Neuro-Fuzzy Algorithm Implemented In Altera’s FPGA For Mobile Robot’s Obstacle Avoidance Mission.

This paper presents the designed obstacle avoidance program for mobile robot that incorporates a neuro-fuzzy algorithm using Altera™ Field Programmable Gate Array (FPGA) development DE2 board

Optimizing Reconfigurable Hardware Resource Usage in System-on-a-Programmable-Chip with Location-Aware Genetic Algorithm

This paper presents static task scheduling using location-aware genetic algorithm techniques to schedule task systems to finite amounts of reconfigurable hardware. This research optimizes the use of limited reconfigurable resources. This scheduling algorithm is built upon our previous work [12- 14]. In this paper, the genetic algorithm has been expanded to include a feature to assign selected tasks to specific functional units. In this reconfigurable hardware environment, multiple sequential processing elements (soft core processors such as Xilinx MicroBlaze [22] or Altera Nios-II [1]), task-specific core (application specific hardware), and communication network within the reconfigurable hardware can be used (such a system is called system-on-a-programmable-chip, SoPC). This paper shows that by pre-assigning (manually or randomly) a percentage of tasks to the desired functional units, the search algorithm is capable of finding acceptable schedules and maintaining high resource utilization (\u3e93 percent, with two processors configuration)

Efficient Architecture and Implementation of Vector Median Filter in Co-Design Context

This work presents an efficient fast parallel architecture of the Vector Median Filter (VMF) using combined hardware/software (HW/SW) implementation. The hardware part of the system is implemented using VHDL language, whereas the software part is developed using C/C++ language. The software part of the embedded system uses the NIOS-II softcore processor and the operating system used is μClinux. The comparison between the software and HW/SW solutions shows that adding a hardware part in the design attempts to speed up the filtering process compared to the software solution. This efficient embedded system implementation can perform well in several image processing applications

Evolutionary computing and particle filtering: a hardware-based motion estimation system

Particle filters constitute themselves a highly powerful estimation tool, especially when dealing with non-linear non-Gaussian systems. However, traditional approaches present several limitations, which reduce significantly their performance. Evolutionary algorithms, and more specifically their optimization capabilities, may be used in order to overcome particle-filtering weaknesses. In this paper, a novel FPGA-based particle filter that takes advantage of evolutionary computation in order to estimate motion patterns is presented. The evolutionary algorithm, which has been included inside the resampling stage, mitigates the known sample impoverishment phenomenon, very common in particle-filtering systems. In addition, a hybrid mutation technique using two different mutation operators, each of them with a specific purpose, is proposed in order to enhance estimation results and make a more robust system. Moreover, implementing the proposed Evolutionary Particle Filter as a hardware accelerator has led to faster processing times than different software implementations of the same algorithm

Pipelined genetic propagation

© 2015 IEEE.Genetic Algorithms (GAs) are a class of numerical and combinatorial optimisers which are especially useful for solving complex non-linear and non-convex problems. However, the required execution time often limits their application to small-scale or latency-insensitive problems, so techniques to increase the computational efficiency of GAs are needed. FPGA-based acceleration has significant potential for speeding up genetic algorithms, but existing FPGA GAs are limited by the generational approaches inherited from software GAs. Many parts of the generational approach do not map well to hardware, such as the large shared population memory and intrinsic loop-carried dependency. To address this problem, this paper proposes a new hardware-oriented approach to GAs, called Pipelined Genetic Propagation (PGP), which is intrinsically distributed and pipelined. PGP represents a GA solver as a graph of loosely coupled genetic operators, which allows the solution to be scaled to the available resources, and also to dynamically change topology at run-time to explore different solution strategies. Experiments show that pipelined genetic propagation is effective in solving seven different applications. Our PGP design is 5 times faster than a recent FPGA-based GA system, and 90 times faster than a CPU-based GA system

Noise-agnostic adaptive image filtering without training references on an evolvable hardware platform

One of the main concerns of evolvable and adaptive systems is the need of a training mechanism, which is normally done by using a training reference and a test input. The fitness function to be optimized during the evolution (training) phase is obtained by comparing the output of the candidate systems against the reference. The adaptivity that this type of systems may provide by re-evolving during operation is especially important for applications with runtime variable conditions. However, fully automated self-adaptivity poses additional problems. For instance, in some cases, it is not possible to have such reference, because the changes in the environment conditions are unknown, so it becomes difficult to autonomously identify which problem requires to be solved, and hence, what conditions should be representative for an adequate re-evolution. In this paper, a solution to solve this dependency is presented and analyzed. The system consists of an image filter application mapped on an evolvable hardware platform, able to evolve using two consecutive frames from a camera as both test and reference images. The system is entirely mapped in an FPGA, and native dynamic and partial reconfiguration is used for evolution. It is also shown that using such images, both of them being noisy, as input and reference images in the evolution phase of the system is equivalent or even better than evolving the filter with offline images. The combination of both techniques results in the completely autonomous, noise type/level agnostic filtering system without reference image requirement described along the paper

Examination of the World3 Model and the Development of a Novel Model of a Multi-market, Multi-regional Economy Driven by Adaptive Heterogeneous Consumer Agents

Ever since the human race developed consciousness we have battled against the elements to bring about prosperity and health. For millennia we closely observed the natural phenomena that seemed to influence future outcomes, gradually building and refining our conceptions of reality, our mental models. We refined the process of observation and discovery with the scientific method, and from that point on our power to control our environment grew immensely. Now our greatest foe is not only Mother Nature, but ourselves. We still act impulsively, and make decisions which seem irrational. We may guiltily watch hour after hour of Antiques Road Show, instead of spending a mere 30 minutes finishing off the final thesis chapter. The tradition of model development is continued herein, with a focus on holistic socio- ecological models. The first part of this thesis examines the pre-existing Limits to Growth model, originally developed by Meadows et. al. in 1972. Uncertainty analysis was per- formed on this model to develop a better understanding of its reliability. This model is also used to better understand the trade-off relationships between common goals humans wish to achieve in the future. A genetic algorithm was used to determine the Pareto front of the seven examined goals. The final part of the thesis presents a novel model designed to allow many simulated human actors to make purchasing decisions in a self determining fashion, based on the cost of various goods. The new model simulates multi-item market- places, floating prices on goods, and spacial effects on trading and resource extraction. A preliminary version of the model is tested under eight different conditions, and the results are presented and discussed