The developments in the field of Very Large Scale Integration (VLSI) and rapid advances of Digital Signal Processing has laid the foundation for implementation of DSP algorithms using digital hardware such as Field Programmable Gate Array. The core function in every DSP system is of digital filtering and the preferred filter is FIR filters due to their advantages such as linear phase response, stability, less quantization noise and round-off noise. There is demand for miniaturization, high speed and low power dissipation in every application. In this work the optimization of FIR filter for these factors using different techniques is discussed. This book discusses that how the filter has been designed using the transposed structure using binary representation for the coefficients, then it is designed using the transposed structure with canonical signed digit (CSD) representation for coefficients and in the last filter has been designed using transposed structure with reduced adder graph (RAG) algorithm.
Digital filters play a key role in the field of digital signal processing. This research presents application of particle swarm optimization (PSO) and its three new variants, dynamic and adjustable particle swarm optimization (DAPSO) and particle swarm optimization with variable acceleration factor (PSO-VAF) and attractive and repulsive particle swarm optimization (ARPSO) in linear phase digital low pass finite impulse response (FIR) filter design. Two fitness functions are considered. The fitness1 is used to find the possible minimum ripples in pass band and stop band. Fitness2 is able to control the ripples in both bands separately.A comparison of simulation results in the form of frequency response in dB plots, normalized frequency response plots, normalized ripples in pass band plots and normalized ripples in stop band plots, convergence behavior of all the algorithms, optimized filter coefficients and statistical results, i.e., minimum, maximum, average, variance and standard deviation for each algorithm and fitness function, illustrate the superiority of the proposed PSO based technique over PSO in digital linear phase FIR filter design.
In any deterministic solution, the convergence is not at all guaranteed, whereas, the stochastic and random search algorithms are 1 shot optimization and it can hit the nearly optimized solution, with guarantee. Therefore the AI dependent evolutionary algorithms (GA, PSO, DE, BFOA) are prescribed for this type of optimization problems. Some selected evolutionary algorithms are presented for digital filter design. If the statistical characteristic of the input data varies with respect to time or the required knowledge about input data is not satisfactory, adaptive filters are needed. Adaptive filters (FIR and IIR) have attractive increasing attention due to their widespread use in many different applications such as system identification, noise cancellation, channel equalization, linear prediction, control, and modeling. In the present book, in order to achieve a global minimum solution to the fitness function related to filter transfer function, biologically inspired algorithm is used. Adaptation to classical Bacterial Foraging Optimization is employed to design stable and optimum digital filter design for signal processing and image processing applications.
Filter designs have number of applications in data transmission systems, perfect reconstruction filter banks, nonuniform sampling, interpolation filters over the past two decades. There are two conventional methods, which are FIR and IIR filter forms, to design filters. FIR filters can be designed with an exact linear phase. However, when the sharp magnitude specifications are required, higher order FIR filters are generally needed, and a larger delay results. On the other hand, IIR filters have two disadvantages: one is the stability that must be considered, and another is that the existing design methods are generally time consuming. For real-time signal applications, the above methods are all linear algebra based methods, therefore, cannot meet the requirements of real-time. Neural networks possessing parallel processing capability have been successfully applied for solving various computationally expensive optimization problems. This is due to the properties of guaranteed convergence to a local minimum of the Lyapunov energy function and the fast computational speed when implemented in hardware.
The integration of production and distribution in the supply chain has changed the way the supply chain is designed and operated. This book presents the innovation in optimization techniques and models for the supply chain. In this book the development of heuristic and exact solution techniques for the integrated model are presented. The details of algorithm design for optimization techniques are also included.
Optimization techniques are the economic decision making tools that optimizes (maximizes or minimizes) the value of an objective function that satisfies the given constraints. Optimization techniques are a powerful set of tools that are important in efficiently managing an enterprise's resources and thereby maximizing shareholder wealth. For example, in a price-output decision making problem, one may be interested in determining the output level that maximizes profits. In a production problem, the goal may be to find the combination of inputs (resources) that minimizes the cost of producing a desired level of output. The common numerical methods of optimization are linear, Dynamic, quadratic, non-linear, integer, stochastic etc. programmings. Hence, application of optimization techniques is of paramount importance for solving complex engineering economics problems.Therefore, it may use for scarce land and irrigation water resource allocation in mitigating the current climate change scenario and establish green economy.
