Power Optimization Techniques in FPGA Devices: A Combination of System- and Low-Levels

This paper presents preliminary results regarding system-level power awareness for FPGA implementations in wireless sensor networks. Re-configurability of field programmable gate arrays (FPGA) allows for significant flexibility in its applications to embedded systems. However, high power consumption in FPGA becomes a significant factor in design considerations. We present several ideas and their experimental verifications on how to optimize power consumption at high level of designing process while maintaining the same energy per operation (low-level methods can be used additionally). This paper demonstrates that it is possible to estimate feasible power consumption savings even at the high level of designing process. It is envisaged that our results can be also applied to other embedded systems applications, not limited to FPGA-based.

Enhanced-Delivery Overlay Multicasting Scheme by Optimizing Bandwidth and Latency Discrepancy Ratios

With optimized bandwidth and latency discrepancy ratios, Node Gain Scores (NGSs) are determined and used as a basis for shaping the max-heap overlay. The NGSs - determined as the respective bandwidth-latency-products - govern the construction of max-heap-form overlays. Each NGS is earned as a synergy of discrepancy ratio of the bandwidth requested with respect to the estimated available bandwidth, and latency discrepancy ratio between the nodes and the source node. The tree leads to enhanceddelivery overlay multicasting – increasing packet delivery which could, otherwise, be hindered by induced packet loss occurring in other schemes not considering the synergy of these parameters on placing the nodes on the overlays. The NGS is a function of four main parameters – estimated available bandwidth, Ba; individual node's requested bandwidth, Br; proposed node latency to its prospective parent (Lp); and suggested best latency as advised by source node (Lb). Bandwidth discrepancy ratio (BDR) and latency discrepancy ratio (LDR) carry weights of α and (1,000 - α ) , respectively, with arbitrary chosen α ranging between 0 and 1,000 to ensure that the NGS values, used as node IDs, maintain a good possibility of uniqueness and balance between the most critical factor between the BDR and the LDR. A max-heap-form tree is constructed with assumption that all nodes possess NGS less than the source node. To maintain a sense of load balance, children of each level's siblings are evenly distributed such that a node can not accept a second child, and so on, until all its siblings able to do so, have already acquired the same number of children. That is so logically done from left to right in a conceptual overlay tree. The records of the pair-wise approximate available bandwidths as measured by a pathChirp scheme at individual nodes are maintained. Evaluation measures as compared to other schemes – Bandwidth Aware multicaSt architecturE (BASE), Tree Building Control Protocol (TBCP), and Host Multicast Tree Protocol (HMTP) - have been conducted. This new scheme generally performs better in terms of trade-off between packet delivery ratio; link stress; control overhead; and end-to-end delays.

Statistical Optimization of Process Variables for Direct Fermentation of 226 White Rose Tapioca Stem to Ethanol by Fusarium oxysporum

Direct fermentation of 226 white rose tapioca stem to ethanol by Fusarium oxysporum was studied in a batch reactor. Fermentation of ethanol can be achieved by sequential pretreatment using dilute acid and dilute alkali solutions using 100 mesh tapioca stem particles. The quantitative effects of substrate concentration, pH and temperature on ethanol concentration were optimized using a full factorial central composite design experiment. The optimum process conditions were then obtained using response surface methodology. The quadratic model indicated that substrate concentration of 33g/l, pH 5.52 and a temperature of 30.13oC were found to be optimum for maximum ethanol concentration of 8.64g/l. The predicted optimum process conditions obtained using response surface methodology was verified through confirmatory experiments. Leudeking-piret model was used to study the product formation kinetics for the production of ethanol and the model parameters were evaluated using experimental data.

Optimization of Gentamicin Production: Comparison of ANN and RSM Techniques

In this work, statistical experimental design was applied for the optimization of medium constituents for Gentamicin production by Micromsonospora echinospora subs pallida (MTCC 708) in a batch reactor and the results are compared with the ANN predicted values. By central composite design, 50 experiments are carried out for five test variables: Starch, Soya bean meal, K2HPO4, CaCO3 and FeSO4. The optimum condition was found to be: Starch (8.9,g/L), Soya bean meal (3.3 g/L), K2HPO4 (0.8 g/L), CaCO3 (4 g/L) and FeSO4 (0.03 g/L). At these optimized conditions, the yield of gentamicin was found to be 1020 mg/L. The R2 values were found to be 1 for ANN training set, 0.9953 for ANN test set, and 0.9286 for RSM.

