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Many of the reports created at assembly lines, where all components of an aircraft are installed, frequently indicate that errors threaten safety. The proposed methodology in this study evaluates error prediction and risk mitigation to prevent failures and their consequences. The results linked to a typical electrical harness manufacture of a military aircraft estimated reductions of 93% in time and 90% in error during the creation of engineering manufacturing processes using AI techniques. However, traditional risk assessments methods struggle to identify and mitigate errors effectively. Thus, developing an advanced methodology to ensure systems safety is needed. This paper addresses how innovative AI technology solutions can overcome these challenges, mitigate error risks, and enhance safety in aerospace. Technologies, such as artificial intelligence, predictive algorithms, machine learning, and automation, can play a key role in enhancing safety. The aim of this study is to develop a model that considers the factors that can potentially contribute to error creation, through an artificial intelligence (AI) approach. The specific AI techniques used such as support vector machine, random forest, logistic regression, K-nearest neighbor, and XGBoost (Python 3.8.5) show good performance for use in error mitigation. We have compared the modeled values obtained in this study with the experimental ones. The results confirm that the best metrics are obtained by using support vector machine and logistic regression. The smallest deviation between the measured and modeled values for these AI methods do not exceed 5%. Furthermore, using advancements in machine learning methods can enhance error mitigation in aerospace. The use of AutoML can play a key role in automatically finding an appropriate model which provides the best performance metrics and therefore the most reliable forecast for data prediction and error mitigation.

Since the dawn of Quantum Computing (QC), theoretical developments like Shor’s algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the techni-cal challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using Parameterized Quantum Circuits (PQCs) in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. In this article we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like Quantum Natural Gradient Descent, Simultaneous Perturbation Stochastic Approximations or Circuit Learning methods, are at least 2× to 4× more energy efficient than their classical counterparts; why Feedback-Based Quantum Optimization is energy-inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of ≥ 20×. Finally, we use the NchooseK high-level programming model to run optimization problems on both gate-based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate-based counterparts., Los Alamos National Laboratory, Grant/Award Number: 20210397ER; Laboratory Directed Research and Development; National Nuclear Security Administration of U.S. Department of Energy, Grant/Award Number: 89233218CNA000001, Peer Reviewed, Postprint (author's final draft)

Being able to keep track of ones assets is a very useful thing, from avoiding losing ones keys or phone to being able to find the needed equipment in a busy hospital or on a construction site. The area of localization is actively evolving to find the best ways to accurately track objects and devices in an energy efficient manner, at any range, and in any type of environment. This thesis focuses on the last aspect of maintaining accurate localization regardless of environment. For radio frequency based systems, challenging environments containing many obstacles, e.g., indoor or urban areas, have a detrimental effect on the measurements used for positioning, making them deceptive. In this work, a method for correcting range measurements is proposed for a narrowband sub-GHz radio frequency based localization system using Received Signal Strength Indicator (RSSI) and Time-of-Flight (ToF) measurements for positioning. Three different machine learning models were implemented: a linear regressor, a least squares support vector machine regressor and a gaussian process regressor. They were compared in their ability to predict the true range between devices based on raw range measurements. Achieved was a 69.96 % increase in accuracy compared to uncorrected ToF estimates and a 88.74 % increase in accuracy compared to RSSI estimates. When the corrected range estimates were used for positioning with a trilateration algorithm using least squares estimation, a 67.84 % increase in accuracy was attained compared to positioning with uncorrected range estimates. This shows that this is an effective method of improving range estimates to facilitate more accurate positioning., Att kunna hålla reda på var ens tillgångar befinner sig kan vara mycket användbart, från att undvika att ens nycklar eller telefon tappas bort till att kunna hitta utrustningen man behöver i ett myllrande sjukhus eller på en byggarbetsplats. Området av lokalisering utvecklas aktivt för att hitta de bästa metoderna och teknologierna för att med precision kunna spåra fysiska objekt på ett energieffektivt sätt, på vilken räckvidd som helst, och i vilken miljö som helst. Detta arbete fokuserar på den sista aspekten av att uppnå precis positionering oavsett miljö. För radiofrekvensbaserade system har utmanande miljöer med många fysiska hinder som till exempel inomhus och stadsområden en negativ effekt på de mätningar som används för positionering, vilket gör dem vilseledande. I detta arbete föreslås en metod för att korrigera avståndsmätningar i ett narrowband sub-GHz radiofrekvensbaserat lokaliseringssystem som använder Received Signal Strength Indicator (RSSI)- och Time-of-Flight (ToF)-mätningar för positionering. Tre olika maskininlärningsmodeller har implementerats: en linear regressor, en least squares support vector machine regressor och en gaussian process regressor. Dessa jämfördes i sin förmåga att förutspå det sanna avståndet mellan enheter baserat på råa avståndsmätningar. De korrigerade avståndsmätningarna uppnådde 69.96 % högre nogrannhet jämfört med okorrigerade ToF-uppskattningar och 88.74 % högre nogrannhet jämfört med RSSI-uppskattningar. Avståndsuppskattningarna användes för positionering med trilateration och minsta kvadratmetoden. De korrigerade uppskattningarna gav 67.84 % mer precis positionering jämfört med de okorrigerde uppskattningarna. Detta visar att detta är en effektiv metod förbättra avståndsuppskattningarna för att i sin tur bidra till mer exakt positionering.

