Volume X, Issue III

Title– COMPARATIVE ANALYSIS OF MULTIPLE FEEDBACK AND SALLEN KEY BAND-PASS FILTERS FOR DEVELOPMENT OF FERRITE BASED HIGH SENSITIVE FLUXGATE SENSOR

Author– Dr. S.D. Yusuf, Dr. I. Umar and Arafat U.H

Abstract

Geomagnetic storms and sub-storms, sudden commencement, and geomagnetic pulsations are a worldwide disturbance of earth’s magnetic field which causes significant damage across the world with a single event. Fluxgate sensor is essential in order to predict such occurrence of the major disturbances. In this study, comparative analysis of Multiple Feedback Band-Pass Filter (MFB-BPF) and Sallen Key Band-Pass Filter (SK BPF) for the implementation of ferrite based high sensitive Fluxgate Sensor (FS) for earth’s magnetic field explorations was carried out using Multisim MATLAB. The harmonics responses of the sensing coil output voltage for the designed fluxgate sensor was tested using the designed second-order MFB-BPF and SK-BPF. The prototype single-axis fluxgate sensor was used to obtain 2nd harmonic magnetic field response with Helmholtz coil. Results show that the 2nd harmonics produced the highest amplitudes of 45.61mV at an excitation current of 18.75mA for MFB-BPF and 51.20mV at excitation current of 50.00mA for the SK-BPF respectively. An indication that the core was strongly saturated at the 2nd harmonics and the MFB-BPF performance proved its superiority over the SK-BPF. The MFB-BPF can be adapted for the development of a high sensitive fluxgate sensor which can be used for earth magnetic field exploration.

Index Term– multiple feedback, sallen key, Helmholtz coil, band pass filter, fluxgate sensor, comparative analysis, magnetic field.

DOI- 10.30696/IJEEA.X.III.2022.01.14

Reference to this paper should be made as follows:  Dr. S.D. Yusuf, Dr. I. Umar, and Arafat U, (2022), “Comparative Analysis of Multiple Feedback and Sallen Key Band Pass Filters for Development of Ferrite Based High Sensitive Fluxgate Sensor” Int. J. Electronics Engineering and Applications, Vol. 10, No. 03, pp. 1-14.

Title– AUTOMATIC DIAGNOSIS OF VARIOUS DISEASE FROM A CHEST X-RAY USING A POWER OF DEEP LEARNING

Author– Arjun Choudhary, Dr. Kalpna Sharma and Dr. Prakash Choudhary

Abstract

Artificial Intelligence has been witnessing a monumental growth in bridging the gap between the capabilities of humans and machines.The diagnosis of pathology in a chest X-ray is generally complicated, even for experienced practitioners. A system that can automatically diagnose the findings in images achieved through X-rays of chest can be useful in the medical examination of the patient as there is a shortage of experienced doctors. Classifying the chest X-ray is a multi-label classification task as a patient may have multiple diseases. In this research, we aim to develop an algorithm using deep learning techniques to identify the condition in the chest X-ray with high accuracy. In this research, we fine-tune a pretrained CNN architecture named DenseNet-121 to extract the features from the chest X-ray and to classify the extracted features into the pathology. The weights of the model are initially set with the weights of a model which is trained on ImageNet then the model is trained on the sample of the” ChestXray14” dataset. They are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer. Each node connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data will be passed along to the next layer of the network.

Index Term– Chest X-ray; CNN; CAD; DenseNet; ChestXray14.

DOI- 10.30696/IJEEA.X.III.2022.15.26

Reference to this paper should be made as follows:  Arjun Choudhary, Dr. Kalpna Sharma and Dr. Prakash Choudhary, (2022), “Automatic Diagnosis of Various Disease from a Chest X-ray Using a Power of Deep Learning” Int. J. Electronics Engineering and Applications, Vol. 10, No. 3, pp. 15-26.