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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

Copied from a disc I had on hand.

Reinforcement learning is considered as a machine learning technique that is anxious with software agents should behave in particular environment. Reinforcement learning (RL) is a division of deep learning concept that assists you to make best use of some part of the collective return. In this paper evolving reinforcement learning algorithms shows possible to learn a fresh and understable concept by using a graph representation and applying optimization methods from the auto machine learning society. In this observe, we stand for the loss function, it is used to optimize an agent’s parameter in excess of its knowledge, as an imputational graph, and use traditional evolution to develop a population of the imputational graphs over a set of uncomplicated guidance environments. These outcomes in gradually better RL algorithms and the exposed algorithms simplify to more multifaceted environments, even though with visual annotations. 

The goal of this project was to investigate algorithm design and program optimization for the functional language architecture. Because the architecture has not yet been realized in hardware, these studies were necessarily either analytical or based on simulations. The problem was attacked from a variety of directions. Most fundamental was that of machine design; since the beginning of the grant period, we have come to understand the implications of the design better, and as a consequence, describe it better and propose better implementations. Additionally, we have refined and extended the design, providing answers to a number of questions that had not been previously addressed. Additionally, we worked on the question of making the machine an appropriate one for solving a broader class of problems than had previously been considered. These investigations were all done in conjunction with our considerations of particular applications that were chosen on the basis of their widespread use and their exhibiting characteristics of fundamental importance.

This is the final report for the Schafer Corporation Genetic Algorithm-Based System Design and Photonics-Based Receiver Technologies Program support under delivery order No. 0018 covering the period of August 2003 through December 2003. This report covers the work accomplished by Team Schafer (Schafer Corporation and Booz Allen Hamilton). Team Schafer supported DARPA with programmatic, technical, and financial assistance support.

This report reviews current fusion techniques used for bathymetry or other geospatial data, as motivated by the Naval Oceanographic Office's (NAVOCEANO) need for intelligent fustion -- combining two or more data sets in a manner that accounts for data uncertainty -- of gridded and in situ bathymetric data sets. Based on this review, the recommended approach for building new bathymetry fusion algorithms uses loss interpolation to obtain a trend surface followed by kriging of residuals to recapture finer details lost from smoothing. In addition, if in situ soundings are used, Monte Carlo simulations are run to estimate depth error induced by position errors. The technique also provides the means to liberally estimate errors for navigation safety. The report concludes with plans to build, validate, and transition the algorithm to the Naval Oceanographic Office, Bathymetry Database Division.

The algorithms described in this report determine the differential corrections to be broadcast to users of the Global Positioning System (GPS) who require higher accuracy navigation or position information than the 30 to 100 meters that GPS normally provides. Properly equipped marine users receiving the broadcasted corrections are expected to achieve better than 10 meters accuracy with the Differential GPS system. The Differential GPS program involves the fabrication of a differential GPS testbed, consisting of an 8-channel GPS receiver for the reference station, data processing hardware and software to generate the corrections and perform channel management and integrity functions, user receiver, and data acquisition hardware and software. The system will be tested in 1987. Keywords: Kalman filtering.

In this report, a second order algorithm and a hybrid second and first order method are developed using sensitivity analysis with error compensation. Test problems are solved and compared with the results obtained by first order method. Also presented is the implementation of the developed algorithms for computer calculations.

Trimester 3 2015/2016

High-speed digital signal processing has a wide range of applications including, radar, sonar, image processing, and target acquisition. The calculations of the Discrete Fourier Transform (DFT) used in these applications has long been a significant bottleneck for high-speed processing. Previous AFIT students have adopted a Prime Factor Algorithm (PFA) method using Winogard Fourier Transform (WFT) processors. Three WFT processors are pipelined into a system capable of computing a 4080-point DFT on complex data approximately every 120 microseconds when operating with a 70 MHz clock. This thesis effort addressed the design and implementation of PFA controller chip and interconnecting memory modules between the WFT processors. The PFA controller is an application specific processor to control the flow of information in the pipeline, interface to the WFT processors, monitor pipeline status, and take corrective action in the presence of faults. the interconnecting memory modules buffer the data coming out of a WFT processor and going into another allowing concurrent reading and writing.