Quantum optimization algorithms offer a promising route to finding the ground states of target Hamiltonians on near-term quantum devices. Nonetheless, it remains necessary to limit the evolution time and circuit depth as much as possible, since otherwise decoherence will degrade the computation. Even when this is done, there always exists a non-negligible error in estimates of the ground state energy. Here we present a scalable extrapolation approach to mitigating this algorithmic error, which significantly improves estimates obtained using three well-studied quantum optimization algorithms: quantum annealing (QA), the variational quantum eigensolver, and the quantum imaginary time evolution at fixed evolution time or circuit depth. The approach is based on extrapolating the annealing time to infinity or the variance of estimates to zero. The method is reasonably robust against noise. For Hamiltonians which only involve few-body interactions, the additional computational overhead is an increase in the number of measurements by a constant factor. Analytic derivations are provided for the quadratic convergence of estimates of energy as a function of time in QA, and the linear convergence of estimates as a function of variance in all three algorithms. We have verified the validity of these approaches through both numerical simulation and experiments on IBM quantum machines. This work suggests a promising new way to enhance near-term quantum computing through classical post-processing.

Besides potentially delivering a huge leap in computational power, quantum computers also offer an essential platform for simulating properties of quantum systems. Consequently, various algorithms have been developed for approximating the dynamics of a target system on quantum computers. But generic quantum simulation algorithms—developed to simulate all Hamiltonians—are unlikely to result in optimal simulations of most physically relevant systems; optimal quantum algorithms need to exploit unique properties of target systems. The aim of this dissertation is to study two prominent properties of physical systems, namely locality and symmetry, and subsequently leverage these properties to design efficient quantum simulation algorithms. In the first part of the dissertation, we explore the locality of quantum systems and the fundamental limits on the propagation of information in power-law interacting systems. In particular, we prove upper limits on the speed at which information can propagate in power-law systems. We also demonstrate how such speed limits can be achieved by protocols for transferring quantum information and generating quantum entanglement. We then use these speed limits to constrain the propagation of error and improve the performance of digital quantum simulation. Additionally, we consider the implications of the speed limits on entanglement generation, the dynamics of correlation, the heating time, and the scrambling time in power-law interacting systems. In the second part of the dissertation, we propose a scheme to leverage the symmetry of target systems to suppress error in digital quantum simulation. We first study a phenomenon called destructive error interference, where the errors from different steps of a simulation cancel out each other. We then show that one can induce the destructive error interference by interweaving the simulation with unitary transformations generated by the symmetry of the target system, effectively providing a faster

