Establishing complete and reliable coverage for a long time-span is a crucial issue in densely surveillance wireless sensor networks (WSNs). Many scheduling algorithms have been proposed to model the problem as a maximum disjoint set covers (DSC) problem. The goal of DSC based algorithms is to schedule sensors into several disjoint subsets. One subset is assigned to be active, whereas, all remaining subsets are set to sleep. An extension to the maximum disjoint set covers problem has also been addressed in literature to allow for more advance sensors to adjust their sensing range. The problem, then, is extended to finding maximum number of overlapped set covers. Unlike all related works which concern with the disc sensing model, the contribution of this paper is to reformulate the maximum overlapped set covers problem to handle the probabilistic sensing model. The problem is addressed as a multi-objective optimization (MOO) problem and the well-known decomposition based multi-objective evolutionary algorithm (MOEA/D) is adopted to solve the stated problem. A Multi-layer MOEA/D is suggested, wherein each layer yields a distinct set cover. Performance evaluations in terms of total number of set covers, total residual energy, and coverage reliability are reported through extensive simulations. The main aspect of the results reveals that the network's lifetime (i.e. total number of set covers) can be extended by increasing number of sensors. On the other hand, the coverage reliability can be increased by increasing sensing ranges but at the expense of decreasing the network's lifetime.
The Fort Munro Formation is well exposed along DG Khan-Fort Munro road, a type locality. The section was measured and sampled for the biostratigraphy, where it has a conformable lower contact with Mughalkot Formation and is underlain by the Pab Sandstone. The total observed thickness was 105 meters, and 26 samples were collected from top to the bottom at random intervals. Fifty thin sections were studied carefully, and five species of large benthic foraminifera, including Orbitoidestissoti, Orbitoidesapiculata, Orbitoides media, Orbitoideshottingeri, and Omphalocyclusmacroporus were identified along with miliolids. Gastropods, Bivalves and Echinoderms were also observed. Based on identified microfossils, the de
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The microbend sensor is designed to experience a light loss when force is applied to the sensor. The periodic microbends cause propagating light to couple into higher order modes, the existing higher order modes become unguided modes. Three models of deform cells are fabricated at (3, 5, 8) mm pitchand tested by using MMF and laser source at 850 nm. The maximum output power of (8, 5, 3)mm model is (3, 2.7, 2.55)nW respectively at applied force 5N and the minimum value is (1.9, 1.65, 1.5)nW respectively at 60N.The strain is calculated at different microbend cells ,and the best sensitivity of this sensor for cell 8mm is equal to 0.6nW/N.
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E-Health care system is one of the great technology enhancements via using medical devices through sensors worn or implanted in the patient's body. Wireless Body Area Network (WBAN) offers astonishing help through wireless transmission of patient's data using agreed distance in which it keeps patient's status always controlled by regular transmitting of vital data indications to the receiver. Security and privacy is a major concern in terms of data sent from WBAN and biological sensors. Several algorithms have been proposed through many hypotheses in order to find optimum solutions. In this paper, an encrypting algorithm has been proposed via using hyper-chaotic Zhou system where it provides high security, privacy, efficiency and
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In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha
... Show MoreIn this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func
Discriminant between groups is one of the common procedures because of its ability to analyze many practical phenomena, and there are several methods can be used for this purpose, such as linear and quadratic discriminant functions. recently, neural networks is used as a tool to distinguish between groups.
In this paper the simulation is used to compare neural networks and classical method for classify observations to group that is belong to, in case of some variables that don’t follow the normal distribution. we use the proportion of number of misclassification observations to the all observations as a criterion of comparison.
The use of heavy ions in the treatment of cancer tumors allows for accurate radiation of the tumor with minimal collateral damage that may affect the healthy tissue surrounding the infected tissue. For this purpose, the stopping power and the range to which these particles achieved of Nitrogen (N) in the skin tissue were calculated by programs SRIM (The Stopping and Range of Ions in Matter),(SRIM Dictionary) [1],(CaSP)(Convolution approximation for Swift Particles )[2]which are famous programs to calculate stopping power of material and Bethe formula , in the energy range (1 - 1000) MeV .Then the semi - empirical formulas to calculate the stopping power and range of Nitrogen io
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In this project we analyze data of a large sample of gas rich dwarfs galaxies including; Low Surface Brightness Galaxies (LSBGs), Blue Compact Galaxies (BCGs), and dwarfs Irregulars (dIr). We then study the difference between properties of these galaxies in the range of radio frequencies (B-band). The data are available in HIPASS catalogue and McGaugh’s Data Page. We depended also NASA/IPACExtragalactic Databes web site http://ned.ipac.caltech.edu in the data reduction. We measured the gas evolution (HI mass), gas mass-to-luminosity ratio, and abundance of the elements such as the oxygen abundance for these galaxies. Our results show a
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