Heritage Institute of Technology

About: Heritage Institute of Technology is a based out in . It is known for research contribution in the topics: Steganography & Support vector machine. The organization has 581 authors who have published 1045 publications receiving 8345 citations.

Planarity does not always mean higher aromaticity - Intriguing metalloaromaticity of three Al13+ isomers.

Abstract: A pair of Al13+ clusters, one perfectly planar (CI) and another quasi-planar structure (CII) have been reported recently by our group [Guin et al. Journal of Molecular Modeling, 2018, 24, 344]. Both these clusters are rare examples of metal-aromatic systems having unique aromatic character. In these clusters, localized strong anti-aromatic deltas are embedded within a strong aromatic sea. The quasi-planar CII structure is a true minimum structure with zero imaginary frequency, but the planar CI structure has been found to have three imaginary frequencies. Further search for a possible transition structure (CT) with single imaginary frequency was successful, which shows that CT structure is also quasi-planar but the tail region of this cluster is puckered downward as compared to that of the CII cluster for which tail region is puckered upward. A comparative electronic structural analysis of these three clusters have been carried out which has provided insight into the way the transformation among the three states take place. Harmonic frequency analysis of these clusters reveals that transition from CT to CII occurs through the planar cluster CI that appears to have an intermediate geometry between the downwardly puckered CT and the upwardly puckered CII. A comparative NICS, ELF, AIM and LOL analysis of all three clusters reveal that the global aromatic nature increases in the sequence CT, CI, CII. A TDDFT study reveals that the oscillator strength of both CT and CII cluster is quite close to each other, which are one order of magnitude higher than the planar CI cluster.

Extraction of efficient electrical parameters of solar cell using firefly and cuckoo search algorithm

Abstract: Energy extraction from the solar irradiance by the use of solar cell Module is very important in the field of renewable energy. The Electrical properties derived from the nonlinear Current-voltage (I-V) curve of the solar cell module play a vital role in the exploration of device performance and overall efficiency. In this paper, two recently developed heuristic algorithm Cuckoo search optimization and Firefly algorithm has been applied for the extraction of parameters of double diode lumped electrical circuit model of a solar cell. The study carried out using data set generated from a MATLAB/SIMULINK model of a PV module. Results suggest that Cuckoo Search and Firefly algorithm outperform other popular evolutionary computing techniques like DE and PSO in terms of convergence speed and excellent final solution. The curves obtained from the extracted values using FA and CS are identical with those of the experimental one at different environmental conditions validates the purpose of this study which is novel also in the solar energy domain.

A Framework for Linear TV Recommendation by Leveraging Implicit Feedback

Abstract: The problem with recommending shows/programs on linear TV is the absence of explicit ratings from the user Unlike video-on-demand and other online media streaming services where explicit ratings can be asked from the user, the linear TV does not support any such option We have to rely only on the data available from the set top box to generate suitable recommendations for the linear TV viewers The set top box data typically contains the number of views (frequency) of a particular show by a user as well as the duration of that view In this paper, we try to leverage the feedback implicitly available from linear TV viewership details to generate explicit ratings, which then can be fed to the existing state-of-the-art recommendation algorithms, in order to provide suitable recommendations to the users In this work, we assign different weightage to both frequency and duration of each user-show interaction pair, unlike the traditional approach in which either the frequency or the duration is considered individually Finally, we compare the results of the different recommendation algorithms in order to justify the effectiveness of our proposed approach

Design of Knowledge-Based Efficient Speed Optimization Algorithm in Unplanned Traffic

Abstract: Speed Optimization in an Unplanned Traffic (SOUT) is a very promising research problem. Searching for an efficient optimization algorithm to increase the degree of speed optimization and thereby increasing the traffic flow in an unplanned zone is a widely concerning issue. However, very few research works have been carried out on the optimization of the lane usage and speed optimization simultaneously. This paper presents a novel SOUT technique to solve the problem optimally using the knowledge derived by analyzing the speed of vehicles, which, in turn will act as a guide in designing lanes optimally to provide better optimized traffic. Also, base model estimates for different features like dimensional factors for the geometric design as well as different traffic control features are being adjusted by the accident factors. Knowledge based analysis technique is applied to the proposed design and speed optimization plan. The experimental results and observations are quite encouraging.

Optimization of intrinsic elements from small signal model of GaN HEMT by using PSO

Abstract: Present paper elaborates a new method for optimization of intrinsic circuit elements of small signal model of GaN/Si HEMT by using Particle Swarm Optimization (PSO) for building a compact model. Measured intrinsic S-parameters are obtained by de-embedding the extrinsic circuit elements from the small signal model having 22 circuit elements. Further, PSO has been employed with unique search space exploration for accurate small signal modeling, and has been verified with measured S-parameters in the frequency range of 1GHz to 21 GHz.