A New Algorithm for the Elimination of Common Subexpressions in Hardware Implementation of Digital Filters by Using Genetic Programming

A new algorithm based on Genetic Programming (GP) for the problem of optimization of Multiple constant Multiplication (MCM) by Common Subexpression Elimination(CSE) is developed. This method is used for hardware optimization for DSP systems. A solution based on Genetic Programming is shown in this paper. The performance of the technique is demonstrated in one- and multi-dimensional digital filters with constant coefficients.     

Rendezvous-guidance trajectory planning for robotic dynamic obstacle avoidance and interception.

This correspondence presents a novel online trajectory-planning method for the autonomous robotic interception of moving targets in the presence of dynamic obstacles, i.e., position and velocity matching (also referred to as rendezvous). The proposed time-optimal interception method is a hybrid algorithm that augments a novel rendezvous-guidance (RG) technique with the velocity-obstacle approach, for obstacle avoidance, first reported by Fiorini and Shiller. The obstacle-avoidance algorithm itself could not be used in its original form and had to be modified to ensure that the online planned path deviates minimally from the one generated by the RG algorithm. Extensive simulation and experimental analyses, some of which are reported in this correspondence, have clearly demonstrated the tangible time efficiency of the proposed interception method.     

On development of a finite dynamic element and solution of associated eigenproblem by a block Lanczos procedure

The paper first presents the details of the development of a new six-noded plane triangular finite dynamic element. A block Lanczos algorithm is developed next for the accurate and efficient solution of the quadratic matrix eigenvalue problem associated with the finite dynamic element formulation. The resulting computer program fully exploits matrix sparsity inherent in such a discretization and proves to be most efficient for the extraction of the usually required first few roots and vectors, including repeated ones. Most importantly, the present eigenproblem solution effort is shown to be comparable to that of the corresponding finite element analysis, thereby rendering the associated dynamic element method rather attractive owing to superior convergence characteristics of such elements, presented herein.     

A generalized continuum theory for multiphase suspension flows

A continuum theory for multiphase particulate suspensions in a fluid media is formulated. The generalized fundamental balance laws for the particulate phases as well as for the continuous suspending fluid phase are presented. The thermodynamics of a multiphase system is studied and the method of Lagrangian multipliers is employed to include the constraints of incompressibility of materials of the constituents and the criterion for fully saturated media. Constitutive equations are developed which include microrotational and microdilatational effects. The basic equations of motion of different phases are derived and discussed. Special considerations are given to the case of incompressible constituents. Examples of applications of the present theory to dilute suspension flows and multiphase sedimentations are also discussed.     

An integrated methodology to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes

Today, air pollution, smoking, use of fatty acids and ready‐made foods, and so on, have exacerbated heart disease. Therefore, controlling the risk of such diseases can prevent or reduce their incidence. The present study aimed at developing an integrated methodology including Markov decision processes (MDP) and genetic algorithm (GA) to control the risk of cardiovascular disease in patients with hypertension and type 1 diabetes. First, the efficiency of GA is evaluated against Grey Wolf optimization (GWO) algorithm, and then, the superiority of GA is revealed. Next, the MDP is employed to estimate the risk of cardiovascular disease. For this purpose, model inputs are first determined using a validated micro‐simulation model for screening cardiovascular disease developed at Tehran University of Medical Sciences, Iran by GA. The model input factors are then defined accordingly and using these inputs, three risk estimation models are identified. The results of these models support WHO guidelines that provide medicine with a high discount to patients with high expected LYs. To develop the MDP methodology, policies should be adopted that work well despite the difference between the risk model and the actual risk. Finally, a sensitivity analysis is conducted to study the behavior of the total medication cost against the changes of parameters.     

Pd/Cu-Free Cobalt-Catalyzed Suzuki and Heck Using Green Bio-Magnetic Hybrid and DFT-Based Theoretical Study

Catalysis Letters - Several highly efficient and magnetically recyclable cobalt catalytic systems were prepared using magnetic chitosan and some safe and available organic compounds...     

