User-centric based vertical handover decision algorithm for telecardiology application in heterogeneous networks

He traditional telecardiology system which is integrated with a single wireless technology is unable to guarantee the patient always get connected to the telecardiology service provider. To overcome this problem, an adaptive user-centric based vertical handover algorithm is proposed to allow the telecardiology system operates in heterogeneous wireless technologies. The proposed algorithm guarantees the quality of service and maintains the user’s satisfaction at the highest level. The algorithm was compared with traditional quality of service based and cost based vertical handover algorithms. The results show that proposed algorithm is performed better than the traditional algorithms

New vertical handover method to optimize utilization of wireless local area network in high-speed environment

In heterogeneous wireless networks, wireless local area network (WLAN) is highly preferred by mobile terminals (MTs) owing to its high transmission bandwidth and low access cost. However, in high-speed environment, handover from a cellular network to a WLAN cell will lead to a high number of handover failures and unnecessary handovers due to the WLAN coverage limitation and will become worse at high speed. A new vertical handover method is proposed to minimize the probability of handover failure and unnecessary handover while maximizing the usage of WLAN in high-speed environment. The simulation results show that the proposed method kept the probability of handover failure and unnecessary handover below 0.5% and 1%, respectively. Compared with previous studies, the proposed method reduced the number of handover failures and unnecessary handovers up to 80.0% and 97.7%, respectively, while the MT is highly mobile. Using the proposed prediction method, the MT can benefit high bandwidth and low network access cost from the WLAN with minimum interruption regardless of speed

Network selection mechanism for telecardiology application in high speed environment

The existing network selection schemes biased either to cost or Quality of Service (QoS) are not efficient enough for telecardiology application in high traveling speed environment. Selection of the candidate network that is fulfilling the telecardiology service requirements as well as user preference is a challenging issue. This is because the preference of telecardiology user might change based on the patient health condition. This research proposed a novel Telecardiology-based Handover Decision Making (THODM) mechanism that consists of three closely integrated algorithms: Adaptive Service Adjustment (ASA), Dwelling Time Prediction (DTP) and Patient Health Condition-based Network Evaluation (PHCNE). The ASA algorithm guarantees the quality of telecardiology service when none of the available networks fulfils the service requirements. The DTP algorithm minimizes the probability of handover failure and unnecessary handover to Wireless Local Area Network (WLAN), while optimizing the connection time with WLAN in high traveling speed environment. The PHCNE algorithm evaluates the quality of available networks and selects the best network based on the telecardiology services requirement and the patient health condition. Simulation results show that the proposed THODM mechanism reduced the number of handover failures and unnecessary handovers up to 80.0% and 97.7%, respectively, compared with existing works. The cost of THODM mechanism is 20% and 85.3% lower than the Speed Threshold-based Handover (STHO) and Bandwidth-based Handover (BWHO) schemes, respectively. In terms of throughput, the proposed scheme is up to 75% higher than the STHO scheme and 370% greater than the BWHO scheme. For telecardiology application in high traveling speed environment, the proposed THODM mechanism has better performance than the existing network selection schemes

A New Method for Minimizing the Unnecessary Handover in High-Speed Scenario

The application of Wireless Local Area Network (WLAN) is limited to indoor or pedestrian walking speed environment because the small WLAN coverage will lead to the growth of unnecessary handover rate in high-speed scenario. The previously proposed traveling distance prediction based handover methods assumed mobile terminal (MT) travels at a constant speed is impractical as most of the MTs may not be traveling at constant speed in real environment. These methods have poor performance in case of acceleration because MT will leave the network earlier than the estimated time. In this paper, a new traveling distance prediction based handover scheme that is aware of MT's speed changes is proposed to overcome the limitation of the existing methods. The proposed scheme is adapted to the MT velocity and acceleration or deceleration rate. The numerical result shows that the performance of the proposed scheme is better than the existing handover methods in high-speed scenario. It keeps the probability of unnecessary handover within the user acceptable level in high-speed scenario

Towards on-site implementation of multi-step air pollutant index prediction in Malaysia industrial area: Comparing the NARX neural network and support vector regression

Malaysia has experienced public health issues and economic losses due to air pollution problems. As the air pollution problem keeps increasing over time, studies on air quality prediction are also advancing. The air quality prediction can help reduce air pollution’s damaging impact on public health and economic activities. This study develops and evaluates the Nonlinear Autoregressive Exogenous (NARX) Neural Network and Support Vector Regression (SVR) for multi-step Malaysia’s Air Pollutant Index (API) prediction, focusing on the industrial areas. The performance of NARX and SVR was evaluated on four crucial aspects of on-site implementation: Input pre-processing, parameter selection, practical predictability limit, and robustness. Results show that both predictors exhibit almost comparable performance, in which the SVR slightly outperforms the NARX. The RMSE and R2 values for the SVR are 0.71 and 0.99 in one-step-ahead prediction, gradually changing to 6.43 and 0.68 in 24-step-ahead prediction. Both predictors can also perform multi-step prediction by using the actual (non-normalized) data, hence are simpler to be implemented on-site. Removing several insignificant parameters did not affect the prediction performance, indicating that a uniform model can be used at all air quality monitoring stations in Malaysia’s industrial areas. Nevertheless, SVR shows more resilience towards outliers and is also stable. Based on the trends exhibited by the Malaysia API data, a yearly update is sufficient for SVR due to its strength and stability. In conclusion, this study proposes that the SVR predictor could be implemented at air quality monitoring stations to provide API prediction information at least nine steps in advance

