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Description
The processing power and storage capacity of portable devices have improved considerably over the past decade. This has motivated the implementation of sophisticated audio and other signal processing algorithms on such mobile devices. Of particular interest in this thesis is audio/speech processing based on perceptual criteria. Specifically, estimation of parameters from human auditory models, such as auditory patterns and loudness, involves computationally intensive operations which can strain device resources. Hence, strategies for implementing computationally efficient human auditory models for loudness estimation have been studied in this thesis. Existing algorithms for reducing computations in auditory pattern and loudness estimation have been examined and improved algorithms have been proposed to overcome limitations of these methods. In addition, real-time applications such as perceptual loudness estimation and loudness equalization using auditory models have also been implemented. A software implementation of loudness estimation on iOS devices is also reported in this thesis. In addition to the loudness estimation algorithms and software, in this thesis project we also created new illustrations of speech and audio processing concepts for research and education. As a result, a new suite of speech/audio DSP functions was developed and integrated as part of the award-winning educational iOS App 'iJDSP." These functions are described in detail in this thesis. Several enhancements in the architecture of the application have also been introduced for providing the supporting framework for speech/audio processing. Frame-by-frame processing and visualization functionalities have been developed to facilitate speech/audio processing. In addition, facilities for easy sound recording, processing and audio rendering have also been developed to provide students, practitioners and researchers with an enriched DSP simulation tool. Simulations and assessments have been also developed for use in classes and training of practitioners and students.
ContributorsKalyanasundaram, Girish (Author) / Spanias, Andreas S (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2013
Description
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.
ContributorsRajan, Adithya (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Bliss, Daniel (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2014
Description
Recently, the location of the nodes in wireless networks has been modeled as point processes. In this dissertation, various scenarios of wireless communications in large-scale networks modeled as point processes are considered. The first part of the dissertation considers signal reception and detection problems with symmetric alpha stable noise which is from an interfering network modeled as a Poisson point process. For the signal reception problem, the performance of space-time coding (STC) over fading channels with alpha stable noise is studied. We derive pairwise error probability (PEP) of orthogonal STCs. For general STCs, we propose a maximum-likelihood (ML) receiver, and its approximation. The resulting asymptotically optimal receiver (AOR) does not depend on noise parameters and is computationally simple, and close to the ML performance. Then, signal detection in coexisting wireless sensor networks (WSNs) is considered. We define a binary hypothesis testing problem for the signal detection in coexisting WSNs. For the problem, we introduce the ML detector and simpler alternatives. The proposed mixed-fractional lower order moment (FLOM) detector is computationally simple and close to the ML performance. Stochastic orders are binary relations defined on probability. The second part of the dissertation introduces stochastic ordering of interferences in large-scale networks modeled as point processes. Since closed-form results for the interference distributions for such networks are only available in limited cases, it is of interest to compare network interferences using stochastic. In this dissertation, conditions on the fading distribution and path-loss model are given to establish stochastic ordering between interferences. Moreover, Laplace functional (LF) ordering is defined between point processes and applied for comparing interference. Then, the LF orderings of general classes of point processes are introduced. It is also shown that the LF ordering is preserved when independent operations such as marking, thinning, random translation, and superposition are applied. The LF ordering of point processes is a useful tool for comparing spatial deployments of wireless networks and can be used to establish comparisons of several performance metrics such as coverage probability, achievable rate, and resource allocation even when closed form expressions for such metrics are unavailable.
ContributorsLee, Junghoon (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Committee member) / Reisslein, Martin (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2014
Description
The problem of cooperative radar and communications signaling is investigated. Each system typically considers the other system a source of interference. Consequently, the tradition is to have them operate in orthogonal frequency bands. By considering the radar and communications operations to be a single joint system, performance bounds on a receiver that observes communications and radar return in the same frequency allocation are derived. Bounds in performance of the joint system is measured in terms of data information rate for communications and radar estimation information rate for the radar. Inner bounds on performance are constructed.
ContributorsChiriyath, Alex (Author) / Bliss, Daniel W (Thesis advisor) / Kosut, Oliver (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2014
Description
Head movement is known to have the benefit of improving the accuracy of sound localization for humans and animals. Marmoset is a small bodied New World monkey species and it has become an emerging model for studying the auditory functions. This thesis aims to detect the horizontal and vertical rotation of head movement in marmoset monkeys.
