Konstantinos Politopoulos

Efficient filtering in biosignals acquisition is challenging. The resistance of the sources exhibits inter- and intra-subject variability or is unknown; thus, using passive filters before the first amplification stage is problematic. Conversely, filtering after amplification does not effectively eliminate the amplified electrical noise, main’s interference, and the artifacts. In this context, the design and utilization of filters in the analog front end of biosensors, in conjunction with the first amplification stage, is not common but offers substantial advantages. In this study, the design of a novel Multi-feedback Differential Filter Instrumentation Amplifier (MFDFIA) is proposed. The design and the equations governing the gain and bandwidth characteristics of the MFDFIA are presented, and relevant topologies are explored. Even though MFDFIA has two op-amps in its first stage, due to its symmetric topology, the analysis can be conducted separately for the differential- and common-mode input signal with a simplified one op-amp equivalent circuit. Notably, MFDFIA’s CMRR is equal and depends only on the CMRR of the second stage. An exemplary simulation for EEG signal acquisition is provided, with a flat band of 1db between 0.7 Hz and 25.4 Hz, a gain of 34.1 db, and an input noise of 70.66 nVrms in the range of 0.1–10 Hz.

Efficient filtering in bio-signals acquisition is challenging. The resistance of the sources exhibits inter- and intra-subject variability or is unknown, thus using passive filters before the first amplification stage is problematic. Conversely, filtering after amplification does not effectively eliminate the amplified electrical noise, Main’s Interference, and the artifacts. In this context, the design and utilization of filters in the Analog Front End of biosensors, in conjunction with the first amplification stage, is not common but offers substantial advantages. In this study, the design of a novel Multi-feedback Differential Filter Instrumentation Amplifier (MFDFIA) is proposed. For addressing the aforementioned issues, the design and the equations governing the gain and bandwidth characteristics of the MFDFIA are presented and relevant topologies are explored. Even though MFDFIA has two op-amps in its first stage, due to its symmetric topology, the analysis can be conducted separately for the differential and common mode input signal with a simplified one op-amp equivalent circuit. Notably, MFDFIA’s CMRR is equal and depends only on the CMRR of the second stage. An exemplary application for EEG signal acquisition is provided, which has bandwidth 0.7 to 98.6 Hz, gain 50.3 db, power consumption 11.583 mW and 0.1-10 Hz noise 462.94 nVrms.

This study aims to investigate the effectiveness of low power red light (661 nm) in accelerating the wound healing process of an in vitro scratch assay model of keratinocytes. Furthermore, the study aims to clarify the role of light irradiation parameters, optimize them and gain additional insight into the mechanisms of wound closure as a result of photobiomodulation. Wound healing was studied using scratch assay model of NCTC 2544 keratinocytes. Cells were irradiated with a laser at various power densities and times. Images were acquired at 0, 24, 48 and 72 h following the laser treatment. Cellular proliferation was studied by MTT. ROS were studied at 0 and 24 h by fluorescence microscopy. Image analysis was used to determine the wound closure rates and quantify ROS. The energy range of 0.18–7.2 J/cm² was not phototoxic, increased cell viability and promoted wound healing. Power and irradiation time proved to be more important than energy. The results indicated the existence of two thresholds in both power and irradiation time that need to be overcome to improve wound healing. An increase in ROS production was observed at 0 h only in the group with the lowest healing rate. This early response seemed to block proliferation and finally wound healing. Low level laser light at 661 nm enhanced both proliferation and migration in keratinocytes, providing evidence that it could possibly stimulate wound healing in vivo. The observed results are dependent on irradiance and irradiation time rather than energy dose in total.

We identify the need for enhanced pedestrian–vehicle simulation tools and build such a tool to explore the interaction among pedestrian “players” and virtual human- and automated-vehicles for different scenarios taking place in an urban environment. We first present contemporary research tools and then propose the design and development of a new desktop application that facilitates pedestrian-point-of-view research. We then conduct a three-step user experience experiment, in which a small number of participants answer questions before and after using the application to interact with virtual human and automated vehicles in diverse road-crossing scenarios. Behavioral results observed in virtuality, especially when motivated by consequence, tend to simulate real life sufficiently well to inform design choices. From the simulation, we observed valuable insights into human–vehicle interactions. Upon completing this preliminary testing, we iterated the tool’s design and ultimately conducted an 89-participant study of human–vehicle interactions for three scenarios taking place in a virtual environment. Our tool raised participant awareness of autonomous vehicles and their capabilities and limitations, which is an important step in overcoming public distrust of AVs. We additionally saw that participants trust humans and technology less as drivers than in other contexts, and that pedestrians feel safer around vehicles with autonomy indicators. Further, we note that study participants increasingly feel safe with automated vehicles with increased exposure. These preliminary results, as well as the efficacy of the tool’s design, may inform future socio-technical design for automated vehicles and their human interactions.

Broader understanding of waste management has the potential to bring about broad societal change impacting the climate crisis and public health. We present existing waste management tools and commercially-available games involving waste management, highlighting the strengths and opportunities left unaddressed by these tools in educational contexts and planning use cases. A survey motivates the need for enhanced interactive tools providing clear feedback through quick-visibility performance indicators. After identifying an opportunity to build upon highly-detailed multi-criteria simulation tools, we explore the need for easy-to-read performance metrics that will bring to the field of waste management easily identifiable and measurable key performance indicators (KPIs) that vary alongside factors affecting waste management policies. Such metrics are introduced and detailed as part of a unified waste management model. We then develop a representative gamified educational tool based upon this model to be used by students, decision makers planning real-world policies, and the public. This simulator is built upon the Unity Game Engine and emulates waste management techniques and resulting KPIs within the context of a virtual city.

We identify the need for an easy-to-use self-driving simulator where game mechanics implicitly encourage high-quality data capture and an associated low-cost physical test platform. We design such a simulator incorporating environmental domain randomization to enhance data generalizability and a low-cost physical test platform running the Robotic Operating System. A toolchain comprising a gamified driving simulator and low-cost vehicle platform is novel and facilitates behavior cloning and domain adaptation without specialized knowledge, supporting crowdsourced data generation. This enables small organizations to develop certain robust and resilient self-driving systems. As proof-of-concept, the simulator is used to capture lane-following data from AI-driven and human-operated agents, with these data training line following Convolutional Neural Networks that transfer without domain adaptation to work on the physical platform.