For the CASA approach, a neural network model is built up to app

For the CASA approach, a neural network model is built up to approximate the desire performance. For the DASA approach, a probabilistic model using the concept of geometry is applied to abstract the properties of the algorithm. Moreover, based on the analysis, the sensor lifetime and cluster lifetime is further explored to show how the operations of the proposed schemes may prolong the network lifetime.The organization of this paper is as follows: Section 2 reviews the current literature on the sensor scheduling management. Section 3 describes the system model and algorithm for sensor scheduling in a cluster-based network topology. In Section 4, a neural network model and a probabilistic model are built up to approximate the desire performance and estimate the sensing rounds of the proposed schemes.

Section 5 summarizes the performance of the proposed scheduling methodology. Finally, Section 6 draws conclusions and shows future research directions.2.?Literature ReviewA large number of sensor scheduling and coverage maintenance protocols have been proposed [8-35]. However, due to the sensing objectives, these management protocols can be different. Yan et al. [1] presented an energy-efficient sensing protocol to achieve the desired sensing coverage. Nodes decide their active periods by exchanging reference points among neighbors. In [2], the authors investigated coverage intensity of the proposed sleep scheduling protocols. Ren et al. [3] provided a generic analytical framework that can be widely used for sensing scheduling protocol design with detection quality requirements.

Turau et al. [4] tried to route packet with the minimum time and energy and aimed to distribute the transmission time slots dynamically among sensor nodes such that the network congestion can be avoided.Hohlt et al. [5] proposed a scheduling scheme for considering energy savings in a data collection process. Schrage et al. [6] applied an ant colony optimization method for scheduling the visiting order of targeted areas in the sensing field such that their energy consumptions are minimized. Decker et al. [7] developed a scheduler to manage Brefeldin_A the competition for resources among different sensing tasks at a single sensor node. Chamberland et al. [8] investigated the relationship between sleeping duration, detection delay and energy consumption in a stationary sensing field.

References [9, 10, 11] are clustering-based protocols that attempt to minimize the energy dissipation in sensor networks.Cheng et al. [12] proposed a bio-inspired scheduling scheme which is a kind of adaptive scheduling scheme which uses only local information for making scheduling decisions. Premkumar et al. [13] considered the problem of quickest detection of an intrusion using a sensor network, keeping only a minimal number of sensors active.

2 3 Accuracy of the QCM BiosensorWe further tried IgE detection

2.3. Accuracy of the QCM BiosensorWe further tried IgE detection in human serum containing a variety of proteins, including different types of immunoglobulins. IgE concentrations in clinical human serum samples were simultaneously measured by the QCM biosensor and the chemoluminescence method. Mean values by the aptamer-based QCM biosensor, antibody-based QCM biosensor and chemoluminescence in 50 clinical human serum samples were 64.0, 62.6 and 64.9 ��g/L, respectively,
Ecological studies provide the necessary background knowledge required to properly interpret a number of paleoclimatic records derived from biological organisms [1,2].

Mountainous and ecotonal regions have been identified as critical zones for understanding eco-hydro-climatic changes over a variety of timescales [3], but dendrochronological records from high latitude and/or high elevation climatic treelines [4] have been the subject of heated controversy regarding their significance for defining human impacts on global surface air temperature [5]. A recent review of climate warming impacts on timberline carbon and water balances in the central European Alps suggested that treeline dynamics respond more to climate extremes than gradual temperature changes [6]. In the western United States, ring-width of bristlecone pine (Pinus longaeva D.K. Bailey) growing within about 150 m of the upper treeline limit have reached unprecedented peaks in the last few decades [7].

This trend is matched by increased air temperature in PRISM data [8], although cause-effect mechanisms would have been easier to identify if in situ hydroclimatic measurements had been available.

While several investigations Cilengitide have focused on environmental controls of wood growth at mid- or high-latitude treelines e.g., [9,10], relatively fewer studies have focused on low-latitude locations e.g., [11,12]. On the other hand, measurements of tropical forest plots at elevations below treeline have been used to assess long-term changes in forest biomass and carbon cycling [13], and stem size changes have provided information on ecological pathways linked to water cycling in these regions [14�C16].

