Relevant bibliographies by topics / Embedded Systems, Algorithms, Optimization Techniques

Academic literature on the topic 'Embedded Systems, Algorithms, Optimization Techniques'

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Published: 14 March 2023

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Journal articles on the topic "Embedded Systems, Algorithms, Optimization Techniques"

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Ajani, Taiwo Samuel, Agbotiname Lucky Imoize, and Aderemi A. Atayero. "An Overview of Machine Learning within Embedded and Mobile Devices–Optimizations and Applications." Sensors 21, no. 13 (June 28, 2021): 4412. http://dx.doi.org/10.3390/s21134412.

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Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.
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Stojanovic, Radovan, Sasa Knezevic, Dejan Karadaglic, and Goran Devedzic. "Optimization and implementation of the wavelet based algorithms for embedded biomedical signal processing." Computer Science and Information Systems 10, no. 1 (2013): 503–23. http://dx.doi.org/10.2298/csis120517013s.

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Existing biomedical wavelet based applications exceed the computational, memory and consumption resources of low-complexity embedded systems. In order to make such systems capable to use wavelet transforms, optimization and implementation techniques are proposed. The Real Time QRS Detector and ?De-noising? Filter are developed and implemented in 16-bit fixed point microcontroller achieving 800 Hz sampling rate, occupation of less than 500 bytes of data memory, 99.06% detection accuracy, and 1 mW power consumption. By evaluation of the obtained results it is found that the proposed techniques render negligible degradation in detection accuracy of -0.41% and SNR of -2.8%, behind 2-4 times faster calculation, 2 times less memory usage and 5% energy saving. The same approach can be applied with other signals where the embedded implementation of wavelets can be beneficial.
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Mhadhbi, Imene, Slim Ben Othman, and Slim Ben Saoud. "An Efficient Technique for Hardware/Software Partitioning Process in Codesign." Scientific Programming 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6382765.

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Codesign methodology deals with the problem of designing complex embedded systems, where automatic hardware/software partitioning is one key issue. The research efforts in this issue are focused on exploring new automatic partitioning methods which consider only binary or extended partitioning problems. The main contribution of this paper is to propose a hybrid FCMPSO partitioning technique, based on Fuzzy C-Means (FCM) and Particle Swarm Optimization (PSO) algorithms suitable for mapping embedded applications for both binary and multicores target architecture. Our FCMPSO optimization technique has been compared using different graphical models with a large number of instances. Performance analysis reveals that FCMPSO outperforms PSO algorithm as well as the Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony Optimization (ACO), and FCM standard metaheuristic based techniques and also hybrid solutions including PSO then GA, GA then SA, GA then ACO, ACO then SA, FCM then GA, FCM then SA, and finally ACO followed by FCM.
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Ramadurgam, Srikanth, and Darshika G. Perera. "An Efficient FPGA-Based Hardware Accelerator for Convex Optimization-Based SVM Classifier for Machine Learning on Embedded Platforms." Electronics 10, no. 11 (May 31, 2021): 1323. http://dx.doi.org/10.3390/electronics10111323.

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Machine learning is becoming the cornerstones of smart and autonomous systems. Machine learning algorithms can be categorized into supervised learning (classification) and unsupervised learning (clustering). Among many classification algorithms, the Support Vector Machine (SVM) classifier is one of the most commonly used machine learning algorithms. By incorporating convex optimization techniques into the SVM classifier, we can further enhance the accuracy and classification process of the SVM by finding the optimal solution. Many machine learning algorithms, including SVM classification, are compute-intensive and data-intensive, requiring significant processing power. Furthermore, many machine learning algorithms have found their way into portable and embedded devices, which have stringent requirements. In this research work, we introduce a novel, unique, and efficient Field Programmable Gate Array (FPGA)-based hardware accelerator for a convex optimization-based SVM classifier for embedded platforms, considering the constraints associated with these platforms and the requirements of the applications running on these devices. We incorporate suitable mathematical kernels and decomposition methods to systematically solve the convex optimization for machine learning applications with a large volume of data. Our proposed architectures are generic, parameterized, and scalable; hence, without changing internal architectures, our designs can be used to process different datasets with varying sizes, can be executed on different platforms, and can be utilized for various machine learning applications. We also introduce system-level architectures and techniques to facilitate real-time processing. Experiments are performed using two different benchmark datasets to evaluate the feasibility and efficiency of our hardware architecture, in terms of timing, speedup, area, and accuracy. Our embedded hardware design achieves up to 79 times speedup compared to its embedded software counterpart, and can also achieve up to 100% classification accuracy.
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Merone, Mario, Alessandro Graziosi, Valerio Lapadula, Lorenzo Petrosino, Onorato d’Angelis, and Luca Vollero. "A Practical Approach to the Analysis and Optimization of Neural Networks on Embedded Systems." Sensors 22, no. 20 (October 14, 2022): 7807. http://dx.doi.org/10.3390/s22207807.

