851 resultados para audio-frequency
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The thesis is the outcome of the experimental and theoretical investigations carried out on a novel slotted microstrip antenna.The antenna excites two resonance frequencies and provides orthogonal polarization. The radiation characteristics of the antenna are studied in detail. The antenna design is optimized using IE3D electromagnetic simulation tool. The frequency-Difference Time-Domain (FDTD) method is employed for the analysis of the antenna.The antenna can be used for personal and satellite communication applications.
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Department of Elecctronics, Cochin University of Science and Technology
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The thesis deals with the preparation and dielectric characterization of Poly aniline and its analogues in ISM band frequency of 2-4 GHz that includes part of the microwave region (300 MHz to 300 GHz) of the electromagnetic spectrum and an initial dielectric study in the high frequency [O.05MHz-13 MHz]. PolyaniIine has been synthesized by an in situ doping reaction under different temperature and in the presence of inorganic dopants such as HCl H2S04, HN03, HCl04 and organic dopants such as camphorsulphonic acid [CSA], toluenesulphonic acid {TSA) and naphthalenesulphonic acid [NSA]. The variation in dielectric properties with change in reaction temperature, dopants and frequency has been studied. The effect of codopants and microemulsions on the dielectric properties has also been studied in the ISM band. The ISM band of frequencies (2-4 GHz) is of great utility in Industrial, Scientific and Medical (ISM) applications. Microwave heating is a very efficient method of heating dielectric materials and is extensively used in industrial as well as household heating applications.
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Department of Electronics, Cochin University of Science & Technology
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The effect of coupling two chaotic Nd:YAG lasers with intracavity KTP crystal for frequency doubling is numerically studied for the case of the laser operating in three longitudinal modes. It is seen that the system goes from chaotic to periodic and then to steady state as the coupling constant is increased. The intensity time series and phase diagrams are drawn and the Lyapunov characteristic exponent is calculated to characterize the chaotic and periodic regions.
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Medical fields requires fast, simple and noninvasive methods of diagnostic techniques. Several methods are available and possible because of the growth of technology that provides the necessary means of collecting and processing signals. The present thesis details the work done in the field of voice signals. New methods of analysis have been developed to understand the complexity of voice signals, such as nonlinear dynamics aiming at the exploration of voice signals dynamic nature. The purpose of this thesis is to characterize complexities of pathological voice from healthy signals and to differentiate stuttering signals from healthy signals. Efficiency of various acoustic as well as non linear time series methods are analysed. Three groups of samples are used, one from healthy individuals, subjects with vocal pathologies and stuttering subjects. Individual vowels/ and a continuous speech data for the utterance of the sentence "iruvarum changatimaranu" the meaning in English is "Both are good friends" from Malayalam language are recorded using a microphone . The recorded audio are converted to digital signals and are subjected to analysis.Acoustic perturbation methods like fundamental frequency (FO), jitter, shimmer, Zero Crossing Rate(ZCR) were carried out and non linear measures like maximum lyapunov exponent(Lamda max), correlation dimension (D2), Kolmogorov exponent(K2), and a new measure of entropy viz., Permutation entropy (PE) are evaluated for all three groups of the subjects. Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. The results shows that nonlinear dynamical methods seem to be a suitable technique for voice signal analysis, due to the chaotic component of the human voice. Permutation entropy is well suited due to its sensitivity to uncertainties, since the pathologies are characterized by an increase in the signal complexity and unpredictability. Pathological groups have higher entropy values compared to the normal group. The stuttering signals have lower entropy values compared to the normal signals.PE is effective in charaterising the level of improvement after two weeks of speech therapy in the case of stuttering subjects. PE is also effective in characterizing the dynamical difference between healthy and pathological subjects. This suggests that PE can improve and complement the recent voice analysis methods available for clinicians. The work establishes the application of the simple, inexpensive and fast algorithm of PE for diagnosis in vocal disorders and stuttering subjects.
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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.
