In a case where it is unsuitable, all experiments will have to be repeated; (ii) RSM is supposed to be a continuous optimization method, since RSM is similar to gradient-based approaches. Hence, unlike other optimizations, RSM is not suitable for discrete optimization; (iii) RSM may find a local optimum, as opposed to other optimizations that search for a global one [16]. On the other hand particle swarm optimization (PSO) doesn’t readjust the initial search domain of the parameters [17]. PSO approaches are proposed for continuous and discrete optimization problems [18]. PSO is a member of the wide category of swarm intelligence methods for solving global optimization problems [19]. Compared with the design optimization of inductive sensor using genetic algorithms [20], PSO has no overlapping and mutation calculations with much simpler implementation.

In this paper, most parameters of the sensor are discussed, but understanding the parameters’ effect on the nonlinearity error is a critical step in designing an effective sensor. Key parameters are chosen on the basis of their influence on the nonlinearity error. The finite element method and particle swarm optimization (PSO) are combined to design the sensor to achieve the minimum nonlinearity error.This paper is organized as follows: in Section 2, the principle of the inductive angle sensor is described. In Section 3, key parameters for the design are selected and the sensor is optimized using PSO-FEM. The results are measured and discussed in Section 4. Finally, our conclusions about the sensor design is drawn in Section 5.

2.?Principle of the Inductive Angle SensorThe proposed inductive angle sensor consists of a stator and a rotor, as illustrated in Figure 1. The stator has two receiving coils and Dacomitinib one excitation coil, and the separation angle between the receiving coil 1 and 2 is 30��. The receiving coil comprises six loops with the same geometric shape. Adjacent loops are wound in the opposition direction. The stator layout has two advantages. The induced voltages in two receiving coils will be periodic when the rotor rotates. The induced voltages in two receiving coils are zero from the excitation coil because adjacent loops are symmetrical and wound in the opposition direction. However, the number of turns in two receiving coils is limited by the number of printed circuit board (PCB) layers.

The multi-layer PCB layout will increase the cost burden. The number of turns in two receiving coils is a compromise between the performance and cost of the sensor.Figure 1.View of inductive angle sensor.A sine-wave voltage is applied to the excitation coil which generates an alternating magnetic field BE. The alternating magnetic field BE induces an eddy current in the rotor, and the current creates an alternating magnetic field BR that opposes the alternating magnetic field BE.

The aggregation model defines how aggregation works, and the attack model defines what kinds of attacks our secure data aggregation scheme should protect against.3.1. Aggregation ModelWe consider large scale WSNs with densely deployed sensors. In WSNs, there are three types of nodes: base station (BS), aggregator, and leaf node. In this paper, we consider the aggregation tree roots at the BS like general data aggregation protocol [1,3]. Sensor nodes have overlapping sensing regions due to the dense deployment, and the same event is often detected by multiple sensors. Hence, data aggregation is proposed to reduce data transmission. The non-leaf nodes, except the BS, may also serve as aggregators. They are responsible for combining answers from their child nodes and forwarding intermediate aggregation results to their parents.

Without loss of generality, we focus on additive aggregation, which can serve as the base of other statistical operations (e.g., count, mean, or variance).3.2. Attack ModelFirst, we categorize the abilities of the adversary as follows:(1)An adversary can eavesdrop on transmission data in a WSN.(2)An adversary can send the forged data to leaf nodes, aggregators, or BS.(3)An adversary can compromise secrets in sensors or aggregators.Then, we define five attacks to qualify the security strength of the secure data aggregation schemes, based on adversary’s abilities and purposes.(1)Ciphertext analysisCiphertext analysis is a very common and basic attack. In such an attack, an adversary wants to deduce the secret key or obtain information only by interpreting ciphertext.

A secure scheme must ensure that it is not possible to gain any information or key, and an adversary cannot decide whether an encrypted ciphertext corresponds to a specific plaintext or not.(2)Chosen plaintext attacksGiven some chosen samples
The detection of pedestrians is a key application in the video surveillance domain [1]. Indeed, a number of surveillance applications require the detection and tracking of people to ensure security and safety [2,3]. The most widespread sensor technology for detecting pedestrians is for sure the use of gray scale [4,5] and color cameras [6,7]. However, using the visible-light information is problematic when facing quick changes in lighting or illumination problems.

Now, thermal-infrared images have a number of distinctive features compared to frames acquired by a visible-light spectrum camera [8�C11].In thermal-infrared video, the gray level value of the objects is set by their temperature GSK-3 and radiated heat, and is independent from lighting conditions. The most intuitive idea when performing a pedestrian detection algorithm in the thermal-infrared spectrum is to take advantage of the fact that humans usually appear warmer than other objects in the scene [12,13]. However, this is not always the case [14].

In the study Ts was derived from band 6 TIR of Landsat TM5 using the model developed by Sobrino et al. in 2004:Ts=TB1+(��?TB/r)ln(?)(5)where �� is the wavelength of emitted radiance (��=11.5), r=h?c?�� equalling 1.438 10-2 mK, where h is Planck’s constant (6.626 10-34 J s), c the velocity of light (2.998 108 m s-1) and �� the Boltzman constant (1.38 10-23 JK-1); emissivity �� was estimated through [28]:?=fv?v+(1?fv)??s(6)where ��v and ��s denote emissivity of vegetation (0.985) and soil (0.960). The fractional vegetation cover fv is related to leaf area index (LAI), fv = 1 ? e?0.5?LAI [9]. By applying the inverse of Plank’s radiation equation, spectral radiance in the thermal band was converted to brightness temperature TB:TB=K2ln(K1L��+1)(7)where K1 and K2 are calibration constants (equal to 607.

76 W m-2 sr-1 ��m-1 and 1260.56 K respectively) defined for Landsat 5 TM sensor [29]; L�� is the pixel value as radiance (W m-2 sr-1 ��m-1), L��=G?(CVDN)+B, with CVDN the pixel value as digital number, G and B the gain and the
The correction of atmospheric path delays in high-resolution spaceborne synthetic aperture radar systems has become increasingly important with continuing improvements to the resolution of SAR systems surveying the Earth. Atmospheric path delays must be taken into account in order to achieve geolocation accuracies better than 1 meter. These effects are mainly due to ionospheric and tropospheric influences. Path delays through the ionosphere are frequency-dependent, proportional to the inverse square of the carrier [1, 2].

At frequencies higher than L-band under average solar conditions, the major contribution of the atmospheric path delay comes from the troposphere [2, 3]. The tropospheric delay is usually divided into hydrostatic, wet and liquid components [4]. The hydrostatic delay is mainly related to the dependency of the refractive index on the air pressure (i.e. target altitude) and the wet delay on the water vapour pressure. The liquid delay is due to clouds and water droplets. While the wet component can be highly variable, the hydrostatic delay normally only changes marginally because of the lack of significant pressure variations within the extent of a typical SAR scene [4].Interferometric radar meteorology produces high resolution maps of integrated water vapour for investigations in atmospheric dynamics and forecasting [4].

Using that knowledge, global and local atmospheric effects (e.g. vortex streets, heterogeneities, turbulences) can be detected or even removed using interferometric and multi-temporal data [5�C7], or by inclusion of global water Anacetrapib vapour maps from the ENVISAT Medium Resolution Imaging Spectrometer (MERIS) sensor [8]. In addition to interferometric applications, there is a growing interest in the correction of atmospheric influences within a single SAR image.