Experiments were conducted in a sound-attenuated acoustic chamber. Head movement of marmoset monkey was studied under various auditory and visual stimulation conditions. With increasing complexity, these conditions are (1) idle, (2) sound-alone, (3) sound and visual signals, and (4) alert signal by opening and closing of the chamber door. All of these conditions were tested with either house light on or off. Infra-red camera with a frame rate of 90 Hz was used to capture of the head movement of monkeys. To assist the signal detection, two circular markers were attached to the top of monkey head. The data analysis used an image-based marker detection scheme. Images were processed using the Computation Vision Toolbox in Matlab. The markers and their positions were detected using blob detection techniques. Based on the frame-by-frame information of marker positions, the angular position, velocity and acceleration were extracted in horizontal and vertical planes. Adaptive Otsu Thresholding, Kalman filtering and bound setting for marker properties were used to overcome a number of challenges encountered during this analysis, such as finding image segmentation threshold, continuously tracking markers during large head movement, and false alarm detection.
The results show that the blob detection method together with Kalman filtering yielded better performances than other image based techniques like optical flow and SURF features .The median of the maximal head turn in the horizontal plane was in the range of 20 to 70 degrees and the median of the maximal velocity in horizontal plane was in the range of a few hundreds of degrees per second. In comparison, the natural alert signal - door opening and closing - evoked the faster head turns than other stimulus conditions. These results suggest that behaviorally relevant stimulus such as alert signals evoke faster head-turn responses in marmoset monkeys.
Experiments were conducted in a sound-attenuated acoustic chamber. Head movement of marmoset monkey was studied under various auditory and visual stimulation conditions. With increasing complexity, these conditions are (1) idle, (2) sound-alone, (3) sound and visual signals, and (4) alert signal by opening and closing of the chamber door. All of these conditions were tested with either house light on or off. Infra-red camera with a frame rate of 90 Hz was used to capture of the head movement of monkeys. To assist the signal detection, two circular markers were attached to the top of monkey head. The data analysis used an image-based marker detection scheme. Images were processed using the Computation Vision Toolbox in Matlab. The markers and their positions were detected using blob detection techniques. Based on the frame-by-frame information of marker positions, the angular position, velocity and acceleration were extracted in horizontal and vertical planes. Adaptive Otsu Thresholding, Kalman filtering and bound setting for marker properties were used to overcome a number of challenges encountered during this analysis, such as finding image segmentation threshold, continuously tracking markers during large head movement, and false alarm detection.
The results show that the blob detection method together with Kalman filtering yielded better performances than other image based techniques like optical flow and SURF features .The median of the maximal head turn in the horizontal plane was in the range of 20 to 70 degrees and the median of the maximal velocity in horizontal plane was in the range of a few hundreds of degrees per second. In comparison, the natural alert signal - door opening and closing - evoked the faster head turns than other stimulus conditions. These results suggest that behaviorally relevant stimulus such as alert signals evoke faster head-turn responses in marmoset monkeys.
ContributorsSimhadri, Sravanthi (Author) / Zhou, Yi (Thesis advisor) / Turaga, Pavan (Thesis advisor) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2014
Description
As the number of devices with wireless capabilities and the proximity of these devices to each other increases, better ways to handle the interference they cause need to be explored. Also important is for these devices to keep up with the demand for data rates while not compromising on industry established expectations of power consumption and mobility. Current methods of distributing the spectrum among all participants are expected to not cope with the demand in a very near future. In this thesis, the effect of employing sophisticated multiple-input, multiple-output (MIMO) systems in this regard is explored. The efficacy of systems which can make intelligent decisions on the transmission mode usage and power allocation to these modes becomes relevant in the current scenario, where the need for performance far exceeds the cost expendable on hardware. The effect of adding multiple antennas at either ends will be examined, the capacity of such systems and of networks comprised of many such participants will be evaluated. Methods of simulating said networks, and ways to achieve better performance by making intelligent transmission decisions will be proposed. Finally, a way of access control closer to the physical layer (a 'statistical MAC') and a possible metric to be used for such a MAC is suggested.
ContributorsThontadarya, Niranjan (Author) / Bliss, Daniel W (Thesis advisor) / Berisha, Visar (Committee member) / Ying, Lei (Committee member) / Arizona State University (Publisher)
Created2014
Description
Audio signals, such as speech and ambient sounds convey rich information pertaining to a user’s activity, mood or intent. Enabling machines to understand this contextual information is necessary to bridge the gap in human-machine interaction. This is challenging due to its subjective nature, hence, requiring sophisticated techniques. This dissertation presents a set of computational methods, that generalize well across different conditions, for speech-based applications involving emotion recognition and keyword detection, and ambient sounds-based applications such as lifelogging.
The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance.
Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems.
Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface.
The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption.
The expression and perception of emotions varies across speakers and cultures, thus, determining features and classification methods that generalize well to different conditions is strongly desired. A latent topic models-based method is proposed to learn supra-segmental features from low-level acoustic descriptors. The derived features outperform state-of-the-art approaches over multiple databases. Cross-corpus studies are conducted to determine the ability of these features to generalize well across different databases. The proposed method is also applied to derive features from facial expressions; a multi-modal fusion overcomes the deficiencies of a speech only approach and further improves the recognition performance.
