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Hybrid of Adaptive Response Surface methodology and Genetic Algorithm Monte Carlo Optimization method and Its Application in the Design of Moderator-Collimator for Accelerator based Thermal Neutron Radiography 作者： Yang, Mr. Chenxiao 1,2 Chen, Dr. Size 1,2 Zhang, Dr. Lianxin 2 Peng, Mr. Chuan 2,4 Xiao, Mr. Dan 3 作者单位： 1. Anhui University 2. Hefei Institutes of Physical Science Chinese Academy of Sciences 3. Hefei Institutes of Physical Science Chinese Academy of Sciences 4. University of Science and Technology of China 通讯作者： Chen, Dr. Size Email:[email protected] 提交时间： 2025-03-09 14:47:29 摘要: A new Monte Carlo (MC) neutronics optimization method named Hybrid of Adaptive Response Surface methodology and Genetic Algorithm Monte Carlo Optimization (HRG-MCO) is proposed to address the strong empirical dependence and low efficiency of global multi-parameter optimization in traditional neutronics design. HRG-MCO integrates the advantages of Response Surface Methodology (RSM) and Genetic Algorithm (GA). Specifically, neutron MC simulation results are iteratively utilized to adaptively construct an RSM model, ensuring the required accuracy. Subsequently, GA is employed to perform multi-parameter optimization based on the constructed RSM model, enabling the rapid determination of optimal design parameters. These optimized parameters are then fed back into the MC simulation model to derive the final design values. Comparative analysis with the traditional enumeration method and GA alone demonstrates the superior optimization efficiency of the proposed approach. To further validate its effectiveness, the method is applied to the optimization of a moderator-collimator system for a thermal neutron radiography system based on an accelerator. Two optimization tasks are performed: (1) determining the optimal efficiency under different source neutron energies and (2) optimization of thermal neutron photon yield ratio. The results highlight the efficiency and applicability of HRG-MCO in neutronics optimization design. Neutronics Design Optimization Monte Carlo simulation Response Surface Methodology Genetic Algorithm Thermal Neutron Radiography 来自： Chen, Dr. Size 分类： 物理学 >> 核物理学 备注: 已向《Nuclear Science and Techniques》投稿 引用： ChinaXiv:202503.00074 (或此版本 ChinaXiv:202503.00074V1 ) DOI:10.12074/202503.00074 CSTR:32003.36.ChinaXiv.202503.00074 推荐引用方式： Yang, Mr. Chenxiao,Chen, Dr. Size,Zhang, Dr. Lianxin,Peng, Mr. Chuan,Xiao, Mr. Dan.Hybrid of Adaptive Response Surface methodology and Genetic Algorithm Monte Carlo Optimization method and Its Application in the Design of Moderator-Collimator for Accelerator based Thermal Neutron Radiography.中国科学院科技论文预发布平台.[DOI:10.12074/202503.00074] 版本历史 [V1] 2025-03-09 14:47:29 ChinaXiv:202503.00074V1 下载全文

In x-ray coherent scatter tomography, tomographic measurements of the forward scatter distribution are used to infer scatter densities within a volume. A radiopaque 2D pattern placed between the object and the detector array enables the disambiguation between different scatter events. The use of a fan beam source illumination to speed up data acquisition relative to a pencil beam presents computational challenges. To facilitate the use of iterative algorithms based on a penalized Poisson log-likelihood function, efficient computational implementation of the forward and backward models are needed. Our proposed implementation exploits physical symmetries and structural properties of the system and suggests a joint system-algorithm design, where the system design choices are influenced by computational considerations, and in turn lead to reduced reconstruction time. Computational-time speedups of approximately 146 and 32 are achieved in the computation of the forward and backward models, respectively. Results validating the forward model and reconstruction algorithm are presented on simulated analytic and Monte Carlo data.

