Abstract

By extending information and communication technologies, a personalizedagricultural advisory system called eSaguTMhas been developed in which the farmersreceive agricultural expert advice for each of their farms at regular intervals. The expertadvice is prepared by agricultural experts based on the crop status information receivedin the form of both digital photographs and text. During 2004-05, the eSaguTMsystemwas operated for 1051 cotton farms covering three villages in the state of AndhraPradesh, India. In eSaguTM, the expert advice had been delivered to every cottonfarm, once in a week. As a result, the data set consisting of about 20,000 such advicetexts had been generated. In this paper, we have carried out the cluster/textualanalysis experiments on the data set and reported interesting results concerning thedynamics of crop problems. Normally, all are cotton farms and belonging to nearbyarea/region should have faced similar problems. However, the cluster analysis of theadvices delivered on each day shows that significant number of farms are suffering fromdistinct crop production problems. The results also indicate that, a cluster of farmswhich face the same crop problem during one week face distinct crop problems duringthe subsequent weeks. Based on the results, we can conclude that it is necessaryto deliver agricultural expert advice to each farm by building agricultural advisorysystems which deliver farm-specific agricultural advices to reduce crop failures andimprove crop productivity.

Abstract

EnergyPlus is an energy simulation tool which models heating, cooling, lighting, ventilation, and other energy flows in buildings. It is based on the popular features of BLAST and DOE-2. Though EnergyPlus is more advanced than DOE-2, it comes with a drawback of larger simulation runtime. A decrease in the simulation runtime of EnergyPlus would increase the usability of the software. In this paper, a concept has been proposed which uses data parallelization to speed up EnergyPlus simulation. Data parallelization is a form of parallelization for computing across multiple processors or multiple computers in a cluster, run under a suitable environment. Data parallelism focuses on distributing the data across different parallel computing nodes by breaking it into smaller chunks, each of which is processed on by the same function, running in parallel on different cores/machines. In the proposed concept, this is …

Abstract

This paper explores the area of creating smart spaces using mobile Internet devices to develop intelligent classrooms. We propose a suite of applications that leverage the recent progress made in the domains of mobile computers and information access, resulting in a richer classroom experience where knowledge is dispensed with greater efficiency while also making it possible to access it efficiently from the Internet as well as peers. We attempt to achieve this by introducing novel means of classroom teaching and management and an intelligent information access system while harnessing the computational capabilities of mobile Internet devices. Though the current work is still in progress, this paper elaborates on our attempts in reducing the gap between technological progress in various domains and the existing pedagogy.

Abstract

The method of calibrated simulation has been used to estimate the energy savings by application of cool roof for the building of Satyam Learning Centre (SLC) at Hyderabad (India). Energy simulation model of the building was created using ‘DesignBuilder v. 1.95’and the building was monitored for few months. Mismatch between the simulation results and actual energy consumption has been reduced to obtain the calibrated model of the building using the method suggested in IPMVP Option-D. The calibrated model is then used to predict energy saving from the cool roof; the annual cooling energy savings were 19.4 kWh/m2. This saving corresponds to 9% reduction in annual air conditioning load and 5% reduction in overall annual energy consumption. Cost/benefit analysis for cool roof at SLC showed a 2.4 year financial payback period.

Abstract

Cool roof is one of the important strategies of energy savings in the tropical climate especially for low rise buildings. Standardization of the possible energy savings for a measure like cool roof would be quite difficult due to availability of various technologies and materials, as well as due to difference in building forms and climatic conditions. This paper talks about the development of an online tool for estimating energy saving through cool roof strategy using Energy Plus v4 as the backend simulation engine. Energy savings are calculated through simulating two identical buildings with different roof reflectivity values. Using this tool, simulations are run for the base case roof reflectivity of 0.3 and proposed case roof reflectivity of 0.75 for various major cities in India for a single floor, daytime use building. An average saving of 6.2% in cooling energy and 2.4% in total energy consumption was obtained using this tool.

Abstract

We have developed a web based integrated platform for the identification of genomic islands in which various measures that capture bias in nucleotide compositions have been implemented, viz., GC content (both at the whole genome and at three codon positions in genes), genomic signature, k-mer distribution (k=2–6), codon usage bias and amino acid usage bias. The analysis carried out in sliding windows (default size 10Kb) is compared with the genomic average for each measure. The output is displayed in a tabular format for each window which may be filtered if the values of the measures differ by 1.5σ (standard deviations) from the genomic average.

Abstract

In this paper, we consider the problem of mining the special proper-ties of a given record in a relational dataset. In our formulation, a property is a combination of multiple attribute-value pairs. The support of a property is the number of records that satisfy it. We consider a property as special if its support occurs to us as a shockand the measure of this shock factor is more than a userdefined thresholdη. We provide a way to define this notion of shock based on en-tropy. We also output the shock factor for records in the dataset in a convenient,easily-interpretable manner. An illustrated example is provided on how users can interpret the results. Experiments on real and synthetic data sets reveal interesting properties of data records that cannot be mined using traditional approaches.

Abstract

The voice onset time (VOT) combines the temporal and frequency structure over very short duration. This makes the VOT detection task difficult. But the VOT is an important temporal feature. In this paper we propose a new method for the detection of VOT in speech utterances. The method uses Fourier-Bessel (FB) expansion followed by amplitude and frequency modulated (AM-FM) signal model. The FB expansion is used to emphasize the vowel and consonant regions of stop consonant vowel (SCV) units (/ka/, /ta/, and /pa/). The emphasized signals using selected range of FB coefficients have been considered narrow-band AM-FM signals. The discrete energy separation algorithm (DESA) has been used to estimate amplitude envelope (AE) function of the AM-FM model. The estimated AE function is explored for the detection of VOT. The VOT detection studies are carried out using the speech corpus consists of utterances of various male and female speakers.

Abstract

Glottal closure instants (GCI) are the instants of significant excitation of the vocal tract system during the production of speech. The performance of many speech analysis and synthesis approaches depends on accurate estimation of GCIs. A new method for detection of GCI locations in speech utterances using Fourier-Bessel (FB) expansion and amplitude and frequency modulated (AM-FM) signal model is proposed in this paper. The inherent filtering property of the FB expansion is used to weaken the effect of formants in the speech utterances. The band-limited signal reconstructed using FB coefficients has been considered as an AM-FM signal. The discrete-energy separation algorithm (DESA) has been used to estimate the amplitude envelope (AE) function of the AM-FM signal model. The estimated AE function is explored for detection of GCIs. The CMU-Arctic database is used in this work for the validation of the proposed method for GCI detection with the electro-glottograph (EGG) as a reference. It has been observed that the detected GCI by the proposed method seems to provide accurate location of GCIs.

Abstract

Sensor networks consist of small motes attached with sensors to measure ambient parameters like temperature, humidity and light. As these motes are unreliable due to wireless link quality and also the data measuring sensors cannot be calibrated accurately for a given applications need. The unique data fusion needs are that parameter being measured is distributed across the network and needs to be computed reliably and with minimum overhead and redundancy due to data value being correlated. We show the asymptotic complexity of topology control when applied to power-aware routing is scalable and argue that the accuracy and reliability of the estimated sensor values can be accurately predicted for the physical value being sensed and aggregating. A prefix-based routing protocol is used for data-centric storage, which allows querying distributed parameters using a KEY, VALUE pairs without the need of the sensor node to know its exact geographic information. Intelligent sensor information processing, which is driven by these requirements, is discussed under the framework INSPIRE-DB.