An efficient telemetry system for restoring sight

PhD ThesisThe human nervous system can be damaged as a result of disease or trauma, causing conditions such as Parkinson’s disease. Most people try pharmaceuticals as a primary method of treatment. However, drugs cannot restore some cases, such as visual disorder. Alternatively, this impairment can be treated with electronic neural prostheses. A retinal prosthesis is an example of that for restoring sight, but it is not efficient and only people with retinal pigmentosa benefit from it. In such treatments, stimulation of the nervous system can be achieved by electrical or optical means. In the latter case, the nerves need to be rendered light sensitive via genetic means (optogenetics). High radiance photonic devices are then required to deliver light to the target tissue. Such optical approaches hold the potential to be more effective while causing less harm to the brain tissue. As these devices are implanted in tissue, wireless means need to be used to communicate with them. For this, IEEE 802.15.6 or Bluetooth protocols at 2.4GHz are potentially compatible with most advanced electronic devices, and are also safe and secure. Also, wireless power delivery can operate the implanted device. In this thesis, a fully wireless and efficient visual cortical stimulator was designed to restore the sight of the blind. This system is likely to address 40% of the causes of blindness. In general, the system can be divided into two parts, hardware and software. Hardware parts include a wireless power transfer design, the communication device, power management, a processor and the control unit, and the 3D design for assembly. The software part contains the image simplification, image compression, data encoding, pulse modulation, and the control system. Real-time video streaming is processed and sent over Bluetooth, and data are received by the LPC4330 six layer implanted board. After retrieving the compressed data, the processed data are again sent to the implanted electrode/optrode to stimulate the brain’s nerve cells

Development of an intelligent self-learning product assembly system using visual identification

Thesis (Master of Engineering in Electrical Engineering) -- Central University of Technology, Free State, 2018Modern automation systems rely on fixed programming to carry out their production routines. These systems are effective for production outputs but do not allow any flexibility within the production routine. Effort is required to change the ongoing production routine through reprogramming, redesign or complete overhaul of the system to cater for new production outputs. These efforts require down time and result in a loss of revenue. If a completely automated flexible system is introduced into such a production line, the complete reprogramming process required to cater for new production needs could be automated without losing production time. Within this study, a real-time KUKA Robotic Control system is introduced. The KUKA Robotic Controller maintains its original programming methods with no reprogramming required when executing a new production assembly. This is achieved through manoeuvring the KUKA Robotic System in real-time to new destinations based on image-processing outputs and feedback. For demonstration purposes and proof of concept, the system learns a design presented to it by an end user and then reproduces this seen design based on the image-processing results in terms of location and orientation. Therefore, instead of reprogramming each new required position, the system takes over real-time control of the KUKA Robotic System and carries out the required steps autonomously. The benefit of such a system would be that the KUKA Robotic System would not require reprogramming to carry out new routines. It is controlled in a real-time environment to carry out new procedures based on external sensors (in this case, image-processing outputs). KUKA Robotic Sensor Interface (RSI) software is used to implement real-time control of the KUKA Robotic System and is explored extensively throughout this study

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Design analysis of levitation facility for space processing applications

Containerless processing facilities for the space laboratory and space shuttle are defined. Materials process examples representative of the most severe requirements for the facility in terms of electrical power, radio frequency equipment, and the use of an auxiliary electron beam heater were used to discuss matters having the greatest effect upon the space shuttle pallet payload interfaces and envelopes. Improved weight, volume, and efficiency estimates for the RF generating equipment were derived. Results are particularly significant because of the reduced requirements for heat rejection from electrical equipment, one of the principal envelope problems for shuttle pallet payloads. It is shown that although experiments on containerless melting of high temperature refractory materials make it desirable to consider the highest peak powers which can be made available on the pallet, total energy requirements are kept relatively low by the very fast processing times typical of containerless experiments and allows consideration of heat rejection capabilities lower than peak power demand if energy storage in system heat capacitances is considered. Batteries are considered to avoid a requirement for fuel cells capable of furnishing this brief peak power demand

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Sensor function virtualization to support distributed intelligence in the internet of things

It is estimated that-by 2020-billion devices will be connected to the Internet. This number not only includes TVs, PCs, tablets and smartphones, but also billions of embedded sensors that will make up the "Internet of Things" and enable a whole new range of intelligent services in domains such as manufacturing, health, smart homes, logistics, etc. To some extent, intelligence such as data processing or access control can be placed on the devices themselves. Alternatively, functionalities can be outsourced to the cloud. In reality, there is no single solution that fits all needs. Cooperation between devices, intermediate infrastructures (local networks, access networks, global networks) and/or cloud systems is needed in order to optimally support IoT communication and IoT applications. Through distributed intelligence the right communication and processing functionality will be available at the right place. The first part of this paper motivates the need for such distributed intelligence based on shortcomings in typical IoT systems. The second part focuses on the concept of sensor function virtualization, a potential enabler for distributed intelligence, and presents solutions on how to realize it

Sleep studies in mice - open and closed loop devices for untethered recording and stimulation

Sleep is an important biological processes that has been studied extensively to date. Research in sleep typically involves mice experiments that use heavy benchtop equipment or basic neural loggers to record ECoG/EMG signals which are then processed offline in workstations. These systems limit the complexity of experiments that can be carried out to only simple open loop recordings, due to either the tethered setup used, which restricts animal movements, or the lack of devices that can offer more advanced features without compromising its portability. With rising popularity in exploring more physiological features that can affect sleep, such as temperature, whose importance has been highlighted in several papers [1][2][3] and advances in optogenetic stimulation, allowing high temporal and spatial neural control, there is now an unprecedented demand for experimental setups using new closed loop paradigms. To address this, this thesis presents compact and lightweight neural logging devices that are not only capable of measuring ECoG and EMG signals for core sleep analysis but also capable of taking high resolution temperature recordings and delivering optogenetic stimulus with fully adjustable parameters. Together with its embedded on-board automatic sleep stage scoring algorithm, the device will allow researchers for the first time to be able to quickly uncover the role a neural circuit plays in sleep regulation through selective neural stimulation when the animal is under the target sleep vigilance state. Original contributions include: the development of two novel multichannel neural logging devices, one for core sleep analysis and another for closed loop experimentation; the development and implementation of a lightweight, fast and highly accurate automatic on-line sleep stage scoring algorithm; and the development of a custom optogenetic coupler that is compatible with most current optogenetic setups for LED-Optical fibre coupling.Open Acces