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Description
Demands in file size and transfer rates for consumer-orientated products have escalated in recent times. This is primarily due to the emergence of high definition video content. Now factor in the consumer desire for convenience, and we find that wireless service is the most desired approach for inter-connectivity. Consumers expect wireless service to emulate wired service with little to virtually no difference in quality of service (QoS). The background section of this document examines the QoS requirements for wireless connectivity of high definition video applications. I then proceed to look at proposed solutions at the physical (PHY) and the media access control (MAC) layers as well as cross-layer schemes. These schemes are subsequently are evaluated in terms of usefulness in a multi-gigabit, 60 GHz wireless multimedia system targeting the average consumer. It is determined that a substantial gap in published literature exists pertinent to this application. Specifically, little or no work has been found that shows how an adaptive PHYMAC cross-layer solution that provides real-time compensation for varying channel conditions might be actually implemented. Further, no work has been found that shows results of such a model. This research proposes, develops and implements in Matlab code an alternate cross-layer solution that will provide acceptable QoS service for multimedia applications. Simulations using actual high definition video sequences are used to test the proposed solution. Results based on the average PSNR metric show that a quasi-adaptive algorithm provides greater than 7 dB of improvement over a non-adaptive approach while a fully-adaptive alogrithm provides over18 dB of improvement. The fully adaptive implementation has been conclusively shown to be superior to non-adaptive techniques and sufficiently superior to even quasi-adaptive algorithms.
ContributorsBosco, Bruce (Author) / Reisslein, Martin (Thesis advisor) / Tepedelenlioğlu, Cihan (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Fully distributed wireless sensor networks (WSNs) without fusion center have advantages such as scalability in network size and energy efficiency in communications. Each sensor shares its data only with neighbors and then achieves global consensus quantities by in-network processing. This dissertation considers robust distributed parameter estimation methods, seeking global consensus on parameters of adaptive learning algorithms and statistical quantities.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
Diffusion adaptation strategy with nonlinear transmission is proposed. The nonlinearity was motivated by the necessity for bounded transmit power, as sensors need to iteratively communicate each other energy-efficiently. Despite the nonlinearity, it is shown that the algorithm performs close to the linear case with the added advantage of power savings. This dissertation also discusses convergence properties of the algorithm in the mean and the mean-square sense.
Often, average is used to measure central tendency of sensed data over a network. When there are outliers in the data, however, average can be highly biased. Alternative choices of robust metrics against outliers are median, mode, and trimmed mean. Quantiles generalize the median, and they also can be used for trimmed mean. Consensus-based distributed quantile estimation algorithm is proposed and applied for finding trimmed-mean, median, maximum or minimum values, and identification of outliers through simulation. It is shown that the estimated quantities are asymptotically unbiased and converges toward the sample quantile in the mean-square sense. Step-size sequences with proper decay rates are also discussed for convergence analysis.
Another measure of central tendency is a mode which represents the most probable value and also be robust to outliers and other contaminations in data. The proposed distributed mode estimation algorithm achieves a global mode by recursively shifting conditional mean of the measurement data until it converges to stationary points of estimated density function. It is also possible to estimate the mode by utilizing grid vector as well as kernel density estimator. The densities are estimated at each grid point, while the points are updated until they converge to a global mode.
ContributorsLee, Jongmin (Electrical engineer) (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2017
Description
The commercial semiconductor industry is gearing up for 5G communications in the 28GHz and higher band. In order to maintain the same relative receiver sensitivity, a larger number of antenna elements are required; the larger number of antenna elements is, in turn, driving semiconductor development. The purpose of this paper is to introduce a new method of dividing wireless communication protocols (such as the 802.11a/b/g
and cellular UMTS MAC protocols) across multiple unreliable communication links using a new link layer communication model in concert with a smart antenna aperture design referred to as Vector Antenna. A vector antenna is a ‘smart’ antenna system and as any smart antenna aperture, the design inherently requires unique microwave component performance as well as Digital Signal Processing (DSP) capabilities. This performance and these capabilities are further enhanced with a patented wireless protocol stack capability.
and cellular UMTS MAC protocols) across multiple unreliable communication links using a new link layer communication model in concert with a smart antenna aperture design referred to as Vector Antenna. A vector antenna is a ‘smart’ antenna system and as any smart antenna aperture, the design inherently requires unique microwave component performance as well as Digital Signal Processing (DSP) capabilities. This performance and these capabilities are further enhanced with a patented wireless protocol stack capability.
