Biologically-inspired Systems Research Articles

A Survey on Modulation Techniques in Molecular Communication via Diffusion

This survey paper focuses on modulation aspects of molecular communication, an emerging field focused on building biologically-inspired systems that embed data within chemical signals. The primary challenges in designing these systems are how to encode and modulate information onto chemical signals, and how to design a receiver that can detect and decode the information from the corrupted chemical signal observed at the destination. In this article, we focus on modulation design for molecular communication via diffusion systems. In these systems, chemical signals are transported using diffusion, possibly assisted by flow, from the transmitter to the receiver. This tutorial presents recent advancements in modulation and demodulation schemes for molecular communication via diffusion. We compare five different modulation types: concentration-based, type-based, timing-based, spatial, and higher-order modulation techniques. The end-to-end system designs for each modulation scheme are presented. In addition, the key metrics used in the literature to evaluate the performance of these techniques are also presented. Finally, we provide a numerical bit error rate comparison of prominent modulation techniques using analytical models. We close the tutorial with a discussion of key open issues and future research directions for design of molecular communication via diffusion systems.

Vision, perception, navigation and ‘cognition’ in honeybees and applications to aerial robotics

This review summarizes research carried out in the author’s laboratory investigating the ways in which honeybees use vision to guide their flight and navigate in their environment, and describes how these principles have been used to design, build and test biologically-inspired systems for the guidance and navigation of unmanned aerial vehicles. It also outlines studies investigating the capacities of honeybees in the areas of visual perception, pattern recognition, and ‘cognition’.

Double-Acting Sleeve Muscle Actuator for Bio-Robotic Systems.

This paper presents a new type of muscle-like actuator, namely double-acting (DA) sleeve muscle actuator, which is suitable for the actuation of biologically-inspired and biomedical robotic systems, especially those serving human-assistance purposes (prostheses, orthoses, etc.). Developed based on the traditional pneumatic muscle actuator, the new DA sleeve muscle incorporates a unique insert at the center. With the insert occupying the central portion of the internal volume, this new actuator enjoys multiple advantages relative to the traditional pneumatic muscle, including a consistent increase of force capacity over the entire range of motion, and a significant decrease of energy consumption in operation. Furthermore, the insert encompasses an additional chamber, which generates an extension force when pressurized. As such, this new actuator provides a unique bi-directional actuation capability, and, thus, has a potential to significantly simplify the design of a muscle actuator-powered robotic system. To demonstrate this new actuator concept, a prototype has been designed and fabricated, and experiments conducted on this prototype demonstrated the enhanced force capacity and the unique bi-directional actuation capability.

Editorial: Biologically-inspired radar and sonar systems

In the last century both radar and sonar active technology developed from inception to the point where high resolution images can be obtained from long ranges. The available technology can exploit Doppler effects, structural resonances, nonlinear scattering, synthetic aperture platforms, and sediment-penetrating modalities. Active sonar and radar share many common approaches, in part because of the similarities of the problems they address, both in the military and commercial research areas. Moreover, in the last two decades, research into both radar and sonar has explored biomimetic and bioinspired solutions [1-5], in recognition of the fact that whilst man-made systems have access to power and bandwidth far beyond that available to any living organism [6], some biological solutions have benefitted from millions of years of natural optimisation to evolve sensing capabilities and strategies and meet the challenges of survival (finding food and mates, avoiding predators, sensing and navigating to and within appropriate habitats, etc.) [7]. Problem solving for survival goes beyond the particular radiation used in sensing, covering also signal processing, detection and classification of targets, use of platforms, and strategies for deploying sensors and interpreting data [8-10]. Bio-inspired approaches follow logically when the problems facing manufactured technology resemble those addressed in nature. For example, in the last 15 years the arena for sonar challenge has changed from the passive detection of large quiet nuclear submarines in the deep, relatively quiet and uncluttered waters, to minehunting by active sonar in shallow coastal waters [11], a problem far closer to that faced by dolphins and bats in their natural environments [12,13].

An interview with Stewart W. Wilson

Stewart W. Wilson is certainly one of the most innovative and functionality-oriented thinkers I have ever had the honor to meet. His research career commenced at the Massachusetts Institute of Technology (MIT), where he received an S.B. degree in physics and an S.M. and PhD degree in electrical engineering. After having completed his formal education, research work at the Polaroid Corporation (starting in 1962) and the Rowland Institute for Science (starting in 1983) led him to the development of practically useful, yet highly complex, biologically-inspired systems and machine learning architectures. In 1998 he founded the research and consultancy company Prediction Dynamics. Since 1999 he has been an adjunct professor at the University of Illinois at Urbana-Champaign, IL.

