A Hybrid Clustering Approach Based Q-Leach in TDMA to Optimize QOS-Parameters

An Orthogonal frequency part multiplexing suffers from a considerable challenge due to a high peak to average power ratio (PAPR). Hence, an effective method such as partial transmits sequence (PTS) can avert this defiance by limit the design of PAPR. Therefore, an improving PAPR reduction performance via a novel approach is proposed by detaching each subblock into two parts furthermore exchanges the first sample with the final selection in each portion of the subblock to generate a new partitioning scheme. Several typical traditional segmentation schemes are used to analyze and apply the presented algorithm, such as adjacent, interleaving, and pseudo-random schemes. Besides, two scenarios are adopted based on simulation software in which the number of subcarriers is set to 128 and 256. Based on the results, a superior PAPR reduction performance is achieved based on the improved segmentation schemes regarding traditional strategies in both systems. Moreover, the enhanced adjusted PTS scheme poses a low computational complexity compared with that of the conventional schemes.

     

A software architecture framework for on-line option pricing

The problem of growing computational complexity in the finance industry demands manageable, high-speed and real-time solutions in solving complex mathematical problems such as option pricing. In current option trading scenarios, determining a fair price for options “any time” and “anywhere” has become vital yet difficult computational problem. In this study, we have designed, implemented, and deployed an architecture for pricing options on-line using a hand-held device that is J2ME-based Mobile computing-enabled and is assisted by web mining tools. In our architecture, the client is a MIDP user interface, and the back end servlet runs on a standalone server bound to a known port address. In addition, the server uses table-mining techniques to mine real-time data from reliable web sources upon the mobile trader’s directive. The server performs all computations required for pricing options since mobile devices have limited battery power, low bandwidth, and low memory. We have parallelized and implemented various computational techniques such as binomial lattice and finite differencing. To the best of our knowledge, this is one of the first studies that facilitate the mobile-enabled-trader to compute the price of an option in ubiquitous fashion. This architecture aims at providing the trader with various computational techniques to avail (to provide results from approximate to accurate results) while on-the-go and to make important and effective trading decisions using the results that will ensure higher returns on investments in options.

Microwave tomography for breast cancer detection on Cell broadband engine processors

Microwave tomography (MT) is a safe screening modality that can be used for breast cancer detection. The technique uses the dielectric property contrasts between different breast tissues at microwave frequencies to determine the existence of abnormalities. Our proposed MT approach is an iterative process that involves two algorithms: Finite-Difference Time-Domain (FDTD) and Genetic Algorithm (GA). It is a compute intensive problem: (i) the number of iterations can be quite large to detect small tumors; (ii) many fine-grained computations and discretizations of the object under screening are required for accuracy. In our earlier work, we developed a parallel algorithm for microwave tomography on CPU-based homogeneous, multi-core, distributed memory machines. The performance improvement was limited due to communication and synchronization latencies inherent in the algorithm. In this paper, we exploit the parallelism of microwave tomography on the Cell BE processor. Since FDTD is a numerical technique with regular memory accesses, intensive floating point operations and SIMD type operations, the algorithm can be efficiently mapped on the Cell processor achieving significant performance. The initial implementation of FDTD on Cell BE with 8 SPEs is 2.9 times faster than an eight node shared memory machine and 1.45 times faster than an eight node distributed memory machine. In this work, we modify the FDTD algorithm by overlapping computations with communications during asynchronous DMA transfers. The modified algorithm also orchestrates the computations to fully use data between DMA transfers to increase the computation-to-communication ratio. We see 54% improvement on 8 SPEs (27.9% on 1 SPE) for the modified FDTD in comparison to our original FDTD algorithm on Cell BE. We further reduce the synchronization latency between GA and FDTD by using mechanisms such as double buffering. We also propose a performance prediction model based on DMA transfers, number of instructions and operations, the processor frequency and DMA bandwidth. We show that the execution time from our prediction model is comparable (within 0.5 s difference) with the execution time of the experimental results on one SPE.     

On-line voltage stability assessment using radial basis function network model with reduced input features

In recent years, voltage instability has become a major threat for the operation of many power systems. This paper presents an artificial neural network (ANN)-based approach for on-line voltage security assessment. The proposed approach uses radial basis function (RBF) networks to estimate the voltage stability level of the system under contingency state. Maximum L-index of the load buses in the system is taken as the indicator of voltage stability. Pre-contingency state power flows are taken as the input to the neural network. The key feature of the proposed method is the use of dimensionality reduction techniques to improve the performance of the developed network. Mutual information based technique for feature selection is proposed to enhance overall design of neural network. The effectiveness of the proposed approach is demonstrated through voltage security assessment in IEEE 30-bus system and Indian practical 76 bus system under various operating conditions considering single and double line contingencies and is found to predict voltage stability index more accurate than feedforward neural networks trained by back propagation algorithm and AC load flow. Experimental results show that the proposed method reduces the training time and improves the generalization capability of the network than the multilayer perceptron networks.     