ContributorsJames, Frank Lee (Author) / Reisslein, Martin (Thesis advisor) / Seeling, Patrick (Thesis advisor) / McGarry, Michael (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2019
Description
Engineering is a multidisciplinary field with a variety of applications. However, since there are so many disciplines of engineering, it is often challenging to find the discipline that best suits an individual interested in engineering. Not knowing which area of engineering most aligns to one’s interests is challenging when deciding on a major and a career. With the development of the Engineering Interest Quiz (EIQ), the goal was to help individuals find the field of engineering that is most similar to their interests. Initially, an Engineering Faculty Survey (EFS) was created to gather information from engineering faculty at Arizona State University (ASU) and to determine keywords that describe each field of engineering. With this list of keywords, the EIQ was developed. Data from the EIQ compared the engineering students’ top three results for the best engineering discipline for them with their current engineering major of study. The data analysis showed that 70% of the respondents had their major listed as one of the top three results they were given and 30% of the respondents did not have their major listed. Of that 70%, 64% had their current major listed as the highest or tied for the highest percentage and 36% had their major listed as the second or third highest percentage. Furthermore, the EIQ data was compared between genders. Only 33% of the male students had their current major listed as their highest percentage, but 55% had their major as one of their top three results. Women had higher percentages with 63% listing their current major as their highest percentage and 81% listing it in the top three of their final results.
ContributorsWagner, Avery Rose (Co-author) / Lucca, Claudia (Co-author) / Taylor, David (Thesis director) / Miller, Cindy (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Engineering is a heavily male-dominated field and females are significantly less likely to choose an engineering-related major or career path. At the age of six years old, females start believing that their male peers are smarter than them, leading them to pursue less ambitious careers. The children's book Lyla B. An Engineering Legacy was created to encourage more young girls to discover their own potential and pursue engineering as a career. To explore the efficacy of the book on its target consumers, a pilot study was performed with first and second grade children. The participants' engineering knowledge; fixed and failure mindset beliefs; STEM (Science, Technology, Engineering, and Math) interest, competency, and career aspirations; and stereotype beliefs were evaluated before and after being read the book to determine if the story has a positive impact on children. Additionally, the satisfaction of the participants towards both the book and main character were analyzed quantitatively and qualitatively. Overall, the results of the study suggest that the book has a positive impact on the interest and competency of STEM fields and the stereotype beliefs that the children had towards engineers. The study also suggests that the book decreases fixed and failure mindsets and that the participants were satisfied with the overall concept of the book and main character, Lyla.
ContributorsPiatak, Catherine (Co-author) / Seelhammer, Marissa Leigh (Co-author) / Torrence, Kelly (Co-author) / Miller, Cindy (Thesis director) / Jordan, Shawn (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
The goal of this study was to understand elementary school children’s perceptions of engineering. A total of 949 elementary school students were surveyed, individually or as a whole group, to examine gender and age differences in achievement-related beliefs (i.e., competency, interest, and importance) pertaining to engineering-related skills and activities. The results of this study found that specific skills and activities showed significant gender and age differences for each of the three measures. Significant findings showed that younger students (kindergarten through second grade) found many of the engineering-related skills and activities more interesting than the older students (third through fifth grade); however, the older students rated more of the skills and activities as being important. Gender differences showed that girls typically rated themselves as being more competent, more interested in, and valuing the skills and activities that pertained more to mindset ideas, such as learning from your mistakes and failures or not giving up, whereas boys rated themselves higher in more of the hands-on activities, such as building with things like legos, blocks, and k’nex.
ContributorsHandlos, Jamie Lynn Harte (Author) / Miller, Cindy (Thesis director) / Reisslein, Martin (Committee member) / School of Life Sciences (Contributor) / Chemical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Since the inception of Internet of Things (IoT) framework, the amount of interaction between electronic devices has tremendously increased and the ease of implementing software between such devices has bettered. Such data exchange between devices, whether between Node to Server or Node to Node, has paved way for creating new business models. Wireless Video Sensor Network Platforms are being used to monitor and understand the surroundings better. Both hardware and software supporting such devices have become much smaller and yet stronger to enable these. Specifically, the invention of better software that enable Wireless data transfer have become more simpler and lightweight technologies such as HTML5 for video rendering, Common Gateway Interface(CGI) scripts enabling interactions between client and server and WebRTC from Google for peer to peer interactions. The role of web browsers in enabling these has been vastly increasing.
Although HTTP is the most reliable and consistent data transfer protocol for such interactions, the most important underlying challenge with such platforms is the performance based on power consumption and latency in data transfer.
In the scope of this thesis, two applications using CGI and WebRTC for data transfer over HTTP will be presented and the power consumption by the peripherals in transmitting the data and the possible implications for those will be discussed.
Although HTTP is the most reliable and consistent data transfer protocol for such interactions, the most important underlying challenge with such platforms is the performance based on power consumption and latency in data transfer.