Despite the difficulty of separating radial growth from hydration Brefeldin_A status of tropical trees [17�C19], intensive monitoring of stem dimension can generate data on soil water availability in seasonally dry tropical environments [20]. Baker et al. [15] found that shade-tolerant woody species in a Ghana forest displayed little diurnal variation in stem size related to water exchanges, and attributed this to low elastic storage in the trunks.

ode group to which they were compared, up regulated transcripts h

ode group to which they were compared, up regulated transcripts had a higher number of homologs to Strongylida parasites only. As with the constitutively expressed transcripts, translation is the most prevalent KEGG category in both C. oncophora and O. ostertagi. Most transcripts are up regulated in more than one stage likely resulting from carryover between consecutive stages. There was a total of 1393 transcripts identified as en coding putatively secreted peptides of which 538 were enriched in at least one stage. It was determined that free living stages tended to have more of these transcripts in common with each other than with the parasitic stages. Parasitic stages tended to have a com mon pool of secreted peptides as well. The exception to this was C.

oncophora L4 which shared more secreted peptides with the free living stages than with the other parasitic stages. The 5% of domains most prevalent in the secreted peptides were very similar between the two species. Transthyretin like, metridin like ShK toxin, saposin B, and CAP domains were among the most prevalent for secreted Batimastat proteins in both species. Two in sulin domains were among the most prevalent in secreted peptides of C. oncophora but were absent from O. ostertagi. Ves allergen was found in 16 secreted peptides of O. ostertagi but was found in only one secreted peptide of C. oncophora. Differences in gene expression and associated functions between free living and parasitic stages Pfam domains were identified in 41% of the peptides in both C. oncophora and O. ostertagi matching 2507 and 2658 different domains, respectively.

In both organisms the most prevalent domain was RNA recognition motif. An examination of transcripts expressed in the free living and parasitic stages of development revealed that some Pfam domains are abundant in both phases of development while others are unique to a single stage or phase. The most abundant Pfam domain in the free living stages of C. oncophora was expressed solely in this phase of development while two of the top three domains in the para sitic stages were not expressed in any of the free living stages. Domains like the RNA recognition motif were found equally in both phases. A total of 35% of C. oncophora peptides and O. ostertagi peptides could be associated with GO terms categorized as biological process, cellular component, and or molecular function.

Examination of GO terms associated with the peptides reveals significant differences between parasitic and free living stages. Significantly enriched molecular functions in the para sitic stages of O. ostertagi and C. oncophora included binding, protein binding, and catalytic activity. In the free living stages, sodium,potassium exchanging ATPase activity and aspartic type endopeptidase activ ity were enriched in C. oncophora while oxygen binding and sequence specific DNA binding were enriched in O. ostertagi. A total of 4,160 and 4,135 unique InterPro domains were detected in 46% of C. oncopho

t250RV1 were designed to amplify an 805 bp cDNA product, at an a

t250RV1 were designed to amplify an 805 bp cDNA product, at an annealing temperature of 60 C. The chicken lysozyme gene was used to determine relative quantities of contaminating host cDNA. The for ward primer RW3F and reverse primer RW4R were designed to amplify a 280 bp host cDNA prod uct at an annealing temperature of 60 C. Semi quantitative PCR The predicted coding regions of each protease gene were examined for potential primer sites within 1 kb of each other where possible. Primers were designed as detailed in Table 5. PCRs were conducted on cDNA samples from E. tenella merozoites, gametocytes, unsporulated and sporulated oocysts. PCR were optimized to produce cDNA sized pro ducts. Negative controls of no DNA template and host cDNA were run alongside a positive genomic DNA control.

