Comparison Study of Pattern Synthesis Techniques Using Neural Networks

In this paper, a comparison study among three neuralnetwork algorithms for the synthesis of array patterns is presented. The neural networks are used to estimate the array elements' excitations for an arbitrary pattern. The architecture of the neural networks is discussed and simulation results are presented. Two new neural networks, based on radial basis functions (RBFs) and wavelet neural networks (WNNs), are introduced. The proposed networks offer a more efficient synthesis procedure, as compared to other available techniques

An investigation into observational characteristics of rainfall and temperature in Central Northeast India—a historical perspective 1889–2008

Mann–Kendall non-parametric test was employed for observational trend detection of monthly, seasonal and annual precipitation of five meteorological subdivisions of Central Northeast India (CNE India) for different 30-year normal periods (NP) viz. 1889–1918 (NP1), 1919–1948 (NP2), 1949–1978 (NP3) and 1979–2008 (NP4). The trends of maximum and minimum temperatures were also investigated. The slopes of the trend lines were determined using the method of least square linear fitting. An application of Morelet wavelet analysis was done with monthly rainfall during June– September, total rainfall during monsoon season and annual rainfall to know the periodicity and to test the significance of periodicity using the power spectrum method. The inferences figure out from the analyses will be helpful to the policy managers, planners and agricultural scientists to work out irrigation and water management options under various possible climatic eventualities for the region. The long-term (1889–2008) mean annual rainfall of CNE India is 1,195.1 mm with a standard deviation of 134.1 mm and coefficient of variation of 11%. There is a significant decreasing trend of 4.6 mm/year for Jharkhand and 3.2 mm/day for CNE India. Since rice crop is the important kharif crop (May– October) in this region, the decreasing trend of rainfall during themonth of July may delay/affect the transplanting/vegetative phase of the crop, and assured irrigation is very much needed to tackle the drought situation. During themonth of December, all the meteorological subdivisions except Jharkhand show a significant decreasing trend of rainfall during recent normal period NP4. The decrease of rainfall during December may hamper sowing of wheat, which is the important rabi crop (November–March) in most parts of this region. Maximum temperature shows significant rising trend of 0.008°C/year (at 0.01 level) during monsoon season and 0.014°C/year (at 0.01 level) during post-monsoon season during the period 1914– 2003. The annual maximum temperature also shows significant increasing trend of 0.008°C/year (at 0.01 level) during the same period. Minimum temperature shows significant rising trend of 0.012°C/year (at 0.01 level) during postmonsoon season and significant falling trend of 0.002°C/year (at 0.05 level) during monsoon season. A significant 4– 8 years peak periodicity band has been noticed during September over Western UP, and 30–34 years periodicity has been observed during July over Bihar subdivision. However, as far as CNE India is concerned, no significant periodicity has been noticed in any of the time series.

Intraseasonal oscillation of total precipitable water over North Indian Ocean and its application in the diagnostic study of coastal rainfall

TRMM Microwave Imager (TMI) is reported to be a useful sensor to measure the atmospheric and oceanic parameters even in cloudy conditions. Vertically integrated specific humidity, Total Precipitable Water (TPW) retrieved from the water vapour absorption channel (22GHz.) along with 10m wind speed and rain rate derived from TMI is used to investigate the moisture variation over North Indian Ocean. Intraseasonal Oscillations (ISO) of TPW during the summer monsoon seasons 1998, 1999, and 2000 over North Indian Ocean is explored using wavelet analysis. The dominant waves in TPW during the monsoon periods and the differences in ISO over Arabian Sea and Bay of Bengal are investigated. The northward propagation of TPW anomaly and its coherence with the coastal rainfall is also studied. For the diagnostic study of heavy rainfall spells over the west coast, the intrusion of TPW over the North Arabian Sea is seen to be a useful tool.

Thermal transport across incommensurate phases in potassium selenate: photo-pyroelectric and calorimetric measurements

The thermal transport properties—thermal diffusivity, thermal conductivity and specific heat capacity—of potassium selenate crystal have been measured through the successive phase transitions, following the photo-pyroelectric thermal wave technique. The variation of thermal conductivity with temperature through the incommensurate (IC) phase of this crystal is measured. The enhancement in thermal conductivity in the IC phase is explained in terms of heat conduction by phase modes, and the maxima in thermal conductivity during transitions is due to enhancement in the phonon mean free path and the corresponding reduction in phonon scattering. The anisotropy in thermal conductivity and its variation with temperature are reported. The variation of the specific heat with temperature through the high temperature structural transition at 745 K is measured, following the differential scanning calorimetric method. By combining the results of photo-pyroelectric thermal wave methods and differential scanning calorimetry, the variation of the specific heat capacity with temperature through all the four phases of K2SeO4 is reported. The results are discussed in terms of phonon mode softening during transitions and phonon scattering by phase modes in the IC phase.

