Scaling the Performance and Cost for Elastic Cloud Web Services

Cloud computing is the latest evolution of computing where the IT resources are offered as services following the “pay-per-usage” pricing model. Cloud’s scalability feature causes variable price for resources governed by the cloud service providers. Therefore, the cloud customers’ main interest is whether the performance scales to the price for the leased resources in the cloud. In this paper we analyze the variable server load impact on the performance and the cost of two web services that utilize memory and CPU resources. In order to determine the real cost of the rented CPU resources, we experimented with different number of concurrent messages with different sizes. The results concerning the memory demanding web service show that the lowest cost is obtained when the web service is hosted on two CPUs, whereas the results concerning the web service which additionally utilizes CPU show that the lowest cost is achieved when it is hosted on one CPU and linearly rises with the resources

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Methods: Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user. Results: Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP. Conclusion: The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation

Non-Invasive Blood Pressure Estimation from ECG Using Machine Learning Techniques

Background: Blood pressure (BP) measurements have been used widely in clinical and private environments. Recently, the use of ECG monitors has proliferated; however, they are not enabled with BP estimation. We have developed a method for BP estimation using only electrocardiogram (ECG) signals. Methods: Raw ECG data are filtered and segmented, and, following this, a complexity analysis is performed for feature extraction. Then, a machine-learning method is applied, combining a stacking-based classification module and a regression module for building systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP) predictive models. In addition, the method allows a probability distribution-based calibration to adapt the models to a particular user. Results: Using ECG recordings from 51 different subjects, 3129 30-s ECG segments are constructed, and seven features are extracted. Using a train-validation-test evaluation, the method achieves a mean absolute error (MAE) of 8.64 mmHg for SBP, 18.20 mmHg for DBP, and 13.52 mmHg for the MAP prediction. When models are calibrated, the MAE decreases to 7.72 mmHg for SBP, 9.45 mmHg for DBP and 8.13 mmHg for MAP. Conclusion: The experimental results indicate that, when a probability distribution-based calibration is used, the proposed method can achieve results close to those of a certified medical device for BP estimation

MAKEDONKA: Applied Deep Learning Model for Text-to-Speech Synthesis in Macedonian Language

This paper presents MAKEDONKA, the first open-source Macedonian language synthesizer that is based on the Deep Learning approach. The paper provides an overview of the numerous attempts to achieve a human-like reproducible speech, which has unfortunately shown to be unsuccessful due to the work invisibility and lack of integration examples with real software tools. The recent advances in Machine Learning, the Deep Learning-based methodologies, provide novel methods for feature engineering that allow for smooth transitions in the synthesized speech, making it sound natural and human-like. This paper presents a methodology for end-to-end speech synthesis that is based on a fully-convolutional sequence-to-sequence acoustic model with a position-augmented attention mechanism—Deep Voice 3. Our model directly synthesizes Macedonian speech from characters. We created a dataset that contains approximately 20 h of speech from a native Macedonian female speaker, and we use it to train the text-to-speech (TTS) model. The achieved MOS score of 3.93 makes our model appropriate for application in any kind of software that needs text-to-speech service in the Macedonian language. Our TTS platform is publicly available for use and ready for integration

Continuous Vital Parameters Monitoring by Using Biosensors and Smart Technology Solution

In this paper we present wireless solution for continuous monitoring of vital parameters by using the leverage of both the biosensors and the smart technology. The proposed solution consists of three commercially available biomedical sensors and a portable smart technology device. The integration allows continuous capture of the heart rate, respiratory rate, part-time blood pressure and oxygen saturation. The application enables insight into the recent history of the parameters, additionally providing information of the shock index, Glasgow comma scale score and the hemodynamic stability of the patient. The solution is suitable for pre-hospital, during the vehicle transport and in-hospital environment. Given all the hardware used is commercially available, the integration is highly cost effective when compared to the hospital equipment. The reliability has been tested in hospital environment

Интероперабилен слоевит модел за поврзано здравство

Овој проект ќе развие нов дизај за опис на парадигмата на поврзано здравство на начин кој ќе ги реши проблемите поврзани со интероперабилност и транзитивност преку воведување на слоеви. Целта на цилиндричниот модел е обезбедување на нов квалитет на системите за телемедицина без разлика дали станува збор за системи што се веќе развиени или се допрва во развој. Со користење на слоевитиот модел, би се обезбедило комплетно мапирање на системите во околината што ја дефинира поврзаното здравство. Со тоа би се обезбедил единствен начин за опис на можните поврзувања со други системи

Evaluation of wearable system for measuring vital parameters in clinical environment

A continuous monitoring of physiological data is especially important for individuals whose chronic condition includes the risk of sudden acute events. Physiological measurements fluctuate over the course of the day, so a once-daily reading might not provide the whole picture. Standard ambulatory systems for monitoring, are not suitable for monitoring over long period of time. The new systems and techniques suitable for hospital environment are investigated over the past years. In this study, we present a wearable system which includes a Zephyr bio-module for measuring patient's vital parameters. Data is collected wirelessly and displayed on mobile device via software. In order to be used in hospitals, the developed system was clinically tested. The data for heart rate (HR) and respiratory rate (RR), obtained with the developed system were compared to the same parameters obtained by the standard medical device, in order to compare their accuracy. Preliminary results from these tests are shown in this paper

Noninvasive Glucose Measurement Using Machine Learning and Neural Network Methods and Correlation with Heart Rate Variability

Diabetes is one of today’s greatest global problems, and it is only becoming bigger. Constant measuring of blood glucose level is a prerequisite for monitoring glucose blood level and establishing diabetes treatment procedures. The usual way of glucose level measuring is by an invasive procedure that requires finger pricking with the lancet and might become painful and obeying, especially if this becomes a daily routine. In this study, we analyze noninvasive glucose measurement approaches and present several classification dimensions according to different criteria: size, invasiveness, analyzed media, sensing properties, applied method, activation type, response delay, measurement duration, and access to results. We set the focus on using machine learning and neural network methods and correlation with heart rate variability and electrocardiogram, as a new research and development trend

Mobile wireless monitoring system for prehospital emergency care

Latest achievement technologies allow engineers to develop medical systems that medical doctors in the health care system could not imagine years ago. The development of signal theory, intelligent systems, biophysics and extensive collaboration between science and technology researchers and medical professionals, open up the potential for preventive, real-time monitoring of patients. With the recent developments of new methods in medicine, it is also possible to predict the trends of the disease development as well the systemic support in diagnose setting. Within the framework of the needs to track the patient health parameters in the hospital environment or in the case of road accidents, the researchers had to integrate the knowledge and experiences of medical specialists in emergency medicine who have participated in the development of a mobile wireless monitoring system designed for real-time monitoring of victim vital parameters. Emergency medicine responders are first point of care for trauma victim providing prehospital care, including triage and treatment at the scene of incident and transport from the scene to the hospital. Continuous monitoring of life functions allows immediate detection of a deterioration in health status and helps out in carrying out principle of continuous e-triage. In this study, a mobile wireless monitoring system for measuring and recording the vital parameters of the patient was presented and evaluated. Based on the measured values, the system is able to make triage and assign treatment priority for the patient. The system also provides the opportunity to take a picture of the injury, mark the injured body parts, calculate Glasgow Coma Score, or insert/record the medication given to the patient. Evaluation of the system was made using the Technology Acceptance Model (TAM). In particular we measured: perceived usefulness, perceived ease of use, attitude, intention to use, patient status and environmental status