While constructing the hierarchical multicast tree, the system of

While constructing the hierarchical multicast tree, the system often chooses the geographical central node as the cluster core or near the core. Hence it can save the transmission distance [8]. However, in sensor grid, the data quantities of different nodes are much different [10]. Usually 80% of the data often is centralized in 20% nodes; naturally these important nodes should be paid more attention to. Generally speaking, the more data the nodes have, the more data transmission will happen from the nodes [11]. If the data scale is the only factor we consider, to choose the node with larger data quantity as root or near the root would undoubtedly improve the efficiency of the data transmission.

As a result, the system should consider not only the space factor, but also the data quantity as the factor [9].

The two factors are independent with each other and related with each other. In other words, their relationship is game and balance. We try to set a group of functions in order to draw an elaborate balance between them in our to-be-presented algorithm. The basic idea goes through the whole process of constructing the hierarchical multicast tree. The space factor and data factor are two factors independent with each other, which have meaning and formation respectively; both of them tend to maximize their result. Namely the two factors game with each other. On the other hand, the two factors also co-exist in a system, common working, mutual interaction and constraint.

Namely they balance with each other. We must synthetically consider the space and data factors while constructing the multicast tree.

The specific implementation of the algorithmsAfter summarizing the context of the algorithms, this subsection discusses the concrete implementation of the Dacomitinib algorithms [12]. The motivation of this paper is to design a multicast scheme in m-D Sensor grid that can achieve not only shorter multicast delay and less resource consumption, but also the efficient data transmission.The network Site URL List 1|]# is partitioned into clusters in terms of some regular Sensor grid area. After group members are initially scattered into different clusters, a tree is built to connect the cluster members within each other. The connection among different clusters is done through hooking the tree roots [13].

In a word, one needs to study the light source optimization choic

In a word, one needs to study the light source optimization choice and design method combination for different detection tasks and work environments.Moreover, the small proportions of the target defects relative to the entire picture in a micrograph, uneven surface illumination for high curvature surfaces and natural metal textures all contribute to make contrast of defect regions and background regions small, and image segmentation comparatively difficult. PCNN has been widely used in every field of image processing, such as denoising [17], segmentation [18], fusion [19] and feature extraction [20], but the elementary PCNN model framework is complex, and there are multiple undetermined parameters such as attenuation constants, amplification coefficients and connect coefficients.

Most parameters are configured by artificial tests, which affect PCNN image processing speed and make it difficult to implement automatic image processing. Richard [21] used a genetic algorithm for setting optimum PCNN parameters, yet the key of genetic algorithms is the accurate setting of parameters such as variation and cross operator, which, if not set properly, will destroy the developmental stability. Particle Swarm Optimization (PSO) is an efficient search strategy [22], which features quick convergence and requires less parameter settings. Chao [23] used a PSO to search for the best parameter value of a generalized diffusion coefficient function that was used for anisotropic diffusion defect detection in low-contrast surface images.

The PSO algorithm is used to automatically set PCNN optimization key parameters by fitness function of maxima between cluster variances, which carries out automatic PCNN image processing.The paper is organized as follows: Section 2 introduces the structure of the Brefeldin_A gyroscope pivot bearing dimension measurement and surface detection system; Section 3 presents task-oriented illumination system design methods; Section 4 presents self-adaptive parameter settings obtained Cilengitide by integrating the PSO algorithm and PCNN; Section 5 describes experimental results and comparisons. Finally, some conclusions and future development are illustrated in Section 6.2.?Detection System Design2.1. System FrameworkThe shape of a gyroscope pivot bearing is shown in Figure 1.

nly at early stage, and thus they belong to the group of early s

nly at early stage, and thus they belong to the group of early stage specific genes. In this early stage specific group, the genes encoding components involved in cell wall metabolism and transcription are of particular interest. First, there are six Probesets that could poten tially represent the genes involved in cell wall biogenesis or property. Second, five Probe sets represent the transcription factors homologous to Arabidopsis ERF5, ATAF1 and ARR9. In addition, Cit. 16537. 1. S1 at represents a GCN5 related N acetyltransferase family protein, which might be involved in global transcriptional control through chromatin remodeling. This result implies that transcriptional control and cell wall property regulation are among the early events in citrus in response to the HLB bacterial attack.

