Microstrip band rejection filter using open loop resonator

Design of a compact microstrip band reject filter is proposed. The device consists of an Open Loop Rectangular Resonator (OLRR) coupled to a microstrip line. The transmission line has a U-bend which enhances the coupling with the OLRR element and reduces the size of the filter. The filter can be made tunable by mounting variable capacitance to the system. Simulated and experimental results are presented.

Modeling and Analysis of Some Heavy Tailed Time Series

The thesis has covered various aspects of modeling and analysis of finite mean time series with symmetric stable distributed innovations. Time series analysis based on Box and Jenkins methods are the most popular approaches where the models are linear and errors are Gaussian. We highlighted the limitations of classical time series analysis tools and explored some generalized tools and organized the approach parallel to the classical set up. In the present thesis we mainly studied the estimation and prediction of signal plus noise model. Here we assumed the signal and noise follow some models with symmetric stable innovations.We start the thesis with some motivating examples and application areas of alpha stable time series models. Classical time series analysis and corresponding theories based on finite variance models are extensively discussed in second chapter. We also surveyed the existing theories and methods correspond to infinite variance models in the same chapter. We present a linear filtering method for computing the filter weights assigned to the observation for estimating unobserved signal under general noisy environment in third chapter. Here we consider both the signal and the noise as stationary processes with infinite variance innovations. We derived semi infinite, double infinite and asymmetric signal extraction filters based on minimum dispersion criteria. Finite length filters based on Kalman-Levy filters are developed and identified the pattern of the filter weights. Simulation studies show that the proposed methods are competent enough in signal extraction for processes with infinite variance.Parameter estimation of autoregressive signals observed in a symmetric stable noise environment is discussed in fourth chapter. Here we used higher order Yule-Walker type estimation using auto-covariation function and exemplify the methods by simulation and application to Sea surface temperature data. We increased the number of Yule-Walker equations and proposed a ordinary least square estimate to the autoregressive parameters. Singularity problem of the auto-covariation matrix is addressed and derived a modified version of the Generalized Yule-Walker method using singular value decomposition.In fifth chapter of the thesis we introduced partial covariation function as a tool for stable time series analysis where covariance or partial covariance is ill defined. Asymptotic results of the partial auto-covariation is studied and its application in model identification of stable auto-regressive models are discussed. We generalize the Durbin-Levinson algorithm to include infinite variance models in terms of partial auto-covariation function and introduce a new information criteria for consistent order estimation of stable autoregressive model.In chapter six we explore the application of the techniques discussed in the previous chapter in signal processing. Frequency estimation of sinusoidal signal observed in symmetric stable noisy environment is discussed in this context. Here we introduced a parametric spectrum analysis and frequency estimate using power transfer function. Estimate of the power transfer function is obtained using the modified generalized Yule-Walker approach. Another important problem in statistical signal processing is to identify the number of sinusoidal components in an observed signal. We used a modified version of the proposed information criteria for this purpose.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Statistical signal extraction using stable processes

The standard models for statistical signal extraction assume that the signal and noise are generated by linear Gaussian processes. The optimum filter weights for those models are derived using the method of minimum mean square error. In the present work we study the properties of signal extraction models under the assumption that signal/noise are generated by symmetric stable processes. The optimum filter is obtained by the method of minimum dispersion. The performance of the new filter is compared with their Gaussian counterparts by simulation.

“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.

Performance analysis of FIR digital filter design: RNS versus traditional

Residue Number System (RNS) based Finite Impulse Response (FIR) digital filters and traditional FIR filters. This research is motivated by the importance of an efficient filter implementation for digital signal processing. The comparison is done in terms of speed and area requirement for various filter specifications. RNS based FIR filters operate more than three times faster and consumes only about 60% of the area than traditional filter when number of filter taps is more than 32. The area for RNS filter is increasing at a lesser rate than that for traditional resulting in lower power consumption. RNS is a nonweighted number system without carry propogation between different residue digits.This enables simultaneous parallel processing on all the digits resulting in high speed addition and multiplication in the RNS domain

