Daljeet Singh

Vehicular traffic noise prediction using soft computing approach.

A new approach for the development of vehicular traffic noise prediction models is presented. Four different soft computing methods, namely, Generalized Linear Model, Decision Trees, Random Forests and Neural Networks, have been used to develop models to predict the hourly equivalent continuous sound pressure level, Leq, at different locations in the Patiala city in India. The input variables include the traffic volume per hour, percentage of heavy vehicles and average speed of vehicles. The performance of the four models is compared on the basis of performance criteria of coefficient of determination, mean square error and accuracy. 10-fold cross validation is done to check the stability of the Random Forest model, which gave the best results. A t-test is performed to check the fit of the model with the field data.

BER Performance of SFBC OFDM System Over TWDP Fading Channel

In this letter, the performance of uncoded and space frequency block coding (SFBC) orthogonal frequency division multiplexing (OFDM) system over two-wave with diffuse power (TWDP) fading environment is analyzed. A general closed-form expressions of average bit error rate for both M-ary phase shift keying and M-ary quadrature amplitude modulation are derived using moment generating function of TWDP fading distribution. Results are verified with the benchmark results available in the literature (i.e., Rayleigh and Rician). Numerical results with various TWDP fading channel parameter (K and A) and with different modulation orders for uncoded and SFBC OFDM systems show excellent agreement with simulation results.

Investigations on Fabrication Techniques of Aluminium-Based Porous Material

The intention of this paper is to investigate the fabrication of aluminium-based porous material. The fabrication is done by two methods: the conventional drilling method and a non-conventional gas-releasing blowing agent technique. The experiments were conducted to compare the optimum value of porosity, compressive strength and metallurgical values. The non-conventional technique results in the aluminium foam with lightweight. The results show that the energy absorption capacity due to constant plateau stress and porosity is high in aluminium foam whereas compressive strength is low in this case. The conventional method has a great advantage to control the pore morphology such as the uniformity of pores, structure of pores. Energy diffraction X-ray spectroscopy and scanning electron microscope techniques are used for characterizing the aluminium metal foam.

Honking noise contribution to road traffic noise prediction

The implementation of Road Traffic Noise predictive Models (RTNMs) is crucial in order to be able to predict noise in urban areas strongly affected by vehicular traffic. These RTNMs can have in input a small or large number of inputs, according to the implemented function. Among these inputs, honking cannot be neglected in some specific areas in which drivers are used to horn in traffic jam or in proximity of intersections or other vehicles. In this paper, starting from a field measurement campaign in India, the authors highlight the shortcomings of standard RTNMs, that are not able to include random noisy events such as low or high pressure honking. Once the differences will be evaluated, the contribution of honking will be estimated and added to the predictions, to achieve a new model that is able to provide results in good agreement with field measurements.The implementation of Road Traffic Noise predictive Models (RTNMs) is crucial in order to be able to predict noise in urban areas strongly affected by vehicular traffic. These RTNMs can have in input a small or large number of inputs, according to the implemented function. Among these inputs, honking cannot be neglected in some specific areas in which drivers are used to horn in traffic jam or in proximity of intersections or other vehicles. In this paper, starting from a field measurement campaign in India, the authors highlight the shortcomings of standard RTNMs, that are not able to include random noisy events such as low or high pressure honking. Once the differences will be evaluated, the contribution of honking will be estimated and added to the predictions, to achieve a new model that is able to provide results in good agreement with field measurements.