Optimal Number Of Nodes Research Articles

Self-organized area coverage in wireless sensor networks by limited node mobility

For wireless sensor networks, monitoring large inaccessible areas where deterministic node deployment is not possible, self-organized techniques are in demand to cover an area using optimal number of nodes. In this paper, given an initial random deployment of mobile sensor nodes, we propose a simple and novel technique for self-organized node movement to satisfy the coverage of the given region of interest using a least number of nodes, such that the maximum node displacement is minimized. We present a simple centralized algorithm and also a distributed version of it for node placement. Moreover, in case of a node failure, a distributed fault recovery algorithm is proposed to replace it locally utilizing the available free nodes. Analysis, simulation, and comparison studies show that the proposed algorithms with less neighborhood information result in significant improvement in terms of average and maximum displacement of a node, rounds of communication, and number of active nodes.

Short-term wind power prediction based on Hybrid Neural Network and chaotic shark smell optimization

By the quick growth of wind power generation in the world, this clean energy becomes an important green electrical source in many countries. However, volatile and non-dispatchable nature of this energy source motivates researchers to find accurate and robust methods to predict its future values. Because of nonlinear and complex behaviors of this signal, more efficient wind power forecast methods are still demanded. In this paper, a new forecasting engine based on Neural Network (NN) and a novel Chaotic Shark Smell Optimization (CSSO) algorithm is proposed. Choosing optimal number of nodes for the hidden layer can enhance the efficiency of the NN’s training performance. Accordingly, a new meta-heuristic algorithm is presented in this paper, which is based on shark abilities in nature, for optimizing the number of hidden nodes pertaining to the NN. Effectiveness of the proposed forecasting strategy is tested on two real-world case studies for predicting wind power. The obtained results demonstrate the capability of the proposed technique to cope with the variability and intermittency of wind power time series for providing accurate predictions of its future values.

Adaptive clustering and routing using fuzzy decision model in WSN

In most of the clustering process, selection of reliable and efficient cluster head selection is important issue. In most of the clustering process, failure of CH occurs due to energy depletion and if the distance between sink and CH is more it ultimately leads to failure in transmission. In order to overcome this issue an adaptive clustering and routing using fuzzy decision model in WSN is proposed. In the proposed technique, first the optimal number of nodes in the cluster is found based on energy level of each node and transmission range is found. Also the size of cluster is found to determine the density of the network. Finally, selection of CH is done using the fuzzy decision model which includes the parameter such as energy level, quality of link, distance of node to sink, data rate and degree of heterogeneity. The advantage of proposed technique is that it provides network with most reliable CH for transmission of the packet.

Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.

The Optimum Number of Nodes and Radius for Distributed Beamforming Networks

This paper presents a ready-made formula to calculate the number of node and network radius for distributed beamforming networks. The calculation is based on guarantee of received signal at destination to be higher than received sensitivity. The proposed calculation model already includes path loss and also imperfection in node location estimation, which can be applied for both indoor and outdoor scenarios.

Number of Mediastinal Lymph Nodes in Non-Small Cell Lung Cancer: A Gaussian Curve, Not a Prognostic Factor

It has been proposed that examining a greater number of lymph nodes (LNs) in patients with non-small-cell lung cancer (NSCLC) treated by surgical resection may increase the likelihood of proper staging and affect outcome. Our purpose was to evaluate the interindividual variability and prognostic relevance of the number of LNs harvested during complete pulmonary and mediastinal lymphadenectomy performed for NSCLC. We prospectively collected and retrospectively reviewed the data from 1,095 patients who underwent lung cancer resection in association with systematic lymphadenectomy and pulmonary and mediastinal LN counts from 2004 to 2009. We analyzed the interindividual variability and prognostic impact of the number of LNs on overall survival (OS). The mean number of harvested pulmonary and mediastinal LNs was 17.4±7.3 (range, 1-65) and was higher in male patients, right lung surgical procedures, lobectomy and pneumonectomy, N2 disease, and pIII stage. The mean number of harvested mediastinal LNs was 10.7±5.6 and was normally distributed (range, 0-49; median, 10). The 5-year survival rate was 53.8%. Overall survival was influenced by the number of involved stations (single-station versus multi-station disease, 5-year survival rates 31.5% versus 16.9%, respectively; p=0.041) but not by the number of harvested LNs, the number of harvested mediastinal LNs, or the number of positive mediastinal LNs. After lung cancer resection and complete lymphadenectomy, the number of LNs is subject to normally distributed interindividual variability, with no significant impact on OS. Recommending an optimal number of nodes is therefore arbitrary. Instead, our recommendation is to perform a complete systematic pulmonary and mediastinal lymphadenectomy following established anatomical boundaries.