In addition, 103 up regulated and 74 down regulated Probesets are specific to the late stage of Las infection. Interestingly, these Probesets repre sent some genes that belong to the categories of metabol ism of carbohydrate, nitrogen and lipids, hormone IAA metabolism, response to chemical stimulus, endomem brane systems and extracellular regions. In addition, while several genes involved in cell wall property regulation are up regulated, some genes encoding transcription factors and protein kinases are down regulated. The most striking feature is that only seven Probesets rep resent the very late stage specific genes. These include the genes that are most closely related to Arabidopsis C domain containing protein 71, a copper binding family protein, a trypsin and protease inhibitor family pro tein Kunitz family protein, a myosin heavy chain related protein, two basic chitinase and one unknown protein encoded by At1g42430.

The small number of genes belonging to this very late stage specific category is likely due to the various experi mental Brefeldin_A conditions because only 26 Probesets are commonly up or down regulated even in the four studies within the same very late stage of Las infec tion. Nevertheless, as this group of genes were identified from four studies specifically at the very late stage compared to only one study for early and late stages respectively, they could be more reliable than groups of early and late stage specific genes.

Construction and characterization of gene coexpression network for citrus response to HLB To provide a systems view of citrus host response to the HLB bacterial infection, the Pearson correlation coeffi cient method was used to infer the gene coexpression network using the four datasets reported in the four transcriptomic studies. A total of 10,668 Probesets, which are present in at least two chips of the transcriptomic stud ies with strong expression and or belong to the group of the HLB responsive genes, were used for network analysis. This number represents 35% of 30,173 Probesets in the citrus Gene Chip. Pcc was computed between each pair of these Probesets. A Pcc threshold of 0. 93 was selected, based on the overall consi

The two electrodes are separated from the test medium (usually l

The two electrodes are separated from the test medium (usually liquid) by a dielectric layer and represent two capacitors characterized by coupling capacitance Ccpl; Ccpl depends predominantly on the thickness and permittivity of the dielectric layer. The electrical behaviour of the test medium appears as a parallel combination of the liquid resistance (Rliq) and the liquid capacitance (Cliq). Part of the electrical energy applied always strays from the test medium, passes along the surface of the dielectric or through its interior and appears as stray (parasitic) capacitance Cx, parallel to the main passageway. The parasitic effect of the stray capacitance is sometimes eliminated by placing a shielding foil between the electrodes [9,10].Figure 1.

A simplified scheme of the equivalent electric circuit for the contactless impedance cell with connections to the input high-frequency voltage source and the output signal meter. For discussion and symbols explanation see the text.The analytical signal is given by the cell impedance, Z, defined by the familiar general equation:Z=R+iX(1)where the real term, resistance R, is a function of the cell geometry and of the electrical conductivity of the test medium. The imaginary term, capacitance X, also depends on the geometric parameters and further on the relative permittivities of the test medium and of the dielectric, and on the angular frequency, ��, or ordinary frequency, f, of the input alternating signal (�� = 2��f); i is the imaginary unit.

It can be seen that the cell behavior depends on a number of experimental parameters and it should be emphasized that all these parameters affect one another, so that they must be considered together when studying the behavior of a particular cell under particular conditions. It is also evident that the set of experimental conditions determines whether the resistance term of Equation (1) predominates��this is the case of the contactless conductivity detection which is mostly used at present, or the capacitance is more important (dielectrometry).

The impedance of the electric GSK-3 equivalent circuit Cilengitide in Figure 1 can be calculated from Equation (2):Z=Z1Z2/(Z1+Z2)(2)where Z1 is the impedance of the bottom branch of equivalent circuit, which equals to:Z1=Rliq?(?i/��Cliq)Rliq?i/��Cliq?2i/��Ccpl(3)If Rliq (?i/��Cliq), the effect of the solution capacitance can be neglected, Equation (3) is simplified to Z1 = Rliq ? 2i/��Ccpl and the sensor works primarily as a conductivity detector. If Rliq (?i/��Cliq), the effect of the solution resistivity can be neglected, Equation (3) is simplified to Z1 = ?i/Cliq ? 2i/��Ccpl and the sensor works primarily as a dielectrometric detector.

However, for the wireless excitation of a microstrip patch antenn

However, for the wireless excitation of a microstrip patch antenna this impedance matching dependence is not required because the patch antenna is not excited using a transmission line (such as a coaxial cable). The quality factor of an antenna can be defined as a representation of the antenna losses [20]. According to [21] the total quality factor for a circular microstrip patch antenna can be calculated from the following equation:1Qt=1Qrad+1Qc+1Qd+1Qsw(2)Figure 1 shows the geometry of the CMPA sensor. For very thin substrates (h<<��0), the loss due to surface waves, 1/Qsw, is very small and can be neglected in calculation of the total quality factor [21]. This indicates that by reducing the thickness of the patch antenna the quality factor due to the surface waves can be improved and as a result of that the total quality factor of the antenna can be improved.

