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+34 946 567 842
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+34 946 567 843
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smazuelas@bcamath.org
Information of interest
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Belief Condensation Filtering For Rssi-Based State Estimation In Indoor Localization
Mehryary, S.; Mazuelas, S.
; Malekzadehz, P.; Spachos, P.; Plataniotisy, K.N.; Mohammadi, A. (2019)
Recent advancements in signal processing and communication systems have resulted in evolution of an intriguing concept referred to as Internet of Things (IoT). By embracing the IoT evolution, there has been a surge of ...
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Crowd-Centric Counting via Unsupervised Learning
Morselli, F.; Bartoletti, S.; Mazuelas, S.; Win, M.; Conti, A. (2019-07-11)
Counting targets (people or things) within a moni-tored area is an important task in emerging wireless applications,including those for smart environments, safety, and security.Conventional device-free radio-based ...
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Deep GEM-based network for weakly supervised UWB ranging error mitigation
Li, Y; Mazuelas, S.; Shen, Y. (2021-12-30)
Ultra-wideband (UWB)-based techniques, while becoming mainstream approaches for high-accurate positioning, tend to be challenged by ranging bias in harsh environments. The emerging learning-based methods for error ...
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Deep Generative Model for Simultaneous Range Error Mitigation and Environment Identification
Li, X.; Mazuelas, S.; Shen, Y. (2021-12-07)
Received waveforms contain rich information for both range information and environment semantics. However, its full potential is hard to exploit under multipath and non-line- of-sight conditions. This paper proposes a ...
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A Deep Learning Approach for Generating Soft Range Information from RF Data
li, Y.; Mazuelas, S.; Shen, Y. (2022-01-24)
Radio frequency (RF)-based techniques are widely adopted for indoor localization despite the challenges in extracting sufficient information from measurements. Soft range information (SRI) offers a promising alternative ...
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Derivation of a Cost-Sensitive COVID-19 Mortality Risk Indicator Using a Multistart Framework
Armañanzas, R.; Diaz, A.; Martinez, M.; Mazuelas, S. (2022-01-14)
The overall global death rate for COVID-19 patients has escalated to 2.13% after more than a year of worldwide spread. Despite strong research on the infection pathogenesis, the molecular mechanisms involved in a fatal ...
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General supervision via probabilistic transformations
Mazuelas, S.; Pérez, A. (2020-08-01)
Different types of training data have led to numerous schemes for supervised classification. Current learning techniques are tailored to one specific scheme and cannot handle general ensembles of training samples. This ...
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Generalized Maximum Entropy for Supervised Classification
Mazuelas, S.; Shen, Y.; Pérez, A. (2022-04)
The maximum entropy principle advocates to evaluate events’ probabilities using a distribution that maximizes entropy among those that satisfy certain expectations’ constraints. Such principle can be generalized for ...
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Location Awareness in Beyond 5G Networks
Conti, A.; Morselli, F.; Liu, Z.; Bartoletti, S.; Mazuelas, S.; Lindsey, W.C.; Win, M.Z. (2021-11-01)
Location awareness is essential for enabling contextual services and for improving network management in 5th generation (5G) and beyond 5G (B5G) networks. This paper provides an overview of the expanding opportunities ...
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Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees
Alvarez, V.; Mazuelas, S.; Lozano, J.A. (2022-07)
The statistical characteristics of instance-label pairs often change with time in practical scenarios of supervised classification. Conventional learning techniques adapt to such concept drift accounting for a scalar rate ...
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Minimax Classification with 0-1 Loss and Performance Guarantees
Mazuelas, S.; Zanoni, A.; Pérez, A. (2020-12-01)
Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample generalization by minimizing surrogate ...
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Probabilistic Load Forecasting Based on Adaptive Online Learning
Álvarez, V.; Mazuelas, S.; Lozano, J.A. (2020)
Load forecasting is crucial for multiple energy management tasks such as scheduling generation capacity, planning supply and demand, and minimizing energy trade costs. Such relevance has increased even more in recent ...
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The role of asymmetric prediction losses in smart charging of electric vehicles
Straka, M.; Buzna, L.; Refa, N.; Mazuelas, S. (2022-07-01)
Climate change prompts humanity to look for decarbonisation opportunities, and a viable option is to supply electric vehicles with renewable energy. The stochastic nature of charging demand and renewable generation requires ...
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A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform
Li, Y.; Mazuelas, S.; Shen, Y. (2021-12-30)
Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. Learning-based methods for error ...
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Soft information for localization-of-things
Conti, A.; Mazuelas, S.; Bartoletti, S.; Lindsey, W.C; Win, M. (2019-11-01)
Location awareness is vital for emerging Internetof- Things applications and opens a new era for Localizationof- Things. This paper first reviews the classical localization techniques based on single-value metrics, such ...
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Soft range information for network localization
Mazuelas, S.; Conti, A.; Allen, J.C.; Win, M.Z. (2018-06-15)
The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on ...
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Spatiotemporal information coupling in network navigation
Mazuelas, S.; Shen, Y.; Win, Z. (2018-12)
Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information ...
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Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables
li, Y.; Mazuelas, S.; Shen, Y. (2022-05-23)
Deep generative models (DGMs) and their conditional counterparts provide a powerful ability for general-purpose generative modeling of data distributions. However, it remains challenging for existing methods to address ...