A New Approach for Covering Wireless Sensor Networks with Optimum Number of Nodes in Order to Prolonging Network Lifetime

In recent years, wireless sensor networks are in great use in applications like disaster management, combat field reconnaissance, border protection and safe care. Although, much research has been done on wireless sensor networks, but in the quality of service (QoS) field there are not enough researches. Since these networks are widely used in many areas, there are different QoS parameters in contrast with traditional networks such as network coverage, optimal number of active nodes, network lifetime and energy consumption. We have proposed an automata-based scheduling method to improve the QoS parameters of the networks. In this method, each node is equipped with a learning automaton to select its correct status (active or passive) at any given time. Simulation results show that the proposed method in comparison with some existing methods such as: CCP, Lacoverage, PEAS and Ottawa reduce energy consumption and increase network’s lifetime. As a result, several QoS parameters are considered in sensor networks, simultaneously.

Breast Tumor Localization Using Simultaneous Perturbation Stochastic-Neural Algorithm

Many impalpable or occult breast cancers cannot be localized using imaging techniques like mam- mography and ultrasound. An accurate localization of the tumor is, however, essential to guide the surgeon to the lesion, and ensure its correct and adequate removal with satisfactory excision margins. Current breast tumor localization techniques are invasive and often result in a cosmetic disfigurement. In this paper, we use the ultrawide band radar-based microwave breast imaging technique to non-invasively localize (impalpable) tumors in the breast. We consider four clinically important lesion features: location, size, depth and spatial orientation within the breast. A comparison of the energy of the received signal from healthy and cancerous breasts exhibits some remarkable differences in some frequency bands. We, therefore, use the energy spectrum of the receiving antenna signal decomposed by wavelet transform as the input to a Simultaneous Perturbation Neural Network (SPNN) classifier. Fur- thermore, we determine the optimum structure and gains of the SPNN, in terms of optimum initial weights and optimum number of nodes in the hidden layer. We use CST Microwave Studio to simulate a data set of 1024 cancer cases with various tumor location, size, depth and direction inside the breast. Our results show that the proposed algorithm gives accurate localization of the breast lesion, and possesses a high sensitivity to small tumor sizes. Additionally, it can accurately detect and classify multiple tumors with short learning and testing time.

Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA

A method to accurately estimate the State-of-Charge (SOC) for LiFePO4 (LFB) batteries is urgently required, to address the issues associated with the increased use of LFP batteries for portable devices. This paper proposes a hybrid method that combines a radial basis function (RBF) neural network, an orthogonal least-squares (OLS) algorithm and an adaptive genetic algorithm (AGA) to estimate the SOC in discharging condition. The OLS algorithm determines the optimal number of nodes in the hidden layer of the RBF neural network. With an optimal RBF neural network structure, the AGA is then used to tune the parameters of the RBF neural network, including the centers and widths of RBF and the connection weights. The trained RBF neural network is then used to estimate the SOC of a LFP battery. In order to demonstrate the effectiveness of the proposed estimation method, the method is tested using LFP batteries under several different discharging conditions. The effectiveness of the proposed method is compared with the Coulomb integration method and a back propagation (BP) neural network. The results show that the proposed method outperforms the other methods.

A Survey on Localization and Covering Techniques in Wireless Sensor Networks

Sensor Network (WSN) has gained popularity in diverse application fields owing to the inexpensive sensor nodes also called as motes. These sensor nodes are either deployed randomly or placed manually depending on the type of applications. The services of WSN are tremendously being used in application areas such as disaster relief, search and rescue, target tracking, battlefield and many more. Due to the application of WSN in such environments, it is not possible to manually deploy sensor nodes and therefore, the nodes are randomly deployed in the area of interest. Due to the random placement of the sensor nodes, it becomes essential to determine the location of the nodes in order to facilitate communication between nodes as well as Base Station (BS). Further, in case of random node deployment, the usual practice is to deploy a very large number of nodes (more than that of the requirement) in the area of interest. This is done in order to achieve fault tolerance, thus opening a large number of research directions to determine the optimal number of nodes participating in WSN 'covering' problem. This paper deals with an insight on the various localization and coverage techniques used in WSN. Further this paper tries to establish the interdependence between the localization and covering techniques.