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The design of a compact, single feed, dual frequency dual polarized and electronically reconfigurable microstrip antenna is presented in this paper. A square patch loaded with a hexagonal slot having extended slot arms constitutes the fundamental structure of the antenna. The tuning of the two resonant frequencies is realized by varying the effective electrical length of the slot arms by embedding varactor diodes across the slots. A high tuning range of 34.43% (1.037–1.394 GHz) and 9.27% (1.359–1.485 GHz) is achieved for the two operating frequencies respectively, when the bias voltage is varied from 0 to −30 V. The salient feature of this design is that it uses no matching networks even though the resonant frequencies are tuned in a wide range with good matching below −10 dB. The antenna has an added advantage of size reduction up to 80.11% and 65.69% for the two operating frequencies compared to conventional rectangular patches.
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Timely detection of sudden change in dynamics that adversely affect the performance of systems and quality of products has great scientific relevance. This work focuses on effective detection of dynamical changes of real time signals from mechanical as well as biological systems using a fast and robust technique of permutation entropy (PE). The results are used in detecting chatter onset in machine turning and identifying vocal disorders from speech signal.Permutation Entropy is a nonlinear complexity measure which can efficiently distinguish regular and complex nature of any signal and extract information about the change in dynamics of the process by indicating sudden change in its value. Here we propose the use of permutation entropy (PE), to detect the dynamical changes in two non linear processes, turning under mechanical system and speech under biological system.Effectiveness of PE in detecting the change in dynamics in turning process from the time series generated with samples of audio and current signals is studied. Experiments are carried out on a lathe machine for sudden increase in depth of cut and continuous increase in depth of cut on mild steel work pieces keeping the speed and feed rate constant. The results are applied to detect chatter onset in machining. These results are verified using frequency spectra of the signals and the non linear measure, normalized coarse-grained information rate (NCIR).PE analysis is carried out to investigate the variation in surface texture caused by chatter on the machined work piece. Statistical parameter from the optical grey level intensity histogram of laser speckle pattern recorded using a charge coupled device (CCD) camera is used to generate the time series required for PE analysis. Standard optical roughness parameter is used to confirm the results.Application of PE in identifying the vocal disorders is studied from speech signal recorded using microphone. Here analysis is carried out using speech signals of subjects with different pathological conditions and normal subjects, and the results are used for identifying vocal disorders. Standard linear technique of FFT is used to substantiate thc results.The results of PE analysis in all three cases clearly indicate that this complexity measure is sensitive to change in regularity of a signal and hence can suitably be used for detection of dynamical changes in real world systems. This work establishes the application of the simple, inexpensive and fast algorithm of PE for the benefit of advanced manufacturing process as well as clinical diagnosis in vocal disorders.
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In this work,we investigate novel designs of compact electronically reconfigurable dual frequency microstrip antennas with a single feed,operating mainly in L-band,without using any matching networks and complicated biasing circuitry.These antennas have been designed to operate in very popular frequency range where a great number of wireless communication applications exist.Efforts were carried out to introduce a successful,low cost reconfigurable dual-frequency microstrip antenna design to the wireless and radio frequency design community.
