Optimizing rate algorithms in wireless networks

Since wireless network optimisations can be typically designed and evaluated independently of one another under the assumption that they can be applied jointly or independently. In this paper, we have analysis some rate algorithms in wireless networks. Since wireless networks have different standards in IEEE with peculiar features, data rate is one of those important parameters that wireless networks depend on for performances. The optimisation of this network is dependent on the behaviour of a particular rate algorithm in a network scenario. We have considered some first and second generation's rate algorithm, and it is all about selecting an appropriate data rate that any available wireless network can utilise for transmission in order to achieve a good performance. We have designed and analysis a wireless network and results obtained for some rate algorithms, like ONOE and AARF.

From the physics of interacting polymers to optimizing routes on the London Underground

Optimizing paths on networks is crucial for many applications, ranging from subway traffic to Internet communication. Because global path optimization that takes account of all path choices simultaneously is computationally hard, most existing routing algorithms optimize paths individually, thus providing suboptimal solutions. We use the physics of interacting polymers and disordered systems to analyze macroscopic properties of generic path optimization problems and derive a simple, principled, generic, and distributed routing algorithm capable of considering all individual path choices simultaneously. We demonstrate the efficacy of the algorithm by applying it to: (i) random graphs resembling Internet overlay networks, (ii) travel on the London Underground network based on Oyster card data, and (iii ) the global airport network. Analytically derived macroscopic properties give rise to insightful new routing phenomena, including phase transitions and scaling laws, that facilitate better understanding of the appropriate operational regimes and their limitations, which are difficult to obtain otherwise.

Optimizing search engines results using linear programming

When a query is passed to multiple search engines, each search engine returns a ranked list of documents. Researchers have demonstrated that combining results, in the form of a "metasearch engine", produces a significant improvement in coverage and search effectiveness. This paper proposes a linear programming mathematical model for optimizing the ranked list result of a given group of Web search engines for an issued query. An application with a numerical illustration shows the advantages of the proposed method. © 2011 Elsevier Ltd. All rights reserved.

Optimizing amino acid groupings for GPCR classification

MOTIVATION: There is much interest in reducing the complexity inherent in the representation of the 20 standard amino acids within bioinformatics algorithms by developing a so-called reduced alphabet. Although there is no universally applicable residue grouping, there are numerous physiochemical criteria upon which one can base groupings. Local descriptors are a form of alignment-free analysis, the efficiency of which is dependent upon the correct selection of amino acid groupings. RESULTS: Within the context of G-protein coupled receptor (GPCR) classification, an optimization algorithm was developed, which was able to identify the most efficient grouping when used to generate local descriptors. The algorithm was inspired by the relatively new computational intelligence paradigm of artificial immune systems. A number of amino acid groupings produced by this algorithm were evaluated with respect to their ability to generate local descriptors capable of providing an accurate classification algorithm for GPCRs.

Processing of Knowledge about Optimization of Classical Optimizing Transformations

The article describes the structure of an ontology model for Optimization of a sequential program. The components of an intellectual modeling system for program optimization are described. The functions of the intellectual modeling system are defined.

An Adaptive Genetic Algorithm with Dynamic Population Size for Optimizing Join Queries

The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-deterministic optimization algorithms. Experiments have been performed optimizing several random queries against a randomly generated data dictionary. The proposed adaptive genetic algorithm with probabilistic selection operator outperforms in a number of test runs the canonical genetic algorithm with Elitist selection as well as two common random search strategies and proves to be a viable alternative to existing non-deterministic optimization approaches.

Optimizing Supply Chain Performance in China with Country-Specific Supply Chain Coordination

The implementation of country-specific supply chain coordination techniques ensures an optimal global supply chain performance. This paper looks at success factors going with a supply chain coordination strategy within the global supply chain of a successful, medium-sized, privately-owned company, one having locations in North America (USA), Europe (Hungary) and Asia (China). Through the shown GSL’s Chinese plant, we will endeavor to argue that increased collaboration in the supply network with appropriate supply chain coordination brings down not only the inventory level but can also improve conformance quality and reduce quoted lead times.

