Performance Improvement of Cooling Systemin T72 Bridge Layer Tank

Modern armoured fighting vehicles (AFVs) are provided with compact and efficient coolingsystems. Any increase in volume warrants the protection which, in turn, increases the weightand size of the vehicle. In a combat vehicle, space is at premium. The AFVs (battle tanks) usedby the Indian Army were found to be overheating when operated in deserts at very high-ambienttemperatures of 323K (50 oC). This imposes severe restrictions in operating the tanks for prolongedtime in deserts. The various subsystems of the cooling pack were investigated scientifically andan optimised solution was arrived at, implemented and proved successful in the subsequentdesert trials. This paper deals with the details of investigations, modifications, and the resultsof studies on bridge layer tank (BLT–T72).Defence Science Journal, 2008, 58(1), pp.77-85, DOI:http://dx.doi.org/10.14429/dsj.58.162

An IoT-Based Beehive Monitoring System for Real-Time Monitoring of Apis cerana indica Colonies

A study was conducted to monitor the bee activity in the colonies of diferente strengths in real time using an IoT-based device. The in-hive temperature and relative humidity were measured in the colonies of Apis cerana indica Fabricius of different strengths using the sensor-laden IoT device that was correlated with the movement of foragers into and out of the hive. A significantly higher movement of foragers was recorded at an in-hive temperature and relative humidity of 27.84 ºC and 61.47% at 5-6 p.m. with an observed activity of 9,638 bees/hive/hour in the strong colonies. In the weak colonies, the mean forager activity was 1,436.3 bees/hive/hour, which was recorded at an in-hive temperature of 26.52 ºC and 61.42% relative humidity. The mean honey area in the strong and weak colonies were 1,300.80±177.61 cm2 and 508.80±156.84 cm2, respectively. Pollen area in the strong and weak colonies were 447.60±112.08 cm2 and 116.20±66.43 cm2, respectively. In the strong and weak colonies, the area under egg brood was 470±53.06 cm2 and 88.20±36.85 cm2, larvae brood was 583.40±11.04 cm2 and 80.00±24.67 cm2 and sealed brood was 684.20±57.98 cm2 and 102.80±16.59 cm2, respectively. The real-time data on the movement of foragers in the colonies of different strengths enabled us to undertake timely intervention in the maintenance of the bee colonies

CSFS OF ERP IMPLEMENTATIONS IN LARGE SCALE INDIAN ORGANIZATIONS: A MULTIPLE CASE STUDY

ABSTRACT The study performs a literature search for critical success factors in ERP implementations. The study classifies the success factors in five groups (Vision, scope and goals; Culture, communication and support; Infrastructure; Approach and Project management). The researcher then study successful ERP implementations in four Indian large scale organizations. Through a detailed survey, we determine which factors in these companies contributed to the success of the implementation. The study then compares these factors to the literature to determine which of the critical success factors in the literature are relevant to the selected organizations. The results show that most of the success factors, found in the literature apply to Indian sectors, although some factors, such as a clear scope definition, management reporting, standardized infrastructure are clearly not important success factors in Indian large scale industries

Estimation of Higher-order Regression via. Sparse Representation Model for Single Image Super-resolution Algorithm

Super-resolution algorithms generate high-resolution (HR) imagery from single or multiple low-resolution (LR) degraded images. In this paper, an efficient single image super-resolution (SR) algorithm using higher-order regression is proposed. Image patches extracted from HR image will have self-similar example patches near its corresponding location in the LR image. A higherorder regression function is learned using these self-similar example patches via. sparse representation model. The regression function is based on local approximations and henceforth estimated from the localized image patches. Taylor series is used as local approximation of the regression function and hence the zeroth order regression co-efficient will yield the local estimate of the regression function and the higher-order regression co-efficient will provide the local estimate of the higher-order derivative of the regression function. The learned higher-order regression mapping function is applied to LR image patches to approximate its corresponding HR version. The proposed super-resolution approach is evaluated with standard test images and is compared against state-of-the-art SR algorithms. It is observed that the proposed technique preserves sharp high-frequency (HF) details and reconstructs visually appealing HR images without introducing andy artifacts

Design Analysis of Brushless Direct Current Generator

In this work, optimisation of a brushless direct current (BLDC) generator design was undertaken by carrying out an electromagnetic and computational fluid dynamic study. The studies were carried out for different loading-overloading conditions and angular speeds, keeping in consideration the required electrical and thermal parameters, firstly for the initial design and then for optimised designs. In the initial phase, transient electromagnetic simulations were done using Ansys Maxwell to estimate power output, flux densities, heat losses et al. In the next phase, steady state conjugate heat transfer simulations using frozen rotor method for rotating domains were carried out in Ansys CFX using the heat loss values obtained from electromagnetic study in the first phase. The results from conjugate heat transfer were obtained in the form of temperature and flow parameters. After a thorough study and comparison of the results for different designs, obtained in the two phases, it was seen one of the optimised designs showed better electromagnetic, thermal and flow parameters as compared to the initial design and satisfied all the optimum electrical and thermal parameters

An Example-Based Super-Resolution Algorithm for Selfie Images

A selfie is typically a self-portrait captured using the front camera of a smartphone. Most state-of-the-art smartphones are equipped with a high-resolution (HR) rear camera and a low-resolution (LR) front camera. As selfies are captured by front camera with limited pixel resolution, the fine details in it are explicitly missed. This paper aims to improve the resolution of selfies by exploiting the fine details in HR images captured by rear camera using an example-based super-resolution (SR) algorithm. HR images captured by rear camera carry significant fine details and are used as an exemplar to train an optimal matrix-value regression (MVR) operator. The MVR operator serves as an image-pair priori which learns the correspondence between the LR-HR patch-pairs and is effectively used to super-resolve LR selfie images. The proposed MVR algorithm avoids vectorization of image patch-pairs and preserves image-level information during both learning and recovering process. The proposed algorithm is evaluated for its efficiency and effectiveness both qualitatively and quantitatively with other state-of-the-art SR algorithms. The results validate that the proposed algorithm is efficient as it requires less than 3 seconds to super-resolve LR selfie and is effective as it preserves sharp details without introducing any counterfeit fine details