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The exponential increase in internet data poses several challenges to cloud systems and data centers, such as scalability, power overheads, network load, and data security. To overcome these limitations, research is focusing on the development of edge computing systems, i.e., based on a distributed computing model in which data processing occurs as close as possible to where the data are collected. Edge computing, indeed, mitigates the limitations of cloud computing, implementing artificial intelligence algorithms directly on the embedded devices enabling low latency responses without network overhead or high costs, and improving solution scalability. Today, the hardware improvements of the edge devices make them capable of performing, even if with some constraints, complex computations, such as those required by Deep Neural Networks. Nevertheless, to efficiently implement deep learning algorithms on devices with limited computing power, it is necessary to minimize the production time and to quickly identify, deploy, and, if necessary, optimize the best Neural Network solution. This study focuses on developing a universal method to identify and port the best Neural Network on an edge system, valid regardless of the device, Neural Network, and task typology. The method is based on three steps: a trade-off step to obtain the best Neural Network within different solutions under investigation; an optimization step to find the best configurations of parameters under different acceleration techniques; eventually, an explainability step using local interpretable model-agnostic explanations (LIME), which provides a global approach to quantify the goodness of the classifier decision criteria. We evaluated several MobileNets on the Fudan Shangai-Tech dataset to test the proposed approach.
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Ahmed, O., S. Areibi, R. Collier, and G. Grewal. "An Impulse-C Hardware Accelerator for Packet Classification Based on Fine/Coarse Grain Optimization." International Journal of Reconfigurable Computing 2013 (2013): 1–23. http://dx.doi.org/10.1155/2013/130765.

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Current software-based packet classification algorithms exhibit relatively poor performance, prompting many researchers to concentrate on novel frameworks and architectures that employ both hardware and software components. The Packet Classification with Incremental Update (PCIU) algorithm, Ahmed et al. (2010), is a novel and efficient packet classification algorithm with a unique incremental update capability that demonstrated excellent results and was shown to be scalable for many different tasks and clients. While a pure software implementation can generate powerful results on a server machine, an embedded solution may be more desirable for some applications and clients. Embedded, specialized hardware accelerator based solutions are typically much more efficient in speed, cost, and size than solutions that are implemented on general-purpose processor systems. This paper seeks to explore the design space of translating the PCIU algorithm into hardware by utilizing several optimization techniques, ranging from fine grain to coarse grain and parallel coarse grain approaches. The paper presents a detailed implementation of a hardware accelerator of the PCIU based on an Electronic System Level (ESL) approach. Results obtained indicate that the hardware accelerator achieves on average 27x speedup over a state-of-the-art Xeon processor.
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Elhossini, Ahmed, Shawki Areibi, and Robert Dony. "Architecture Exploration Based on GA-PSO Optimization, ANN Modeling, and Static Scheduling." VLSI Design 2013 (September 26, 2013): 1–22. http://dx.doi.org/10.1155/2013/624369.

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Embedded systems are widely used today in different digital signal processing (DSP) applications that usually require high computation power and tight constraints. The design space to be explored depends on the application domain and the target platform. A tool that helps explore different architectures is required to design such an efficient system. This paper proposes an architecture exploration framework for DSP applications based on Particle Swarm Optimization (PSO) and genetic algorithms (GA) techniques that can handle multiobjective optimization problems with several hybrid forms. A novel approach for performance evaluation of embedded systems is also presented. Several cycle-accurate simulations are performed for commercial embedded processors. These simulation results are used to build an artificial neural network (ANN) model that can predict performance/power of newly generated architectures with an accuracy of 90% compared to cycle-accurate simulations with a very significant time saving. These models are combined with an analytical model and static scheduler to further increase the accuracy of the estimation process. The functionality of the framework is verified based on benchmarks provided by our industrial partner ON Semiconductor to illustrate the ability of the framework to investigate the design space.
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Guardado, J. L., F. Rivas-Davalos, J. Torres, S. Maximov, and E. Melgoza. "An Encoding Technique for Multiobjective Evolutionary Algorithms Applied to Power Distribution System Reconfiguration." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/506769.

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Network reconfiguration is an alternative to reduce power losses and optimize the operation of power distribution systems. In this paper, an encoding scheme for evolutionary algorithms is proposed in order to search efficiently for the Pareto-optimal solutions during the reconfiguration of power distribution systems considering multiobjective optimization. The encoding scheme is based on the edge window decoder (EWD) technique, which was embedded in the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and the Nondominated Sorting Genetic Algorithm II (NSGA-II). The effectiveness of the encoding scheme was proved by solving a test problem for which the true Pareto-optimal solutions are known in advance. In order to prove the practicability of the encoding scheme, a real distribution system was used to find the near Pareto-optimal solutions for different objective functions to optimize.
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Branco, Sérgio, André G. Ferreira, and Jorge Cabral. "Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey." Electronics 8, no. 11 (November 5, 2019): 1289. http://dx.doi.org/10.3390/electronics8111289.

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The number of devices connected to the Internet is increasing, exchanging large amounts of data, and turning the Internet into the 21st-century silk road for data. This road has taken machine learning to new areas of applications. However, machine learning models are not yet seen as complex systems that must run in powerful computers (i.e., Cloud). As technology, techniques, and algorithms advance, these models are implemented into more computational constrained devices. The following paper presents a study about the optimizations, algorithms, and platforms used to implement such models into the network’s end, where highly resource-scarce microcontroller units (MCUs) are found. The paper aims to provide guidelines, taxonomies, concepts, and future directions to help decentralize the network’s intelligence.
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Matusiak, Mariusz. "Optimization for Software Implementation of Fractional Calculus Numerical Methods in an Embedded System." Entropy 22, no. 5 (May 18, 2020): 566. http://dx.doi.org/10.3390/e22050566.

