Human-competitive evolved antennas

Despite the prevalence of small satellites in recent years, the systems flown to date have very limited propulsion capability. SmallSats are typically secondary payloads and have significant constraints for volume, mass, and power in addition to limitations on the use of hazardous propellants or stored energy. These constraints limit the options for SmallSat maneuverability. NASA's Space Technology Mission Directorate approved the iodine Satellite flight project for a rapid demonstration of iodine Hall thruster technology in a 12U (cubesat units) configuration under the Small Spacecraft Technology Program. The mission is a partnership between NASA MSFC, NASA GRC, and Busek Co, Inc., with the Air Force supporting the propulsion technology maturation. The team is working towards the critical design review in the final design and fabrication phase of the project. The current design shows positive technical performance margins in all areas. The iSat project is planned for launch readiness in the spring of 2017.

An examination of designer and user perceptions of JAD and the traditional IS design methodology

Evolutionary algorithms (EAs) for solving constraint satisfaction problems (CSPs) can be roughly divided into two classes: EAs with adaptive fitness functions and heuristic-based EAs. A.E. Eiben et al. (1998) compared effective EAs of the first class experimentally using a large set of benchmark instances consisting of randomly-generated binary CSPs. In this paper, we complete this comparison by performing the same experiments using three of the most effective heuristic-based EAs. The results of our experiments indicate that the three heuristic-based EAs have similar performances on random binary CSPs. Comparing these results with those of A.E. Eiben et al., we are able to identify the best EA for binary CSPs as the algorithm introduced by G. Dozier et al. (1994), which uses a heuristic as well as an adaptive fitness function.

Fuzzy performance evaluation of Evolutionary Algorithms based on extreme learning classifier

VariableZ0, a new concept in antenna design and optimization, is applied to two optimization problems: a multi-stub matching network (MSMN) using biogeography-based optimization (BBO), and an ultra wideband meander monopole antenna (MMA) using central force optimization (CFO). BBO is a newly-proposed stochastic global search and optimization evolutionary algorithm (EA) used to determine MSMN stub lengths and locations for optimum (minimum) reflection coefficient. CFO is a deterministic EA used to optimize the MMA's impedance bandwidth (IBW) while maintaining good average gain without considering the radiation pattern in detail. Two cases are investigated for both problems: (a) fixed characteristic impedanceZ0, and (b) variable characteristic impedance. In the first case,Z0is a fixed user-specified parameter (the traditional methodology), whereas in the second, it is a true variable quantity whose value is determined by the optimization methodology, which is a new technology. VariableZ0is a fundamentally different design approach in optimization problems. BBO's fixedZ0results for MSMN are compared to published data computed using Nelder–Mead optimization with BBO exhibiting better performance. BBO's results are improved even more using VariableZ0technology. A similar performance improvement is seen for VariableZ0applied to the CFO-optimized MMA.

Utilizing evolutionary algorithms to achieve a low axial ratio of a circularly polarized microstrip antenna over a wide bandwidth and optimal impedance matching is the main aim of this study. A circularly polarized cross aperture coupled microstrip antenna using a 50Ω microstrip feed line is designed. A software simulation program (CST microwave studio) is used to design and compare the performance of the antenna in terms of return loss and axial ratio. Two distinct evolutionary algorithms; Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) have been applied to optimize the fitness function to ensure optimal axial ratio and impedance matching. The optimal patch dimensions, the aperture length, and the microstrip feed line length are obtained from both methods and their performances are compared. Moreover, the antenna is fabricated and the return loss is measured using a network analyzer. A comparative analysis is carried out demonstrating a high agreement between measured and simulated return loss.

