Monitoring body motions related to Huntington disease by exploiting the 5G Paradigm
- Authors: Haider, Daniyal , Romain, Olivier , Kernec, Julien Le , Shah, Syed Yaseen , Farooq, Malik Muhammad Umer , Qadus, Zunaira
- Date: 2019
- Type: Text , Conference proceedings
- Relation: 2019 UK/ China Emerging Technologies (UCET); Glasgow UK; 21-22 August 2019 p. 1-4
- Full Text: false
- Reviewed:
- Description: The modern wireless technology exploiting the full potential of 5G IoT is the future for healthcare sector. In the healthcare sector, the 5G technology will maximize the performance and will reduce the chances of damage to the patient by providing careful and advance activity monitoring scenarios. We have proposed the idea of monitoring different body posture in Huntington disease by exploiting the low cost wireless devices operating at 4.8 GHz frequency. The system captures the wireless channel information for three body motions and classification of these motions was performed by using support vector machine. The SVM used 10 time-domain features for the classification process by using three main kernel functions, such as, Linear, Polynomial and Radial basis function. The system minimizes all the external noise by using the microwave absorbing materials. This increases the performance and feasibility of sensing body motions.
Investigation into maritime military platform hull defects automation of sensors and processing : Research-in-progress
- Authors: Mead, Ronald , Chattopadhyay, Gopi
- Date: 2017
- Type: Text , Conference proceedings , Conference paper
- Relation: 2017 IEEE International Conference on Mechatronics, ICM 2017; Gippsland, Australia; 13th-15th February 2017 p. 492-497
- Full Text: false
- Reviewed:
- Description: Management of maritime military platform life is a real challenge for operators and maintainers. One of the major problems is how to capture cost effective information about the hull defect and predict the remaining life? This paper will focus on the automation process for calculation of the failure rate and prediction from collated hull defects information. This data is assessed for accuracy, sensitivity and correctness of defect location and failure modes. An outline of the automation techniques investigated to determine the hull failures rates is discussed. Five major failure modes are analysed and a high level approach for integrating failure data collation is presented. The paper then discusses some of the issues and challenges with obtaining reliable maintenance data and opportunities for further research in finding a better solution.
New artificial intelligence based tire size identification for fast and safe inflating cycle
- Authors: Kahandawa, Gayan , Choudhury, Tanveer , Ibrahim, Yousef , Dzitac, Pavel , Mazid, Abdul Md
- Date: 2015
- Type: Text , Conference proceedings
- Full Text: false
- Description: Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper. © 2015 IEEE.
Optimal sensing requirement for slippage prevention in robotic grasping
- Authors: Dzitac, Pavel , Mazid, Abdul Md , Ibrahim, Yousef , Appuhamillage, Gayan , Choudhury, Tanveer
- Date: 2015
- Type: Text , Conference proceedings
- Full Text: false
- Description: This paper presents a new theoretical development and modelling related to the requirement of the minimum number of sensors necessary for slippage prevention in robotic grasping. A fundamental experimental investigation has been conducted to support the newly developed postulate. A series of basic experiments proved that it is possible to evaluate the contributions of various sensors to slippage prevention and control in robotic grasping. The use of three discrete physical sensors, one for each of the three sensing functions (normal, tangential and slippage), has been proven to be the most reliable combination for slippage prevention in robotic grasping. It was also proven that the best performance from a two-sensor combination can be achieved when normal grasp force and tangential force are both monitored in the grasping process. © 2015 IEEE.
Empirical study of decision trees and ensemble classifiers for monitoring of diabetes patients in pervasive healthcare
- Authors: Kelarev, Andrei , Stranieri, Andrew , Yearwood, John , Jelinek, Herbert
- Date: 2012
- Type: Text , Conference proceedings
- Full Text: false
- Description: Diabetes is a condition requiring continuous everyday monitoring of health related tests. To monitor specific clinical complications one has to find a small set of features to be collected from the sensors and efficient resource-aware algorithms for their processing. This article is concerned with the detection and monitoring of cardiovascular autonomic neuropathy, CAN, in diabetes patients. Using a small set of features identified previously, we carry out an empirical investigation and comparison of several ensemble methods based on decision trees for a novel application of the processing of sensor data from diabetes patients for pervasive health monitoring of CAN. Our experiments relied on an extensive database collected by the Diabetes Complications Screening Research Initiative at Charles Sturt University and concentrated on the particular task of the detection and monitoring of cardiovascular autonomic neuropathy. Most of the features in the database can now be collected using wearable sensors. Our experiments included several essential ensemble methods, a few more advanced and recent techniques, and a novel consensus function. The results show that our novel application of the decision trees in ensemble classifiers for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the outcomes obtained previously in the literature. © 2012 IEEE.
- Description: 2003009675
Addressing the confidentiality and integrity of assistive care loop framework using wireless sensor networks
- Authors: Balasubramanian, Venki , Hoang, Doan , Zia, Tanveer
- Date: 2011
- Type: Text , Conference proceedings
- Relation: 21st International Conference on Systems Engineering, ICSEng 2011; Las Vegas, NV; United States; 16th-18th Aug, published in Proceedings - ICSEng 2011: International Conference on Systems Engineering; p. 416-421
- Full Text: false
- Reviewed:
- Description: In-house healthcare monitoring applications are continuous time-critical applications often built upon Body Area Wireless Sensor Networks (BAWSNs). Our Assistive Care Loop Framework (ACLF) is an in-house healthcare application capable of monitoring the health conditions of aged/patients over a dedicated period of time by deploying the BAWSN as the monitoring component. However, the wireless medium used in the BAWSN for communications is prone to vulnerabilities that could open a door to attackers tampering with or compromising the user's data privacy. Hence, it is imperative to maintain the privacy and integrity of the data to gain the confidence and hence, the acceptance of the users of the healthcare applications. Furthermore, in time-critical applications, the vital health conditions must be monitored at regular intervals within their specified critical time. Therefore, the security model proposed for the BAWSN must not incur undue overheads when meeting the critical time requirements of the application. In this paper, we propose and implement a secure adaptive triple-key scheme (aTKS) for the BAWSN to achieve the privacy and integrity of the monitored data with minimal overheads. We then present the performance results of our scheme for the BAWSN, using real-time test-bed implementations and simulations. © 2011 IEEE.
- Description: Proceedings - ICSEng 2011: International Conference on Systems Engineering
Provisioning delay sensitive service in cognitive radio networks with multiple radio interfaces
- Authors: Hasan, Rashidul , Murshed, Manzur
- Date: 2011
- Type: Text , Conference proceedings
- Relation: Proceedings of the 2011 IEEEE Wireless Communications and Networking Conference (WCNC 2011), 28th-31st March, 2011, New York p 162-167
- Full Text: false
- Reviewed:
- Description: Cognitive radio network (CRN) users are inherently expected to experience widely-varied delays due to the uncertainty in wireless channel availability. Supporting delay sensitive real-time services through CRNs, so that visitors are allowed to experience full-scale networking services by opportunistically sharing the spectrum from a number of existing networks without impacting on the primary users, thus remains a challenging task. This paper presents a novel technique to provision QoS guarantee for delay-sensitive services in CRNs having secondary users equipped with multiple radio interfaces. The technique relies on modeling spectrums holes from multiple primary networks through a resultant channel to enable implementing a single server queuing model with random service interruption. Simulation results using ns-2.33 show that using multiple radio interfaces has sheer strength to reduce CRN delay with fewer number of primary channel sensing.