The following two phases are involved during any classification p

The following two phases are involved during any classification process: training and decision. Really, the combination of the outputs provided by the two individual classifiers is carried out during the decision phase, as we will explain later. Given a set of training data, scattered through the tri-dimensional RGB data space and assuming known the number of clusters and the distribution of the samples into the clusters, both BP and FC individual classifiers estimate their associated parameters. Based on these estimated parameters, during the decision phase, each individual classifier provides for each pixel to be classified, a support of belonging to a cluster, BP provides probabilities and FC membership degrees, i.

e., continuous outputs.

Because the number of classes is known, we build a network of nodes netj for each class wj, where each node i in the netj is identified as a pixel location i �� (x, y) in the image which is to be classified. Each node i is initialized in the netj with the output probability, provided by BP, that the node belongs to the class wj. This is the initial state value for the node i in the netj. Each state is later iteratively updated through the Deterministic Simulated Annealing (DSA) optimization strategy taking into account the previous states and two types of external influences exerted by other nodes on its neighbourhood. The external influences are mapped as consistencies under two terms: regularization and contextual.

These terms are clique potentials of an underlying Markov Random Field model [23] and they both involve a kind of human perception.

Indeed, the tri-dimensional scenes are captured by the imaging sensor and mapped in the bi-dimensional space, although the third dimension is lost under this mapping, the spatial grouping of the regions is preserved, and they are Brefeldin_A Anacetrapib visually perceived grouped together like in the real scene.The above allows the application of the Gestalt principles of psychology [24,25], specifically: similarity, proximity and connectedness. The similarity principle states that similar pixels tend to be grouped together. The proximity principle states that pixels near to one another tend to be grouped together.

The connectedness states that the pixels belonging to the same region are spatially connected. The proximity and connectedness principles justify the choice of the neighbourhood for defining the regularization and contextual terms and the similarity establishes the analogies in the supports received by the pixels in the neighbourhood coming from the individual classifiers. From the point of view of the combination of classifiers the most relevant term is the regularization one.

According to Equation (1), geometrical variables involving the th

According to Equation (1), geometrical variables involving the thickness of the metal layer and the refractive index of a prism can be tuned to manage the SPR waveband or resonance angle in the Kretschmann configuration. In addition, the propagation constant of excited SPP ��spp responds sensitively to the variation in the environmental refractive index. This property is typically adopted in order to improve the performance of SPR based sensors.This resonance condition is also applicable for waveguide coupling based SPR sensors. Light injected into an optical fiber propagates into the core through total internal reflection and generates an evanescent field in the vicinity of the waveguide boundary, which induces SPR at the interface between the metal film and the sensing, as presented in Figure 1(b).

A small portion of the sensing region in the fiber-optic sensor can be approximated as a 2D flat dielectric-metal-dielectric structure similar to a Kretschmann configuration. Meanwhile, the spectral response of fiber-optic SPR sensors is slightly different from the Kretschmann configuration. When an optical fiber is used as the sensor body, the spatial-frequency bandwidth of the angular spectrum of incident light at a point on the metal surface in the sensing region is quite wide and the control of incidence angle becomes difficult to implement. Because of these characteristics, many researchers have attempted to develop analytical procedures for estimating the performance of fiber-optic sensors [2].

The grating coupling method for SPP excitation is slightly different from the above described methods.

SPPs can be produced by the direct illumination of a metal surface of a grating structure, as shown in Figure 1(c). To attain SPR, primary conditions are required. The component of the wave vector in the plane parallel to the grating surface is altered by diffraction (m?2��/��). The propagation constant of the wave vector in the plane of grating must be the same as the propagation Brefeldin_A constant of the SPPs, as described in the equation below [5]:2��nd sin ��+m2��=�� Re (��spp); ��spp=��spp0+����,(2)where m is an integer representing the diffraction order, nd is the refractive index of the sensing material, and �� is the grating period.

GSK-3 Here, ���� accounts for the change in the SPPs propagation constant due to the presence of the grating structure.The optical system of an SPR based refractive index sensor consists of a light source, an SPR coupler with a sensor chip, and a light detector. Various coupling methods are used to design an SPR coupler.

