Relevant bibliographies by topics / Personalised Signal Processing

Academic literature on the topic 'Personalised Signal Processing'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Personalised Signal Processing"

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Wolf, KatieAnna E., and Rebecca Fiebrink. "Personalised interactive sonification of musical performance data." Journal on Multimodal User Interfaces 13, no. 3 (March 13, 2019): 245–65. http://dx.doi.org/10.1007/s12193-019-00294-y.

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Shpakova, Tatiana, and Nataliya Sokolovska. "Probabilistic personalised cascade with abstention." Pattern Recognition Letters 147 (July 2021): 8–15. http://dx.doi.org/10.1016/j.patrec.2021.03.029.

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Kasemsuppakorn, Piyawan, and Hassan A. Karimi. "Personalised routing for wheelchair navigation." Journal of Location Based Services 3, no. 1 (March 2009): 24–54. http://dx.doi.org/10.1080/17489720902837936.

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Mills, Jed, Jia Hu, and Geyong Min. "Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing." IEEE Transactions on Parallel and Distributed Systems 33, no. 3 (March 1, 2022): 630–41. http://dx.doi.org/10.1109/tpds.2021.3098467.

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Millonig, A., and G. Gartner. "Identifying motion and interest patterns of shoppers for developing personalised wayfinding tools." Journal of Location Based Services 5, no. 1 (November 29, 2010): 3–21. http://dx.doi.org/10.1080/17489725.2010.535029.

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Kasabov, Nikola. "Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach." Pattern Recognition Letters 28, no. 6 (April 2007): 673–85. http://dx.doi.org/10.1016/j.patrec.2006.08.007.

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Pandithevan, Ponnusamy, and Gurunathan Saravana Kumar. "Personalised bone tissue engineering scaffold with controlled architecture using fractal tool paths in layered manufacturing." Virtual and Physical Prototyping 4, no. 3 (September 2009): 165–80. http://dx.doi.org/10.1080/17452750903055512.

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Cavallo, Francesca Romana, Khalid Baig Mirza, Sara de Mateo, Luca Miglietta, Jesus Rodriguez-Manzano , Konstantin Nikolic, and Christofer Toumazou. "A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR." Biosensors 12, no. 7 (July 19, 2022): 537. http://dx.doi.org/10.3390/bios12070537.

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This paper presents a fully automated point-of-care device for protein quantification using short-DNA aptamers, where no manual sample preparation is needed. The device is based on our novel aptamer-based methodology combined with real-time polymerase chain reaction (qPCR), which we employ for very sensitive protein quantification. DNA amplification through qPCR, sensing and real-time data processing are seamlessly integrated into a point-of-care device equipped with a disposable cartridge for automated sample preparation. The system’s modular nature allows for easy assembly, adjustment and expansion towards a variety of biomarkers for applications in disease diagnostics and personalised medicine. Alongside the device description, we also present a new algorithm, which we named PeakFluo, to perform automated and real-time quantification of proteins. PeakFluo achieves better linearity than proprietary software from a commercially available qPCR machine, and it allows for early detection of the amplification signal. Additionally, we propose an alternative way to use the proposed device beyond the quantitative reading, which can provide clinically relevant advice. We demonstrate how a convolutional neural network algorithm trained on qPCR images can classify samples into high/low concentration classes. This method can help classify obese patients from their leptin values to optimise weight loss therapies in clinical settings.
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Villarán, Carlos, and Marta Beltrán. "User-Centric Privacy for Identity Federations Based on a Recommendation System." Electronics 11, no. 8 (April 14, 2022): 1238. http://dx.doi.org/10.3390/electronics11081238.

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Specifications such as SAML, OAuth, OpenID Connect and Mobile Connect are essential for solving identification, authentication and authorisation in contexts such as mobile apps, social networks, e-commerce, cloud computing or the Internet of Things. However, end-users relying on identity providers to access resources, applications or services lose control over the Personally Identifiable Information (PII) they share with the different providers composing identity federations. This work proposes a user-centric approach based on a recommendation system to support users in making privacy decisions such as selecting service providers or choosing their privacy settings. The proposed Privacy Advisor gives end-users privacy protection by providing personalised recommendations without compromising the identity federations’ functionalities or requiring any changes in their underlying specifications. A proof of concept of the proposed recommendation system is presented to validate and evaluate its utility and feasibility.
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Adeluyi, Olufemi, Miguel A. Risco-Castillo, María Liz Crespo, Andres Cicuttin, and Jeong-A. Lee. "A Computerized Bioinspired Methodology for Lightweight and Reliable Neural Telemetry." Sensors 20, no. 22 (November 12, 2020): 6461. http://dx.doi.org/10.3390/s20226461.

