Implementation of Fuzzy Decision Based Mobile Robot Navigation Using Stereo Vision

AbstractIn this article, we discuss implementation phases for an autonomous navigation of a mobile robotic system using SLAM data, while relying on the features of learned navigation maps. The adopted SLAM based learned maps, was relying entirely on an active stereo vision for observing features of the navigation environment. We show the framework for the adopted lower-level software coding, that was necessary once a vision is used for multiple purposes, distance measurements, and obstacle discovery. In addition, the article describes the adopted upper-level of system intelligence using fuzzy based decision system. The proposed map based fuzzy autonomous navigation was trained from data patterns gathered during numerous navigation tasks. Autonomous navigation was further validated and verified on a mobile robot platform

Zhou Method for the Solutions of System of Proportional Delay Differential Equations

In this paper, we consider a viable semi-analytical approach for the approximate-analytical solutions of certain system of functional differential equations (SFDEs) engendered by proportional delays. The proposed semi-analytical technique is built on the basis of the classical Differential Transform Method (DTM). The effectiveness and robustness of the proposed technique is illustratively demonstrated and the results are compared with their exact forms. We note also that using this method, the SFDEs with proportional delays need not be converted to SFDEs with constant delays before obtaining their solutions, and no symbolic calculation or initial guesstimates are required

leveraging artificial intelligence to improve voice disorder identification through the use of a reliable mobile app

The evolution of the Internet of Things, cloud computing and wireless communication has contributed to an advance in the interconnectivity, efficiency and data accessibility in smart cities, improving environmental sustainability, quality of life and well-being, knowledge and intellectual capital. In this scenario, the satisfaction of security and privacy requirements to preserve data integrity, confidentiality and authentication is of fundamental importance. In particular, this is essential in the healthcare sector, where health-related data are considered sensitive information able to reveal confidential details about the subject. In this regard, to limit the possibility of security attacks or privacy violations, we present a reliable mobile voice disorder detection system capable of distinguishing between healthy and pathological voices by using a machine learning algorithm. This latter is totally embedded in the mobile application, so it is able to classify the voice without the necessity of transmitting user data to or storing user data on any server. A Boosted Trees algorithm was used as the classifier, opportunely trained and validated on a dataset composed of 2003 voices. The most frequently considered acoustic parameters constituted the inputs of the classifier, estimated and analyzed in real time by the mobile application

Dysphonia Detection Index (DDI): A New Multi-Parametric Marker to Evaluate Voice Quality

The rapid diffusion of voice disorders and the lack of appropriate knowledge about the problem have prompted the search for novel and reliable approaches to detect dysphonia, through easy and accessible instruments such as mobile devices. These systems represent, in fact, valid instruments to improve the patient care not only to facilitate the monitoring of symptoms of any diseases but also supporting the correct diagnosis of pathology, such as the dysphonia. In this paper, we propose a new marker, namely the dysphonia detection index, able to support the evaluation of voice disorders, which can be embedded in a mobile health solution. Four acoustic parameters are combined in a single marker to globally evaluate the state of the health of the voice and to assess the presence or not of a voice disorder. A model tree regression algorithm has been applied to define the relationship between these parameters, and the Youden analysis has been used to define the threshold value to distinguish a pathological from a healthy voice. The reliability of the proposed index has been tested in terms of correct classification of accuracy, sensitivity, and specificity. A dataset of 2003 voices has been used to evaluate the performance of our proposed index, composed of samples selected from three different databases: the Massachusetts Eye and Ear Infirmary, the Saarbruecken Voice, and the VOice ICar fEDerico II databases. Our approach achieved the best performances in comparison with other algorithms, and accuracy equals to 82.2%, while sensitivity and specificity are 82% and 82.6%, respectively

Al-Guluw Wa Al-Mawqif Al-Islamiy/ Al-Manshuri

273 p. ; 22 c

Al-Guluw Wa Al-Mawqif Al-Islamiy/ Al-Manshuri

273 p. ; 22 c