Multiple-input multiple-output (MEMO) systems with antenna selection are practical ones that can intuitively alleviate the computational complexity at the receiver and achieve good reception performance. Channel corre...
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ISBN:
(纸本)9781467318808
Multiple-input multiple-output (MEMO) systems with antenna selection are practical ones that can intuitively alleviate the computational complexity at the receiver and achieve good reception performance. Channel correlation, not just carrier-to-noise ratio (CNR), has a great impact on the reception performance in MEMO channels. We propose a simple antenna selection algorithm that exploits the condition number of the channel matrix and a predetermined threshold CNR. This paper describes the hardware implementation of the proposed algorithm and its performance evaluation, which was conducted in an indoor measurement using received signals obtained in the actual mobile outdoor experiment. The results confirm that our proposed method provides good bit error rate performance by setting a threshold CNR properly.
This paper presents various solutions for organizing an accumulator battery. It examines three different architectures: series-parallel, parallel-series and C3C architecture, which spread the cell output current flux ...
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This paper presents various solutions for organizing an accumulator battery. It examines three different architectures: series-parallel, parallel-series and C3C architecture, which spread the cell output current flux to three other cells. Alternatively, to improve a several cell system reliability, it is possible to insert more cells than necessary and soliciting them less. Classical RAMS (Reliability, Availability, Maintainability, Safety) solutions can be deployed by adding redundant cells or by tolerating some cell failures. With more cells than necessary, it is also possible to choose active cells by a selection algorithm and place the others at rest. Each variant is simulated for the three architectures in order to determine the impact on battery-operative dependability, that is to say the duration of how long the battery complies specifications. To justify that the conventional RAMS solutions are not deployed to date, this article examines the influence on operative dependability. If the conventional variants allow to extend the moment before the battery stops to be operational, using an algorithm with a suitable optimization criterion further extend the battery mission time.
Extreme Learning Machine (ELM) is a new paradigm for using Single-hidden Layer Feedforward Networks (SLFNs) with a much simpler training method. The input weights and the bias of the hidden layer are randomly chosen a...
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ISBN:
(纸本)9781467314886
Extreme Learning Machine (ELM) is a new paradigm for using Single-hidden Layer Feedforward Networks (SLFNs) with a much simpler training method. The input weights and the bias of the hidden layer are randomly chosen and output weights are analytically determined. One of the open problems in ELM research is how to automatically determine network architectures for given tasks. In this paper, it is taken as a model selection problem, a modified fast recursive algorithm (MFRA) is introduced to quickly and efficiently estimate the contribution of each hidden layer node to the decrease of the net function, and then a leave one out (LOO) cross validation is used to select the optimal number of hidden layer nodes. Simulation results on both artificial and real world benchmark datasets indicate the effectiveness of the proposed method.
In this paper, we introduce a new algorithm for feature selection for two-class classification problems, called l_1-StaR. The algorithm consists of first extracting the statistically relevant features using the Studen...
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ISBN:
(纸本)9781467320658
In this paper, we introduce a new algorithm for feature selection for two-class classification problems, called l_1-StaR. The algorithm consists of first extracting the statistically relevant features using the Student t-test, and then passing the reduced feature set to an l_1-norm support vector machine (SVM) with recursive feature elimination (RFE). The final number of features chosen by the l_1-StaR algorithm can be smaller than the number of samples, unlike with l_1-norm regression where the final number of features is bounded below by the number of samples. The algorithm is illustrated by applying it to the problem of determining which endometrial cancer patients are at risk of having the cancer spreading to their lymph nodes. The data consisted of 1,428 micro-RNAs measured on a data set of 94 patient samples (divided evenly between those with lymph node metastasis and those without). Using the algorithm, we identified a subset of just 15 micro-RNAs and a linear classifier based on these, that achieved two-fold cross validation accuracies in excess of 80%, and combined accuracy, sensitivity and specificity in excess of 93%.
The brain's neuromodulatory systems play a key role in regulating decision-making and responding to environmental challenges. Attending to the appropriate sensory signal, filtering out noise, changing moods, and s...
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ISBN:
(纸本)9781467314886
The brain's neuromodulatory systems play a key role in regulating decision-making and responding to environmental challenges. Attending to the appropriate sensory signal, filtering out noise, changing moods, and selecting behavior are all influenced by these systems. We introduce a neural network for action selection that is based on principles of neuromodulatory systems. The algorithm, which was tested on an autonomous robot, demonstrates valuable features such as fluid switching of behavior, gating in important sensory events, and separating signal from noise.
Cloud computing has emerged as an alternate infrastructure, computation and service platform. Several user jobs and applications concurrently compete for cloud resources. Cloud environment exploits virtualisation to i...
