Article
Computer Science, Hardware & Architecture
Aditi Paul, Somnath Sinha
Summary: Location verification is crucial in WSNs applications, and a novel pattern-matching approach is proposed in this study to verify sensors' location without using additional hardware like GPS. The algorithm uses Spline curve and Cubic Bezier Curves to efficiently identify node's location change and estimate the amount of difference. The implementation in the Cooja simulator shows satisfactory performance with up to 90% accuracy in location verification and up to 99% accuracy in estimating location change.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Engineering, Electrical & Electronic
Lismer Andres Caceres Najarro, Iickho Song, Slavisa Tomic, Muhammad Salman, Youngtae Noh, Kiseon Kim
Summary: This article addresses the target tracking problem in wireless sensor networks using received signal strength and angle of arrival. It proposes a tracking algorithm based on evolutionary techniques that does not require approximation of the cost function, resulting in improved tracking accuracy compared to existing algorithms.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Zhao Xiaoqian, Wang Wei
Summary: In this study, a three-dimensional iterative centroid localization algorithm based on received signal strength indication is proposed to improve the positioning accuracy in wireless sensor networks. By simplifying the reference nodes and utilizing spatial geometric relationships, the algorithm effectively reduces the positioning error of three-dimensional positioning. Simulation results demonstrate that the proposed algorithm can significantly enhance the positioning accuracy compared to traditional algorithms.
LASER & OPTOELECTRONICS PROGRESS
(2021)
Article
Computer Science, Information Systems
Wencheng Yang, Song Wang
Summary: Authentication is crucial for securing communication in wireless body sensor networks. This article introduces a privacy-preserving ECG-based authentication system that not only provides secure intranode authentication but also protects sensitive information in ECG data.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Multidisciplinary
Wei Han, Xiongzhu Bu, Miaomiao Xu, Yunpu Zhu
Summary: This study focuses on the mechanism analysis and model establishment of SAW sensing systems based on RSSI detection principle, and validates the model's effectiveness through experiments. By optimizing the system's timing parameters, an optimized torque measurement prototype is designed and the system's effectiveness is verified.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Laura Flueratoru, Viktoriia Shubina, Dragos Niculescu, Elena Simona Lohan
Summary: This paper presents a measurement-based analysis of Bluetooth Low Energy (BLE) signal Received Signal Strength (RSS) in Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios. The study found that snapshot RSS measurements typically have high variability, while aggregated RSS data is more informative statistically and aligns better with current theoretical models.
IEEE SENSORS JOURNAL
(2022)
Article
Automation & Control Systems
Ali Ihsan Tas, Mehmet Iscan, Berkay Gurkan, Cuneyt Yilmaz
Summary: This paper proposes an adaptive novel elliptic trajectory formula-based tracking algorithm for received signal strength indicator (ANETF-RSSI) for continuous telemetry transmission between unmanned aerial vehicles (UAVs) and ground control stations. The algorithm generates a two-dimensional RSSI map to identify optimal paths under challenging flight conditions.
TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL
(2023)
Article
Environmental Sciences
Xiaojun Mei, Dezhi Han, Nasir Saeed, Huafeng Wu, Fahui Miao, Jiangfeng Xian, Xinqiang Chen, Bing Han
Summary: This study proposes a coarse-to-fine localization method (CFLM) for underwater wireless sensor networks (UWSNs), which improves the localization accuracy by integrating block principal pivoting and Taylor series expansion techniques. Simulation results demonstrate that the proposed CFLM outperforms other methods in various scenarios.
FRONTIERS IN MARINE SCIENCE
(2023)
Article
Engineering, Electrical & Electronic
Tomas Bravenec, Michael Gould, Tomas Fryza, Joaquin Torres-Sospedra
Summary: The popularity of indoor positioning and navigation has led to the development of various approaches using different technologies. Most of these approaches require the creation of a signal propagation radio map (RM) by analyzing the environment. This article explores the options for reducing the time needed to acquire RSSI information and suggests using linear interpolation and Gaussian process regression (GPR) to balance positioning accuracy, computational complexity, and data collection time.
