Linear Filtering in MDCT domain

In this paper, expressions for convolution multiplication properties of MDCT are derived starting from the equivalent DFT representations. Using these expressions, methods for implementing linear filtering through block convolution in the MDCT domain are presented. The implementation is exact for symmetric filters and approximate for non-symmetric filters in the case of rectangular window based MDCT. For a general MDCT window function, the filtering is done on the windowed segments and hence the convolution is approximate for symmetric as well as non-symmetric filters. This approximation error is shown to be perceptually insignificant for symmetric impulse response filters. Moreover, the inherent $50 \%$ overlap between adjacent frames used in MDCT computation does reduce this approximation error similar to smoothing of other block processing errors. The presented techniques are useful for compressed domain processing of audio signals.

Environmental audit of Municipal Solid Waste Management

The management of municipal solid waste has become an acute problem due to enhanced economic activities and rapid urbanisation. Increased attention has been given by the government in recent years to handle this problem in a safe and hygienic manner. In this regard, Municipal Solid Waste Management (MSWM) environmental audit has been carried out for Bangalore city through the collection of secondary data from government agencies, and interviews with stakeholders and field surveys. Field surveys were carried out in seven wards (representative samples of the city) to understand the practice and identify the lacunae. The MSWM audit that was carried out functional-element-wise in selected wards to understand the efficacy and shortfalls, if any, is discussed in this paper.

Adaptive Filtering Technique and DSP Based Implementation for High-Speed Distance Protection

The paper presents an adaptive Fourier filtering technique and a relaying scheme based on a combination of a digital band-pass filter along with a three-sample algorithm, for applications in high-speed numerical distance protection. To enhance the performance of above-mentioned technique, a high-speed fault detector has been used. MATLAB based simulation studies show that the adaptive Fourier filtering technique provides fast tripping for near faults and security for farther faults. The digital relaying scheme based on a combination of digital band-pass filter along with three-sample data window algorithm also provides accurate and high-speed detection of faults. The paper also proposes a high performance 16-bit fixed point DSP (Texas Instruments TMS320LF2407A) processor based hardware scheme suitable for implementation of the above techniques. To evaluate the performance of the proposed relaying scheme under steady state and transient conditions, PC based menu driven relay test procedures are developed using National Instruments LabVIEW software. The test signals are generated in real time using LabVIEW compatible analog output modules. The results obtained from the simulation studies as well as hardware implementations are also presented.

Explicit filtering LES for turbulent non-premixed combustion

Two models for large eddy simulation of turbulent reacting flow in homogeneous turbulence were studied. The sub-grid stress arising out of non-linearities of the Navier-Stokes equations were modeled using an explicit filtering approach. A filtered mass density function (FMDF) approach was used for closure of the sub-grid scalar fluctuations. A posteriori calculations, when compared with the results from the direct numerical simulation, indicate that the explicit filtering is adequate in representing the effect of sub-grid stress on the filtered velocity field in the absence of reaction. Discrepancies arise when reactions occur, but the FMDF approach suffices to account for sub-grid scale fluctuations of the reacting scalars, accurately.

Pseudo-time particle filtering for diffuse optical tomography

We recast the reconstruction problem of diffuse optical tomography (DOT) in a pseudo-dynamical framework and develop a method to recover the optical parameters using particle filters, i.e., stochastic filters based on Monte Carlo simulations. In particular, we have implemented two such filters, viz., the bootstrap (BS) filter and the Gaussian-sum (GS) filter and employed them to recover optical absorption coefficient distribution from both numerically simulated and experimentally generated photon fluence data. Using either indicator functions or compactly supported continuous kernels to represent the unknown property distribution within the inhomogeneous inclusions, we have drastically reduced the number of parameters to be recovered and thus brought the overall computation time to within reasonable limits. Even though the GS filter outperformed the BS filter in terms of accuracy of reconstruction, both gave fairly accurate recovery of the height, radius, and location of the inclusions. Since the present filtering algorithms do not use derivatives, we could demonstrate accurate contrast recovery even in the middle of the object where the usual deterministic algorithms perform poorly owing to the poor sensitivity of measurement of the parameters. Consistent with the fact that the DOT recovery, being ill posed, admits multiple solutions, both the filters gave solutions that were verified to be admissible by the closeness of the data computed through them to the data used in the filtering step (either numerically simulated or experimentally generated). (C) 2011 Optical Society of America

A DCT Based Filtering of Biomedical Images

Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a-priori knowledge about the type of noise corrupting the image and image features. This makes the standard filters to be application and image specific. The most popular filters such as average, Gaussian and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design filters based on discrete cosine transform (DCT) is proposed in this study for optimal medical image filtering. This algorithm exploits the better energy compaction property of DCT and re-arrange these coefficients in a wavelet manner to get the better energy clustering at desired spatial locations. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions.

Zero-phase inverse filtering for extraction of source characteristics

The instants at which significant excitation of vocal tract take place during voicing are referred to as epochs. Epochs and strengths of excitation pulses at epochs are useful in characterizing voice source. Epoch filtering technique proposed by the authors determine epochs from speech waveform. In this paper we propose zero-phase inverse filtering to obtain strengths of excitation pulses at epochs. Zero-phase inverse filter compensates the gross spectral envelope of short-time spectrum of speech without affecting phase characteristics. Linear prediction analysis is used to realize the zero-phase inverse filter. Source characteristics that can be derived from speech using this technique are illustrated with examples.

Environmental remediation by photocatalysis

Photocatalysis refers to the oxidation and reduction reactions on semiconductor surfaces, mediated by the valence band holes and conduction band electrons, which are generated by the absorption of ultraviolet or visible light radiation. Photocatalysis is widely being practiced for the degradation and mineralization of hazardous organic compounds to CO2 and H2O, reduction of toxic metal ions to their non-toxic states, deactivation and destruction of water borne microorganisms, decomposition of air pollutants like volatile organic compounds, NOx, CO and NH3, degradation of waste plastics and green synthesis of industrially important chemicals. This review attempts to showcase the well established mechanism of photocatalysis, the use of photocatalysts for water and air pollution control,visible light responsive modified-TiO2 and non-TiO2 based materials for environmental and energy applications, and the importance of developing reaction kinetics for a comprehensive understanding and design of the processes.