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In this article, some practical software optimization methods for implementations of fractional order backward difference, sum, and differintegral operator based on Grünwald–Letnikov definition are presented. These numerical algorithms are of great interest in the context of the evaluation of fractional-order differential equations in embedded systems, due to their more convenient form compared to Caputo and Riemann–Liouville definitions or Laplace transforms, based on the discrete convolution operation. A well-known difficulty relates to the non-locality of the operator, implying continually increasing numbers of processed samples, which may reach the limits of available memory or lead to exceeding the desired computation time. In the study presented here, several promising software optimization techniques were analyzed and tested in the evaluation of the variable fractional-order backward difference and derivative on two different Arm® Cortex®-M architectures. Reductions in computation times of up to 75% and 87% were achieved compared to the initial implementation, depending on the type of Arm® core.
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Dissertations / Theses on the topic "Embedded Systems, Algorithms, Optimization Techniques"

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LOIACONO, CARMELO. "Algorithm Optimization and Applications for Embedded Systems." Doctoral thesis, Politecnico di Torino, 2016. http://hdl.handle.net/11583/2642819.

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Optimizations for Embedded Systems imply ad-hoc tunings and performance improvements, as well as development of specific algorithms and data structures, well suited for embedded platforms. This work first focuses on efficient and accurate implementation of visual search algorithms on embedded GPUs adopting the OpenGL ES and OpenCL languages. Considering embedded GPUs that support only low precision computations, we discuss the problems arising during the design phase, and we detail our implementation choices, focusing on two well-known key-point detectors SIFT and ALP. We illustrate how to re-engineering standard Gaussian Scale Space computations to mobile multi-core parallel GPUs using the OpenGL ES language transforming standard, i.e., single (or double) precision floating-point computations, to reduced-precision GPU arithmetic without precision loss. We also concentrate on an efficient and accurate OpenCL implementation of the main MPEG Compact Descriptors for Visual Search (CDVS) stages. We introduce new techniques to adapt sequential algorithms to parallel processing. Furthermore, to reduce the memory accesses, and efficiently distribute the OpenCL kernels workload, we use new approaches to store and retrieve CDVS information on proper GPU data structures. Secondly, we focus on improving the scalability of formal verification algorithms for Embedded System design models. We address the problem of reducing the size of Craig interpolants generated within inner steps of SAT-based Unbounded Model Checking. Craig interpolants are obtained from refutation proofs of unsatisfiable SAT runs, in terms of and/or circuits of linear size, w.r.t. the proof. We also consider the issue of property grouping, property decomposition, and property coverage in model checking problems. Property grouping, i.e., clustering, is a valuable solution whenever (very) large sets of properties have to be proven for a given model. On the other hand, property decomposition can be effective whenever a given property turns-out (or it is expected) to be “hard-to-prove”. Overall, experimental results are promising and demonstrate that our solutions have a speed-up over the existing ones and can be really beneficial if appropriately integrated in a new environment.
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Ahmadinia, Ali. "Optimization algorithms for dynamically reconfigurable embedded systems." Berlin : Köster, 2006. http://deposit.ddb.de/cgi-bin/dokserv?id=2793299&prov=M&dok_var=1&dok_ext=htm.

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Levy, Renato. "Optimization Techniques for Energy-Aware Memory Allocation in Embedded Systems." Diss., Computer Science, George Washington University, 2004. http://hdl.handle.net/1961/116.

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Degree awarded (2004): DScCS, Computer Science, George Washington University
A common practice to save power and energy in embedded systems is to "put to sleep" or disable parts of the hardware. The memory system consumes a significant portion of the energy budget of the overall system, so it is a natural target for energy optimization techniques. The principle of software locality makes the memory subsystem an even better choice, since all memory blocks but the ones immediately required can be disabled at any given time. This opportunity is the motivation for developing energy optimization techniques to dynamically and selectively control the power state of the different parts of the memory system. This dissertation develops a set of algorithms and techniques that can be organized into a hardware/software co-development tool to help designers apply the selective powering of memory blocks to minimize energy consumption. In data driven embedded systems, most of the data memory is used either by global static variables or by dynamic variables. Although techniques already exist for energy-aware allocation of global static arrays under certain constraints, very little work has focused on dynamic variables, which are actually more important to event driven/data driven embedded systems than their static counterparts. This dissertation addresses this gap, and extends and consolidates previous allocation techniques in a unique framework. A formal model for memory energy optimization for dynamic and global static variables and efficient algorithms for energy aware allocation of variables to memory are presented. Dependencies between generic code and data are uncovered, and this information is exploited to fine-tune a system. A framework is presented for retrieving this profile information which is then used to design energy aware allocation algorithms for dynamic variables, including heuristics for segmentation and control of the memory heap. By working at the assembly code level, these techniques can be integrated into any compiler regardless of the source language. The proposed techniques were implemented and tested against data intensive benchmarks, and experimental results indicate significant savings of up to 50% in the memory system energy consumption.
Advisory Committee: Professor Bhagirath Narahari, Professor Hyoeong-Ah Choi (Chair), Professor Rahul Simha, Professor Shmuel Rotenstreich, Professor Can E. Korman, Dr. Yul Williams
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Bautista-Quintero, Ricardo. "Techniques for the implementation of control algorithms using low-cost embedded systems." Thesis, University of Leicester, 2009. http://hdl.handle.net/2381/8220.

