Papers by Javier Andreu-Perez
IEEE Transactions on Computational Social Systems
Johns Hopkins University Press eBooks, 2020
Since Robotics is the field concerned with the connection of perception to action, Artificial Int... more Since Robotics is the field concerned with the connection of perception to action, Artificial Intelligence must have a central role in Robotics if the connection is to be intelligent. Artificial Intelligence addresses the crucial questions of: what knowledge is required in any aspect of thinking; ...
Computing with Words (CWW) methodology has been used to design intelligent systems which make dec... more Computing with Words (CWW) methodology has been used to design intelligent systems which make decisions by manipulating the linguistic information, like human beings. Human beings naturally understand (and express) themselves linguistically, and hence can reason (and make decision) just with linguistic information without any numerical measure. Perceptual Computing makes use of type 2 fuzzy sets for modeling the words in the CWW paradigm. This use of type-2 fuzzy sets enables better representation of the inherent uncertainty in the fuzzy linguistic semantics on numerous problems. To realise the potential of Perceptual Computing, its MATLAB implementation has been made freely available to the end-users/ researchers, and MATLAB is a proprietary development environment. Therefore, this contribution aims at proposing a python implementation of the Perceptual Computing, or its main processing element the perceptual computer that consists of three components viz., encoder, CWW engine and decoder. Our python implementation provides the end user with a seamless blending amongst all three components, which does not exist yet, to the best of our knowledge.
Springer eBooks, 2014
In the previous chapters, we have discussed issues concerning hardware, communication and network... more In the previous chapters, we have discussed issues concerning hardware, communication and network topologies for the practical deployment of Body Sensor Networks (BSNs). The pursuit of low power miniaturised distributed sensing under a patient’s natural physiological conditions has also imposed significant technical challenges on integrating information from what is often heterogeneous, incomplete and error-prone sensor data. For BSNs, the nature of errors can be attributed to a number of sources; but motion artefacts, inherent limitations and possible malfunctions of the sensors along with communication errors are the main causes of concern. In practice, it is desirable to rely on sensors with redundant or complementary data to maximise the information content and reduce both systematic errors and random artefacts. This, in essence, is the main drive for multi-sensor fusion, which is concerned with the synergistic use of multiple sources of information.
Internet of things is projected to make its way into all spheres of human life in the near future... more Internet of things is projected to make its way into all spheres of human life in the near future. This has been compounded with the growing demand for contactless solutions in the wake of the recent pandemic. A potential solution could involve a privacy-preserving gesture-based control system that could control a wide range of appliances. Implementing such gesture-based control systems is mainly conducted using opaque box Artificial Intelligence (AI) models. Systems based on such opaque box AI models have shown high-performance metrics on in-distribution data in a lab environment. However, they are prone to failure when exposed to real-world out-of-distribution data where they cannot be tuned or calibrated due to their complexity and opaqueness. Interval Type-2 Fuzzy Logic-based explainable AI models offer an alternative to opaque box models showing comparable performance on lab in-distribution data. In contrast, in the real world, out-of-distribution data, the type-2 fuzzy models could be easily calibrated and tuned (thanks for their explainability) to provide similar performance to those achieved on the lab in-distribution data.
IEEE transactions on artificial intelligence, Jun 1, 2023
Explainable Artificial Intelligence (XAI) is a paradigm that delivers transparent models and deci... more Explainable Artificial Intelligence (XAI) is a paradigm that delivers transparent models and decisions, which are easy to understand, analyze, and augment by a non-technical audience. Fuzzy Logic Systems (FLS) based XAI can provide an explainable framework, while also modeling uncertainties present in real-world environments, which renders it suitable for applications where explainability is a requirement. However, most real-life processes are not characterized by high levels of uncertainties alone; they are inherently time-dependent as well, i.e., the processes change with time. To account for the temporal component associated with a process, in this work, we present novel Temporal Type-2 FLS Based Approach for time-dependent XAI (TXAI) systems, which can account for the likelihood of a measurement's occurrence in the time domain using (the measurement's) frequency of occurrence. In Temporal Type-2 Fuzzy Sets (TT2FSs), a four-dimensional (4D) time-dependent membership function is developed where relations are used to construct the interrelations between the elements of the universe of discourse and its frequency of occurrence. The proposed TXAI system with TT2FSs is exemplified with a step-by-step numerical example and an empirical study using a real-life intelligent environments dataset to solve a time-dependent classification problem (predict whether or not a room is occupied depending on the sensors readings at a particular time of day). The TXAI system performance is also compared with other state-of-the-art classification methods with varying levels of explainability. The TXAI system manifested better classification prowess, with 10-fold test datasets, with a mean recall of 95.40% than a standard XAI system (based on non-temporal general type-2 (GT2) fuzzy sets) that had a mean recall of 87.04%. TXAI also performed significantly better than most nonexplainable AI systems between 3.95%, to 19.04% improvement gain in mean recall. Temporal convolution network (TCN) was marginally better than TXAI (by 1.98% mean recall improvement) although with a major computational complexity. In addition, TXAI can also outline the most likely time-dependent trajectories using the frequency of occurrence values embedded in the TXAI model; viz. given a rule at a determined time interval, what will be the next most likely rule at a subsequent time interval. In this regard, the proposed TXAI system can have profound implications for delineating the evolution of real-life time-dependent processes, such as behavioural or biological processes.