A PARTIAL IMPLEMENTATION OF THE BICA COGNITIVE DECATHLON USING THE PSYCHOLOGY EXPERIMENT BUILDING LANGUAGE (PEBL)

The Cognitive Decathlon is a proposed set of tasks that can be tested on both human and artificially intelligent agents, and which constitutes a modern specification for the Turing Test. In this paper, a partial implementation of the Cognitive Decathlon is described using the Psychology Experiment Building Language (PEBL). The tasks focus not simply on generic human abilities, but on critical skills that highlight aspects of human performance that are at odds with common artificial intelligence approaches. The differences between human and algorithmic behavior in such tasks can reveal properties of the human cognitive architecture, and production of similar behavior by artificial systems can help constrain and validate biologically-inspired systems.

Path finding in the tile assembly model

Swarm robotics, active self-assembly, and amorphous computing are fields that focus on designing systems of large numbers of small, simple components that can cooperate to complete complex tasks. Many of these systems are inspired by biological systems, and all attempt to use the simplest components and environments possible, while still being capable of achieving their goals. The canonical problems for such biologically-inspired systems are shape assembly and path finding. In this paper, we demonstrate path finding in the well-studied tile assembly model, a model of molecular self-assembly that is strictly simpler than other biologically-inspired models. As in related work, our systems function in the presence of obstacles and can be made fault-tolerant. The path-finding systems use Θ ( 1 ) distinct components and find minimal-length paths in time linear in the length of the path.

A review of biosensors and biologically-inspired systems for explosives detection

This article provides a review of the published literature describing the use of biosensors and biologically-inspired systems for explosives detection. The review focusses on the use of antibodies, enzymes, biologically-inspired synthetic ligands and whole-cell biosensors, providing a flavour of the range of technology, formats and approaches that can be used to detect explosives using biological systems.

Machines mimicking humans

The more we come to understand human biological systems, the more biomemesis, the study of imitating nature, is proving to be a powerful inspiration to roboticists. The research has potential as a human prosthetic and in robotics applications in which a machine needs visual input to do a job, such as in space or a dangerous environment. Much robotics research seeks ways to create machines that can go where humans can't go and do things human can't do safely. It seems the way to such discoveries increasingly means studying human biological systems for inspiration and tools for creating robustness and efficiency. The challenges biological systems present offer us new ways to look at organization and communication - as well as the very definitions of human and mechanical.

Microwave‐driven thunder materials

AbstractAn experimental study of a smart‐materials actuator driven by a microwave is presented in this paper. A proof‐of‐concept experiment using a smart material, such as thin layer composite unimorph ferroelectric driver and sensor (THUNDER), has been demonstrated with wirelessly transmitted microwave power. Such an advance system will offer a new class of smart devices that are remotely controlled via wirelessly transmitted power. Thus, a system of this nature autonomously functions without an onboard power infrastructure. Potential applications are integrated sensors, actuators, and smart flight control in space applications, as well as biologically‐inspired systems. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 36: 331–333, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10757

Towards a neurally-inspired computer architecture

The first two main rounds of neural computing focused on adaptation and self-organization in neural networks, and on use of analog VLSI for compartmental modeling of the neuron, respectively. This paper is a prospectus for a third round of neural computing: analyzing the architecture of the primate brain to extract neural information processing principles and translate them into biologically-inspired operating systems and computer architectures. The way in which the cerebellum interacts with other brain regions in learning how to better control and coordinate movements provides a case study to introduce key ideas for these three rounds of neural computation. It is argued that the third round will develop and exploit general insights into brain architectures, their function and dynamics, that will provide a principled combination of cooperative computation, learning and perceptual robotics (i.e., the integration of action, perception and computation). This new effort is motivated by recent advances in computational neuroscience research, studying examples of system evolution ranging from low-level vision to how the interactions between frontal and parietal cortices serve action recognition (the mirror system), and language. The paper also notes the vast difference between the slow biological evolution of diverse brains and the needs of computer technology for explicit tools for the design and testing of novel programs and architectures, and suggests the challenges of developing a reflection methodology for wrapping modules with descriptions that can be automatically updated as the module itself adapts through learning. The Appendix provides a road map for approaching the voluminous literature on brain theory and neural networks.

Message from Editors-in-Chief

Message from Editors-in-Chief