Besides affecting the acoustic properties of speech, emotions have a strong influence over speech articulation kinematics. A learning approach, which constrains a classifier trained over acoustic descriptors, to also model articulatory data is proposed here. This method requires articulatory information only during the training stage, thus overcoming the challenges inherent to large-scale data collection, while simultaneously exploiting the correlations between articulation kinematics and acoustic descriptors to improve the accuracy of emotion recognition systems.
Identifying context from ambient sounds in a lifelogging scenario requires feature extraction, segmentation and annotation techniques capable of efficiently handling long duration audio recordings; a complete framework for such applications is presented. The performance is evaluated on real world data and accompanied by a prototypical Android-based user interface.
The proposed methods are also assessed in terms of computation and implementation complexity. Software and field programmable gate array based implementations are considered for emotion recognition, while virtual platforms are used to model the complexities of lifelogging. The derived metrics are used to determine the feasibility of these methods for applications requiring real-time capabilities and low power consumption.
ContributorsShah, Mohit (Author) / Spanias, Andreas (Thesis advisor) / Chakrabarti, Chaitali (Thesis advisor) / Berisha, Visar (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2015
Description
The electric power system is one of the largest, most complicated, and most important cyber-physical systems in the world. The link between the cyber and physical level is the Supervisory Control and Data Acquisition (SCADA) systems and Energy Management Systems (EMS). Their functions include monitoring the real-time system operation through state estimation (SE), controlling the system to operate reliably, and optimizing the system operation efficiency. The SCADA acquires the noisy measurements, such as voltage angle and magnitude, line power flows, and line current magnitude, from the remote terminal units (RTUs). These raw data are firstly sent to the SE, which filters all the noisy data and derives the best estimate of the system state. Then the estimated states are used for other EMS functions, such as contingency analysis, optimal power flow, etc.
In the existing state estimation process, there is no defense mechanism for any malicious attacks. Once the communication channel between the SCADA and RTUs is hijacked by the attacker, the attacker can perform a man-in-middle attack and send data of its choice. The only step that can possibly detect the attack during the state estimation process is the bad data detector. Unfortunately, even the bad data detector is unable to detect a certain type of attack, known as the false data injection (FDI) attacks.
Diagnosing the physical consequences of such attacks, therefore, is very important to understand system stability. In this thesis, theoretical general attack models for AC and DC attacks are given and an optimization problem for the worst-case overload attack is formulated. Furthermore, physical consequences of FDI attacks, based on both DC and AC model, are addressed. Various scenarios with different attack targets and system configurations are simulated. The details of the research, results obtained and conclusions drawn are presented in this document.
In the existing state estimation process, there is no defense mechanism for any malicious attacks. Once the communication channel between the SCADA and RTUs is hijacked by the attacker, the attacker can perform a man-in-middle attack and send data of its choice. The only step that can possibly detect the attack during the state estimation process is the bad data detector. Unfortunately, even the bad data detector is unable to detect a certain type of attack, known as the false data injection (FDI) attacks.
Diagnosing the physical consequences of such attacks, therefore, is very important to understand system stability. In this thesis, theoretical general attack models for AC and DC attacks are given and an optimization problem for the worst-case overload attack is formulated. Furthermore, physical consequences of FDI attacks, based on both DC and AC model, are addressed. Various scenarios with different attack targets and system configurations are simulated. The details of the research, results obtained and conclusions drawn are presented in this document.
ContributorsLiang, Jingwen (Author) / Sankar, Lalitha (Thesis advisor) / Kosut, Oliver (Thesis advisor) / Hedman, Kory (Committee member) / Arizona State University (Publisher)
Created2015
Description
This work considers the problem of multiple detection and tracking in two complex time-varying environments, urban terrain and underwater. Tracking multiple radar targets in urban environments is rst investigated by exploiting multipath signal returns, wideband underwater acoustic (UWA) communications channels are estimated using adaptive learning methods, and multiple UWA communications users are detected by designing the transmit signal to match the environment. For the urban environment, a multi-target tracking algorithm is proposed that integrates multipath-to-measurement association and the probability hypothesis density method implemented using particle filtering. The algorithm is designed to track an unknown time-varying number of targets by extracting information from multiple measurements due to multipath returns in the urban terrain. The path likelihood probability is calculated by considering associations between measurements and multipath returns, and an adaptive clustering algorithm is used to estimate the number of target and their corresponding parameters. The performance of the proposed algorithm is demonstrated for different multiple target scenarios and evaluated using the optimal subpattern assignment metric. The underwater environment provides a very challenging communication channel due to its highly time-varying nature, resulting in large distortions due to multipath and Doppler-scaling, and frequency-dependent path loss. A model-based wideband UWA channel estimation algorithm is first proposed to estimate the channel support and the wideband spreading function coefficients. A nonlinear frequency modulated signaling scheme is proposed that is matched to the wideband characteristics of the underwater environment. Constraints on the signal parameters are derived to optimally reduce multiple access interference and the UWA channel effects. The signaling scheme is compared to a code division multiple access (CDMA) scheme to demonstrate its improved bit error rate performance. The overall multi-user communication system performance is finally analyzed by first estimating the UWA channel and then designing the signaling scheme for multiple communications users.