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Personalized health monitoring of neural signals usually results in a very large dataset, the processing and transmission of which require considerable energy, storage, and processing time. We present bioinspired electroceptive compressive sensing (BeCoS) as an approach for minimizing these penalties. It is a lightweight and reliable approach for the compression and transmission of neural signals inspired by active electroceptive sensing used by weakly electric fish. It uses a signature signal and a sensed pseudo-sparse differential signal to transmit and reconstruct the signals remotely. We have used EEG datasets to compare BeCoS with the block sparse Bayesian learning-bound optimization (BSBL-BO) technique—A popular compressive sensing technique used for low-energy wireless telemonitoring of EEG signals. We achieved average coherence, latency, compression ratio, and estimated per-epoch power values that were 35.38%, 62.85%, 53.26%, and 13 mW better than BSBL-BO, respectively, while structural similarity was only 6.295% worse. However, the original and reconstructed signals remain visually similar. BeCoS senses the signals as a derivative of a predefined signature signal resulting in a pseudo-sparse signal that significantly improves the efficiency of the monitoring process. The results show that BeCoS is a promising approach for the health monitoring of neural signals.
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Dissertations / Theses on the topic "Personalised Signal Processing"

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Galozy, Alexander. "Data-driven personalized healthcare : Towards personalized interventions via reinforcement learning for Mobile Health." Licentiate thesis, Högskolan i Halmstad, CAISR Centrum för tillämpade intelligenta system (IS-lab), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44091.

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Medical and technological advancement in the last century has led to the unprecedented increase of the populace's quality of life and lifespan. As a result, an ever-increasing number of people live with chronic health conditions that require long-term treatment, resulting in increased healthcare costs and managerial burden to the healthcare provider. This increase in complexity can lead to ineffective decision-making and reduce care quality for the individual while increasing costs. One promising direction to tackle these issues is the active involvement of the patient in managing their care. Particularly for chronic diseases, where ongoing support is often required, patients must understand their illness and be empowered to manage their care. With the advent of smart devices such as smartphones, it is easier than ever to provide personalised digital interventions to patients, help them manage their treatment in their daily lives, and raise awareness about their illness. If such new approaches are to succeed, scalability is necessary, and solutions are needed that can act autonomously without costly human intervention. Furthermore, solutions should exhibit adaptability to the changing circumstances of an individual patient's health, needs and goals. Through the ongoing digitisation of healthcare, we are presented with the unique opportunity to develop cost-effective and scalable solutions through Artificial Intelligence (AI). This thesis presents work that we conducted as part of the project improving Medication Adherence through Person-Centered Care and Adaptive Interventions (iMedA) that aims to provide personalised adaptive interventions to hypertensive patients, supporting them in managing their medication regiment. The focus lies on inadequate medication adherence (MA), a pervasive issue where patients do not take their medication as instructed by their physician. The selection of individuals for intervention through secondary database analysis on Electronic Health Records (EHRs) was a key challenge and is addressed through in-depth analysis of common adherence measures, development of prediction models for MA and discussions on limitations of such approaches for analysing MA. Furthermore, providing personalised adaptive interventions is framed in the contextual bandit setting and addresses the challenge of delivering relevant interventions in environments where contextual information is significantly corrupted.        The contributions of the thesis can be summarised as follows: (1) Highlighting the issues encountered in measuring MA through secondary database analysis and providing recommendations to address these issues, (2) Investigating machine learning models developed using EHRs for MA prediction and extraction of common refilling patterns through EHRs and (3) formal problem definition for a novel contextual bandit setting with context uncertainty commonly encountered in Mobile Health and development of an algorithm designed for such environments.
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Saha, Simanto. "Personalised Signal Processing for Cortical and Cardiac Applications." Thesis, 2020. http://hdl.handle.net/2440/127293.

