Research

18 pages, 24552 KiB

Open AccessArticle

A Genetic Algorithm-Enhanced Sensor Marks Selection Algorithm for Wavefront Aberration Modeling in Extreme-UV (EUV) Photolithography

by Aris Magklaras, Panayiotis Alefragis, Christos Gogos, Christos Valouxis and Alexios Birbas

Information 2023, 14(8), 428; https://doi.org/10.3390/info14080428 - 28 Jul 2023

Viewed by 1048

Abstract

In photolithographic processes, nanometer-level-precision wavefront-aberration models enable the machine to be able to meet the overlay (OVL) drift and critical dimension (CD) specifications. Software control algorithms take as input these models and correct any expected wavefront imperfections before reaching the wafer. In such [...] Read more.

In photolithographic processes, nanometer-level-precision wavefront-aberration models enable the machine to be able to meet the overlay (OVL) drift and critical dimension (CD) specifications. Software control algorithms take as input these models and correct any expected wavefront imperfections before reaching the wafer. In such way, a near-optimal image is exposed on the wafer surface. Optimizing the parameters of these models, however, involves several time costly sensor measurements which reduce the throughput performance of the machine in terms of exposed wafers per hour. In that case, photolithography machines come across the trade-off between throughput and quality. Therefore, one of the most common optimal experimental design (OED) problems in photolithography machines (and not only) is how to choose the minimum amount of sensor measurements that will provide the maximum amount of information. Additionally, each sensor measurement corresponds to a point on the wafer surface and therefore we must measure uniformly around the wafer surface as well. In order to solve this problem, we propose a sensor mark selection algorithm which exploits genetic algorithms. The proposed solution first selects a pool of points that qualify as candidates to be selected in order to meet the uniformity constraint. Then, the point that provides the maximum amount of information, quantified by the Fisher-based criteria of G-, D-, and A-optimality, is selected and added to the measurement scheme. This process, however, is considered “greedy”, and for this reason, genetic algorithms (GA) are exploited to further improve the solution. By repeating in parallel the “greedy” part several times, we obtain an initial population that will be the input to our GA. This meta-heuristic approach outperforms the “greedy” approach significantly. The proposed solution is applied in a real life semiconductors industry use case and achieves interesting industry as well as academical results. Full article

(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2022))

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32 pages, 3421 KiB

Open AccessArticle

Federated Edge Intelligence and Edge Caching Mechanisms

by Aristeidis Karras, Christos Karras, Konstantinos C. Giotopoulos, Dimitrios Tsolis, Konstantinos Oikonomou and Spyros Sioutas

Information 2023, 14(7), 414; https://doi.org/10.3390/info14070414 - 18 Jul 2023

Cited by 6 | Viewed by 1798

Abstract

Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine learning contexts, particularly in edge intelligence and edge caching applications. Recognizing the prevalent challenges of imbalanced and noisy data impacting scalability and resilience, [...] Read more.

Federated learning (FL) has emerged as a promising technique for preserving user privacy and ensuring data security in distributed machine learning contexts, particularly in edge intelligence and edge caching applications. Recognizing the prevalent challenges of imbalanced and noisy data impacting scalability and resilience, our study introduces two innovative algorithms crafted for FL within a peer-to-peer framework. These algorithms aim to enhance performance, especially in decentralized and resource-limited settings. Furthermore, we propose a client-balancing Dirichlet sampling algorithm with probabilistic guarantees to mitigate oversampling issues, optimizing data distribution among clients to achieve more accurate and reliable model training. Within the specifics of our study, we employed 10, 20, and 40 Raspberry Pi devices as clients in a practical FL scenario, simulating real-world conditions. The well-known FedAvg algorithm was implemented, enabling multi-epoch client training before weight integration. Additionally, we examined the influence of real-world dataset noise, culminating in a performance analysis that underscores how our novel methods and research significantly advance robust and efficient FL techniques, thereby enhancing the overall effectiveness of decentralized machine learning applications, including edge intelligence and edge caching. Full article

(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2022))

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24 pages, 599 KiB

Open AccessArticle

A Novel Authentication Method That Combines Honeytokens and Google Authenticator

by Vassilis Papaspirou, Maria Papathanasaki, Leandros Maglaras, Ioanna Kantzavelou, Christos Douligeris, Mohamed Amine Ferrag and Helge Janicke

Information 2023, 14(7), 386; https://doi.org/10.3390/info14070386 - 07 Jul 2023

Cited by 2 | Viewed by 2373

Abstract

Despite the rapid development of technology, computer systems still rely heavily on passwords for security, which can be problematic. Although multi-factor authentication has been introduced, it is not completely effective against more advanced attacks. To address this, this study proposes a new two-factor [...] Read more.