The field of algorithm design is concerned with the development of efficient methods for solving computational problems. Although the field traces its roots to theoretical computer science, recent algorithmic advances have drastically reduced the costs of real computations. For this reason it is important that anyone involved with computing have at least a cursory knowledge of the area. This paper surveys the field of algorithm design in two ways: first by the study of a few problems in detail, and then by a systematic view of the field. The orientation of this paper is towards the practitioner of computing (in either software or hardware); the goals of the paper are to provide both an understanding of the field and a feeling for what it can do for me. (Author)

During the ascent flight phase of NASA s Constellation Program, the Ares launch vehicle propels the Orion crew vehicle to an agreed to insertion target. If a failure occurs at any point in time during ascent then a system must be in place to abort the mission and return the crew to a safe landing with a high probability of success. To achieve continuous abort coverage one of two sets of effectors is used. Either the Launch Abort System (LAS), consisting of the Attitude Control Motor (ACM) and the Abort Motor (AM), or the Service Module (SM), consisting of SM Orion Main Engine (OME), Auxiliary (Aux) Jets, and Reaction Control System (RCS) jets, is used. The LAS effectors are used for aborts from liftoff through the first 30 seconds of second stage flight. The SM effectors are used from that point through Main Engine Cutoff (MECO). There are two distinct sets of Guidance and Control (G&C) algorithms that are designed to maximize the performance of these abort effectors. This paper will outline the necessary inputs to the G&C subsystem, the preliminary design of the G&C algorithms, the ability of the algorithms to predict what abort modes are achievable, and the resulting success of the abort system. Abort success will be measured against the Preliminary Design Review (PDR) abort performance metrics and overall performance will be reported. Finally, potential improvements to the G&C design will be discussed.

Multidisciplinary design, analysis, and optimization using a genetic algorithm is being developed at the National Aeronautics and Space Administration Dryden Flight Research Center (Edwards, California) to automate analysis and design process by leveraging existing tools to enable true multidisciplinary optimization in the preliminary design stage of subsonic, transonic, supersonic, and hypersonic aircraft. This is a promising technology, but faces many challenges in large-scale, real-world application. This report describes current approaches, recent results, and challenges for multidisciplinary design, analysis, and optimization as demonstrated by experience with the Ikhana fire pod design.!

In this paper, a linear phase Low Pass FIR filter is designed and proposed based on Firefly algorithm. We exploit the exploitation and exploration mechanism with a local search routine to improve the convergence and get higher speed computation. The optimum FIR filters are designed based on the Firefly method for which the finite word length is used to represent coefficients. Furthermore, Particle Swarm Optimization (PSO) and Differential Evolution algorithm (DE) will be used to show the solution. The results will be compared with PSO and DE methods. Firefly algorithm and Parks–McClellan (PM) algorithm are also compared in this paper thoroughly. The design goal is successfully achieved in all design examples using the Firefly algorithm. They are compared with that obtained by using the PSO and the DE algorithm. For the problem at hand, the simulation results show that the Firefly algorithm outperforms the PSO and DE methods in some of the presented design examples. It also performs well in a portion of the exhibited design examples particularly in speed and quality.

This thesis uses Multi-agent systems (MAS), and Genetic Algorithm (GA) techniques to develop a Bluetooth radio system simulation that is called \"Wireless World\". Typically, wireless world is a simple two-dimensional (2D) toy model of Bluetooth Technology implemented in the Java programming language version 1.2.1 and Borland jbuilder3 university edition editor environment. In addition, the wireless model is designed for outdoor environment for the six different weather conditions. And in the environment, there may be situated three types of interference systems. Within these systems, the IEEE 802.11b WLAN, an alternative to the BT, is implemented as interference in the simulation environment. The goal of the wireless world simulation is to explore the performance limitations and restrictions on the basis of the current Bluetooth technology specifications. This simulation will hopefully help Bluetooth system designers and decision-makers in gaining insight into the system performance analysis and enable them to make more informed decisions in the future.

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

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

Haskell lukkas

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.

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.

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.

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.