When genomic DNA products were not amplified, a repeat PCR was performed at longer annealing times to produce the often much larger genomic DNA product. A typical PCR was as follows, 1uL of standardized Dacomitinib cDNA sample, 0. 2 uM forward primer, 0. 2 uM reverse primer, 1 �� Accu Prime reaction mix, and AccuPrime Pfx DNA poly merase. Cycling conditions typically involved an initial denaturation at 95 C for 3 min, followed by 25 cycles of denaturation 95 C for 30 s, annealing at Tm 5 for 1 min, extension at 68 C for 1. 5 min. When products were to be sequenced, a final extension at 68 C for 10 min was performed at the end of the PCR reaction. PCRs were per formed at least twice and, generally, three times for each gene product by a different researcher each time.

All amplified products were gel purified using a QIAquickW Gel Extraction Kit according to the manufacturers instructions and sequenced. When cDNA pro ducts were amplified from different parasite stages, these were pooled and used in sequencing reactions. When cDNA products were not obtained, additional primers were designed and used. If a cDNA product was still unable to be amplified with the second primer pair, genomic DNA products were sequenced to confirm primer specificity. Sequences were analysed using DNASTAR Lasergene 9 Core suite. GAM56 processing assay A frozen sample of purified E. tenella gametocytes was resuspended in PBS to a final volume of 500 uL. Glass beads were added to the suspension and vortexed at full speed for three 1 min pulses with a 1 min pause on ice between each pulse.

After three vortex cycles, the sample was centrifuged and the lysate trans ferred to a clean tube. Equal aliquots of the gametocyte extract were immediately added to either 2 uL of 10�� protease inhibitor or PBS. A zero time sample was taken from the PBS control and immediately added to Laemmli sample buffer and frozen. The assay tubes were incubated at 37 C for 2, 4, 6, 8, 10, 12, 16 or 24 h, after which Laemmli sample buffer was added and samples stored at ?20 C for further assessment. SDS PAGE and immunoblotting were carried out as described previously. Briefly, gametocyte assay samples, resuspended in Laemmli sample

75 s, which was adequate for the temperature measurements Figure

75 s, which was adequate for the temperature measurements. Figure 3 shows the fluorescence intensity of each dye versus the pulse count. The fluorescence intensity was normalized by the initial intensity for each dye. The fluorescence intensities of RhB and Rh110 after 20 pulses were 101% and 98% of the initial dye intensities, respectively. Thus, almost no photobleaching occurred using pulse excitation for several tens of pulses. Consequently, pulse excitation was used in the temperature measurements. The un
The emphasis of vision sensor technology becomes more and more evident in various visual measurements, such as automotive, human machine interface, surveillance and security, and industry control.

For example, if we introduce a vision sensor for high-speed visual information [1] and proposed an appropriate control algorithm for the vision sensor utilizing some unique features, real-time visual measurement [2] and wearable biometrics devices will be achieved. Ideally, synchronization can be achieved without any external triggers or references in the computer vision field. Multiple groups of images bring much more valuable additional information, such as the depth parameter, to perform accurate measurements in the real world, without the limitation of one-view measurement techniques [3].Firstly, there are a group of studies in which geometric correspondences such as points are used for synchronization [4�C11]. Although these methods can carry out geometric calibration and synchronization simultaneously, a sufficient number of correspondences across images are necessary.

This is not appropriate depending on applications. Also, estimating simultaneously geometric parameters and time delays, which are inherently independent of each other, might sacrifice accuracy to some degree.Therefore, AV-951 it is more desirable to synchronize without using image correspondences. Yan and Pollefeys proposed a method for video synchronization [12] that uses the space-time interest points defined by Laptev and Lindeberg [13]. This method also fails to synchronize images in the case of foreground objects [14]. When the feature points are not available or reliable, some alternative algorithms that use the object outline or silhouette as the reliable image feature exploit into the epipolar tangents [15], i.e., points on the silhouette contours in which the tangent to the silhouette is an epipolar line [16].