JA phytohormone signaling Two proteins which also showed increas

JA phytohormone signaling. Two proteins which also showed increased expression in egg induced elms are patatin like protein and heat shock protein 81. Patatin proteins are related to the major storage protein known from potato tubers and have the enzymatic activity of phospholipases and re lease fatty acids from membrane lipids. These proteins have been identified in many plant species and were shown to be involved inter alia in pathogen triggered cell death and to be induced by wound stimuli. They might also be associated with the herbivore induced defense pathway via the mobilization of lino lenic acid from the cell membrane, which activates the octadecanoid pathway and finally leads to the synthesis Brefeldin_A of JA and other oxylipins.

HSPs meanwhile, are molecular chaperones which can modulate the folding of a variety of other specific target proteins involved, for in stance, in cell cycle control and signal transduction. HSP 81 belongs to the HSP 90 family of stress proteins, which are known to influence several resistance gene signaling pathways, the inhibition of which lead to decreased resistance to pathogens and increased resist ance to insect herbivores. Thus, a suite of defense response genes, that work together to protect the plant from insect attack appears to be coordinately activated by egg laying on elm. Transcripts of jasmonic acid biosynthesis genes are present in high abundance JA has been determined to be an integral part of the plant signal transduction pathway, which leads to the ac tivation of direct and indirect defenses against herbivor ous insects.

Decreased resistance to herbivores and enhanced egg laying activity has been observed in tomato mutants with impaired JA biosyn thesis. Moreover, transcriptome analyses using microarrays indicated that a large portion of herbivory induced responses are mediated through the JA pathway. In egg induced elms, we found high levels of tran scripts of genes encoding key enzymes involved in the biosynthesis of JA including lipoxygenase and allene oxide synthase. Our findings support the expected in volvement of the octadecanoid signal transduction path way in egg induced plant defense, as the treatment of elms with MeJA leads to the release of volatiles that are attractive to egg parasitoids. Genes involved in JA bio synthesis were also upregulated after pierid eggs laying on A. thaliana.

However, we also found enhanced transcript abundances after egg laying in comparison to the other treatments for jasmonate ZIM domain pro teins, which are known to repress JA responsive genes. Auxin might be another phytohormone involved in elm responses to eggs, and transcripts of both positive and negative regulators of auxin signal transduction, an auxin receptor and an auxin repressed protein, were also found. After JA treatment of poplar, down regulation of genes involved in auxin signaling was observed. Auxin interferes with JA and SA signaling, and the negative regulation of auxin is supposed to mediate

hest value and was in a similar range as the curcuma DMSO e trac

hest value and was in a similar range as the curcuma DMSO e tract. Changes in gene e pression with curcumin Based on the above mentioned findings, curcumin was investigated at different concentrations in more detail at the 6 hour time point. Treat ment with curcumin caused a significant reduction of MMP1 and MMP3 at 10 uM and 20 uM. For MMP13, all concentrations of curcumin caused a significant reduction. E pression of IL 1B and IL 6 was significantly inhibited at both, 10 uM and 20 uM, while the lowest Analysis of NF ��B Immunoblotting of p65 in nuclear e tracts of untreated, IL 1B treated and IL 1B curcumin treated cells revealed that IL 1B treatment caused nuclear translocation of p65 after 60 min. However, compared to IL 1B stimulated samples, curcumin treatment did not reduce levels of the target protein in nuclear e tracts.

Using the NF ��B p65 transcription factor assay, we provide further evidence that IL 1B strongly induced NF ��B DNA bind ing, while cur cumin was not able to reduce levels after IL 1B stimulation. Internal assay controls ensured valid ity of the test. concentration caused a slight increase of IL 6. IL 8 e pression was Batimastat also decreased at 20 uM. In con trast, TNF e pression was significantly increased at all three curcumin concentrations, with the most prominent effects at 20 uM. Furthermore, TNF e pression was also increased upon curcumin treatment alone, while all other target genes remained unaltered under these conditions. TLR2 e pression was sig nificantly reduced with each concentration. For summarized values see Additional file 4 Table S4.