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Biomedical signals reflect alterations in human physiological parameters in both healthy and pathological conditions. Their inherent variability over time and across individuals reduces the reproducibility of results and utility of biomedical signals. Personalisation of signal processing schemes by including parameters associated with the sources of inter-session and inter-subject variability can promote the usability of biomedical signals for larger cohorts. This thesis explores strategies for personalising signal processing techniques for the assessment of cortical and cardiac electrophysiological phenomena. A sensorimotor rhythm-based brain-computer interface (BCI) exploits changes in electroencephalogram (EEG) during motor imagery tasks and can establish a direct communication link between the brain and a computer, which may augment motor performance. Dealing with the variability inherent in EEG signals is not trivial and yet to be understood comprehensively to deliver BCI technology for practical use. A waveletbased signal processing method has been applied to model inter-subject associative source activations, leading to a more generalised BCI design. Intracardiac electrograms (EGM) are important for mapping electrical activation across the heart. Multiple variables, including bipolar vector orientation relative to the wave propagation vector, inter-electrode spacing, impact EGM recording. In this thesis, intracardiac EGM recorded with a customised array of electrodes were analysed to assess the impact of bipolar vector orientation and inter-electrode spacing on atrial fibrillation mapping. A novel spatial filtering method has been proposed to reduce the measurement uncertainty due to bipolar vector orientation. Besides, an independent component analysis-based filtering has been proposed as a potential preprocessing method for eliminating ventricular far-field artefact.
Thesis (MPhil) -- University of Adelaide, School of Electrical & Electronic Engineering, 2020
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Book chapters on the topic "Personalised Signal Processing"

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Yeh, Kuo-Hui, Nai-Wei Lo, and Chun-Kai Wang. "A NFC-Based Authentication Scheme for Personalized IPTV Services." In Advances in Intelligent Information Hiding and Multimedia Signal Processing, 273–81. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63859-1_34.

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Bianchi, Anna Maria, Stefania Coelli, Riccardo Lolatto, Pierluigi Reali, and Giuseppe Baselli. "Signal processing for cardiovascular applications in p-health." In Personalized Health Systems for Cardiovascular Disease, 85–118. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-12-818950-4.00007-0.

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Rajagopal, Sivakumar, and Babu Gopal. "Effective and Accurate Diagnosis Using Brain Image Fusion." In Applications of Deep Learning and Big IoT on Personalized Healthcare Services, 197–217. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2101-4.ch012.

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Medical imaging techniques are routinely employed to create images of the human system for clinical purposes. Multi-modality medical imaging is a widely used technology for diagnosis, detection, and prediction of various tissue abnormalities. This chapter is focused on the development of an improved brain image processing technique for the removal of noise from a magnetic resonance image (MRI) for accurate image restoration. Feature selection and extraction of MRI brain images are processed using image fusion. The medical images suffer from motion blur and noise for which image denoising is developed through non-local means (NLM) filtering for smoothing and shrinkage rule for sharpening. The peak signal to noise ratio (PSNR) of improved curvelet based self-similarity NLM method is better than discrete wavelet transform with an NLM filter.
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Chow, Lawrence, Nicholas Bambos, Alex Gilman, and Ajay Chander. "Personalized Monitors for Real-Time Detection of Physiological States." In Wearable Technologies, 931–51. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5484-4.ch042.

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The authors introduce an algorithmic framework to process real-time physiological data using nonparametric Bayesian models under the context of developing and testing personalized wellness monitors. A wearable device aggregates signals from various sensors while periodically transmitting the collected data to a backend server, which builds custom user profiles based on inferred hidden Markov states. They discuss how these user profiles can be used in various contexts as proxies for fluctuating physiological states and leveraged for various longitudinal classification tasks. Using data collected in a two-week study hosted at Jaslok Hospital, the authors show how physiological changes induced by different environments with various levels of stress can be quantified by the authors' platform. To minimize the dependence on continuous connectivity with the backend server, they introduce a heuristic to enable real-time state identification using the modest processing capabilities of the wearable device.
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Hossain, Gahangir. "Design Analytics of Complex Communication Systems Involving Two Different Sensory Disabilities." In Ophthalmology, 300–316. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5195-9.ch018.