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The main challenges in the deposition of cathode materials in thin film form are the reproduction of stoichiometry close to the bulk material and attaining higher rates of deposition and excellent crystallinity at comparatively lower annealing temperatures. There are several methods available to develop stoichiometric thin film cathode materials including pulsed laser deposition; plasma enhanced chemical vapor deposition, electron beam evaporation, electrostatic spray deposition and RF magnetron sputtering. Among them the most versatile method is the sputtering technique, owing to its suitability for micro-fabricating the thin film batteries directly on chips in any shape or size, and on flexible substrates, with good capacity and cycle life. The main drawback of the conventional sputtering technique using RF frequency of 13.56MHz is its lower rate of deposition, compared to other deposition techniques A typical cathode layer for a thin film battery requires a thickness around one micron. To deposit such thick layers using convention RF sputtering, longer time of deposition is required, since the deposition rate is very low, which is typically 10-20 Å/min. This makes the conventional RF sputtering technique a less viable option for mass production in an economical way. There exists a host of theoretical and experimental evidences and results that higher excitation frequency can be efficiently used to deposit good quality films at higher deposition rates with glow discharge plasma. The effect of frequencies higher than the conventional one (13.56MHz) on the RF magnetron sputtering process has not been subjected to detailed investigations. Attempts have been made in the present work, to sputter deposit spinel oxide cathode films, using high frequency RF excitation source. Most importantly, the major challenge faced by the thin film battery based on the LiMn2O4 cathode material is the poor capacity retention during charge discharge cycling. The major causes for the capacity fading reported in LiMn2O4cathode materials are due to, Jahn-Teller distortion, Mn2+ dissolution into the electrolyte and oxygen loss in cathode material during cycling. The work discussed in this thesis is an attempt on overcoming the above said challenges and developing a high capacity thin film cathode material.
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Tropical cyclones genesis, movement and intensification are highly dependent on its environment both oceanic and atmospheric. This thesis has made a detailed study on the environmental factors related to tropical cyclones of North Indian Ocean basin. This ocean basin has produced only 6% of the global tropical cyclones annually but it has caused maximum loss of human life associated with the strong winds, heavy rain and particularly storm surges that accompany severe cyclones as they strike the heavily populated coastal areas. Atmospheric factors studied in the thesis are the moisture content of the atmosphere, instability of the atmosphere that produces thunderstorms which are the main source of energy for the tropical cyclone, vertical wind shear to which cyclones are highly sensitive and the Sub-Tropical westerly Jetsteram and its Asian high speed center. The oceanic parameters studied are sea surface temperature and heat storage in the top layer of the ocean. A major portion of the thesis has dealt with the three temporal variabilities of tropical cyclone frequency namely intra-seasonal (mainly the influence of Madden Julian Oscillation), inter- annual (the relation with El Nino Southern Oscillation) and decadal variabilities. Regarding decadal variability, a prominent four decade oscillation in the frequency of both tropical cyclones and monsoon depressions unique to the Indian Ocean basin has been brought out. The thesis consists of 9 chapters.
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The modern telecommunication industry demands higher capacity networks with high data rate. Orthogonal frequency division multiplexing (OFDM) is a promising technique for high data rate wireless communications at reasonable complexity in wireless channels. OFDM has been adopted for many types of wireless systems like wireless local area networks such as IEEE 802.11a, and digital audio/video broadcasting (DAB/DVB). The proposed research focuses on a concatenated coding scheme that improve the performance of OFDM based wireless communications. It uses a Redundant Residue Number System (RRNS) code as the outer code and a convolutional code as the inner code. The bit error rate (BER) performances of the proposed system under different channel conditions are investigated. These include the effect of additive white Gaussian noise (AWGN), multipath delay spread, peak power clipping and frame start synchronization error. The simulation results show that the proposed RRNS-Convolutional concatenated coding (RCCC) scheme provides significant improvement in the system performance by exploiting the inherent properties of RRNS.
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Any automatically measurable, robust and distinctive physical characteristic or personal trait that can be used to identify an individual or verify the claimed identity of an individual, referred to as biometrics, has gained significant interest in the wake of heightened concerns about security and rapid advancements in networking, communication and mobility. Multimodal biometrics is expected to be ultra-secure and reliable, due to the presence of multiple and independent—verification clues. In this study, a multimodal biometric system utilising audio and facial signatures has been implemented and error analysis has been carried out. A total of one thousand face images and 250 sound tracks of 50 users are used for training the proposed system. To account for the attempts of the unregistered signatures data of 25 new users are tested. The short term spectral features were extracted from the sound data and Vector Quantization was done using K-means algorithm. Face images are identified based on Eigen face approach using Principal Component Analysis. The success rate of multimodal system using speech and face is higher when compared to individual unimodal recognition systems