RNS based programmable multi-mode decimation filter for WCDMA and WiMAX

The recent trends envisage multi-standard architectures as a promising solution for the future wireless transceivers to attain higher system capacities and data rates. The computationally intensive decimation filter plays an important role in channel selection for multi-mode systems. An efficient reconfigurable implementation is a key to achieve low power consumption. To this end, this paper presents a dual-mode Residue Number System (RNS) based decimation filter which can be programmed for WCDMA and 802.16e standards. Decimation is done using multistage, multirate finite impulse response (FIR) filters. These FIR filters implemented in RNS domain offers high speed because of its carry free operation on smaller residues in parallel channels. Also, the FIR filters exhibit programmability to a selected standard by reconfiguring the hardware architecture. The total area is increased only by 24% to include WiMAX compared to a single mode WCDMA transceiver. In each mode, the unused parts of the overall architecture is powered down and bypassed to attain power saving. The performance of the proposed decimation filter in terms of critical path delay and area are tabulated.

Dual-mode RNS based programmable decimation filter for WCDMA and WLANa

The recent trends envisage multi-standard architectures as a promising solution for the future wireless transceivers. The computationally intensive decimation filter plays an important role in channel selection for multi-mode systems. An efficient reconfigurable implementation is a key to achieve low power consumption. To this end, this paper presents a dual-mode Residue Number System (RNS) based decimation filter which can be programmed for WCDMA and 802.11a standards. Decimation is done using multistage, multirate finite impulse response (FIR) filters. These FIR filters implemented in RNS domain offers high speed because of its carry free operation on smaller residues in parallel channels. Also, the FIR filters exhibit programmability to a selected standard by reconfiguring the hardware architecture. The total area is increased only by 33% to include WLANa compared to a single mode WCDMA transceiver. In each mode, the unused parts of the overall architecture is powered down and bypassed to attain power saving. The performance of the proposed decimation filter in terms of critical path delay and area are tabulated

k-NN based On-Line Handwritten Character recognition system

On-line handwriting recognition has been a frontier area of research for the last few decades under the purview of pattern recognition. Word processing turns to be a vexing experience even if it is with the assistance of an alphanumeric keyboard in Indian languages. A natural solution for this problem is offered through online character recognition. There is abundant literature on the handwriting recognition of western, Chinese and Japanese scripts, but there are very few related to the recognition of Indic script such as Malayalam. This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using K-NN algorithm. It would help in recognizing Malayalam text entered using pen-like devices. A novel feature extraction method, a combination of time domain features and dynamic representation of writing direction along with its curvature is used for recognizing Malayalam characters. This writer independent system gives an excellent accuracy of 98.125% with recognition time of 15-30 milliseconds

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

On-Line Handwritten Character Recognition using Kohonen Networks

This paper presents an efficient Online Handwritten character Recognition System for Malayalam Characters (OHR-M) using Kohonen network. It would help in recognizing Malayalam text entered using pen-like devices. It will be more natural and efficient way for users to enter text using a pen than keyboard and mouse. To identify the difference between similar characters in Malayalam a novel feature extraction method has been adopted-a combination of context bitmap and normalized (x, y) coordinates. The system reported an accuracy of 88.75% which is writer independent with a recognition time of 15-32 milliseconds

High Selectivity Filter Employing Stepped Impedance Resonators, Series Capacitors and Defected Ground Structures for Ultra Wide Band Applications

The paper presents a compact planar Ultra Wide Band ¯lter employing folded stepped impedance resonators with series capacitors and dumb bell shaped defected ground structures. An interdigital quarter wavelength coupled line is used for achieving the band pass characteristics. The transmission zeros are produced by stepped impedance resonators. The ¯lter has steep roll o® rate and good attenuation in its lower and upper stop bands, contributed by the series capacitor and defected ground structures respectively.