Despite the rapid development of technology, computer systems still rely heavily on passwords for security, which can be problematic. Although multi-factor authentication has been introduced, it is not completely effective against more advanced attacks. To address this, this study proposes a new two-factor authentication method that uses honeytokens. Honeytokens and Google Authenticator are combined to create a stronger authentication process. The proposed approach aims to provide additional layers of security and protection to computer systems, increasing their overall security beyond what is currently provided by single-password or standard two-factor authentication methods. The key difference is that the proposed system resembles a two-factor authentication but, in reality, works like a multi-factor authentication system. Multi-factor authentication (MFA) is a security technique that verifies a user’s identity by requiring multiple credentials from distinct categories. These typically include knowledge factors (something the user knows, such as a password or PIN), possession factors (something the user has, such as a mobile phone or security token), and inherence factors (something the user is, such as a biometric characteristic like a fingerprint). This multi-tiered approach significantly enhances protection against potential attacks. We examined and evaluated our system’s robustness against various types of attacks. From the user’s side, the system is as friendly as a two-factor authentication method with an authenticator and is more secure. Full article

(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2022))

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19 pages, 4189 KiB

Open AccessArticle

Online Professional Development on Educational Neuroscience in Higher Education Based on Design Thinking

by Stylianos Mystakidis, Athanasios Christopoulos, Maria Fragkaki and Konstantinos Dimitropoulos

Information 2023, 14(7), 382; https://doi.org/10.3390/info14070382 - 03 Jul 2023

Viewed by 1889

Abstract

Higher education teaching staff members need to build a scientifically accurate and comprehensive understanding of the function of the brain in learning as neuroscience evidence can constitute a way to optimize teaching and achieve learning excellence. An international consortium developed a professional development [...] Read more.

Higher education teaching staff members need to build a scientifically accurate and comprehensive understanding of the function of the brain in learning as neuroscience evidence can constitute a way to optimize teaching and achieve learning excellence. An international consortium developed a professional development six-module course on educational neuroscience and online community of practice by applying design thinking. A mixed methods research design was employed to investigate the attitudes of thirty-two (N = 32) participating academics using a survey comprising eleven closed and open questions. Data analysis methods included descriptive statistics, correlation, generalized additive model and grounded theory. The overall evaluation demonstrated a notable satisfaction level with regard to the quality of the course. Given the power of habits, mentoring and peer interactions are recommended to ensure the effective integration of theoretical neuroscientific evidence into teaching practice. Full article

(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2022))

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17 pages, 926 KiB

Open AccessArticle

Mind the Move: Developing a Brain-Computer Interface Game with Left-Right Motor Imagery

by Georgios Prapas, Kosmas Glavas, Katerina D. Tzimourta, Alexandros T. Tzallas and Markos G. Tsipouras

Information 2023, 14(7), 354; https://doi.org/10.3390/info14070354 - 21 Jun 2023

Cited by 2 | Viewed by 1753

Abstract

Brain-computer interfaces (BCIs) are becoming an increasingly popular technology, used in a variety of fields such as medical, gaming, and lifestyle. This paper describes a 3D non-invasive BCI game that uses a Muse 2 EEG headband to acquire electroencephalogram (EEG) data and OpenViBE [...] Read more.

Brain-computer interfaces (BCIs) are becoming an increasingly popular technology, used in a variety of fields such as medical, gaming, and lifestyle. This paper describes a 3D non-invasive BCI game that uses a Muse 2 EEG headband to acquire electroencephalogram (EEG) data and OpenViBE platform for processing the signals and classifying them into three different mental states: left and right motor imagery and eye blink. The game is developed to assess user adjustment and improvement in BCI environment after training. The classification algorithm used is Multi-Layer Perceptron (MLP), with 96.94% accuracy. A total of 33 subjects participated in the experiment and successfully controlled an avatar using mental commands to collect coins. The online metrics employed for this BCI system are the average game score, the average number of clusters and average user improvement. Full article

(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2022))

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21 pages, 13556 KiB

Open AccessArticle

Irradiance Non-Uniformity in LED Light Simulators

by Vasiliki Naskari, Gregory Doumenis and Ioannis Masklavanos

Information 2023, 14(6), 316; https://doi.org/10.3390/info14060316 - 30 May 2023

Viewed by 1214

Abstract

Photovoltaic (PV) cells are a technology of choice for providing power to self-sufficient Internet of Things (IoT) devices. These devices’ declining power demands can now be met even in indoor environments with low light intensity. Correspondingly, light simulation systems need to cover a [...] Read more.