The other quality factors can be calculated using following equations [21]:Qc=h��f��0��(3)Qd=1tan��(4)and for the dominant mode of operation [22]:Qrad=30[(ka)2?1]hf��0(k0a)2I1(5)where:I1=��0��/2[J1��2(k0asin��)+cos2��J12(k0asin��)/(k0asin��)2]sin��d��(6)Figure 1.Geometry of the CMPA sensor.The quality factor due to dielectric losses can be improved by using low loss materials for the antenna substrate. From Equation (3), the conductive loss can be reduced by increasing the conductivity of the patch and the ground plane. In practice, the Qc will be lower than Equation (3) because of the surface roughness of the patch and the ground plane. According to [20], for very thin substrates the dominant factor is the radiation quality factor.

This part of the total quality factor is proportional to the substrate dielectric constant and the inverse of the substrate thickness.2.1. Effect of Substrate Material on Quality FactorIn order to improve the quality factor of the CMPA, a numerical investigation into the effect of each parameter of the total quality factor was carried out. To this end, two commercially available substrates were selected, namely a FR4 (��r = 4.5, tan�� = 0.025) and Rogers? RT/duroid 6010.2LM? (��r = 10.2, tan�� = 0.0023 [23]; Rogers Corporation, Brooklyn, CT, USA). The FR4 substrate was selected because it was used in previous studies on CMPA sensors [8�C10,17] and the Rogers? substrate (which is called high Q material in the rest of the paper) was used because it has much lower tangent loss and much higher permittivity compared to FR4.

For the numerical study the thickness of each substrate was varied within the range of commercially Cilengitide available laminate thicknesses, i.e., 0.127, 0.254, 0.635, 1.27, 1.90, 2.50 mm for high Q material and 0.8, 1.0, 1.2, 1.5, 2.0, 2.4 mm for FR4. The antennas were designed to resonate at 1.5 GHz (similar to previous studies on CMPAs [8�C10,17]).

As a strain sensor for wearable computing, the need is to have a

As a strain sensor for wearable computing, the need is to have a flexible sensor, which changes shape with the body it is placed upon (torso, arm, leg, etc.), and produces a reproducible change in resistance in response to mechanical strain, ideally in a linear way for easy integration into a system design. Furthermore, the signal must be reliable over the lifecycle of the product it is used in with regards to signal stability, aging, drift, etc. after subjected repeated mechanical cyclic loads. Conventional tensile strain sensors have an operating range of a few percent, with large strain variations attaining 10% strain as with the HBM LD20 high strain gauge [12]. In the conventional metal strain gauge design, a change in electrical conductivity in a thin wire or foil in response to mechanical elongation is measured to determine the strain on the structure being investigated [13].

The performance of strain sensors is generally reported by referencing the gauge factor (k) according to Equation (1):��RR=k��ll(1)where R is the resistance at a strain of zero, l is the length at a strain of zero, while DR and Dl are the changes in resistance and length due to an applied mechanical strain. The gauge factor (k) for conventional strain gauges is generally close to 2 [12]. Beyond the mechanical strain limit of the sensor the resistance signal becomes unstable due to excessive strain of the sensing element and eventual mechanical failure of the structure occurs. In the current application, a strain of 20%�C100% is required, and for this reason traditional strain sensors could be not be employed.

Polymer-based materials would be able to fulfill the mechanical deformation requirements. Monofilament fiber strain sensors are an ideal form for wearable computing applications since they can be integrated into clothing in an unobtrusive way. Piezoresistive materials are often used in sensor designs where relationships between deformation and electrical resistance can be characterized and used in circuit design. However, research into piezoresistive sensors has GSK-3 mainly focused on the doping of silicon structures, and not on larger scale flexible sensors. Flexible elastomeric sensors can be achieved by using dielectrics and electro-active polymers (EAPs), elastomers with carbon-based electrodes have been investigated, but generally with the desire to develop flexible force actuators.

Alternatively, a conductive polymer composite (CPC) can be created by combining a conductive filler (e.g., silver, carbon nanotubes, carbon black) and a polymer matrix (elastomer, thermoplastic, etc.).1.2. Elastomer Capacitor SensorEAPs based on elastomers coated with conductive layers have been investigated for flexible force actuator applications. In a sensor configuration, highly flexible capacitive sensors can be created [14].