ContributorsZhou, Meng (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Kovvali, Narayan (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2014
Description
The large distributed electric power system is a hierarchical network involving the
transportation of power from the sources of power generation via an intermediate
densely connected transmission network to a large distribution network of end-users
at the lowest level of the hierarchy. At each level of the hierarchy (generation/ trans-
mission/ distribution), the system is managed and monitored with a combination of
(a) supervisory control and data acquisition (SCADA); and (b) energy management
systems (EMSs) that process the collected data and make control and actuation de-
cisions using the collected data. However, at all levels of the hierarchy, both SCADA
and EMSs are vulnerable to cyber attacks. Furthermore, given the criticality of the
electric power infrastructure, cyber attacks can have severe economic and social con-
sequences.
This thesis focuses on cyber attacks on SCADA and EMS at the transmission
level of the electric power system. The goal is to study the consequences of three
classes of cyber attacks that can change topology data. These classes include: (i)
unobservable state-preserving cyber attacks that only change the topology data; (ii)
unobservable state-and-topology cyber-physical attacks that change both states and
topology data to enable a coordinated physical and cyber attack; and (iii) topology-
targeted man-in-the-middle (MitM) communication attacks that alter topology data
shared during inter-EMS communication. Specically, attack class (i) and (ii) focus on
the unobservable attacks on single regional EMS while class (iii) focuses on the MitM
attacks on communication links between regional EMSs. For each class of attacks,
the theoretical attack model and the implementation of attacks are provided, and the
worst-case attack and its consequences are exhaustively studied. In particularly, for
class (ii), a two-stage optimization problem is introduced to study worst-case attacks
that can cause a physical line over
ow that is unobservable in the cyber layer. The long-term implication and the system anomalies are demonstrated via simulation.
For attack classes (i) and (ii), both mathematical and experimental analyses sug-
gest that these unobservable attacks can be limited or even detected with resiliency
mechanisms including load monitoring, anomalous re-dispatches checking, and his-
torical data comparison. For attack class (iii), countermeasures including anomalous
tie-line interchange verication, anomalous re-dispatch alarms, and external contin-
gency lists sharing are needed to thwart such attacks.
transportation of power from the sources of power generation via an intermediate
densely connected transmission network to a large distribution network of end-users
at the lowest level of the hierarchy. At each level of the hierarchy (generation/ trans-
mission/ distribution), the system is managed and monitored with a combination of
(a) supervisory control and data acquisition (SCADA); and (b) energy management
systems (EMSs) that process the collected data and make control and actuation de-
cisions using the collected data. However, at all levels of the hierarchy, both SCADA
and EMSs are vulnerable to cyber attacks. Furthermore, given the criticality of the
electric power infrastructure, cyber attacks can have severe economic and social con-
sequences.
This thesis focuses on cyber attacks on SCADA and EMS at the transmission
level of the electric power system. The goal is to study the consequences of three
classes of cyber attacks that can change topology data. These classes include: (i)
unobservable state-preserving cyber attacks that only change the topology data; (ii)
unobservable state-and-topology cyber-physical attacks that change both states and
topology data to enable a coordinated physical and cyber attack; and (iii) topology-
targeted man-in-the-middle (MitM) communication attacks that alter topology data
shared during inter-EMS communication. Specically, attack class (i) and (ii) focus on
the unobservable attacks on single regional EMS while class (iii) focuses on the MitM
attacks on communication links between regional EMSs. For each class of attacks,
the theoretical attack model and the implementation of attacks are provided, and the
worst-case attack and its consequences are exhaustively studied. In particularly, for
class (ii), a two-stage optimization problem is introduced to study worst-case attacks
that can cause a physical line over
ow that is unobservable in the cyber layer. The long-term implication and the system anomalies are demonstrated via simulation.
For attack classes (i) and (ii), both mathematical and experimental analyses sug-
gest that these unobservable attacks can be limited or even detected with resiliency
mechanisms including load monitoring, anomalous re-dispatches checking, and his-
torical data comparison. For attack class (iii), countermeasures including anomalous
tie-line interchange verication, anomalous re-dispatch alarms, and external contin-
gency lists sharing are needed to thwart such attacks.
ContributorsZhang, Jiazi (Author) / Sankar, Lalitha (Thesis advisor) / Hedman, Kory (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2015