An RBF Neural Network Combined with OLS Algorithm and Genetic Algorithm for Short-Term Wind Power Forecasting

An accurate forecasting method for power generation of the wind energy conversion system (WECS) is urgently needed under the relevant issues associated with the high penetration of wind power in the electricity system. This paper proposes a hybrid method that combines orthogonal least squares (OLS) algorithm and genetic algorithm (GA) to construct the radial basis function (RBF) neural network for short-term wind power forecasting. The RBF neural network is composed of three-layer structures, which contain the input, hidden, and output layers. The OLS algorithm is used to determine the optimal number of nodes in a hidden layer of RBF neural network. With an appropriate RBF neural network structure, the GA is then used to tune the parameters in the network, including the centers and widths of RBF and the connection weights in second stage. To demonstrate the effectiveness of the proposed method, the method is tested on the practical information of wind power generation of a WECS installed in Taichung coast of Taiwan. Comparisons of forecasting performance are made to the persistence method and back propagation neural network. The good agreements between the realistic values and forecasting values are obtained; the test results show the proposed forecasting method is accurate and reliable.

Multivariate Quadrature-Based Moments Methods for turbulent polydisperse gas–liquid systems

The Conditional Quadrature Method of Moments (CQMOM) and the Direct Quadrature Method of Moments (DQMOM) are compared with Direct Simulation Monte Carlo (DSMC) for the description of gas bubble coalescence, breakage and mass transfer with the surrounding continuous liquid phase. CQMOM and DQMOM are both moment methods based on the idea of overcoming the closure problem by using a quadrature approximation. The methods are compared and performances evaluated for spatially homogeneous and inhomogeneous systems. Eventually CQMOM and DQMOM are implemented in a commercial CFD code to simulate a realistic two-dimensional bubble column. Particular attention is paid to the impossibility of conserving moments with DQMOM in the presence of numerical diffusion. To cure this problem a fully-conservative DQMOM formulation is presented and tested. The relationship between the two methods is investigated, showing that under particular conditions CQMOM is identical to DQMOM. The different methods are employed under a number of different conditions including very fast chemical reactions, in order to highlight if the problem of bubble coalescence, breakage and mass transfer really needs a bivariate population balance to be tackled and what is the optimal number of nodes for the quadrature approximation.

In Vitro Propagation of Muña-Muña (Clinopodium odorum (Griseb.) Harley)

A micropropagation protocol was developed which may assist in the safeguarding and augmentation of dwindling natural populations of Clinopodium odorum (Griseb.) Harley, a critically and endangered medicinal plant. Factors affecting culture initiation bud sprouting and growth, rooting, and acclimatization were studied, using nodal segments of in vitro germinated seedling as primary explants on six media supplemented with different concentrations and combinations of 6-benzylaminopurine (BAP) (0.5–1.5 and 2-Naphthalene acetic acid (NAA) (0.5–1.5). Best results for culture initiation with sustainable multiplication rates (100%) were obtained on WP medium without any growth regulator. WP with the addition of 0.5 : 1 or 0.5 : 1.5) of BAP and NAA promoted a higher elongation; however, the optimum number of nodes were obtained in plantlets grown on 1/2 MS with the addition of 1 : 1.5 of BAP and NAA. Culture of sectioned individual nodes transferred to the media with different rates of BAP and NAA 1/2 MS-9 (1.5 : 1.5), SH-8 (1.5 : 1.0), and 1/2 B5-4 (1.0 : 0.5) media resulted in no proliferated shoots. The in vitro plants were successfully acclimatized garden soil and sand (2 : 1) in the greenhouse, with over 90% survival rate. The in vitro-grown plants could be transferred to ex vitro conditions and the efficacy in supporting ex vitro growth was assessed, with a view to develope longer-term strategies for the transfer and reintroduction into natural habitats.