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The feedback control literature has reported success in numerous implementations of systems that employ state-of-the-art components. In such systems, the quality of computer controller, actuators and sensors are largely unaffected by nonlinear effects, external disturbances and finite precision of the digital computer. Overall, this type of control systems can be designed and implemented with comparative ease. By contrast, in cases when the implementation is based on limited resources, such as, low-cost computer hardware along with simple actuators and sensors, there are significant challenges for the developer. This thesis has the goal of simplifying the design of mechatronic systems implemented using low-cost hardware. This approach involves design techniques that enhance the links between feedback control algorithms (in theory) and reliable real-time implementation (in practice). The outcome of this research provides a part of a framework that can be used to design and implement efficient control algorithms for resource-constrained embedded computers. The scope of the thesis is limited to situations where 1) the computer hardware has limited memory and CPU performance; 2) sensor-related uncertainties may affect the stability of the plant and 3) unmodelled dynamic of actuator(s) limit the performance of the plant. The thesis concludes by emphasising the importance of finding mechanisms to integrate low-cost components with nontrivial robust control algorithms in order to satisfy multi-objective requirements simultaneously.
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McCurrey, Michael. "Probabilistic Algorithms, Lean Methodology Techniques, and Cell Optimization Results." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/7939.

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There is a significant technology deficiency within the U.S. manufacturing industry compared to other countries. To adequately compete in the global market, lean manufacturing organizations in the United States need to look beyond their traditional methods of evaluating their processes to optimize their assembly cells for efficiency. Utilizing the task-technology fit theory this quantitative correlational study examined the relationships among software using probabilistic algorithms, lean methodology techniques, and manufacturer cell optimization results. Participants consisted of individuals performing the role of the systems analyst within a manufacturing organization using lean methodologies in the Southwestern United States. Data were collected from 118 responses from systems analysts through a survey instrument, which was an integration of two instruments with proven reliability. Multiple regression analysis revealed significant positive relationships among software using probabilistic algorithms, lean methodology, and cell optimization results. These findings may provide management with information regarding the skillsets required for systems analysts to implement software using probabilistic algorithms and lean manufacturing techniques to improve cell optimization results. The findings of this study may contribute to society through the potential to bring sustainable economic improvement to impoverished communities through the implementation of efficient manufacturing solutions with lower capital expenditures.
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DeBardelaben, James Anthony. "An optimization-based approach for cost-effective embedded DSP system design." Diss., Georgia Institute of Technology, 1998. http://hdl.handle.net/1853/15757.

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Kandi, Jayavardhan R. "Embedded Cryptography: An Analysis and Evaluation of Performance and Code Optimization Techniques for Encryption and Decryption in Embedded Systems." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000151.

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CAZZANIGA, PAOLO. "Stochastic algorithms for biochemical processes." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2010. http://hdl.handle.net/10281/7820.

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After the completion of the human genome sequencing (and of a lot of other genomes), the main challenge for the modern biology is to understand complex biological processes such as metabolic pathways, gene regulatory networks and cell signalling pathways, which are the basis of the functioning of living cells. This goal can only be achieved by using mathematical modelling tools and computer simulation techniques, to integrate experimental data and to make predictions on the system behaviour that will be then experimentally checked, so as to gain insights into the working and the general principles of organization of biological systems. In the study of biological systems, the use of stochastic methods is motivated by the fact that these systems are usually composed by many chemical interactions among a large number of chemical species, but the molecular quantities involved can be small (few tens of molecules), and the noise plays a major role in the system’s dynamics. One major problem related to stochastic methods is that they are difficult to implement analytically; hence, they are implemented by means of numerical simulations whose computation time is usually very expensive. In this thesis we provide a discrete and stochastic framework for the modelling, simulation and analysis of biological and chemical systems, which overcomes the limitations of a variant of membrane systems, called DPPs, and of the classic stochastic algorithms. This novel method combines, in particular, the descriptive power of DPPs with the efficiency of tau-leaping algorithm. This approach, called tau-DPP, exploits the membrane structure and the system definition of DPPs, with the aim of describing multiple volume systems, and uses a modified version of the tau-leaping algorithm for the efficient description of the system behaviour. The framework of tau-DPP has been applied to ecological, biological and chemical systems. In general, the study of such kind of models requires the knowledge of many numerical factors for a complete and accurate description of biological systems, like molecular species quantities and reaction rates, which represent an indispensable quantitative information to perform computational investigations of the system behaviour. The lack and the inaccuracy of these information bring about the challenging problem of developing suitable techniques to automatically estimate the correct values to all parameters in order to reproduce the expected dynamics in the best possible way. In this thesis, we consider the application of two optimisation techniques, genetic algorithms and particle swarm optimizer, to tackle this problem. In particular, we test and compare the performances of genetic algorithms and particle swarm optimization to the aim of identify the most suitable optimisation technique for the parameter estimation. Finally, the problem related to the exploration of the parameters space of a biochemical system is described. Usually, this kind of analysis is achieved by means of large numbers of independent simulations where each execution is performed with a particular parametrisation. To efficiently tackle this problem, we present the implementation of a parameter sweep application on a grid framework.
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Pratap, Rana Jitendra. "Design and Optimization of Microwave Circuits and Systems Using Artificial Intelligence Techniques." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7225.

