| 1. |
- Athanasiou, Vasileios, 1988, et al.
(författare)
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Memristor Models for Early Detection of Sepsis in ICU patients
- 2019
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Ingår i: Computing in Cardiology. - 2325-8861 .- 2325-887X.
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Konferensbidrag (refereegranskat)abstract
- A supervised learning technique is used to carefully train memristor models to predict at an early stage whether a patient in intensive care unit (ICU) has the sepsis. A memristor behaves as a resistor, with a (mem)resistance that changes over time within a bounded interval. The resistance value depends on the full history of an applied voltage difference across the element, in the same way as the state of the brain depends on what a person has experienced in the past. The information contained in a voltage difference time series can be encoded in the resistance value. Clinical variables measured subsequently each hour since the patient's admittance in ICU are transformed into voltage difference signals with transformation functions. The training procedure involves the optimization of the transformation functions. The decision of whether to predict sepsis or not is taken by reading the value of the resistance. The authors have participated in the Physionet 2019 challenge with the name called "the memristive agents" and their best submission resulted to a utility score 0.20 on a hidden test data-set.
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| 2. |
- Athanasiou, Vasileios, 1988, et al.
(författare)
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On a generic theory of the organic electrochemical transistor dynamics
- 2019
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Ingår i: Organic Electronics: physics, materials, applications. - : Elsevier BV. - 1566-1199. ; 72:September, s. 39-49
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Tidskriftsartikel (refereegranskat)abstract
- In the recent years, the organic electrochemical transistors (OECT) have attracted considerable attention for biosensing applications due to the biocompatibility of their materials and their low operating voltages. Upon exposure to an electrolyte, the drain current becomes ion-dependent. This can be exploited for sensing ion applications. To facilitate the process of designing such powerful ion sensing devices one needs the ability to simulate the transient dynamical behavior of many OECT elements connected in a network. We have developed a generic theoretical model of the OECT element that can be used for such purposes. Our OECT element resembles a typical FET three-port element with the response function parameterized with an additional time-dependent variable, T, which describes how far the element operates from the stationary state. We have constructed a dynamical equation that describes how T changes in time when the element is exposed to arbitrary external voltages. This makes the element model highly interoperable with generic electrical circuit simulators. We provide an example of possible numerical implementation using the modified nodal analysis. We tested the underlying theoretical assumptions by comparing model predictions with experimental data and found a reasonable agreement. Our model describes the typical current spikes observed in the literature.
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| 3. |
- Athanasiou, Vasileios, 1988, et al.
(författare)
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On Improving the Computing Capacity of Dynamical Systems
- 2020
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Ingår i: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322 .- 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- Reservoir Computing has emerged as a practical approach for solving temporal pattern recognition problems. The procedure of preparing the system for pattern recognition is simple, provided that the dynamical system (reservoir) used for computation is complex enough. However, to achieve a sufficient reservoir complexity, one has to use many interacting elements. We propose a novel method to reduce the number of reservoir elements without reducing the computing capacity of the device. It is shown that if an auxiliary input channel can be engineered, the drive, advantageous correlations between the signal one wishes to analyse and the state of the reservoir can emerge, increasing the intelligence of the system. The method has been illustrated on the problem of electrocardiogram (ECG) signal classification. By using a reservoir with only one element, and an optimised drive, more than 93% of the signals have been correctly labelled.
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| 4. |
- Athanasiou, Vasileios, 1988, et al.
(författare)
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On Mathematics of Universal Computation with Generic Dynamical Systems
- 2019
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Ingår i: From Parallel to Emergent Computing. - Boca Raton, Florida : CRC Press, [2019] | Produced in celebration of the 25th anniversary of the International Journal of Parallel, Emergent, and Distributed Systems. : CRC Press. - 9781138054011 ; , s. 385-405
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter presents some challenging ideas regarding the possible use of dynamical systems for general-purpose computation, addressing possible strategies of inferring information about the computing capacity of such machines. It discusses some possibilities for developing a unifying theory of reservoir computing, which bridges rigorous mathematical thinking, the state of the art numerical sampling techniques, and the theory of dynamical systems. The chapter addresses the question of what one can actually compute with a reservoir computer. It provides a map of desired mathematical theorems that would be extremely useful for addressing the broad issues, and it as terra incognita of practical theorems. The chapter also presents some numerical examples, as an illustration of how validity tests of the probabilistic conditions from the previous section might be carried out. It explores the usual way of thinking about reservoir computing. The chapter also discusses generic open problems, focuses on a few specific ones, and also provides some fictitious theorems.