A  SoC  (System-on-Chip)  interconnection  requires  crossbar  switches to connect inputs to outputs. These devices must be  configured  by  a  scheduling  algorithm  that  allows  arriving  packets  to  be  delivered  across  the  switch  efficiently  and quickly. A suitable scheduling algorithm  has  to achieve the  maximum  throughput  while  maintaining  stability  and  eliminating starvation. Thus,  in  this  paper,  we  present  a  design  and  an  implementation  of  a  scheduler  for  a  32x32  SoC  interconnexion  based  on  an  iterative  scheduling  algorithm  that  we  called  Credited-iSLIP  (iterative  Serial  Line  Internet  Protocol).  It  is  a  variation  of  the  iSLIP  algorithm.  A  description  of  the  scheduler  designed  is  presented  with  simulation results to indicate its performance. The design has been  implemented in VHDL RTL, simulated  with Modelsim 6.2 and synthesized using Xilinx-ISE and the  Virtex-4 device XC4VFX100-12FFG1517 of 90 nm technology  to  achieve  a  maximum  frequency  of  265.88  MHz,  a  minimum slices utilization of 771 and total  estimated power  consumption  about  915  mW.  The  number  of  iterations  is  fixed to i = 8.

Tall buildings with irregular shapes and considerable heights are gaining popularity in creating the vertical cities around the world. They also considered one of the major energy consumers with little regards to sustainability. Tall building is wind-sensitive structure and shape plays major role in determining wind loads, which usually govern the design of its lateral resisting system. Thus, evaluating wind loads properly and designing an optimal lateral system accordingly are the main challenges attributed to the design process of irregular tall building. This paper presents a computational procedure for the optimal design of wind-resistant irregular tall building to minimize the total weight of structure within design requirements in single digital environment. That is achieved firstly by creating a digital system of computational fluid dynamic (CFD) analysis that is coupled with pressure-load translation (PLT) algorithm to evaluate the wind motions on irregular tall buildings and generate the design wind loads accordingly. Genetic Algorithm (GA) with enhance design constrains function of lateral displacements, inter-story drifts and top acceleration is then developed to perform structural optimization. A numerical example using 70-story twisting reinforced concrete building is implemented to verify the feasibility of the developed computational procedures. Steady and incompressible flow applied at (

This article is from BMC Research Notes , volume 4 . Abstract Background: The "off-target" silencing effect hinders the development of siRNA-based therapeutic and research applications. Existing solutions for finding possible locations of siRNA seats within a large database of genes are either too slow, miss a portion of the targets, or are simply not designed to handle a very large number of queries. We propose a new approach that reduces the computational time as compared to existing techniques. Findings: The proposed method employs tree-based storage in a form of a modified truncated suffix tree to sort all possible short string substrings within given set of strings (i.e. transcriptome). Using the new algorithm, we pre-computed a list of the best siRNA locations within each human gene ("siRNA seats"). siRNAs designed to reside within siRNA seats are less likely to hybridize off-target. These siRNA seats could be used as an input for the traditional "set-of-rules" type of siRNA designing software. The list of siRNA seats is available through a publicly available database located at http://web.cos.gmu.edu/~gmanyam/siRNA_db/search.php Conclusions: In attempt to perform top-down prediction of the human siRNA with minimized off-target hybridization, we developed an efficient algorithm that employs suffix tree based storage of the substrings. Applications of this approach are not limited to optimal siRNA design, but can also be useful for other tasks involving selection of the characteristic strings specific to individual genes. These strings could then be used as siRNA seats, as specific probes for gene expression studies by oligonucleotide-based microarrays, for the design of molecular beacon probes for Real-Time PCR and, generally, any type of PCR primers.

In a recent past, research activities have been carried out for developing a six-legged robot at LARM: Laboratory of Robotics and Mechatronics in Cassino. The proposed designs have been conceived with the aim of achieving suitable mechanism structures and architectures with low-cost and user-friendly features. Simulations and experimental tests have been developed by referring to a novel leg design in order to characterize both the walking operation and design characteristics for a suitable control strategy. In this work, we propose a control architecture that has been set up by using a proper algorithm based on evolution strategies. Successful simulation results have been reported to validate the proposed control architecture.