Design methodologies: Industrial and educational applications

This paper presents a comparison between conventional and multi-objective Cartesian Genetic Programming evolved designs for a 2-bit adder and a 2-bit multiplier. Each design is converted from a gate-level schematic to a transistor level implementation, through the use of an open-source standard cell library, and simulated in NGSPICE in order to generate industry standard metrics, such as propagation delay and dynamic power. Additionally, a statistical intrinsic variability analysis is performed, in order to see how each design is affected by intrinsic variability when fabricated at a cutting-edge technology node. The results show that the evolved design for the 2-bit adder is slower and consumes more power than the conventional design. The evolved design for the 2-bit multiplier was found to be faster but consumed more power than the conventional design, and that it was also more tolerant to the effects of intrinsic variability in both timing and power. This provides evidence that in the future, evolutionary-based approaches could be a feasible alternative for optimising designs at cutting-edge technology nodes, where traditional design methodologies are no longer appropriate, providing speed and power information about the standard cell library is used.KeywordsClock CyclePropagation DelayDynamic PowerConventional DesignIntrinsic VariabilityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

With the rapid evolution of commercial space markets in the new space era, satellite design philosophy is transitioning from "performance supremacy" toward "cost-effectiveness priority." This research concentrates on solar arrays, a critical component representing 15%-20% of satellite costs, addressing limitations in traditional design methodologies that inadequately coordinate user requirements and lack systematic frameworks for cost-performance collaborative optimization. We propose a solar array optimization methodology predicated on the integration of Quality Function Deployment (QFD) and the Theory of Inventive Problem Solving (TRIZ). This methodology incorporates cost dimensions through an extended QFD matrix, employs TRIZ to resolve technical contradictions, and develops a multi-constraint optimization model utilizing the NSGA-II multi-objective optimization algorithm. Through empirical validation with a commercial remote sensing satellite case study, results demonstrate that the optimized solar array design achieves a 12.5% cost reduction while simultaneously realizing a 20.8% improvement in specific power ratio, 0.04% enhancement in reliability, and 39.4% extension in operational lifespan. This cost-performance balanced optimization methodology provides practical engineering guidance for the commercial aerospace sector and offers valuable reference for promoting innovative satellite design in cost-sensitive environments.

Transitioning away from traditional design methodology, for example, paper sketching, CAAD works, and 'flat screen' rendering, this paper proposes a new methodological ecosystem of which tests its validity within a studio-based case study. The focus will prove whether dynamic implementation and interconnectivity of evolving design tools can create richness and complexity of a design outcome through arbitrary phases of a generative design methodology ecosystem. Processes tested include combinations of agent simulations, artistic image processing analysis, site photogrammetry, 3D immersive sketching both abstract and to site-scale, parametric design generation, and virtual reality style presentations. Enhancing the process of design with evolving techniques in a generative way which dynamically interconnects will stimulate a digital culture of design generation that includes new aspects of interest and introduces innovative opportunities within all corners of the architectural realm. Methodology components within this ecosystem of interaction prove that the architecture cannot be as rich and complex without the utilisation of all strengths within each unique design tool.

<div class="section abstract"><div class="htmlview paragraph">New optimization methods that are intended as an improvement over traditional design methodology often require the design model itself to be developed in a nontraditional manner. This paper describes the tailoring of a supersonic business jet design model to the Bi-Level Integrated System Synthesis (BLISS) optimization method. Included is a brief discussion of BLISS, the development and implementation of the design model, application of the design constraints, and a survey of favorable results. For discussion purposes, the design model is ‘tailored’ to the optimization method, not vice versa, to illustrate the model’s unique development.</div></div>

With the advancement of socioeconomic policies aiming at the efficiency constructive and sustainable development in recent decades, recently there is a constant need of the dematerialisation of civil construction, i.e., build more using the minimum material and human resources with maximum reuse. In this regard, normative regulations of civil engineering must be constantly updated and in line with the challenges of sustainable construction and current technological development. The study of the wind action and its interaction with the structures, are preponderant and must be taken into account even in the project phase. These must be designed to withstand the forces imposed by the wind. With the advancement of computer technology and numerical techniques, Computational Fluid Dynamics (CFD) have played a fundamental role for solution of wind-structure interaction problems. Aiming at the optimization of resources for the execution of structures and efficiency in the design of the projects, this work presents a comparative study of the calculation of the aerodynamic forces due to the wind action in buildings obtained through CFD with the calculation models used in the Brazilian Standard NBR 6123:1988 and Japanese Standard -Recommendations for Loads on Buildings. These three calculation approaches are compared with the experimental data from wind tunnel reduced scale models and the results have been found to be satisfactory. The results of the CFD simulations were coupled in a structural analysis program to evaluate the forces and displacements of the buildings through the Finite Element Method (FEM). The results obtained by the CFD-FEM coupling are compared with the traditional design methodologies in which Computational Fluid Dynamics is presented as an alternative to estimate the aerodynamic forces in buildings. The numerical simulations of the wind-structure interaction were performed using the open source OpenFOAM.