The configurable fault-tolerant processor (CFTP) project was intended to develop the means for a system to operate in areas which include frequent single-effect events (SEEs) similar to those caused by ionized radiation colliding with logic gates. Such errors are capable of degrading the functionality of a system and completely changing a state machine, such as is at the heart of most spacecrafts’ processors. The method for this consisted of a field-programmable gate array (FPGA) being designed into a system which is capable of detecting and then correcting SEEs. The system was designed by many students. This project will take that design, which launched into space earlier this year, and reduce it to modules which can be uploaded individually, built around a core which will be part of the existing triple fault-tolerant design (TFTD). Modularizing the code allows more experiments to be simultaneously performed in the future by changing the architecture of the system to upload specific modules to specified addresses. This will allow smaller uploads and code tweaks, without incurring long upload times, and more frequent updates to run specific tests ad hoc. Research and development conducted for this thesis has demonstrated the capability to inject configuration errors into the current design and the TFTD’s ability to detect those and similar errors, contributing to a better understanding of the TFTD., http://archive.org/details/modularizationof1094563984, Lieutenant, United States Navy, Approved for public release; distribution is unlimited.

Coriolis Vibratory Gyroscope (CVG) is considered the most critical component in a strap downinertial navigation unit. It measures the rate of rotation through the Coriolis coupling, or exchangeof angular momentum, between a pair of resonance modes with very close resonance frequency,often referred to as a degenerate mode pair. The measurement error, however, is closely related tohow close the degenerate mode pair match in frequencies. Further the errors also drifts over time,making precise calibration nearly impossible.There is a particular class of CVGs with radial symmetry in their geometric design. Suchsymmetry permits the simultaneous actuation and measurement of multiple Coriolis mode pairs.Such design opens up the possibility of operating more than one pair of degenerate modes onthe same resonator. The UCLA resonator (URES) was designed to exploit the benefits of thispossibility. The first challenge to overcome is the simultaneous modal frequency match of twoCoriolis mode pairs. The two mode pairs of interest are the n = 2 mode, which is ellipticalin shape, and the n = 3 mode, which is trilobal in shape. The as etched resonator typicallyexhibit frequency mismatch on the order of 10Hz for both mode pairs. A technique using massperturbation has been developed to simultaneously reduce the frequency mismatch of both modepairs to within 0.1 Hz. Once tuned, a system identification procedure is performed to determine themodal properties of both mode pairs, from which the drive and sense electrodes and the peripheralelectronics can be optimally configured to maximize signal to noise ratio, and reduce quadratureerror.iiA new control architecture is then implemented to simultaneously operate both mode pairson the tuned resonator as two independent Coriolis sensors, thus allowing the extraction of twoindependent rate measurements from the same resonator. The noise and long term stability performanceof both mode pairs are characterized. Angle random walk (ARW), bias stabi

Faced with the exponential growth in computing requirements, programmable hardware accelerators, such as GPUs and FPGAs, are becoming increasingly popular in high performance computing systems. In deference to energy efficiency and scalability challenges in these systems, it is crucial to efficiently use hardware resources while maintaining their reliability requirements. To meet system reliability requirements, traditional methods add redundancy in hardware or software. However, these redundancy-based error mitigation techniques suffer from inefficient use of hardware resources. The goal in this dissertation is to devise low-overhead approaches to mitigate the fault-susceptibility of hardware accelerators, and use their available resources efficiently. For fault-susceptibility mitigation in GPU accelerators, this dissertation proposes a software-based approach that enables isolation of faulty components through task migration. Due to lack of configurable scheduler for GPUs, the proposed solution makes use of introspective kernels to enable effective task migration for isolating faulty components. This technique has very low overhead in terms of performance and energy and improves the accelerator lifetime and overall system cost. For FPGA accelerators, faulty component isolation is handled with a directive-based method through the synthesis tool. This dissertation presents practical optimization methods to efficiently use the available resources on programmable hardware accelerators. These optimizations are performed at different levels of abstractions that are useful for GPUs and FPGAs, and the trade-offs among them are elaborated. For GPUs, optimization opportunities are explored in hardware-level and source-level. For FPGAs, optimizations are studied at the compiler-level, source-level, and algorithm-level. These optimization methods seek to remove unnecessary redundancies from program or hardware. This dissertation demonstrates practical and efficient approaches

