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Incorporating auditory models in speech/audio applications

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Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception.

Following the success in incorporating perceptual models in audio coding algorithms, their application in other speech/audio processing systems is expanding. In general, all perceptual speech/audio processing algorithms involve minimization of an objective function that directly/indirectly incorporates properties of human perception. This dissertation primarily investigates the problems associated with directly embedding an auditory model in the objective function formulation and proposes possible solutions to overcome high complexity issues for use in real-time speech/audio algorithms. Specific problems addressed in this dissertation include: 1) the development of approximate but computationally efficient auditory model implementations that are consistent with the principles of psychoacoustics, 2) the development of a mapping scheme that allows synthesizing a time/frequency domain representation from its equivalent auditory model output. The first problem is aimed at addressing the high computational complexity involved in solving perceptual objective functions that require repeated application of auditory model for evaluation of different candidate solutions. In this dissertation, a frequency pruning and a detector pruning algorithm is developed that efficiently implements the various auditory model stages. The performance of the pruned model is compared to that of the original auditory model for different types of test signals in the SQAM database. Experimental results indicate only a 4-7% relative error in loudness while attaining up to 80-90 % reduction in computational complexity. Similarly, a hybrid algorithm is developed specifically for use with sinusoidal signals and employs the proposed auditory pattern combining technique together with a look-up table to store representative auditory patterns. The second problem obtains an estimate of the auditory representation that minimizes a perceptual objective function and transforms the auditory pattern back to its equivalent time/frequency representation. This avoids the repeated application of auditory model stages to test different candidate time/frequency vectors in minimizing perceptual objective functions. In this dissertation, a constrained mapping scheme is developed by linearizing certain auditory model stages that ensures obtaining a time/frequency mapping corresponding to the estimated auditory representation. This paradigm was successfully incorporated in a perceptual speech enhancement algorithm and a sinusoidal component selection task.

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2011

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Adaptive learning and unsupervised clustering of immune responses using microarray random sequence peptides

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Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can

Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can potentially provide pre-symptomatic diagnosis for infectious diseases or detection of biological threats. Currently, traditional bioinformatics tools, such as data mining classification algorithms, are used to process the large amount of peptide microarray data. However, these methods generally require training data and do not adapt to changing immune conditions or additional patient information. This work proposes advanced processing techniques to improve the classification and identification of single and multiple underlying immune response states embedded in immunosignatures, making it possible to detect both known and previously unknown diseases or biothreat agents. Novel adaptive learning methodologies for un- supervised and semi-supervised clustering integrated with immunosignature feature extraction approaches are proposed. The techniques are based on extracting novel stochastic features from microarray binding intensities and use Dirichlet process Gaussian mixture models to adaptively cluster the immunosignatures in the feature space. This learning-while-clustering approach allows continuous discovery of antibody activity by adaptively detecting new disease states, with limited a priori disease or patient information. A beta process factor analysis model to determine underlying patient immune responses is also proposed to further improve the adaptive clustering performance by formatting new relationships between patients and antibody activity. In order to extend the clustering methods for diagnosing multiple states in a patient, the adaptive hierarchical Dirichlet process is integrated with modified beta process factor analysis latent feature modeling to identify relationships between patients and infectious agents. The use of Bayesian nonparametric adaptive learning techniques allows for further clustering if additional patient data is received. Significant improvements in feature identification and immune response clustering are demonstrated using samples from patients with different diseases.

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2013

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Analysis, design, simulation, and measurements of flexible high impedance surfaces

Description

High Impedance Surfaces (HISs), which have been investigated extensively, have proven to be very efficient ground planes for low profile antenna applications due to their unique reflection phase characteristics. Another emerging research field among the microwave and antenna technologies is

High Impedance Surfaces (HISs), which have been investigated extensively, have proven to be very efficient ground planes for low profile antenna applications due to their unique reflection phase characteristics. Another emerging research field among the microwave and antenna technologies is the design of flexible antennas and microwave circuits to be utilized in conformal applications. The combination of those two research topics gives birth to a third one, namely the design of Conformal or Flexible HISs (FHISs), which is the main subject of this dissertation. The problems associated with the FHISs are twofold: characterization and physical realization. The characterization involves the analysis of scattering properties of FHISs in the presence of plane wave and localized sources. For this purpose, an approximate analytical method is developed to characterize the reflection properties of a cylindrically curved FHIS. The effects of curvature on the reflection phase of the curved FHISs are examined. Furthermore, the effects of different types of currents, specifically the ones inherent to finite sized periodic structures, on the reflection phase characteristics are observed. After the reflection phase characterization of curved HISs, the performance of dipole antennas located in close proximity to a curved HIS are investigated, and the results are compared with the flat case. Different types of resonances that may occur for such a low-profile antenna application are discussed. The effects of curvature on the radiation performance of antennas are examined. Commercially available flexible materials are relatively thin which degrades the bandwidth of HISs. Another practical aspect, which is related to the substrate thickness, is the compactness of the surface. Because of the design limitations of conventional HISs, it is not possible to miniaturize the HIS and increase the bandwidth, simultaneously. To overcome this drawback, a novel HIS is proposed with a periodically perforated ground plane. Copper plated through holes are extremely vulnerable to bending and should be avoided at the bending parts of flexible circuits. Fortunately, if designed properly, the perforations on the ground plane may result in suppression of surface waves. Hence, metallic posts can be eliminated without hindering the surface wave suppression properties of HISs.