Reconstruction of the dark energy equation of state

One of the main challenges of modern cosmology is to investigate the nature of dark energy in our Universe. The properties of such a component are normally summarised as a perfect fluid with a (potentially) time-dependent equation-of-state parameter w(z). We investigate the evolution of this parameter with redshift by performing a Bayesian analysis of current cosmological observations. We model the temporal evolution as piecewise linear in redshift between 'nodes', whose w-values and redshifts are allowed to vary. The optimal number of nodes is chosen by the Bayesian evidence. In this way, we can both determine the complexity supported by current data and locate any features present in w(z). We compare this node-based reconstruction with some previously well-studied parameterisations: the Chevallier-Polarski-Linder (CPL), the Jassal-Bagla-Padmanabhan (JBP) and the Felice-Nesseris-Tsujikawa (FNT). By comparing the Bayesian evidence for all of these models we find an indication towards possible time-dependence in the dark energy equation-of-state. It is also worth noting that the CPL and JBP models are strongly disfavoured, whilst the FNT is just significantly disfavoured, when compared to a simple cosmological constant w = −1. We find that our node-based reconstruction model is slightly disfavoured with respect to the ΛCDM model.

Localized policy-based target tracking using wireless sensor networks

Wireless Sensor Networks (WSN)-based surveillance applications necessitate tracking a target's trajectory with a high degree of precision. Further, target tracking schemes should consider energy consumption in these resource-constrained networks. In this work, we propose an energy-efficient target tracking algorithm, which minimizes the number of nodes in the network that should be activated for tracking the movement of the target. We model the movement of a target based on the Gauss Markov Mobility Model [Camp et al. 2002]. On detecting a target, the cluster head which detects it activates an optimal number of nodes within its cluster, so that these nodes start sensing the target. A Markov Decision Process (MDP)-based framework is designed to adaptively determine the optimal policy for selecting the nodes localized with each cluster. As the distance between the node and the target decreases, the Received Signal Strength (RSS) increases, thereby increasing the precision of the readings of sensing the target at each node. Simulations show that our proposed algorithm is energy-efficient. Also, the accuracy of the tracked trajectory varies between 50% to 1% over time.

P3-07-17: Optimal Number of Sentinel Lymph Nodes That Could Avoid Completion of Axillary Lymph Node Dissection in Operable Breast Cancer.

Abstract Background Axillary lymph node dissection (ALND) has been the standard of care for breast cancer patients with sentinel lymph node (SLN) metastasis. It is now under discussion since the publications of ACOZOG Z0011 and NSABP-32. The 1–3% rate of axillary recurrence is very low in these studies, whereas the rate of additional nodal metastasis after the completion of ALND varies from 15% to 35%. The purpose of this study was to determine the optimal number of nodes to be removed in order to obtain an axilla free of disease after initial surgery. Patients and methods: 4928 consecutive patients treated for breast cancer ≤ 2 cm with primary breast surgery with SNB between 2000 and 2009 were reviewed from the Institut Curie breast cancer prospective database. 1165 patients with a positive SLN (macrometastasis, micrometastasis and isolated tumor cells) underwent ALND. The proportion of patients with positive ALND was analysed according to the total number of sentinel lymph nodes biopsied. Results were compared with Chi-square test for qualitative variables and with Kruskal-Wallis for quantitative variables. Results: Among the 1165 patients, 308 (26.4%) had a positive ALND. Among them, 81.1% of patients had a macrometastasis in the SLN versus 45.6% in the 857 patients with negative ALND (p<0.0001); 10.8% had a micrometastasis versus 31.4% (p<0.001) and 8.1% of patients had isolated tumor cells in the SLN versus 23% (p<0.001). The proportion of patients with positive ALND decreased with the number of sentinel nodes removed (Figure 1), independently of the number of positive SLN. Among the 1165 patients, the rate of patients with 5 nodes removed or more and with residual disease in the axilla was < 1% (n=11). Those patients could have avoided completion ALND, representing 3.6% of the total positive ALND that would have been neglected. Conclusions: We showed that patients with positive SLN have a very low risk (<1%) of residual disease in the axilla when 5 nodes or more have been removed. This number which is a major component of nomograms, could be used as a threshold to avoid completion ALND. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr P3-07-17.