Tri 1 2018/2019

This article is from BMC Bioinformatics , volume 15 . Abstract Background: The ability of science to produce experimental data has outpaced the ability to effectively visualize and integrate the data into a conceptual framework that can further higher order understanding. Multidimensional and shape-based observational data of regenerative biology presents a particularly daunting challenge in this regard. Large amounts of data are available in regenerative biology, but little progress has been made in understanding how organisms such as planaria robustly achieve and maintain body form. An example of this kind of data can be found in a new repository (PlanformDB) that encodes descriptions of planaria experiments and morphological outcomes using a graph formalism. Results: We are developing a model discovery framework that uses a cell-based modeling platform combined with evolutionary search to automatically search for and identify plausible mechanisms for the biological behavior described in PlanformDB. To automate the evolutionary search we developed a way to compare the output of the modeling platform to the morphological descriptions stored in PlanformDB. We used a flexible connected component algorithm to create a graph representation of the virtual worm from the robust, cell-based simulation data. These graphs can then be validated and compared with target data from PlanformDB using the well-known graph-edit distance calculation, which provides a quantitative metric of similarity between graphs. The graph edit distance calculation was integrated into a fitness function that was able to guide automated searches for unbiased models of planarian regeneration. We present a cell-based model of planarian that can regenerate anatomical regions following bisection of the organism, and show that the automated model discovery framework is capable of searching for and finding models of planarian regeneration that match experimental data stored in PlanformDB. Conclusion: The work presented here, including our algorithm for converting cell-based models into graphs for comparison with data stored in an external data repository, has made feasible the automated development, training, and validation of computational models using morphology-based data. This work is part of an ongoing project to automate the search process, which will greatly expand our ability to identify, consider, and test biological mechanisms in the field of regenerative biology.

This paper presents the active suspension system (ASS) control method using the adaptive cascade control scheme. The control scheme is implemented by two control loops, the inner control loop and outer control loop are designed respectively. The inner control loop uses the pole assignment method in order to move the poles of the original system to desired poles respect to the required performance of the suspension system. To design the controller in the inner loop, the model without the noise caused by the road profile and velocity of the car is used. The outer control loop then designed with an adaptive mechanism calculates the active control force to compensate for the vibrations caused by the road profile and velocity of the car. The control force is determined by the error between states of the reference model and states of suspension systems, the reference model is the model of closed loop with inner control loop without the noise. The simulation results implemented by using the practice date of the road profile show that the capability of oscillation decrease for ASS is quite efficient.

Line plotting is the one of the basic operations in the scan conversion. Bresenham’s line drawing algorithm is an efficient and high popular algorithm utilized for this purpose. This algorithm starts from one end-point of the line to the other end-point by calculating one point at each step. As a result, the calculation time for all the points depends on the length of the line thereby the number of the total points presented. In this paper, we developed an approach to speed up the Bresenham algorithm by partitioning each line into number of segments, find the points belong to those segments and drawing them simultaneously to formulate the main line. As a result, the higher number of segments generated, the faster the points are calculated. By employing 32 cores in the Field Programmable Gate Array, a line of length 992 points is formulated in 0.31μs only. The complete system is implemented using Zybo board that contains the Xilinx Zynq-7000 chip (Z-7010).  

Bibliography: leaf 37

Companion Website http://www.cs.princeton.edu/~wayne/kleinberg-tardos/ Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science. Chapter 1 Introduction: Some Representative Problems Chapter 2 Basics of Algorithm Analysis Chapter 3 Graphs Chapter 4 Greedy Algorithms Chapter 5 Divide and Conquer Chapter 6 Dynamic Programming Chapter 7 Network Flow Chapter 8 NP and Computational Intractability Chapter 9 PSPACE: A Class of Problems beyond NP Chapter 10 Extending the Limits of Tractability Chapter 11 Approximation Algorithms Chapter 12 Local Search Chapter 13 Randomized Algorithms

Companion Website http://www.cs.princeton.edu/~wayne/kleinberg-tardos/ Algorithm Design introduces algorithms by looking at the real-world problems that motivate them. The book teaches students a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science. Chapter 1 Introduction: Some Representative Problems Chapter 2 Basics of Algorithm Analysis Chapter 3 Graphs Chapter 4 Greedy Algorithms Chapter 5 Divide and Conquer Chapter 6 Dynamic Programming Chapter 7 Network Flow Chapter 8 NP and Computational Intractability Chapter 9 PSPACE: A Class of Problems beyond NP Chapter 10 Extending the Limits of Tractability Chapter 11 Approximation Algorithms Chapter 12 Local Search Chapter 13 Randomized Algorithms

Finite field arithmetic logic is central in the implementation of some error-correcting coders and some cryptographic devices. There is a need for good multiplication algorithms which can be easily realized. Massey and Omura recently developed a new multiplication algorithm for finite fields based on a normal basis representation. Using the normal basis representation, the design of the finite field multiplier is simple and regular. The fundamental design of the Massey-Omura multiplier is based on a design of a product function. In this article, a generalized algorithm to locate a normal basis in a field is first presented. Using this normal basis, an algorithm to construct the product function is then developed. This design does not depend on particular characteristics of the generator polynomial of the field.