In the scope of this thesis, two applications using CGI and WebRTC for data transfer over HTTP will be presented and the power consumption by the peripherals in transmitting the data and the possible implications for those will be discussed.
ContributorsRentala, Sri Harsha (Author) / Reisslein, Martin (Thesis advisor) / Kitchen, Jennifer (Committee member) / McGarry, Michael (Committee member) / Arizona State University (Publisher)
Created2016
Description
Video capture, storage, and distribution in wireless video sensor networks
(WVSNs) critically depends on the resources of the nodes forming the sensor
networks. In the era of big data, Internet of Things (IoT), and distributed
demand and solutions, there is a need for multi-dimensional data to be part of
the Sensor Network data that is easily accessible and consumable by humanity as
well as machinery. Images and video are expected to become as ubiquitous as is
the scalar data in traditional sensor networks. The inception of video-streaming
over the Internet, heralded a relentless research for effective ways of
distributing video in a scalable and cost effective way. There has been novel
implementation attempts across several network layers. Due to the inherent
complications of backward compatibility and need for standardization across
network layers, there has been a refocused attention to address most of the
video distribution over the application layer. As a result, a few video
streaming solutions over the Hypertext Transfer Protocol (HTTP) have been
proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion
Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These
frameworks, do not address the typical and future WVSN use cases. A highly
flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH)
are introduced. The platform's goal is to usher video as a data element that
can be integrated into traditional and non-Internet networks. A low cost,
scalable node is built from the ground up to be fully compatible with the
Internet of Things Machine to Machine (M2M) concept, as well as the ability to
be easily re-targeted to new applications in a short time. Flexi-WVSNP design
includes a multi-radio node, a middle-ware for sensor operation and
communication, a cross platform client facing data retriever/player framework,
scalable security as well as a cohesive but decoupled hardware and software
design.
(WVSNs) critically depends on the resources of the nodes forming the sensor
networks. In the era of big data, Internet of Things (IoT), and distributed
demand and solutions, there is a need for multi-dimensional data to be part of
the Sensor Network data that is easily accessible and consumable by humanity as
well as machinery. Images and video are expected to become as ubiquitous as is
the scalar data in traditional sensor networks. The inception of video-streaming
over the Internet, heralded a relentless research for effective ways of
distributing video in a scalable and cost effective way. There has been novel
implementation attempts across several network layers. Due to the inherent
complications of backward compatibility and need for standardization across
network layers, there has been a refocused attention to address most of the
video distribution over the application layer. As a result, a few video
streaming solutions over the Hypertext Transfer Protocol (HTTP) have been
proposed. Most notable are Apple’s HTTP Live Streaming (HLS) and the Motion
Picture Experts Groups Dynamic Adaptive Streaming over HTTP (MPEG-DASH). These
frameworks, do not address the typical and future WVSN use cases. A highly
flexible Wireless Video Sensor Network Platform and compatible DASH (WVSNP-DASH)
are introduced. The platform's goal is to usher video as a data element that
can be integrated into traditional and non-Internet networks. A low cost,
scalable node is built from the ground up to be fully compatible with the
Internet of Things Machine to Machine (M2M) concept, as well as the ability to
be easily re-targeted to new applications in a short time. Flexi-WVSNP design
includes a multi-radio node, a middle-ware for sensor operation and
communication, a cross platform client facing data retriever/player framework,
scalable security as well as a cohesive but decoupled hardware and software
design.
ContributorsSeema, Adolph (Author) / Reisslein, Martin (Thesis advisor) / Kitchen, Jennifer (Committee member) / Seeling, Patrick (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2017
Description
Visual attention (VA) is the study of mechanisms that allow the human visual system (HVS) to selectively process relevant visual information. This work focuses on the subjective and objective evaluation of computational VA models for the distortion-free case as well as in the presence of image distortions.
Existing VA models are traditionally evaluated by using VA metrics that quantify the match between predicted saliency and fixation data obtained from eye-tracking experiments on human observers. Though there is a considerable number of objective VA metrics, there exists no study that validates that these metrics are adequate for the evaluation of VA models. This work constructs a VA Quality (VAQ) Database by subjectively assessing the prediction performance of VA models on distortion-free images. Additionally, shortcomings in existing metrics are discussed through illustrative examples and a new metric that uses local weights based on fixation density and that overcomes these flaws, is proposed. The proposed VA metric outperforms all other popular existing metrics in terms of the correlation with subjective ratings.