The semiconductor industry plays a vital role in driving technological advancements, and the incorporation of AI (Artificial Intelligence) can greatly enhance its efficiency and productivity. Through optimizing material usage and reducing defects, AI can significantly reduce costs and enhance production efficiency and product quality. However, despite the increasing interest in AI applications in the semiconductor industry, comprehensive reviews are lacking to systematically analyze existing research and identify the challenges and opportunities in this field. This review aims to bridge this gap by providing a thorough overview of AI-driven techniques in optimizing semiconductor manufacturing and offering valuable insights for future research directions. The integration of Artificial Intelligence (AI) into chip design marks a transformative phase for the semiconductor industry. Traditional design methodologies, often labor-intensive and time-consuming, are increasingly constrained by human expertise and iterative processes. Generative AI, utilizing advanced machine learning models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), offers innovative approaches to automate and optimize various stages of chip design. This paper examines how generative AI can revolutionize chip design by automating complex tasks, including architecture exploration, circuit optimization, and layout generation. Through case studies, we demonstrate significant improvements in design efficiency, performance optimization, and reduced time-to-market. Additionally, we address challenges such as data availability, model interpretability, and the integration of AI-generated designs into existing verification workflows. The findings highlight the potential of generative AI to enhance design capabilities, reduce development costs, and accelerate innovation in semiconductor technology.

With the growing popularity of mobile devices, the trend in the field of system-on-chip has shifted from high performance to low power operation. However, traditional design methodology is limited by the design margins reserved for process, voltage and temperature variations. Therefore, a systematic solution that enables real-time timing error detection and correction was proposed to eliminate redundant margins in the design of an ARM microprocessor. A prototype stochastic ARM1136 processor was implemented in TSMC 90nm technology. Two circuit-level techniques, Razor and Surger, are exploited to form a hybrid error detection mechanism by observing both global and local timing information. To enable the deployment of aggressive voltage scaling with hardware-based error tolerance mechanism, we propose an activity-driven optimization flow to reshape the slack distribution based on path-activation probability. The chip achieves a frequency of 250MHz at worst case with 48.82mW power consumption. The overall power overhead of the proposed error tolerance mechanism is about 25% (hold-fixing latches 15.25% plus Razor 10.53%). The energy saving through design margins elimination is 51% (an average of the three corner cases) and a 42.8% saving was measured at the lowest operation voltage.

Problem statement: Most of the information retrieval systems used only client-server architectures. The client-server model though powerful, had some limitations. In mobile computing environment which has both wired network and wireless networks with limited communication capabilities, the performance of the system was very low. Approach: Mobile agents are considered a suitable technology to develop applications such as information retrieval system for mobile computing environment. Mobile agents are autonomous and dynamic entities that can migrate between various nodes in the network. They offer many advantages over traditional design methodologies like: reduction in network load, overcoming network latency and disconnected operations. Since the mobile agents do not need continuous communication with the mobile host, they are not affected by the sudden disconnection of wireless network and the situation of turning mobile host off for power saving. In order to get the complete benefit of mobile agent system, the system must be fault tolerant. In the context of mobile agents, fault-tolerance prevents a partial or complete loss of the agent. Results: Our system in mobile computing environment ensured that the agent arrived at its destination with result and performance of the system improved by the way of reduction in the response time. And also, the system allowed sending more requests by the way of creating many mobile agents without affecting the performance. Conclusion: Our research compared the performance of client-server architecture and fault tolerant mobile agent information retrieval system and proved that our system solved the limitations faced by the client server architecture. The system can also be extended to adhoc networks.