The E911 Initiative in the mid-1990s established an opportunity to obtain location specific digital evidence of subscriber activity from cellular carriers. Call Detail Records (CDR) containing Cell Site Location Information (CSLI) evidence production was made available from cellular carriers in response to the CALEA, 911 and ECPA acts. In the late 1990s, cellular carriers began to produce evidence for investigative and litigation purposes. CDR/CSLI evidence has become an important evidentiary focus in the courtroom. This research project resulted in the creation of a method of validating cellular carrier records accuracy and mitigating errors in forensic cell site analyst conclusions. The process establishes a scientific foundation critical to satisfying key Daubert requirements. The United States Patent and Trademark Office (USPTO) awarded a patent for this methodology.

This paper is a postprint of a paper submitted to and accepted for publication in [journal] and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library, In free space optical (FSO) communications, several factors can strongly affect the link quality. Among them, one of the most important impairments that can degrade the FSO link quality and its reliability even under the clear sky conditions consists of optical turbulence. In this work, the authors investigate the generation of both weak and moderate turbulence regimes in an indoor environment to assess the FSO link quality. In particular, they show that, due to the presence of the turbulence, the link experiences both erasure errors and packet losses during transmission, and also compare the experimental statistical distribution of samples with the predicted Gamma Gamma model. Furthermore, the authors demonstrate that the application of the RaptorQ codes noticeably improves the link quality decreasing the packet error rate (PER) by about an order of magnitude, also offering in certain cases an error-free transmission with a PER of ∼10−2 at Rytov variance value of 0.5. The results show that the recovery rate increases with the redundancy, the packet length and the number of source packets, and it decreases with increasing data rates.

The requirements of safety-critical wireless sensornetwork (WSN) applications, such as closed-loop control andtraffic safety, cannot be met by the IEEE 802.15.4-2006 standardnor its industrial WSN (IWSN) derivatives. The main problem inthat respect is the communication reliability, which is seriouslycompromised by 2.4-GHz interference, as well as multipathfading and attenuation (MFA) at industrial facilities. Meanwhile,communication blackouts in critical WSN applications maylead to devastating consequences, including production halts,damage to production assets and can pose a threat to safetyof human personnel. This work presents PREED, a method toimprove the reliability by exploiting the determinism in IWSNcommunication. The proposed solution is based on the analysisof bit error traces collected in real transmissions at four differentindustrial environments. A case study on WirelessHART packetformat shows that PREED recovers 42%-134% more packetsthan the competing approaches on links compromised by WLANinterference. In addition, PREED reduces one of the most trivialcauses of packet loss in IWSN, i.e. the corruption offrame lengthbyte, by 88% and 99%, for links exposed to WLAN interferenceand MFA, respectively., INSPEC Accession Number: 15310425

Space is a harsh environment, full of high-energy radiation that can cause single-event effects (SEE) in orbiting satellites. Some of these effects, such as single-event upsets and single-event functional interrupts, occur when ionizing radiation causes the logical value in a memory element to change and are detectable and correctable through various error mitigation techniques. In this thesis, we develop and implement a hardware solution to combat these SEE for deployment as an experimental module on Naval Postgraduate School Satellite 1 (NPSAT-1). Based on the relatively benign orbit of NPSAT-1, industrial-grade, commercial-off-the-shelf components that have shown tolerance to radiation were selected to keep costs low. The primary source of mitigation relies on a globally triple-modular redundant microprocessor system instantiated inside of a XILINX Kintex-7 field-programmable gate array. The system consists of an open-source microprocessor without interlocked pipeline stages (MIPS) based processor softcore, a cached memory structure capable of accessing double-data rate type three and secure digital card memories, an interface to the main satellite bus, and XILINX’s soft error mitigation softcore. The hardware was tested both in and out of the system and verified to work on the ground with faults injected. Other techniques to mitigate errors due to SEE, such as memory scrubbing, are intended to be added before launch., http://archive.org/details/implementationof1094548536, Lieutenant, United States Navy, Approved for public release; distribution is unlimited.