A rich literature exists on exploiting epipolar tangents, both for orthographic cameras [15,17] and perspective cameras [18]. There are also factorization-based methods to recover 3D models from multiple perspective views with uncalibrated cameras, performing a projective reconstruction using a bilinear factorization algorithm and then converting the projective solution to a Euclidean one by enforcing metric constrains, but they are based on static scenes and moving objects [11, 19�C 21].

Furthermore, it is proposed a design methodology over a FGPA for

Furthermore, it is proposed a design methodology over a FGPA for the TDNN. The tool used to simulate the system in floating point format was Matlab and Simulink, and especially the Neural Network Toolbox was used [20].Initially the bit rate (Rb) was set to one kilobit per second and ten samples were taken per bit (n = 10), therefore the sampling frequency (fm) was set to 10 kHz. The value of the bit rate has not transcendence, the important parameter is the number of samples per bit. In the final system the bit rate can increase as much as the technology permits, according to the maximum clock frequency. Figure 2 shows the original data signal and the sampled received signal with +10 dB of SNR.Figure 2.(a) Original data signal and (b) sampled received signal.3.

?The Floating Point ModellingFigure 3 shows a Time Delay Neural Network (TDNN), it is a neural network which includes input cascaded delay elements. In this case, each element delays the signal a sampling interval (Tm seconds). For processing n samples, (n �C 1) delay cells are necessary. This architecture has a transitory period for the first input symbol until the first n samples arrive. Without the delay cells the system is a Multilayer Perceptron Neural Network type.Figure 3.Time delay neural network.The question is whether this TDNN will improve the SNR of the sampled signal. This TDNN is trained with its input noisy sampled signal and the target is the original data signal. The signal received at the input of the sampler is called r(t), which is equal to the data signal d(t) plus noise signal n(t); that is, r(t) = d(t) + n(t).

At a given time, called t0, the delay elements of the neural network store the samples r(to �C kTm), where k is equal to 0, 1, 2, ��, 9. For these values the target for the neural network training is d(to), the original data value in t0. The observation interval is Tb seconds.Initially to train, validate and test t
In fullerene (C60), each carbon atom is bound to three other carbons and is sp2-hybridized. The C60 molecule has two types of bonds: the double bond of 6:6 ring bonds and the shorter bond in 6:5 bonds. C60 behaves like electron-deficient alkenes, and readily reacts with electron-rich species [1]. Their small size, inert behavior, and stable structure account for the low toxicity of fullerenes, even at relatively high concentrations.

Their electrochemical characteristics combined with unique physiochemical properties enable the application of fullerenes in the design AV-951 of novel biosensor systems. Given their possible protein and enzyme functionalization, as well as their signal mediation and light-induced switching, fullerenes can potentially provide new and powerful tools in the fabrication of electrochemical biosensors [2].Urea is one of the metabolic products of protein metabolism.

After that, chemical contents of palm oil are analyzed by using P

After that, chemical contents of palm oil are analyzed by using Partial Least Squares Regression (PLSR) models [30].Besides imaging technology, a capacitive-based concept of grading method is proposed in [31]. The capacitive concept is applied to measure the dielectric properties of the oil palm fruit. This measurement method yielded 5% accuracy for the dielectric constant (��) and 3% for the dielectric loss (��). The capacitive method is similar to the other methods, where supporting equipment is needed and it is not suitable for outdoor testing.In the prevailing research works, no grading methods using an inductive concept is proposed. An inductive concept non-destructive grading method is proposed in this research work, based on the moisture content of the oil palm fruit.

The permeability value of water is 1.2566270 �� 10?6. Thus, with a low permeability value compared to other materials, such as metals, a high frequency range is used in the measurement. This proposed inductive method has great potential for use in outdoor testing [32�C34]In this paper, investigation on the oil palm ripeness sensor based on the resonant frequency (fr) is presented. Inductance values in the high frequency range are used to determine the ripeness of the oil palm fruits which are then categorized into ripe and unripe fruits. The frequency characteristics of the sensors are studied and the fr of air (fra), ripe fruit (frr) and unripe fruit (fru) are analyzed. Initially, the value of frr and fru is normalized to fra.

