to solve the problem of CSI, simulation–optimization method finds the minimum target of CSI and concentration which meet the simulation value and detected value of sensors. This paper proposes an improved genetic algorithm to solve the CSI problem under uncertainty water demand and comparative experiments are placed on two water distribution networks of different sizes.     

Nanoporous impedemetric biosensor for detection of trace atrazine from water samples

Trace contamination of ground water sources has been a problem ever since the introduction of high-soil-mobility pesticides, one such example is atrazine. In this paper we present a novel nanoporous portable bio-sensing device that can identify trace contamination of atrazine through a label-free assay. We have designed a pesticide sensor comprising of a nanoporous alumina membrane integrated with printed circuit board platform. Nanoporous alumina in the biosensor device generates a high density array of nanoscale confined spaces. By leveraging the size based immobilization of atrazine small molecules we have designed electrochemical impedance spectroscopy based biosensor to detect trace amounts of atrazine. We have calibrated the sensor using phosphate buffered saline and demonstrated trace detection from river and bottled drinking water samples. The limit of detection in all the three cases was in the femtogram/mL (fg/mL) (parts-per-trillion) regime with a dynamic range of detection spanning from 10 fg/mL to 1 ng/mL (0.01 ppt to 1 ppm). The selectivity of the device was tested using a competing pesticide; malathion and selectivity in detection was observed in the fg/mL regime in all the three cases.     

Practical biophysics: Sensors for rapid detection of biological targets utilizing target-induced oligonucleotide annealing

Detection and quantitation of biomolecules is one of the most commonly performed measurements in biomedical research and clinical diagnostics. There is high demand for convenient, rapid and sensitive biomolecule detection methodologies. In this review we discuss a family of sensors that have been developed in our laboratory that share a common simple biophysical mechanism of action and that are capable of rapid detection of a diverse range of biological targets. The sensors generate fluorescence signal in the presence of the target molecule through target-induced association of short fluorochrome-labeled complementary oligonucleotides that are attached to target recognition elements of the sensors (antibodies, aptamers, etc.) via nanometer scale flexible linkers. This sensor design can be used for detecting proteins, antibodies, nucleic acids and whole cells. The assays using these sensors require only adding a sample to the sensor mix followed by simple fluorescence intensity readout. The simplicity, the speed of detection and the potential for miniaturization are the main assets of these sensors.     

Distributed Clone Detection in Static Wireless Sensor Networks: Random Walk with Network Division

Wireless Sensor Networks (WSNs) are vulnerable to clone attacks or node replication attacks as they are deployed in hostile and unattended environments where they are deprived of physical protection, lacking physical tamper-resistance of sensor nodes. As a result, an adversary can easily capture and compromise sensor nodes and after replicating them, he inserts arbitrary number of clones/replicas into the network. If these clones are not efficiently detected, an adversary can be further capable to mount a wide variety of internal attacks which can emasculate the various protocols and sensor applications. Several solutions have been proposed in the literature to address the crucial problem of clone detection, which are not satisfactory as they suffer from some serious drawbacks. In this paper we propose a novel distributed solution called Random Walk with Network Division (RWND) for the detection of node replication attack in static WSNs which is based on claimer-reporter-witness framework and combines a simple random walk with network division. RWND detects clone(s) by following a claimer-reporter-witness framework and a random walk is employed within each area for the selection of witness nodes. Splitting the network into levels and areas makes clone detection more efficient and the high security of witness nodes is ensured with moderate communication and memory overheads. Our simulation results show that RWND outperforms the existing witness node based strategies with moderate communication and memory overheads.     

Distributed sensor activation algorithm for target tracking with binary sensor networks

Target tracking with wireless sensor networks (WSNs) has been a hot research topic recently. Many works have been done to improve the algorithms for localization and prediction of a moving target with smart sensors. However, the results are frequently difficult to implement because of hardware limitations. In this paper, we propose a practical distributed sensor activation algorithm (DSA²) that enables reliable tracking with the simplest binary-detection sensors. In this algorithm, all sensors in the field are activated with a probability to detect targets or sleep to save energy, the schedule of which depends on their neighbor sensors’ behaviors. Extensive simulations are also shown to demonstrate the effectiveness of the proposed algorithm. Great improvement in terms of energy-quality tradeoff and excellent robustness of the algorithm are also emphasized in the simulations.     

