Arjun Paramarthalingam
@aucev.edu.in
Assistant Professor, Department of Computer Science and Engineering
University College of Engineering Villupuram
Arjun Paramarthalingam currently works at the department of CSE, University College of Engineering Villupuram, Tamilnadu, India. Arjun does research in Computer Vision, Artificial Intelligence and IoT.
EDUCATION
B.Tech., M.E., Ph.D
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Vision and Pattern Recognition, Computer Science, Computer Science Applications, Artificial Intelligence
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Ashokkumar Janarthanan, Arjun Paramarthalingam, Amirthasaravanan Arivunambi, and P. M. Durai Raj Vincent
IEEE
Globalization and industrialization have brought about their reverberation in the form of different simultaneous harmful components that affects environment's equilibrium conditions. The improper management of such materials has resulted in the contamination of essential ecosystem elements that serve as the foundation for human survival. The solution here is evaluating the degree of ferocity and then initiating preventive and eradication measures based on that information. In this work, air quality-based internet of things (IoT) nodes with MQ135 sensors were set to sense the presence and intensity of air pollutants, then transmit the recorded information to a mobile app and web via GSM module or WiFi system for data analysis and display. The MQ135 sensor monitors air quality since it detects the majority of dangerous gases and can correctly quantify their level. The air quality is consistently monitored and will raise an alarm when the air quality drops below a specific range, it indicates that there are significant amounts of dangerous pollutants like CO, CO2, C2H4, NO, NO2, SO2 and CH4 present in the air. The air quality readings taken in PPM are regularly updated to the user through a mobile application to take necessary preventive action.
Amirthasaravanan Arivunambi, Arjun Paramarthalingam, P. Sanju, S. Uthayashangar, and Keerthi. V. L
IEEE
Virtual mind inquiries about hastening the improvement of reasonable incessant Brain Computer Interface (BCI). Equipment developments the expansion ability of Virtual mind dissect and Brain PC-wearable sensors have made possible a few novel programming systems for engineers to use and make applications joining BCI and IoT(Internet of Things). At present, a comprehensive study on BCI in IoT from dissimilar viewpoints; together with Electroencephalogram (EEG) based BCI models, and current dynamic stages. In view of this analyses, the fundamental discoveries of study eye on three substantial improvement patterns of BCI, which are EEG, IoT, and cloud computing. Utilizing this it is totally helpful for finding the genuine condition regardless of whether the cerebrum is alive or dead. In the incident that it is active, at that point the movement of the mind is checked and put away. Through this anybody can arrive at resolution that whether the activity done is legitimate or illegal. This has a favorable position for two situations. First is for Adulteration in bank subtleties and secondly fabrication in resource archives. The principle point is to transfer human cerebrum private things in the cloud, if there are any adjustments in the status of the mind, the virtual cerebrum site will go about as the human mind.
Arjun Paramarthalingam and Mirnalinee Thankanadar
Institution of Engineering and Technology (IET)
Shape recognition and retrieval is a complex task on non-rigid objects and it can be effectively performed by using a set of compact shape descriptors. This paper presents a new technique for generating normalised contour points from shape silhouettes, which involves the identification of object contour from images and subsequently the object area normalisation (OAN) method is used to partition the object into equal part area segments with respect to shape centroid. Later, these contour points are used to derive six descriptors such as compact centroid distance (CCD), central angle (ANG), normalized points distance (NPD), centroid distance ratio (CDR), angular pattern descriptor (APD) and multi-triangle area representation (MTAR). These descriptors are a 1D shape feature vector which preserve contour and region information of the shapes. The performance of the proposed descriptors is evaluated on MPEG-7 Part-A, Part-B and multi-view curve dataset images. The present experiments are aimed to check proposed shape descriptor’s robustness to affine invariance property and image retrieval performance. Comparative study has also been carried out for evaluating our approach with other state of the art approaches. The results show that image retrieval rate in OAN approach performs significantly better than that in other existing shape descriptors.
Arjun Paramarthalingam, Amirthasaravanan Arivunambi, and Sreedhar Thapasimony
Springer International Publishing
P. Arjun, S. Stephenraj, N.Naveen Kumar, and K.Naveen Kumar
IEEE
Today’s modern world people preferred to live the sophisticated life with all facilities. The science and technological developments are growing rapidly to meet the above requirements. With advanced innovations, Internet of Things (IoT) plays a major role to automate different areas like health monitoring, traffic management, agricultural irrigation, street lights, class rooms, etc., Currently we use manual system to operate the street lights, this leads to the enormous energy waste in all over the world and it should be changed. In this survey we studied about, how IoT is used to develop the street lights in the smart way for our modern era. It is an important fact to solve the energy crises and also to develop the street lights to the entire world. In addition, with the study on smart street lighting systems we analyzed and described different sensors and components which are used inIoT environment. All the components of this survey are frequently used and very modest but effective to make the unswerving intelligence systems.
P. Arjun and T. T. Mirnalinee
World Scientific Pub Co Pte Lt
This paper describes a multi-scale feature integration framework using angular pattern (AP), binary AP (BAP) and sequential backward selection (SBS) algorithms. These angular descriptors are represented by multi-scale features from which the best subsets of the scales are chosen using five-fold cross-validation technique along with SBS algorithm for efficient image retrieval. The SBS algorithm reduces the dimensionality of feature space which in turn reduces the matching time complexity. The extracted AP and BAP features are represented in histograms and are compared by the Chi-square distance metric. The experimental analysis is performed on the MPEG-7 CE-1 Part-B dataset images to demonstrate the effectiveness of multi-scale feature integration using SBS algorithm. The image retrieval performance of this framework is compared with state-of-the-art shape descriptors. Being multi-scale global shape descriptors, the proposed framework captures complete information about the shape and are invariant to scaling and rotation transformations.
P. Arjun, T.T. Mirnalinee, and M. Tamilarasan
IEEE
Shape descriptors are more powerful to discern objects present in the images. The present work is focused on simple contour based shape descriptor using centroid distance function and it works on closed contour objects. Object area normalization is performed to obtain `N' normalized contour points. The centroid distance feature extraction is performed on all normalized points. It forms simple 1-D feature vector of size `N'. For similarity matching correlation coefficient metric is used. This shape descriptor satisfies affine invariance property. The proposed idea is tested on MPEG-7 CE Shape-1 Part-B dataset images to validate its effectiveness. Experimental results shows that proposed compact centroid distance shape descriptor is more accurate than basic centroid distance shape descriptor and it saves space and time requirements at processing.
P. Arjun and T. T. Mirnalinee
Hikari, Ltd.
Simple and fast feature extraction methods are in need today for Content Based Image Retrieval (CBIR) and object recognition applications. The work presented in this paper is contour based one dimensional shape feature extraction technique for closed contour objects. The continuous contour is normalized into ‘N’ representative points. The sector area based object area normalization (OAN) technique is used for contour normalization. The centroid distance from all normalized points forms 1-D Compact Centroid Distance (CCD) feature vector. The experiments are conducted in MPEG-7 CE Shape-1 Part-B dataset images to test affine invariance property and image retrieval accuracy. Experimental results show the effectiveness of the proposed method in content-based image retrieval tasks.
P. Arjun, T. T. Mirnalinee, S. Sindhuja, and G. Bharathi Raja
Springer India