PSWT Based Linear Predictive Coding and Development of a Parallel Multiple Subsequence Structure for DWT Computation

During 1990's the Wavelet Transform emerged as an important signal processing tool with potential applications in time-frequency analysis and non-stationary signal processing.Wavelets have gained popularity in broad range of disciplines like signal/image compression, medical diagnostics, boundary value problems, geophysical signal processing, statistical signal processing,pattern recognition,underwater acoustics etc.In 1993, G. Evangelista introduced the Pitch- synchronous Wavelet Transform, which is particularly suited for pseudo-periodic signal processing.The work presented in this thesis mainly concentrates on two interrelated topics in signal processing,viz. the Wavelet Transform based signal compression and the computation of Discrete Wavelet Transform. A new compression scheme is described in which the Pitch-Synchronous Wavelet Transform technique is combined with the popular linear Predictive Coding method for pseudo-periodic signal processing. Subsequently,A novel Parallel Multiple Subsequence structure is presented for the efficient computation of Wavelet Transform. Case studies also presented to highlight the potential applications.

Development of Time - Frequency Techniques for Sonar Applications

Sonar signal processing comprises of a large number of signal processing algorithms for implementing functions such as Target Detection, Localisation, Classification, Tracking and Parameter estimation. Current implementations of these functions rely on conventional techniques largely based on Fourier Techniques, primarily meant for stationary signals. Interestingly enough, the signals received by the sonar sensors are often non-stationary and hence processing methods capable of handling the non-stationarity will definitely fare better than Fourier transform based methods.Time-frequency methods(TFMs) are known as one of the best DSP tools for nonstationary signal processing, with which one can analyze signals in time and frequency domains simultaneously. But, other than STFT, TFMs have been largely limited to academic research because of the complexity of the algorithms and the limitations of computing power. With the availability of fast processors, many applications of TFMs have been reported in the fields of speech and image processing and biomedical applications, but not many in sonar processing. A structured effort, to fill these lacunae by exploring the potential of TFMs in sonar applications, is the net outcome of this thesis. To this end, four TFMs have been explored in detail viz. Wavelet Transform, Fractional Fourier Transfonn, Wigner Ville Distribution and Ambiguity Function and their potential in implementing five major sonar functions has been demonstrated with very promising results. What has been conclusively brought out in this thesis, is that there is no "one best TFM" for all applications, but there is "one best TFM" for each application. Accordingly, the TFM has to be adapted and tailored in many ways in order to develop specific algorithms for each of the applications.

Synthesis And Reactions Of Pyrazoldouinoxalines

The present work reports the synthesis of 2-ary1—3—oxo-3—pyrazolino[3,4-b]quinoxalines for the first time. These compounds have been prepared by the reaction of ethyl 2-chloroquinoxaline—3—carboxylate with different phenylhydrazines. 2-Aryl—3-oxo—3—pyrazolino[3,4—b]quinoxalines are generally light yellow in either neutral or acid solutions but changed the colour to deep violet or green in basic media. The change in colour appears to be sharp and therefore these compounds may be used as acid base indicators. Their UV absorption maxima under acidic and basic media are also very different. However, the actual conditions under which these compounds may be used as indicators have not been worked out. The synthesis and reactions of a new heterocyclic system, lH—l,5—benzodiazepino[2,3—b]quinoxaline is also reported here. This novel nitrogen heterocycke was prepared by the condensation of ethyl 2-chloroquinoxaline-3—carboxylate with o-phenylene diamine and subsequent manipulationsa to give the parent compound. Several derivatives which are expected tx> have valuable biological properties have also beenlreported. The structures of all new compounds have been established by elemental analysis and also by analysing their spectral data smch as ultraviolet, infrared, nuclear magnetic resonance and mass spectrometry. Compounds obtained from this work will be submitted for screening their biological properties.

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.