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In this thesis, a new approach combining neural networks and genetic algorithms is presented for microwave design. In this method, an accurate neural network model is developed from the experimental data. This neural network model is used to perform sensitivity analysis and derive response surfaces. An innovative technique is then applied in which genetic algorithms are coupled with the neural network model to assist in synthesis and optimization. The proposed method is used for modeling and analysis of circuit parameters for flip chip interconnects up to 35 GHz, as well as for design of multilayer inductors and capacitors at 1.9 GHz and 2.4 GHz. The method was also used to synthesize mm wave low pass filters in the range of 40-60 GHz. The devices obtained from layout parameters predicted by the neuro-genetic design method yielded electrical response close to the desired value (95% accuracy). The proposed method also implements a weighted priority scheme to account for tradeoffs in microwave design. This scheme was implemented to synthesize bandpass filters for 802.11a and HIPERLAN wireless LAN applications in the range of 5-6 GHz. This research also develops a novel neuro-genetic design centering methodology for yield enhancement and design for manufacturability of microwave devices and circuits. A neural network model is used to calculate yield using Monte Carlo methods. A genetic algorithm is then used for yield optimization. The proposed method has been used for yield enhancement of SiGe heterojunction bipolar transistor and mm wave voltage-controlled oscillator. It results in significant yield enhancement of the SiGe HBTs (from 25 % to 75 %) and VCOs (from 8 % to 85 %). The proposed method is can be extended for device, circuit, package, and system level integrated co-design since it can handle a large number of design variables without any assumptions about the component behavior. The proposed algorithm could be used by microwave community for design and optimization of microwave circuits and systems with greater accuracy while consuming less computational time.
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Bao, Min. "System-Level Techniques for Temperature-Aware Energy Optimization." Licentiate thesis, Linköpings universitet, ESLAB - Laboratoriet för inbyggda system, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-60855.

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Energy consumption has become one of the main design constraints in today’s integrated circuits. Techniques for energy optimization, from circuit-level up to system-level, have been intensively researched. The advent of large-scale integration with deep sub-micron technologies has led to both high power densities and high chip working temperatures. At the same time, leakage power is becoming the dominant power consumption source of circuits, due to continuously lowered threshold voltages, as technology scales. In this context, temperature is an important parameter. One aspect, of particular interest for this thesis, is the strong inter-dependency between leakage and temperature. Apart  from leakage power, temperature also has an important impact on circuit delay and, implicitly, on the frequency, mainly through its influence on carrier mobility and threshold voltage. For power-aware design techniques, temperature has become a major factor to be considered. In this thesis, we address the issue of system-level energy optimization for real-time embedded systems taking temperature aspects into consideration. We have investigated two problems in this thesis: (1) Energy optimization via temperature-aware dynamic voltage/frequency scaling (DVFS). (2) Energy optimization through temperature-aware idle time (or slack) distribution (ITD). For the above two problems, we have proposed off-line techniques where only static slack is considered. To further improve energy efficiency, we have also proposed online techniques, which make use of both static and dynamic slack. Experimental results have demonstrated that considerable improvement of the energy efficiency can be achieved by applying our temperature-aware optimization techniques. Another contribution of this thesis is an analytical temperature analysis approach which is both accurate and sufficiently fast to be used inside an energy optimization loop.
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Books on the topic "Embedded Systems, Algorithms, Optimization Techniques"

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Leupers, Rainer. Code Optimization Techniques for Embedded Processors: Methods, Algorithms, and Tools. Boston, MA: Springer US, 2000.

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Bhuvaneswari, M. C., ed. Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-1958-3.

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Malepati, Hazarathaiah. Digital media processing: DSP algorithms using C. Burlington, MA: Newnes/Elsevier, 2010.

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Digital media processing: DSP algorithms using C. Burlington, MA: Newnes, 2010.

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Gogniat, Guy. Algorithm-Architecture Matching for Signal and Image Processing: Best papers from Design and Architectures for Signal and Image Processing 2007 & 2008 & 2009. Dordrecht: Springer Science+Business Media B.V., 2011.

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Code Optimization Techniques for Embedded Processors - Methods, Algorithms, and Tools. Springer, 2000.

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Marwedel, Peter, and Manish Verma. Advanced Memory Optimization Techniques for Low-Power Embedded Processors. Springer, 2010.

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Marwedel, Peter, and Manish Verma. Advanced Memory Optimization Techniques for Low-Power Embedded Processors. Springer London, Limited, 2007.