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Cloud computing has emerged as an alternate infrastructure, computation and service platform. Several user jobs and applications concurrently compete for cloud resources. Cloud environment exploits virtualisation to instantiate virtual machine (VM) for subscribers. VMs are like producers and consumers in cloud environment. VMs consume computational, storage and network resources in cloud on behalf of jobs and tasks. Jobs of subscribers are represented as VM or VMs. In this view, VMs are producers. Another concern in clouds remains energy efficiency. This paper presents a novel SLA aware VM selection scheme. Proposed selection scheme has been compared with minimum migration time, linear regression, maximum utilisation and threshold-based selection schemes. Simulations-based analysis established that proposed approach (SLA consciousness) outperforms other selection schemes using various allocation strategies. During simulation some real world workload traces from PlanetLab has been used.
Biological networks, like most engineered networks, are not the product of a singular design but rather are the result of a long process of refinement and optimization. Many large real-world networks are comprised of ...
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Biological networks, like most engineered networks, are not the product of a singular design but rather are the result of a long process of refinement and optimization. Many large real-world networks are comprised of well-defined and meaningful smaller modules. While engineered networks are designed and refined by humans with particular goals in mind, biological networks are created by the selective pressures of evolution. In this paper, we seek to define aspects of network architecture that are shared among different types of evolved biological networks. First, we developed a new mathematical model, the Stochastic Block Model with Path selection (SBM-PS) that simulates biological network formation based on the selection of edges that increase clustering. SBM-PS can produce modular networks whose properties resemble those of real networks. Second, we analyzed three real networks of very different types, and showed that all three can be fit well by the SBM-PS model. Third, we showed that modular elements within the three networks correspond to meaningful biological structures. The networks chosen for analysis were a proteomic network composed of all proteins required for mitochondrial function in budding yeast, a mesoscale anatomical network composed of axonal connections among regions of the mouse brain, and the connectome of individual neurons in the nematode C. elegans. We find that the three networks have common architectural features, and each can be divided into subnetworks with characteristic topologies that control specific phenotypic outputs. (C) 2017 Elsevier Ltd. All rights reserved.
作者：
Mutturi, SarmaCSIR
Cent Food Technol Res Inst Dept Microbiol & Fermentat Technol Mysuru 570020 Karnataka India
Although handful tools are available for constraint-based flux analysis to generate knockout strains, most of these are either based on bilevel-MIP or its modifications. However, metaheuristic approaches that are know...
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Although handful tools are available for constraint-based flux analysis to generate knockout strains, most of these are either based on bilevel-MIP or its modifications. However, metaheuristic approaches that are known for their flexibility and scalability have been less studied. Moreover, in the existing tools, sectioning of search space to find optimal knocks has not been considered. Herein, a novel computational procedure, termed as FOCuS (Flower-pOllination coupled Clonal selection algorithm), was developed to find the optimal reaction knockouts from a metabolic network to maximize the production of specific metabolites. FOCuS derives its benefits from nature-inspired flower pollination algorithm and artificial immune system-inspired clonal selection algorithm to converge to an optimal solution. To evaluate the performance of FOCuS, reported results obtained from both MIP and other metaheuristic-based tools were compared in selected case studies. The results demonstrated the robustness of FOCuS irrespective of the size of metabolic network and number of knockouts. Moreover, sectioning of search space coupled with pooling of priority reactions based on their contribution to objective function for generating smaller search space significantly reduced the computational time.
The design of production systems at chemical plants is considered. Attention focuses on the selection of the target transformations of materials within a virtual system. The information approach is adopted in an algor...
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The design of production systems at chemical plants is considered. Attention focuses on the selection of the target transformations of materials within a virtual system. The information approach is adopted in an algorithm for step-by-step selection of the target processes and their optimal integration within the virtual system. The key feature of this algorithm is that it allows to use general principles for the distribution of the system's functions between its elements and subsystems to ensure optimal integration of the target processes. Those principles were described in previous works. As an example, the algorithm is employed in the optimal organization of a system for converting lignite to synthesis gas, with specified proportions of the key components.
Traditionally, a cell phone remains on a single primary mobile network operator (MNO) as long as it is available, and uses another MNO only when the primary is unavailable and a roaming agreement exists. Multi-network...
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ISBN:
(纸本)9781538663592;9781538663585
Traditionally, a cell phone remains on a single primary mobile network operator (MNO) as long as it is available, and uses another MNO only when the primary is unavailable and a roaming agreement exists. Multi-network access (MNA), where a cell phone can use any one of multiple MNOs at any point in space and time, can greatly increase cellular network capacity. This paper investigates how much MNA can improve cellular network capacity in the context of a multi-operator mobile virtual network operator (MO-MVNO), such as Google's Project Fi, and explores how the capacity gain varies with the MNOs' resource allocation scheme, and the MO-MVNO's market share and MNO selection algorithm. Simulations show that MNA can expand cellular network capacity by as much as 80% without additional spectrum or infrastructure. Resource allocation schemes affect both total capacity gain and how the gain is shared among operators. We also show that an MNO selection algorithm that is rational for an individual MO-MVNO subscriber can hurt the overall performance of both the MO-MVNO and MNOs.
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