Salvatore Sessa

Bio: Salvatore Sessa is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: Fuzzy logic & Fuzzy set. The author has an hindex of 30, co-authored 202 publications receiving 4714 citations. Previous affiliations of Salvatore Sessa include University of Catania & Egypt-Japan University of Science and Technology.

Fixed point theorems by altering distances between the points

Abstract: Let R be the set of nonnegative real numbers and N the set ofpositive integers.Delbosco [/] and Skof [&] have established fixed point theorems forselfmaps of complete metric spaces by altering the distances between thepoints withproperties: the use of a function (0 : R -*• R satisfying the following1. cp is continuous and strictly increasing in R ;2. ip(t) = 0 if and only if t = 0 ;3.

Fuzzy Relation Equations and Their Applications to Knowledge Engineering

Abstract: 1: Introductory Remarks on Fuzzy Sets.- 2: Fuzzy Relation Equations in Residuated Lattices.- 3: Lower Solutions of Max-Min Fuzzy Equations.- 4: Measures of Fuzziness of Solutions of Max-Min Fuzzy Relation Equations on Linear Lattices.- 5: Boolean Solutions of Max-Min Fuzzy Equations.- 6: ?-Fuzzy Relation Equations and Decomposable Fuzzy Relations.- 7: Max-Min Decomposition Problem of a Fuzzy Relation in Linear Lattices.- 8: Fuzzy Relation Equations with Lower and Upper Semicontinuous Triangular Norms.- 9: Fuzzy Relation Equations with Equality and Difference Composition Operators.- 10: Approximate Solutions of Fuzzy Relation Equations.- 11: Handling Fuzziness in Knowledge-Based Systems.- 12: Construction of Knowledge Base, Its Validation and Optimization.- 13: Inference Algorithms in Knowledge-Based Systems.- 14: A Fuzzy Controller and Its Realization.- 15: Bibliographies.- Author Index.

Towards Miniaturization of a MEMS-Based Wearable Motion Capture System

Abstract: This paper presents a modular architecture to develop a wearable system for real-time human motion capture. The system is based on a network of smart inertial measurement units (IMUs) distributed on the human body. Each of these modules is provided with a 32-bit RISC microcontroller (MCU) and miniaturized MEMS sensors: three-axis accelerometer, three-axis gyroscopes, and three-axis magnetometer. The MCU collects measurements from the sensors and implement the sensor fusion algorithm, a quaternion-based extended Kalman filter to estimate the attitude and the gyroscope biases. The design of the proposed IMU, in order to overcome the problems of the commercial solution, aims to improve performance and to reduce size and weight. In this way, it can be easily embedded in a tracksuit for total body motion reconstruction with considerable enhancement of the wearability and comfort. Furthermore, the main achievements will be presented with a performance comparison between the proposed IMU and some commercial platforms.

Compatible mappings and common fixed points

Abstract: A common fixed point theorem of S.L. and S.P. Singh is generalized by weakening commutativity hypotheses and by increasing the number of functions involved.

Putting Rough Sets and Fuzzy Sets Together

Abstract: In this paper we argue that fuzzy sets and rough sets aim to different purposes and that it is more natural to try to combine the two models of uncertainty (vagueness for fuzzy sets and coarseness for rough sets) in order to get a more accurate account of imperfect information. First, the upper and lower approximations of a fuzzy set are defined, when the universe of discourse of a fuzzy sets is coarsened by means of an equivalence relation. We then come close to Caianiello’s C-calculus. Shafer’s concept of coarsened belief functions also belongs to the same line of thought and is reviewed here. Another idea is to turn the equivalence relation relation into a fuzzy similarity relation, for a more expressive modeling of coarseness. New results on the representation of similarity relations by means of a fuzzy partition of fuzzy clusters of more or less indiscernible points are surveyed. The properties of upper and lower approximations of fuzzy sets by similarity relations are thoroughly studied. Lastly the potential usefulness of the fuzzy rough set notions for logical inference in the presence of both fuzzy predicates and graded indiscernibility is indicated. Especially fuzzy rough sets may provide a nice semantic background for modal logic involving fuzzy modalities and/or fuzzy sentences.

Unobtrusive Sensing and Wearable Devices for Health Informatics

Abstract: The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major challenges of our present-day society. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, health informatics, which deals with the acquisition, transmission, processing, storage, retrieval, and use of health information, has emerged as an active area of interdisciplinary research. In particular, acquisition of health-related information by unobtrusive sensing and wearable technologies is considered as a cornerstone in health informatics. Sensors can be weaved or integrated into clothing, accessories, and the living environment, such that health information can be acquired seamlessly and pervasively in daily living. Sensors can even be designed as stick-on electronic tattoos or directly printed onto human skin to enable long-term health monitoring. This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobtrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion, and then to identify some future directions of research.

Density-based clustering based on hierarchical density estimates

Abstract: We propose a theoretically and practically improved density-based, hierarchical clustering method, providing a clustering hierarchy from which a simplified tree of significant clusters can be constructed. For obtaining a “flat” partition consisting of only the most significant clusters (possibly corresponding to different density thresholds), we propose a novel cluster stability measure, formalize the problem of maximizing the overall stability of selected clusters, and formulate an algorithm that computes an optimal solution to this problem. We demonstrate that our approach outperforms the current, state-of-the-art, density-based clustering methods on a wide variety of real world data.