Abstract

This study demonstrates an experimental example of application of time-lapse high dynamic range images for analyzing variations in correlated color temperature (CCT) and circadian lighting in an interior space daylit using a tubular daylight device (TDD). Previous studies have shown that the spectral composition of daylight affects human circadian rhythms, with emphasis on wavelengths towards the blue region of the visible spectrum. Spectrally based metrics such as CCT can therefore aid in evaluating the circadian impact of daylight. TDDs redirect daylight towards interior spaces with changing light levels and color temperatures during the daylight hours. This paper details the color temperature and circadian lighting metrics (circadian luminance and illuminance) in an interior space throughout a period of several days in a mid-latitude, temperate, sunny climate. An automated timelapse HDR photography apparatus was modified to allow for calculation of CCT and circadian lighting metrics using state-of-the art techniques available in the literature. Prior to the measurements in the space with the TDD, this device was calibrated against a spectrophotometer. Results indicate the level of daily variation in CCT and circadian lighting metrics that can be expected in interior spaces daylit by TDDs without any other sources of daylight. Besides showcasing the use of HDR techniques, these results could be used to support design decisions involving daylighting in interior spaces. These techniques can also be used outside the laboratory in the evaluation of the circadian impact of lighting throughout the built environment.

Abstract

A building is a boundary between an outdoor climate and indoor conditions. The building energy consumption, notably the energy used for air-conditioning is closely related to outdoor climate. The Solar Radiation, Drybulb Temperature and Relative Humidity are the important climatic parameters which affect building’s thermal and visual conditions. The energy codes development analyses and evaluation of new technologies with respect to energy savings and financial viability have been carried out for selected cities that are supposed to represent any particular climate zone. There is a significant change in thermal loads in different cities of a climate zone. Selection of a representative city for a climatic zone is important because it needs to reflect average climatic conditions for a particular climate zone accurately. This study presents a new method that has been developed for the selection of a representative city in any climate zone, using the principle of Euclidean distance approach having a dry-bulb temperature, wet bulb temperature and solar radiation as three axes. The city having minimum Euclidean distance with respect to remaining cities of the same climatic zone is proposed to be taken as a representative city. The proposed method has been applied on each of the five existing climatic zones of India according to National Building Code (NBC) of India and eight climate zones according to ASHRAE Standard 169-2013.

Abstract

Intensity and duration are both pertinent aspects of an emotional experience, yet how they are related is unclear. Though stronger emotions usually last longer, sometimes they abate faster than the weaker ones. We present a quantitative model of affective adaptation, the process by which emotional responses to unchanging affective stimuli weaken with time, that addresses this intensity-duration problem. The model, described by three simple linear algebraic equations, assumes that the relationship between an affective stimulus and its experiencer can be broken down into three parameters. Self-relevance and explanation level combine multiplicatively to determine emotion intensity whereas the interaction of these with explanatory ease determines its duration. The model makes predictions, consistent with available empirical data, about emotion intensity, its duration, and adaptation speed for different scenarios. It predicts when the intensity-duration correlation is positive, negative or even absent, thus offering a solution to the intensity-duration problem. The model also addresses the shortcomings of past models of affective adaptation with its enhanced predictive power and by offering a more complete explanation to empirical observations that earlier models explain inadequately or fail to explain altogether. The model has potential applications in areas such as virtual reality training, games, human-computer interactions, and robotics.

Abstract

Formants (peaks in the spectrum) are typically found in vowel regions.However, resonances of vocal tract are present in both voiced and unvoiced regions.It is important to focus on extracting vocal tract resonances from speech signal toprovide compact and effective representation. This work applies multilayered neuralnetworks for the estimation of vocal tract resonances from linear prediction cepstralcoefficients. To train such a neural network, the voiced regions of speech signal are firstprobed forreliable spectral prominent regions. A sequence of spectral peaks detectedby imposing continuity constraints in voiced regions is referred to as reliable spectralprominent regions. A multilayer feedforward neural network is then trained to model amapping function from linear prediction cepstral coefficients to vocal tract resonancesestimated in these reliable spectral prominent regions. This modeled function is usedto estimate vocal tract resonances in all speech regions including unvoiced sections

Abstract

In this work, we explore the combination of MFCCand RCC features for Indian Language identification(LID) task.MFCC represents the vocal tract information while RCCrepresents the excitation source information, which arecomplementary to each other. We use Deep neural network(DNN) framework for end to end learning. To combine these twoinformation we uses different fusion techniques such as Min,Max, Average and Min-Max for decision level fusion. We performexperiments on 13 Indian languages data set. We compare ourresults with state of art I-vector based approach. Min-max basedfusion approach gives 9.64% ERR compare to 10.18% of 39D I-vector based approach.

