An analogue approach for the processing of biomedical signals

Constant device scaling has signifcantly boosted electronic systems design in the digital domain enabling incorporation of more functionality within small silicon area and at the same time allows high-speed computation. This trend has been exploited for developing high-performance miniaturised systems in a number of application areas like communication, sensor network, main frame computers, biomedical information processing etc. Although successful, the associated cost comes in the form of high leakage power dissipation and systems reliability. With the increase of customer demands for smarter and faster technologies and with the advent of pervasive information processing, these issues may prove to be limiting factors for application of traditional digital design techniques. Furthermore, as the limit of device scaling is nearing, performance enhancement for the conventional digital system design methodology cannot be achieved any further unless innovations in new materials and new transistor design are made. To this end, an alternative design methodology that may enable performance enhancement without depending on device scaling is much sought today.Analogue design technique is one of these alternative techniques that have recently gained considerable interests. Although it is well understood that there are several roadblocks still to be overcome for making analogue-based system design for information processing as the main-stream design technique (e.g., lack of automated design tool, noise performance, efficient passive components implementation on silicon etc.), it may offer a faster way of realising a system with very few components and therefore may have a positive implication on systems performance enhancement. The main aim of this thesis is to explore possible ways of information processing using analogue design techniques in particular in the field of biomedical systems

Controlling underwater robots with electronic nervous systems

We are developing robot controllers based on biomimetic design principles. The goal is to realise the adaptive capabilities of the animal models in natural environments. We report feasibility studies of a hybrid architecture that instantiates a command and coordinating level with computed discrete-time map-based (DTM) neuronal networks and the central pattern generators with analogue VLSI (Very Large Scale Integration) electronic neuron (aVLSI) networks. DTM networks are realised using neurons based on a 1-D or 2-D Map with two additional parameters that define silent, spiking and bursting regimes. Electronic neurons (ENs) based on Hindmarsh-Rose (HR) dynamics can be instantiated in analogue VLSI and exhibit similar behaviour to those based on discrete components. We have constructed locomotor central pattern generators (CPGs) with aVLSI networks that can be modulated to select different behaviours on the basis of selective command input. The two technologies can be fused by interfacing the signals from the DTM circuits directly to the aVLSI CPGs. Using DTMs, we have been able to simulate complex sensory fusion for rheotaxic behaviour based on both hydrodynamic and optical flow senses. We will illustrate aspects of controllers for ambulatory biomimetic robots. These studies indicate that it is feasible to fabricate an electronic nervous system controller integrating both aVLSI CPGs and layered DTM exteroceptive reflexes

Development of a Myoelectric Detection Circuit Platform for Computer Interface Applications

Personal computers and portable electronics continue to rapidly advance and integrate into our lives as tools that facilitate efficient communication and interaction with the outside world. Now with a multitude of different devices available, personal computers are accessible to a wider audience than ever before. To continue to expand and reach new users, novel user interface technologies have been developed, such as touch input and gyroscopic motion, in which enhanced control fidelity can be achieved. For users with limited-to-no use of their hands, or for those who seek additional means to intuitively use and command a computer, novel sensory systems can be employed that interpret the natural electric signals produced by the human body as command inputs. One of these novel sensor systems is the myoelectric detection circuit, which can measure electromyographic (EMG) signals produced by contracting muscles through specialized electrodes, and convert the signals into a usable form through an analog circuit. With the goal of making a general-purpose myoelectric detection circuit platform for computer interface applications, several electrical circuit designs were iterated using OrCAD software, manufactured using PCB fabrication techniques, and tested with electrical measurement equipment and in a computer simulation. The analog circuit design culminated in a 1.35” x 0.8” manufactured analog myoelectric detection circuit unit that successfully converts a measured EMG input signal from surface skin electrodes to a clean and usable 0-5 V DC output that seamlessly interfaces with an Arduino Leonardo microcontroller for further signal processing and logic operations. Multiple input channels were combined with a microcontroller to create an EMG interface device that was used to interface with a PC, where simulated mouse cursor movement was controlled through the voluntary EMG signals provided by a user. Functional testing of the interface device was performed, which showed a long battery life of 44.6 hours, and effectiveness in using a PC to type with an on-screen keyboard