Analysis of MAP kinases Effects of curcumin on MAP kinase activity were investi gated by detection of levels of phosphorylated and unphosporylated p38, ERK and JNK using immunoblotting technique of whole cell e tracts. Results demonstrate that IL 1B treat ment increased levels of phosporylated p38, ERK and JNK after 15 min, which is indicative of activation of these MAP kinases. Treatment with curcumin reduced activity of JNK compared to IL 1B treatment, but further increased levels of p ERK and p p38 compared to IL 1B treatment. Levels of unphosporylated p38, ERK and JNK were similar in all groups. Equal protein loading was confirmed by tubulin detection. Discussion Changes in gene e pression Curcuma is not only an ancient spice, but also a trad itional remedy that has been used in Indian and Chinese medicine to treat indigestion and many other medical issues.

Since the 1970s, the anti inflammatory com pounds called curcuminoids were discovered in the spice, with one being curucmin. Because of its anti inflammatory properties, curcuma and its components have been investigated in osteoarthritis and rheumatoid arthritis during the past one to two decades, while only one paper has been published on the effects of curcumin on intervertebral disc cells so far. Our results clearly show that the different curcuma e tracts influenced cellular behavior in a different manner. While the curcuma

To overcome this shortage, an operation procedure of two steps ha

To overcome this shortage, an operation procedure of two steps has been proposed [2]. In the first step which is also called the MIMO mode, each radar transmits a probing signal (usually orthogonal waveforms) to estimate the time delay differences and total phase differences between sub-radars and the master radar, and they are referred to as coherence parameters (CPs) [2�C5]. In the second step which is also called the coherent mode, all radars transmit coherent waveforms adjusted by the estimated CPs from MIMO mode. Clearly, the estimation accuracy of CPs greatly impacts the coherence gain that can be obtained by NGR, which raises two important questions: What is the best estimation accuracy for the CPs? How much coherence gain can we get assuming that estimation accuracy is achievable?Another problem in NGR lies in the constraint of system size, i.

e., the number of radars cannot be arbitrarily large in practice. Thus, the maximum SNR gain that can be obtained merely through the spatially coherent processing of distributed radars is limited, which is unfavorable in detecting and tracking long-range weak targets. To settle this problem, it is natural and essential to emit pulse trains in NGR, which means we will accumulate the energy of echoes not only from different radars but also from multiple pulses.

In NGR transmitting pulse trains, new questions immediately emerge: How will the introduction of pulse trains affect the estimation accuracy of aforementioned CPs? Are there any new parameters that need to be estimated? If any, what is the best estimation accuracy for those parameters? What is the optimal coherence performance for NGR with pulse trains?A thorough review of the existing literature on NGR reveals that the present signal models in NGR are all based on single pulse schemes [2�C5], whereas the transmission of pulse trains has not been considered yet. From the aspect of parameter estimation, [4] derived the CRBs of time delay differences and T/R phase differences for a general NGR architecture, but the CRBs of total phase differences are not given. Therefore, the CRBs of CPs have not been thoroughly worked out so far, according to the definition of CPs. In the field of performance analysis, [4] derived the performance bound of NGR based only on the CRBs of T/R phase differences, assuming that all time delay differences are ideally compensated.

In [5] a formula of coherence gain taking all types of estimation errors into consideration Cilengitide was presented, but the performance bounds were not analyzed. Therefore, the performance bound analysis of NGR still remains an unresolved problem.In addition, it is worth pointing out that the parameter estimation of NGR should be distinguished from the parameter estimation of MIMO radar which has been studied in [6�C17], despite their superficial similarities in emitting orthogonal waveforms.

However, during their work on estimating ground subsidence from s

However, during their work on estimating ground subsidence from satellite data, Fujiwara et al. [26] and Shimada [27] noted that it was effectively quite difficult to differentiate the atmospheric delay phase due to water vapor from the phase due to changes in the Earth’s crust. The atmospheric delay itself is found to be a direct cause of remarkable observational error. For this reason, in order to increase the precision of change detection in the Earth’s crust, research on the effects of water vapor on microwaves, and their mitigation, is essential [5,27�C30].Changes in atmospheric water vapor are extremely complex, with 3-dimensional changes taking place not only in the vertical, but also in the horizontal directions [5,29,31].