The electric vehicle (EV) has nowadays become a suitable alternative to clean and sustainable energy emissions in transportation, so researchers have become interested in modeling and controlling the electric vehicle.in this paper, an optimal robust integral signum of the error (ORISE) controller is designed to control the actuator speed of an electric vehicle. The actuator type of this vehicle is three-phase induction motor (IM). By reducing the discrepancy between the desired and actual output, the standard salp swarm algorithm (SSA) is utilized to find the optimal suggested ORISE parameter. The suggested controller tested by different desired velocity trajectory. Simulation results demonstrate that the ORISE have high performance, fast and accurate tracking for the EV speed, compare with PID controller that the output speed suffer from chattering and has higher oscillation. In particular, the SSA-based ORISE controller is superior to the proportional-integral-derivative (PID)-based SSA method in terms of no steady-state error and smallest overshoot (0.002% with ORISE while 0.05% with PID) prevention for electric vehicle (EV) speed despite the better results of settling time and rising time obtained in PID (1.532s and 0.785s) respectively while these values were (1.574s and 1.915s) respectively, in ORISE. The MATLAB (R2020a)/Simulink environment is used for all projects.

This paper describes the design of the robust PI/PID controller for the higher order interval system via its reduced order model using the differential evolution (DE) algorithm. A stable reduced interval model is generated from a higher order interval system using the DE in order to minimize the cost and reduce the complexity of the system. This reduced order interval numerator and denominator polynomials are determined by minimizing the Integral Squared Error (ISE) using the DE. Then, using reduced order interval model, a robust PI/PID controller is designed based on the stability conditions for determining robust stability of interval system. Finally, using these stability conditions, a set of inequalities in terms of controller parameters is obtained from the reduced order closed loop characteristic polynomial. Then these inequalities are solved to obtain robust controller parameters with the help of a DE algorithm. The designed, robust controller from the reduced order interval model will be attributed to the higher order interval system. The designed PI/PID controller from our proposed method not only stabilizes the reduced order model, but also stabilizes the original higher order system. The viability of the proposed methodology is illustrated through the numerical example of its successful implementation. The efficacy of the proposed methodology is also evaluated against the available approaches presented in the literature and the results were successfully implemented.

ADA148025

Optimal design of structural engineering problems, particularly truss structures, is highly challenging due to the presence of design constraints such as stress and displacement. These constraints are often defined in a nonlinear and dynamic manner, which causes optimization algorithms to become trapped in local optima. In contrast, metaheuristic algorithms generate acceptable solutions for such problems using specialized programming techniques and operators. The standard Special Relativity Search (SRS) algorithm fails to solve these types of problems effectively due to the lack of coordination between two crucial parameters: exploitation and exploration, leading to convergence to local optima. In this study, two new techniques are introduced and implemented to enhance the performance of this algorithm. The gradient descent algorithm is employed as a complementary method to the SRS algorithm, with the movement step controlled using specific coefficients. To demonstrate the effectiveness of the proposed method compared to other advanced approaches, their results are compared. The findings indicate that the proposed algorithm has a higher convergence rate than previous methods and successfully establishes a proper balance between exploitation and exploration parameters.

Optimal design of structural engineering problems, particularly truss structures, is highly challenging due to the presence of design constraints such as stress and displacement. These constraints are often defined in a nonlinear and dynamic manner, which causes optimization algorithms to become trapped in local optima. In contrast, metaheuristic algorithms generate acceptable solutions for such problems using specialized programming techniques and operators. The standard Special Relativity Search (SRS) algorithm fails to solve these types of problems effectively due to the lack of coordination between two crucial parameters: exploitation and exploration, leading to convergence to local optima. In this study, two new techniques are introduced and implemented to enhance the performance of this algorithm. The gradient descent algorithm is employed as a complementary method to the SRS algorithm, with the movement step controlled using specific coefficients. To demonstrate the effectiveness of the proposed method compared to other advanced approaches, their results are compared. The findings indicate that the proposed algorithm has a higher convergence rate than previous methods and successfully establishes a proper balance between exploitation and exploration parameters.

