Abstract

The scan-line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed in May 2003, and this abnormal functioning of SLC resulted in about 22% of the pixels per scene without being scanned. By filling the un-scanned gap by a good technique will help in more use of ETM+ data for many scientific applications. While there have been a number of approaches developed to fill in the data gaps in ETM+ imagery, each method has shortcomings, especially they require SLC-on (images acquired before SLC-off anomaly) imagery for the same location to fill the gaps in SLC-off (images acquired after SLC anomaly) image. To overcome such shortcomings this study proposes an alternative interpolation method based on the partial derivative. This case study shows that this technique is very much useful to interpolate the missing pixel values in the SLC-off ETM+ data.

Abstract

In this paper, we present a new multistage approach for SfM reconstruction of a single component. Our method begins with building a coarse 3D reconstruction using highscale features of given images. This step uses only a fraction of features and is fast. We enrich the model in stages by localizing remaining images to it and matching and triangulating remaining features. Unlike traditional incremental SfM, localization and triangulation steps in our approach are made efficient and embarrassingly parallel using geometry of the coarse model. The coarse model allows us to use 3D-2D correspondences based direct localization techniques to register remaining images. We further utilize the geometry of the coarse model to reduce the pair-wise image matching effort as well as to perform fast guided feature matching for majority of features. Our method produces similar quality models as compared to incremental SfM methods while being notably fast and parallel. Our algorithm can reconstruct a 1000 images dataset in 15 hours using a single core, in about 2 hours using 8 cores and in a few minutes by utilizing full parallelism of about 200 cores.

Abstract

Most of the existing vowel onset point (VOP) detection methods are developed for clean speech. In this chapter, we propose methods for detection of VOPs in the presence of speech coding and background noise conditions. VOP detection method for coded speech is based on the spectral energy between 500 and 2,500 Hz frequency band of the speech segments present in glottal closure region. In case of noisy speech, the proposed VOP detection method exploits the spectral energy at the formant locations of the speech segments present in glottal closure region. The proposed VOP detection methods are evaluated using objective measures and consonant vowel (CV) unit recognition experiments.

Abstract

We present various robust precoder designs for two-way relaying in a cognitive radio network, where a pair of cognitive (or secondary) transceiver nodes communicate with each other assisted by a set of cognitive two-way relays. The secondary nodes share the spectrum with a licensed primary user (PU) node while keeping the interference to the PU below a specified threshold. The PU node and the cognitive transceivers employ single transmit/receive antennas whereas the secondary relay nodes employ multiple transmit/receive antennas. The proposed precoder designs ensure robust performance in the presence of errors in the channel state information (CSI). Such robust designs are of significant interest since in practice it is very difficult to obtain perfect CSI. We consider CSI errors with two different types of characterization and corresponding robust designs. First, we consider robust relay precoder designs that are applicable when CSI errors have known first and second moments. Next, we consider robust designs that are applicable when the CSI error can be characterized in terms of spherical uncertainty region. We show that the proposed designs can be reformulated as convex optimization problems that can be solved efficiently. Through numerical simulations and comparisons we illustrate the performance of the proposed designs.

Abstract

In order to help architects and designers make early design decisions, an online tool (WinOpt) has been developed. WinOpt optimizes various components associated with the design of windows such as Azimuth Angle (angle with respect to North), Aspect Ratio, WWR (Window Wall Ratio), SHGC (Solar Heat Gain Coefficient) of glass used for windows, and various local shading strategies such as overhang and side fins. WinOpt performs annual energy simulations for a given location using EnergyPlus. It uses an optimization tool in the back end (GenOpt) to reduce the few million simulations to a few hundred, thus helping in making perfect rapid design decisions. WinOpt optimizes the design to minimize the operational energy for conditioned buildings and maximize thermal comfort for unconditioned buildings. It also has a parametric option where parametric simulations are performed for selected values for various input parameters. This can help the user understand the spectrum of design solutions. Since EnergyPlus can evaluate thermal comfort conditions besides energy consumption in a building, WinOpt can help in design decisions for low energy/ net zero energy buildings.