It is extremely difficult to correct for the local effects of water vapor when local aerological data are not available [32]. The PSInSAR method attempts to handle this issues by temporal averaging of up to 30 SAR images [9]. Conventional researches with respect to the atmospheric impacts on InSAR mainly examine the effects of changes in atmospheric water vapor with altitude, limiting therefore the precision of this methodology in most of the past studies on atmospheric delay using InSAR techniques [33,34].There is therefore a great urge nowadays for quantitatively obtaining 3-dimensional spatial distributions of water vapor in order to estimate the atmospheric delay on InSAR data, as due to the changes in atmospheric water vapor contents. This is a very challenging issue, especially when one has to quantitatively assess these distributions and the related atmospheric impacts in order to use them with the JERS-1 SAR data.

We have to note here that these satellite data were observed from 1992.01.11 to 1998.10.12, a period when no archived upper-level meteorological data are available over Japan.In this study, using GIS as analytical platform, we aim at estimating the spatial variation and the temporal changes in ground subsidence over the Nobi Plain using Batimastat both ground level measurements data and InSAR data. However, notwithstanding the availability of weather charts and detailed information of ground surface atmospheric conditions (temperature, pressure, water vapor, wind, etc.

) over Japan during the JERS-1 period (1992�C1998), detailed information for upper atmospheric layers has been made available by the Japan Meteorological Agency only after 2002 with its multi-layer and multi-temporal Grid Point Value of Meso-Scale Model (GPV-MSM) data set (see Section 2.2 for details). We therefore propose to use the Analog Weather Chart (hereafter, AWC) method [35,36] in order to estimate from the analog GPV-MSM weather charts and datasets those water vapor inputs needed for calculating water vapor effects on the JERS-1 SAR interferometry data.

The computational complexity of the method is evaluated in Secti

The computational complexity of the method is evaluated in Section 4. In Section 5, simulation results are provided to verify the performance of the proposed algorithm. Finally, Section 6 concludes this paper.Notation:Scalars, column vectors, matrices and tensor are expressed by regular, bold lowercase, bold uppercase and bold calligraphic letters, respectively. [A]i,j and i,j,k stand for the (i,j) and (i,j, k) element of a matrix, A, and a tensor, . (?)H, (?)T, (?))?1 and (?)* denote the Hermitian transpose, transpose, inverse and complex conjugation without transposition, respectively. and denote the Kronecker operator and the Khatri-Rao product, respectively. diag(?) denotes the diagonalization operation, and arg(��) denotes the phase of ��.2.?Tensor Basics and Signal Model2.1.

Tensor BasicsFor the readers’ convenience, several tensor operations are introduced firstly, which refer to [15,16].Definition 1 (Matrix Unfolding):The three standard unfoldings of a third-order tensor, I��J��K, denoted by [](1) I��JK [](2) J��IK and [](3) K��IJ, can be expressed as [[](1)]i,(k?1)J+j = []i,j,k, [[](2)]j,(i?1)K+k = []i,j,k and [[](3)]k,(j?1)I+i = []i,j,k, respectively.Definition 2 (Mode-n Tensor-Matrix Product):The mode-n product of I1��I2����IN by a matrix, A Jn��In, is denoted by = ��nA, where I1��I2����In?1��Jn��In+1����IN and [Y]i1,i2,?,in?1,jn,jn+1,?,iN=��in=1In[X]i1,i2,?,in?1,in,in+1,?,iN?[A]jn,inDefinition 3 (The Properties of the Mode Product):The properties of the mode product are shown as follows:X��nA��mB=X��mB��nA,m��nX��nA��nB=X��n(BA)(1)[X��1A1��2A2��?��KAK](n)=An?[X](n)?[An+1?An+2??AK?A1??An?1]T(2)2.

