Can my chip behave like my brain?

Many decades ago, Carver Mead established the foundations of neuromorphic systems. Neuromorphic systems are analog circuits that emulate biology. These circuits utilize subthreshold dynamics of CMOS transistors to mimic the behavior of neurons. The objective is to not only simulate the human brain, but also to build useful applications using these bio-inspired circuits for ultra low power speech processing, image processing, and robotics. This can be achieved using reconfigurable hardware, like field programmable analog arrays (FPAAs), which enable configuring different applications on a cross platform system. As digital systems saturate in terms of power efficiency, this alternate approach has the potential to improve computational efficiency by approximately eight orders of magnitude. These systems, which include analog, digital, and neuromorphic elements combine to result in a very powerful reconfigurable processing machine.Ph.D

KYPO4INDUSTRY: A Testbed for Teaching Cybersecurity of Industrial Control Systems

There are different requirements on cybersecurity of industrial control systems and information technology systems. This fact exacerbates the global issue of hiring cybersecurity employees with relevant skills. In this paper, we present KYPO4INDUSTRY training facility and a course syllabus for beginner and intermediate computer science students to learn cybersecurity in a simulated industrial environment. The training facility is built using open-source hardware and software and provides reconfigurable modules of industrial control systems. The course uses a flipped classroom format with hands-on projects: the students create educational games that replicate real cyber attacks. Throughout the semester, they learn to understand the risks and gain capabilities to respond to cyber attacks that target industrial control systems. Our described experience from the design of the testbed and its usage can help any educator interested in teaching cybersecurity of cyber-physical systems

Nonlinear Circuit Analysis via Perturbation Methods and Hardware Prototyping

Nonlinear signal processing is necessary in many emerging applications where form factor and power are at a premium. In order to make such complex computation feasible under these constraints, it is necessary to implement the signal processors as analog circuits. Since analog circuit design is largely based on a linear systems perspective, new tools are being introduced to circuit designers that allow them to understand and exploit circuit nonlinearity for useful processing. This paper discusses two such tools, which represent nonlinear circuit behavior in a graphical way, making it easy to develop a qualitative appreciation for the circuits under study

Machine learning in weather prediction and climate analyses : applications and perspectives

In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research - photovoltaic and wind energy, atmospheric physics and processes; in climate research - parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting

Study and design of an interface for remote audio processing

This project focused on the study and design of an interface for remote audio processing, with the objective of acquiring by filtering, biasing, and amplifying an analog signal before digitizing it by means of two MCP3208 ADCs to achieve a 24-bit resolution signal. The resulting digital signal was then transmitted to a Raspberry Pi using SPI protocol, where it was processed by a Flask server that could be accessed from both local and remote networks. The design of the PCB was a critical component of the project, as it had to accommodate various components and ensure accurate signal acquisition and transmission. The PCB design was created using KiCad software, which allowed for the precise placement and routing of all components. A major challenge in the design of the interface was to ensure that the analog signal was not distorted during acquisition and amplification. This was achieved through careful selection of amplifier components and using high-pass and low-pass filters to remove any unwanted noise. Once the analog signal was acquired and digitized, the resulting digital signal was transmitted to the Raspberry Pi using SPI protocol. The Raspberry Pi acted as the host for a Flask server, which could be accessed from local and remote networks using a web browser. The Flask server allowed for the processing of the digital signal and provided a user interface for controlling the gain and filtering parameters of the analog signal. This enabled the user to adjust the signal parameters to suit their specific requirements, making the interface highly flexible and adaptable to a variety of audio processing applications. The final interface was capable of remote audio processing, making it highly useful in scenarios where the audio signal needed to be acquired and processed in a location separate from the user. For example, it could be used in a recording studio, where the audio signal from the microphone could be remotely processed using the interface. The gain and filtering parameters could be adjusted in real-time, allowing the sound engineer to fine-tune the audio signal to produce the desired recording. In conclusion, the project demonstrated the feasibility and potential benefits of using a remote audio processing system for various applications. The design of the PCB, selection of components, and use of the Flask server enabled the creation of an interface that was highly flexible, accurate, and adaptable to a variety of audio processing requirements. Overall, the project represents a significant step forward in the field of remote audio processing, with the potential to benefit many different applications in the future

Hybrid experimental and model approaches for the caracterization of Lymnaea stagnalis neural activity. Part. I: Experimental and theoretical characterisation of CPG activity in Lymnaea stagnalis

Máster en Investigación e Innovación en las TICsCentral Pattern Generators (CPG) are neural circuits that produce and coordinate rhythmic motor activity. Their robust rhythms consist of sequences of neuron activations, which result in e ective motor patterns. These rhythms are at the same time exible and can adapt as a function of the behavioral context. This work characterises the intervals that build up the rhythm and the associated sequence of the feeding CPG of the mollusc Lymnaea Stagnalis. The study entails both the activity obtained in electrophysiolocal recordings of living neurons and in a realistic conductance-based model. The analysis reported here assesses the quanti cation of the variability of the intervals and the existence of relationships between some of these intervals and the period in the form of dynamical invariants

Performance analysis of single board computer clusters

The past few years have seen significant developments in Single Board Computer (SBC) hardware capabilities. These advances in SBCs translate directly into improvements in SBC clusters. In 2018 an individual SBC has more than four times the performance of a 64-node SBC cluster from 2013. This increase in performance has been accompanied by increases in energy efficiency (GFLOPS/W) and value for money (GFLOPS/$). We present systematic analysis of these metrics for three different SBC clusters composed of Raspberry Pi 3 Model B, Raspberry Pi 3 Model B+ and Odroid C2 nodes respectively. A 16-node SBC cluster can achieve up to 60GFLOPS, running at 80W. We believe that these improvements open new computational opportunities, whether this derives from a decrease in the physical volume required to provide a fixed amount of computation power for a portable cluster; or the amount of compute power that can be installed given a fixed budget in expendable compute scenarios. We also present a new SBC cluster construction form factor named Pi Stack; this has been designed to support edge compute applications rather than the educational use-cases favoured by previous methods. The improvements in SBC cluster performance and construction techniques mean that these SBC clusters are realising their potential as valuable developmental edge compute devices rather than just educational curiosities