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Annual IEEE Northeast workshop on Circuits and Systems (NEWCAS)

作者： Stephan H. Chagas João B. Martins Leonardo L. de Oliveira UFSM PPGI Santa Maria Brazil UFSM GMICRO Santa Maria Brazil

Localization of nodes in wireless sensor networks without the use of GPS is important for applications such as military surveillance, environmental monitoring, robotics, domotics, animal tracking, and many others. Low cost and energy efficient sensors require methods that compute their position using indirect information such as RSSI (Received Signal Strength Indicator). This work presents an artificial neural networks (ANNs) approach to localization in wireless sensor networks through the adjustment of the ANNs structures using Genetic algorithms. A population of feedforward ANNs containing their structure in a genetic code is evolved during 20 generations. Each individual is evaluated through the training of the artificial neural network and further calculation of its root mean square error for all the testing set. The RSSI measurements were used as the artificial neural networks inputs to localize the nodes. The approach was tested using the MATLAB-based Probabilistic Wireless Network Simulator (Prowler) to collect the artificial neural networks input data, under simulated static indoor network environment of 26×26 meters with 8 anchor nodes, i.e., nodes with awareness of their positions. The MATLAB's genetic algorithms and artificial neural networks toolboxes were used. Results using the best artificial neural network structure found after optimization had a root mean square error of 0.41 meters, a maximum error of 1.07 meters and a minimum error of 0.014 meters.

关键词： Wireless sensor networks Sensors Artificial neural networks Training optimization Biological neural networks

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Millimeter-wave technology for medical applications

Millimeter-wave technology for medical applications

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IEEE MTT-S international Microwave workshop Series (IMWS)

作者： Fujiang Lin Wei Hu Ao Li University of Science and Technology Hefei Anhui China

All living cells generate alternating electromagnetic fields. Cells communicate within our body in millimeter waves (mmWave), or frequencies centre at 42.2, 53.6, and 61.2 GHz. Unbalances in these waves are a basic cause for health problem. If an external low power (mW/cm2) mmWave source with right frequency is brought to right exposure site, it can stimulates and establish communicate with body cells and cell membranes. Harmonic and balanced resonance contact with the frequencies of the body can thus correct the unbalances in the organism. This non-thermal low-intensity electromagnetic energy treatment is millimeter-wave therapy (MWT), a new revolutionary method in health care. Scientifically it is based on quantum physics. Over 40 years of researches and clinic trials have been conducted, in particular in Russia. It has shown clear healing effect for cardiovascular disorders, diabetes, dermatitis, gastrointestinal disorders, wound healing, pain relief, and the reduction of toxic side effects of chemotherapy in cancer patients, depression, etc. In order to use millimeter-wave as an effective medical treatment method, we must first explore the different nature of the millimeter-wave source and a variety of specific effects on organisms, and based on them, doing specific treatment research and design optimization of millimeter-wave instruments, including millimeter-wave source, instrument hardware platform and treatment methods (algorithms). In addition, in order to using it in family occasions, there is a need for miniaturized medical devices, and do low-power design, and thus making it portable and wearable. In this talk, the most recently early staege MWT study will be shared, including possible mechanism and mininature MWT solutions in this new R&D direction for the next integrated waves of biology, neurology and electromagnetics coming together.

关键词： Millimeter wave technology Resonant frequency Cells (biology) Organisms Medical treatment

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Adaptive reference update (ARU) algorithm. A stochastic search algorithm for efficient optimization of multi-drug cocktails

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BMC GENOMICS 2012年第6-Sup期13卷 S12-S12页

作者： Kim, Mansuck Yoon, Byung-Jun Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

Background: Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing. Results: In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms. Conclusions: Simulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications.

关键词： Drug Combination Drug Response Drug Cocktail Reference Concentration Finite State Automaton

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Modified artificial bee colony algorithms for numerical optimization

Modified artificial bee colony algorithms for numerical opti...