Multi-objective Optimization of Vehicle Passive Suspension with a Two-Terminal Mass Using Chebyshev Goal Programming

To improve the dynamics response of the vehicle passive suspension, a two-terminal mass is suggested to connect in parallel with the suspension strut. Three performance criteria, tire grip, ride comfort and suspension deflection, are taken into consideration to optimize the suspension parameters. However, the three criteria are conflicting and non-commensurable. For this reason, the Chebyshev goal programming method is applied to find the best tradeoff among the three objectives. A simulation case is presented to describe the multi-objective optimization procedure. For comparison, the Chebyshev method is also employed to optimize the design of a conventional passive suspension. The effectiveness of the proposed design method has been clearly demonstrated by the result. It is also shown that the suspension with a two-terminal mass in parallel has better performance in terms of the three objectives.

Motion Control of a Ball Throwing Robot with a Flexible Robotic Arm

Motion control of flexible arms is more difficult than that of rigid arms, however utilizing its dynamics enables improved performance such as a fast motion in short operation time. This paper investigates a ball throwing robot with one rigid link and one flexible link. This robot throws a ball at a set speed with a proper control torque. A mathematical model of this ball throwing robot is derived through Hamilton’s principle. Several patterns of torque input are designed and tested through the proposed simulation models. The parameters of each torque input pattern is optimized and determined by chaos embedded vector evaluated particle swarm optimization (CEVEPSO). Then, the residual vibration of the manipulator after throwing is suppressed with input shaping technique. Finally, a real experiment is set up for the model checking.

Codon-optimized Carbonic Anhydrase from Dunaliella species: Expression and Characterization

Carbonic anhydrases (CAs) has been focused as biological catalysis for CO2 sequestration process because it can catalyze the conversion of CO2 to bicarbonate. Here, codon-optimized sequence of α type-CA cloned from Duneliala species. (DsCAopt) was constructed, expressed, and characterized. The expression level in E. coli BL21(DE3) was better for codon-optimized DsCAopt than intact sequence of DsCAopt. DsCAopt enzyme shows high-stability at pH 7.6/10.0. In final, we demonstrated that in the Ca2+ solution, DsCAopt enzyme can catalyze well the conversion of CO2 to CaCO3, as the calcite form.

Quantification of Technology Innovation Usinga Risk-Based Framework

There is significant interest in achieving technology innovation through new product development activities. It is recognized, however, that traditional project management practices focused only on performance, cost, and schedule attributes, can often lead to risk mitigation strategies that limit new technology innovation. In this paper, a new approach is proposed for formally managing and quantifying technology innovation. This approach uses a risk-based framework that simultaneously optimizes innovation attributes along with traditional project management and system engineering attributes. To demonstrate the efficacy of the new riskbased approach, a comprehensive product development experiment was conducted. This experiment simultaneously managed the innovation risks and the product delivery risks through the proposed risk-based framework. Quantitative metrics for technology innovation were tracked and the experimental results indicate that the risk-based approach can simultaneously achieve both project deliverable and innovation objectives.

Thermodynamic Optimization of Turboshaft Engine using Multi-Objective Genetic Algorithm

In this paper multi-objective genetic algorithms are employed for Pareto approach optimization of ideal Turboshaft engines. In the multi-objective optimization a number of conflicting objective functions are to be optimized simultaneously. The important objective functions that have been considered for optimization are specific thrust (F/m& 0), specific fuel consumption ( P S ), output shaft power 0 (& /&) shaft W m and overall efficiency( ) O η . These objectives are usually conflicting with each other. The design variables consist of thermodynamic parameters (compressor pressure ratio, turbine temperature ratio and Mach number). At the first stage single objective optimization has been investigated and the method of NSGA-II has been used for multiobjective optimization. Optimization procedures are performed for two and four objective functions and the results are compared for ideal Turboshaft engine. In order to investigate the optimal thermodynamic behavior of two objectives, different set, each including two objectives of output parameters, are considered individually. For each set Pareto front are depicted. The sets of selected decision variables based on this Pareto front, will cause the best possible combination of corresponding objective functions. There is no superiority for the points on the Pareto front figure, but they are superior to any other point. In the case of four objective optimization the results are given in tables.