Optimization of PbS thin films using different metal ion sources for photovoltaic applications

An extensive study on the influence of metal ion sources on the properties of chemical bath deposited lead sulphide thin films is reported in this paper. Four different lead sources namely lead nitrate, lead acetate, lead chloride and lead sulphate in alkaline medium have been used for the synthesis along with thiourea as sulphur source. The influence of lead sources on structural, surface morphological optical and electrical properties is investigated for photovoltaic applications. According to X-ray diffraction studies, all the films are poly crystalline with face centered cubic structure. The average crystallite sizes are found to be in the range 13–24 nm. The SEM photographs showed diverse morphology. The optical band gap is found to be very sensitive to the metal sources used. The direct band gap energy values obtained are in the range of 1.862–2.609 eV. The electrical conductivity varies in the range 33.6 ?7.62 × 10^?9 (Ω cm)^?1. Photosensitivity is closely linked to surface morphology. In this work, we established that the cationic precursor sources have significant role in physical properties of as-deposited PbS thin films. Samples prepared using nitrate as metal source are found to be most suitable to be used as solar control coating and the samples with lead acetate can be used as absorber layers for solar cell fabrication.     

Effect of deposition time on optical and luminescence properties of ZnS thin films prepared by photo assisted chemical deposition technique

Photo assisted chemical deposition method (PCD) is a new procedure for the deposition of compound semiconducting materials which is less explored. In this method the deposition is carried out with the irradiation of UV light on the reaction bath. PCD scores advantages for its low cost, use of flexible substrates and capability of large area deposition compared to other chemical methods like chemical bath deposition (CBD), electro chemical deposition (ECD), etc. Zinc sulfide films have been deposited on glass substrate by aqueous alkaline solution comprised of zinc nitrate, hydrazine hydrate, ammonium nitrate, ammonia and thiourea. The samples were prepared under UV illumination for different durations and characterized. The thickness of the samples increases with the deposition time. XRD patterns revealed the crystalline nature of samples with more number of dips. Optical study showed a low absorbance and constant transparency throughout the visible region disclosing the stiochiometric nature of the film. Obtained band gap energies were in good agreement with the theoretical value. Photoluminescence spectra showed two blue emission bands around 450 and 470 nm, and the intensity was found to depend on the thickness of the films.     

Finding a Path Subject to Many Additive QoS Constraints

A fundamental problem in quality-of-service (QoS) routing is to find a path between a source-destination node pair that satisfies two or more end-to-end QoS constraints. We model this problem using a graph with n vertices and m edges with K additive QoS parameters associated with each edge, for any constant Kges2. This problem is known to be NP-hard. Fully polynomial time approximation schemes (FPTAS) for the case of K=2 have been reported in the literature. We concentrate on the general case and make the following contributions. 1) We present a very simple (Km+nlogn) time K-approximation algorithm that can be used in hop-by-hop routing protocols. 2) We present an FPTAS for one optimization version of the QoS routing problem with a time complexity of O(m(n/epsi)^K-1). 3) We present an FPTAS for another optimization version of the QoS routing problem with a time complexity of O(nlogn+m(H/epsi)^K-1) when there exists an H-hop path satisfying all QoS constraints. When K is reduced to 2, our results compare favorably with existing algorithms. The results of this paper hold for both directed and undirected graphs. For ease of presentation, undirected graph is used     

Enhancing performance of failure-prone clusters by adaptive provisioning of cloud resources

In this paper, we investigate Cloud computing resource provisioning to extend the computing capacity of local clusters in the presence of failures. We consider three steps in the resource provisioning including resource brokering, dispatch sequences, and scheduling. The proposed brokering strategy is based on the stochastic analysis of routing in distributed parallel queues and takes into account the response time of the Cloud provider and the local cluster while considering computing cost of both sides. Moreover, we propose dispatching with probabilistic and deterministic sequences to redirect requests to the resource providers. We also incorporate checkpointing in some well-known scheduling algorithms to provide a fault-tolerant environment. We propose two cost-aware and failure-aware provisioning policies that can be utilized by an organization that operates a cluster managed by virtual machine technology, and seeks to use resources from a public Cloud provider. Simulation results demonstrate that the proposed policies improve the response time of users’ requests by a factor of 4.10 under a moderate load with a limited cost on a public Cloud.     

Pathological brain detection in MRI scanning via Hu moment invariants and machine learning

Abstract

Background: We proposed a new automatic and rapid computer-aided diagnosis system to detect pathological brain images obtained in the scans of magnetic resonance imaging (MRI). Methods: For simplification, we transformed the problem to a binary classification task (pathological or normal). It consisted of two steps: first, Hu moment invariants (HMI) were extracted from a specific MR brain image; then, seven HMI features were fed into two classifiers: twin support vector machine (TSVM) and generalised eigenvalue proximal SVM (GEPSVM). Results: Then, a 5 × 5-fold cross validation on a data set containing 90 MR brain images, demonstrated that the proposed methods “HMI + GEPSVM” and “HMI + TSVM” achieved classification accuracy of 98.89%, higher than eight state-of-the-art methods: “DWT + PCA + BP-NN”, “DWT + PCA + RBF-NN”, “DWT + PCA + PSO-KSVM”, “WE + BP-NN”, “WE + KSVM”, “DWT + PCA + GA-KSVM”, “WE + PSO-KSVM” and “WE + BBO-KSVM”. Conclusion: The proposed methods are superior to other methods on pathological brain detection (p < 0.05).