Optimizing the radiation pattern of smart antennas using genetic algorithms

This work is directed towards optimizing the radiation pattern of smart antennas using genetic algorithms. The structure of the smart antennas based on Space Division Multiple Access (SDMA) is proposed. It is composed of adaptive antennas, each of which has adjustable weight elements for amplitudes and phases of signals. The corresponding radiation pattern formula available for the utilization of numerical optimization techniques is deduced. Genetic algorithms are applied to search the best phase-amplitude weights or phase-only weights with which the optimal radiation pattern can be achieved. ^ One highlight of this work is the proposed optimal radiation pattern concept and its implementation by genetic algorithms. The results show that genetic algorithms are effective for the true Signal-Interference-Ratio (SIR) design of smart antennas. This means that not only nulls can be put in the directions of the interfering signals but also simultaneously main lobes can be formed in the directions of the desired signals. The optimal radiation pattern of a smart antenna possessing SDMA ability has been achieved. ^ The second highlight is on the weight search by genetic algorithms for the optimal radiation pattern design of antennas having more than one interfering signal. The regular criterion for determining which chromosome should be kept for the next step iteration is modified so as to improve the performance of the genetic algorithm iteration. The results show that the modified criterion can speed up and guarantee the iteration to be convergent. ^ In addition, the comparison between phase-amplitude perturbations and phase-only perturbations for the radiation pattern design of smart antennas are carried out. The effects of parameters used by the genetic algorithm on the optimal radiation pattern design are investigated. Valuable results are obtained. ^

An integrated multi-agent framework for optimizing time, cost and environmental impact of construction processes

Environmentally conscious construction has received a significant amount of research attention during the last decades. Even though construction literature is rich in studies that emphasize the importance of environmental impact during the construction phase, most of the previous studies failed to combine environmental analysis with other project performance criteria in construction. This is mainly because most of the studies have overlooked the multi-objective nature of construction projects. In order to achieve environmentally conscious construction, multi-objectives and their relationships need to be successfully analyzed in the complex construction environment. The complex construction system is composed of changing project conditions that have an impact on the relationship between time, cost and environmental impact (TCEI) of construction operations. Yet, this impact is still unknown by construction professionals. Studying this impact is vital to fulfill multiple project objectives and achieve environmentally conscious construction. This research proposes an analytical framework to analyze the impact of changing project conditions on the relationship of TCEI. This study includes green house gas (GHG) emissions as an environmental impact category. The methodology utilizes multi-agent systems, multi-objective optimization, analytical network process, and system dynamics tools to study the relationships of TCEI and support decision-making under the influence of project conditions. Life cycle assessment (LCA) is applied to the evaluation of environmental impact in terms of GHG. The mixed method approach allowed for the collection and analysis of qualitative and quantitative data. Structured interviews of professionals in the highway construction field were conducted to gain their perspectives in decision-making under the influence of certain project conditions, while the quantitative data were collected from the Florida Department of Transportation (FDOT) for highway resurfacing projects. The data collected were used to test the framework. The framework yielded statistically significant results in simulating project conditions and optimizing TCEI. The results showed that the change in project conditions had a significant impact on the TCEI optimal solutions. The correlation between TCEI suggested that they affected each other positively, but in different strengths. The findings of the study will assist contractors to visualize the impact of their decision on the relationship of TCEI.

Optimizing storage and memory systems for energy and performance

Electrical energy is an essential resource for the modern world. Unfortunately, its price has almost doubled in the last decade. Furthermore, energy production is also currently one of the primary sources of pollution. These concerns are becoming more important in data-centers. As more computational power is required to serve hundreds of millions of users, bigger data-centers are becoming necessary. This results in higher electrical energy consumption. Of all the energy used in data-centers, including power distribution units, lights, and cooling, computer hardware consumes as much as 80%. Consequently, there is opportunity to make data-centers more energy efficient by designing systems with lower energy footprint. Consuming less energy is critical not only in data-centers. It is also important in mobile devices where battery-based energy is a scarce resource. Reducing the energy consumption of these devices will allow them to last longer and re-charge less frequently. Saving energy in computer systems is a challenging problem. Improving a system's energy efficiency usually comes at the cost of compromises in other areas such as performance or reliability. In the case of secondary storage, for example, spinning-down the disks to save energy can incur high latencies if they are accessed while in this state. The challenge is to be able to increase the energy efficiency while keeping the system as reliable and responsive as before. This thesis tackles the problem of improving energy efficiency in existing systems while reducing the impact on performance. First, we propose a new technique to achieve fine grained energy proportionality in multi-disk systems; Second, we design and implement an energy-efficient cache system using flash memory that increases disk idleness to save energy; Finally, we identify and explore solutions for the page fetch-before-update problem in caching systems that can: (a) control better I/O traffic to secondary storage and (b) provide critical performance improvement for energy efficient systems.