Photovoltaic (PV) cells are a technology of choice for providing power to self-sufficient Internet of Things (IoT) devices. These devices’ declining power demands can now be met even in indoor environments with low light intensity. Correspondingly, light simulation systems need to cover a wide spectrum of irradiance intensity to emulate a PV cell’s working conditions while meeting cost targets. In this paper, we propose a method for calculating the irradiance distribution for a given number and position of LED sources to meet irradiance and uniformity requirements in LED-based light simulators. In addition, we establish design guidelines for minimizing non-uniformity under specific constraints and utilize a function to evaluate the degree of non-uniformity and determine the optimal distance from the illuminated surface. We demonstrate that even with a small number of low-cost LED sources, high levels of irradiance can be achieved with bounded non-uniformities. The presented guidelines serve as a resource for designing tailored, low-cost light simulators that meet users’ area/intensity/uniformity specifications. Full article

(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2022))

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24 pages, 3138 KiB

Open AccessArticle

Exploiting Security Issues in Human Activity Recognition Systems (HARSs)

by Sofia Sakka, Vasiliki Liagkou and Chrysostomos Stylios

Information 2023, 14(6), 315; https://doi.org/10.3390/info14060315 - 30 May 2023

Cited by 1 | Viewed by 1400

Abstract

Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem [...] Read more.

Human activity recognition systems (HARSs) are vital in a wide range of real-life applications and are a vibrant academic research area. Although they are adopted in many fields, such as the environment, agriculture, and healthcare and they are considered assistive technology, they seem to neglect the aspects of security and privacy. This problem occurs due to the pervasive nature of sensor-based HARSs. Sensors are devices with low power and computational capabilities, joining a machine learning application that lies in a dynamic and heterogeneous communication environment, and there is no generalized unified approach to evaluate their security/privacy, but rather only individual solutions. In this work, we studied HARSs in particular and tried to extend existing techniques for these systems considering the security/privacy of all participating components. Initially, in this work, we present the architecture of a real-life medical IoT application and the data flow across the participating entities. Then, we briefly review security and privacy issues and present possible vulnerabilities of each system layer. We introduce an architecture over the communication layer that offers mutual authentication, solving many security and privacy issues, particularly the man-in-the-middle attack (MitM). Relying on the proposed solutions, we manage to prevent unauthorized access to critical information by providing a trustworthy application. Full article

(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2022))

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16 pages, 14957 KiB

Open AccessArticle

Virtual and Augmented Experience in Virtual Learning Tours

by Fotios Bosmos, Alexandros T. Tzallas, Markos G. Tsipouras, Evripidis Glavas and Nikolaos Giannakeas

Information 2023, 14(5), 294; https://doi.org/10.3390/info14050294 - 16 May 2023

Viewed by 1440

Abstract

The aim of this work is to highlight the possibilities of using VR applications in the informal learning process. This is attempted through the development of virtual reality cultural applications for historical monuments. For this purpose, the theoretical framework of virtual and augmented [...] Read more.

The aim of this work is to highlight the possibilities of using VR applications in the informal learning process. This is attempted through the development of virtual reality cultural applications for historical monuments. For this purpose, the theoretical framework of virtual and augmented reality techniques is presented, developing as a showcase of the virtual environment of the historical bridge of Arta, in Greece. The bridge model is created through 3D software, which is then imported into virtual world environment by employing the Unity engine. The main objective of the research is the technical and empirical evaluation of the VR application by specialists, in comparison with the real environment of the monument. Accordingly, the use of the application in the learning process is evaluated by high school students. Using the conclusions of the evaluation, the environment will be enriched with multimedia elements and the application will be evaluated by secondary school students as a learning experience and process, using electroencephalography (EEG). The recording and analysis of research results can be generalized and lead to safe conclusions for the use of similar applications in the field of culture and learning. Full article

(This article belongs to the Special Issue Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2022))

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