The paper is organised as follows: after the introduction, the se

The paper is organised as follows: after the introduction, the second part describes the database and the methodology; the third one is dedicated to the detection of tropical cyclones and describes the specific treatments developed for altimeter data; the fourth part gives results about the analysis of ETDs cases and more precisely on the possibility of retrieving a SLP signal from altimeter measurements during ETDs. The last part gives a complementary analysis of the SLP-SLA relationship in the case of ETDs from barotropic model outputs (MOG2D, [1]).2.?Database and Methodology2.1. DatabaseThe 2003/2004 time period has been chosen for the analysis because it is covered by several independent databases:- the ENVISAT, Topex/Poseidon and Jason-1 altimeter missions;- an extensive observing network deployed in the Atlantic ocean by the National Oceanic and Atmospheric Administration (NOAA).

The NOAA hosts the National Hurricane Center (NHC) and the Hurricane Research Division (HRD), which has defined an experimental wind analysis tool to provide regular high-resolution wind fields for tropical cyclones ([13]; http://www.solar.ifa.hawaii.edu/Tropical/tropical.html). This database gives an extensive list of tropical storms which have occurred on all ocean basins, with information on the track of the storm and estimates of the maximum sustain winds, wind gusts and the minimum central pressure. However these estimates give a measure of the storm��s intensity but not of the wind or SLP field which can be easily compared with the altimeter ground track measurements;- a collocated JASON/buoy database: buoy data include the NDBC network, data available via M��t��o-France, and the TAO array;- the ECMWF pressure analyses at 0.

5 degree-6 hour resolution;- the QuikSCAT scatterometer wind measurements; QuikSCAT winds have been assimilated into the ECMWF Numerical Weather Prediction (NWP) model since 2002.The ECWMF global pressure Drug_discovery fields are used to provide long time series of surface pressure with global space/time coverage. However, in this study, we are mostly interested in low and very low pressure systems. In such conditions, NWP models such as ECMWF suffer from limitations related to their coarse space and time resolution, to very few assimilated SLP measurements (aside from those of ships of opportunity limited to the ships��main tracks), and the fact that the dense scatterometer winds are severely under-sampled when assimilated in the NWP. However, we can derive SLP fields from scatterometer wind measurements using an atmospheric planetary boundary layer (PBL) model [14,15]. These QuikSCAT-derived SLP fields have the advantage of retaining the fine scale structures present in the QuikSCAT wind fields.

Such an expectation cannot be achieved without carefully scheduli

Such an expectation cannot be achieved without carefully scheduling the energy utilization, especially when sensors are densely deployed (up to 20 nodes/m3 [1]), which causes severe problems such as scalability, redundancy, and radio channel contention. Due to the high density, multiple nodes may generate and transmit redundant data about the same event to the sink node, causing unnecessary energy consumption and hence a significant reduction in network lifetime. For a sensor node, energy consumption includes three parts: data sensing, data processing, and data transmission/reception, amongst which, the energy consumed for communication is the most critical. Reducing the amount of communication by eliminating or aggregating redundant sensed data and using the energy-saving link would save large amount of energy, thus prolonging the lifetime of the WSNs.

Data gathering is a typical operation in many WSN applications, and data aggregation in a hierarchical manner is widely used for prolonging network lifetime. Data aggregation can eliminate data redundancy and reduce the communication load. Hierarchical mechanisms (especially clustering algorithms) are helpful to reduce data latency and increase network scalability, and they have been extensively exploited in previous works [2-8]. In this paper, we propose a distributed and energy-efficient protocol, called EAP for data gathering in wireless sensor networks. In EAP, a node with a high ratio of residual energy to the average residual energy of all the neighbor nodes in its cluster range will have a large probability to become the cluster head.

This can better handle heterogeneous energy circumstances than existing clustering algorithms which elect the cluster head only based on a node’s own residual energy. After the cluster formation phase, EAP constructs a spanning tree over the set of cluster heads. Only the root node of this tree can communicate with the sink node by single-hop communication. Because the energy consumed for all communications in in-network can be computed by the free space model, the energy will be extremely saved and thus leading to sensor network longevity. EAP also utilizes a simple but efficient approach to solve the area coverage problem. With the increase in node density, this approach can guarantee that the network lifetime will be linear with the number of deployed nodes, which significantly outperforms the previous works designed for data gathering application.

The remainder of this paper is organized as follows: GSK-3 Section 2 reviews related works. Section 3 describes the system model and the motivation of our work. Section 4 presents the detailed design of EAP. Section 5 reports the result of EAP effectiveness and performance via simulations and a comparison made with LEACH and HEED. Section 6 concludes the paper.2.