Traditional brain tumor classification methods face limitations regarding time consuming manual analysis and the potential for inaccuracies. The study proposes a novel approach for brain tumor classification using a hybrid deep learning model incorporating U Net and CNNs. The model will be trained on a large dataset of labeled MRI images to learn features and classify tumors automatically. This approach promises faster diagnoses, improved accuracy compared to traditional methods, and reduced workload for radiologists. The research aims to explore the application of other imaging modalities and expand the scope of the tumor detection system. Dr. M. V. Vijaya Saradhi | Gali Lahari Reddy | Ale Laxminarayana | Alladi Yuvaraj Kumar | Tanishq Duddi "Design and Development of Brain Tumor Classification using Hybrid Deep Learning Algorithm" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-1 , February 2025, URL: https://www.ijtsrd.com/papers/ijtsrd75030.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/75030/design-and-development-of-brain-tumor-classification-using-hybrid-deep-learning-algorithm/dr-m-v-vijaya-saradhi

A newly hybrid nature-inspired algorithm called HSSGWOA is presented with the combination of the salp swarm algorithm (SSA) and grey wolf optimizer (GWO). The major idea is to combine the salp swarm algorithm's exploitation ability with the grey wolf optimizer's exploration ability to generate both variants' strength. The proposed algorithm uses to tune the parameters of the integral sliding mode controller (ISMC) that design to improve the dynamic performance of the two-link flexible joint manipulator. The efficiency and the capability of the proposed hybrid algorithm are evaluated based on the selected test functions. It is clear that when compared to other algorithms like SSA, GWO, differential evolution (DE), gravitational search algorithm (GSA), particles swarm optimization (PSO), and whale optimization algorithm (WOA). The ISMC parameters were tuned using the SSA, which was then compared to the HSSGWOA algorithm. The simulation results show the capabilities of the proposed algorithm, which gives an enhancement percentage of 57.46% compared to the standard algorithm for one of the links, and 55.86% for the other.

In present years, the increasing use of power electronic based nonlinear devices in various power system applications causes harmonic distortions in source side voltages and currents. The unwanted harmonic distortion can introduce detrimental effects on electrical equipment and temperature increases in neutral conductors and distribution transformers. In this paper, a new optimization method of Meta-heuristic firefly algorithm is proposed for the optimal design of passive harmonic filters to mitigate the harmonics existing in the power system environment. The performance of the proposed firefly algorithm based passive filter evidences better result compared with the other algorithm based methods.

Intelligent Innovation Platform Design of Chinese Film and Television Music Based on Multi-Dimensional Information Fusion Algorithm

Information Platform Design and IOS Software Implementation of Sino-Japanese Cultural Exchange Based on the Hierarchical Fusion Algorithm of Internet Information

Book Source: Digital Library of India Item 2015.194589 dc.contributor.author: Ajeet Kumar dc.date.accessioned: 2015-07-08T04:44:25Z dc.date.available: 2015-07-08T04:44:25Z dc.date.digitalpublicationdate: 2005-09-27 dc.identifier.barcode: 1990010093373 dc.identifier.origpath: /rawdataupload/upload/0093/373 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/194589 dc.description.scannerno: 14 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 146 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher: Indian Institute Of Technology Kanpur dc.rights: Out_of_copyright dc.source.library: Indian Institute Of Technology Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Civil Engineering dc.title: Development Of A Standard Design Algorithm For Intze Tank

A novel algorithm for working out the Kink energy of quantum-dot cellular automata (QCA) circuits and their fault tolerability is introduced. In this algorithm at first with determining the input values on a specified design, the calculation between cells makes use of Kink physical relations will be managed. Therefore, the polarization of any cell and consequently output cell will be set. Then by determining missed cell(s) on the discussed circuit, the polarization of output cell will be obtained and by comparing it with safe state or software simulation, its fault tolerability will be proved. The proposed algorithm was implemented on a novel and advance fault tolerance full adder whose performance has been demonstrated. This algorithm could be implemented on any QCA circuit. Noticeably higher speed of the algorithm than simulation and traditional manual methods, expandability of this algorithm for variable circuits, beyond of four-dot square of QCA circuits, and the investigation of several damaged cells instead just one and special cell are the advantages of algorithmic action.