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| 5. |
- Athanasiou, Vasileios, 1988, et al.
(författare)
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On Sensing Principles Using Temporally Extended Bar Codes
- 2020
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Ingår i: IEEE Sensors Journal. - 1558-1748 .- 1530-437X. ; 20:13, s. 6782-6791
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Tidskriftsartikel (refereegranskat)abstract
- The detection of ionic variation patterns could be a significant marker for the diagnosis of neurological and other diseases. This paper introduces a novel idea for training chemical sensors to recognise patterns of ionic variations. By using an external voltage signal, a sensor can be trained to output distinct time-series signals depending on the state of the ionic solution. Those sequences can be analysed by a relatively simple readout layer for diagnostic purposes. The idea is demonstrated on a chemical sensor that is sensitive to zinc ions with a simple goal of classifying zinc ionic variations as either stable or varying. The study features both theoretical and experimental results. By extensive numerical simulations, it has been shown that the proposed method works successfully in silico. Distinct time-series signals are found which occur with a high probability under only one class of ionic variations. The related experimental results point in the right direction.
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| 6. |
- Athanasiou, Vasileios, 1988, et al.
(författare)
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On the efficient simulation of electrical circuits with constant phase elements: The Warburg element as a test case
- 2018
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Ingår i: International Journal of Circuit Theory and Applications. - : Wiley. - 0098-9886 .- 1097-007X. ; 46:5, s. 1072-1090
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Tidskriftsartikel (refereegranskat)abstract
- The constant phase Element (CPE) concept naturally emerges as a model for describing a range of electrical phenomena where ionic diffusion is involved. We suggest a new method for modelling the transient behavior of electrical circuits that contain CPE elements. Without loss of generality, we study the Warburg element to demonstrate the method, but the method can be easily extended to any CPE. Transient simulations of such elements require the numerical evaluation of a computationally expensive convolution integral that links the voltage drop across the element, with the current that passed through it. In our work we suggest a new method for reducing the computational cost of the numerical evaluation of the convolution integral.We show that the computational cost can be reduced by one order of magnitude.
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| 7. |
- Athanasiou, Vasileios, 1988, et al.
(författare)
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On the use of collaborative interactions for embedded sensing applications: Memristor networks as intelligent sensing substrates
- 2021
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Ingår i: AISB Convention 2021: Communication and Conversations.
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Konferensbidrag (refereegranskat)abstract
- A novel sensing approach has been investigated in which environment-sensitive memristor networks are used as intelligent sensing substrates. A substrate collects pieces of environment-related information over time and encodes this information into its state. The stored information can be extracted by monitoring how the substrate responds to an external drive signal. An advantage of this indirect sensing approach is that the drive signal can be optimised to make the inference process efficient: even small pieces of information (which might go unnoticed in the traditional sensing setup) are collected. To demonstrate the main ideas an instance of a binary classification problem has been investigated. A separability index has been used as a measure of the substrate quality. By simulating the dynamics of a large number of memristor networks and computing their separability indices, it has been found that heterogeneous networks with delayed feedback elements make good sensing substrates.
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| 8. |
- Athanasiou, Vasileios, 1988, et al.
