• Ethanol Effect on Graphene Drop Casting for Acetone Vapor Sensors Operating at Room Temperature

    Ethanol Effect on Graphene Drop Casting for Acetone Vapor Sensors Operating at Room Temperature

    Acetone vapor sensors find great use in many areas as they are being used for non-invasive diabetes detection and monitoring, fat burning rate monitoring, general industrial applications or even detection of explosive environments. Even though several sensors have been proposed, there is a continuous need for low power and low-cost ones, ideally implemented on flexible substrates. Thus, 2D nanomaterials such as graphene, due to their large specific surface area and exceptional electrical properties, have attracted broad attention for their gas sensing potential even at low temperature range of operation. As an example, recently, an acetone sensor was shown using graphene nanoplatelets combined with Zinc ferrite (ZnFe2O4) and achieved a sensitivity of 7% at 200 ppm acetone vapor while operating at 275°C. In this work, we compare various graphene-based gas sensors, capable of detecting acetone vapors, that are implemented on glass substrates and operate at room temperature. We demonstrate that such sensors exhibit good repeatability and sensitivity at 200 ppm of acetone vapor. All acetone sensing measurements were conducted by exposing the sensors in repeating cycles of several acetone concentrations diluted in nitrogen gas. To test the repeatability of the sensors, they were exposed to three cycles of 200 ppm acetone vapors followed by nitrogen gas during recovery. The sensors were tested in room temperature.

    Authors
    Michael Georgas, George Zardalidis, Filippos Farmakis

    Conference
    9th Micro Nano International Conference
    Availability Date
    TBA

  • Systematic Techno-Economic Analysis of Medium-Voltage PV-BES Prosumers Operating Under NEM Policy

    Systematic Techno-Economic Analysis of Medium-Voltage PV-BES Prosumers Operating Under NEM Policy

    Net-metering (NEM) is one of the most widely known support mechanisms aiming to promote the installation of distributed photovoltaic (PV) systems. However, due to the increasing penetration of PVs, challenges related to the secure operation of the power system are emerged. For this reason, battery energy storage (BES) systems are installed alongside PVs to tackle these technical problems. In this paper, a systematic assessment analysis of NEM policy in medium-voltage (MV) prosumers with PV-BES systems, e.g., university campuses, hospitals, etc., is conducted in both technical and economic terms. In the analysis, annual generation and consumption timeseries of university campuses of the Democritus University of Thrace, Greece, are indicatively employed and various scenarios of PV-BES systems are investigated, to evaluate the profitability of NEM policy in MV PV-BES prosumers and consequently determine the optimal investment plan in monetary terms.

    Authors
    Kalliopi D. Pippi, Evangelos D. Kyriakopoulos, Theofilos A. Papadopoulos, Georgios C. Kryonidis

    Conference
    2nd International Conference on Energy Transition in the Mediterranean Area
    Availability Date
    TBA

  • Temperature Sensors by Inkjet-Printing Compatible with Flexible Substrates: A Review

    Temperature Sensors by Inkjet-Printing Compatible with Flexible Substrates: A Review

    During the past decade, microelectronics incorporated inkjet-printing technology as a versatile tool for industrial applications, as it combines high printing quality and resolution along with low cost compared to conventional microelectronics techniques that require high cost and complex equipment. In addition, inherently, inkjet printing requires no photolithography steps. In this review paper we present temperature sensors that have been manufactured by inkjet printing. Based on the main active sensing material, the research studies are classified into four different types, i.e. metal and metal oxide-based, carbon-based, polymer-based and composite that combine properties of multiple active materials. It is demonstrated that silver ink has been, by far, the most popular material for metal based temperature sensors with a temperature coefficient of resistance (TCR) of around 2 × 10 -3 ◦C -1 . Silver sensors as well as almost all metal, carbon and polymer based sensors were either resistance temperature detectors (RTDs) or thermistors. Regarding polymer and carbon-based sensors, it was found that, in some cases, they outperformed metal-based sensors in terms of TCR, although fabrication using such materials had a less predictable result with a TCR ranging from -16 × 10 -3 ◦C -1 to 2 × 10 -3 ◦C -1 . Finally, in the case of composite materials as temperature sensors, several combinations of active materials exhibited interesting results and yielded a variety of sensing technologies such as thermocouples and radio frequency based.