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2013

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Energy-efficient distributed estimation by utilizing a nonlinear amplifier

Description

Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation,

Distributed estimation uses many inexpensive sensors to compose an accurate estimate of a given parameter. It is frequently implemented using wireless sensor networks. There have been several studies on optimizing power allocation in wireless sensor networks used for distributed estimation, the vast majority of which assume linear radio-frequency amplifiers. Linear amplifiers are inherently inefficient, so in this dissertation nonlinear amplifiers are examined to gain efficiency while operating distributed sensor networks. This research presents a method to boost efficiency by operating the amplifiers in the nonlinear region of operation. Operating amplifiers nonlinearly presents new challenges. First, nonlinear amplifier characteristics change across manufacturing process variation, temperature, operating voltage, and aging. Secondly, the equations conventionally used for estimators and performance expectations in linear amplify-and-forward systems fail. To compensate for the first challenge, predistortion is utilized not to linearize amplifiers but rather to force them to fit a common nonlinear limiting amplifier model close to the inherent amplifier performance. This minimizes the power impact and the training requirements for predistortion. Second, new estimators are required that account for transmitter nonlinearity. This research derives analytically and confirms via simulation new estimators and performance expectation equations for use in nonlinear distributed estimation. An additional complication when operating nonlinear amplifiers in a wireless environment is the influence of varied and potentially unknown channel gains. The impact of these varied gains and both measurement and channel noise sources on estimation performance are analyzed in this paper. Techniques for minimizing the estimate variance are developed. It is shown that optimizing transmitter power allocation to minimize estimate variance for the most-compressed parameter measurement is equivalent to the problem for linear sensors. Finally, a method for operating distributed estimation in a multipath environment is presented that is capable of developing robust estimates for a wide range of Rician K-factors. This dissertation demonstrates that implementing distributed estimation using nonlinear sensors can boost system efficiency and is compatible with existing techniques from the literature for boosting efficiency at the system level via sensor power allocation. Nonlinear transmitters work best when channel gains are known and channel noise and receiver noise levels are low.

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2013

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Energy and quality-aware multimedia signal processing

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Today's mobile devices have to support computation-intensive multimedia applications with a limited energy budget. In this dissertation, we present architecture level and algorithm-level techniques that reduce energy consumption of these devices with minimal impact on system quality. First, we present

Today's mobile devices have to support computation-intensive multimedia applications with a limited energy budget. In this dissertation, we present architecture level and algorithm-level techniques that reduce energy consumption of these devices with minimal impact on system quality. First, we present novel techniques to mitigate the effects of SRAM memory failures in JPEG2000 implementations operating in scaled voltages. We investigate error control coding schemes and propose an unequal error protection scheme tailored for JPEG2000 that reduces overhead without affecting the performance. Furthermore, we propose algorithm-specific techniques for error compensation that exploit the fact that in JPEG2000 the discrete wavelet transform outputs have larger values for low frequency subband coefficients and smaller values for high frequency subband coefficients. Next, we present use of voltage overscaling to reduce the data-path power consumption of JPEG codecs. We propose an algorithm-specific technique which exploits the characteristics of the quantized coefficients after zig-zag scan to mitigate errors introduced by aggressive voltage scaling. Third, we investigate the effect of reducing dynamic range for datapath energy reduction. We analyze the effect of truncation error and propose a scheme that estimates the mean value of the truncation error during the pre-computation stage and compensates for this error. Such a scheme is very effective for reducing the noise power in applications that are dominated by additions and multiplications such as FIR filter and transform computation. We also present a novel sum of absolute difference (SAD) scheme that is based on most significant bit truncation. The proposed scheme exploits the fact that most of the absolute difference (AD) calculations result in small values, and most of the large AD values do not contribute to the SAD values of the blocks that are selected. Such a scheme is highly effective in reducing the energy consumption of motion estimation and intra-prediction kernels in video codecs. Finally, we present several hybrid energy-saving techniques based on combination of voltage scaling, computation reduction and dynamic range reduction that further reduce the energy consumption while keeping the performance degradation very low. For instance, a combination of computation reduction and dynamic range reduction for Discrete Cosine Transform shows on average, 33% to 46% reduction in energy consumption while incurring only 0.5dB to 1.5dB loss in PSNR.