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Marwedel, Peter, and Manish Verma. Advanced Memory Optimization Techniques for Low-Power Embedded Processors. Springer, 2007.

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Bhuvaneswari, M. C. Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems. Springer, 2016.

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Book chapters on the topic "Embedded Systems, Algorithms, Optimization Techniques"

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Aliee, Hananeh, Michael Glaß, Faramarz Khosravi, and Jürgen Teich. "Uncertainty-Aware Compositional System-Level Reliability Analysis." In Dependable Embedded Systems, 457–77. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52017-5_19.

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AbstractContinuous technology scaling has increased the susceptibility of today’s electronic devices to manufacturing tolerances and environmental changes. The resulting uncertainty in component reliability can be only approximated or estimated at design time and might propagate to system level. Therefore, uncertainty must be considered to enable the design of robust systems. In this chapter, we propose a methodology for cross-level reliability analysis to tame the ever increasing analysis complexity of contemporary systems under the influence of uncertainties. The presented methodology combines various reliability analysis techniques across different levels of abstraction while providing an explicit modeling of uncertainties. It introduces mechanisms for (a) the composition and decomposition of the system during analysis and (b) converting analysis data between different levels of abstraction through adapters. The developed analysis techniques are integrated in an automatic electronic system-level reliability analysis tool to allow for the evaluation of reliability-increasing techniques and for DSE!. The tool thereby uses meta-heuristic algorithms for optimization and enables the comparison of system implementation candidates with objectives represented by uncertainty distributions.
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Wang, Hong, Liyuan Chang, Lina Yu, and Xiangli Dong. "Optimization and Embedded Implementation of Gesture Recognition Algorithm Based on Convolutional Neural Network." In 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems, 1587–92. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1726-3_210.

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Beck, Antonio Carlos Schneider. "Dynamic Optimization Techniques." In Adaptable Embedded Systems, 163–210. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-1746-0_6.

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Marwedel, Peter. "Optimization." In Embedded Systems, 349–79. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-60910-8_7.

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AbstractEmbedded systems have to be efficient (at least) with respect to the objectives considered in this book. In particular, this applies to resource-constrained mobile systems, including sensor networks embedded in the Internet of Things. In order to achieve this goal, many optimizations have been developed. Only a small subset of those can be mentioned in this book. In this chapter, we will present a selected set of such optimizations. This chapter is structured as follows: first of all, we will present some high-level optimization techniques, which could precede compilation of source code or could be integrated into it. We will then describe concurrency management for tasks. Section 7.3 comprises advanced compilation techniques. The final Sect. 7.4 introduces power and thermal management techniques.
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Zouggar, Souad Taleb, and Abdelkader Adla. "Optimization Techniques for Machine Learning." In Algorithms for Intelligent Systems, 31–50. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-0994-0_3.

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Ahmadinia, Ali. "Optimization Algorithms for Dynamic Reconfigurable Embedded Systems." In Field Programmable Logic and Application, 1168. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30117-2_158.

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Pop, Paul. "Embedded Systems Design: Optimization Challenges." In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, 16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11493853_2.

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Zhu, Ming, Jinian Bian, and Weimin Wu. "Model Optimization Techniques in a Verification Platform for Classified Properties." In Embedded Software and Systems, 542–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11535409_79.

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Jarndal, Anwar, Sadeque Hamdan, Sanaa Muhaureq, and Maamar Bettayeb. "Neural Networks Modeling Based on Recent Global Optimization Techniques." In Algorithms for Intelligent Systems, 65–75. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5243-4_6.

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Sowmith, P., N. Vamsi Krishna, and B. Varunkumar. "Conventional and Heuristic Optimization Techniques Comparison for Economic Load Dispatch." In Algorithms for Intelligent Systems, 405–20. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2109-3_38.

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Conference papers on the topic "Embedded Systems, Algorithms, Optimization Techniques"

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Yang, Chulho, and Douglas E. Adams. "Identification of Multiple Damages in a Structure Using Embedded Sensitivity Functions and Optimization Techniques." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-62277.

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A new method for identifying multiple damages in a structure using embedded sensitivity functions and optimization algorithms is presented in this work. Optimization techniques are used to minimize the difference between the measured frequency response functions from a damaged structure and the predicted FRFs from the baseline structure. The predicted FRF functions are calculated directly from the undamaged system response data using the embedded sensitivity functions and their Taylor series expansions. The optimal damage parameters are identified in engineering units as changes in stiffness, damping, or mass through the optimization process for minimizing the difference between those two FRFs. The method is applied to a two degree of freedom analytical model to determine the accuracy of the diagnostic results. Finite element analyses are then conducted on a three-story structure with damages in the form of stiffness and mass perturbations to demonstrate the applicability of this method to more complicated structural systems. It is shown that the suggested technique can detect and quantify multiple damages in a structure with high numerical accuracy in the level of the estimated damages.
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Sivamurugan, V., P. Indumathi, and R. Rajakumar. "Performance analysis and comparison of telephone speech enhancement algorithm for HOH listeners using OMAP processor based embedded systems." In 2017 IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing (INCOS). IEEE, 2017. http://dx.doi.org/10.1109/itcosp.2017.8303070.