In practice, the image quality is affected by a host of factors at several stages of the image processing pipeline such as acquisition, compression, and transmission. However, none of the existing studies have discussed the subjective and objective evaluation of visual saliency models in the presence of distortion. In this work, a Distortion-based Visual Attention Quality (DVAQ) subjective database is constructed to evaluate the quality of VA maps for images in the presence of distortions. For creating this database, saliency maps obtained from images subjected to various types of distortions, including blur, noise and compression, and varying levels of distortion severity are rated by human observers in terms of their visual resemblance to corresponding ground-truth fixation density maps. The performance of traditionally used as well as recently proposed VA metrics are evaluated by correlating their scores with the human subjective ratings. In addition, an objective evaluation of 20 state-of-the-art VA models is performed using the top-performing VA metrics together with a study of how the VA models’ prediction performance changes with different types and levels of distortions.
Existing VA models are traditionally evaluated by using VA metrics that quantify the match between predicted saliency and fixation data obtained from eye-tracking experiments on human observers. Though there is a considerable number of objective VA metrics, there exists no study that validates that these metrics are adequate for the evaluation of VA models. This work constructs a VA Quality (VAQ) Database by subjectively assessing the prediction performance of VA models on distortion-free images. Additionally, shortcomings in existing metrics are discussed through illustrative examples and a new metric that uses local weights based on fixation density and that overcomes these flaws, is proposed. The proposed VA metric outperforms all other popular existing metrics in terms of the correlation with subjective ratings.
In practice, the image quality is affected by a host of factors at several stages of the image processing pipeline such as acquisition, compression, and transmission. However, none of the existing studies have discussed the subjective and objective evaluation of visual saliency models in the presence of distortion. In this work, a Distortion-based Visual Attention Quality (DVAQ) subjective database is constructed to evaluate the quality of VA maps for images in the presence of distortions. For creating this database, saliency maps obtained from images subjected to various types of distortions, including blur, noise and compression, and varying levels of distortion severity are rated by human observers in terms of their visual resemblance to corresponding ground-truth fixation density maps. The performance of traditionally used as well as recently proposed VA metrics are evaluated by correlating their scores with the human subjective ratings. In addition, an objective evaluation of 20 state-of-the-art VA models is performed using the top-performing VA metrics together with a study of how the VA models’ prediction performance changes with different types and levels of distortions.
ContributorsGide, Milind Subhash (Author) / Karam, Lina J (Thesis advisor) / Abousleman, Glen (Committee member) / Li, Baoxin (Committee member) / Reisslein, Martin (Committee member) / Arizona State University (Publisher)
Created2016
Description
Ethernet based technologies are emerging as the ubiquitous de facto form of communication due to their interoperability, capacity, cost, and reliability. Traditional Ethernet is designed with the goal of delivering best effort services. However, several real time and control applications require more precise deterministic requirements and Ultra Low Latency (ULL), that Ethernet cannot be used for. Current Industrial Automation and Control Systems (IACS) applications use semi-proprietary technologies that provide deterministic communication behavior for sporadic and periodic traffic, but can lead to closed systems that do not interoperate effectively. The convergence between the informational and operational technologies in modern industrial control networks cannot be achieved using traditional Ethernet. Time Sensitive Networking (TSN) is a suite of IEEE standards designed by augmenting traditional Ethernet with real time deterministic properties ideal for Digital Signal Processing (DSP) applications. Similarly, Deterministic Networking (DetNet) is a Internet Engineering Task Force (IETF) standardization that enhances the network layer with the required deterministic properties needed for IACS applications. This dissertation provides an in-depth survey and literature review on both standards/research and 5G related material on ULL. Recognizing the limitations of several features of the standards, this dissertation provides an empirical evaluation of these approaches and presents novel enhancements to the shapers and schedulers involved in TSN. More specifically, this dissertation investigates Time Aware Shaper (TAS), Asynchronous Traffic Shaper (ATS), and Cyclic Queuing and Forwarding (CQF) schedulers. Moreover, the IEEE 802.1Qcc, centralized management and control, and the IEEE 802.1Qbv can be used to manage and control scheduled traffic streams with periodic properties along with best-effort traffic on the same network infrastructure. Both the centralized network/distributed user model (hybrid model) and the fully-distributed (decentralized) IEEE 802.1Qcc model are examined on a typical industrial control network with the goal of maximizing scheduled traffic streams. Finally, since industrial applications and cyber-physical systems require timely delivery, any channel or node faults can cause severe disruption to the operational continuity of the application. Therefore, the IEEE 802.1CB, Frame Replication and Elimination for Reliability (FRER), is examined and tested using machine learning models to predict faulty scenarios and issue remedies seamlessly.
ContributorsNasrallah, Ahmed (Author) / Reisslein, Martin (Thesis advisor) / Syrotiuk, Violet R. (Committee member) / LiKamWa, Robert (Committee member) / Thyagaturu, Akhilesh (Committee member) / Arizona State University (Publisher)
Created2022