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2012

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Energy efficient RF transmitter design using enhanced breakdown voltage SOI-CMOS compatible MESFETs

Description

The high cut-off frequency of deep sub-micron CMOS technologies has enabled the integration of radio frequency (RF) transceivers with digital circuits. However, the challenging point is the integration of RF power amplifiers, mainly due to the low breakdown voltage of

The high cut-off frequency of deep sub-micron CMOS technologies has enabled the integration of radio frequency (RF) transceivers with digital circuits. However, the challenging point is the integration of RF power amplifiers, mainly due to the low breakdown voltage of CMOS transistors. Silicon-on-insulator (SOI) metal semiconductor field effect transistors (MESFETs) have been introduced to remedy the limited headroom concern in CMOS technologies. The MESFETs presented in this thesis have been fabricated on different SOI-CMOS processes without making any change to the standard fabrication steps and offer 2-30 times higher breakdown voltage than the MOSFETs on the same process. This thesis explains the design steps of high efficiency and wideband RF transmitters using the proposed SOI-CMOS compatible MESFETs. This task involves DC and RF characterization of MESFET devices, along with providing a compact Spice model for simulation purposes. This thesis presents the design of several SOI-MESFET RF power amplifiers operating at 433, 900 and 1800 MHz with ~40% bandwidth. Measurement results show a peak power added efficiency (PAE) of 55% and a peak output power of 22.5 dBm. The RF-PAs were designed to operate in Class-AB mode to minimize the linearity degradation. Class-AB power amplifiers lead to poor power added efficiency, especially when fed with signals with high peak to average power ratio (PAPR) such as wideband code division multiple access (W-CDMA). Polar transmitters have been introduced to improve the efficiency of RF-PAs at backed-off powers. A MESFET based envelope tracking (ET) polar transmitter was designed and measured. A low drop-out voltage regulator (LDO) was used as the supply modulator of this polar transmitter. MESFETs are depletion mode devices; therefore, they can be configured in a source follower configuration to have better stability and higher bandwidth that MOSFET based LDOs. Measurement results show 350 MHz bandwidth while driving a 10 pF capacitive load. A novel polar transmitter is introduced in this thesis to alleviate some of the limitations associated with polar transmitters. The proposed architecture uses the backgate terminal of a partially depleted transistor on SOI process, which relaxes the bandwidth and efficiency requirements of the envelope amplifier in a polar transmitter. The measurement results of the proposed transmitter demonstrate more than three times PAE improvement at 6-dB backed-off output power, compared to the traditional RF transmitters.

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2012

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Efficient test strategies for Analog/RF circuits

Description

Test cost has become a significant portion of device cost and a bottleneck in high volume manufacturing. Increasing integration density and shrinking feature sizes increased test time/cost and reduce observability. Test engineers have to put a tremendous effort in order

Test cost has become a significant portion of device cost and a bottleneck in high volume manufacturing. Increasing integration density and shrinking feature sizes increased test time/cost and reduce observability. Test engineers have to put a tremendous effort in order to maintain test cost within an acceptable budget. Unfortunately, there is not a single straightforward solution to the problem. Products that are tested have several application domains and distinct customer profiles. Some products are required to operate for long periods of time while others are required to be low cost and optimized for low cost. Multitude of constraints and goals make it impossible to find a single solution that work for all cases. Hence, test development/optimization is typically design/circuit dependent and even process specific. Therefore, test optimization cannot be performed using a single test approach, but necessitates a diversity of approaches. This works aims at addressing test cost minimization and test quality improvement at various levels. In the first chapter of the work, we investigate pre-silicon strategies, such as design for test and pre-silicon statistical simulation optimization. In the second chapter, we investigate efficient post-silicon test strategies, such as adaptive test, adaptive multi-site test, outlier analysis, and process shift detection/tracking.

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2012

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On the ordering of communication channels

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This dissertation introduces stochastic ordering of instantaneous channel powers of fading channels as a general method to compare the performance of a communication system over two different channels, even when a closed-form expression for the metric may not be available.