(författare)
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On using reservoir computing for sensing applications: exploring environment-sensitive memristor networks
- 2018
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Ingår i: International Journal of Parallel, Emergent and Distributed Systems. - : Informa UK Limited. - 1744-5779 .- 1744-5760. ; 33:4, s. 367-386
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Tidskriftsartikel (refereegranskat)abstract
- Recently, the SWEET sensing setup has been proposed as a way of exploiting reservoir computing for sensing. The setup features three components: an input signal (the drive), the environment and a reservoir, where the reservoir and the environment are treated as one dynamical system, a superreservoir. Due to the reservoir-environment interaction, the information about the environment is encoded in the state of the reservoir. This information can be inferred (decoded) by analysing the reservoir state. The decoding is done by using an external drive signal. This signal is optimised to achieve a separation in the space of the reservoir states: Under different environmental conditions, the reservoir should visit distinct regions of the configuration space. We examined this approach theoretically by using an environment-sensitive memristor as a reservoir, where the memristance is the state variable. The goal has been to identify a suitable drive that can achieve the phase space separation, which was formulated as an optimization problem, and solved by a genetic optimization algorithm developed in this study. For simplicity reasons, only two environmental conditions were considered (describing a static and a varying environment). A suitable drive signal has been identified based on intuitive analysis of the memristor dynamics, and by solving the optimization problem. Under both drives the memristance is driven to two different regions of the onedimensional state space under the influence of the two environmental conditions, which can be used to infer about the environment. The separation occurs if there is a synchronisation between the drive and the environmental signals. To quantify the magnitude of the separation, we introduced a quality of sensing index: The ability to sense depends critically on the synchronisation between the drive and environmental conditions. If this synchronisation is not maintained the quality of sensing deteriorates
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| 9. |
- Athanasiou, Vasileios, 1988
(författare)
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Theoretical simulations of dynamical systems for advanced reservoir computing applications
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- There are computational problems that are simply too complex and cannot be handled by traditional CMOS technologies due to practical engineering limitations related to either fundamental physical behavior of devices at small scales, or various energy consumption issues. The field of unconventional computation has emerged as a response to these challenges. Up to date unconventional computation encompasses a plethora of computing frameworks, such as neuromorphic computing, molecular computing, reaction-diffusion computing, or quantum computing, and is ever increasing in its scope. This thesis is biased towards developing sensing applications in the unconventional computing context. This initiative is further extended towards developing novel machine learning applications. The possibility of building intelligent dynamical systems that collect information and analyze it in real-time has been investigated theoretically. The basic idea is to expose a dynamical system to the environment one wishes to analyze over time. The system operates as an environment sensitive reservoir computer. Since the state of the reservoir depends on the environment, the information about the environment one wishes to retrieve gets encoded in the state of the system. The key idea exploited in the thesis is that if the state of the reservoir is highly correlated with the state of the environment then the information about the environment can be inferred with a modest engineering overhead. A typical dynamical system is assumed to be a network of environment sensitive elements. Each element can be something simple, but taken together, the elements acquire collective intelligence that can be harvested. These ideas have been examined theoretically (and verified experimentally) by simulating various networks of environment-sensitive elements: the memristor, the capacitor, the constant phase element and the organic field effect transistor element. The simulations were done in the context of ion sensing, which is an extremely complex, many-body, and multi-scale modeling problem.
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| 10. |
- Athanasiou, Vasileios, 1988
(författare)
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Theoretical simulations of environment-sensitive dynamical systems for advanced reservoir computing applications
- 2018
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The possibility of building intelligent sensing substrates that both collect information about an environment and analyze it in real-time has been investigated theoretically. In a typical setup, a dynamical system is assumed to interact with the environment over time. The system operates as a reservoir computer acting as a reservoir of states. Due to the reservoir-environment interaction, the information about the environment is encoded in the state of the reservoir. The information stored in the system can be inferred (decoded) by analyzing the reservoir state, which is done by observing how a system responds to an external stimulus being an external drive signal. This signal is optimized to ensure that under different environmental conditions the reservoir visits distinct regions of the configuration space. If such a behavior is possible, then a relatively simple readout layer can be used to achieve efficient sensing. These ideas have been examined theoretically by simulating various networks of environment-sensitive elements: the memristor, the capacitor, the constant phase element, and the organic field effect transistor element. It was found that heterogeneity of the network is important for sensing. The simulations were done in the context of ion sensing, which is an extremely complex, many-body, and multi-scale modeling problem. A generic electrical circuit simulator has been developed with a focus on understanding transient dynamics. The constant phase element has been identified as an important primitive that is essential for modeling the experimental data. A new algorithm has been develop to model its transient behavior. Likewise, the same was done for the organic electrochemical transistor. To quantify the sensing capacity of an environment sensitive network a precise mathematical measure has been introduced, the state separability index, and evaluated in numerical experiments. The theoretical work has been supported by the related set of experiments.
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