IEEE SENSORS JOURNAL
(2023)
Article
Computer Science, Interdisciplinary Applications
Apidet Booranawong, Peeradon Thammachote, Yoschanin Sasiwat, Jutamanee Auysakul, Kiattisak Sengchuai, Dujdow Buranapanichkit, Sawit Tanthanuch, Nattha Jindapetch, Hiroshi Saito
Summary: This paper presents the implementation and validation of a target tracking system based on RSSI in an indoor corridor environment. Six tracking methods using RSSI signals measured from stationary reference nodes are proposed and a filter is applied for better accuracy. Experimental results demonstrate that the proposed methods can efficiently track moving targets in real time with reduced distance errors.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2022)
Article
Chemistry, Analytical
Weizhong Ding, Qiubo Zhong, Yan Wang, Chao Guan, Baofu Fang
Summary: A new positioning algorithm based on RSS measurement is proposed in this paper. The algorithm utilizes maximum likelihood estimation and semi-definite programming to transform the received signal strength model into a non-convex estimator and then into a convex estimator, obtaining the global minimum of the target location estimation. This algorithm addresses the L-0 known problem and extends its application to the case of L-0 unknown, demonstrating better accuracy compared to existing positioning algorithms through simulations and experimental results.
Article
Computer Science, Information Systems
Mohd Fazuwan Ahmad Fauzi, Rosdiadee Nordin, Nor Fadzilah Abdullah, Haider A. H. Alobaidy
Summary: The need for wider and higher-quality mobile network coverage is crucial in today's society due to the increased internet penetration. The Covid-19 pandemic has further accelerated the digital transformation, making the traditional prediction models less reliable. This study explores the use of machine learning models for mobile network coverage prediction, and the evaluation results show that the GPR model is the most accurate, while the ET model is the most practical for real-world mobile network planning applications.
Article
Engineering, Electrical & Electronic
Ankit Mittal, Nikita Mirchandani, Giuseppe Michetti, Luca Colombo, Tanbir Haque, Matteo Rinaldi, Aatmesh Shrivastava
Summary: This paper presents a new technique of RF signal strength detection in an IoT network using a RSSI circuit. The circuit achieves direct conversion of RF to digital code indicating the signal strength and incorporates a feedback circuit to correct detection inaccuracies. The circuit offers high detection accuracy with ultra-low power consumption, making it suitable for IoT applications.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS
(2022)
Article
Computer Science, Information Systems
Hong Zhao, Yuexin Zhang, Xinyi Huang, Yang Xiang, Chunhua Su
Summary: Due to the characteristics of wireless network transmission, smart home devices are vulnerable to malicious attacks, which pose security risks to people's daily lives. In order to ensure the security of transmitted data, this article proposes an adaptive physical-layer key generation scheme based on received signal strength, which improves the randomness and performance of the generated keys through group quantization and adaptive quantization.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Engineering, Electrical & Electronic
Yulong Wang, Shenghong Li, Wei Ni, Minghui Zhao, Abbas Jamalipour, Bochun Wu
Summary: This paper introduces a new approach for accurately and efficiently identifying a large number of wireless devices based on received signal strengths (RSSs). By using a probabilistic graphical model and belief propagation algorithm in a structured environment, the approach achieves accurate device positioning and assignment.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2021)
Article
Computer Science, Theory & Methods
Juan Carlos Salinas-Hilburg, Marina Zapater, Jose M. Moya, Jose L. Ayala
Summary: The text discusses a fast energy estimation framework for long-running applications in data center facilities, which uses application signatures to estimate CPU and memory energy consumption without complete execution. The framework achieves low estimation errors and high Compression Ratio values, demonstrating its effectiveness in improving energy efficiency.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Interdisciplinary Applications
Luis Garcia-Terriza, Jose L. Risco-Martin, Gemma Reig Rosello, Jose L. Ayala
Summary: This study presents a novel and promising approach to the clinical management of acute stroke by developing accurate diagnosis and prediction real-time models using machine learning techniques. Results show high precision in stroke diagnosis, exitus prediction, and stroke recurrence prediction, with accuracies ranging from 98% to 99.8%.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2021)
Article
Neurosciences
Vanesa Pytel, Maria Nieves Cabrera-Martin, Alfonso Delgado-Alvarez, Jose Luis Ayala, Paloma Balugo, Cristina Delgado-Alonso, Miguel Yus, Maria Teresa Carreras, Jose Luis Carreras, Jorge Matias-Guiu, Jordi A. Matias-Guiu
Summary: This study found that using personalized targeting repetitive transcranial magnetic stimulation can improve language ability, patient and caregiver perception of change, apathy, and depression in patients with PPA, and there is an increase in metabolism in various brain regions after treatment, indicating enhancement of synaptic activity.