This dissertation introduces stochastic ordering of instantaneous channel powers of fading channels as a general method to compare the performance of a communication system over two different channels, even when a closed-form expression for the metric may not be available. Such a comparison is with respect to a variety of performance metrics such as error rates, outage probability and ergodic capacity, which share common mathematical properties such as monotonicity, convexity or complete monotonicity. Complete monotonicity of a metric, such as the symbol error rate, in conjunction with the stochastic Laplace transform order between two fading channels implies the ordering of the two channels with respect to the metric. While it has been established previously that certain modulation schemes have convex symbol error rates, there is no study of the complete monotonicity of the same, which helps in establishing stronger channel ordering results. Toward this goal, the current research proves for the first time, that all 1-dimensional and 2-dimensional modulations have completely monotone symbol error rates. Furthermore, it is shown that the frequently used parametric fading distributions for modeling line of sight exhibit a monotonicity in the line of sight parameter with respect to the Laplace transform order. While the Laplace transform order can also be used to order fading distributions based on the ergodic capacity, there exist several distributions which are not Laplace transform ordered, although they have ordered ergodic capacities. To address this gap, a new stochastic order called the ergodic capacity order has been proposed herein, which can be used to compare channels based on the ergodic capacity. Using stochastic orders, average performance of systems involving multiple random variables are compared over two different channels. These systems include diversity combining schemes, relay networks, and signal detection over fading channels with non-Gaussian additive noise. This research also addresses the problem of unifying fading distributions. This unification is based on infinite divisibility, which subsumes almost all known fading distributions, and provides simplified expressions for performance metrics, in addition to enabling stochastic ordering.

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2014

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Improving the reliability of NAND Flash, phase-change RAM and spin-torque transfer RAM

Description

Non-volatile memories (NVM) are widely used in modern electronic devices due to their non-volatility, low static power consumption and high storage density. While Flash memories are the dominant NVM technology, resistive memories such as phase change access memory (PRAM) and

Non-volatile memories (NVM) are widely used in modern electronic devices due to their non-volatility, low static power consumption and high storage density. While Flash memories are the dominant NVM technology, resistive memories such as phase change access memory (PRAM) and spin torque transfer random access memory (STT-MRAM) are gaining ground. All these technologies suffer from reliability degradation due to process variations, structural limits and material property shift. To address the reliability concerns of these NVM technologies, multi-level low cost solutions are proposed for each of them. My approach consists of first building a comprehensive error model. Next the error characteristics are exploited to develop low cost multi-level strategies to compensate for the errors. For instance, for NAND Flash memory, I first characterize errors due to threshold voltage variations as a function of the number of program/erase cycles. Next a flexible product code is designed to migrate to a stronger ECC scheme as program/erase cycles increases. An adaptive data refresh scheme is also proposed to improve memory reliability with low energy cost for applications with different data update frequencies. For PRAM, soft errors and hard errors models are built based on shifts in the resistance distributions. Next I developed a multi-level error control approach involving bit interleaving and subblock flipping at the architecture level, threshold resistance tuning at the circuit level and programming current profile tuning at the device level. This approach helped reduce the error rate significantly so that it was now sufficient to use a low cost ECC scheme to satisfy the memory reliability constraint. I also studied the reliability of a PRAM+DRAM hybrid memory system and analyzed the tradeoffs between memory performance, programming energy and lifetime. For STT-MRAM, I first developed an error model based on process variations. I developed a multi-level approach to reduce the error rates that consisted of increasing the W/L ratio of the access transistor, increasing the voltage difference across the memory cell and adjusting the current profile during write operation. This approach enabled use of a low cost BCH based ECC scheme to achieve very low block failure rates.

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2014

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Adaptive methods within a sequential Bayesian approach for structural health monitoring

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Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to

Structural integrity is an important characteristic of performance for critical components used in applications such as aeronautics, materials, construction and transportation. When appraising the structural integrity of these components, evaluation methods must be accurate. In addition to possessing capability to perform damage detection, the ability to monitor the level of damage over time can provide extremely useful information in assessing the operational worthiness of a structure and in determining whether the structure should be repaired or removed from service. In this work, a sequential Bayesian approach with active sensing is employed for monitoring crack growth within fatigue-loaded materials. The monitoring approach is based on predicting crack damage state dynamics and modeling crack length observations. Since fatigue loading of a structural component can change while in service, an interacting multiple model technique is employed to estimate probabilities of different loading modes and incorporate this information in the crack length estimation problem. For the observation model, features are obtained from regions of high signal energy in the time-frequency plane and modeled for each crack length damage condition. Although this observation model approach exhibits high classification accuracy, the resolution characteristics can change depending upon the extent of the damage. Therefore, several different transmission waveforms and receiver sensors are considered to create multiple modes for making observations of crack damage. Resolution characteristics of the different observation modes are assessed using a predicted mean squared error criterion and observations are obtained using the predicted, optimal observation modes based on these characteristics. Calculation of the predicted mean square error metric can be computationally intensive, especially if performed in real time, and an approximation method is proposed. With this approach, the real time computational burden is decreased significantly and the number of possible observation modes can be increased. Using sensor measurements from real experiments, the overall sequential Bayesian estimation approach, with the adaptive capability of varying the state dynamics and observation modes, is demonstrated for tracking crack damage.

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2013