Electrolytic cell engineering and device optimization for electrosynthesis of e-biofuels via co-valorisation of bio-feedstocks and captured CO2

Utilizing CO₂ in an electro-chemical process and synthesizing value-added chemicals are amongst the few viable and scalable pathways in carbon capture and utilization technologies.CO₂ electro-reduction is also counted as one of the main options entailing less fossil fuel consumption and as a future electrical energy storage strategy.The current study aims at developing a new electrochemical platform to produce low-carbon e-biofuel through multifunctional electrosynthesis and integrated co-valorisation of biomass feedstocks with captured CO₂.In this approach,CO₂ is reduced at the cathode to produce drop-in fuels(e.g.,methanol)while value-added chemicals(e.g.,selective oxidation of alcohols,aldehydes,carboxylic acids and amines/amides)are produced at the anode.In this work,a numerical model of a continuous-flow design considering various anodic and cathodic reactions was built to determine the most techno-economically feasible configurations from the aspects of energy efficiency,environment impact and economical values.The reactor design was then optimized via parametric analysis.     

An improved Differential Evolution algorithm using learning automata and population topologies

A new variant of Differential Evolution (DE), called ADE-Grid, is presented in this paper which adapts the mutation strategy, crossover rate (CR) and scale factor (F) during the run. In ADE-Grid, learning automata (LA), which are powerful decision making machines, are used to determine the proper value of the parameters CR and F, and the suitable strategy for the construction of a mutant vector for each individual, adaptively. The proposed automata based DE is able to maintain the diversity among the individuals and encourage them to move toward several promising areas of the search space as well as the best found position. Numerical experiments are conducted on a set of twenty four well-known benchmark functions and one real-world engineering problem. The performance comparison between ADE-Grid and other state-of-the-art DE variants indicates that ADE-Grid is a viable approach for optimization. The results also show that the proposed ADE-Grid improves the performance of DE in terms of both convergence speed and quality of final solution.     

A game attention model for efficient bit rate allocation in cloud gaming

The widespread availability of broadband internet access and the growth in server-based processing have provided an opportunity to run games away from the player into the cloud and offer a new promising service known as cloud gaming. The concept of cloud gaming is to render a game in the cloud and stream the resulting game scenes to the player as a video sequence over a broadband connection. To meet the stringent network bandwidth requirements of cloud gaming and support more players, efficient bit rate reduction techniques are needed. In this paper, we introduce the concept of game attention model (GAM), which is basically a game context-based visual attention model, as a means for reducing the bit rate of the streaming video more efficiently. GAM estimates the importance of each macro-block in a game frame from the player’s perspective and allows encoding the less important macro-blocks with lower bit rate. We have evaluated nine game video sequences, covering a wide range of game genre and a spectrum of scene content in terms of details, motion and brightness. Our subjective assessment shows that by integrating this model into the cloud gaming framework, it is possible to decrease the required bit rate by nearly 25 % on average, while maintaining a relatively high user quality of experience. This clearly enables players with limited communication resources to benefit from cloud gaming with acceptable quality.     

Bumpy Particle Adhesion and Removal in Turbulent Flows Including Electrostatic and Capillary Forces

The effect of electrostatic and capillary forces on bumpy particle adhesion and removal in turbulent flows is studied. We use the JKR theory and account for the increase of adhesion by capillary force. The effects of electrostatic forces and nonlinear hydrodynamic drag are included in the analyses. The criteria for incipient rolling and sliding detachments and electrostatic lifting removal are evaluated. A turbulence burst model is used for evaluating the peak air velocity near the substrate. The critical shear velocities for detaching particles of different sizes under different conditions are evaluated. The electric field strength needed for electrostatic removal of particles with different charges is also estimated. The results are compared with those obtained in the absence of the capillary force. Comparisons of the model predictions with the available experimental data are also presented.