Abstract

This article presents the effect of envelope measures on thermal environmental conditions of a naturally ventilated building block in composite climate of India. Envelope measures such as roof U-value, wall U-value, glass U-value and glass solar heat gain coefficient, roof reflectance and two combinations of these measures (total seven cases) are analysed as recommended by National Energy Conservation Building Code-India (ECBC) through prescriptive route. Subject building is modelled and simulated using dynamic thermal simulation tool. Two approaches of thermal comfort have been used in this study such as heat balance model based on laboratory study and adaptive model of thermal comfort based on field findings. Thermal environmental conditions are evaluated based on operative temperature with and without considering the effect of thermal adaptation. This study found that comfortable temperature …

Abstract

Increasing roof reflectance reduces absorption of solar radiation, roof surface temperature and heat ingress into the building. This leads to an increase in indoor comfort and reduction in room air temperature that in turn results in energy saving by reducing AC demand. To assess this impact of cool roofs on buildings in rural and peri-urban areas of India, a study was performed on two un-conditioned school buildings in Hyderabad (ZPH School) and Nagpur (VNHM School). Two classrooms of same size, function, and occupancy from both the school buildings were monitored for a period of ten weeks starting from March 2014 till May 2014. The data from the experiments and their analyses showed promising results. The average reduction and peak reduction in indoor air, roof underdeck and roof overdeck surface temperatures are 2.1 ◦C, 5.0 ◦C and 12.3 ◦C and 4.3 ◦C, 10.0 ◦C and 26.3 ◦C respectively for the room with white roof as opposed to grey roof in ZPH School and 1.5 ◦C, 4.0 ◦C and 9.5 ◦C, and 3.3 ◦C, 4.2 ◦C, and 25.2 ◦C respectively in VNHM School. The results from the experiment prove that white roof has a significant effect in reducing the indoor air temperatures in buildings due to reduction in outdoor and indoor roof surface temperature.

Abstract

As soon as natural disaster events happen, users are eager to know more about them. However, search engines currently provide a ten blue links interface for queries related to such events. Relevance of results for such queries can be significantly improved if users are shown a structured summary of the fresh events related to such queries. This would not just reduce the number of user clicks to get the relevant information but would also help users get updated with more fine grained attribute-level information. Twitter is a great source that can be exploited for obtaining such fine-grained structured information for fresh natural disaster events. Such events are often reported on Twitter much earlier than on other news media. However, extracting such structured information from tweets is challenging because: 1. tweets are noisy and ambiguous; 2. there is no well defined schema for various types of natural disaster events; 3. it is not trivial to extract attribute-value pairs and facts from unstructured text; and 4. it is difficult to find good mappings between extracted attributes and attributes in the event schema. We propose algorithms to extract attribute-value pairs, and also devise novel mechanisms to map such pairs to manually generated schemas for natural disaster events. Besides the tweet text, we also leverage text from URL links in the tweets to fill such schemas. Our schemas are temporal in nature and the values are updated whenever fresh information flows in from human sensors on Twitter. Evaluation on ~58000 tweets for 20 events shows that our system can fill such event schemas with an F1 of ~0.6.

Abstract

Our computing demands have grown so much that we need a robust distributed computing platform to process data. To feed such data hungry systems, we need an equally robust distributed file systems that span across multiple geographically separate locations. Most distributed file systems break any file into a set of blocks or chunks which are spread across the cluster. The major bottleneck is to identify where to place the blocks and where to place the replicas so that cluster is optimized on certain parameters like disk utilization, minimizing network congestion, maximizing throughput, optimal power utilization, etc. This paper proposes assigning a distance measure for each one of the data sources with respect to the other and placing the blocks on the specified disk on the node that minimizes the total distance of the last few requests made for that block. As the request pattern and parameters change, the distances are updated and the blocks are moved dynamically to minimized the distance, in effect optimizing on the required parameters. The distance function is to be modeled based on the cluster and the parameters you wish to optimize on. It can be a function of just bandwidth or bandwidth and latency or other miscellaneous features like disk utilization, processing power, disk speed, power utilization, cooling requirements, temperature, etc. A detailed performance analysis was carried out with disk bandwidth, network bandwidth and disk utilization as the parameters and the performance is far better in comparison to the reference system (10% or more depending on specification differences) which has no understanding of the different type of disks present and the nature of the cluster. Due to the performance aware nature, the system was inherently able to utilize memory for speeding up performance through the use of in-memory partitions.

Abstract

Given a knowledge base, annotating any text with entities in the knowledge base enhances automated understanding of the text. Entities provide extra contextual information for the automated system to understand and interpret the text better. In the special case when the text is in the form of short text queries, automated understanding can be critical in improving the quality of search results and recommendations. Annotation of queries helps semantic retrieval, ensuring diversity of search results including retrieval of relevant news stories. In this paper, we present SIEL@ERD, a system for automated stamping of entity information in short query text. Our system builds from the state-of-the-art TAGME system and is optimized for time and performance efficiency. Our system achieved an F1 measure of 0.53 and the latency of 0.31 seconds on a dataset of 500 queries and a Freebase snapshot provided for the short track in the Entity Recognition and Disambiguation Challenge at SIGIR 2014.