便携式生物光学传感器的研究进展

生物光学传感器是一种对生物物质敏感并将其浓度转换为光信号,再由光电器件转换成电信号进行检测的仪器。由于随着微加工技术和纳米技术的进步,生物光学传感器将不断的微型化,各种便携式生物光学传感器的出现使得人们在家中进行疾病诊断、在市场上直接检测食品及在野外快速检测环境污染成为可能。便携式生物光学传感器一般由光源、光学通路和光电元件三部分组成。传感器结构中各个组件的优化处理将有利于检测设备在实际运用中的便利性和在复杂环境中的适用性,同时也有利于提高生物检测的灵敏度。主要从激励光源的选择、生物光学检测的原理、用于传感光源分析的半导体光敏元件3方面,描述近年来常见的便携式生物光学传感器的研究进展。未来生物检测器件将趋于成本低廉、便携快捷、智能高效等特点,基于生物光学反应特性的研究和传感器结构制备的优化将使得便携式生物光学传感器在未来传感检测应用中具有巨大的商业价值和广泛的实用价值。     

Model-based identification of motion sensor placement for tracking retraction and elongation of the tongue

Electromagnetic articulography (EMA) is designed to track facial and tongue movements. In practice, the EMA sensors for tracking the movement of the tongue’s surface are placed heuristically. No recommendation exists. Within this paper, a model-based approach providing a mathematical analysis and a computational-based recommendation for the placement of sensors, which is based on the tongue’s envelope of movement, is proposed. For this purpose, an anatomically detailed Finite Element (FE) model of the tongue has been employed to determine the envelope of motion for retraction and elongation using a forward simulation. Two optimality criteria have been proposed to identify a set of optimal sensor locations based on the pre-computed envelope of motion. The first one is based on the assumption that locations exhibiting large displacements contain the most information regarding the tongue’s movement and are less susceptible to measurement errors. The second one selects sensors exhibiting each the largest displacements in the anterior-posterior, superior-inferior, medial-lateral and overall direction. The quality of the two optimality criteria is analysed based on their ability to deduce from the respective sensor locations the corresponding muscle activation parameters of the relevant muscle fibre groups during retraction and elongation by solving the corresponding inverse problem. For this purpose, a statistical analysis has been carried out, in which sensor locations for two different modes of deformation have been subjected to typical measurement errors. Then, for tongue retraction and elongation, the expectation value, the standard deviation, the averaged bias and the averaged coefficient of variation have been computed based on 41 different error-afflicted sensor locations. The results show that the first optimality criteria is superior to the second one and that the averaged bias and averaged coefficient of variation decrease when the number of sensors is increased from 2, 4 to 6 deployable sensors.     

Development of an electrochemical polypyrrole-based DNA sensor and subsequent studies on the effects of probe and target length on performance

DNA sensors have a wide scope of applications in the present and emerging medical and scientific fields, such as medical diagnostics and forensic investigations. However, much research-to-date on DNA sensor development has focused on short target DNA strands as model genes. In this communication we study the effect of the length of oligonucleotide probe and target strands as a significant step towards real world applications for DNA detection. The sensor technology described uses the conducting polymer polypyrrole as both a sensing element and transducer of sensing events - namely the hybridization of complementary target oligonucleotide to probe oligonucleotide. Detection is performed using electrical impedance spectroscopy. Initially sensor development is performed, wherein we demonstrate an improvement in stability and sensitivity as well as show a reduction in non-specific DNA binding for fabricated sensors, through use of a specific dopant and post-growth treatment. Subsequently, we show that longer target DNA strands display increased response, as do sensors containing longer probe DNA strands. It is suggested that these results are a feature of the increase in negative charges associated with the longer DNA strands. The results of this comparative study are aimed to guide future design of analogous sensors.     

Recent advances in ZnO nanostructure as a gas-sensing element for an acetone sensor: a short review

Air pollution is a severe concern globally as it disturbs the health conditions of living beings and the environment because of the discharge of acetone molecules. Metal oxide semiconductor (MOS) nanomaterials are crucial for developing efficient sensors because of their outstanding chemical and physical properties, empowering the inclusive developments in gas sensor productivity. This review presents the ZnO nanostructure state of the art and notable growth, and their structural, morphological, electronic, optical, and acetone-sensing properties. The key parameters, such as response, gas detection limit, sensitivity, reproducibility, response and recovery time, selectivity, and stability of the acetone sensor, have been discussed. Furthermore, gas-sensing mechanism models based on MOS for acetone sensing are reported and discussed. Finally, future possibilities and challenges for MOS (ZnO)-based gas sensors for acetone detection have also been explored.     