Effect of Distance and Direction on Distress Keyword Recognition using Ensembled Bagged Trees with a Ceiling-Mounted Omnidirectional Microphone

Audio surveillance can provide an effective alternative to video surveillance in situations where the latter is impractical. Nevertheless, it is essential to note that audio recording raises privacy and legal concerns that require unambiguous consent from all parties involved. By utilizing keyword recognition, audio recordings can be filtered, allowing for the creation of a surveillance system that is activated by distress keywords. This paper investigates the performance of the Ensemble Bagged Trees (EBT) classifier in recognizing the distress keyword "Please" captured by a ceiling-mounted omnidirectional microphone in a room measuring 4.064m (length) x 2.54m (width) x 2.794m (height). The study analyzes the impact of different distances (0m, 1m, and 2m) and two directions (facing towards and away from the microphone) on recognition performance. Results indicate that the system is more sensitive and better able to identify targeted signals when they are farther away and facing toward the microphone. The validation process demonstrates excellent accuracy, precision, and recall values exceeding 98%. In testing, the EBT achieved a satisfactory recall rate of 86.7%, indicating moderate sensitivity, and a precision of 97.7%, implying less susceptibility to false alarms, a crucial feature of any reliable surveillance system. Overall, the findings suggest that a single omnidirectional microphone equipped with an EBT classifier is capable of detecting distress keywords in a low-noise enclosed room measuring up to 4.0 meters in length, 4.0 meters in width, and 2.794 meters in height. This study highlights the potential of employing an omnidirectional microphone and EBT classifier as an edge audio surveillance system for indoor environments

The Implementation of Application Software to Improve Verbal Communication in Children with Autism Spectrum Disorder: A Review

Autism-assistive apps offer therapists and caregivers new approaches for educating and assisting individuals with autism spectrum disorder (ASD), mainly in social interaction. Even though these apps are deemed effective, they are not. These autism-assistive apps are not highly customizable, which limits their usefulness. This article examined the application software that was applied to encourage verbal communication in the intervention for children with ASD. The aim was to determine the minimum requirements for a verbal communication intervention app that adequately satisfies children with ASD, caregivers, and therapists. Databases were searched, including Scopus, Springer, PubMed, Education Resources Information Centre, and Google Scholar, with the following free-text terms combining Boolean operators: autism, children, intervention, verbal communication, software, app, and technology. A total of fifteen studies were found relevant, and the following information was collected: participant characteristics, information on the devices and apps, target behaviors, intervention procedures, and intervention outcomes. The findings suggest that the autism-assistive apps effectively improve verbal communication of children with ASD. For that, the apps should be attractive and engaging to the children with ASD, able to identify the child’s capability and suggest appropriate lesson activities, as well as encompass specific learning outcomes with multilevel lesson strategy. The apps should also use systematic evidence-based intervention procedures in the activities, be able to evaluate the child’s learning progress, and allow caregivers or therapists to keep track of application usage and performance. The use of apps in intervention does provide many benefits. However, they should never replace qualified therapists. App-based interventions make home-based treatment more focused, systematic, and economical

Internet of things based real-time coronavirus 2019 disease patient health monitoring system

The coronavirus disease (COVID-19) outbreak has led to many infected worldwide and has become a global crisis. COVID-19 manifests in the form of shortness of breath, coughing and fever. More people are getting infected and healthcare systems worldwide are overwhelmed as healthcare workers become exhausted and infected. Thus, remote monitoring for COVID-19 patients is required. An internet of things (IoT) based real-time health monitoring system for COVID-19 patients was proposed. It features monitoring of five physiological parameters, namely electrocardiogram (ECG), heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2) and body temperature. These vitals are processed by the main controller and transmitted to the cloud for storage. Healthcare professionals can read real-time patient vitals on the web-based dashboard which is equipped with an alert service. The proposed system was able to transmit and display all parameters in real-time accurately without any packet loss or transmission errors. The accuracy of body temperature readings, RR, SpO2 and HR, is up to 99.7%, 100%, 97.97% and 98.34%, respectively. Alerts were successfully sent when the parameters reached unsafe levels. With the proposed system, healthcare professionals can remotely monitor COVID-19 patients with greater ease, lessen their exposure to the pathogen, and improve patient monitoring

User-centric based vertical handover decision algorithm for telecardiology application in heterogeneous networks

The traditional telecardiology system which is integrated with a single wireless technology is unable to guarantee the patient always get connected to the telecardiology service provider. To overcome this problem, an adaptive user-centric based vertical handover algorithm is proposed to allow the telecardiology system operates in heterogeneous wireless technologies. The proposed algorithm guarantees the quality of service and maintains the user’s satisfaction at the highest level. The algorithm was compared with traditional quality of service based and cost based vertical handover algorithms. The results show that proposed algorithm is performed better than the traditional algorithms