Abstract

wo-view stereo problem is a well researchedproblem in 3D computer vision. Algorithms proposedin the past have focussed on rectified stereo imageswhere the epipolar lines are parallel to the horizon-tal axis.The general problem of computing stereocorrespondences for unrectified images without anyknowledge of calibration parameters is an importantproblem but unexplored as yet. Our idea in this paperis to predict depth maps from two unrectified stereoimages using a modified Flownet architecture. Since,datasets for depth map reconstruction for unrectifiedstereo images for deep learning do not exist, we havecreated a dataset of turn table sequences of 3D modelsfrom Google 3D warehouse. Following the conceptsof Attention modelling, we implement an architecturefor combining correlations computed at multipleresolutions using a simple element-wise multiplicationof the correlations to aid the architecture to resolvecorrespondences for textureless and repeated texturedsurfaces. Our experiments show both qualitaitve andquantitative improvements of depth maps over theoriginal Flownet architecture.

Abstract

Video highlights are a selection of the most interesting parts of a video. The problem of highlight de-tection has been explored for video domains like egocentric,sports, movies, and surveillance videos. Existing methods are limited to finding visually important parts of the video but does not necessarily learn semantics. Moreover, the available benchmark datasets contain audio muted, single activity,short videos, which lack any context apart from a few keyframes that can be used to understand them. In this work,we explore highlight detection in the TV series domain, which features complex interactions with the surroundings. The existing methods would fare poorly in capturing the video semantics in such videos. To incorporate the importance of dialogues/audio, we propose using the descriptions of shots of the video as cues to learning visual importance.Note that while the audio information is used to determine visual importance during training, the highlight detection still works using only the visual information from videos.We use publicly available text ranking algorithms to rank the descriptions. The ranking scores are used to train a visual pair wise shot ranking model (VPSR) to find the highlights of the video. The results are reported on TV series videos of the Video Set dataset and a season of Buffy the Vampire SlayerTV series.

Abstract

This paper proposes a distributed implementation of spatial modulation (SM) using cognitive radios. In distributed spatial modulation (DSM), multiple relays form a virtual antenna array and assist a source to transmit its information to a destination. The source broadcasts its signal, which is independently demodulated by all the relays. Each of the relays then divides the received data in two parts: the first part is used to decide which one of the relays will be active, and the other part decides what data it will transmit to the destination. An analytical expression for symbol error probability is derived for DSM in independent and identically distributed (i.i.d.) Rayleigh fading channels. The analytical results are later compared with Monte Carlo simulations. Further, an instantaneous symbol error rate (SER) based selection combining is proposed to incorporate the direct link between the source and destination with existing DSM. Next, DSM implementation is extended to a cognitive network scenario where the source, relays, and destination are all cognitive radios. A dynamic frequency allocation scheme is proposed to improve the performance of DSM in this scenario. The frequency allocation is modeled through a bipartite graph with end-to-end SER as a weight function. The optimal frequency allocation problem is formulated as minimum weight perfect matching problem and is solved using the Hungarian method. Finally, numerical results are provided to illustrate the efficacy of the proposed scheme.

Abstract

Cognitive radio offers a novel solution to over-come the problem of spectrum underutilization by providing spectral access to secondary users. The television (TV) bands are of particular interest for secondary usage due to their high penetration power and greater coverage. These licensed bands are occupied only in few regions while in most of the other regions they are unoccupied and are termed asTV white space(TVWS). The estimation of TVWS has mostly been done by usingthe statistical and empirical propagation models. In this paper,terrain data is incorporated into the estimation of TVWS and shown to improve the quantitative estimation of TVWS. Using the relevant transmitter information and terrain data in the Indian state of Telangana, the efficacy of the proposed approach is demonstrated. The performance of the proposed approach is compared to that of widely used H at a propagation model. It is shown that the accuracy in TV coverage estimation increases onan average by 45% while incorporating terrain data as compared to using only empirical propagation model. As area outside theTV coverage is TVWS, the increased accuracy in the estimation of TV coverage directly translates to improved accuracy in the estimation of TVWS, which in turn translates into more efficient use of spectrum and better interference management

Abstract

Data fusion is a prevalent method to extract the best combination of satellite images from different modalities - spectral,spatial, temporal. A new method of object-based fusion of high resolution multi spectral (MS) and panchromatic (PAN)images is proposed in this paper, which emphasizes on spectral characteristics preservation. It is a hybrid approach where individual objects detected in the images are considered for mapping data and information transfer is done on a per-pixel basis. In addition, the paper proposes a quantitative assessment measure to assess the spectral distortion of the fused outcomes. The quantitative results demonstrate that the pro-posed method is better in terms of preserving spectral characteristics as compared to other widely used methods such as Principle Component (PC) based fusion, Intensity Hue Saturation (IHS) based fusion and Color Normalization (CN) or Brovey