Integration of Image and Patient Data, Software and International Coding Systems for Use in a Mammography Research Project

Mammographic images and data analysis to facilitate modelling or computer aided diagnostic (CAD) software development should best be done using a common database that can handle various mammographic image file formats and relate these to other patient information. This would optimize the use of the data as both primary reporting and enhanced information extraction of research data could be performed from the single dataset. One desired improvement is the integration of DICOM file header information into the database, as an efficient and reliable source of supplementary patient information intrinsically available in the images. The purpose of this paper was to design a suitable database to link and integrate different types of image files and gather common information that can be further used for research purposes. An interface was developed for accessing, adding, updating, modifying and extracting data from the common database, enhancing the future possible application of the data in CAD processing. Technically, future developments envisaged include the creation of an advanced search function to selects image files based on descriptor combinations. Results can be further used for specific CAD processing and other research. Design of a user friendly configuration utility for importing of the required fields from the DICOM files must be done.

Optimization of Enzymatic Activities in Malting of Oat

Malting is usually carried out on intact barley seed, while hull is still attached to it. In this study, oat grain with and without hull was subjected to controlled germination to optimize its enzymes activity, in such a way that lipase has the lowest and α- amylase and proteinase the highest activities. Since pH has a great impact on the activity of the enzymes, the pH of germination media was set up to 3 to 8. In dehulled oats, lipase and α-amylase had the lowest and highest activities in pHs 3 and 6, respectively whereas the highest proteinase activity was evidenced at pH 7 and 4 in the oats with and without hull respectively. While measurements indicated that the effect of hull on the enzyme activities particularly in lipase and amylase at each level of the pH are significantly different, the best results were obtained in those samples in which their hull had been removed. However, since the similar lipase activity in germinated dehulled oat were recorded at the pHs 4 and 5, therefore it was concluded that pH 5 in dehulled oat seed may provide the optimum enzyme activity for all the enzymes.

A Hybrid Model of ARIMA and Multiple Polynomial Regression for Uncertainties Modeling of a Serial Production Line

Uncertainties of a serial production line affect on the production throughput. The uncertainties cannot be prevented in a real production line. However the uncertain conditions can be controlled by a robust prediction model. Thus, a hybrid model including autoregressive integrated moving average (ARIMA) and multiple polynomial regression, is proposed to model the nonlinear relationship of production uncertainties with throughput. The uncertainties under consideration of this study are demand, breaktime, scrap, and lead-time. The nonlinear relationship of production uncertainties with throughput are examined in the form of quadratic and cubic regression models, where the adjusted R-squared for quadratic and cubic regressions was 98.3% and 98.2%. We optimized the multiple quadratic regression (MQR) by considering the time series trend of the uncertainties using ARIMA model. Finally the hybrid model of ARIMA and MQR is formulated by better adjusted R-squared, which is 98.9%.

Genetic Content-Based MP3 Audio Watermarking in MDCT Domain

In this paper a novel scheme for watermarking digital audio during its compression to MPEG-1 Layer III format is proposed. For this purpose we slightly modify some of the selected MDCT coefficients, which are used during MPEG audio compression procedure. Due to the possibility of modifying different MDCT coefficients, there will be different choices for embedding the watermark into audio data, considering robustness and transparency factors. Our proposed method uses a genetic algorithm to select the best coefficients to embed the watermark. This genetic selection is done according to the parameters that are extracted from the perceptual content of the audio to optimize the robustness and transparency of the watermark. On the other hand the watermark security is increased due to the random nature of the genetic selection. The information of the selected MDCT coefficients that carry the watermark bits, are saves in a database for future extraction of the watermark. The proposed method is suitable for online MP3 stores to pursue illegal copies of musical artworks. Experimental results show that the detection ratio of the watermarks at the bitrate of 128kbps remains above 90% while the inaudibility of the watermark is preserved.

A Multiple-Objective Environmental Rationalization and Optimization for Material Substitution in the Production of Stone-Washed Jeans- Garments

As the Textile Industry is the second largest industry in Egypt and as small and medium-sized enterprises (SMEs) make up a great portion of this industry therein it is essential to apply the concept of Cleaner Production for the purpose of reducing pollution. In order to achieve this goal, a case study concerned with ecofriendly stone-washing of jeans-garments was investigated. A raw material-substitution option was adopted whereby the toxic potassium permanganate and sodium sulfide were replaced by the environmentally compatible hydrogen peroxide and glucose respectively where the concentrations of both replaced chemicals together with the operating time were optimized. In addition, a process-rationalization option involving four additional processes was investigated. By means of criteria such as product quality, effluent analysis, mass and heat balance; and cost analysis with the aid of a statistical model, a process optimization treatment revealed that the superior process optima were 50%, 0.15% and 50min for H2O2 concentration, glucose concentration and time, respectively. With these values the superior process ought to reduce the annual cost by about EGP 105 relative to the currently used conventional method.