This paper presents the design of a genetic algorithm (GA) based full order state observer for a compressor system. The observer measures the compressor system parameters and computes the difference between the measured output and the setpoint, which is used as feedback to the active surge controller. The proposed control structure with a GA tuned compressor characteristic is required to minimize system oscillations and energy losses induced by the piston actuation gas recycling technique. The design method for GA based full order observer begins with a state observer modelling, observer error estimation based on the magnitude of fluid friction and mechanical effects on measured parameters. The aim of the controller is to regulate the compressor driver speed such that the measured output achieves its required set point. In order to achieve a maximum compressor operating capacity, the compressor characteristics were optimized using GA, where the mass flow was maximized to improve the compressor efficiency. Simulations were carried out, and results showed the viability of GA based full order state observer compared with the piston actuation recycle method to control active surge in centrifugal compressor systems

Platform Design and Algorithm Tracking for Empirical Analysis of Team Cognition and Enterprise Performance in the Background of Big Data

Modern design intelligent training assistance platform based on the Lingnan architectural image generation algorithm is studied in the paper. In order to make the network better train unpaired image samples, this paper introduces a loss function composed of the same mapping loss term and perceptual loss term on the basis of the original loss function. Then, the novel style transfer algorithm is designed. We propose CycleGAN without training on paired datasets, and the network employs two general generators and two discriminators, respectively. Two generators are used to then convert between the two domains, and two discriminators are used to distinguish the true and false images in the two domains. Then, the designed model is implemented with the novel intelligent training assistance platform and consider the factor of the Lingnan issues. Through the experimental simulation, the performance is validated.

Book Source: Digital Library of India Item 2015.192825 dc.contributor.author: Vikas Saxena dc.date.accessioned: 2015-07-08T01:41:34Z dc.date.available: 2015-07-08T01:41:34Z dc.date.digitalpublicationdate: 2005-08-20 dc.identifier.barcode: 1990010091216 dc.identifier.origpath: /rawdataupload/upload/0091/216 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/192825 dc.description.scannerno: 14 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 105 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher: Indian Institute Of Technology Kanpur dc.rights: Out_of_copyright dc.source.library: Indian Institute Of Technology Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Mechanical Engineering In General. Nuclear Technology. Electrical Engineering. Machinery dc.title: Optimal Design Of Car Suspension System Using Genetic Algorithm

This CD includes manuals, lectures, source code, and a digital copy of the complete book to go along with the book.

The Poisson equation occurs in many areas of science and engineering. Here we focus on its numerical solution for an equation in d dimensions. In particular we present a quantum algorithm and a scalable quantum circuit design which approximates the solution of the Poisson equation on a grid with error \varepsilon. We assume we are given a supersposition of function evaluations of the right hand side of the Poisson equation. The algorithm produces a quantum state encoding the solution. The number of quantum operations and the number of qubits used by the circuit is almost linear in d and polylog in \varepsilon^{-1}. We present quantum circuit modules together with performance guarantees which can be also used for other problems.

This letter considers waveform design of orthogonal frequency division multiplexing (OFDM) signal for radar applications, and aims at mitigating the envelope fluctuation in OFDM. A novel method is proposed to reduce the peak-to-mean envelope power ratio (PMEPR), which is commonly used to evaluate the fluctuation. The proposed method is based on the tone reservation approach, in which some bits or subcarriers of OFDM are allocated for decreasing PMEPR. We introduce the coefficient of variation of envelopes (CVE) as the cost function for waveform optimization, and develop an iterative least squares algorithm. Minimizing CVE leads to distinct PMEPR reduction, and it is guaranteed that the cost function monotonically decreases by applying the iterative algorithm. Simulations demonstrate that the envelope is significantly smoothed by the proposed method.