We present efficient realization of Householder Transform (HT) based QR factorization through algorithm-architecture co-design where we achieve performance improvement of 3-90x in-terms of Gflops/watt over state-of-the-art multicore, General Purpose Graphics Processing Units (GPGPUs), Field Programmable Gate Arrays (FPGAs), and ClearSpeed CSX700. Theoretical and experimental analysis of classical HT is performed for opportunities to exhibit higher degree of parallelism where parallelism is quantified as a number of parallel operations per level in the Directed Acyclic Graph (DAG) of the transform. Based on theoretical analysis of classical HT, an opportunity re-arrange computations in the classical HT is identified that results in Modified HT (MHT) where it is shown that MHT exhibits 1.33x times higher parallelism than classical HT. Experiments in off-the-shelf multicore and General Purpose Graphics Processing Units (GPGPUs) for HT and MHT suggest that MHT is capable of achieving slightly better or equal performance compared to classical HT based QR factorization realizations in the optimized software packages for Dense Linear Algebra (DLA). We implement MHT on a customized platform for Dense Linear Algebra (DLA) and show that MHT achieves 1.3x better performance than native implementation of classical HT on the same accelerator. For custom realization of HT and MHT based QR factorization, we also identify macro operations in the DAGs of HT and MHT that are realized on a Reconfigurable Data-path (RDP). We also observe that due to re-arrangement in the computations in MHT, custom realization of MHT is capable of achieving 12% better performance improvement over multicore and GPGPUs than the performance improvement reported by General Matrix Multiplication (GEMM) over highly tuned DLA software packages for multicore and GPGPUs which is counter-intuitive.

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

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 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

In this paper a three phase induction motor controller for a photovoltaic powered water pump without the use of chemical storage element is presented. The use of a three-phase induction motor is a better solution to the dc motor water pumping system. This system is use to achieve a more efficient, reliable, maintenance-free, and cheaper solution than that use dc motors or low-voltage synchronous motors. The developed system is based on a non inverting buck-boost converter and a full-bridge three-phase inverter. Maximum Power Point Tracking (MPPT) algorithm has been developed and implemented to maximize the use of solar power generated at any given instant. By using MATLAB/ Simulink model is implemented. The system is expected to have a high lifetime due to the inexistence of storage element. As a result, the system is a economical solution to deliver water to household, industrial and agricultural activities which require water supply. Swati Killedar | Dr. S. N. Patil | Prof. A. P. Kinge"Design and Simulation of MPPT Algorithm for Three Phase Induction Motor Solar Water Pump" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-2 , February 2018, URL: http://www.ijtsrd.com/papers/ijtsrd9682.pdf Article URL: http://www.ijtsrd.com/engineering/electrical-engineering/9682/design-and-simulation-of-mppt-algorithm-for-three-phase-induction-motor-solar-water-pump/swati-killedar

The primary objective of this document is to provide a detailed description of the In-Trail Procedure (ITP) algorithm, which is part of the Airborne Traffic Situational Awareness In-Trail Procedure (ATSA-ITP) application. To this end, the document presents a high level description of the ITP Algorithm and a prototype implementation of this algorithm in the programming language C.

We describe a parallel genetic algorithm (GA) that automatically generates circuit designs using evolutionary search. A circuit-construction programming language is introduced and we show how evolution can generate practical analog circuit designs. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. We present experimental results as applied to analog filter and amplifier design tasks.

Realization of a bit-serial multiplication algorithm for the encoding of Reed-Solomon (RS) codes on a single VLSI chip using NMOS technology is demonstrated to be feasible. A dual basis (255, 223) over a Galois field is used. The conventional RS encoder for long codes ofter requires look-up tables to perform the multiplication of two field elements. Berlekamp's algorithm requires only shifting and exclusive-OR operations.

Realization of a bit-serial multiplication algorithm for the encoding of Reed-Solomon (RS) codes on a single VLSI chip using NMOS technology is demonstrated to be feasible. A dual basis (255, 223) over a Galois field is used. The conventional RS encoder for long codes ofter requires look-up tables to perform the multiplication of two field elements. Berlekamp's algorithm requires only shifting and exclusive-OR operations.

Recently, the Gravitational-Search Algorithm (GSA) has been presented as a promising physics-inspired stochastic global optimization technique. It takes its derivation and features from laws of gravitation. This paper applies the GSA to design optimal controllers of a nonlinear system consisting of a doubly-fed induction generator (DFIG) driven by a wind turbine. Both the active and the reactive power are controlled and processed through a back-to-back converter. The active power control loop consists of two cascaded proportional integral (PI) controllers. Another PI controller is used to set the q-component of the rotor voltage by compensating the generated reactive power. The GSA is used to simultaneously tune the parameters of the three PI controllers. A time-weighted absolute error (ITAE) is used in the objective function to stabilize the system and increase its damping when subjected to different disturbances. Simulation results will demonstrate that the optimal GSA-based coordinated controllers can efficiently damp system oscillations under severe disturbances. Moreover, simulation results will show that the designed optimal controllers obtained using the GSA perform better than the optimal controllers obtained using two commonly used global optimization techniques, which are the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).

