SRM University

About: SRM University is a education organization based out in Chennai, India. It is known for research contribution in the topics: Computer science & Population. The organization has 10787 authors who have published 11704 publications receiving 103767 citations. The organization is also known as: Sri Ramaswamy Memorial University.

Bidirectional communication between the neuroendocrine system and the immune system: relevance to health and diseases

Abstract: In the past century, physiological, molecular, and cellular-based studies have proved that the functions of the nervous system, endocrine system, and immune system are dependent upon each other and that this interaction among these systems determines the maintenance of health or susceptibility to infections. The release of neurotransmitters and neuropeptides from the brain is a response to external environmental stimuli that influences the release of hormones from the pituitary in order to regulate the functions such as metabolism and growth, reproduction, etc. In addition, there are direct sympathetic noradrenergic and peptidergic innervations of primary (bone marrow and thymus) and secondary (spleen, lymph nodes, and lymphoid tissues) lymphoid organs. The neurotransmitters and neuropeptides released in these lymphoid organs then bind to specific receptors on the cells of the immune system to modulate their functions. Another circuit in this bidirectional communication involves the products of the immune system, for e.g., cytokines that can cross the blood-brain barrier to alter the activities of the neuronal function in the central nervous system especially during fever and inflammation in infectious diseases and cancer. Dysregulation of the interactions between the neuroendocrine and immune system due to alterations in the neural activity, secretion of hormones and cytokines, and synthesis of growth factors has been demonstrated to promote the pathogenesis and progression of infectious and autoimmune diseases, cancer, and neurodegenerative diseases. It is imperative that further research is carried out to understand the mechanisms of neuroendocrine-immune interactions to facilitate development of better treatment strategies for neurodegenerative diseases.

CRAIoT: Concept, Review and Application(s) of IoT

Abstract: IoT is a fast evolving concept. In IoT the message need to be delivered instantly between neighboring nodes in IoT enabled network and for this to happen the nodes need to be in overlapping transmission range of each other. Literature Survey reveals that not much work has been done in the direction of understanding the concept of IoT and exploring it's applications. In our proposed research work, effort has been made to explore the new and innovative application of IoT along with a detailed systematic literature survey.

Electromagnetic interference (EMI) shielding, microwave absorption, and optical sensing properties of BaM/CCTO composites in Ku-band

Abstract: Sol-gel autocombustion technique was used to synthesized composites of barium hexaferrites (BaM) and calcium copper titanate (CCTO) with chemical composition Ba1−xCoxFe12−x−yDyxLayO19 (x = 0.0, 0.1, 0.2, 0.3 and y = 0.0, 0.4, 0.5, 0.6) and Ca1-xErxCu3Ti4−yMnyO12 (x = 0.0, 0.1, 0.2, 0.3 and y = 0.0, 0.4, 0.5, 0.6) respectively. The BaM/CCTO composites exhibit a single crystalline phases of both BaM and CCTO, this observation is supported by Raman spectroscopy. Characteristics peaks of both BaM and CCTO were observed at 439 and 592 cm−1; these peaks give a hint on the formation of both BaM and CCTO phase in the composites. Morphology analysis show that the grains of the BaM nanoparticles exhibit uneven distribution with morphology close to hexagonal structure while the CCTO microparticles shows cubic-like grains with homogenous distribution and the stoichiometry of the prepared composites was also observed. Lattice fringes of BaM nanoparticles and CCTO microparticles with sizes of 0.2629 and 0.2613 nm corresponding to (1 1 4) and (2 2 0) hkl planes respectively have been observed. The observed band-gap increases with increase in crystallite size. The coercivity increases as a result of the presence of CCTO phase. BaMCCTO4 composite shows maximum reflection loss (RL) of −27.9 dB (99.83% absorption) at 16.5 GHz and matching thickness of 3 mm with effective absorption (RL 10 dB) bandwidth of 1.7 GHz (between 15.7 and 17.4). The EMI shielding capacity of the BaMCCTO4 composite shows a total shielding effectiveness of 41.8 dB at 16.5 GHz corresponding to 99.99% shielding of EM waves with effective shielding (SET > 20 dB) bandwidth of 4.2 GHz.

Automatic detection and classification of EEG artifacts using fuzzy kernel SVM and wavelet ICA (WICA)

Abstract: Electroencephalography (EEG) is almost contaminated with many artifacts while recording the brain signal activity. Clinical diagnostic and brain computer interface applications frequently require the automated removal of artifacts. In digital signal processing and visual assessment, EEG artifact removal is considered to be the key analysis technique. Nowadays, a standard method of dimensionality reduction technique like independent component analysis (ICA) and wavelet transform combination can be explored for removing the EEG signal artifacts. Manual artifact removal is time-consuming; in order to avoid this, a novel method of wavelet ICA (WICA) using fuzzy kernel support vector machine (FKSVM) is proposed for removing and classifying the EEG artifacts automatically. Proposed method presents an efficient and robust system to adopt the robotic classification and artifact computation from EEG signal without explicitly providing the cutoff value. Furthermore, the target artifacts are removed successfully in combination with WICA and FKSVM. Additionally, proposes the various descriptive statistical features such as mean, standard deviation, variance, kurtosis and range provides the model creation technique in which the training and testing the data of FKSVM is used to classify the EEG signal artifacts. The future work to implement various machine learning algorithm to improve performance of the system.