This letter considers waveform design of orthogonal frequency division multiplexing (OFDM) signal for radar applications, and aims at mitigating the envelope fluctuation in OFDM. A novel method is proposed to reduce the peak-to-mean envelope power ratio (PMEPR), which is commonly used to evaluate the fluctuation. The proposed method is based on the tone reservation approach, in which some bits or subcarriers of OFDM are allocated for decreasing PMEPR. We introduce the coefficient of variation of envelopes (CVE) as the cost function for waveform optimization, and develop an iterative least squares algorithm. Minimizing CVE leads to distinct PMEPR reduction, and it is guaranteed that the cost function monotonically decreases by applying the iterative algorithm. Simulations demonstrate that the envelope is significantly smoothed by the proposed method.

Trimester 1 2017/2018

Trimester 2 2017/2018

Trimester 2 2018/2019

Trimester 1 2019/2020

This article is from BMC Bioinformatics , volume 14 . Abstract Background: Primer design for highly variable DNA sequences is difficult, and experimental success requires attention to many interacting constraints. The advent of next-generation sequencing methods allows the investigation of rare variants otherwise hidden deep in large populations, but requires attention to population diversity and primer localization in relatively conserved regions, in addition to recognized constraints typically considered in primer design. Results: Design constraints include degenerate sites to maximize population coverage, matching of melting temperatures, optimizing de novo sequence length, finding optimal bio-barcodes to allow efficient downstream analyses, and minimizing risk of dimerization. To facilitate primer design addressing these and other constraints, we created a novel computer program (PrimerDesign) that automates this complex procedure. We show its powers and limitations and give examples of successful designs for the analysis of HIV-1 populations. Conclusions: PrimerDesign is useful for researchers who want to design DNA primers and probes for analyzing highly variable DNA populations. It can be used to design primers for PCR, RT-PCR, Sanger sequencing, next-generation sequencing, and other experimental protocols targeting highly variable DNA samples.

This article is from Sensors (Basel, Switzerland) , volume 14 . Abstract An improved method based on a genetic algorithm (GA) is developed to design a broadband electrical impedance matching network for piezoelectric ultrasound transducer. A key feature of the new method is that it can optimize both the topology of the matching network and perform optimization on the components. The main idea of this method is to find the optimal matching network in a set of candidate topologies. Some successful experiences of classical algorithms are absorbed to limit the size of the set of candidate topologies and greatly simplify the calculation process. Both binary-coded GA and real-coded GA are used for topology optimization and components optimization, respectively. Some calculation strategies, such as elitist strategy and clearing niche method, are adopted to make sure that the algorithm can converge to the global optimal result. Simulation and experimental results prove that matching networks with better performance might be achieved by this improved method.

This article is from BioMedical Engineering OnLine , volume 13 . Abstract Background: Real-time spectral analyzers can be difficult to implement for PC computer-based systems because of the potential for high computational cost, and algorithm complexity. In this work a new spectral estimator (NSE) is developed for real-time analysis, and compared with the discrete Fourier transform (DFT). Method: Clinical data in the form of 216 fractionated atrial electrogram sequences were used as inputs. The sample rate for acquisition was 977 Hz, or approximately 1 millisecond between digital samples. Real-time NSE power spectra were generated for 16,384 consecutive data points. The same data sequences were used for spectral calculation using a radix-2 implementation of the DFT. The NSE algorithm was also developed for implementation as a real-time spectral analyzer electronic circuit board. Results: The average interval for a single real-time spectral calculation in software was 3.29 μs for NSE versus 504.5 μs for DFT. Thus for real-time spectral analysis, the NSE algorithm is approximately 150× faster than the DFT. Over a 1 millisecond sampling period, the NSE algorithm had the capability to spectrally analyze a maximum of 303 data channels, while the DFT algorithm could only analyze a single channel. Moreover, for the 8 second sequences, the NSE spectral resolution in the 3-12 Hz range was 0.037 Hz while the DFT spectral resolution was only 0.122 Hz. The NSE was also found to be implementable as a standalone spectral analyzer board using approximately 26 integrated circuits at a cost of approximately $500. The software files used for analysis are included as a supplement, please see the Additional files 1 and 2. Conclusions: The NSE real-time algorithm has low computational cost and complexity, and is implementable in both software and hardware for 1 millisecond updates of multichannel spectra. The algorithm may be helpful to guide radiofrequency catheter ablation in real time.