Diverse water quality responses to extreme climate events: an introduction

We synthesize and summarize main findings from a special issue examining the origins, evolution, and resilience of diverse water quality responses to extreme climate events resulting from a Chapman Conference of the American Geophysical Union (AGU). Origins refer to sequences of interactive disturbances and antecedent conditions that influence diversification of water quality responses to extreme events. Evolution refers to the amplification, intensification, and persistence of water quality signals across space and time in watersheds. Resilience refers to strategies for managing and minimizing extreme water quality impacts and ecosystem recovery. The contributions of this special issue, taken together, highlight the following: (1) there is diversification in the origins of water quality responses to extreme climate events based on the intensity, duration, and magnitude of the event mediated by previous historical conditions; (2) interactions between climate variability and watershed disturbances (e.g., channelization of river networks, land use change, and deforestation) amplify water quality ‘pulses,’ which can manifest as large changes in chemical concentrations and fluxes over relatively short time periods. In the context of the evolution of water quality responses, results highlight: (3) there are high intensity and long-term climate events, which can generate unique sequences in water quality, which have differential impacts on persistence of water quality problems and ecosystem recovery rates; and (4) ‘chemical cocktails’ or novel mixtures of elements and compounds are transported and transformed during extreme climate events. The main findings regarding resilience to extreme climate events are that: (5) river restoration strategies for reducing pollution from extreme events can be improved by preserving and restoring floodplains, wetlands, and oxbow ponds, which enhance hydrologic and biogeochemical retention, and lengthen the distribution of hydrologic residence times; and (6) the biogeochemical capacity for stream and river ecosystems to retain and transform pollution from landscapes can become “saturated” during floods unless watershed pollution sources are reduced. Finally, the unpredictable occurrence of extreme climate events argues for wider deployment of high-frequency, in situ sensors for monitoring, managing, and modeling diverse water quality responses. These sensors can be used to develop robust proxies for chemical cocktails, detect water quality violations following extreme climate events, and effectively trace the trajectory of water quality recovery in response to managing ecosystem resilience.     

Distance estimation by mining characteristics in anisotropic sensor networks

Localization is useful for many position-dependent applications in wireless sensor networks, where distance estimation from sensor nodes to beacon nodes plays a fundamental role. Most current ranging methods rely on an assumption that deployed WSNs are isotropic. Hence, adjustments on measured distances are the same in all directions. Unfortunately, this assumption does not hold in practice. Present methods introduce such great ranging errors that they are not feasible for real applications. In order to obtain better distance estimation in anisotropic WSNs, we propose a new metric, Dominating Degree, to describe the local deployment characteristics of sensor nodes, and to identify turning nodes along paths. We further propose a method to scale deployment irregularities of WSNs as global characteristics. Finally, appropriate adjustments to distance measurements are performed by synthesizing both local and global characteristics. Simulation results show that the proposed method outperforms PDM and DV-distance especially when beacon nodes are not deployed uniformly.     

State Estimation of the Time-Varying and Spatially Localized Concentration of Signal Molecules from the Stochastic Adsorption Dynamics on the Carbon Nanotube-Based Sensors and Its Application to Tumor Cell Detection

This paper addresses a problem of estimating time-varying, local concentrations of signal molecules with a carbon-nanotube (CNT)-based sensor array system, which sends signals triggered by monomolecular adsorption/desorption events of proximate molecules on the surfaces of the sensors. Such sensors work on nano-scale phenomena and show inherently stochastic non-Gaussian behavior, which is best represented by the chemical master equation (CME) describing the time evolution of the probabilities for all the possible number of adsorbed molecules. In the CME, the adsorption rate on each sensor is linearly proportional to the local concentration in the bulk phase. State estimators are proposed for these types of sensors that fully address their stochastic nature. For CNT-based sensors motivated by tumor cell detection, the particle filter, which is nonparametric and can handle non-Gaussian distributions, is compared to a Kalman filter that approximates the underlying distributions by Gaussians. In addition, the second-order generalized pseudo Bayesian estimation (GPB2) algorithm and the Markov chain Monte Carlo (MCMC) algorithm are incorporated into KF and PF respectively, for detecting latent drift in the concentration affected by different states of a cell.     

Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks

Detecting spreading outbreaks in social networks with sensors is of great significance in applications. Inspired by the formation mechanism of humans’ physical sensations to external stimuli, we propose a new method to detect the influence of spreading by constructing excitable sensor networks. Exploiting the amplifying effect of excitable sensor networks, our method can better detect small-scale spreading processes. At the same time, it can also distinguish large-scale diffusion instances due to the self-inhibition effect of excitable elements. Through simulations of diverse spreading dynamics on typical real-world social networks (Facebook, coauthor, and email social networks), we find that the excitable sensor networks are capable of detecting and ranking spreading processes in a much wider range of influence than other commonly used sensor placement methods, such as random, targeted, acquaintance and distance strategies. In addition, we validate the efficacy of our method with diffusion data from a real-world online social system, Twitter. We find that our method can detect more spreading topics in practice. Our approach provides a new direction in spreading detection and should be useful for designing effective detection methods.     

