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- Genre: Masters Thesis
- Creators: Papandreou-Suppappola, Antonia
Description
Constant false alarm rate is one of the essential algorithms in a RADAR detection system. It allows the RADAR system to dynamically set thresholds based on the data power level to distinguish targets with interfering noise and clutters.
To have a better acknowledgment of constant false alarm rate approaches performance, three clutter models, Gamma, Weibull, and Log-normal, have been introduced to evaluate the detection's capability of each constant false alarm rate algorithm.
The order statistical constant false alarm rate approach outperforms other conventional constant false alarm rate methods, especially in clutter evolved environments. However, this method requires high power consumption due to repeat sorting.
In the automotive RADAR system, the computational complexity of algorithms is essential because this system is in real-time. Therefore, the algorithms must be fast and efficient to ensure low power consumption and processing time.
The reduced computational complexity implementations of cell-averaging and order statistic constant false alarm rate were explored. Their big O and processing time has been reduced.
To have a better acknowledgment of constant false alarm rate approaches performance, three clutter models, Gamma, Weibull, and Log-normal, have been introduced to evaluate the detection's capability of each constant false alarm rate algorithm.
The order statistical constant false alarm rate approach outperforms other conventional constant false alarm rate methods, especially in clutter evolved environments. However, this method requires high power consumption due to repeat sorting.
In the automotive RADAR system, the computational complexity of algorithms is essential because this system is in real-time. Therefore, the algorithms must be fast and efficient to ensure low power consumption and processing time.
The reduced computational complexity implementations of cell-averaging and order statistic constant false alarm rate were explored. Their big O and processing time has been reduced.
ContributorsChu, Huiwen (Author) / Bliss, Daniel W. (Thesis advisor) / Alkhateeb, Ahmed (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2020
Description
Infants born before 37 weeks of pregnancy are considered to be preterm. Typically, preterm infants have to be strictly monitored since they are highly susceptible to health problems like hypoxemia (low blood oxygen level), apnea, respiratory issues, cardiac problems, neurological problems as well as an increased chance of long-term health issues such as cerebral palsy, asthma and sudden infant death syndrome. One of the leading health complications in preterm infants is bradycardia - which is defined as the slower than expected heart rate, generally beating lower than 60 beats per minute. Bradycardia is often accompanied by low oxygen levels and can cause additional long term health problems in the premature infant.The implementation of a non-parametric method to predict the onset of brady- cardia is presented. This method assumes no prior knowledge of the data and uses kernel density estimation to predict the future onset of bradycardia events. The data is preprocessed, and then analyzed to detect the peaks in the ECG signals, following which different kernels are implemented to estimate the shared underlying distribu- tion of the data. The performance of the algorithm is evaluated using various metrics and the computational challenges and methods to overcome them are also discussed.
It is observed that the performance of the algorithm with regards to the kernels used are consistent with the theoretical performance of the kernel as presented in a previous work. The theoretical approach has also been automated in this work and the various implementation challenges have been addressed.
It is observed that the performance of the algorithm with regards to the kernels used are consistent with the theoretical performance of the kernel as presented in a previous work. The theoretical approach has also been automated in this work and the various implementation challenges have been addressed.
ContributorsMitra, Sinjini (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Moraffah, Bahman (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2020
Description
Lattice-based Cryptography is an up and coming field of cryptography that utilizes the difficulty of lattice problems to design lattice-based cryptosystems that are resistant to quantum attacks and applicable to Fully Homomorphic Encryption schemes (FHE). In this thesis, the parallelization of the Residue Number System (RNS) and algorithmic efficiency of the Number Theoretic Transform (NTT) are combined to tackle the most significant bottleneck of polynomial ring multiplication with the hardware design of an optimized RNS-based NTT polynomial multiplier. The design utilizes Negative Wrapped Convolution, the NTT, RNS Montgomery reduction with Bajard and Shenoy extensions, and optimized modular 32-bit channel arithmetic for nine RNS channels to accomplish an RNS polynomial multiplication. In addition to a full software implementation of the whole system, a pipelined and optimized RNS-based NTT unit with 4 RNS butterflies is implemented on the Xilinx Artix-7 FPGA(xc7a200tlffg1156-2L) for size and delay estimates. The hardware implementation achieves an operating frequency of 47.043 MHz and utilizes 13239 LUT's, 4010 FF's, and 330 DSP blocks, allowing for multiple simultaneously operating NTT units depending on FGPA size constraints.