Characteristics of oscillation in surface layer and above over Cochin

The oscillations in the Atmospheric Boundary Layer (ABL) are important because the transport mechanism from the surface to the upper atmosphere is governed by the ABL characteristics. The study was carried out using wind and temperature data observed at surface, 925 hPa and 850 hPa levels over Cochin and the different frequencies embedded in the boundary layer parameters are identified by employing wavelet technique. Surface boundary layer characteristics over the monsoon region are closely linked to the upper layer monsoon features. In this perception it is important to study the various oscillations in the surface boundary layer and the layer above. It is found that the wind and temperature at different levels show oscillations in Quasi Biweekly Mode (QBM) and Intra Seasonal Oscillation (ISO) bands as observed in a typical monsoon system. Amplitude of the oscillation varies with height. The amplitude of the QBM periodicity is more in the surface levels but in the upper levels the amplitude of the ISO periodicity is more than that of the QBM. From this, it is obvious that the controlling mechanism of QBM band is surface parameters such as surface friction and that for ISO band is associated with the active-break cycles of monsoon system

SST–convection relation over tropical oceans

According to current knowledge, convection over the tropical oceans increases with sea surface temperature (SST) from 26 to 29 °C, and at SSTs above 29 °C, it sharply decreases. Our research shows that it is only over the summer warm pool areas of Indian and west Pacific Oceans (monsoon areas) where the zone of maximum SST is away from the equator that this kind of SST-convection relationship exists. In these areas (1) convection is related to the SST gradient that generates low-level moisture convergence and upward vertical motion in the atmosphere. This has modelling support. Regions of SST maxima have low SST gradients and therefore feeble convection. (2) Convection initiated by SST gradient produces strong wind fields particularly cross-equatorial low-level jetstreams (LLJs) on the equator-ward side of the warm pool and both the convection and LLJ grow through a positive feedback process. Thus, large values of convection are associated with the cyclonic vorticity of the LLJ in the atmospheric boundary layer. In the inter-tropical convergence zone (ITCZ) over the east Pacific Ocean and the south Pacific convergence zone (SPCZ) over the west Pacific Ocean, low-level winds from north and south hemisphere converge in the zone of maximum SST, which lies close to the equator producing there elongated bands of deep convection, where we find that convection increases with SST for the full range of SSTs unlike in the warm pool regions. The low-level wind divergence computed using QuikSCAT winds has large and significant linear correlation with convection in both the warm pool and ITCZ/SPCZ areas. But the linear correlation between SST and convection is large only for the ITCZ/SPCZ. These findings have important implications for the modelling of largescale atmospheric circulations and the associated convective rainfall over the tropical oceans

Characteristic Study of the Boundary Layer Parameters over the Arabian Sea and the Bay of Bengal Using the QuikSCAT Dataset

The marine atmospheric boundary layer (MABL) plays a vital role in the transport of momentum and heat from the surface of the ocean into the atmosphere. A detailed study on the MABL characteristics was carried out using high-resolution surface-wind data as measured by the QuikSCAT (Quick scatterometer) satellite. Spatial variations in the surface wind, frictional velocity, roughness parameter and drag coe±cient for the di®erent seasons were studied. The surface wind was strong during the southwest monsoon season due to the modulation induced by the Low Level Jetstream. The drag coe±cient was larger during this season, due to the strong winds and was lower during the winter months. The spatial variations in the frictional velocity over the seas was small during the post-monsoon season (»0.2 m s¡1). The maximum spatial variation in the frictional velocity was found over the south Arabian Sea (0.3 to 0.5 m s¡1) during the southwest monsoon period, followed by the pre-monsoon over the Bay of Bengal (0.1 to 0.25 m s¡1). The mean wind-stress curl during the winter was positive over the equatorial region, with a maximum value of 1.5£10¡7 N m¡3, but on either side of the equatorial belt, a negative wind-stress curl dominated. The area average of the frictional velocity and drag coe±cient over the Arabian Sea and Bay of Bengal were also studied. The values of frictional velocity shows a variability that is similar to the intraseasonal oscillation (ISO) and this was con¯rmed via wavelet analysis. In the case of the drag coe±cient, the prominent oscillations were ISO and quasi-biweekly mode (QBM). The interrelationship between the drag coe±cient and the frictional velocity with wind speed in both the Arabian Sea and the Bay of Bengal was also studied.