A Fuzzy Preprocessing Module for Optimizing the Access Network Selection in Wireless Networks

A heterogeneous wireless network is characterized by the presence of different wireless access technologies that coexist in an overlay fashion. These wireless access technologies usually differ in terms of their operating parameters. On the other hand, Mobile Stations (MSs) in a heterogeneous wireless network are equipped with multiple interfaces to access different types of services from these wireless access technologies. The ultimate goal of these heterogeneous wireless networks is to provide global connectivity with efficient ubiquitous computing to these MSs based on the Always Best Connected (ABC) principle. This is where the need for intelligent and efficient Vertical Handoffs (VHOs) between wireless technologies in a heterogeneous environment becomes apparent. This paper presents the design and implementation of a fuzzy multicriteria based Vertical Handoff Necessity Estimation (VHONE) scheme that determines the proper time for VHO, while considering the continuity and quality of the currently utilized service, and the end-users' satisfaction.

On Optimizing Compatible Security Policies in Wireless Networks

This paper deals with finding the maximum number of security policies without conflicts. By doing so we can remove security loophole that causes security violation. We present the problem of maximum compatible security policy and its relationship to the problem of maximum acyclic subgraph, which is proved to be NP-hard. Then we present a polynomial-time approximation algorithm and show that our result has approximation ratio for any integer with complexity .

Optimizing the Synthesis of AZN

Azulenyl nitrone (AZN) is a bright green compound that can be used to stain different compounds, including plastics. When these stained plastics are irradiated, as they commonly are in the sterilization of medical devices, AZN changes color from green to red, constituting a permanent change. This would make obsolete the current methods of radioactive labeling and maintain the integrity of medical equipment. Although a method of synthesis is already in place, the aim was to improve the yield significantly and find a more efficient and cost-effective procedure. Last year, the procedure used resulted in 18 to 20% of AZN synthesized at the most favorable conditions. With that in mind, this year modifications were done in the hopes of improving the yield. The solvent was changed to a mixture of isopropanol and triethylamine, a stronger base, and a catalytic amount of N-tertbutyl hydroxylamine hydrochloride was used (around 4 equivalents). The reaction time was also increased to 7 days, rather than 2. After several trials, the samples were run through column chromatography and the average yield was 70%, a much more promising result than that obtained last year. There is still research to be done to improve the technicalities of the procedure, including altering the amounts of N-tertbutyl hydroxylamine hydrochloride to try and obtain similar data with fewer amounts. This portion of the research will be done in the second half of the year. In the meantime, however, a novel and more efficient method of synthesis has been established for the production of AZN that can be potentially commercialized.

Optimizing the Synthesis of AZN

Azulenyl nitrone (AZN) is a bright green compound that can be used to stain different compounds, including plastics. When these stained plastics are irradiated, as they commonly are in the sterilization of medical devices, AZN changes color from green to red, constituting a permanent change. This would make obsolete the current methods of radioactive labeling and maintain the integrity of medical equipment. Although a method of synthesis is already in place, the aim was to improve the yield significantly and find a more efficient and cost-effective procedure. Last year, the procedure used resulted in 18 to 20% of AZN synthesized at the most favorable conditions. With that in mind, this year modifications were done in the hopes of improving the yield. The solvent was changed to a mixture of isopropanol and triethylamine, a stronger base, and a catalytic amount of N-tertbutyl hydroxylamine hydrochloride was used (around 4 equivalents). The reaction time was also increased to 7 days, rather than 2. After several trials, the samples were run through column chromatography and the average yield was 70%, a much more promising result than that obtained last year. There is still research to be done to improve the technicalities of the procedure, including altering the amounts of N-tertbutyl hydroxylamine hydrochloride to try and obtain similar data with fewer amounts. This portion of the research will be done in the second half of the year. In the meantime, however, a novel and more efficient method of synthesis has been established for the production of AZN that can be potentially commercialized.

Compilation of downward flux observations from sediment trap deployments in the Atlantic Ocean - Contribution to EURO-BASIN's Data integration

We provide a compilation of downward fluxes (total mass, POC, PON, BSiO2, CaCO3, PIC and lithogenic/terrigenous fluxes) from over 6000 sediment trap measurements distributed in the Atlantic Ocean, from 30 degree North to 49 degree South, and covering the period 1982-2011. Data from the Mediterranean Sea are also included. Data were compiled from different sources: data repositories (BCO-DMO, PANGAEA), time series sites (BATS, CARIACO), published scientific papers and/or personal communications from PI's. All sources are specifed in the data set. Data from the World Ocean Atlas 2009 were extracted to provide each flux observation with contextual environmental data, such as temperature, salinity, oxygen (concentration, AOU and percentage saturation), nitrate, phosphate and silicate.