Indian Sign Language is one of the most important and widely used forms of communication for people with speaking and hearing impairments. Many people or communities have attempted to create systems that read the sign language symbols and convert the same to text, but text or audio to sign language is still infrequent. This project mainly focuses on developing a translating system consisting of many modules that take English audio and convert the input to English text, which is further parsed to structure grammar representation on which grammar rules of Indian Sign Language are applied. Stop words are removed from the reordered sentence. Since the Indian Sign Language does not support conjugation in words, stemming and lemmatization will transform the provided word into its root or original word. Then all the individual words are checked in a dictionary holding videos of each word. If the system does not find words in the dictionary, then the most suitable synonym replaces them. The system proposed by us is inventive as the current systems are bound to direct conversion of words into Indian Sign Language on-the-other-hand our system aims to convert the sentences in Indian Sign Language grammar and effectively display it to the user.
Designing wireless sensor networks (WSNs) that will sustain for longer time have been a challenging issue. For achieving this, efficient utilization of available energy should be anticipated from energy-constrained sensors running autonomously for long periods. In the field of agricultural research, farming related activities, assistance to farmers using advanced IoT and sensor network technologies is a need of time. Initially this paper briefed about smartphone based application systems that forewarns the farmer about pests/diseases that can occur on the crop. The designed system assists farmers to foresee the crop disease using machine learning algorithm. The disease can be predicted based on the environmental parameters as soil moisture, temperature and humidity obtained from sensor node and other parameters like rain fall, evaporation rate and sun shine hour obtained from open weather map application programming interfaces. A prototype is developed using ultra low powered micro-controller with interconnected sensor’s module. While comparing with the Arduino micro-controller based system, it was observed that the developed prototype system utilized less energy with effectively forewarned the farmers about crop diseases using Internet of Things (IoT). Thus it would assist the farmers proactively to reduce the losses due to crop diseases.
Fetal heart rate (FHR) monitoring is done for accessing fetal wellbeing during antepartum and intrapartum phases. Although noninvasive fetal electrocardiogram (NIfECG) is a potential data acquisition method for FHR, extraction of fetal electrocardiogram (ECG) from the abdominal ECG (aECG) is one of the major challenging research areas. This chapter proposed and assessed a method suitable for single channel based on principal component analysis (PCA) for extracting fetal ECG. Maternal R peaks and fetal R peaks were detected using Pan Tomkins algorithm (PTA) and improved Pan Tomkins algorithm (IPTA), respectively. Performance of fetal QRS detection is assessed using two open-access databases available online. The method shows satisfactory performance when compared with similar methods and makes it suitable for using a single channel system.
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