Ulkuhan Guler

Abu Masum, Mehran Moghadam, M. Hassan Najafi et al.

Altitude sickness is a potentially life-threatening condition that impacts many individuals traveling to elevated altitudes. Timely detection is critical as symptoms can escalate rapidly. Early recognition enables simple interventions such as descent, oxygen, or medication, and prompt treatment can save lives by significantly lowering the risk of severe complications. Although conventional machine learning (ML) techniques have been applied to identify altitude sickness using physiological signals, such as heart rate, oxygen saturation, respiration rate, blood pressure, and body temperature, they often struggle to balance predictive performance with low hardware demands. In contrast, hyperdimensional computing (HDC) remains under-explored for this task with limited biomedical features, where it may offer a compelling alternative to existing classification models. Its vector symbolic framework is inherently suited to hardware-efficient design, making it a strong candidate for low-power systems like wearables. Leveraging lightweight computation and efficient streamlined memory usage, HDC enables real-time detection of altitude sickness from physiological parameters collected by wearable devices, achieving accuracy comparable to that of traditional ML models. We present AMS-HD, a novel system that integrates tailored feature extraction and Hadamard HV encoding to enhance both the precision and efficiency of HDC-based detection. This framework is well-positioned for deployment in wearable health monitoring platforms, enabling continuous, on-the-go tracking of acute altitude sickness.

Vladimir Vakhter, Betul Soysal, Patrick Schaumont et al.

The landscape of miniaturized wireless biomedical devices (MWBDs) is rapidly expanding as proactive mobile healthcare proliferates. MWBDs are diverse and include various injectable, ingestible, implantable, and wearable devices. While the growth of MWBDs increases the flexibility of medical services, the adoption of these technologies brings privacy and security risks for their users. MWBDs can operate with sensitive, private information and affect patients through the use of stimulation and drug delivery. Therefore, these devices require trust and need to be secure. Embedding protective mechanisms into MWBDs is challenging because they are restricted in size, power budget, as well as processing and storage capabilities. Nevertheless, MWBDs need to be at least minimally securable in the face of evolving threats. The main intent of this work is to make the primary stakeholders of MWBDs aware of associated risks and to help the architects and the manufacturers of MWBDs protect their emerging designs in a repeatable and structured manner. Making MWBDs securable begins with performing threat modeling. This paper introduces a domain-specific qualitative-quantitative threat model dedicated to MWBDs. The proposed model is then applied to representative case studies from each category of MWBDs.

Yecheng Lyu, Ming Li, Xinming Huang et al.

General graphs are difficult for learning due to their irregular structures. Existing works employ message passing along graph edges to extract local patterns using customized graph kernels, but few of them are effective for the integration of such local patterns into global features. In contrast, in this paper we study the methods to transfer the graphs into trees so that explicit orders are learned to direct the feature integration from local to global. To this end, we apply the breadth first search (BFS) to construct trees from the graphs, which adds direction to the graph edges from the center node to the peripheral nodes. In addition, we proposed a novel projection scheme that transfer the trees to image representations, which is suitable for conventional convolution neural networks (CNNs) and recurrent neural networks (RNNs). To best learn the patterns from the graph-tree-images, we propose TreeRNN, a 2D RNN architecture that recurrently integrates the image pixels by rows and columns to help classify the graph categories. We evaluate the proposed method on several graph classification datasets, and manage to demonstrate comparable accuracy with the state-of-the-art on MUTAG, PTC-MR and NCI1 datasets.