2. Bistatic MIMO Radar Signal ModelConsider a narrowband bistatic MIMO radar system with M colocated antennas for the transmit array and N colocated antennas for the receive array, shown in Figure 1.Figure 1.Bistatic multiple-input multiple-output (MIMO) radar scenario.Both the transmit array and receive array are uniform linear arrays (UALs), and the inter-element spaces of the transmit and receive arrays are half-wavelength. At the transmit array, the transmit antennas emit Anacetrapib the orthogonal waveforms S = [s1, s2, , sM]T M��K, where K is the number of samples per pulse period. All the targets are modeled as a point-scatterer in the far-field, and it is assumed that there are P uncorrelated targets in the same range-bin of interest. ��pp=1P and ��pp=1P are the DOD and DOA with respect to the transmit and receive array normal, respectively.

The measurement noise and sensor faults are likely to be stochast

The measurement noise and sensor faults are likely to be stochastically unrelated, while event measurements are likely to be spatially correlated. The Bayesian detection scheme in [14] selects the minimum neighbors for a given detection error boundary such that the communication volume is minimized during the fault correction. Luo et al. in [14] did not explicitly attempt to detect faulty sensors, instead the schemes they proposed improve the event detection accuracy in the presence of faulty sensors.Article [15] presents a distributed fault detection algorithm for wireless sensor networks. Each sensor node identifies its own status based on
The increasing availability of commercial high-resolution satellite imaging sensors such as SPOT5, IKONOS, QuickBird and TerraSAR, requires the availability of suitable automatic interpretation tools to extract and identify cartographic features, especially in rapidly changing urban areas.

Roads are one of the most important linear cartographic features. Particularly, extraction of road networks from remotely sensed imagery is not only meaningful for cartography and topography [1], but also significant for various applications of geodata such as automatically aligning two spatial datasets [2] or automated vehicle navigation [3]. Therefore, research on the automatic extraction of road networks from remotely sensed imagery has been a topical research theme in the various fields of photogrammetry, remote sensing, geographic information systems, pattern recognition, and computer vision.

As a result, many strategies, methodologies and algorithms for road network extraction have been presented since the 1970s, which have achieved varying degrees of success [4]. According to the level of automation, AV-951 the techniques for road extraction with the aid of computer vision can be coarsely classified into automatic and semi-automatic approaches.The automatic methods attempt to seek an analysis and interpretation of the image similar to that of a human operator. Nevatia and Babu [5] utilized an edge detection method to identify ribbon roads with lateral and parallel anti-edges. Radon transform was employed to locate the roadsides and to measure the width of a road [6]. Due to the variation of the complexity of image contents, the above low level edge detection methods are insufficient to extract the road features with high completeness, correctness, quality and accuracy. Therefore, more high level techniques have been developed. For example, angular texture signatures can make use of the characteristics of road’s texture, and this is utilized to find candidate road centerline points [7] or to discriminate road surfaces from the parking lots [8].

Cells were pelleted then resuspended in 200 ��L of FACS buffer (P

Cells were pelleted then resuspended in 200 ��L of FACS buffer (PBS containing 2% FCS, 2 mM EDTA, and 0.05% NaN3), stained on ice with fluorescent antibodies for 30 minutes, washed with FACS buffer and then fixed with 4% parafomaldehyde in PBS. Appropriate isotype control antibodies were used to assess the level of specific labelling.2.4. Intracellular detection of IFN��NK cells alone or with DC were incubated for 24 hours at 37��C. Brefeldin-A (10 ��g/mL, Sigma-Aldrich, Poole, UK) was added for the last 5 hours of culture. Cells were then fixed with 2% paraformaldehyde in PBS for 15 minutes at room temperature, washed and stained for 20 minutes at room temperature with anti-CD56-PE, anti-CD69-PerCP, anti-HLA-DR-PerCP or anti-CD3-PerCP, and anti-hIFN��-FITC (BD Pharmingen, Oxford, UK) antibodies in the presence of 0.