    Authors
    Michael Georgas, Petros Selinis, George Zardalidis, Filippos Farmakis

    Journal
    IEEE Sensors Journal
    Publication Date
    October 21st, 2022

  • A Smart Energy Management System for Elderly Households

    A Smart Energy Management System for Elderly Households

    The rapid growth of aging population dictates the necessity of sophisticated monitoring and actuation systems for the smart control and management of elderly households. This paper proposes a state-of-the-art energy management system aiming at increased energy efficiency, lower electricity cost and improved user comfort. The study focuses on a Greek residency with elderly people incorporating controllable and uncontrollable loads, an energy storage system, and photovoltaic generation. A smart home energy management system under the net-metering policy is proposed consisting of three control mechanisms. The reported results offer insights into the optimal residential management practices and evaluate the performance of the proposed control strategy in comparison to other alternative demand response solutions.

    Authors
    Christos L. Athanasiadis, Kalliopi D. Pippi, Theofilos A. Papadopoulos, Christos Korkas, Christos Tsaknakis, Vasiliki Alexopoulou, Vasileios Nikolaidis, Elias Kosmatopoulos

    Conference
    57th International Universities Power Engineering Conference
    Availability Date
    October 18th, 2022

  • Hitting times of quantum and classical random walks in potential spaces

    Hitting times of quantum and classical random walks in potential spaces

    The spatial search problem is an interesting and important problem in computer science and especially the area of algorithms. The objective is a marked site to be found in a finite physical space, that can be modeled as a finite lattice or a graph. Many approaches have been developed to address this problem. Classical random walks and quantum walks are efficient models that address the spatial search problem. Quantum walks is a universal model of quantum computation and can be mapped directly to quantum circuits and consequently executed on quantum computers. Quantum walks utilized for quantum search proved to achieve significantly lower hitting times than their classical counterpart, classical random walks. The evolution space for the quantum walks as well as the classical random walks is up until now a free space. In our approach, we introduce external electrical potentials to the evolution space. We study the evolution of discrete time quantum and classical random walks in such potential spaces and the probability — hitting time on finding marked sites. We considered the differences in applied potential among neighboring sites as weights for the lattice — graph. We introduce these weights to the evolution space as an operator for the discrete time quantum walk and as coin probabilities for the classical random walk. Our results show that quantum walks again, evolve faster in the evolution space with the applied potential. Quantum walks also achieve better probability — hitting time on finding the marked site in the potential space. With the introduction of electrical potentials, quantum walks evolving in potential spaces, can lead to the development of novel quantum algorithms, where input parameters can be introduced as external potentials.

    Authors
    Georgios D. Varsamis, Ioannis G. Karafyllidis, Georgios Ch. Sirakoulis

    Journal
    Physica A: Statistical Mechanics and its Applications
    Publication Date
    August 27th, 2022

  • A Framework for Active Vision-Based Robot Planning using Spiking Neural Networks

    A Framework for Active Vision-Based Robot Planning using Spiking Neural Networks

    Robust and energy-efficient robot planning is of utmost importance for mobile robots since the dynamic changes of the environment entail robotic agents with high adaptation capacities, so as to excel in their tasks. In this work, we introduce a hybrid spiking and deep neural network architecture for actor-critic control of a 6-DOF robot arm. Our method firstly involves autonomous object detection via active vision exploration and thereafter, the entire hybrid architecture is described. In specific, the actor utilises an integrated-and-fire model for action generation, while the critic a deep neural one for action evaluation. Lastly, the benefits of this approach in terms of energy efficiency are extensively discussed.

    Authors
    Katerina Maria Oikonomou, Ioannis Kansizoglou, Antonios Gasteratos

    Conference
    30th Mediterranean Conference on Control and Automation
    Availability Date
    August 1st, 2022
  • Image Shifting Tracking Leveraging Memristive Devices

    Image Shifting Tracking Leveraging Memristive Devices

    Unconventional circuits with built-in memory and computing functionalities are becoming the cornerstones of artificial intelligence (AI) at the edge. In the currently deployed systems, sensing and computing occur in separate physical locations, imposing a vast amount of data shuttling between the sensor module and the cloud-computing platforms. Regarding the acceleration of image processing at the edge, in this work, a memristive computing circuit has been designed. By exploiting the non-linear behavior and memory capabilities of memristor devices, a memristive circuit, capable of tracking the shifting of an image is proposed. The presented circuit design can be also combined with an array of sensors, aiming to implement a discrete image tracking module.

