Long period gratings in multimode optical fibers: application in chemical sensing

We propose and demonstrate a new technique for evanescent wave chemical sensing by writing long period gratings in a bare multimode plastic clad silica fiber. The sensing length of the present sensor is only 10 mm, but is as sensitive as a conventional unclad evanescent wave sensor having about 100 mm sensing length. The minimum measurable concentration of the sensor reported here is 10 nmol/l and the operating range is more than 4 orders of magnitude. Moreover, the detection is carried out in two independent detection configurations viz., bright field detection scheme that detects the core-mode power and dark field detection scheme that detects the cladding mode power. The use of such a double detection scheme definitely enhances the reliability and accuracy of the results. Furthermore, the cladding of the present fiber need not be removed as done in conventional evanescent wave fiber sensors.

Speaker identification using models for phonemes

Motivation for Speaker recognition work is presented in the first part of the thesis. An exhaustive survey of past work in this field is also presented. A low cost system not including complex computation has been chosen for implementation. Towards achieving this a PC based system is designed and developed. A front end analog to digital convertor (12 bit) is built and interfaced to a PC. Software to control the ADC and to perform various analytical functions including feature vector evaluation is developed. It is shown that a fixed set of phrases incorporating evenly balanced phonemes is aptly suited for the speaker recognition work at hand. A set of phrases are chosen for recognition. Two new methods are adopted for the feature evaluation. Some new measurements involving a symmetry check method for pitch period detection and ACE‘ are used as featured. Arguments are provided to show the need for a new model for speech production. Starting from heuristic, a knowledge based (KB) speech production model is presented. In this model, a KB provides impulses to a voice producing mechanism and constant correction is applied via a feedback path. It is this correction that differs from speaker to speaker. Methods of defining measurable parameters for use as features are described. Algorithms for speaker recognition are developed and implemented. Two methods are presented. The first is based on the model postulated. Here the entropy on the utterance of a phoneme is evaluated. The transitions of voiced regions are used as speaker dependent features. The second method presented uses features found in other works, but evaluated differently. A knock—out scheme is used to provide the weightage values for the selection of features. Results of implementation are presented which show on an average of 80% recognition. It is also shown that if there are long gaps between sessions, the performance deteriorates and is speaker dependent. Cross recognition percentages are also presented and this in the worst case rises to 30% while the best case is 0%. Suggestions for further work are given in the concluding chapter.

A Prototype for a Multimodal Biometric Security system based on Face and Audio Signatures

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

Novel Electrochemical and Fluorescence Sensors for Food Additives and Neurotransmitters

There is an enormous demand for chemical sensors in many areas and disciplines including chemistry, biology, clinical analysis, environmental science. Chemical sensing refers to the continuous monitoring of the presence of chemical species and is a rapidly developing field of science and technology. They are analytical devices which transform chemical information generating from a reaction of the analyte into an measurable signal. Due to their high selectivity, sensitivity, fast response and low cost, electrochemical and fluorescent sensors have attracted great interest among the researchers in various fields. Development of four electrochemical sensors and three fluorescent sensors for food additives and neurotransmitters are presented in the thesis. Based on the excellent properties of multi walled carbon nanotube (MWCNT), poly (L-cysteine) and gold nanoparticles (AuNP) four voltammetric sensors were developed for various food additives like propyl gallate, allura red and sunset yellow. Nanosized fluorescent probes including gold nanoclusters (AuNCs) and CdS quantum dots (QDs) were used for the fluorescent sensing of butylated hydroxyanisole, dopamine and norepinephrine. A total of seven sensors including four electrochemical sensors and three fluorescence sensors have been developed for food additives and neurotransmitters.