arXiv subjects

Jan Erik Håkegård

Publications and source records attributed to Jan Erik Håkegård.

4 recordsLinked to original sources

arXiv2025

A dual bistatic optical forward transceiver configuration for determining the position of an acoustic communication source detected by optical communication fibers

Optical fibers have long been employed as sensors in a wide range of commercial systems. Distributed Acoustic Sensing (DAS) extends this concept by enabling the detection and localization of acoustic sources along the fiber, using backscattered light from small segments to achieve spatial resolution on the order of meters. Recently, DAS has also been explored as a component in underwater acoustic communication systems. Emerging interest in bidirectional configurations where both transmitter and receiver are placed at opposite ends of the fiber has opened new possibilities. However, in such setups, source localization is not inherently integrated into the signal decoding process. For scenarios where source positioning is valuable, we propose an approach inspired by bi-static radar principles. This configuration utilizes acoustic signals received at both ends of the fiber to estimate source position based on propagation delay differences. Although the localization accuracy is lower than that of DAS due to reduced sampling rates, the method offers a viable alternative for integrated communication and positioning. We present the system topology and configuration for a dual-fiber layout, each end equipped with optical transmitters and receivers. The position estimation is derived from the time difference of arrival (TDOA) between the two receivers. The Cramér-Rao Bound is derived to characterize the theoretical limits of localization accuracy, highlighting dependencies on system parameters such as optical power loss. Our analysis shows that increased acoustic bandwidth and higher carrier frequencies enhance spatial resolution. We formulate the Cross Ambiguity Function as a maximum likelihood estimator for TDOA and provide simulation results illustrating its performance under varying system conditions. Finally, we discuss key challenges that must be addressed for practical implementation.

eess.SP↗

arXiv2025

A Conceptual Algorithm for Applying Ethical Principles of AI to Medical Practice

Artificial Intelligence (AI) is poised to transform healthcare delivery through revolutionary advances in clinical decision support and diagnostic capabilities. While human expertise remains foundational to medical practice, AI-powered tools are increasingly matching or exceeding specialist-level performance across multiple domains, paving the way for a new era of democratized healthcare access. These systems promise to reduce disparities in care delivery across demographic, racial, and socioeconomic boundaries by providing high-quality diagnostic support at scale. As a result, advanced healthcare services can be affordable to all populations, irrespective of demographics, race, or socioeconomic background. The democratization of such AI tools can reduce the cost of care, optimize resource allocation, and improve the quality of care. In contrast to humans, AI can potentially uncover complex relationships in the data from a large set of inputs and lead to new evidence-based knowledge in medicine. However, integrating AI into healthcare raises several ethical and philosophical concerns, such as bias, transparency, autonomy, responsibility, and accountability. In this study, we examine recent advances in AI-enabled medical image analysis, current regulatory frameworks, and emerging best practices for clinical integration. We analyze both technical and ethical challenges inherent in deploying AI systems across healthcare institutions, with particular attention to data privacy, algorithmic fairness, and system transparency. Furthermore, we propose practical solutions to address key challenges, including data scarcity, racial bias in training datasets, limited model interpretability, and systematic algorithmic biases. Finally, we outline a conceptual algorithm for responsible AI implementations and identify promising future research and development directions.

cs.AI↗

arXiv2023

Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions

With the advent of the IoT, AI, ML, and DL algorithms, the landscape of data-driven medical applications has emerged as a promising avenue for designing robust and scalable diagnostic and prognostic models from medical data. This has gained a lot of attention from both academia and industry, leading to significant improvements in healthcare quality. However, the adoption of AI-driven medical applications still faces tough challenges, including meeting security, privacy, and quality of service (QoS) standards. Recent developments in \ac{FL} have made it possible to train complex machine-learned models in a distributed manner and have become an active research domain, particularly processing the medical data at the edge of the network in a decentralized way to preserve privacy and address security concerns. To this end, in this paper, we explore the present and future of FL technology in medical applications where data sharing is a significant challenge. We delve into the current research trends and their outcomes, unravelling the complexities of designing reliable and scalable \ac{FL} models. Our paper outlines the fundamental statistical issues in FL, tackles device-related problems, addresses security challenges, and navigates the complexity of privacy concerns, all while highlighting its transformative potential in the medical field. Our study primarily focuses on medical applications of \ac{FL}, particularly in the context of global cancer diagnosis. We highlight the potential of FL to enable computer-aided diagnosis tools that address this challenge with greater effectiveness than traditional data-driven methods. We hope that this comprehensive review will serve as a checkpoint for the field, summarizing the current state-of-the-art and identifying open problems and future research directions.

cs.LG↗

arXiv2022

COROID: A Crowdsourcing-based Companion Drones to Tackle Current and Future Pandemics

Due to the current COVID-19 virus, which has already been declared a pandemic by the World Health Organization (WHO), we are witnessing the greatest pandemic of the decade. Millions of people are being infected, resulting in thousands of deaths every day across the globe. Even it was difficult for the best healthcare-providing countries could not handle the pandemic because of the strain of treating thousands of patients at a time. The count of infections and deaths is increasing at an alarming rate because of the spread of the virus. We believe that innovative technologies could help reduce pandemics to a certain extent until we find a definite solution from the medical field to handle and treat such pandemic situations. Technology innovation has the potential to introduce new technologies that could support people and society during these difficult times. Therefore, this paper proposes the idea of using drones as a companion to tackle current and future pandemics. Our COROID drone is based on the principle of crowdsourcing sensors data of the public's smart devices, which can correlate the reading of the infrared cameras equipped on the COROID drones. To the best of our knowledge, this concept has yet to be investigated either as a concept or as a product. Therefore, we believe that the COROID drone is innovative and has a huge potential to tackle COVID-19 and future pandemics.

cs.CY↗