ContributorsBrist, Logan Alan (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Bliss, Daniel (Committee member) / Arizona State University (Publisher)
Created2020
Description
In the recent times, traffic congestion and motor accidents have been a major problem for transportation in major cities. Intelligent Transportation Systems has the potential to be an effective solution in order to tackle this issue. Connected Autonomous Vehicles can cooperate at intersections, ramp merging, lane change and other conflicting scenarios in order to resolve the conflicts and avoid collisions with other vehicles. A lot of works has been proposed for specific scenarios such as intersections, ramp merging or lane change which partially solve the conflict resolution problem. Also, one of the major issues in autonomous decision making - deadlocks have not been considered in some of the works. The existing works either do not consider deadlocks or lack a safety proof. This thesis proposes a cooperative driving solution that provides a complete navigation, conflict resolution and deadlock resolution for connected autonomous vehicles. A graph-based model is used to resolve the deadlocks between vehicles and the responsibility sensitive safety (RSS) rules have been used in order to ensure safety of the autonomous vehicles during conflict detection and resolution. This algorithm provides a complete navigation solution for an autonomous vehicle from its source to destination. The algorithm ensures that accidents do not occur even in the worst-case scenario and the decision making is deadlock free.
ContributorsAllamsetti, Harshith (Author) / Shrivastava, Aviral (Thesis advisor) / Sen, Arunabha (Committee member) / Ren, Fengbo (Committee member) / Arizona State University (Publisher)
Created2020
Description
Detecting areas of change between two synthetic aperture radar (SAR) images of the same scene, taken at different times is generally performed using two approaches. Non-coherent change detection is performed using the sample variance ratio detector, and displays a good performance in detecting areas of significant changes. Coherent change detection can be implemented using the classical coherence estimator, which does better at detecting subtle changes, like vehicle tracks. A two-stage detector was proposed by Cha et al., where the sample variance ratio forms the first stage, and the second stage comprises of Berger's alternative coherence estimator.
A modification to the first stage of the two-stage detector is proposed in this study, which significantly simplifies the analysis of the this detector. Cha et al. have used a heuristic approach to determine the thresholds for this two-stage detector. In this study, the probability density function for the modified two-stage detector is derived, and using this probability density function, an approach for determining the thresholds for this two-dimensional detection problem has been proposed. The proposed method of threshold selection reveals an interesting behavior shown by the two-stage detector. With the help of theoretical receiver operating characteristic analysis, it is shown that the two-stage detector gives a better detection performance as compared to the other three detectors. However, the Berger's estimator proves to be a simpler alternative, since it gives only a slightly poorer performance as compared to the two-stage detector. All the four detectors have also been implemented on a SAR data set, and it is shown that the two-stage detector and the Berger's estimator generate images where the areas showing change are easily visible.
A modification to the first stage of the two-stage detector is proposed in this study, which significantly simplifies the analysis of the this detector. Cha et al. have used a heuristic approach to determine the thresholds for this two-stage detector. In this study, the probability density function for the modified two-stage detector is derived, and using this probability density function, an approach for determining the thresholds for this two-dimensional detection problem has been proposed. The proposed method of threshold selection reveals an interesting behavior shown by the two-stage detector. With the help of theoretical receiver operating characteristic analysis, it is shown that the two-stage detector gives a better detection performance as compared to the other three detectors. However, the Berger's estimator proves to be a simpler alternative, since it gives only a slightly poorer performance as compared to the two-stage detector. All the four detectors have also been implemented on a SAR data set, and it is shown that the two-stage detector and the Berger's estimator generate images where the areas showing change are easily visible.