Then, the deviation between the mean value in the normalized resonant frequency (Nfr) between the air (Nfra) and ripe fruit (Nfrr) as well as between air and unripe fruit (Nfru) are observed and analyzed for the effect of the size of the sensor and the coil diameter size affecting the sensitivity of the sensor, which is determined by the deviation in the mean value between Nfra and Nfrr as well as in between Nfra and Nfru. The larger the deviation from the mean value the more sensitive is the sensor. In this study, twenty sensors with different sensor sizes as well as different coil diameter sizes are built. Looking into the effects of coil diameter, the results portray a uniform pattern throughout the testing. It is observed that the Nfrr leads the Nfru. The value of the Nfr decreases as the air coil length is increased.

As for the effects of air coil length, the differences between the ripe to unripe samples increase as the Drug_discovery air coil length increases. The results from this study play an important role in designing the air coil structure as it will improve the sensitivity of the oil palm sensor to determine the maturity of the oil palm FFB as well as the ripening process of the fruitlets. Nevertheless, the inductive oil palm ripeness sensor method offers a few advantages such as it is a passive type sensor, reduces time consumption and is an accurate grading system.

This approach resulted in two distinctly different paths in sens

This approach resulted in two distinctly different paths in sensor design, depending on whether the sensor was designed with the different responses to be generated in parallel or serially. Essentially, the dual mechanism allowed either: (1) a reduction in false-positive signals or (2) an enhancement in the detection signal. For example, one ch
The documentation of archeological elements through advanced imaging techniques, finally leading to a detailed 3D model of the objects of interest, is currently a hot topic both in commercial as well as in scientific communities. Some examples of using the 3D technology in this field are listed in [1] and there is even a conference series devoted to this topic [2]. Such a 3D representation is used for visualization, archaeological documentation, restoration, or preservation purposes.

In general we use the term image based modeling [3,4] if we refer to the entire workflow from image acquisition, to image calibration and orientation, to image matching and meshing, or to CAD-like object reconstruction and parameterization.Although software tools which offer support for the entire workflow are available nowadays, a good planning of the initial image acquisition is still necessary in many applications such as in archeology in order to achieve the desired accuracy and reliability of subsequent image processing steps. Moreover, portable physical cultural finds need to be documented first in-situ locations before preserving them in heritage collections and museums [5].

This mission of field data capture in excavation sites is preferred to be automated in order to save time during the capture and ensure adequate data [6]. The mentioned photo acquisition planning demands experience and knowledge in the field of photogrammetry.In the literature few recently presented papers deal with the proper selection and acquisition of images among a large dataset for 3D image-based modeling. Hosseininaveh, et al. [7] introduced a method called Image Network Designer (IND) for the modeling of a museum artifact. Carfilzomib That paper lacks a deep investigation on the accuracy indices after the network reduction and the experiment was tested on small size artifact with only a few images. Wenzel, et al. [8] presented a guideline for image data acquisition called ��one panorama each step��.

They discussed extensively how to find a compromise between large and short base imaging configurations.Previously, we introduced the minimal camera network technique [9] by reducing a pre-designed (simulated) dense imaging network. The redundancy in the information from the dense network will provide freedom for selecting the suitable and sufficient images for the subsequent 3D modeling. This technique is useful, but it fails to accommodate better intersection geometry of rays between cameras and object points and this can result in gaps in the final 3D models.

As optical field equipments have become available, the bio-optic

As optical field equipments have become available, the bio-optical model has appeared and is being widely used, which make it possible to accurately remove the contribution due to water column [22, 23] and then to separate between the signatures coming from water column and bottom.This paper aims to present an approach to estimate the past biomass of aquatic vegetation in a shallow inland lake using the historical satellite imageries without any corresponding field investigations, based on the present satellite imagery provided along with concurrent or quasi-concurrent ground field investigations, and then to determine the historical change of the aquatic vegetation in Taihu Lake.