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international workshop on Intelligent Systems and applications

作者： Zhang, Dongli Guan, Xinping Tang, Yinggan Tang, Yong Institute of Electrical Engineering Yanshan University Qinhuangdao China Institute of Information Science and Engineering Yanshan University Qinhuangdao China

ISBN: (纸本)9781424498574

The artificial bee colony algorithm is a swarm intelligence optimization algorithm inspired by the intelligent foraging behavior of honeybees. In this paper, modified ABC algorithms are proposed for numerical optimization. We have compared the performance of our ABC approach against the basic ABC algorithm, results show that the proposed methods have somewhat improved the convergence rate and global searching capability in some optimization problems. © 2011 IEEE.

关键词： Swarm intelligence

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Engineering applications of Neural Networks 2011

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丛书名： IFIP Advances in Information and Communication Technology

2011年

作者： Lazaros S. Iliadis Chrisina Jayne

ISBN: (数字)9783642239571

ISBN: (纸本)9783642239564

The two-volume set IFIP AICT 363 and 364 constitutes the refereed proceedings of the 12th international Conference on Engineering applications of Neural Networks, EANN 2011, and the 7th IFIP WG 12.5 international Conference, AIAI 2011, held jointly in Corfu, Greece, in September 2011. The 52 revised full papers and 28 revised short papers presented together with 31 workshop papers were carefully reviewed and selected from 150 submissions. The first volume includes the papers that were accepted for presentation at the EANN 2011 conference. They are organized in topical sections on computer vision and robotics, self organizing maps, classification/pattern recognition, financial and management applications of AI, fuzzy systems, support vector machines, learning and novel algorithms, reinforcement and radial basis function ANN, machine learning, evolutionary genetic algorithms optimization, Web applications of ANN, spiking ANN, feature extraction minimization, medical applications of AI, environmental and earth applications of AI, multi layer ANN, and bioinformatics. The volume also contains the accepted papers from the workshop on applications of Soft Computing to Telecommunication (ASCOTE 2011), the workshop on Computational Intelligence applications in Bioinformatics (CIAB 2011), and the Second workshop on Informatics and Intelligent Systems applications for Quality of Life Information Services (ISQLIS 2011).

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Utilization-controlled Task Consolidation for Power optimization in Multi-Core Real-Time Systems

Utilization-controlled Task Consolidation for Power Optimiza...

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1st international workshop on Cyber-Physical Systems, Networks, and applications (CPSNA) held during 17th IEEE international Conference on Embedded and Real-Time Computing Systems and applications (RTCSA)

作者： Fu, Xing Wang, Xiaorui Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

ISBN: (纸本)9780769545028

Since multi-core processors have become a primary trend in processor development, new scheduling algorithms are needed to minimize power consumption while achieving the desired timeliness guarantees for multi-core (and many-core) real-time embedded systems. Although various power/energy-efficient scheduling algorithms have recently been proposed, existing studies may have degraded run-time performance in terms of power/energy efficiency and real-time guarantees when applied to real-time embedded systems with uncertain execution times. In this paper, we propose a novel online solution that integrates core-level feedback control with processor-level optimization to minimize both the dynamic and leakage power consumption of a multi-core real-time embedded system. Our solution monitors the utilization of each CPU core in the system and dynamically responds to unpredictable execution time variations by conducting per-core voltage and frequency scaling. We then perform task consolidation on a longer timescale and shut down unused cores for maximized power savings. Both empirical results on a hardware multi-core testbed with well-known benchmarks and simulation results in many-core systems show that our solution provides the desired real-time guarantees while achieving more power savings than three state-of-the-art algorithms.

关键词： Multicore processing Real time systems Power demand optimization Embedded systems Resource management Frequency control

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CONVEX optimization FOR EXACT RANK RECOVERY IN TOPIC MODELS

CONVEX OPTIMIZATION FOR EXACT RANK RECOVERY IN TOPIC MODELS

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21st IEEE international workshop on Machine Learning for Signal Processing (MLSP)

作者： Behmardi, Behrouz Raich, Raviv Oregon State Univ Sch EECS Corvallis OR 97331 USA

ISBN: (纸本)9781457716232

Topic models are widely used in a variety of applications including document classification and computer vision. The number of topics in the model plays an important role in terms of accuracy. We consider the problem of estimating the number of topics. In [1], a convex optimization approach was proposed to solve the problem via a constrained nuclear norm minimization. A standard semidefinite programming (SDP) was applied to solve the convex optimization only for a small size problem (e. g. 100x 100 matrix) due to its high computational complexity. To extend the applicability of the approach to large scale problems, we propose an accelerated gradient algorithm (AGA). Numerical results show that proposed algorithm can reliably solve a wide range of large scale problems in a shorter time than SDP solvers. Moreover, algorithms applied to a fairly large size real world dataset and results are provided.