    Authors
    Theodoros Panagiotis Chatzinikolaou, Iosif-Angelos Fyrigos, Georgios Ch. Sirakoulis

    Conference
    2022 11th International Conference on Modern Circuits and Systems Technologies (MOCAST)
    Availability Date
    July 28th, 2022

  • Joint-Aware Action Recognition for Ambient Assisted Living

    Joint-Aware Action Recognition for Ambient Assisted Living

    As the aged population is rapidly increased, the need for efficient and low-cost ambient systems becomes vital. The effectiveness of such systems lies upon the accurate and fast motion analysis in order to predict the elderly’s action and develop systems to act in need. To achieve that, the precise estimation of the entire human body pose is often exploited, providing the required motion-related information. Yet, the exploitation of the entire human pose can present several limitations. The paper at hand exploits state-of-the-art data-driven classifiers and compares their efficiency in action recognition based on a specific set of joints or coordinates, i.e., the x, y and z-axis. The above rests upon the notion that each action in real life can be effectively perceived by observing only a specific set of joints. Considering that, we aim to investigate the capacity of such a joint analysis and its ability to deliver an enhanced pose-based action recognition system. To that end, we correlate specific joints with each action, indicating the joints that contribute the most. We evaluate our findings on two different senior subjects using two different classifiers, viz., support vector machine (SVM) and convolutional neural network (CNN), showing that the above strategy can improve recognition rates.

    Authors
    Katerina Maria Oikonomou, Ioannis Kansizoglou, Pelagia Manaveli, Athanasios Grekidis, Dimitrios Menychtas, Nikolaos Aggelousis, Georgios Ch. Sirakoulis, Antonios Gasteratos

    Conference
    2022 IEEE International Conference on Imaging Systems and Techniques
    Availability Date
    July 20th, 2022
  • Dimensionality reduction through visual data resampling for low-storage loop-closure detection

    Dimensionality reduction through visual data resampling for low-storage loop-closure detection

    As loop-closure detection plays a fundamental role in any simultaneous localization and mapping (SLAM) system, through its ability to recognize previously visited locations, one of its main objectives is to permit consistent map generation for an extended period. Within large-scale SLAM autonomy, the scalability in terms of timing needed for database search and the storage requirements has to be addressed. In this paper, a low-storage visual loop-closure detection technique is proposed. Our system is based on the incremental bag-of-tracked-words scheme for the trajectory mapping still, the generated visual representations are reduced to lower dimensions through a resampling process. This way, we achieve to shorten the overall database size and searching time, while at the same time preserving the high performance. The evaluation, which took place on different well-known datasets, exhibits the system’s low-storage requirements and high recall scores compared to the baseline version and other state-of-the-art approaches.

    Authors
    Konstantinos A. Tsintotas, Shan An, Ioannis Tsampikos Papapetros, Fotios K. Konstantinidis, Georgios Ch. Sirakoulis, Antonios Gasteratos

    Conference
    2022 IEEE International Conference on Imaging Systems and Techniques
    Availability Date
    July 20th, 2022
  • Continuous Emotion Recognition for Long-Term Behavior Modeling through Recurrent Neural Networks

    Continuous Emotion Recognition for Long-Term Behavior Modeling through Recurrent Neural Networks

    One’s internal state is mainly communicated through nonverbal cues, such as facial expressions, gestures and tone of voice, which in turn shape the corresponding emotional state. Hence, emotions can be effectively used, in the long term, to form an opinion of an individual’s overall personality. The latter can be capitalized on in many human–robot interaction (HRI) scenarios, such as in the case of an assisted-living robotic platform, where a human’s mood may entail the adaptation of a robot’s actions. To that end, we introduce a novel approach that gradually maps and learns the personality of a human, by conceiving and tracking the individual’s emotional variations throughout their interaction. The proposed system extracts the facial landmarks of the subject, which are used to train a suitably designed deep recurrent neural network architecture. The above architecture is responsible for estimating the two continuous coefficients of emotion, i.e., arousal and valence, following the broadly known Russell’s model. Finally, a user-friendly dashboard is created, presenting both the momentary and the long-term fluctuations of a subject’s emotional state. Therefore, we propose a handy tool for HRI scenarios, where robot’s activity adaptation is needed for enhanced interaction performance and safety.

