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Background: Automatic human emotion recognition system is an active area of research due to its wide applications in the field of Human Computer Interaction(HCI) systems, driver fatigue monitoring systems, surveillance systems, human assistance systems, smile detectors etc. Objective: The study presents a fuzzy based approach to extract facial features from input image and builds different classification models to classify the image into two emotion classes i.e. happy and neutral. The system has potential implications in the field of smile detection systems, customer experience analysis and patient monitoring systems. Methods: The proposed system determines the dimensional attributes (l-attribute and w-attribute) of mouth region extracted from the facial image using viola-jones algortithm. The feature set is generated by using a total of 136 images from JAFFE, NimStim and MUG dataset. The differentiating power of the attribures is then evaluated using five different classification models. Results: The accuracy, precision and recall is determined for each classification model. The results show good accuracy of 70% for the grayscale JAFFE and NimStim databases and 95% for the coloured MUG database. Conclusion: The mouth features calculated in the study are based on the geometric coordinates which eliminates the possibility of false distance measurements due to presence of noise or shadows.

Spectro-temporal features of speech are the basis of phonological comprehension and production in the brain. Thus, these features provide a relevant framework to study speech and language development in children. In this paper, we present a novel framework to study the statistics of spectro-temporal features of speech that are encoded at different timescales. These timescales correspond to different linguistic units such as prosodic or syllabic components. The framework is tested on a speech corpus consisting of 169 speech samples. The paper demonstrates usage of the proposed framework in finding milestones of speech development in children. The results indicate the presence of more number of spectro-temporal features encoded at short timescales in adults as compared to children. However, no significant difference is observed in the spectro-temporal features encoded at long timescales between these groups. The proposed framework is also used in studying the speech impairments of children and adults with mild to moderate intellectual disabilities. The results reveal the absence of some spectro-temporal features encoded at both the timescales and their absence is more prominent in shorter timescales. The suggested framework can be used for studying speech development and impairment in different disorders.

This study presents a geometric feature based automatic facial expression recognition system. The proposed system utilises the facial landmark points to determine the relative distances between the facial features in order to capture deformities caused by the movement of facial muscles due to different expressions. Three feature sets are generated by using landmark coordinates, relative distances between the facial points and a combination of both. Discriminating power of each feature set is determined by training different classification models for classifying an image into six basic emotions or neutral state. The proposed system is validated on two publically available facial expression databases. Experimental results show good accuracy of 95.5% for MUG database on the combined features by using ensemble neural network.

Sharing medical data with numerous stakeholders for varied purposes is a perplexing problem facing healthcare systems throughout the world. All this needs to be done, whilst ensuring patient and health data privacy and data integrity. Through this paper, an attempt has been made to present a novel distributed digital health record management system. The blockchain framework has been built using the tools provided by hyper ledger, an open source project. This system empowers patients with a comprehensive, secure, immutable, and easily accessible digital record of their health. Also, this information is available across multiple practitioners and healthcare providers throughout the world. This system can therefore empower the emergence of health data economics, empowering researchers with the health data and giving patients an opportunity to share their health data without losing anonymity. The purpose of this paper is to introduce a prototype of a health data management system utilising blockchain.

This paper reviews the technology used in Speech-to-Speech Translation that is the phrases spoken in one language are immediately spoken in another language by the device. Speech-to-Speech Translation is a three step software process which includes Automatic speech Recognition, Machine Translation and voice synthesis. This paper includes the major speech translation projects using different approaches for speech recognition, translation and text to speech synthesis highlighting the major pros and cons for the approach being used.

The objective of this study is to analyze the developmental changes in the speech of children and adult. We quantified speech in terms of the mean power across 10 frequency bins between 0–2000 Hz in the population of 90 children (4–8 years old) and 27 adults. We found that with age, the harmonic structures in the speech increases, as the power spectra of adult is more distributed and has peaks at higher frequency bins than the younger children. To further support our findings, we have performed classification by different classification techniques and found the significant differences in the power spectra's of children and adults. This result can be used as a database for Indian children for doing comparative studies to find out speech abnormalities in the children with speech, language or brain disorder.

