Abstract

The goal of this paper is to obtain the robust transistor sizing of the digital logic circuits by minimizing the leakage power keeping the other performance parameters such as propagation delays and area in the bound. To perform such action, Simulated Annealing based Multi-Objective Optimization algorithm has been implemented and further improved the reduction in leakage power using Gradient Descent (MO-SAGRAD).The algorithms are implemented in python and the performance parameters are computed using HSPICE tool at 45nm, 22nmtechnology nodes with Metal Gate High K PTM model cards. Toensure the unruffled functioning of the cells in the automotive applications, the temperature range has been taken between−55oCto125oCrespectively and the supply voltage range istaken as 0.95V to 1.05V for 45nm and 0.76V to 0.84V for22nm technology node. The Overall reduction in leakage power achieved in the logic cells is up to 58% without any penalty in the critical path delay.

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

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

Conditional von Neumann entropy is an intriguing concept in quantum information theory. In the present work, we examine the effect of global unitary operations on the conditional entropy of the system. We start with the set containing states with non-negative conditional entropy and find that some states preserve the non-negativity under unitary operations on the composite system. We call this class of states as Absolute Conditional von Neumann entropy Non Negative class (ACVENN). We are able to characterize such states for 2 ⊗ 2 dimensional systems. On a different perspective the characterization accentuates the detection of states whose conditional entropy becomes negative after the global unitary action. Interestingly, we show that this ACVENN class of states forms a set which is convex and compact. This feature enables the existence of hermitian witness operators. With these we can distinguish the unknown states which will have negative conditional entropy after the global unitary operation. We also show that this has immediate application in super dense coding and state merging as negativity of conditional entropy plays a key role in both these information processing tasks. Some illustrations followed by analysis are also provided to probe the connection of such states with absolutely separable (AS) states and absolutely local (AL) states.

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

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

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 paper, we propose a feature set by con-catenating Mel-Frequency Cepstral Coefficients (MFCC) andSpectral Centroid Magnitude Coefficients (SCMC) features forAcoustic Scene Classification (ASC) using Deep Neural Networks(DNN). MFCC features are used to hold the acoustic characteris-tics such as spectral envelope of an acoustic scene in each frame.It also carries the sub-band average energy as a single dimension.SCMC features are used to hold the distribution of energy in asub-band effectively. A test is carried out on Tampere Universityof Technology (TUT) Acoustic Scenes 2017 Dataset. The DNNarchitecture for utterance level classification has been used. Theproposed system’s performance on a 4-fold cross-validation setupis 80.2% and it gives 5.4% relative improvement in performancewhen compared to the baseline system that uses log-Mel bandenergies with Multi-Layer Perceptron model.

Abstract

Herein, we propose an Acoustic Scene Classification(ASC) based on Deep Neural Networks (DNN). The design of Mel-filer bank helps in capturing the acoustic scene characteristicsin the low-frequency regions during MFCC extraction. In thispaper, inverse MFCC are used as interdependent to structureof Mel filter bank. We can effectively capture the acousticinformation in the total audio frequency range using MFCCand IMFCC features. An experiment is carried on TampereUniversity of Technology (TUT) Acoustic Scenes 2017 Dataset.DNN architecture at utterance level classification with supervisedlearning is adopted. Scores from the DNN models correspondingto MFCC and IMFCC features are combined for testing themodel. The relative improvement of 5.22% wih respect to baselinesystem is achieved by the proposed system on setup of 4-foldcross-validation. We participated in the DCASE 2017 challengefor ASC task, also got 45.9% accuracy on given evaluationdataset. This approach got 76thrank out of 97 submissions.

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.