JOURNAL OF ALZHEIMERS DISEASE
(2021)
Article
Computer Science, Hardware & Architecture
Juan Carlos Salinas-Hilburg, Marina Zapater, Jose M. Moya, Jose L. Ayala
Summary: Energy-aware task scheduling approaches are crucial for improving energy savings in data centers, with the use of application signatures to estimate energy consumption without complete execution of applications. Different scheduling approaches can be combined with application signatures to optimize the makespan of the scheduling process and enhance energy savings, with high accuracy compared to oracle methods.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Geriatrics & Gerontology
Fernando Garcia-Gutierrez, Alfonso Delgado-Alvarez, Cristina Delgado-Alonso, Josefa Diaz-Alvarez, Vanesa Pytel, Maria Valles-Salgado, Maria Jose Gil, Laura Hernandez-Lorenzo, Jorge Matias-Guiu, Jose L. Ayala, Jordi A. Matias-Guiu
Summary: This study developed machine learning models using neuropsychological tests for the diagnosis of neurodegenerative disorders. Results showed high levels of accuracy, supporting the usefulness of cognitive assessment in diagnosis.
INTERNATIONAL JOURNAL OF GERIATRIC PSYCHIATRY
(2022)
Article
Geriatrics & Gerontology
Josefa Diaz-Alvarez, Jordi A. Matias-Guiu, Maria Nieves Cabrera-Martin, Vanesa Pytel, Ignacio Segovia-Rios, Fernando Garcia-Gutierrez, Laura Hernandez-Lorenzo, Jorge Matias-Guiu, Jose Luis Carreras, Jose L. Ayala, Alzheimer's Dis Neuroimaging Initiative
Summary: Genetic algorithms can be used for automated and accurate diagnosis of Alzheimer's disease, frontotemporal dementia, and related disorders in FDG-PET imaging. By selecting the most meaningful features, genetic algorithms achieve high accuracy in diagnosis and require fewer features for assessment.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Neurosciences
Carlos Moral-Rubio, Paloma Balugo, Adela Fraile-Pereda, Vanesa Pytel, Lucia Fernandez-Romero, Cristina Delgado-Alonso, Alfonso Delgado-Alvarez, Jorge Matias-Guiu, Jordi A. Matias-Guiu, Jose Luis Ayala
Summary: EEG was evaluated as a biomarker for PPA diagnosis, showing high accuracy in distinguishing PPA from controls. However, the ability to differentiate between PPA variants was lower. Future studies should explore the potential of high-density EEG in distinguishing PPA variants.
Article
Computer Science, Information Systems
Juan Manuel Carrera Garcia, Joaquin Recas Piorno, Maria Guijarro Mata-Garcia
Summary: This study proposes a solution based on artificial vision analysis of zenith images, which can automatically analyze the available parking spaces and real-time occupancy in a parking lot. The system can accurately detect the presence of vehicles in parking spaces and the area occupied by them, and assign a suitable parking space based on the dimensions of a new vehicle and the location of parked cars nearby.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Geriatrics & Gerontology
Noelia Esteban-Garcia, Luis C. Fernandez-Beltran, Juan Miguel Godoy-Corchuelo, Jose L. Ayala, Jordi A. Matias-Guiu, Silvia Corrochano
Summary: This study found that different body lipid metabolic traits are associated with the risk of frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). The findings suggest that controlling body lipid metabolism may be a potential approach for the treatment of FTD and ALS, and identified a potential link between circulating lipid levels and these disorders through HNRNPK.