5% saponin. The cells were washed and fixed in 4% paraformaldehyde before being analysed by FACS.2.5. Detection of CD107a on NK cellsThe percentage of degranulating NK cells was measured as previously described by Alter et al. [23]. Briefly, NK cells were incubated alone, or with immature or mature DC either cultured together or separated by 4 ��m transwells (R&D Systems), or treated with immature or mature pDC supernatants for 24 hours. Cells were then harvested and incubated with K562 cells for 4 hours at an E:T ratio of 5:1 in the presence of monensin (6 ��g/mL, Sigma-Aldrich, UK) and anti-human CD107a-FITC antibody (BD Pharmingen, Oxford, UK). Cells were then surface labelled with anti-CD3-PerCP and anti-CD56-APC antibodies (BD Pharmingen).

The cells were washed and fixed in 4% paraformaldehyde before being analysed by FACS.2.6. ELISA assaysThe levels of IFN��, IFN��, and IL12p70 in DC/NK cell co-culture supernatants were quantified by sandwich ELISA. IFN�� was detected using monoclonal mouse-anti-human IFN�� antibody (R&D Systems, UK), polyclonal sheep anti human IFN�� antibody (R&D systems, UK), and rabbit-anti-sheep antibody-HRP (Dako, Ely, UK). IFN�� was measured using IFN�� matched antibodies (R&D systems, UK) in accordance with the manufacturer’s protocol. IL-12p70 was detected using rat anti-human IL-12p70 antibody Brefeldin_A (BD Pharmingen), biotinylated-mouse-anti-human IL-12p40/70 antibody (BD Pharmingen), and horse
Geometric primitives, such as surface normal vector, curvature, and the change of curvature and so on, may provide additional and useful information to recover the correspondence of two point clouds.

A method to find the correspondence of two point clouds using geometric primitives and a local search algorithm, named Geometric Primitive ICP (GP-ICP), is proposed. Since this paper aims to present the evaluation results of the convergence region of the GP-ICP, the precision and accuracy of the relative transformation between point clouds are treated in the paper.

In particular, starting from the results obtained in [4], new ex

In particular, starting from the results obtained in [4], new experiments have been carried out using a cylindrical geometry with two different coating: Damival and UR5041, showing that it is not the shape but the transversal dimension and the material characteristics of the coating that influence the sensitivity.2.?Operating PrincipleThe relation between the normalized Bragg wavelength shift ����B/��B and a spatial uniform sound pressure P(t) = p?sin(��St) around the FBG (where p and ��S are the amplitude and angular frequency of the sound pressure, respectively), is given by [5]:����B��B(t)=[?(1?2��)E+n22(1?2��)E(2p12+p11)]P(t)(1)where n = 1.465, E = 70 GPa, �� = 0.17 and p11 = 0.121 and p12 = 0.270 are the effective refractive index of the guided mode, the Young’s modulus, Poisson ratio and the elasto-optic coefficients of the optical fiber, respectively.

Thus, the spectral response of the FBG moves without changing its shape at the same frequency of the applied acoustic pressure. For a GE-doped FBG at 1550 nm, ����B/��P was measured as -3��10-3 nm/MPa over a pressure range of 70 MPa [6]. This means that with interrogation units able to perform wavelength shift measurements with a resolution of 10-4 pm in the investigated acoustic frequency range, an acoustic pressure limit of detection of hundreds of Pa can be obtained.When optical fibers are coated with a plastic material, they exhibit some order of magnitude increase in their pressure sensitivity [7-8].

In fact, according to the Hocker analysis [9], if the FBG is coated with a thick layer of polymer, the normalized wavelength Carfilzomib pressure sensitivity is given by:����B��B(t)=[?1+n22[p12?��(p11+p12)]](1?2��coa)EcoaP(t)(2)where Ecoa and ��coa are the Young’s modulus and the Poisson ratio of the coating, respectively.It can be seen from Equation 2 that for coatings with small Young’s modulus compared with the fiber one, the wavelength pressure sensitivity of the FBG can be increased significantly. The experimental demonstration of the pressure gain sensitivity was proven in the static case [10], but the concept can be extended in the case of acoustic fields if the coating dimensions are small compared with the acoustic wavelength.In addition, the extension of results is valid if the acoustic damping within the overlay is low in order to not affect the dynamic strain amplitude within the coating itself. Also, the acoustic impedance, related to the coating thickness and elastic modulus, should be very close to that of the water, in order to minimize the acoustic reflection at the water-coating interface.3.