Adaptive Channel Equalization Using Multiplicative Neural Network for Rayleigh Faded Channel

Problem statement: Digital transmission over band-limited communication channel largely suffers from ISIS and various noise sources. The presence of ISI and noise causes bit errors in the received signal. Equalization is necessary at the receiver to overcome these channel impairment to recover the original transmitted sequence. Traditionally equalization is considered as equivalent to inverse filtering and implemented using linear-perform under severe distortion conditions when Signal to Noise Ratio (SNR) is poor. Equalization can be considered as a non-linear classification problem and optimum solution is given by Bayesian solution. Non-linear techniques like Artificial Neutral Networks (ANN) are very good choice for non-linear classification problems. Several non-lines are equalizers have been implemented using ANN which outperformed LTE and solved the problem of equalization to the varying degree of sources. Approach: Forward neural network architecture with optimum number of nodes was used to achieve adaptive channel equalization. Summation at each node was replaced by multiplications which result in powerful mapping. The equalizer was tested on Rayleigh fading channel with a BPSK signal. Results: Results showed that proposed equalizer provides simplified architecture and improvement in the bit error rate at various levels of signal to noise ratio for Rayleigh faded channel. Conclusion: A high order feed forward network equalizer with multiplicative neuron is proposed in this study. Use of Multiplication allows direct computing of polynomial inputs and approximation with fewer nodes. Performance comparison in terms of network architecture and BER performance suggest the better classification capability of the proposed MNN equalizer over RRBF.

Cross-species common regulatory network inference without requirement for prior gene affiliation

Cross-species meta-analyses of microarray data usually require prior affiliation of genes based on orthology information that often relies on sequence similarity. We present an algorithm merging microarray datasets on the basis of co-expression alone, without any requirement for orthology information to affiliate genes. Combining existing methods such as co-inertia analysis, back-transformation, Hungarian matching and majority voting in an iterative non-greedy hill-climbing approach, it affiliates arrays and genes at the same time, maximizing the co-structure between the datasets. To introduce the method, we demonstrate its performance on two closely and two distantly related datasets of different experimental context and produced on different platforms. Each pair stems from two different species. The resulting cross-species dynamic Bayesian gene networks improve on the networks inferred from each dataset alone by yielding more significant network motifs, as well as more of the interactions already recorded in KEGG and other databases. Also, it is shown that our algorithm converges on the optimal number of nodes for network inference. Being readily extendable to more than two datasets, it provides the opportunity to infer extensive gene regulatory networks. Source code (MATLAB and R) freely available for download at http://www.mchips.org/supplements/moghaddasi_source.tgz.

A better way for finding the optimal number of nodes in a distributed database management system

Distributed Database Management System (DDBMS) is one of the prime concerns in distributed computing. The driving force of development of DDBMS is the demand of the applications that need to query very large databases (order of terabytes). Traditional Client- Server database systems are too slower to handle such applications. This paper presents a better way to find the optimal number of nodes in a distributed database management systems. Keywords: DDBMS, Data Fragmentation, Linear Search, RMI. DOI: 10.3329/diujst.v4i2.4362 Daffodil International University Journal of Science and Technology Vol.4(2) 2009 pp.19-22

Optimum lymphadenectomy for esophageal cancer.

Using Worldwide Esophageal Cancer Collaboration data, we sought to (1) characterize the relationship between survival and extent of lymphadenectomy, and (2) from this, define optimum lymphadenectomy. What constitutes optimum lymphadenectomy to maximize survival is controversial because of variable goals, analytic methodology, and generalizability of the underpinning data. A total of 4627 patients who had esophagectomy alone for esophageal cancer were identified from the Worldwide Esophageal Cancer Collaboration database. Patient-specific risk-adjusted survival was estimated using random survival forests. Risk-adjusted 5-year survival was averaged for each number of lymph nodes resected and its relation to cancer characteristics explored. Optimum number of nodes that should be resected to maximize 5-year survival was determined by random forest multivariable regression. For pN0M0 moderately and poorly differentiated cancers, and all node-positive (pN+) cancers, 5-year survival improved with increasing extent of lymphadenectomy. In pN0M0 cancers, no optimum lymphadenectomy was defined for pTis; optimum lymphadenectomy was 10 to 12 nodes for pT1, 15 to 22 for pT2, and 31 to 42 for pT3/T4, depending on histopathologic cell type. In pN+M0 cancers and 1 to 6 nodes positive, optimum lymphadenectomy was 10 for pT1, 15 for pT2, and 29 to 50 for pT3/T4. Greater extent of lymphadenectomy was associated with increased survival for all patients with esophageal cancer except at the extremes (TisN0M0 and >or=7 regional lymph nodes positive for cancer) and well-differentiated pN0M0 cancer. Maximum 5-year survival is modulated by T classification: resecting 10 nodes for pT1, 20 for pT2, and >or=30 for pT3/T4 is recommended.