Vlsi Implementation of Olfactory Cortex Model

This thesis attempts to implement the building blocks required for the realization of the biologically motivated olfactory neural model in silicon as the special purpose hardware. The olfactory model is originally developed by R. Granger, G. Lynch, and Ambros-Ingerson. CMOS analog integrated circuits were used for this purpose. All of the building blocks were fabricated using the MOSIS service and tested at our site. The results of this study can be used to realize a system level integration of the olfactory model.Electrical Engineerin

Spiking Neural Networks: Modification and Digital Implementation

Real-time large-scale simulation of biological systems is a challenging task due to nonlinear functions describing biochemical reactions in the cells. Being fast, cost and power efficient alongside of capability to work in parallel have made hardware an attractive choice for simulation platform. This thesis proposes a neuromorphic platform for online Spike Timing Dependant Plasticity (STDP) learning, based on the COordinate Rotation DIgital Computer (CORDIC) algorithms. The implemented platform comprises two main components. First, the Izhikevich neuron model is modified for implementation using the CORDIC algorithm and simulated to ensure the model accuracy. Afterwards, the model was described as hardware and implemented on Field Programmable Gate Array (FPGA). Second, the STDP learning algorithm is adapted and optimized using the CORDIC method, synthesized for hardware, and implemented to perform on-FPGA online learning on a network of CORDIC Izhikevich neurons to demonstrate competitive Hebbian learning. The implementation results are compared with the original model and state-of-the-art to verify accuracy, effectiveness, and higher speed of the system. These comparisons confirm that the proposed neuromorphic system offers better performance and higher accuracy while being straightforward to implement and suitable to scale. New findings show that astrocytes are important parts of the information processing in brain and believed to be responsible for some brain diseases such as Alzheimer and Epilepsy. Astrocytes generate Ca$^{2+}$ waves and release neuro-transmitters over a large area. To study astrcoytes, one need to simulate large number of biologically realistic models of these cells alongside neuron models. Software simulation is flexible but slow. This thesis proposes a high-speed and low-cost digital hardware to replicate biological-plausible astrocyte and glutamate release mechanism. The nonlinear terms of these models were calculated using high-precision and cost-efficient algorithms. Subsequently, the modified models were simulated to study and validate their functions. Several hardware were developed by setting different constraints to investigate trade-offs and achieve best possible design. As proof of concept, the design was implemented on a FPGA device. Hardware implementation results confirmed the ability of the design to replicate biological cells in detail with high accuracy. As for performance, the proposed design turned out to be far more faster and area efficient than previously published works that targeted digital hardware for biological-plausible astrocytes. Spiking neurons, the models that mimic the biological cells in the brain, are described using ordinary differential equations. A common method to numerically solve these equations is Euler\u27s method. An important factor that has a significant impact on the performance and cost of the hardware implementation or software simulation of spiking neural networks and yet its importance has been neglected in the published literature, is the time step in Euler\u27s method. In this thesis, first the Izhikevich neuron\u27s accuracy as a function of the time step was measured. It was uncovered that the threshold time step that Izhikevich neuron becomes unstable is an exponential function of the input current. Software simulation performance, including total computational time and memory usage were compared for different time steps. Afterwards, the model was synthesized and implemented on the FPGA. Hardware performance metrics such as speed, area and power consumption were measured for each time step. Results indicated that time step has a negative linear effect on the performance. It was concluded that by determining maximum input current to the neuron, larger time steps comparable to those used in the previous works could be employed

The application of artificial neural networks to interpret acoustic emissions from submerged arc welding

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Automated fusion welding processes play a fundamental role in modern manufacturing industries. The proliferation of joint geometries together with the large permutation of associated process variable configurations has given rise to research into complex system modelling and control strategies. Many of these techniques have involved monitoring of not only the electrical characteristics of the process but visual and acoustic information. Acoustic information derived from certain welding processes is well documented as it is an established fact that skilled manual welders utilise such information as an aid to creating an optimum weld. The experimental investigation presented in this thesis is dedicated to the feasibility of monitoring airborne acoustic emissions of Submerged Arc Welding (SAW) for diagnostic and real time control purposes. The experimental method adopted for this research takes a cybernetic approach to data processing and interpretation in an attempt to replicate the robustness of human biological functions. A custom designed audio hardware system was used to analyse signals obtained from bead on mild steel plate fusion welds. Time and frequency domains were used in an attempt to establish salient characteristics or identify the signatures associated with changes of the process variables. The featured parameters were voltage / current and weld travel speed, due to their ease of validation. However, consideration has also been given to weld defect prediction due to process instabilities. As the data proved to be highly correlated and erratic when subjected to off line statistical analysis, extensive investigation was given to the application of artificial neural networks to signal processing and real time control scenarios. As a consequence, a dedicated neural based software system was developed, utilising supervised and unsupervised neural techniques to monitor the process. The research was aimed at proving the feasibility of monitoring the electrical process parameters and stability of the welding process in real time. It was shown to be possible, by the exploitation of artificial neural networks, to generate a number of monitoring parameters indicative of the welding process state. The limitations of the present neural method and proposed developments are discussed, together with an overview of applied neural network technology and its impact on artificial intelligence and robotic control. Further developments are considered together with recommendations for future areas of research

Computer Science & Technology Series : XIX Argentine Congress of Computer Science. Selected papers

CACIC’13 was the nineteenth Congress in the CACIC series. It was organized by the Department of Computer Systems at the CAECE University in Mar del Plata. The Congress included 13 Workshops with 165 accepted papers, 5 Conferences, 3 invited tutorials, different meetings related with Computer Science Education (Professors, PhD students, Curricula) and an International School with 5 courses. CACIC 2013 was organized following the traditional Congress format, with 13 Workshops covering a diversity of dimensions of Computer Science Research. Each topic was supervised by a committee of 3-5 chairs of different Universities. The call for papers attracted a total of 247 submissions. An average of 2.5 review reports were collected for each paper, for a grand total of 676 review reports that involved about 210 different reviewers. A total of 165 full papers, involving 489 authors and 80 Universities, were accepted and 25 of them were selected for this book.Red de Universidades con Carreras en Informática (RedUNCI

Colour and Colorimetry Multidisciplinary Contributions Vol. XIb

It is well known that the subject of colour has an impact on a range of disciplines. Colour has been studied in depth for many centuries, and as well as contributing to theoretical and scientific knowledge, there have been significant developments in applied colour research, which has many implications for the wider socio-economic community. At the 7th Convention of Colorimetry in Parma, on the 1st October 2004, as an evolution of the previous SIOF Group of Colorimetry and Reflectoscopy founded in 1995, the "Gruppo del Colore" was established. The objective was to encourage multi and interdisciplinary collaboration and networking between people in Italy that addresses problems and issues on colour and illumination from a professional, cultural and scientific point of view. On the 16th of September 2011 in Rome, in occasion of the VII Color Conference, the members assembly decided to vote for the autonomy of the group. The autonomy of the Association has been achieved in early 2012. These are the proceedings of the English sessions of the XI Conferenza del Colore