Discrete wavelet transform de-noising in eukaryotic gene splicing

This paper compares the most common digital signal processing methods of exon prediction in eukaryotes, and also proposes a technique for noise suppression in exon prediction. The specimen used here which has relevance in medical research, has been taken from the public genomic database - GenBank.Here exon prediction has been done using the digital signal processing methods viz. binary method, EIIP (electron-ion interaction psuedopotential) method and filter methods. Under filter method two filter designs, and two approaches using these two designs have been tried. The discrete wavelet transform has been used for de-noising of the exon plots.Results of exon prediction based on the methods mentioned above, which give values closest to the ones found in the NCBI database are given here. The exon plot de-noised using discrete wavelet transform is also given.Alterations to the proven methods as done by the authors, improves performance of exon prediction algorithms. Also it has been proven that the discrete wavelet transform is an effective tool for de-noising which can be used with exon prediction algorithms

Characterization of Novel Fabry Pérot Filter Arrays for Nanospectrometers in Medical Applications

Optische Spektroskopie ist eine sehr wichtige Messtechnik mit einem hohen Potential für zahlreiche Anwendungen in der Industrie und Wissenschaft. Kostengünstige und miniaturisierte Spektrometer z.B. werden besonders für moderne Sensorsysteme “smart personal environments” benötigt, die vor allem in der Energietechnik, Messtechnik, Sicherheitstechnik (safety and security), IT und Medizintechnik verwendet werden. Unter allen miniaturisierten Spektrometern ist eines der attraktivsten Miniaturisierungsverfahren das Fabry Pérot Filter. Bei diesem Verfahren kann die Kombination von einem Fabry Pérot (FP) Filterarray und einem Detektorarray als Mikrospektrometer funktionieren. Jeder Detektor entspricht einem einzelnen Filter, um ein sehr schmales Band von Wellenlängen, die durch das Filter durchgelassen werden, zu detektieren. Ein Array von FP-Filter wird eingesetzt, bei dem jeder Filter eine unterschiedliche spektrale Filterlinie auswählt. Die spektrale Position jedes Bandes der Wellenlänge wird durch die einzelnen Kavitätshöhe des Filters definiert. Die Arrays wurden mit Filtergrößen, die nur durch die Array-Dimension der einzelnen Detektoren begrenzt werden, entwickelt. Allerdings erfordern die bestehenden Fabry Pérot Filter-Mikrospektrometer komplizierte Fertigungsschritte für die Strukturierung der 3D-Filter-Kavitäten mit unterschiedlichen Höhen, die nicht kosteneffizient für eine industrielle Fertigung sind. Um die Kosten bei Aufrechterhaltung der herausragenden Vorteile der FP-Filter-Struktur zu reduzieren, wird eine neue Methode zur Herstellung der miniaturisierten FP-Filtern mittels NanoImprint Technologie entwickelt und präsentiert. In diesem Fall werden die mehreren Kavitäten-Herstellungsschritte durch einen einzigen Schritt ersetzt, die hohe vertikale Auflösung der 3D NanoImprint Technologie verwendet. Seit dem die NanoImprint Technologie verwendet wird, wird das auf FP Filters basierende miniaturisierte Spectrometer nanospectrometer genannt. Ein statischer Nano-Spektrometer besteht aus einem statischen FP-Filterarray auf einem Detektorarray (siehe Abb. 1). Jeder FP-Filter im Array besteht aus dem unteren Distributed Bragg Reflector (DBR), einer Resonanz-Kavität und einen oberen DBR. Der obere und untere DBR sind identisch und bestehen aus periodisch abwechselnden dünnen dielektrischen Schichten von Materialien mit hohem und niedrigem Brechungsindex. Die optischen Schichten jeder dielektrischen Dünnfilmschicht, die in dem DBR enthalten sind, entsprechen einen Viertel der Design-Wellenlänge. Jeder FP-Filter wird einer definierten Fläche des Detektorarrays zugeordnet. Dieser Bereich kann aus einzelnen Detektorelementen oder deren Gruppen enthalten. Daher werden die Seitenkanal-Geometrien der Kavität aufgebaut, die dem Detektor entsprechen. Die seitlichen und vertikalen Dimensionen der Kavität werden genau durch 3D NanoImprint Technologie aufgebaut. Die Kavitäten haben Unterschiede von wenigem Nanometer in der vertikalen Richtung. Die Präzision der Kavität in der vertikalen Richtung ist ein wichtiger Faktor, der die Genauigkeit der spektralen Position und Durchlässigkeit des Filters Transmissionslinie beeinflusst.