FRONTIERS IN AGING NEUROSCIENCE
(2022)
Article
Computer Science, Information Systems
Javier Galvez-Goicurla, Josue Pagan, Ana B. Gago-Veiga, Jose M. Moya, Jose L. Ayala
Summary: Chronic diseases benefit from personalized medicine advancements resulting from the integration of systems biology, the Internet of Things, and Artificial Intelligence. Current healthcare costs in the EU and US are largely spent on chronic diseases, emphasizing the need for personalized treatments to reduce risks of overmedication.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Kevin Henares, Jose L. Risco-Martin, Jose L. Ayala, Roman Hermida
Summary: The rise of the Internet of Things has led to an exponential increase in the number of connected devices, enabling data collection and improving various services. However, this demands more powerful storage and processing capabilities. Modeling and Simulation technology plays a vital role in deploying IoT infrastructure, providing flexible mechanisms to study and compare different strategies. Micro Data Centers offer an effective solution to alleviate the burden on Cloud Data Centers. This paper explores a modeling and simulation methodology to analyze the power consumption of a healthcare IoT scenario and compares various data center configurations.
JOURNAL OF SIMULATION
(2022)
Article
Medicine, General & Internal
Jordi A. Matias-Guiu, Cristina Delgado-Alonso, Maria Diez-Cirarda, Alvaro Martinez-Petit, Silvia Oliver-Mas, Alfonso Delgado-Alvarez, Constanza Cuevas, Maria Valles-Salgado, Maria Jose Gil, Miguel Yus, Natividad Gomez-Ruiz, Carmen Polidura, Josue Pagan, Jorge Matias-Guiu, Jose Luis Ayala
Summary: Fatigue is a common disabling symptom in neurological disorders with an important cognitive component. This study aimed to develop predictive models for fatigue using neuropsychological assessments and evaluate the relationship between cognitive fatigue and objective neuropsychological assessment results. However, the study did not find reliable predictors of cognitive fatigue and suggests different pathophysiological mechanisms of fatigue and cognitive dysfunction in post-COVID syndrome.
JOURNAL OF CLINICAL MEDICINE
(2022)
Article
Psychiatry
Jordi A. Matias-Guiu, Elena Herrera, Maria Gonzalez-Nosti, Kamini Krishnan, Cristina Delgado-Alonso, Maria Diez-Cirarda, Miguel Yus, Alvaro Martinez-Petit, Josue Pagan, Jorge Matias-Guiu, Jose Luis Ayala, Robyn Busch, Bruce P. Hermann
Summary: The objective of this study was to develop objective criteria for cognitive dysfunction associated with the post-COVID syndrome. Four hundred and four patients with post-COVID syndrome were evaluated using comprehensive neuropsychological batteries. The developed criteria classified 41.2% and 17.3% of the sample as having at least one impaired cognitive domain using-1 and-1.5 standard deviations as cutoff points. Cognitive impairment was associated with younger age and lower education levels, but not hospitalization.
PSYCHIATRY RESEARCH
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Javier Galvez-Goicuria, Josue Pagan, Lucia Perez, Julian Catalina-Gomez, Jose M. Moya, Jose L. Ayala
Summary: Maintaining good sleep hygiene is essential for preventing sleep disorders and worsening symptoms of other diseases. Polysomnography, a study of sleep conducted by professionals at hospitals, allows for diagnosis but lacks continuous monitoring. This study examines the reliability of using a wrist-worn wearable device to monitor body posture during sleep. Through the development of classification models, the researchers improve the accuracy by 0.011 points, achieving F-values of 0.966 and 0.989 for Random Forest and k-Nearest Neighbors algorithms respectively.
2022 IEEE INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS (IEEE COINS 2022)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Laura Hernandez-Lorenzo, Inigo Sanz Ilundain, Jose L. Ayala Rodrigo
Summary: This study applies the Dynamic Time Warping technique combined with hierarchical clustering to analyze time series datasets of Alzheimer's Disease. The results obtained from both unidimensional and multidimensional datasets are consistent with clinical expectations, highlighting the potential of time series clustering in discovering new knowledge in time-dependent diseases such as AD.
2022 IEEE INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS (IEEE COINS 2022)
(2022)