APPLICATION OF GENE AMPLIFICATION IN CONJUNCTION WITH A HYBRIDIZATION SENSOR FOR RAPID DETECTION OF SALMONELLA SPP. AND FECAL CONTAMINATION INDICATORS IN ANIMAL FEEDS

The rapid detection of pathogenic microbial species in feed is of paramount importance considering its implications for animal production and food safety. To demonstrate the feasibility of rapidly detecting Salmonella spp. and fecal pollution microbial indicators in feed using gene amplification protocols, commercial and mixed feed samples were inoculated with two levels of a marker strain of S. typhimurium. Liquid extracts of the feed samples were used as templates in gene amplification reactions to amplify sequences associated with fecal contamination indicators. The sequence specificity of the amplification products (amplicons) were confirmed using biotin and fluorescein labeled probes in a navel dual probe based hybridization sensor. Using the combination of gene amplification and the hybridization sensor, the presence of sequences associated with fecal contamination were detected in 15 different feed matrices without employing preenrichment steps. Using this detection methodology, fecal pollution can be confirmed in feed at naturally occurring concentrations. The study demonstrates that it is possible to rapidly detect and confirm the presence of pathogenic bacterial genera in feed matrices by combining robust gene amplification reactions with appropriate post amplification detection systems.     

A New Method for Rapid Screening of End-Point PCR Products: Application to Single Genome Amplified HIV and SIV Envelope Amplicons

PCR is the most widely applied technique for large scale screening of bacterial clones, mouse genotypes, virus genomes etc. A drawback of large PCR screening is that amplicon analysis is usually performed using gel electrophoresis, a step that is very labor intensive, tedious and chemical waste generating. Single genome amplification (SGA) is used to characterize the diversity and evolutionary dynamics of virus populations within infected hosts. SGA is based on the isolation of single template molecule using limiting dilution followed by nested PCR amplification and requires the analysis of hundreds of reactions per sample, making large scale SGA studies very challenging. Here we present a novel approach entitled Long Amplicon Melt Profiling (LAMP) based on the analysis of the melting profile of the PCR reactions using SYBR Green and/or EvaGreen fluorescent dyes. The LAMP method represents an attractive alternative to gel electrophoresis and enables the quick discrimination of positive reactions. We validate LAMP for SIV and HIV env-SGA, in 96- and 384-well plate formats. Because the melt profiling allows the screening of several thousands of PCR reactions in a cost-effective, rapid and robust way, we believe it will greatly facilitate any large scale PCR screening.     

Optimal Sensor Placement in Smart Home Using Building Information Modeling: A Home Support Application

ObjectivesIn this paper, we present a plugin for the optimal placement of sensors in a smart home. Our approach includes the Building Information Modeling (BIM) which is a plan that describes the building layout.Material and methodsThis plugin uses the CSTB EveBim viewer for loading IFC file representing the digital building's model. We use then, a mathematical model based on a mixed integer linear program, to determine the optimal sensor placement according to building and sensors characteristics.ResultsThe results show the efficiency of the proposed algorithm and the developed plugin. We obtain an optimal solution after few seconds, and we show the sensor placement on the building digital model.ConclusionWe show the relevance of the proposed plugin to equip room of retirement home or ambient assisted living in order to identify occupant activity for home support application.     

Disposable TSM-biosensor based on viscosity changes of the contacting medium

The thickness shear mode (TSM)-sensor responds to changes of mechanical properties of the material contacting the surface of the sensor. One of the material properties is the viscosity of a liquid. Abiosensor based on the TSM-resonator for the detection of endotoxin has been developed. It exploits the viscosity–density change during the reaction of endotoxin with limulus amebocyte lysate (LAL). The effect of surface properties of the sensor has been investigated to achieve better output signals. It is shown that the sensor requires a hydrophilic surface to get a better coupling between the sensor and the LAL–endotoxin solution. The TSM biosensor is able to detect an endotoxin concentration as low as 100 fg/ml by using only 50-μl standard LAL solution. The disadvantages of reusable sensors, such as the contamination from previous measurement of endotoxin and the cost of the regeneration or reclining processes of the sensor, have been eliminated by using a cost effective disposable TSM-sensor.     

生物传感器快速测定BOD的研究

生化需氧量(biochemicaloxygendemand,BOD)是一种表征水体有机污染程度的综合指标,广泛应用于水体监测和废水处理厂的运行控制。由于BOD的标准测定方法需时5天,不能及时地反映水质状况和反馈处理信息,因此快速测定BOD的方法和仪器化研究近年来得到广泛的重视。利用生物传感器测定BOD是一种有效地快速测定废水中可生化降解有机物的方法。介绍生物传感器的工作原理及其生物敏感材料,讨论BOD传感器的性能参数以及BOD快速测定值(BODst)与标准BOD5值的一致性问题。对现阶段市场上常见的几种BOD快速测定仪进行简单的介绍,并对它们的性能进行比较 。