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The design of a robust communication among two different sensory disabilities (Deaf vs. Blind) remains an emerging field of research in disability healthcare communication system design. As an important part of modern technology, android and iPhone applications are frequently used in designing such communication systems. However, there is no 'one-size-fits-all' in case of different sensory disability health communication design. Hence, an in-depth understanding of their requirement, media preferences, similarity and difference and up-to-date technology usability are plausible towards universal and personalized communication system design. This research addresses such complex issues and performs a study involving two different types of disabilities (deaf and blind) communication. As a part of healthcare analytics, critical incidences are recorded and corresponding complexities are measured in order to evaluate communication protocol with social signal processing. Communication flow diagram, complexity analysis and critical incidence are quantified to improve communication protocols. Moreover, the uniqueness of disability can be personalized through this process which has valuable implications in rehabilitation and multi-purpose healthcare communication device development.
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Ruotsalainen, Pekka, and Bernd Blobel. "How a Service User Knows the Level of Privacy and to Whom Trust in pHealth Systems?" In pHealth 2021. IOS Press, 2021. http://dx.doi.org/10.3233/shti210571.

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pHealth is a data (personal health information) driven approach that use communication networks and platforms as technical base. Often it’ services take place in distributed multi-stakeholder environment. Typical pHealth services for the user are personalized information and recommendations how to manage specific health problems and how to behave healthy (prevention). The rapid development of micro- and nano-sensor technology and signal processing makes it possible for pHealth service provider to collect wide spectrum of personal health related information from vital signs to emotions and health behaviors. This development raises big privacy and trust challenges especially because in pHealth similarly to eCommerce and Internet shopping it is commonly expected that the user automatically trust in service provider and used information systems. Unfortunately, this is a wrong assumption because in pHealth’s digital environment it almost impossible for the service user to know to whom to trust, and what the actual level of information privacy is. Therefore, the service user needs tools to evaluate privacy and trust of the service provider and information system used. In this paper, the authors propose a solution for privacy and trust as results of their antecedents, and for the use of computational privacy and trust. To answer the question, which antecedents to use, two literature reviews are performed and 27 privacy and 58 trust attributes suitable for pHealth are found. A proposal how to select a subset of antecedents for real life use is also provided.
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Conference papers on the topic "Personalised Signal Processing"

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Gavankar, Chetana, Aditya Phatak, Nikita Thakkar, Vaidehi Patel, Bhoomi Pragda, and Rutuja Lathkar. "A Utility Tool for Personalised Medicine." In ICVISP 2018: The 2nd International Conference on Vision, Image and Signal Processing. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3271553.3271562.

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Pahar, Madhurananda, Marisa Klopper, Byron Reeve, Rob Warren, Grant Theron, Andreas Diacon, and Thomas Niesler. "Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring." In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909522.

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Fajtl, Jiri, Lorenzo Vitali, Vasileios Argyriou, Dorothy Monekosso, and Paolo Remagnino. "Unsupervised Methods for a Personalised Route Recommendation System." In 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP). IEEE, 2020. http://dx.doi.org/10.1109/csndsp49049.2020.9249544.

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Chaczko, Zenon, Christopher Chiu, and Anup Kale. "Cooperative agent-based SANET architecture for personalised healthcare monitoring." In 2010 4th International Conference on Signal Processing and Communication Systems (ICSPCS 2010). IEEE, 2010. http://dx.doi.org/10.1109/icspcs.2010.5709690.

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Baird, Alice, Shahin Amiriparian, and Bjorn Schuller. "Can Deep Generative Audio be Emotional? Towards an Approach for Personalised Emotional Audio Generation." In 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2019. http://dx.doi.org/10.1109/mmsp.2019.8901785.

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Xiong, Feifei, Jon Barker, and Heidi Christensen. "Phonetic Analysis of Dysarthric Speech Tempo and Applications to Robust Personalised Dysarthric Speech Recognition." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8683091.

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Ofli, Ferda, Engin Erzin, Yucel Yemez, and A. Murat Tekalp. "Estimation of Personalized Facial Gesture Patterns." In 2007 IEEE 15th Signal Processing and Communications Applications. IEEE, 2007. http://dx.doi.org/10.1109/siu.2007.4298615.

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Yu, Ying, Chengling Zhao, and Qi Luo. "Research on Personalized Service System in E-supermarket." In 2006 8th international Conference on Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/icosp.2006.346124.

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Rubinstein, Bar, Yoav Filin, Nir Shlezinger, and Nariman Farsad. "Personalized Sleep State Classification via Learned Factor Graphs." In 2022 30th European Signal Processing Conference (EUSIPCO). IEEE, 2022. http://dx.doi.org/10.23919/eusipco55093.2022.9909523.

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Kim, Jangho, Simyung Chang, Sungrack Yun, and Nojun Kwak. "Prototype-Based Personalized Pruning." In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021. http://dx.doi.org/10.1109/icassp39728.2021.9414526.

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