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Abstract: Cardiac Hypertrophy commonly known as abnormal thickening of the heart muscle. It results from the increase in the cardiomyocyte size and other heart muscle component changes, like extracellular matrix. There can be physiological and pathological causes for the cardiac hypertrophy like amount of strenuous physical activity performed by an athlete and hypertension respectively. Most patients today survive hypertrophy, thanks to a number of efficient treatment options. In ECG, the QRS amplitude and duration plays an important role in determination of Cardiac Hypertrophy. Raw ECG data has been obtained from MIT PTB database and analysed using MATLAB. De-noising of the ECG signal has been done using filters. The clinically important parameters for Cardiac Hypertrophy have been evaluated using Fast Walsh Hadamard Transform. Statistical Analysis is done by comparing the parameters thus obtained with those of normal ECG parameters to gain a deeper understanding of Cardiac Hypertrophy. This research in turn can help the physicians to diagnose the preliminary signs of cardiac hypertrophy which might otherwise go un-noticed.

Keywords: Electrocardiogram (ECG), Cardiac Hypertrophy, Hypertrophy, Statistical Analysis, Cardiac Analysis, QRS amplitude, QRS duration, ECG Monitoring, MATLAB, Fast Walsh Hadamard Transform.

References:

1. Hall, A. G. (2006): Textbook of Medical Physiology,pp. 103-156. Elsevier Saunders.
2. Sörnmo, L. a. (2006): Bioelectrical signal processing in cardiac and neurological applications,pp. 411-449. Elsevier Academic Press.
3. Besma Khelil, A. K. (March 19-22, 2007): P Wave Analysis in ECG Signals using Correlation for Arrhythmias Detection. Fourth International Multi-Conference on Systems, Signals & Devices . Hammamet, Tunisia.
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6. Pal, S. (2010): Detection of Premature Ventricular Contraction Beats Using ANN. International Journal of Recent Trends in Engineering and Technology, vol. 3, no. 4.
7. Anusha F.G, J. S. (2015): Automatic Identification ECG Anomalous Using Xml Data Processing . IJEDR | NC3N 2015 | ISSN: 2321-9939.
8. Medina Hadjem, O. S.-A. (2014): An ECG Monitoring System For Prediction of Cardiac Anomalies Using WBAN. 2014 IEEE 16th International Conference on e-Health Networking, Applications and Services (Healthcom), pp. 365-370.
9. Agnes Aruna John, A. P. (2015): Evaluation of cardiac signals using discrete wavelet transform with MATLAB graphical user interface. Indian Heart Journal 67 (2015), pp. 549-551.
10. Tanmaya, N. P. (2016): Analysis of ECG Signal for Detecting Heart Blocks Using Signal Processing Techniques. International Journal of Innovative Research in Computer and Communication Engineering, pp. 3778-3784.
11. Çankaya, H. A.-Z. (2015): Heart Rate Monitoring and PQRST Detection Based on Graphical User Interface with Matlab . International Journal of Information and Electronics Engineering, Vol. 5, No. 4, 311-316.
12. Jyoti Gupta, V. R. (2015): Analyzing Tachycardia, Bradycardia and Transmitting ECG Data Using MATLAB. American International Journal of Research in Science, Technology,Engineering & Mathematics, pp. 207-214.
13. Apoorva Mahajan, A. B. (2015): Acquisition, Filtering and Analysis of ECG Using MATLAB. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), Volume 4, Issue 5, pp. 1170-1173.
14. Jaylaxmi C Mannurmath, P. R. (2014): MATLAB Based ECG Signal Classification. International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 7, pp. 1946-1951.
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16. Anand Kumar Joshi, A. T. (2014): A Review Paper on Analysis of Electrocardiograph (ECG) Signal for the Detection of Arrhythmia Abnormalities. International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, pp. 12466-12475.
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Abstract: This paper centers around an Intelligent Light Emitting Diode (LED) road lighting framework which offers extensive favorable circumstances over the regular road lighting frameworks. The present framework resembles, the road lights will be exchanged on at night after the sun sets and they are turned off the following day morning consequently which totally dispense with manual task. Encourage the street light's energy is controlled by different time breaks in the midst of night which also makes it most sharp and extras most prominent essentialness. This paper gives the best response for electrical power or imperativeness wastage. In this paper the two sensors are used which are Light Dependent Resistor LDR sensor to demonstrate a day/evening time and the photoelectric sensors to recognize the obstacle all over the place.

Keywords: LDR, Photoelectric Sensor, Arduino Uno, Vitality Sparing and Circuit Configuration, LED. 

References:

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Abstract: As young people pass through puberty and adolescence, health needs related to sexual and reproductive health arise. Adolescents and youth have been perceived to have few health needs and little income to access to health services. As a result, they have generally been neglected by the health systems. In Kenya, inadequate dissemination and implementation of existing policies have further hampered the successful implementation of adolescent and youth sexual and reproductive health (AYSRH) programs. If left out, the youth will lack of information may result in behaviors that may affect their future lives. Several attempts have been made by the government and another stakeholder to ensure adequate dissemination of reproductive health information to the youth; however, due to limited resources, the information is still inadequate. With web 2.0 technologies wide adoption by the youth, these technologies can be harnessed to fill the gap. This study sought to find the use of web 2.0 technologies in the provision of the of reproductive health information to the youth. The study found out that Facebook is the most widely used web 2.0 technology followed by WhatsApp. the study also found out that as much as the technologies have a potential in reaching the youth, caution must be exercised not to expose the youth to security breaches while online. 

Keywords: Web 2.0, Social Networking Sites, Reproductive Health, Preference.

References:

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