COMBINED FEATURE EXTRACTION TECHNIQUES AND NAIVE BAYES CLASSIFIER FOR SPEECH RECOGNITION

Speech processing and consequent recognition are important areas of Digital Signal Processing since speech allows people to communicate more natu-rally and efficiently. In this work, a speech recognition system is developed for re-cognizing digits in Malayalam. For recognizing speech, features are to be ex-tracted from speech and hence feature extraction method plays an important role in speech recognition. Here, front end processing for extracting the features is per-formed using two wavelet based methods namely Discrete Wavelet Transforms (DWT) and Wavelet Packet Decomposition (WPD). Naive Bayes classifier is used for classification purpose. After classification using Naive Bayes classifier, DWT produced a recognition accuracy of 83.5% and WPD produced an accuracy of 80.7%. This paper is intended to devise a new feature extraction method which produces improvements in the recognition accuracy. So, a new method called Dis-crete Wavelet Packet Decomposition (DWPD) is introduced which utilizes the hy-brid features of both DWT and WPD. The performance of this new approach is evaluated and it produced an improved recognition accuracy of 86.2% along with Naive Bayes classifier.

IRIS BIOMETRIC RECOGNITION SYSTEM EMPLOYING CANNY OPERATOR

Biometrics has become important in security applications. In comparison with many other biometric features, iris recognition has very high recognition accuracy because it depends on iris which is located in a place that still stable throughout human life and the probability to find two identical iris's is close to zero. The identification system consists of several stages including segmentation stage which is the most serious and critical one. The current segmentation methods still have limitation in localizing the iris due to circular shape consideration of the pupil. In this research, Daugman method is done to investigate the segmentation techniques. Eyelid detection is another step that has been included in this study as a part of segmentation stage to localize the iris accurately and remove unwanted area that might be included. The obtained iris region is encoded using haar wavelets to construct the iris code, which contains the most discriminating feature in the iris pattern. Hamming distance is used for comparison of iris templates in the recognition stage. The dataset which is used for the study is UBIRIS database. A comparative study of different edge detector operator is performed. It is observed that canny operator is best suited to extract most of the edges to generate the iris code for comparison. Recognition rate of 89% and rejection rate of 95% is achieved

PERFORMANCE OF DIFFERENT CLASSIFIERS IN SPEECH RECOGNITION

Speech is the most natural means of communication among human beings and speech processing and recognition are intensive areas of research for the last five decades. Since speech recognition is a pattern recognition problem, classification is an important part of any speech recognition system. In this work, a speech recognition system is developed for recognizing speaker independent spoken digits in Malayalam. Voice signals are sampled directly from the microphone. The proposed method is implemented for 1000 speakers uttering 10 digits each. Since the speech signals are affected by background noise, the signals are tuned by removing the noise from it using wavelet denoising method based on Soft Thresholding. Here, the features from the signals are extracted using Discrete Wavelet Transforms (DWT) because they are well suitable for processing non-stationary signals like speech. This is due to their multi- resolutional, multi-scale analysis characteristics. Speech recognition is a multiclass classification problem. So, the feature vector set obtained are classified using three classifiers namely, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Naive Bayes classifiers which are capable of handling multiclasses. During classification stage, the input feature vector data is trained using information relating to known patterns and then they are tested using the test data set. The performances of all these classifiers are evaluated based on recognition accuracy. All the three methods produced good recognition accuracy. DWT and ANN produced a recognition accuracy of 89%, SVM and DWT combination produced an accuracy of 86.6% and Naive Bayes and DWT combination produced an accuracy of 83.5%. ANN is found to be better among the three methods.

Ni filled flexible multi-walled carbon nanotube–polystyrene composite films as efficient microwave absorbers

In this letter, we report flexible, non corrosive, and light weight nickel nanoparticle@multi-walled carbon nanotube–polystyrene (Ni@MWCNT/PS) composite films as microwave absorbing material in the frequency range of S band (2-4 GHz). Dielectric permittivity and magnetic permeability of composites having 0.5 and 1.5 wt. % filler amount were measured using the cavity perturbation technique. Reflection loss maxima of 33 dB (at 2.7 GHz) and 24 dB (at 2.7 GHz) were achieved for 0.5 and 1.5 wt. % Ni@MWCNT/PS composite films of 6 and 4 mm thickness, respectively, suggesting that low concentrations of filler provide significant electromagnetic interference shielding