Development of NOx Emission Model for a Tangentially Fired Acid Incinerator

This paper aims to develop a NOx emission model of an acid gas incinerator using Nelder-Mead least squares support vector regression (LS-SVR). Malaysia DOE is actively imposing the Clean Air Regulation to mandate the installation of analytical instrumentation known as Continuous Emission Monitoring System (CEMS) to report emission level online to DOE . As a hardware based analyzer, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive technique is often preferred and considered as a feasible alternative to replace the CEMS for regulatory compliance. The LS-SVR model is built based on the emissions from an acid gas incinerator that operates in a LNG Complex. Simulated Annealing (SA) is first used to determine the initial hyperparameters which are then further optimized based on the performance of the model using Nelder-Mead simplex algorithm. The LS-SVR model is shown to outperform a benchmark model based on backpropagation neural networks (BPNN) in both training and testing data.

Application of Artificial Intelligence for Tuning the Parameters of an AGC

This paper deals with the tuning of parameters for Automatic Generation Control (AGC). A two area interconnected hydrothermal system with PI controller is considered. Genetic Algorithm (GA) and Particle Swarm optimization (PSO) algorithms have been applied to optimize the controller parameters. Two objective functions namely Integral Square Error (ISE) and Integral of Time-multiplied Absolute value of the Error (ITAE) are considered for optimization. The effectiveness of an objective function is considered based on the variation in tie line power and change in frequency in both the areas. MATLAB/SIMULINK was used as a simulation tool. Simulation results reveal that ITAE is a better objective function than ISE. Performances of optimization algorithms are also compared and it was found that genetic algorithm gives better results than particle swarm optimization algorithm for the problems of AGC.

Traffic Signal Coordinated Control Optimization: A Case Study

In the urban traffic network, the intersections are the “bottleneck point" of road network capacity. And the arterials are the main body in road network and the key factor which guarantees the normal operation of the city-s social and economic activities. The rapid increase in vehicles leads to seriously traffic jam and cause the increment of vehicles- delay. Most cities of our country are traditional single control system, which cannot meet the need for the city traffic any longer. In this paper, Synchro6.0 as a platform to minimize the intersection delay, optimizesingle signal cycle and split for Zhonghua Street in Handan City. Meanwhile, linear control system uses to optimize the phase for the t arterial road in this system. Comparing before and after use the control, capacities and service levels of this road and the adjacent road have improved significantly.

Modeling and Performance Evaluation of LTE Networks with Different TCP Variants

Long Term Evolution (LTE) is a 4G wireless broadband technology developed by the Third Generation Partnership Project (3GPP) release 8, and it's represent the competitiveness of Universal Mobile Telecommunications System (UMTS) for the next 10 years and beyond. The concepts for LTE systems have been introduced in 3GPP release 8, with objective of high-data-rate, low-latency and packet-optimized radio access technology. In this paper, performance of different TCP variants during LTE network investigated. The performance of TCP over LTE is affected mostly by the links of the wired network and total bandwidth available at the serving base station. This paper describes an NS-2 based simulation analysis of TCP-Vegas, TCP-Tahoe, TCPReno, TCP-Newreno, TCP-SACK, and TCP-FACK, with full modeling of all traffics of LTE system. The Evaluation of the network performance with all TCP variants is mainly based on throughput, average delay and lost packet. The analysis of TCP performance over LTE ensures that all TCP's have a similar throughput and the best performance return to TCP-Vegas than other variants.

Phenomenological and Semi-microscopic Analysis for Elastic Scattering of Protons on 6,7Li

Analysis of the elastic scattering of protons on 6,7Li nuclei has been done in the framework of the optical model at the beam energies up to 50 MeV. Differential cross sections for the 6,7Li + p scattering were measured over the proton laboratory–energy range from 400 to 1050 keV. The elastic scattering of 6,7Li+p data at different proton incident energies have been analyzed using singlefolding model. In each case the real potential obtained from the folding model was supplemented by a phenomenological imaginary potential, and during the fitting process the real potential was normalized and the imaginary potential optimized. Normalization factor NR is calculated in the range between 0.70 and 0.84.