Abstract

This article attempts to look at the manner in which the leftwing in Indian politics has dealt with the globalisation of India's economy and the consequent changes in foreign policy over the two decades since 1991. The left in India opposed the new economic policies, of which globalisation was a central aspect, and worked to build a wide consensus against it within the polity. The left's opposition to globalisation was premised on the fact that it would lead to a watering-down of India's sovereignty and will lead to the imposition of economic and foreign policies that would be detrimental to the nation. Despite deep internal differences on a range of issues within the left in India, this was a shared understanding of what globalisation meant and what its consequences would be. This article looks at how the left built up its opposition to globalisation of the Indian economy and how has it steered this opposition over the two decades since. It argues that the left's analysis of both the major characteristics of globalisation as well as its consequences for India's economy and foreign policy have proved to be grossly inadequate, if not entirely erroneous. However, despite mounting evidence that globalisation was not a disaster for the Indian economy, nor did it whittle down the State's sovereignty, there was no revision in the left's analysis or political stand. The article argues that the Indian radical has been unable to come to terms with or define globalisation, and remains unsure of how to engage with it. This has led to theoretical confusions, which have opened the door to the left's hegemonisation by the idea of nationalism, to the extent where its positions often become indistinguishable from the radical right. The article concludes by suggesting that this failure to analyse and engage with globalisation has been both a symptom as well as the cause of the weakening of the leftwing in Indian politics

Abstract

Unplanned large scale developmental projects in recent times have been causing alterations in land use land cover [LULC] at spatial and temporal scales with the substantial changes in the landscape. Uttara Kannada district has the distinction of having highest forest cover in India.Forest ecosystems in the district have witnessed major transformations due to industrialization during the post-independence. Impact of developmental activities during the post-independence era, is evident from barren hill tops due to deforestation. The analysis of temporal spatial data acquired through space borne sensors highlights the decline of evergreen-semi evergreen forest cover from 67.73% (1973) to 32.08% (2013). The ad-hoc approaches adopted in the implementation of developmental projects in the ecologically sensitive regions has impaired the ecosystem services affecting the people’s livelihood. The changes in the landscape structure with LULC changes have altered the functional abilities of an ecosystem evident from lowered hydrological yield, disappearing perennial streams, higher instances of human–animal conflicts, declined ecosystem goods, etc. About 64355 Ha of forest land is diverted for various non-forestry activities during the last four decades by the government apart from encroachment of 7072 Ha of forest area for agriculture, horticulture activities, etc. Encroachment of forest land has resulted in the land degradation leading to reduce dproductivity. Alterations in bio-geo chemical and hydrological cycles have necessitated the restoration of native forests in the region to ensure water and food security, and livelihood of the local people. Monitoring and visualization of landscape dynamics helps in evolving appropriate manage-ment strategies. LULC dynamics analyses considering agents (developmental projects) through fuzzy analytic hierarchy process (AHP) integrated with Markov cellular automata (CA) indicate the developmental projects such as West Coast Paper Mill (WCPM) and Kaiga Nuclear PowerHouse (KNPH) will lead to further fragmentation of forests with spurt in urbanization by 2022.This necessitates framing appropriate policy measures to sustain natural resources focusing on the landscape’s ecological, hydrological, economic and social factors.

Abstract

This paper describes a software system for automatically generating a reference (baseline) building energy model from the proposed (as-designed) building energy model. This system is built using the OpenStudio Software Development Kit (SDK) and is designed to operate on building energy models in the OpenStudio file format.

Abstract

The radiant systems consume less energy and provide better thermal comfort as compared to the Conventional system, but the main drawback of this technology is condensation and inability to cater the latent load. These challenges have directed many researchers to move their emphasis towards Personalized Conditioning System which aims to generate a microclimate zone near the vicinity of occupant according to need. This study is focused on Personalized Conditioning System when it is combined with a radiant system. The objectives of this study are 1) Evaluate the performance analysis of the system in cooling mode and, 2) Evaluate the performance of PRCS for cooling mode on different radiant cooling systems. An experimental setup of an office cubicle has been designed and fabricated using radiant panels at MNIT Jaipur, India. Three cases of the experiment were designed to evaluate and compare the performance of the system which are: Only Radiant Cooled Ceiling System (RCCS), Only Personalized Radiant Cooling System (PRCS), and both PRCS and RCCS coupled. It was observed that PRCS alone is sufficient to reach the thermal comfort criteria with low energy consumption, but it was unable to maintain the background temperature. When PRCS coupled with RCCS, it gives maximum thermal comfort for high supply water temperature ranging from 17 °C to 21 °C.

Abstract

Energy utilization can be improved by precise plug load monitoring and control. Plug load energy consumption is nearly 30% of the total building energy consumption. Therefore, plug load identification is a key requirement for energy conservation in buildings. Intrusive load monitoring techniques identify loads precisely but have not been tested widely so far for their performance in changing operating conditions. Hence, the present research proposes a robust low frequency intrusive load monitoring technique to identify load accurately. A smart power strip using proposed load identification technique is designed and developed. Linear regression is applied on the acquired data to capture the behavioral trends of a particular device more explicitly and concisely. Further, weighted K-NN classifier is applied on the transformed data set for device. Experimental results show that the proposed algorithm performs better than the standard classifiers, and can offer tangible savings.