Graduate Course Descriptions, 2006 Winter

Wright State University graduate course descriptions from Winter 2006

Graduate Course Descriptions, 2005 Fall

Wright State University graduate course descriptions from Fall 2005

Development of PVDF tactile dynamic sensing in a behaviour-based assembly robot

The research presented in this thesis focuses on the development of tactile event sig¬ nature sensors and their application, especially in reactive behaviour-based robotic assembly systems.In pursuit of practical and economic sensors for detecting part contact, the application ofPVDF (polyvinylidene fluoride) film, a mechanical vibration sensitive piezo material, is investigated. A Clunk Sensor is developed which remotely detects impact vibrations, and a Push Sensor is developed which senses small changes in the deformation of a compliant finger surface. The Push Sensor is further developed to provide some force direction and force pattern sensing capability.By being able to detect changes of state in an assembly, such as a change of contact force, an assembly robot can be well informed of current conditions. The complex structure of assembly tasks provides a rich context within which to interpret changes of state, so simple binary sensors can conveniently supply a lot more information than in the domain of mobile robots. Guarded motions, for example, which require sensing a change of state, have long been recognised as very useful in part mating tasks. Guarded motions are particularly well suited to be components of assembly behavioural modules.In behaviour-based robotic assembly systems, the high level planner is endowed with as little complexity as possible while the low level planning execution agent deals with actual sensing and action. Highly reactive execution agents can provide advantages by encapsulating low level sensing and action, hiding the details of sensori-motor complexity from the higher levels.Because behaviour-based assembly systems emphasise the utility of this kind of quali¬ tative state-change sensor (as opposed to sensors which measure physical quantities), the robustness and utility of the Push Sensor was tested in an experimental behaviourbased system. An experimental task of pushing a ring along a convoluted stiff wire is chosen, in which the tactile sensors developed here are aided by vision. Three differ¬ ent methods of combining these different sensors within the general behaviour-based paradigm are implemented and compared. This exercise confirms the robustness and utility of the PVDF-based tactile sensors. We argue that the comparison suggests that for behaviour-based assembly systems using multiple concurrent sensor systems, bottom-level motor control in terms of force or velocity would be more appropriate than positional control. Behaviour-based systems have traditionally tried to avoid symbolic knowledge. Considering this in the light of the above work, it was found useful to develop a taxonomy of type of knowledge and refine the prohibition

Fabrication and Application of a Polymer Neuromorphic Circuitry Based on Polymer Memristive Devices and Polymer Transistors

Neuromorphic engineering is a discipline that aims to address the shortcomings of today\u27s serial computers, namely large power consumption, susceptibility to physical damage, as well as the need for explicit programming, by applying biologically-inspired principles to develop neural systems with applications such as machine learning and perception, autonomous robotics and generic artificial intelligence. This doctoral dissertation presents work performed fabricating a previously developed type of polymer neuromorphic architecture, termed Polymer Neuromorphic Circuitry (PNC), inspired by the McCulloch-Pitts model of an artificial neuron. The major contribution of this dissertation is a development of processing techniques necessary to realize the Polymer Neuromorphic Circuitry, which required a development of individual polymer electronics elements, as well as customization of fabrication processes necessary for the realization of the circuitry on separate substrates as well as on a single substrate. This is the first demonstration of a fabrication of an entire neuron, and more importantly, a network of such neurons, that includes both the weighting functionality of a synapse and the somatic summing, all realized with polymer electronics technology. Polymer electronics is a new branch of electronics that is based on conductive and semi-conductive polymers. These new elements hold a great advantage over the conventional, inorganic electronics in the form of physical flexibility, low cost and ease of fabrication, manufacturing compatibility with many substrate materials, as well as greater biological compatibility. These advantages were the primary motivation for the choice to fabricate all of the electrical components required to realize the PNC, namely polymer transistors, polymer memristive devices, and polymer resistors, with polymer electronics components. The efficacy of this design is validated by demonstrating that the activation function of a single neuron approximates the sigmoidal function commonly employed by artificial neural networks. The utility of the neuromorphic circuitry is further corroborated by illustrating that a network of such neurons, and even a single neuron, are capable of performing linear classification for a real-life problem

An experimental design framework for evolutionary robotics

Based on the failures of work in the area of machine intelligence in the past, a new paradigm has been proposed: for a machine to develop intelligence it should be able to interact with and survive within a hostile dynamic environment. It should therefore be able to display adaptive behaviour and respond correctly to changes in its situation. This means that before higher cognitive properties can be modeled, the modeling of the lower levels of intelligence would be achieved first. Only by building on this platform of physical and mental abilities may it be possible to develop true intelligence. One train of thought for implementing this is to control and design a robot by modeling the neuroethology of simpler animals such as insects. This thesis outlines one approach to the design and development of such a robot, controlled by a neural network, by combining the work of a number of researchers in the areas of machine intelligence and artificial life. It involves Rodney Brooks’ subsumption architecture, Randall D. Beer’s work in the area of computational neuroethology, Richard Dawkins’ work in the area of biomorphs and computational embryology and finally the work of John Holland and David Goldberg in genetic algorithms. This thesis will demonstrate the method and reasoning behind the combination of the work of the above named researchers. It will also detail and analyse the results obtained by their application