    Authors
    Ioannis Kansizoglou, Evangelos Misirlis, Konstantinos Tsintotas, Antonios Gasteratos

    Journal
    Technologies
    Publication Date
    May 12th, 2022

  • Computing the lowest eigenstate of tight-binding Hamiltonians using quantum walks

    Computing the lowest eigenstate of tight-binding Hamiltonians using quantum walks

    Finding or estimating the lowest eigenstate of quantum system Hamiltonians is an important problem for quantum computing, quantum physics, quantum chemistry, and material science. Several quantum computing approaches have been developed to address this problem. The most frequently used method is variational quantum eigensolver (VQE). Many quantum systems, and especially nanomaterials, are described using tight-binding Hamiltonians, but until now no quantum computation method has been developed to find the lowest eigenvalue of these specific, but very important, Hamiltonians. We address the problem of finding the lowest eigenstate of tight-binding Hamiltonians using quantum walks. Quantum walks is a universal model of quantum computation equivalent to the quantum gate model. Furthermore, quantum walks can be mapped to quantum circuits comprising qubits, quantum registers, and quantum gates and, consequently, executed on quantum computers. In our approach, probability distributions, derived from wave function probability amplitudes, enter our quantum algorithm as potential distributions in the space where the quantum walk evolves. Our results showed the quantum walker localization in the case of the lowest eigenvalue is distinctive and characteristic of this state. Our approach will be a valuable computation tool for studying quantum systems described by tight-binding Hamiltonians.

    Authors
    Georgios D. Varsamis, Ioannis G. Karafyllidis

    Journal
    International Journal of Quantum Information
    Publication Date
    April 25th, 2022

  • Visual Loop-Closure Detection via Prominent Feature Tracking

    Visual Loop-Closure Detection via Prominent Feature Tracking

    Loop-closure detection (LCD) has become an essential part of any simultaneous localization and mapping (SLAM) framework. It provides a means to rectify the drift error, which is typically accumulated along a robot’s trajectory. In this article we propose an LCD method based on tracked visual features, combined with a signal peak-trace filtering approach for loop-closure identification. In particular, local binary features are firstly extracted and tracked through consecutive frames. This way online visual words are generated, which in turn form an incremental bag of visual words (BoVW) vocabulary. Loop-closures (LCs) result from a classification method, which considers current and past state peaks on the similarity matrix. The system discerns the movement of the peaks to identify whether they come about to be true-positive detections or background noise. The suggested peak-trace filtering technique provides exceeding robustness to noisy signals, enabling the usage of only a handful of visual local features per image; thus resulting into a considerably downsized visual vocabulary.

    Authors
    Ioannis Tsampikos Papapetros, Vasiliki Balaska, Antonios Gasteratos

    Journal
    Journal of Intelligent & Robotic Systems
    Publication Date
    March 12th, 2022

  • Do Neural Network Weights Account for Classes Centers?

    Do Neural Network Weights Account for Classes Centers?

    The exploitation of deep neural networks (DNNs) as descriptors in feature learning challenges enjoys apparent popularity over the past few years. The above tendency focuses on the development of effective loss functions that ensure both high feature discrimination among different classes, as well as low geodesic distance between the feature vectors of a given class. The vast majority of the contemporary works rely their formulation on an empirical assumption about the feature space of a network’s last hidden layer, claiming that the weight vector of a class accounts for its geometrical center in the studied space. This article at hand follows a theoretical approach and indicates that the aforementioned hypothesis is not exclusively met. This fact raises stability issues regarding the training procedure of a DNN, as shown in our experimental study. Consequently, a specific symmetry is proposed and studied both analytically and empirically that satisfies the above assumption, addressing the established convergence issues. More specifically, the aforementioned symmetry suggests that all weight vectors are unit, coplanar, and their vector summation equals zero. Such a layout is proven to ensure a more stable learning curve compared against the corresponding ones succeeded by popular models in the field of feature learning.

    Authors
    Ioannis Kansizoglou, Loukas Bampis, Antonios Gasteratos

    Journal
    IEEE Transactions on Neural Networks and Learning Systems
    Publication Date
    March 8th, 2022