Abstract

The paper emphasizes on mingling Information and Communication Technologies (ICTs) with Remote Sensing schemes for disaster management not only for the relief and rescue operations, but also for preparedness prior the disaster and recovery beyond the disaster. Remote Sensing and ICTs upon prudent mobilization, can counter the probable loss due to natural disasters.

ICTs can be used to real time tracking of incidents prior to disaster with the aid of sensors and the stakeholders are in unceasing communication with the situation. In case of flood, this can help by providing flood level data continuously to the stakeholders so that they can be alert of the anticipated flood. These data along with the Remote Sensing images before and after disasters are processed by the proficient to extract information, which can be disseminated via graphs and visualized via maps. The concerned authorities like governing agencies and humanitarian organizations henceforth can use these products for rescue and relief operations. Mass media and Short Message Service (SMS) technologies can be efficacious tool both in search and rescue planning along with disaster preparedness and recovery approach. The Remote Sensing images, blended with Global Positioning System (GPS) data collected on the field can be used in numerical quantification and appraisal of losses for ecological recovery tactic and recuperation.

Hence, Remote Sensing coupled with ICTs is one of the modest technologies, which can play substantial role in disaster management and planning aftermath. They can be blended wisely for emergency warning and quick response for diminution of the disastrous effects, if not nullify. Mass media and SMS technologies are other two important factors aiding disaster regulation and management.

Abstract

Landslides pose a great risk to life and property, therefore landslide susceptibility assessment is of vital importance, especially, in the hilly terrain. The key objective of this study is to generate a landslide susceptibility map through integrating weightages of different categories of the landslide causative factors derived from Statistical Information Value Model (SIVM) under Geographic Information Systems (GIS) environment. Several causative factors, such as slope, slope aspect, geology, drainage proximity, structural feature proximity, Landu Use/ Land Cover (LU/LC), NDVI, curvature, topographic wetness index, stream power index, road proximity, and relative relief were identified in this study area resulting in slope failures to a great extent. The existing landslides were mapped using remotely sensed data and field survey which were then divided into 70% (model training, i.e. calibration) and 30% (model testing, i.e. validation) data sets. Finally, the Landslide susceptibility map derived from statistical information value model has been divided into five equal classes, namely Very Low, Low, Moderate, High, and Very High. The accuracy of the model was evaluated using Receiver Operation Characteristic (ROC) curve, which resulted in 0.86 areas under curve. The area under curve figure reflects that the prediction accuracy of model is 86% and the results obtained will be useful for the policymakers in the study area for the generation of key plans and decision-making tasks.

Abstract

In this research work, wavelet domain method is designed to filter noise in medical images. This method adapts to
various types of noise, which is dependent on the user or medical expert. Here, a single parameter can be used to
balance the preservation of relevant details and the level of noise reduction. This method needs the subsequent
information of the related image details across the resolution scales to perform a preliminary coefficient classification.
The statistical distributions of the coefficients can be estimated by using preliminary coefficient classification that
characterize the valuable image features and noise levels. Wavelet domain indicator is used to achieve the conversion
to the image features and noise level. The experimental results demonstrated noise suppression in Magnet Resonance
(MR) and Ultra Sound (US) images and its performance is validated by using quantitative and qualitative methods.

Abstract

Foot ulcers are the common innate mechanism of poorly controlled diabetes, forming as a result of skin tissue
breakdown in the lower leg or feet. When the sugar levels are high in blood for a diabetic patient, a wound may not be
cured so easily because of nerve damage. In this paper, the author has proposed a method to analyze the wounds in
diabetic patients suffering from foot ulcers. Mean Shift algorithm is used to find the maximum density of the wound in the
affected area and Color Clustering technique was also implemented in this system. This color clustering method helps to
classify the affected region in different phases and the wound tissues are shown by various colors like red, yellow, and
blue. The datasets collected from Saraswati Hospital, Parassala in India were used as input and the results provide good
accuracy and high efficiency.

Abstract

In order to develop a system which is versatile there is a need to achieve a dual channel communication, i.e. from deaf
to hearing and vice-versa. Basically, Sign Language Recognition System is a system which helps the stakeholders, i.e. the
deaf and the dumb persons and those with normal senses to communicate with each others quickly and efficiently
without a need of conventional interpreters. In this review, the authors have tried to study the works, which have been
carried out over the years and tried to present the insight gained along with the identified research gap.