Our research focuses on collecting high quality EEG signals without conductive gel and wires. During the demonstration we will show that the subject can take a normal looking cap, and then a tablet displays the EEG signals.

The number of large sensor systems are rapidly growing nowadays in many fields. Well-designed Big Data solutions are able to manage the enormous data flow and create real business benefits. One dynamically growing application area is precision farming. It requires robust and energy-efficient sensors, because the devices are placed outdoors, often in harsh conditions, and there is no power outlet in the middle of a corn field. Power efficiency is one of the major themes of the Internet of Things (IoT). According to the IoT vision, embedded sensors send their data to processing units (either located near to the sensor or on some intermediate gateway device or in the cloud) using heterogeneous transport networks. Some sensors employ short-range network like Bluetooth and some gateway device like a tablet. Other sensors directly connect to wide-area networks like cellular networks. This paper will analyse different communication patterns accomplished over GSM network from the viewpoint of the energy consumption of the sensor device with the assumption that the sensor is stationary. The measurements were done using two different GSM modems designed for embedded systems to ensure that the results represent a wider picture and not some implementation property of a particular GSM modem. Recommendations are given about the strategies applications should follow in order to minimize the energy consumption of their GSM subsystems.

Artificial Intelligence (AI) systems, particularly large language models, have shown remarkable advancements, revolutionising various fields across industries. However, the sustainability of building large AI models with billions of parameters has become a subject of concern due to their significant environmental and social impact. The training of such models consumes enormous amounts of water and energy and emits substantial carbon emissions, contributing to climate change as data centres heavily rely on fossil fuels. This article summarises the current situation and explores the benefits and challenges of large AI models, emphasising the environmental impact and proposing strategies towards sustainability. Special attention is given to the social challenges, including accessibility, job displacement, biases, and data privacy concerns. Finally, the article advocates for the formulation of green and good AI practices standards for the future. To achieve sustainability, regulations are suggested to ensure transparency and accountability while promoting innovation-friendly frameworks. The authors see that while there is more progress in technology and infrastructure to address environmental impacts, social impacts are more neglected, and they are arguing for more detailed regulation as a solution.