A new and unique directional weighted error function was introduced into the backpropagation algorithm used in Artificial Neural Networks (ANNs) for applications where yes or no decisions are made on the output. A continuous error function based on a weighted curve is suggested for use in the backpropagation algorithm in an effort to increase the number of correct decisions. Results were compared to the standard and weighted error methods. A higher number of correct decisions were made with the new method. / Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical and Computer Engineering. / "July 2006." / Includes bibliographic references (leaves 36-37).

In this paper, an analysis of the clock-feedthrough effects in switched-current (SI)' circuits will be presented. The clock-feedthrough effects caused by the non-ideal characteristic of MOS switches when they are turned off limit the accuracy of the analog track-and-hold (T/H) circuits. A model to analyze and characterize this effect is established. A current-feedthrough cancellation technique is developed for analog/digital applications. This circuit allows SI filters to be implemented with small transistor sizes and still performs relatively well compared with the existing techniques. A SI lowpass biquadratic filter with a cutoff frequency of 5 KHz and a SI T/H circuit were implemented and fabricated using a two micron P-well CMOS process technology from MOSIS (MOS Integration Service). / Graduation date: 1992

Digital information och kommunikation genom digitala medier är ett växande område. E-post och andra kommunikationsmedel används dagligen över hela världen. Parallellt med att området växer så växer även intresset av att hålla informationen säker. Transmission via antenner är inom signalbehandling ett välkänt område. Transmission från en sändare till en mottagare genom fri rymd är ett vanligt exempel. I en tuff miljö som till exempel ett rum med reflektioner och oberoende elektriska apparater kommer det att finnas en hel del distorsion i systemet och signalen som överförs kan, på grund av systemets egenskaper och buller förvrängas.Systemidentifiering är ett annat välkänt begrepp inom signalbehandling. Denna avhandling fokuserar på systemidentifiering i en tuff miljö med okända system. En presentation ges av matematiska verktyg från den linjära algebran samt en tillämpning inom signalbehandling. Denna avhandling grundar sig främst på en matrisfaktorisering känd som Singular Value Decomposition (SVD). SVD’n används här för att lösa komplicerade matrisinverser och identifiera system.Denna avhandling utförs i samarbete med Combitech AB. Deras expertis inom signalbehandling var till stor hjälp när teorin praktiserades. Med hjälp av ett välkänt programmeringsspråk känt som LabView praktiserades de matematiska verktygen och kunde synkroniseras med diverse instrument som användes för att generera signaler och system. / Digital information transmission is a growing field. Emails, videos and so on are transmitting around the world on a daily basis. Along the growth of using digital devises there is in some cases a great interest of keeping this information secure. In the field of signal processing a general concept is antenna transmission. Free space between an antenna transmitter and a receiver is an example of a system. In a rough environment such as a room with reflections and independent electrical devices there will be a lot of distortion in the system and the signal that is transmitted might, due to the system characteristics and noise be distorted. System identification is another well-known concept in signal processing. This thesis will focus on system identification in a rough environment and unknown systems. It will introduce mathematical tools from the field of linear algebra and applying them in signal processing. Mainly this thesis focus on a specific matrix factorization called Singular Value Decomposition (SVD). This is used to solve complicated inverses and identifying systems. This thesis is formed and accomplished in collaboration with Combitech AB. Their expertise in the field of signal processing was of great help when putting the algorithm in practice. Using a well-known programming script called LabView the mathematical tools were synchronized with the instruments that were used to generate the systems and signals.

Dysphagia (swallowing disorder) is a common health problem that degrades the quality of life of many people. The videofluoroscopic swallowing study (VFSS) is the current gold standard in dysphagia assessment but is associated with high cost, long wait times, and a lack of portability. As a result, there is a pining need for an alternative technique that can serve day-to-day monitoring of dysphagia as well as screening for VFSS referral. The primary objective of this thesis was to investigate three non-invasive signal modalities, namely dual-axis accelerometry, submental mechanomyography (MMG), and nasal airflow, for their potential as alternatives to VFSS. To this end, signals were acquired from 17 healthy individuals and 24 patients with dysphagia, with various stimuli. In a characterization study, the anterior-posterior (A-P) and superior-inferior (S-I) axes in dual-axis accelerometry were found to contain non-overlapping information about swallowing, justifying the extension of single-axis (A-P only) to dual-axis (A-P and S-I) accelerometry. Also, several dual-axis accelerometry signal features were found to be stimulus dependent, and the observed stimulus effects were linked to slower swallowing function with increasing bolus viscosity. Age and stimulus effects on submental MMG were scrutinized, as an analogy to previous electromyography (EMG) studies of similar design. Similarities to EMG confirmed the validity of MMG as a muscle activity measurement tool in swallowing research. Automatic swallow segmentation, which is a crucial precursory step to swallow diagnosis, was investigated with artificial neural networks. Segmentation performance was shown to improve as more signal modalities were included, verifying the value of multi-sensor fusion. When all signal modalities were utilized, an adjusted accuracy of 89.6% was achieved. Automatic discrimination between healthy and abnormal swallows was investigated in two studies. Using previously collected pediatric data, a radial basis classifier based only on A-P accelerometry resulted in an adjusted accuracy of 81.3% in aspiration detection. In an adult study, linear discriminant classifiers resulted in adjusted accuracies of 74.7%, 83.7%, and 84.2% for aspiration, valleculae residue, and pyriform sinus residue detection, respectively. It was concluded that the three signal modalities analyzed in this thesis possess promising potential for abnormal swallow detection.