The objective of this work is to design and implement a self-adaptive parallel GPU optimized Monte Carlo algorithm for the simulation of adsorption in porous materials. We focus on Nvidia's GPUs and CUDA's Fermi architecture specifically. The resulting package supports the different ensemble methods for the Monte Carlo simulation, which will allow for the simulation of multi-component adsorption in porous solids. Such an algorithm will have broad applications to the development of novel porous materials for the sequestration of CO2 and the filtration of toxic industrial chemicals. The primary objective of this work is the release of a massively parallel open source Monte Carlo simulation engine implemented using GPUs, called GOMC. The code will utilize the canonical ensemble, and the Gibbs ensemble method, which will allow for the simulation of multiple phenomena, including liquid-vapor phase coexistence, and single and multi-component adsorption in porous materials. In addition, the grand canonical ensemble and the configurational-bias algorithms have been implemented so that polymeric materials and small proteins may be simulated.
As the transistors became smaller in size and the systems became faster, issues like power consumption, signal integrity, soft error tolerance, and testing became serious challenges. There is an increasing demand to put CAD tools in the design flow to address these issues at every step of the design process. First part of this research investigates circuit level techniques to reduce power consumption in digital systems. In second part, improving soft error tolerance of digital systems is considered as a trade off problem between power and reliability and a power aware dynamic soft error tolerance control strategy is developed. The objective of this research is to provide CAD tools and circuit design techniques to optimize power consumption and to increase soft error tolerance of digital circuits. Multiple supply and threshold voltages are used to reduce power consumption. Variable supply and threshold voltages are used together with variable capacitances to develop a dynamic soft error tolerance control scheme.
This book presents an efficient meta-heuristic method for distribution systems reconfiguration for lower losses and better voltage profile. A modified Tabu Search (MTS) algorithm is used to reconfigure distribution systems so that active power losses are minimized with turning on/off sectionalizing switches. A new method to check the radial topology of the system is presented. Also, the reconfiguration problem is solved using the particle swarm optimizer (PSO), a member of the recently growing swarm intelligent-based algorithms. In most of the PSO publications, the algorithm is used for solving problems of unconstrained optimization. Consequently, to address the feeder reconfiguration problem, some modifications to the standard PSO are proposed to allow dealing with such highly constrained optimization problem. To verify the effectiveness of the proposed methods, comparative studies are conducted on four test systems with encouraging results. The proposed methods are applied to 16-node, 32-node, 69-node, and 119-node distribution systems. The obtained results are compared with results obtained using other approaches in the previous literature work to examine the performance.
Recent years have witnessed a huge increase in the power consumption of computing systems ranging from embedded systems to supercomputers. Power issues now drive major design decisions in businesses and large-scale enterprises and hence, the knowledge of how power concerns influence processor architecture is important for both researchers and business policy-makers. This book discusses basic concepts, algorithms and architectures for cache energy optimization in single-core and multi-core systems, multitasking, real-time and QoS systems. It presents dynamic reconfiguration based energy saving techniques for caches designed with both conventional SRAM devices and emerging non-volatile devices such as STT-RAM. It also provides both qualitative and quantitative comparison with state-of-the-art cache energy saving techniques to stimulate the readers to take the frontiers beyond what has been presented here. This book will be useful for all those who are interested in power-aware computing and architecture design, be they students or researchers in computer architecture, chip designers, software engineers, technical marketing professionals or managers interested in low-power design.
The tremendous growth of Information and Communication Technology (ICT) has unknowingly influenced our lifestyle. We are making more phone calls, exchanging more emails and spending more time on the World Wide Web than we did a decade ago. The nature of information content is also evolving. High definition video streaming, online shopping, online banking and content sharing libraries are some of the new paradigms that have replaced the simple web applications of early days. In such circumstances, the objectives for future networks are more challenging. This book focuses on the IEEE 802.11 MAC and identifies several challenges that require further research. The IEEE 802.11 standard for local area networks, specifically the IEEE 802.11e, is discussed in a tutorial style. The primary focus is on rate-adaptation schemes for multi-rate WLANs stations and design of QoS enabled routing protocol by incorporating information from the IEEE 802.11e compliant MAC. The book serves as a valuable reference for researchers, professionals and students who would like to gain a more formal understanding of Cross-layer optimization techniques and specifically the IEEE 802.11 standard.