Instead, windows are located from the holes on the wall features

Instead, windows are located from the holes on the wall features. Finally, outline polygons are fitted from feature segments, and combined to a complete polyhedron model. A significant advantage of this approach is that semantic feature types are extracted and linked to the resulting models, so that i) it is possible to get faster visualization by sharing the same texture for same feature type; ii) polygons can be associated with various attributes according to its feature type.Figure 2 shows a building facade model which is reconstructed with the above approach. Most facade features are successfully extracted and modeled. However, if take a close look, it is easy to identify several mistakes from the model. By analyzing more models, two main reasons for the modeling errors are deduced.

They are:Limitations of outline generation method. For example, side wall’s eave can ��attract�� the side boundary edges of the facade, and result in a slight wider polygon in horizontal direction. The almost vertical or horizontal edges are forced to be vertical or horizontal; however, this is not always beneficial.Poor scanning quality. Due to the scanning strategy of stationary laser scanner, complete scanning of a scene seems impossible. There are always some parts which contain very sparse laser points, because of the visibility is poor to all scan stations. Occluded zones without any laser points are also usual in laser point clouds. The lack of reference laser information leads to gaps in the final model. Sometimes these gaps are removed using knowledge, but this is not as accurate as data driven modeling.

Figure 2.A reconstructed building facade model, show
Detection of pathogenic bacteria in food, water, and air has been an important issue for scientists because of its critical impact on public health. Although standard microbiological methods of cell culture and plating are confirmative to identify bacterial strains [1], it often takes several days to complete the processes. In addition, most of conventional methods require intricate instrumentation and cannot be used on-site. Thus, both private and government sectors strongly need biosensors that can detect pathogens in a fast and accurate manner.Pathogen sensors must meet several requirements. First, they should show high sensitivity and a low detection limit.

Since the speed of multiplication of bacteria is very high, even low numbers of bacteria cells (<10 cells) can be a risk to a patient's health [2]. USDA requires zero tolerance of certain strains of bacteria, Brefeldin_A such as E. coli O157:H7, Salmonella, and L. monocytogenes, in food products [3,4]. Second, rapid analysis time is essential. This is especially important to take immediate measures for curing victims of pathogens and restricting the spread of pathogens. Third, simultaneous detection and identification of different strains of bacteria is also critical.

Therefore, a dot usually provides an error between discontinuous

Therefore, a dot usually provides an error between discontinuous coordinate systems printed in the original coordinate system of the line and the screen, and the key concept of Bresenham algorithm is to select a dot which best minimizes this error by drawing the straight lines (or another figures). The process of drawing lines using Bresenham algorithm is as follows:Array p1 and p2, the two dots representing the line in the order of coordinate axes. At this point, if the slope of the line is less than 1, array in increasing order of coordinate x and if greater than 1, array in increasing order of coordinate y. (Here assumed to be arrayed to increasing order of x)Begin with the first dot out of the dots arrayed.

Make a dot at the present location.Provide setting the next location.

Then, increase the location of the present pixel by one to increasing order of coordinate x.Calculate the error value at the next location. Here, the error term is the addition of the differences between y coordinate values of p1 and p2.Compare the error terms and examine if the error portion is greater than one pixel. That is, after comparing the error terms up to now and the difference between x coordinate values of p1 and p2, increase the coordinate value by one to increasing order of coordinate y if the error term is greater than the difference.Repeat (3) to (6) until the last coordinate is dotted.For drawing of a quadrangle, the process of drawing four lines using Bresenham algorithm is repeated.

The process of drawing a circle represented by the equation, x2 + y2 = r2 the fundamental of algorithm to be used in Carfilzomib this paper is as follows:Begin with a fixed point on the top of the circle. Here, draw a quarter circle clockwise and repeat this circle four times.Make a dot in the present coordinate.Increase the coordinate by one to increasing order of coordinate x.Then decide y coordinate. Decide one out of y or y?1 for y coordinate. If x2 + (y ? 1)2 < x2 + y2 < r2 is valid, y becomes the next coordinate and if r2 < x2 + (y ? 1)2 < x2 + y2 is valid, y?1 becomes the one. In other cases except for
In this work we present various designs for nanowire arrays, their fabrication, their optical characterization and their potential in (bio-)electrochemical sensing applications. Existing combined electrochemical sensor systems, such as electrochemical optical waveguide Anacetrapib lightmode spectroscopy (EC-OWLS) and electrochemical quartz crystal microbalance with dissipation (EC-QCM-D), have clearly demonstrated their individual uniqueness and usefulness [1�C3].