Optimal design of water distribution networks, which are governed by a series of linear and nonlinear equations, has been extensively studied in the past decades. Due to their NP-hardness, methods to solve the optimization problem have changed from traditional mathematical programming to modern intelligent optimization techniques. In this study, with respect to the model formulation, we have demonstrated that the network system can be reduced to the dimensionality of the number of closed simple loops or required independent paths, and the reduced nonlinear system can be solved efficiently by the Newton-Raphson method. Regarding the optimization technique, a discrete state transition algorithm (STA) is introduced to solve several cases of water distribution networks. In discrete STA, there exist four basic intelligent operators, namely, swap, shift, symmetry and substitute as well as the "risk and restore in probability" strategy. Firstly, we focus on a parametric study of the restore probability $p_1$ and risk probability $p_2$. To effectively deal with the head pressure constraints, we then investigate the effect of penalty coefficient and search enforcement on the performance of the algorithm. Based on the experience gained from the training of the Two-Loop network problem, the discrete STA has successfully achieved the best known solutions for the Hanoi and New York problems. A detailed comparison of our results with those gained by other algorithms is also presented.

We consider a single allocation hub-and-spoke network design problem which allocates each non-hub node to exactly one of given hub nodes so as to minimize the total transportation cost. This paper deals with a case in which the hubs are located in a cycle, which is called a cycle-star hub network design problem. The problem is essentially equivalent to a cycle-metric labeling problem. The problem is useful in the design of networks in telecommunications and airline transportation systems.We propose a $2(1-1/h)$-approximation algorithm where $h$ denotes the number of hub nodes. Our algorithm solves a linear relaxation problem and employs a dependent rounding procedure. We analyze our algorithm by approximating a given cycle-metric matrix by a convex combination of Monge matrices.

Trimester 2 2016/2017

This collection of matlab scripts and functions was used to design and optimize shaped pulses for CEST MRI

DNA (deoxyribonucleic acid), is the hereditary material in humans and almost all other organisms. Nearly every cell in a person’s body has the same DNA. The information in DNA is stored as a code made up of four chemical bases: adenine (A), guanine (G), cytosine (C), and thymine (T). With continuous technology development and growth of sequencing data, large amount of biological data is generated. This large amount of generated data causes difficulty to store, analyse and process DNA sequences. So there is a wide need of reducing the size, for this reason, DNA Compression is employed to reduce the size of DNA sequence. Therefore there is a huge need of compressing the DNA sequence. In this paper, we have proposed an efficient and fast DNA sequence compression algorithm based on differential direct coding and variable look up table (LUT).

Algorithms and data structures are among the primary constituents of computer software and thus are among basic objects of study in Computer Science. This project is concerned with the structure and automated design of algorithms and data structures. Our scientific hypothesis is the there exist general algorithm, data structure, and design concepts that underlie and explain most of the detailed structure of conventional software systems. By abstracting and formalizing these concepts and showing how to mechanize their application, we can prepare the way for the coming generation of automated software design environments. Our approach involves identifying classes of algorithms that solve a broad range of useful problems. In particular we have emphasized formalizing abstract algorithms that make minimal assumptions about the structure of a problem. Once a class of algorithms has been identified we represent its essence as a theory, called an algorithm theory.

Book Source: Digital Library of India Item 2015.191775 dc.contributor.author: B. Kumara Swamy dc.date.accessioned: 2015-07-08T01:10:12Z dc.date.available: 2015-07-08T01:10:12Z dc.date.digitalpublicationdate: 2005-08-31 dc.identifier.barcode: 1990010090261 dc.identifier.origpath: /rawdataupload/upload/0090/261 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/191775 dc.description.scannerno: 12 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 99 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher: Indian Institute Of Technology Kanpur dc.rights: Out_of_copyright dc.source.library: Indian Institute Of Technology Kanpur dc.subject.classification: Technology dc.subject.classification: Engineering. Technology In General dc.subject.classification: Mechanical Engineering In General. Nuclear Technology. Electrical Engineering. Machinery dc.title: Optimal Design Of Pseudo-intrinsic Surfaces Using Genetic Algorithm Technique