Traditional signal processing systems, based on linear modeling principles, face a stifling pressure to meet present-day demands caused by the deluge of data generated, transmitted and processed across the globe. Fortunately, recent advances have resulted in the emergence of more sophisticated, nonlinear signal models. Such nonlinear models have inspired fundamental changes in which information processing systems are designed and analyzed. For example, the sparse signal model serves as the basis for Compressive Sensing (CS), an exciting new framework for signal acquisition. In this thesis, we advocate a geometry-based approach for nonlinear modeling of signal ensembles. We make the guiding assumption that the signal class of interest forms a nonlinear low-dimensional manifold belonging to the high-dimensional signal space. A host of traditional data models can be essentially interpreted as specific instances of such manifolds. Therefore, our proposed geometric approach provides a common framework that can unify, analyze, and significantly extend the scope of nonlinear models for information acquisition and processing. We demonstrate that the geometric approach enables new algorithms and analysis for a number of signal processing applications. Our specific contributions include: (i) new convex formulations and algorithms for the design of linear systems for data acquisition, compression, and classification; (ii) a general algorithm for reconstruction, deconvolution, and denoising of signals, images, and matrix-valued data; (iii) efficient methods for inference from a small number of linear signal samples, without ever resorting to reconstruction; and, (iv) new signal and image representations for robust modeling and processing of large-scale data ensembles.

No description available.

Progressive developments in computing and sensor technologies during the past decades have enabled the formulation of increasingly advanced problems in statistical inference and signal processing. The thesis is concerned with statistical estimation methods, and is divided into three parts with focus on two different areas: sensor fusion and sparse signal processing. The first part introduces the well-established Bayesian, Fisherian and least-squares estimation frameworks, and derives new estimators. Specifically, the Bayesian framework is applied in two different classes of estimation problems: scenarios in which (i) the signal covariances themselves are subject to uncertainties, and (ii) distance bounds are used as side information. Applications include localization, tracking and channel estimation. The second part is concerned with the extraction of useful information from multiple sensors by exploiting their joint properties. Two sensor configurations are considered here: (i) a monocular camera and an inertial measurement unit, and (ii) an array of passive receivers. New estimators are developed with applications that include inertial navigation, source localization and multiple waveform estimation. The third part is concerned with signals that have sparse representations. Two problems are considered: (i) spectral estimation of signals with power concentrated to a small number of frequencies,and (ii) estimation of sparse signals that are observed by few samples, including scenarios in which they are linearly underdetermined. New estimators are developed with applications that include spectral analysis, magnetic resonance imaging and array processing. / <p>QC 20130426</p>

In forensic investigations, verification of digital recordings is an important as-pect. There are numerous methods to verify authentication of recordings, but itis difficult to determine when the media was recorded. By studying the electricalnetwork frequency, one can find a unique signature and then match the recordingto this signature. By matching a recorded signal to a database, which contains allnecessary information, one can find the time when the recording was made.

No description available.

This work examines error rate performance of Multi-Hop communication systems, employing Single Input Single Output (SISO) transmissions over Nakagami-m fading channel. Mobile multi-hop relaying (MMR) system has been adopted in several Broadband Wireless Access Networks (BWAN) as a cost-effective means of extending the coverage and improving the capacity of these wireless networks. In a MMR system, communication between the source node and destination node is achieved through an intermediate node (i.e., Relay Station). It is widely accepted that multi-hop relaying communication can provide higher capacity and can reduce the interference in BWANs. Such claims though have not been quantified. Quantification of such claims is an essential step to justify a better opportunity for wide deployment of relay stations.In this thesis, Bit Error Rate (BER) of multi-hop communication systems has been analysed. Different kinds of fading channels have been used to estimate the error rate performance for wireless transmission. Binary Phase Shift Keying (BPSK) has been employed as the modulation technique and Additive White Gaussian Noise (AWGN) has been used as the channel noise. The same Signal to Noise Ratio (SNR) was used to estimate the channel performance. Three channels were compared by simulating their BER, namely, Rayleigh, Rician and Nakagami. Matlab has been used for simulation.