Speech is the primary means of human communication. Speech production starts in early ages and matures as children grow. People with intellectual or learning disabilities have deficit in speech production and faces difficulties in communication. These people need tailor-made therapies or trainings for rehabilitation to lead their lives independently. To provide these special trainings , it is important to know the exact nature of impairment in the speech through acoustic analysis. This study calculated the spectro-temporal features relevant to brain structures, encoded at short and long timescales in the speech of 82 subjects including 32 typically developing children, 20 adults and 30 participants with intellectual disabilities (severity ranges from mild to moderate). The results revealed that short timescales, which encoded information like formant transition in typically developing group were significantly different from intellectually disabled group, whereas long timescales were similar amongst groups. The short timescales were significantly different even within typically developing group but not within intellectually disabled group. The findings suggest that the features encoded at short timescales and ratio (short/long) play a significant role in classifying the group. It is shown that the classifier models with good accuracy can be constructed using acoustic features under investigation. This indicates that these features are relevant in differentiating normal and disordered speech. These classification models can help in early diagnostics of intellectual or learning disabilities.

Basic needs of humans are met by conveying information to each other. Most efficient way to do this is via speech signal. But looking in the society we find many people with mental impairment, their essential infrastructure of articulation, word stress, accent, pitch and other speech features are deeply affected by damage in their brain. There are many factors that affect speech but the fundamental frequency (perceived form: pitch) plays major role in prosodic development. This study is an attempt to observe the pattern of pitch development in normal developing and mentally retarded children and adults. It also investigates possible differences between them. Results reveal that younger children have a slighter difference in their pitch as compared to age matched MR (Mentally Retard), but significant difference is found between normal developing adults and those suffering from neurodevelopment disorder. These criteria can be considered as first step in diagnosis of these neurodevelopment disorders.

In speech development research, it's important to know how speech acoustic features vary as a function of age and the age when the variability and magnitude of acoustic features start to exhibit adult-like patterns. During the first few years of life, a child's speech changes from the cries and babbles of an infant to adult-like words and phrases of a young child. A number of acoustic studies observed that, adult's speech compared to children's speech, exhibits lower pitch and formant frequencies, shorter segmental durations, and lesser temporal and spectral variability. In this research we extracted acoustic, spectral and temporal features of a speech signal and then classify these features to predict the age of the subjects using different classification techniques. The feature vector comprised of fundamental frequency, formants, lpc coefficients and segmental duration. This study investigated the developmental patterns and the varying trends observed in speech acoustics with advancement in age and gender. The investigation then contributed in predicting the age of the speakers by analyzing these extracted features using various classification techniques and the result revealed maximum recognition rate by using neuro-fuzzy classifiers. This prediction of age may further help us in analyzing the speech samples in order to predict early speech disorders or language delay in children with neuro-developmental disorder.

Speech sound is information rich signal which impart knowledge about the speaker. In this research we have done a comparative study of speech signals of speakers of varying age group by analyzing their voice samples which were recorded by us. The Database of the speakers has been dissected in this investigation with the help of which the spectrum analysis and the pitch analysis have been performed. Pitch is an attribute of high importance for voiced speech. Further, application of Pitch estimation may prove of great use in fields of Health Care, Music recording enhancements and security purposes by helping in speaker identification.

An ad hoc network is a group of wireless mobile computers or nodes in which individual nodes cooperate by forwarding packets for each other to allow nodes to communicate beyond direct wireless transmission range. In this paper I presented the design and performance evaluation of a new on demand ad hoc routing protocol, called SWARM. SWARM used mechanisms of swarm intelligence to based select good routes to destinations. SWARM was Scalable, Adaptable, Autonomous and have good Fault tolerance capability. This paper presents the simulated performance comparison of SWARM with AODV and DSR in different CBR traffic flow. Performance evaluation of AODV, DSR and SWARM was evaluated on packet delivery ratio, network throughput, end-to-end latency and packet transmission rate.