We first present some general information about Taihu Lake and the field investigation, including instruments and sampling methods, and satellite imageries together with its preprocessing method.

Then we describe the method for developing the quantitative estimation model for aquatic vegetation in Taihu Lake. Finally, some conclusions are drawn on the basis of the full discussion.2.?Study areaTaihu Lake, a large shallow lake with an average depth of 1.9 m (max. 2.6 m), and covering an area of 2427.8 km2 (including 51 islands), is one of the five largest fresh-water lakes of China and is located in the core of the Yangtze Delta in the lower reaches of Yangtze River, in East China, an area that has a developed economy (Figure 1).

Unfortunately, in recent years, water quality has deteriorated and cyanobacteria blooms have covered large areas of the lake every summer since the 1990s.

There exist four types of aquatic vegetation: (a) emerged vegetation, including Phragnites communis, Typha angustifolia, Zizania latifolia and so on, (b) submerged vegetation, including Ceratophyllum demersum, Vallisneria spiralis, P. malaianus and so on, (c) floating vegetation, including Eichhornia crassipes, Lemna minor and so on, and (d) floating-leaved vegetation, including Brefeldin_A Nyphar pumilum, Nymphaea tetragona and so on. They are mainly distributed in the several bays of the east and southeast and in the south littoral zones, where the water is clear and cyanobacteria blooms are rare.Figure 1.Location of Taihu Lake in China.

3.?Material3.1. Field dataThe campaign was carried out along the preset transects Dacomitinib for 94 samples on 10-18 June 2007 (Figure 2), which were positioned by differential GPS (Global Positioning System, Trimble Ltd., USA) with a positioning precision of 2-5 m. Before the field investigations, the sampling locations were preset according to the prior knowledge of the aquatic vegetation distribution with help of a 1:50,000 scale digital topographic map.

For example, by simulating hyperspectral data with different spat

For example, by simulating hyperspectral data with different spatial resolution, Luo [12] evaluated the adaptability of linear spectral unmixing to different levels of spatial resolution. Jiao [13] simulated hyperspectral data to evaluate the influence of spatial and spectral resolution to vegetation classification. In addition, simulated data are often used to evaluate and test novel algorithms such as target detection and identification algorithms in hyperspectral remote sensing. There is no easy method to simulate hyperspectral data for testing the performance of these algorithms. If simulated hyperspectral data can be easily obtained, it will greatly help the testing and development of new algorithms.

The universal pattern decomposition method (UPDM) is a sensor-independent method which can be considered as a spectral reconstruction approach, in which each satellite pixel is expressed as the linear sum of fixed, standard spectral patterns for water, vegetation, and soil, and the same normalized spectral patterns can be used for different solar-reflected spectral satellite sensors [14]. Sensor independence requires that analysis results for the same sample are the same or nearly the same regardless of the sensor used. Based on this trait, here we present a method based on the universal pattern decomposition method (UPDM) to achieve the goal of simulating hyperspectral data from multispectral data, which can be considered either a method of spectral construction or spectral transform. The hyperspectral and multispectral data are NASA EO-1 satellite/Hyperion and EO-1/ALI data, respectively (see Section 3.

2 for a brief introduction). First, we obtained ALI and Hyperion data covering the same area and AV-951 performed atmospheric correction to obtain surface reflectance data; here Hyperion data served as standard or real data to evaluate the results in the subsequent analysis. Then, we obtained the decomposition coefficients thought to be sensor-independent for the same sample by applying the UPDM to ALI data; these coefficients were subsequently used to construct Hyperion data. Before performing UPDM, standard pattern matrices of both sensors were calculated based on the standard spectral patterns (see Section 2 for details). Finally, the simulated Hyperion data were compared with the real Hyperion data, i.e., test data, to evaluate and assess this method.2.?Spectral Reconstruction Approach2.1. Review of the Universal Pattern Decomposition Method (UPDM)The spectral reconstruction approach is based on the UPDM, which is a sensor-independent method derived from PDM that has been successfully applied in many studies [14�C21].