IEEE Transactions on Emerging Topics in Computational Intelligence
Neural Processing Letters
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Internet of things is projected to make its way into all spheres of human life in the near future... more Internet of things is projected to make its way into all spheres of human life in the near future. This has been compounded with the growing demand for contactless solutions in the wake of the recent pandemic. A potential solution could involve a privacy-preserving gesture-based control system that could control a wide range of appliances. Implementing such gesture-based control systems is mainly conducted using opaque box Artificial Intelligence (AI) models. Systems based on such opaque box AI models have shown high-performance metrics on in-distribution data in a lab environment. However, they are prone to failure when exposed to real-world out-of-distribution data where they cannot be tuned or calibrated due to their complexity and opaqueness. Interval Type-2 Fuzzy Logic-based explainable AI models offer an alternative to opaque box models showing comparable performance on lab in-distribution data. In contrast, in the real world, out-of-distribution data, the type-2 fuzzy models could be easily calibrated and tuned (thanks for their explainability) to provide similar performance to those achieved on the lab in-distribution data.
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Computing with Words (CWW) methodology has been used to design intelligent systems which make dec... more Computing with Words (CWW) methodology has been used to design intelligent systems which make decisions by manipulating the linguistic information, like human beings. Human beings naturally understand (and express) themselves linguistically, and hence can reason (and make decision) just with linguistic information without any numerical measure. Perceptual Computing makes use of type 2 fuzzy sets for modeling the words in the CWW paradigm. This use of type-2 fuzzy sets enables better representation of the inherent uncertainty in the fuzzy linguistic semantics on numerous problems. To realise the potential of Perceptual Computing, its MATLAB implementation has been made freely available to the end-users/ researchers, and MATLAB is a proprietary development environment. Therefore, this contribution aims at proposing a python implementation of the Perceptual Computing, or its main processing element the perceptual computer that consists of three components viz., encoder, CWW engine and decoder. Our python implementation provides the end user with a seamless blending amongst all three components, which does not exist yet, to the best of our knowledge.
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021
Neurons usually converse through electrochemical signals and pooled neuronal firings feasibly be ... more Neurons usually converse through electrochemical signals and pooled neuronal firings feasibly be recorded on the scalp through the medium of electroencephalogram (EEG). EEG waveforms are recorded, analysed and categorized across directives concerning a Brain-Computer Interface (BCI). Deteriorated signal to noise ratio and non-stationarities stand as a paramount obstacle in steady decoding of EEG. Appearance of non-stationarities across EEG patterns notably upset the feature waveforms thus worsening the functioning of detection block and as a whole the Brain Computer Interface. Stationary Subspace schemes bring to light subspaces within which data distribution persists stably over time. Current work focuses on the development of a novel spatial transform based feature extraction scheme to address nonstationarity in EEG signals recorded against a drowsiness detection problem (a machine learning regression scenario). The presented approach: F-DIV-IT-JAD-WS derived features distinctly surpassed DivOVR-FuzzyCSP-WS based standard features across RMSE and CC performance criteria pair. We construe that the propounded feature derivation approach based on F-DIV-IT-JAD-WS will usher a significant attention in researchers who are developing algorithms for signal processing, specifically, for BCI regression scenarios.
Recent advances in the reliability of the eye-tracking methodology as well as the increasing avai... more Recent advances in the reliability of the eye-tracking methodology as well as the increasing availability of affordable non-intrusive technology have opened the door to new research opportunities in a variety of areas and applications. This has raised an increasing interest within disciplines such as medicine, business and education for analysing human perceptual and psychological processes based on eye-tracking data. However, most of the currently available software requires programming skills and focuses on the analysis of a limited set of eyemovement measures (e.g. saccades and fixations), thus excluding other measures of interest to the classification of a determined state or condition. This paper describes ‘EALab’, a MATLAB analytics toolbox aimed at easing the extraction, multivariate analysis and classification stages of eye-activity data collected from commercial and independent eye trackers. The processing implemented in this toolbox enables to evaluate variables extracted ...
Journal of Experimental & Theoretical Artificial Intelligence
2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Information Sciences, 2021
Highlights • A formal discussion about the links and similarities between Yager's Computing with ... more Highlights • A formal discussion about the links and similarities between Yager's Computing with Words (CWW) framework, the linguistic computational models based on the extension principle and, the symbolic method. • An augmented version of extension principle based linguistic computational model is proposed to solve the problem of unequally weighted linguistic information. • Two new CWW methodologies are proposed: intuitionistic fuzzy sets based CWW methodology and rough sets based CWW methodology. • Step-by-step numerical examples are provided for each proposed method to allow reproducibility of the presented methodologies.
International Journal of Dynamics and Control, 2021
The open-loop drive of the MEMS tunable capacitor does not guarantee accurate output voltage in a... more The open-loop drive of the MEMS tunable capacitor does not guarantee accurate output voltage in an AC voltage reference source (VRS). For a precise regulation, the capacitor movable plate should track the pull-in point trajectory harmlessly and should be kept at a certain distance from the fixed plate. Achievement of this objective is a highly challenging issue, particularly when measurement noise, unmodeled dynamics, and external disturbance are deemed. In addition, the control effort consumed energy requires minimization in MEMS applications. This paper contemplates different modern control strategies for the capacitance regulation, in a step-by-step manner. The addressed and designed controllers include the simple pole placement state feedback controller (PPSFC), PPSFC equipped with the Luenberger observer (LO), linear quadratic regulator (LQR), LQR equipped with an LO, linear quadratic integrator (LQI), and the Kalman filter-based PPSFC. The design process is considered in an ev...
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Papers by Javier Andreu-Perez