Narrow Results

SINGAPORE – Singapore eHealth Innovations Summit Announces the First EMRAM Stage 7 Hospital in Singapore and Emphasized Technology as Transformative Agent in Specialty Functions.

TAIWAN – Health2Sync Strategically Partners with Taiwan's Ministry of Health and Welfare in Asia's First Government Supported Online Diabetes Care Program.

UNITED STATES – Scientists Identify Protein Involved in Restoring Effectiveness of Common Treatment for Breast Cancer.

UNITED STATES – Scientists Reveal How Signals from Pathogenic Bacteria Reach Danger Sensors of Cells.

UNITED STATES – Scientists Find New Path in Brain to Ease Depression.

UNITED STATES – Tips for Living a Heart Healthy Lifestyle.

CANADA – Review Suggests Eating Oats Can Lower Cholesterol as Measured by a Variety of Markers.

SOUTH KOREA – CSA Group Opens Highly Advanced Electro - Medical Laboratory in Seoul.

AUSTRALIA – Cynata’s Technology Significant Efficacy in Preclinical Asthma Study.

INDIA – Essilor Launches ‘Love to See Change’ Campaign to Educate People about Need to Preserve Visual Health.

In industrial installations, the piezoelectric sensor plays a very important role in the monitoring of electromechanical systems and the detection of their early defects. Modeling is the mathematical presentation of the operating principle of the piezoelectric sensor, it allows to transform this principle to equations, these equations allow to improve the performances of this sensor and to propose new designs. In this work, the effects of piezoelectric materials are explained and the piezoelectric sensor is described. The physical behavior of the sensor is modeled and extracted a formula relates the accuracy as a function of relative movement (vibratory displacement). The model developed is validated by simulation and by experimental tests and the appropriate choice of the damping rate makes it possible to improve the parameters of the piezoelectric sensor and to progress the vibratory analysis technique.

With a tremendous increase in mobile and wearable devices, the study of sensor-based activity recognition has drawn a lot of attention in the past years. In recent years, the applications of Human Activity Recognition are getting more and more attention, especially in eldercare and healthcare as an assistive technology when combined with the Internet of Things. In this paper, we propose three deep learning approaches to improve the accuracy of activity detection on the WISDM dataset. Particularly, we apply a convolutional neural network to extract the interesting features, then we use softmax function, support vector machine, and random forest for classification tasks. The results show that the hybrid algorithm, convolutional neural network combined with the support vector machine, outperforms all the previous methods in classifying every activity. In addition, not only the support vector machine but also the random forest shows better accuracy in classification task than the neural network classification and the former approaches do.

A cantilever beam and fiber Bragg grating is used to measure acceleration. The cantilever induces strain on the grating resulting in a Bragg wavelength modification that is subsequently detected. The output signal is insensitive to temperature variations and for a temperature change from −20°C to 40 °C, the output signal fluctuated less than 5% without any temperature compensation schemes. Because the sensor does not utilize expensive and complex demodulation techniques it is potentially inexpensive. For the experimental system a linear output range of 8 g could be detected.