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Yang, Chulho, Young Bae Chang, Jongsung Sa, and Junyoung Park. "Enhancement of an Optimization-Based Damage Detection Technique." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-52775.

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Various structural health monitoring (SHM) techniques utilizing vibration signals have been developed for identification of damages in a structure. Many of these studies are based on sensitivity analysis, finite element model (FEM) updating, and optimization techniques. FEM updating technique is one of the major techniques that iteratively minimizes the difference between the modal parameters measured from the real structure and the corresponding analytical predictions. This method would be more beneficial for typical continuous systems such as beams, plates, and shells which cannot be reasonably discretized. One of the drawbacks of these techniques is the large number of unknowns to be estimated. These techniques in the literature that use FEM updating to estimate perturbed parameters for all elements in the model can be time-consuming and ill-conditioned, even for relatively simple structures. The technique also requires a full and accurate finite element model for each monitored structure. A new method to identify damages in a structure using embedded sensitivity functions and optimization algorithms is described and its performance is demonstrated in this paper. The perturbed frequency response function (FRF) is calculated using Taylor series expansion in terms of the baseline system and the embedded sensitivity functions. The optimization process minimizes the difference between the measured FRFs of the damaged structure and the perturbed FRFs calculated from the baseline structure. Structural damages are often characterized by changes in mechanical parameters such as stiffness, mass, and damping. Embedded sensitivity functions offer a means of determining the path that is followed from the baseline to the perturbed FRF of the structure. The robustness and efficiency of suggested structural health monitoring method are discussed in this paper. The accuracy of damage estimation is investigated with respect to various types and values of damages, objective functions, frequency ranges, scale factors, procedures, and noise levels. Precise measurement and monitoring of vibration signals are critical for accurate detection of the location, type, and level of damage. However, in most practical mechanical systems, vibration tests may result in noise on the input or output measurements. Noise on the measurement affects the accuracy of the FRFs and identification of damages in a structure. Based on the results of the study, several parameters and factors in the optimization process and structural dynamics are suggested to enhance the efficiency and robustness of damage identification process. It is shown that the iteration number of the optimization process is significantly reduced. Accurate estimate of damages can be obtained within the range of 2∼5% error with various enhancements applied to the technique.
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Shetty, Devdas, Naresh Poudel, and Esther Ososanya. "Design of Robust Mechatronics Embedded Systems by Integration of Virtual Simulation and Mechatronics Platform." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-52784.

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Increasing demands on the productivity of complex systems, such as machine tools and their steadily growing technological importance will require the application of new methods in the product development process. This paper shows that the analysis of the simulation results from the simulation based mechatronic model of a complex system followed by a procedure that allows a better understanding of the dynamic behavior and interactions of the components. Mechatronics is a design philosophy, which is an integrating approach to engineering design. Through a mechanism of simulating interdisciplinary ideas and techniques, mechatronics provides ideal conditions to raise the synergy, thereby providing a catalytic effect for the new solutions to technically complex situations. This paper shows how the mechatronic products can exhibit performance characteristics that were previously difficult to achieve without the synergistic combination. The paper further examines an approach used in modeling, simulation and optimization of dynamic machine tools and adopts it for general optimized design of mechatronics instrumentation and portable products. By considering the machine tool as a complete mechatronic system, which can be broken down into subsystems, forms the fundamental basis for the procedure. Starting from this point of view it is necessary to establish appropriate simulation models, which are capable of representing the relevant properties of the subsystems and the dynamic interactions between the machine components. Many real-world systems can be modeled by the mass-spring-damper system and hence considering one such system, namely Mechatronics Technology Demonstrator (MTD) is discussed here. MTD is a portable low cost, technology demonstrator, developed and refined by the authors. It is suitable for studying the key elements of mechatronic systems including; mechanical system dynamics, sensors, actuators, computer interfacing, and application development. An important characteristic of mechatronic devices and systems is their built-in intelligence that results through a combination of precision, mechanical and electrical engineering, and real time programming integrated to the design process. The synergy can be generated by the right combination of parameters, that is, the final product can be better than just the sum of its parts. The paper highlights design optimization of several mechatronic products using the procedures derived by the use of mass spring damper based mechatronic system. The paper shows step by step development of a mechatronic product and the use of embedded software for portability of hand held equipment. A LabVIEW based platform was used as a control tool to control the MTD, perform data acquisition, post-processing, and optimization. In addition to the use of LabVIEW software, the use of embedded control system has been proposed for real-time control and optimization of the mass-spring-damper system. Integrating embedded control system with the mass-spring-damper system makes the MTD a multi-concepts Mechatronics platform. This allows interface with external sensors and actuators with closed-loop control and real-time monitoring of the physical system. This teaches students the skill set required for embedded control: design control algorithms (model-based embedded control software development, signal processing, communications), Computer Software (real-time computation, multitasking, interrupts), Computer hardware (interfacing, peripherals, memory constraints), and System Performance Optimization. This approach of deriving a mathematical model of system to be controlled, developing simulation model of the system, and using embedded control for rapid prototyping and optimization, will practically speed product development and improve productivity of complex systems.
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Mollaei, Mohammadreza, and Stephen Mascaro. "Optimal Control Algorithm for Multi-Input Binary-Segmented SMA Actuators Applied to a Multi-DOF Robot Manipulator." In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-4094.

