Wildfire detection in the densely forested and remote regions of Finland presents substantial challenges. This paper introduces a new publicly available dataset, FireMan-UAV-RGBT, comprising UAV-captured RGB and thermal video data to advance wildfire detection methodologies. The dataset includes high-resolution images of boreal forests that have been carefully annotated both manually and using a semi-automatic method that leverages thermal information for improved RGB image segmentation. The utility of the dataset is assessed by applying established deep learning models (ResN et50 and YOLOv8), and comparing their performance in unimodal and multimodal detection approaches. The performance is evaluated using both intra-set validation on the novel dataset and inter-set evaluation through cross-validation with the Flame-1 and Flame-2 datasets, demonstrating the usability of our dataset in wildfire detection scenarios. The FireMan-UAV-RGBT dataset represents a step forward in wildfire management, offering a resource that may contribute to cost-effective and environmentally sensitive solutions in remote sensing and emergency response strategies.

Remote photoplethysmography (rPPG) offers a state-of-the-art, non-contact methodology for estimating human pulse by analyzing facial videos. Despite its potential, rPPG methods can be susceptible to various artifacts, such as noise, occlusions, and other obstructions caused by sunglasses, masks, or even involuntary face touching. In this study, we apply image processing transformations to intentionally degrade video quality, mimicking these challenging conditions, and subsequently evaluate the performance of both non-learning and learning-based rPPG methods on the deteriorated data. Our results reveal a significant decrease in accuracy in the presence of these artifacts, prompting us to propose the application of restoration techniques, such as denoising and inpainting, to improve heart-rate estimation outcomes. By addressing these challenging conditions and occlusion artifacts, our approach aims to make rPPG methods more robust and adaptable to real-world situations. To assess the effectiveness of our proposed methods, we undertake comprehensive experiments on three publicly available datasets, encompassing a wide range of scenarios and artifact types. Our findings underscore the potential to construct a robust rPPG system by employing an optimal combination of restoration algorithms and rPPG techniques. Moreover, our study contributes to the advancement of privacy-conscious rPPG methodologies, thereby bolstering the overall utility and impact of this innovative technology in the field of remote heart-rate estimation under realistic and diverse conditions.

In the age of the Internet of Things (IoT) and the expanding computing continuum, it’s crucial to manage and share resources at the edges of networks. This position paper presents a new concept known as ’semantic slicing’. This approach harnesses the power of artificial intelligence (AI), wireless networks, edge computing, and sensing technologies to enable novel applications, optimize resource allocation, and streamline data processing and decision-making across complex systems spanning the computing continuum. Semantic slicing applies a deep understanding of the data and specific application requirements to intelligently allocate resources and distribute processing tasks in the computing continuum. This strategy allows for the creation of systems that are not only more efficient and responsive, but also better equipped to adapt to a variety of applications and services.

Ambient intelligence demands collaboration schemes for distributed constrained devices which are not only highly energy efficient in distributed sensing, processing and communication, but which also respect data privacy. Traditional algorithms for distributed processing suffer in Ambient intelligence domains either from limited data privacy, or from their excessive processing demands for constrained distributed devices. In this paper, we present Camouflage learning, a distributed machine learning scheme that obscures the trained model via probabilistic collaboration using physical-layer computation offloading and demonstrate the feasibility of the approach on backscatter communication prototypes and in comparison with Federated learning. We show that Camouflage learning is more energy efficient than traditional schemes and that it requires less communication overhead while reducing the computation load through physical-layer computation offloading. The scheme is synchronization-agnostic and thus appropriate for sharply constrained, synchronization-incapable devices. We demonstrate model training and inference on four distinct datasets and investigate the performance of the scheme with respect to communication range, impact of challenging communication environments, power consumption, and the backscatter hardware prototype.

We explore the possibility of leveraging radar-based sensing systems to analyze vital signs for classification, user identification, and regression tasks. Specifically, we extract time-domain and frequency-domain features from distance, respiration, and pulse signals obtained by filtering radio-frequency signals. Our Random Forest classification models are trained on these features to recognize scenarios in which the radar data were collected, categorize individuals into age groups, and classify human activities. For classification, we achieved up to 94.7% of accuracy when distinguishing apnea and normal breathing in the lying position. We then show the feasibility of identifying individuals in a small group using vital signs, which can support model fine-tuning with data acquired from new users. Furthermore, we used a Random Forest regression model to estimate the Body Mass Index, height, and weight of subjects. These classification, identification, and regression models benefit smart systems that can simultaneously identify users, recognize their behaviours, and extract their vital signs from radar sensors.

Visualization is a key point in data exploration. In this paper we have emphasis in adding dynamic features by constructing exploration animations. We use Principal Component Analysis (PCA) in dimensionality reduction and K-means clustering algorithm in defining states. In predicting state transitions, we use Hidden Markov Model (HMM). Analyzed physical data is got from self-healing autonomous data centers. Our research methodology is to animate state transitions for data exploration in modern computerized environment. We use Jupyter tool and Python 3 programming language in our experimental realization. As results we get PCA animations for exploration purposes. Our approach is based on state discovery, where it is possible to find some physical interpretations for the defined states and state transitions. State structure and behaviour depend strongly on analyzed data.

We describe a methodology to measure ballistocardiography (BCG) signals from the body surface, using body-worn digital accelerometers to extract medically relevant information for Pervasive Healthcare. We are able to measure measuring heart rate with an 95% accuracy as well as other cardiac metrics, such as the S1-S2 interval, deviating from ECG by only 1.3%. Our results show that BCG can be a viable alternative to an electrocardiogram to provide complementary information on the heart's condition in mobile and pervasive use cases. We further show that BCG information can be detected from arm as reliably as from chest, which is especially convenient for measuring from supine positions in Pervasive healthcare applications.

Gait has been proposed as a feature for mobile device pairing across arbitrary positions on the human body. Results indicate that the correlation in gait-based features across different body locations is sufficient to establish secure device pairing. However, the population size of the studies is limited and powerful attackers with e.g., capability of video recording are not considered. We present a concise discussion of security properties of gait-based pairing schemes including quantization, classification and analysis of attack surfaces, of statistical properties of generated sequences, an entropy analysis, as well as possible threats and security weaknesses. For one of the schemes considered, we present modifications to fix an identified security flaw. As a general limitation of gait-based authentication or pairing systems, we further demonstrate that an adversary with video support can create key sequences that are sufficiently close to on-body generated acceleration sequences to breach gait-based security mechanisms.