ContributorsBondre, Akshay Sunil (Author) / Richmond, Christ D (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Bliss, Daniel W (Committee member) / Arizona State University (Publisher)
Created2020
Description
With the formation of next generation wireless communication, a growing number of new applications like internet of things, autonomous car, and drone is crowding the unlicensed spectrum. Licensed network such as LTE also comes to the unlicensed spectrum for better providing high-capacity contents with low cost. However, LTE was not designed for sharing spectrum with others. A cooperation center for these networks is costly because they possess heterogeneous properties and everyone can enter and leave the spectrum unrestrictedly, so the design will be challenging. Since it is infeasible to incorporate potentially infinite scenarios with one unified design, an alternative solution is to let each network learn its own coexistence policy. Previous solutions only work on fixed scenarios. In this work we present a reinforcement learning algorithm to cope with the coexistence between Wi-Fi and LTE-LAA agents in 5 GHz unlicensed spectrum. The coexistence problem was modeled as a Dec-POMDP and Bayesian approach was adopted for policy learning with nonparametric prior to accommodate the uncertainty of policy for different agents. A fairness measure was introduced in the reward function to encourage fair sharing between agents. We turned the reinforcement learning into an optimization problem by transforming the value function as likelihood and variational inference for posterior approximation. Simulation results demonstrate that this algorithm can reach high value with compact policy representations, and stay computationally efficient when applying to agent set.
ContributorsSHIH, PO-KAN (Author) / Moraffah, Bahman (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Dasarathy, Gautam (Committee member) / Shih, YiChang (Committee member) / Arizona State University (Publisher)
Created2021
Description
The work presented in this manuscript has the overarching theme of radiation. The two forms of radiation of interest are neutrons, i.e. nuclear, and electric fields. The ability to detect such forms of radiation have significant security implications that could also be extended to very practical industrial applications. The goal is therefore to detect, and even image, such radiation sources.
The method to do so revolved around the concept of building large-area sensor arrays. By covering a large area, we can increase the probability of detection and gather more data to build a more complete and clearer view of the environment. Large-area circuitry can be achieved cost-effectively by leveraging the thin-film transistor process of the display industry. With production of displays increasing with the explosion of mobile devices and continued growth in sales of flat panel monitors and television, the cost to build a unit continues to decrease.
Using a thin-film process also allows for flexible electronics, which could be taken advantage of in-house at the Flexible Electronics and Display Center. Flexible electronics implies new form factors and applications that would not otherwise be possible with their single crystal counterparts. To be able to effectively use thin-film technology, novel ways of overcoming the drawbacks of the thin-film process, namely the lower performance scale.
The two deliverable devices that underwent development are a preamplifier used in an active pixel sensor for neutron detection and a passive electric field imaging array. This thesis will cover the theory and process behind realizing these devices.
The method to do so revolved around the concept of building large-area sensor arrays. By covering a large area, we can increase the probability of detection and gather more data to build a more complete and clearer view of the environment. Large-area circuitry can be achieved cost-effectively by leveraging the thin-film transistor process of the display industry. With production of displays increasing with the explosion of mobile devices and continued growth in sales of flat panel monitors and television, the cost to build a unit continues to decrease.
Using a thin-film process also allows for flexible electronics, which could be taken advantage of in-house at the Flexible Electronics and Display Center. Flexible electronics implies new form factors and applications that would not otherwise be possible with their single crystal counterparts. To be able to effectively use thin-film technology, novel ways of overcoming the drawbacks of the thin-film process, namely the lower performance scale.
The two deliverable devices that underwent development are a preamplifier used in an active pixel sensor for neutron detection and a passive electric field imaging array. This thesis will cover the theory and process behind realizing these devices.
ContributorsChung, Hugh E (Author) / Allee, David R. (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2015
Description
Researchers have observed that the frequencies of leading digits in many man-made and naturally occurring datasets follow a logarithmic curve, with digits that start with the number 1 accounting for 30% of all numbers in the dataset and digits that start with the number 9 accounting for 5% of all numbers in the dataset. This phenomenon, known as Benford's Law, is highly repeatable and appears in lists of numbers from electricity bills, stock prices, tax returns, house prices, death rates, lengths of rivers, and naturally occurring images. This paper will demonstrate that human speech spectra also follow Benford's Law. This observation is used to motivate a new set of features that can be efficiently extracted from speech and demonstrate that these features can be used to classify between human speech and synthetic speech.
ContributorsHsu, Leo (Author) / Berisha, Visar (Thesis advisor) / Spanias, Andreas (Committee member) / Papandreou-Suppappola, Antonia (Committee member) / Arizona State University (Publisher)
Created2022