This article is from Bioinformatics , volume 30 . Abstract Motivation: Structure-based computational protein design (SCPR) is an important topic in protein engineering. Under the assumption of a rigid backbone and a finite set of discrete conformations of side-chains, various methods have been proposed to address this problem. A popular method is to combine the dead-end elimination (DEE) and A* tree search algorithms, which provably finds the global minimum energy conformation (GMEC) solution.Results: In this article, we improve the efficiency of computing A* heuristic functions for protein design and propose a variant of A* algorithm in which the search process can be performed on a single GPU in a massively parallel fashion. In addition, we make some efforts to address the memory exceeding problem in A* search. As a result, our enhancements can achieve a significant speedup of the A*-based protein design algorithm by four orders of magnitude on large-scale test data through pre-computation and parallelization, while still maintaining an acceptable memory overhead. We also show that our parallel A* search algorithm could be successfully combined with iMinDEE, a state-of-the-art DEE criterion, for rotamer pruning to further improve SCPR with the consideration of continuous side-chain flexibility.Availability: Our software is available and distributed open-source under the GNU Lesser General License Version 2.1 (GNU, February 1999). The source code can be downloaded from http://www.cs.duke.edu/donaldlab/osprey.php or http://iiis.tsinghua.edu.cn/∼compbio/software.html.Contact:[email protected] .cnSupplementary information:Supplementary data are available at Bioinformatics online.

This article is from SpringerPlus , volume 3 . Abstract This paper proposes designing of Static Synchronous Series Compensator (SSSC) based damping controller to enhance the stability of a Single Machine Infinite Bus (SMIB) system by means of Invasive Weed Optimization (IWO) technique. Conventional PI controller is used as the SSSC damping controller which takes rotor speed deviation as the input. The damping controller parameters are tuned based on time integral of absolute error based cost function using IWO. Performance of IWO based controller is compared to that of Particle Swarm Optimization (PSO) based controller. Time domain based simulation results are presented and performance of the controllers under different loading conditions and fault scenarios is studied in order to illustrate the effectiveness of the IWO based design approach.

This article is from SpringerPlus , volume 3 . Abstract This paper proposes designing of Static Synchronous Series Compensator (SSSC) based damping controller to enhance the stability of a Single Machine Infinite Bus (SMIB) system by means of Invasive Weed Optimization (IWO) technique. Conventional PI controller is used as the SSSC damping controller which takes rotor speed deviation as the input. The damping controller parameters are tuned based on time integral of absolute error based cost function using IWO. Performance of IWO based controller is compared to that of Particle Swarm Optimization (PSO) based controller. Time domain based simulation results are presented and performance of the controllers under different loading conditions and fault scenarios is studied in order to illustrate the effectiveness of the IWO based design approach.

This article is from SpringerPlus , volume 3 . Abstract This paper proposes designing of Static Synchronous Series Compensator (SSSC) based damping controller to enhance the stability of a Single Machine Infinite Bus (SMIB) system by means of Invasive Weed Optimization (IWO) technique. Conventional PI controller is used as the SSSC damping controller which takes rotor speed deviation as the input. The damping controller parameters are tuned based on time integral of absolute error based cost function using IWO. Performance of IWO based controller is compared to that of Particle Swarm Optimization (PSO) based controller. Time domain based simulation results are presented and performance of the controllers under different loading conditions and fault scenarios is studied in order to illustrate the effectiveness of the IWO based design approach.

Title from cover

Thesis advisors, Murali Tummala, Roberto Cristi

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.

Design and implementation of a cryptographic algorithm based on the AES advanced encryption standard for UHF RFID systems

An important consideration in the design of high performance multiprocessor systems is to ensure the correctness of the results computed in the presence of transient and intermittent failures. Concurrent error detection and correction have been applied to such systems in order to achieve reliability. Algorithm Based Fault Tolerance (ABFT) was suggested as a cost-effective concurrent error detection scheme. The research was motivated by the complexity involved in the analysis and design of ABFT systems. To that end, a matrix-based model was developed and, based on that, algorithms for both the design and analysis of ABFT systems are formulated. These algorithms are less complex than the existing ones. In order to reduce the complexity further, a hierarchical approach is developed for the analysis of large systems.

