Computational aerodynamics and artificial intelligence

The general principles of artificial intelligence are reviewed and speculations are made concerning how knowledge based systems can accelerate the process of acquiring new knowledge in aerodynamics, how computational fluid dynamics may use expert systems, and how expert systems may speed the design and development process. In addition, the anatomy of an idealized expert system called AERODYNAMICIST is discussed. Resource requirements for using artificial intelligence in computational fluid dynamics and aerodynamics are examined. Three main conclusions are presented. First, there are two related aspects of computational aerodynamics: reasoning and calculating. Second, a substantial portion of reasoning can be achieved with artificial intelligence. It offers the opportunity of using computers as reasoning machines to set the stage for efficient calculating. Third, expert systems are likely to be new assets of institutions involved in aeronautics for various tasks of computational aerodynamics

Teaching Hardware Reverse Engineering: Educational Guidelines and Practical Insights

Since underlying hardware components form the basis of trust in virtually any computing system, security failures in hardware pose a devastating threat to our daily lives. Hardware reverse engineering is commonly employed by security engineers in order to identify security vulnerabilities, to detect IP violations, or to conduct very-large-scale integration (VLSI) failure analysis. Even though industry and the scientific community demand experts with expertise in hardware reverse engineering, there is a lack of educational offerings, and existing training is almost entirely unstructured and on the job. To the best of our knowledge, we have developed the first course to systematically teach students hardware reverse engineering based on insights from the fields of educational research, cognitive science, and hardware security. The contribution of our work is threefold: (1) we propose underlying educational guidelines for practice-oriented courses which teach hardware reverse engineering; (2) we develop such a lab course with a special focus on gate-level netlist reverse engineering and provide the required tools to support it; (3) we conduct an educational evaluation of our pilot course. Based on our results, we provide valuable insights on the structure and content necessary to design and teach future courses on hardware reverse engineering

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Modeling the Bat Spatial Navigation System: A Neuromorphic VLSI Approach

Autonomously navigating robots have long been a tough challenge facing engineers. The recent push to develop micro-aerial vehicles for practical military, civilian, and industrial use has added a significant power and time constraint to the challenge. In contrast, animals, from insects to humans, have been navigating successfully for millennia using a wide range of variants of the ultra-low-power computational system known as the brain. For this reason, we look to biological systems to inspire a solution suitable for autonomously navigating micro-aerial vehicles. In this dissertation, the focus is on studying the neurobiological structures involved in mammalian spatial navigation. The mammalian brain areas widely believed to contribute directly to navigation tasks are the Head Direction Cells, Grid Cells and Place Cells found in the post-subiculum, the medial entorhinal cortex, and the hippocampus, respectively. In addition to studying the neurobiological structures involved in navigation, we investigate various neural models that seek to explain the operation of these structures and adapt them to neuromorphic VLSI circuits and systems. We choose the neuromorphic approach for our systems because we are interested in understanding the interaction between the real-time, physical implementation of the algorithms and the real-world problem (robot and environment). By utilizing both analog and asynchronous digital circuits to mimic similar computations in neural systems, we envision very low power VLSI implementations suitable for providing practical solutions for spatial navigation in micro-aerial vehicles

Hardware/Software Co-Design Architecture and Implementations of MIMO Decoders on FPGA

During the last years, multiple-input multiple-output (MIMO) technology has attracted great attentions in the area of wireless communications. The hardware implementation of MIMO decoders becomes a challenging task as the complexity of the MIMO system increases. This thesis presents hardware/software co-design architecture and implementations of two typical lattice decoding algorithms, including Agrell and Vardy (AV) algorithm and Viterbo and Boutros (VB) algorithm. Three levels of parallelisms are analyzed for an efficient implementation with the preprocessing part on embedded MicroBlaze soft processor and the decoding part on customized hardware. The decoders for a 4 by 4 MIMO system with 16-QAM modulation scheme are prototyped on a Xilinx XC2VP30 FPGA device. The hardware implementations of the AV and VB decoders show that they support up to 81 Mbps and 37 Mbps data rate respectively. The performances in terms of resource utilizations and BER are also compared between these two decoders

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

New concepts in tele-autonomous systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76226/1/AIAA-1987-1686-200.pd