Mikromechanisch durchstimmbare, dielektrische Fabry-Pérot-Filter im nahen Infrarot-Bereich: Konzept, Herstellung und Charakterisierung

Das Ziel der vorliegenden Arbeit war die Herstellung und Charakterisierung mikromechanisch durchstimmbarer, dielektrischer Fabry-Pérot-Filter im nahen Infrarot-Bereich bei einer Zentralwellenlänge von λc = 950 nm. Diese Bauelemente wurden auf Basis kostengünstiger Technologien realisiert, dank deren Entwicklung extreme Miniaturisierung und gleichzeitig hohe spektrale Anforderungen möglich sind. Der Vorteil solcher Filter liegt darin, dass sie direkt in einen Photodetektor integriert werden können und mit ganz wenigen Komponenten zu einem kompakten Spektrometermodul zusammengesetzt werden können. Die Baugröße ist nur durch die Größe des Photodetektors limitiert und die gesamte Intensität des einfallenden Lichts kann vorteilhaft auf eine einzelne Filtermembran des Fabry-Pérot-Filters fokussiert werden. Für den Filteraufbau werden zwei hochreflektierende, dielektrische DBR-Spiegel, ein organisches Opferschichtmaterial, welches zur Erzeugung einer Luftkavität im Filter dient, und zwei unterschiedliche Elektroden aus ITO und Aluminium verwendet. Die mikromechanische Auslenkung der freigelegten Filtermembran geschieht mittels elektrostatischer Aktuation, wobei auf diese Weise die Kavitätshöhe des Fabry-Pérot-Filters geändert wird und somit dieser im erforderlichen Spektralbereich optisch durchgestimmt wird. Das in dieser Arbeit gewählte Filterkonzept stellt eine Weiterentwicklung eines bereits bestehenden Filterkonzepts für den sichtbaren Spektralbereich dar. Zum Einen wurden in dieser Arbeit das vertikale und das laterale Design der Filterstrukturen geändert. Eine entscheidende Änderung lag im mikromechanisch beweglichen Teil des Fabry-Pérot-Filters. Dieser schließt den oberen DBR-Spiegel und ein aus dielektrischen Schichten und der oberen Aluminium-Elektrode bestehendes Membranhaltesystem ein, welches später durch Entfernung der Opferschicht freigelegt wird. Die Fläche des DBR-Spiegels wurde auf die Fläche der Filtermembran reduziert und auf dem Membranhaltesystem positioniert. Zum Anderen wurde im Rahmen dieser Arbeit der vertikale Schichtaufbau des Membranhaltesystems variiert und der Einfluss der gewählten Materialien auf die Krümmung der freistehenden Filterstrukturen, auf das Aktuationsverhalten und auf die spektralen Eigenschaften des gesamten Filters untersucht. Der Einfluss der mechanischen Eigenschaften dieser Materialien spielt nämlich eine bedeutende Rolle bei der Erhaltung der erforderlichen optischen Eigenschaften des gesamten Filters. Bevor Fabry-Pérot-Filter ausgeführt wurden, wurde die mechanische Spannung in den einzelnen Materialien des Membranhaltesystems bestimmt. Für die Messung wurde Substratkrümmungsmethode angewendet. Es wurde gezeigt, dass die Plasmaanregungsfrequenzen der plasmaunterstützten chemischen Gasphasenabscheidung bei einer Prozesstemperatur von 120 °C die mechanische Spannung von Si3N4 enorm beeinflussen. Diese Ergebnisse wurden im Membranhaltesystem umgesetzt, wobei verschiedene Filter mit unterschiedlichen mechanischen Eigenschaften des Membranhaltesystems gezeigt wurden. Darüber hinaus wurden optische Eigenschaften der Filter unter dem Einfluss des lateralen Designs der Filterstrukturen untersucht. Bei den realisierten Filtern wurden ein optischer Durchstimmbereich von ca. 70 nm und eine spektrale Auflösung von 5 nm erreicht. Die erreichte Intensität der Transmissionslinie liegt bei 45-60%. Diese Parameter haben für den späteren spektroskopischen Einsatz der realisierten Fabry-Pérot-Filter eine hohe Bedeutung. Die Anwendung soll erstmalig in einem „Proof of Concept“ stattfinden, wobei damit die Oberflächentemperatur eines GaAs-Wafers über die Messung der spektralen Lage seiner Bandlücke bestimmt werden kann.