This book aims for beginners who want to learn Artificial Intelligent Optimization Techniques and their implementation with MATLAB. If you follow the book chapter by chapter, you can manage those techniques and even develop your own techniques in a less than one year. Despite our focusing on beginners, advanced user can also benefit from the comprehensive models from part two of this book.
This edition of A Novel High Speed FPGA Architecture Design for FIR Filter presents the details of hardware implementation of linear phase FIR filter using merged MAC architecture. Speed of convolution operation of FIR filter is improved using merged MAC architecture.In order to improve the speed of the multiplication process within the computational unit;there is a major bottleneck that is needed to be considered that is the partial products reduction network which is used in the multiplication block.For implementation of this stage require addition of large operands that involve long paths for carry propagation.The proposed architecture is based on binary tree constructed using modified 4:2 and 5:2 compressor circuits.My objective of work is, to increase the speed of multiplication operation by minimizing the number of combinational gates using higher n:2 compressors. The experimental test of the proposed modified compressor is done using Spartan-3FPGA device(XC3S400 PQ-208).The implemented FPGA architecture should help to design new efficient FIR architecture for high speed computation operation in Microprocessor &in DSP,and should be especially useful to students in VLSI field.
This book is concerned with the recent trend in optimization techniques. The first chapter is introductory in nature. The second chapter provides classification of optimization problems. An overview of the multi-objective optimization and classification of optimization techniques are given in chapter three and four respectively. Fifth chapter provides reviews of literature on different optimization techniques.
In this book, the main significant contributions are formulation of a new non-linear membership function using fuzzy approach to capture and describe vagueness in the technological coefficients of constraints and intelligent solution methodology using hybrid meta-heuristics optimization techniques in an industrial production planning problems. The novel and innovative hybrid intelliegent such has genetic algorithms, simulated annealing, pattern search and fuzzy logic have been extensively used in this research work. Finally, it is concluded that hybrid optimization techniques are robust, less time-consuming, dependable, high quality solutions and an efficient productive tool for solving the non-linear real world problem in an uncertain industrial engineering environment. The hybrid line search with genetic algorithms and hybrid line search with simulated annealing techniques developed in this study are user friendly, easy-to-use and can serve as a teaching and research tool, besides being useful for practicing scientist, decision makers, managers and researchers in the area of industrial engineering, finance, economics and management science.
Achieving optimal design of phase-locked loop (PLL) is a major challenge in WiMax technology in order to improve system behavior against noise and to enhance Quality of Service (QOS). A new loop filter design method for phase locked loop (PLLs) is introduced taking into consideration various design objectives: small settling time, small overshoot and meeting Mobile WiMax requirements. Optimizing conflicting objectives is accomplished via linear programming and semidefinite programming (especially Linear Matrix Inequality (LMI)) in conjunction with appropriate adjustment of certain design parameters. Digital filters, Infinite Impulse Response (IIR) and Finite Impulse Response (FIR) are designed using linear programming and convex programming.Simulations show that IIR digital lowpass filter with narrow transition band could not work properly with mobile WiMax system. Simulations show that FIR digital lowpass filter utilizing linear programming managed to improve the transient behavior. The FIR digital lowpass filter utilizing semidefinite programming (LMI) will much improve the transient behavior; therefore it is recommended for mobile WiMax systems.
Communication plays a vital role in every phase of our life. Its development has shrunk the world into a smaller place to live in. Every face of the globe is accessible as a result of the numerous inventions in the field of communications. The communication channel is subject to various interferences like fading, bursts which threaten reliable reception of data. The use of an appropriate and robust coding technique helps to minimize the adverse effects of channel noise. The Viterbi algorithm (VA) is an optimal method in decoding convolutional codes. By using this algorithm as the constraint length increases the accuracy in decoding the data improves. In this book the various techniques, for optimizing the Viterbi algorithm is analyzed for better BER and SNR at various constraint lengths.
Turbomachinery design and optimization using numerical technique has been presented in this book. Reynolds Average Navier-Stokes (RANS) equation based solution approach has been explained to design the system. Single as well as multi-objective optimization techniques are presented along with the analysis of flow field. Several surrogate based optimization techniques including a weighted average method was used for optimization. The Turbomachinery designers trying to enhance the system performance can follow the procedure explained in this book. In short, the books can be helpful for the Turbomachinery designers and academicians. This book may also be of general interest of designers and researchers who want CFD simulation of any system via RANS and want to apply systematic approach for optimization.