关键词： low rank matrix recovery nuclear norm minimization topic models convex optimization

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The Sixth international workshop on Automatic Performance Tuning (iWAPT2011)

The Sixth International Workshop on Automatic Performance Tu...

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international Conference on Computational Science (ICCS) on the Ascent of Computational Excellence

作者： Katagiri, Takahiro Vuduc, Richard Univ Tokyo Ctr Informat Technol Bunkyo Ku 2-11-16 Yayoi Tokyo 1138658 Japan Georgia Inst Technol Sch Comp Sci & Engn Atlanta GA 30332 USA

This preface summarizes The Sixth international workshop on Automatic Performance Tuning (iWAPT2011).

关键词： Performance modeling Adaptive algorithms Numerical algorithms and libraries Scientific applications Parallel and distributed computing Computing with GPGPU and accelerators Database management system Numerical precision and stability Resource restrictions (real-time, memory etc.) Empirical Compilation Automatically-tuned Code Generation Frameworks and theories of automatic tuning and software optimization Autonomic computing and context-aware computing Low-power computing and green computing Position papers

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Artificial neural network weights optimization using ICA, GA, ICA-GA and R-ICA-GA: Comparing performances

Artificial neural network weights optimization using ICA, GA...

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IEEE workshop on Hybrid Intelligent Models and applications

作者： Khorani, Vahid Forouzideh, Nafiseh Nasrabadi, Ali Motie Islamic Azad University Qazvin Branch Qazvin Iran University of Tehran Kish International Campus Kish Island Iran Shahed University Biomedical Engineering Department Tehran Iran

ISBN: (纸本)9781424499090

Artificial neural networks (ANN) and evolutionary algorithms are two relatively young research areas that were subject to a steadily growing interest during the past years. This paper examines the use of different evolutionary algorithms, imperialist competitive algorithm (ICA), genetic algorithm (GA), ICA-GA and recursive ICA-GA (R-ICA-GA) to train a classification problem on a multi layer perceptron (MLP) neural network. All of named evolutionary training algorithms are compared together in this paper. The first goal of the paper is to apply new evolutionary optimization algorithms ICA-GA and R-ICA-GA for training the ANN and the second goal of the paper is to compare different evolutionary algorithms. It is shown that the ICA-GA has the best performance, in number of epochs, compared to the other algorithms. For this purpose, learning algorithms are applied on six known datasets (WINE, PIMA, WDBC, IRIS, SONAR and GLASS) which are used for classification problems. © 2011 IEEE.

关键词： Neural networks

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Local as View Information Integration in the Semantic Model Approach

Local as View Information Integration in the Semantic Model ...

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12th IEEE international Conference on Information Reuse and Integration (IRI)/1st international workshop on Issues and Challenges in Social Computing (WICSOC)

作者： Sadri, Fereidoon Univ N Carolina Dept Comp Sci Greensboro NC 27402 USA

ISBN: (纸本)9781457709661

The problem of large-scale, decentralized data sharing, which is relevant for a wide range of applications, is still a source of major theoretical and practical challenges in spite of many years of sustained research. The Semantic-Model approach to information integration was introduced recently. In this approach, the mediator schema is a simple ontological view of the sources' data, consisting of a collection of binary relations. This paper extends query processing and optimization in the Semantic Model approach with local-as-view (LAV) mappings. It has been argued that local-as-view is the approach of choice for information integration applications, providing significant flexibility and better query optimization opportunities. We will concentrate on XML information sources, provide relational-to-XML mappings to describe source contents in terms of the mediator schema, and present query processing and optimization algorithms for this framework.

关键词： Data models Ontologies optimization Query processing Semantics XML

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