Nitte Meenakshi Institute of Technology

About: Nitte Meenakshi Institute of Technology is a based out in . It is known for research contribution in the topics: Computer science & Ultimate tensile strength. The organization has 846 authors who have published 644 publications receiving 2702 citations.

Computational Fluid Dynamics Analysis of an Evaporative Cooling System

Abstract: Abstract The use of chlorofluorocarbon based refrigerants in the air-conditioning system increases the global warming and causes the climate change. The climate change is expected to present a number of challenges for the built environment and an evaporative cooling system is one of the simplest and environmentally friendly cooling system. The evaporative cooling system is most widely used in summer and in rural and urban areas of India for human comfort. In evaporative cooling system, the addition of water into air reduces the temperature of the air as the energy needed to evaporate the water is taken from the air. Computational fluid dynamics is a numerical analysis and was used to analyse the evaporative cooling system. The CFD results are matches with the experimental results.

Comparative performance analysis of various digital modulation schemes in AWGN channel

Abstract: It is well known that noise in the communication channel is a menace for transmission of digital bits leading to several errors in the bit level. To understand this more deeply, various modulation schemes such as FSK, MSK, BPSK, QPSK, QAM, MPSK and MQAM are analysed in terms of BER, Probability of error, SNR, MSE and rate distortion. From this analysis arises an interesting result which recommends the use of certain modulation schemes under the presence of certain levels of noise in the communication channel.

Speech enhancement and encoding by combining SS-VAD and LPC

Abstract: In this paper, the encoding of noisy and enhanced speech data is demonstrated. To encode and enhance the speech data under an uncontrolled environment, the linear predictive coding (LPC) and spectral subtraction with voice activity detection (SS-VAD) methods are studied individually. The noisy speech data is obtained by considering the amalgamation of the clean speech signal and noise model and it is encoded using the LPC technique. The LPC uses a lossy compression procedure to encode the speech data which converts the data rate from 64 to 2.4 Kbps. Due to reverberations and degradations in noisy speech data, the quality of encoded noisy speech data is very less. Therefore, an algorithm is proposed to enhance and encode the speech data by combining SS-VAD and LPC under degraded conditions. In the first step, the encoding of noisy speech data is done using LPC and its performance is evaluated using signal-to-ratio. The noisy speech data is given as input to the SS-VAD algorithm and the output of SS-VAD is given as input to the LPC encoder is followed in the second step. In the LPC encoder, the coefficients are extracted from the input speech data to design all-pole filters. The cross correlation process is also done for differentiating the voiced and unvoiced samples at the analysis step. The pitch information and extracted coefficients are used in the synthesis step. The experiments are conducted for different types of noisy speech data which are degraded by musical noise, F16 noise, factory noise, and car noise. The experimental results show that there is a significant improvement in the quality of enhanced encoded speech data obtained by the proposed method compared to encoded noisy speech data. The schematic representation of outputs of LPC and proposed combined SS-VAD and LPC waveforms are also given in this work.

A novel data mining approach for soil Classification

Abstract: Decision tree is a well known approach for classification in data mining. C4.5 and Classification and Regression Trees (CART) are two widely used decision tree algorithms for classification. The main drawback of C4.5 algorithm is that, it is biased towards attributes with more values while CART algorithm produces misclassification errors when the domain of the target attribute is very large. In view of these limitations, this paper presents a modified decision tree algorithm. The C4.5, CART and the proposed classifier are trained using data set containing soil samples by considering optimal soil parameters namely pH (power of Hydrogen), Ec (Electrical Conductivity) and ESP (Exchangeable Sodium Percentage). The model is tested with test data set of soil samples. The test proves that the modified decision tree algorithm has higher classification accuracy when compared to C4.5 and CART algorithms.