Abstract

This contribution presents an efficient techniquefor the speech enhancement of a signal using Kepstrum basedapproach. In any speech enhancement system, the noise is beingsuppressed in spectral domain assuming the degraded componentare stationary or slowly varying for few frames of the signal.As conventional methods for noise elimination fails due to non-stationary properties of the speech signal, in this context, adistortion filter and speech remover filter are being introduced.The main focus of this paper is involved in developing a newspeech enhancement system based on speech characteristics andit’s modelling provide high quality and intelligibility on mobilesor embedded platforms that have two or more microphones.The proposed method was evaluated for machinery, babble, andcar noise types, using both objective and subjective measuresfor both quality and intelligibility of the enhanced speech.The results show significant improvements in comparison with single microphone speech enhancement, kepstrum and Distortion Correlation Filter methods

Abstract

Internationally, the cooling degree-days (CDD) and heating degree-days (HDD) have been commonly used as indicators of cooling and heating load occurring due to outdoor temperature. These indicators have also applied for classification of climate zones as referred in ASHRAE Standard 169-2013. As of now various base temperatures has been used in the different part of the world. Additionally, the analysis for base temperature had been done in 1980 by ASHRAE. This study aims to determine the base temperature for cities in India. The simulation approach has been used to identify base temperature using Energy Signature and Performance line method for 60 cities of India. The US-DOE developed 16 reference buildings which have been used for eight climate zones of India. These eight climate zones have been classified using the methodology of ASHRAE Standard 169–2013 on weather data of Indian cities. It was observed that the cooling base temperature is varying from 6.8 °C to 28.6 °C for the large hotel and 8.7–28.1 °C for office buildings while the heating base temperature is ranging from 13.8 °C to 21.4 °C for the large hotel and 10.4–18.7 °C respectively for different cities. The estimated base temperature for cooling and heating is 18 °C for India.

Abstract

Nations have set-up Critical Information Infrastruc-ture (CII) protection centers to provide safety of organizations which are deemed to be critical for the nation. In this context,this paper proposes a framework to identify Critical Information Infrastructures (CIIs). This framework has been adopted to identify the banks which are critical for Indian economy. Previously,Reserve Bank of India, the central bank of the country, has announced three banks as systemically important banks. The proposed approach, in this paper, considers a publicly available data set publishing the number (volume) and the amount (value)of financial transactions in the Indian domestic market involving all the banks. The proposed approach finds the individual ranks of each participating bank based on these two parameters.Finally, rank aggregation method combines these two rank lists(based on the value and the volume) to generate a final rank of the banks. Several rank aggregation methods have been compared in this context.

Abstract

Weather-based decision support systems (DSSs) are being built to improve the efficiency of the production systems in the domains of healthcare, agriculture, transport, governance and so on. Normally, a weather con-dition (WC) is represented by the statistical values of weather variables for a given duration (e.g. a day or a week). In a weather-based DSS, given a WC, the domain experts prepare the appropriate suggestions to improve the efficiency of the stakeholders. Normally, once the domain experts prepare a suggestion for a given WC that belongs to certain period (e.g. year or season), there is a scope to reuse the same suggestion for the similar WCs of other period(s). As a result, the performance of the DSS could be improved. In this paper, to improve reuse, we have proposed a notion of category-based WC (CWC) which is formed by using the categories of weather variables in the respective domain. By considering the context of agromet advisory service operated by India Meteorological Department (IMD) and the corresponding weather categories provided by IMD, we have ana-lyzed the extent of reuse among CWCs by conducting the experiments on 30 years of weather data collected at Rajendranagar, Hyderabad, Telangana state. The experiments are conducted by considering two types of CWCs with the duration equal to one day and five days. By varying the number of weather variables in CWC from one to five, we have computed the extent of reuse among CWCs of different periods of the following period types: year, season, and phenophases (i.e., growth stages) of the Rice crop. The results show that there is a significant similarity among the CWCs of the given period and the CWCs of the preceding periods of each period type. For any domain including agriculture, the results provide an opportunity to improve the efficiency of weather-based agricultural DSSs by improving the reuse of the weather-based suggestions

Abstract

The popularity and applicability of mobile crowdsensing applications are continuously increasing due to the widespread of mobile devices and their sensing and processing capabilities. However, we need to offer appropriate incentives to the mobile users who contribute their resources and preserve their privacy. Blockchain technologies enable semi-anonymous multi-party interactions and can be utilized in crowdsensing applications to maintain the privacy of the mobile users while ensuring first-rate crowdsensed data. In this work, we propose to use blockchain technologies and smart contracts to orchestrate the interactions between mobile crowdsensing providers and mobile users for the case of spatial crowdsensing, where mobile users need to be at specific locations to perform the tasks. Smart contracts, by operating as processes that are executed on the blockchain, are used to preserve users’ privacy and make payments. Furthermore, for the assignment of the crowdsensing tasks to the mobile users, we design a truthful, cost-optimal auction that minimizes the payments from the crowdsensing providers to the mobile users. Extensive experimental results show that the proposed privacy preserving auction outperforms state-of-the-art proposals regarding cost by ten times for high numbers of mobile users and tasks.