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In this paper, we present an optimal design and control algorithm for multi-input binary-segmented Shape Memory Alloy (SMA) actuator arrays applied to a multi-degree-of-freedom (DOF) robot manipulator as it tracks a desired trajectory. The multi-DOF manipulator used for this paper is a 3-DOF-robot finger. A multi-input binary-segmented SMA actuator drives each DOF. SMA wires are embedded into a compliant vessel, such that both electric and fluidic (hot/cold) input can be applied to the actuators. By segmenting the SMA actuators, each segment can be controlled in a binary fashion (fully contracted/extended) to create a set of discrete displacements for each joint of the manipulator. To design the number of segments and length of each segment, an algorithm is developed to optimize the workspace. To optimize the workspace, it is desired to have a uniform distribution of reachable points in Cartesian space. Moreover, the number of neighbors (points that can be reached just by one control command from the current configuration) and the computational cost are important in workspace optimization. Graph theory search techniques based on the A* algorithm are employed to develop the control algorithm. A path-cost function is proposed to optimize the cost, which is a combination of actuation time, energy usage, and kinematic error. The kinematic error is estimated as the deviation between the actual and desired trajectory. The performance of the search algorithm and cost function are validated through simulation.
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Scarcia, Umberto, Giovanni Berselli, Claudio Melchiorri, Manuele Ghinelli, and Gianluca Palli. "Optimal Design of 3D Printed Spiral Torsion Springs." In ASME 2016 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/smasis2016-9218.

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Spiral Torsion Springs (STS) are generally manufactured employing medium/high-carbon steel alloys shaped as thin rods with rectangular cross section. Meanwhile, plastic materials (e.g. ABS or PLA), currently used in freeform manufacturing processes, may not be suited for several applications, owing to the low material yield strength and the rather poor fatigue life. Despite the above-mentioned limitations, the main advantages of a 3D printing process, as compared to more traditional manufacturing techniques, are the design flexibility and the possibility to directly integrate elastic components within a joint mechanism produced as a single (monolithic) part. In particular, provided that the external forces acting on the spring coils are maintained within a certain threshold and that the spring geometry is suitably optimized, a reliable 3D-printed STS alternative to traditional steel springs is actually feasible. Given these premises, the main purpose of the present paper is to propose a model-based optimization algorithm that allows to optimally size STS for user-specified torque-deflection characteristics. Optimal STS geometries are then realized in ABS via Fused Deposition Manufacturing, and subsequently tested with a purposely-designed experimental set-up. Furthermore, the behavior of each STS sample (in terms of stiffness and equivalent Von Mises stress) is evaluated by means of non-linear finite elements analysis, in order to check the correspondence with the expected behavior. Finally, numerical and experimental results are provided, which demonstrate the prediction capabilities of the proposed modeling/optimization techniques, and confirm that well-behaved STS can be conceived and produced. Envisaged applications concern the development of smart structures for robot design, such as multi-articulated compliant robotic chains that can be used as low-cost manipulators (i.e. arm) or as mini-manipulators (i.e. fingers). The proposed approach effectively simplifies the production and the assembly of the mechanism, also allowing for an easier integration of embedded sensory-actuation systems.
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Qian, Zhongyan, and G. K. Ananthasuresh. "Optimal Embedding in the Topology Design of Structures." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34148.

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Extensive literature exists on the topology design of single-component structures while multi-component structural systems have received much less attention. In this paper, we present a technique for optimizing the topology of a structure that should be connected to one or more pre-designed components to maximize the stiffness of the overall assembly. We call it an embedding problem because pre-designed components are to be optimally positioned and oriented within a design region while the connecting structure’s topology is optimized simultaneously. Continuous design variables are used to vary the locations of the embedded objects smoothly along with the topology of the connecting structure in order to apply gradient-based optimization algorithms. A new material interpolation function on the basis of normal distribution function is used for this purpose. Optimality criteria method combined with the steepest descent method is used to minimize mean compliance to obtain the stiffest structure for a given volume of material. As a special case of this method, topology optimization of multi-component structural systems is also considered. Illustrative examples are presented.
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Oliveira, Lizandro, Julio C. B. Mattos, and Lisane Brisolara. "Survey of Memory Optimization Techniques for Embedded Systems." In 2013 III Brazilian Symposium on Computing Systems Engineering (SBESC). IEEE, 2013. http://dx.doi.org/10.1109/sbesc.2013.35.

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Nivodhini, M. K., K. Kousalya, and S. Malliga. "Algorithms to improve scheduling techniques in IaaS cloud." In 2013 International Conference on Information Communication and Embedded Systems (ICICES 2013). IEEE, 2013. http://dx.doi.org/10.1109/icices.2013.6508188.

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Kim, Chunghee, Luciano Lavagno, and Alberto Sangiovanni-Vincentelli. "Free MDD-based software optimization techniques for embedded systems." In the conference. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/343647.343686.

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