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.

This paper discusses the simulation of an automated guided vehicle (AGV) with the differential-drive mobile robot (DDMR) concept. Using this wheel configuration, the AGV can maneuver in tight workspaces. However, controlling a self-driving AGV with obstacle avoidance is not easy. Therefore, this paper proposes a control system to drive an AGV with several process stages. First, a kinematic model is formulated to represent the AGV with the concept of two wheels that can be controlled differentially. In the second stage, the pure pursuit control method is applied to the model so that the AGV can follow the waypoint coordinates determined and combine them with obstacle avoidance. Finally, the effectiveness of the control system was verified using simulation. The look-ahead parameter with a value of 0.2 meters shows optimal results so that pure pursuit control can reach all waypoint coordinates. Based on this simulation, the AGV prototype was then designed, assembled, and equipped with an internet of things-based obstacle avoidance system. While the simulation proves promising, the anticipated challenges identified in the AGV field test, such as GPS inaccuracies and signal obstructions, underscore the need for ongoing improvements in real-world applications.

The United States Navy requires radical and innovative ways to model and design multifunction phased array radars. This thesis puts forth the concept that Genetic Algorithms, computer simulations that mirror the natural selection process to develop creative solutions to complex problems, would be extremely well suited in this application. The capability of a Genetic Algorithm to predict adequately the behavior of an array antenna with randomly located elements was verified with expected results through the design, construction, development and evaluation of a test-bed array. The test-bed array was constructed of commercially available components, including a unique and innovative application of a quadrature modulator microchip used in commercial communications applications. Corroboration of predicted beam patterns from both Genetic Algorithm and Method of Moments calculations was achieved in anechoic chamber measurements conducted with the test-bed array. Both H-plane and E-plane data runs were made with several phase steered beams. In all cases the measured data agreed with that predicted from both modeling programs. Although time limited experiments to beam forming and steering with phase shifting, the test-bed array is fully capable of beam forming and steering though both phase shifting and amplitude tapering.

The Geoscience Laser Altimeter System (GLAS) is the primary instrument for the ICESat (Ice, Cloud and Land Elevation Satellite) laser altimetry mission. ICESat was the benchmark Earth Observing System (EOS) mission for measuring ice sheet mass balance, cloud and aerosol heights, as well as land topography and vegetation characteristics. From 2003 to 2009, the ICESat mission provided multi-year elevation data needed to determine ice sheet mass balance as well as cloud property information, especially for stratospheric clouds common over polar areas. It also provided topography and vegetation data around the globe, in addition to the polar-specific coverage over the Greenland and Antarctic ice sheets.This document describes the detailed design of GLAS Science Algorithm Software (GSAS). The GSAS is used to create the ICESat GLAS standard data products. The National Snow and Ice Data Center (NSDIC) distribute these products. The document contains descriptions, flow charts, data flow diagrams, and structure charts for each major component of the GSAS. The purpose of this document is to present the detailed design of the GSAS. It is intended as a reference source to assist the maintenance programmer in making changes that fix or enhance the documented software.

With technology scaling down, hundreds and thousands processing elements (PEs) can be integrated on a single chip. Network-on-chip (NoC) has been proposed as an efficient solution to handle this distinctive challenge. In this thesis, we have explored the high performance NoC design for MPSoC and CMP structures from the performance modeling in the offline design phase to the routing algorithm and NoC architecture optimization. More specifically, we first deal with the issue of how to estimate an NoC design fast and accurately in the synthesis inner loop. For this purpose, we propose a machine learning based latency regression model to evaluate the NoC designs with respect to different configurations. Then, for high performance NoC designs, we tackle one of the most important problems, i.e., the routing algorithms design. For avoiding temperature hotspots, a thermal-aware routing algorithm is proposed to achieve an even temperature profile for application-specific Network-on-chips (NoCs). For improving the reliability, a routing algorithm to achieve maximum performance under fault is proposed. Finally, in the architecture level, we propose two new NoC structures using bi-directional links for the performance optimization. In particular, we propose a flit-level speedup scheme to enhance the network-on-chip(NoC) performance utilizing bidirectional channels. We also propose a flexible NoC architecture which takes advantage of a dynamic distributed routing algorithm and improves the NoC communication performance with moderate energy overhead. From the simulation results on both synthetic traffic and real workload traces, significant performance improvement in terms of latency and throughput can be achieved.

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