Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Computer science. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

Synthesis of linear aperiodic array using Cauchy mutated cat swarm optimization

Abstract: A novel Cauchy mutated cat swarm optimization (CMCSO) that features effective global search capabilities with fast convergence is introduced in this paper. The Cauchy mutation enables the cats of the cat swarm optimization (CSO) algorithm to seek their positions in directions that avoid the problem of premature convergence and local optima. In this communication, CMCSO is applied to the synthesis of linear aperiodic arrays for minimizing sidelobe level and controlling the null positions. Various synthesis examples are considered and the obtained results are compared with linear aperiodic array designs from literature. Numerical results demonstrate that the proposed method is superior to existing methods in terms of accuracy and convergence speed. Some of the synthesized aperiodic array designs are implemented with wire dipole antenna elements using a full-wave electromagnetic simulator. Furthermore, experiments are conducted on several standard benchmark complex multimodal problems to demonstrate the effectiveness of the proposed method. The sensitivity analysis is performed on different parameters of CMCSO to demonstrate their influence on the overall performance of the benchmark and antenna array synthesis problems.

Surveillance scene representation and trajectory abnormality detection using aggregation of multiple concepts

Abstract: Use of CCTV is growing rapidly in surveillance applications. Rapid advancement in machine learning and camera hardware has opened-up adequate scopes to build next generation of expert systems aiming at understanding surveillance environments automatically by detection of trajectory abnormality through analyzing object behavior. Such intelligent surveillance systems should be able to learn and combine multiple concepts of abnormality in real-life scenario and classify the events of interest as normal or abnormal. Primary challenges of such systems are to represent and learn patterns in surveillance scenes and combine multiple concepts of abnormalities to activate the alarm system. This paper presents a graph-based representation of a given surveillance scene and learning of relevant features including origin, destination, path, speed, size, etc. These features are combined and correlated with target behaviors to detect abnormalities in moving object trajectories. We also propose an aggregation method that reduces the number of missed alarms during aggregation. Several cases using publicly available surveillance video datasets have been presented and the results indicate that the proposed method can be useful to design intelligent and expert surveillance systems.

Geochemical evaluation of thermal springs in Odisha, India

Abstract: The Indian state of Odisha has a number of thermal springs. These thermal springs are located at eight places (Attri, Tarabalo, Deulajhari, Magarmuhan, Bankhol, Badaberena, Taptapani and Boden) and belong to Mahanadi Geothermal Province, which is an Archean/Pre-Cambrian Geothermal Province. The thermal water discharging from these springs shows moderately acidic to moderately alkaline character (pH: 5.05–8.93) and the temperature ranges from 28 (Boden) to 58 °C (Tarabalo). Total dissolved solids (TDS) also shows a wide variation between 16.9 (Bankhol) and 595 mg/L (Deulajhari). A wide variation in the chemical characteristics of the thermal waters has been observed as they are located in different geological settings. Based on water chemistry, all the thermal springs can broadly be grouped into three water types: Na–Cl, Ca–HCO3 and Na–HCO3. The thermal spring water from Attri, Tarabalo and Deulajhari belongs to Na–Cl water type which is due to the circulation through granitic rocks. Higher concentrations of Cl and F in these thermal waters further suggest limited mixing and longer residence time as compared to the other areas where Ca–HCO3 and Na–HCO3 water types were found. Anion variation diagram clearly indicates that the thermal waters from Attri, Tarabalo and Deulajhari are fast ascending and fall in the mature water field; thus, their chemical signatures can be used to determine the reservoir temperature. However, in other areas, water chemistry is shaped by near-surface groundwater mixing processes and thus the chemical geothermometers may not be applicable to determine the reservoir temperature. No appreciable temporal variations have been observed in the water chemistry of the thermal waters.