This paper discusses a methodology to manage wireless sensor networks (WSN) with self-organising feature maps, using cooperative Extended Kohonen Maps (EKMs). EKMs have been successfully demonstrated in other machine-learning contexts such as learning sensori-motor control and feedback tasks. Through a quantitative analysis of the algorithmic process, an indirect-mapping EKM can self-organise from a given input space, such as the WSN's external factors, to administer the WSN's routing and clustering functions with a control parameter space. Preliminary results demonstrate indirect mapping with EKMs provide an economical control and feedback mechanism by operating in a continuous sensory control space when compared with direct mapping techniques. By training the control parameter, a faster convergence is made with processes such as the recursive least squares method. The management of a WSN's clustering and routing procedures are enhanced by the cooperation of multiple self-organising EKMs to adapt to actively changing conditions in the environment.
The quality of sleep affects the patient’s health, along with the observation of vital life signs such as body temperature and sweat in sleep, is essential in the monitoring of sleep as well as clinical diagnosis. However, traditional methods in recording physiological change amidst sleep is difficult without being intrusive. The smart pillow is developed to provide a relatively easy way to observe one’s sleep condition, employing temperature and humidity sensors by implanting them inside the pillow in strategic positions. With the patient’s head on the pillow, the roles of sensors are identified as main, auxiliary or environmental temperature, based on the differences of value from three temperature sensors, thus the pattern of sleep can be extracted by statistical analysis, and the body temperature is inferred by a specially designed Fuzzy Logic System if the head-on position is stable for more than 15 min. Night sweat is reported on data from the humidity sensor. Therefore, a cloud-based health-sensing system is built in the smart pillow to collect and analyze data. Experiments from various individuals prove that statistical and inferred results reflect normal and abnormal conditions of sleep accurately. The daily sleeping information of patients from the pillow is helpful in the decision-making of diagnoses and treatment, and users can change their habits of sleep gradually by observing the data with their health professional.
In the proposed work we present a combination of two paradigms: Wireless Sensor Networks (WSN) and Computer Vision applied for Motion Analysis. In this work the Computer Vision provides high-level behavioural monitoring and analysis, whereas Wireless Sensors capture detailed parameters of a moving object. Fusion of sensory information received from both types of sensors provides micro-level and macro-level details. These combined details can be used in various application areas. In considered applications, one of the areas can be Robotics. In this case this strategy can be used to monitor health of robots under certain actions and situations. Another important application domain is health care and rehabilitation of injured persons. In this application, movement of an injured body portion is measured after its treatment. Apart from the analysis of motion we also propose optimized movement advice to patients. Optimum motion advice is very useful in case of sports injury to recover strength and performance. In this paper we produce experimental work performed by simulating different movements of hands and legs in free space. The experimental simulation provides a broad range of data on motion analysis with visualization. The third area of application that is explored is elderly patient condition monitoring and motion analysis for health monitoring.
Abstract-In the development of large ad-hoc Wireless Sensor and Actuator Agent Networks (SANETS), a multitude of disparate problems are faced. In order for these networks to function, software must be able to effectively manage: unreliable dynamic distributed communication, the power constraints of un-wired devices, failure of hardware devices in hostile environments and the remote allocation of distributed processing tasks throughout the network. The solutions to these problems must be solved in a highly scalable manner. The paper describes the process of analysis of the requirements and presents a design of a serviceoriented software infrastructure (middleware) solution for scalable ad-hoc networks, in a context of a system made of mobile sensors and actuators.
This survey was conducted in the Kingdom of Saudi Arabia (KSA) to investigate the level of awareness of BPR. Respondents (customers, employees, and managers) had different educational backgrounds and were from private and public sectors. Findings of the study indicate a general awareness of BPR in KSA.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.