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Quantum Error Correction in Repetition Codes
No full textThis paper explores the extension of classical error correction techniques to quantum codes, elucidating the opportunities and challenges therein. By using a Repetition code, the author shows how the formalism of encoding, error propagation, error recovery via a MWPM decoder, and decoding work in the regime of quantum circuits. Some pitfalls are mentioned, and the work concludes with potential future research avenues for partial completion of a masters degree in computer science at AFIT
Benchmark Performance of a Rust-Based Python Extension
No full textThis paper presents a benchmarking analysis comparing the performance of data structures implemented in PyO3 Rust plugins against native Python implementations. The benchmarked data structures include Structure of Arrays (SoA), Array of Structures (AoS), linked lists, and vectors. Rust and Python implementations of each structure were compared, facilitated by PyO3’s wrapping functionality. Benchmarks assessed data access and manipulation, running for 100 iterations across varying element sizes. Results revealed the Rust data structures outperformed the native python implementations with the exception of the Rust AoS plugin
Neural Image Sharpening: A Framework for Volumetric Wavefront Sensing and Imaging
No full textThis paper introduces a novel framework, to our knowledge, referred to here as neural image sharpening (NIS). NIS leverages implicit neural representations to sense and correct for anisoplanatic phase errors. In this paper, these phase errors manifest from the distributed-volume aberrations caused by atmospheric turbulence in digital-holographic (DH) measurements. With that said, NIS is not limited to this specific test case and has numerous potential benefits in applications where researchers gain access to the complex-valued optical field (e.g., microscopy, metrology, lidar, remote sensing, etc.). Nonetheless, using single-shot DH data, we employ NIS via unsupervised learning to perform volumetric wavefront sensing and imaging through turbulence (i.e., we sense and correct for the phase errors distributed along the turbulent propagation path). In so doing, we overcome the limitations of traditional pixel-based image sharpening (IS) frameworks. Using both wave-optics simulations and scaled-laboratory experiments, we demonstrate that NIS outperforms a traditional IS framework, achieving higher peak Strehl ratios over weak-, moderate-, and deep-turbulence conditions. Our findings establish NIS as a promising approach for volumetric wavefront sensing and imaging across numerous applications
Deployable Origami Structure
Get PDFThe present invention relates to deployable origami structures and methods of making and using same. Such deployable origami structures can be folded into a compact state utilizing origami, yet easily deployed as the structures comprise pseudo-elastic connectors. Additive manufacturing techniques can be used to make such deployable origami structures as such techniques can lower cost and structure weight
Event-Based Sensor Noise Modeling for Space-Based Space Domain Awareness
Get PDFBuilding off the foundation of a physics-based end-to-end model for event-based vision sensors (EVS) observing resident space objects (RSOs), we apply new techniques to model realistic low-light sensor noise. While previous approaches simulate memorized current leakage and apply temporal noise models, our methods improve on these approaches and additionally account for current-following noise as an event source. These improvements are key components for accurate event-generating simulators which can advise requirements and concepts of operations for dedicated event-based Space Domain Awareness (SDA) architectures. The EVS pixel’s independent and asynchronous recording of changes in photocurrent produces data with high temporal resolution and dynamic range making it an attractive technology for SDA. EVS are particularly appealing as a space-based payload because of their sparse data output reducing the need for downlink, computational, and power resources. However, truth data is limited for space-based architectures. Therefore, simulation of the underlying physics is particularly important. Our physics-based end-to-end model now includes noise based on induced photocurrent to generate simulated events closer to known truth. We introduce a new Poisson-based method to model the noise generated by the temporal variation of the dark current and a method to tune high-frequency white noise on the induced photocurrent to model the noise on signals above the dark current. These techniques demonstrably improve EVS noise modeling by closely matching observed event rate and polarity behavior, moving EVS one step closer to operational space-based SDA usage
Identification of Authentic GNSS Signals in Time-Differenced Carrier-Phase Measurements with a Software-Defined Radio Receiver
Get PDFThe time-differenced carrier phase can be computed from measurements recorded by a multi-global navigation satellite system software-defined radio receiver such as PyChips, from which the user displacement and receiver clock drift can be solved. PyChips is able to simultaneously track authentic and inauthentic signals in separate channels, which makes it possible to observe both types of measurements with corresponding navigation data. A random sample consensus algorithm has been introduced to assess the consistency between the measurements and data. This algorithm successfully separated authentic channels from inauthentic channels when they are broadcast simultaneously
Detection and Identification of Inauthentic Signals Based on the Frequency Drift of the GNSS Receiver Clock
No full textMin-Time Escape of a Dubins Car from a Polygon
No full textA turn constrained vehicle is initially located inside a region described as a convex polygon and desires to escape in minimum time. First, the method of characteristics is used to describe the time-optimal strategies for reaching a line. Next, the approach is extended to polygons constructed of a series of line segments. Using this construction technique, the min-time path to reach each edge is obtained; the resulting minimum of the set of optimal trajectories is then selected for escaping the polygon
Impact of Retrieval Augmented Generation and Large Language Model Complexity on Undergraduate Exams Created and Taken by AI Agents
Get PDFThe capabilities of large language models (LLMs) have advanced to the point where entire textbooks can be queried using retrieval-augmented generation (RAG), enabling AI to integrate external, up-to-date information into its responses. This study evaluates the ability of two OpenAI models, GPT-3.5 Turbo and GPT-4 Turbo, to create and answer exam questions based on an undergraduate textbook. 14 exams were created with four true-false, four multiple-choice, and two short-answer questions derived from an open-source Pacific Studies textbook. Model performance was evaluated with and without access to the source material using text-similarity metrics such as ROUGE-1, cosine similarity, and word embeddings. Fifty-six exam scores were analyzed, revealing that RAG-assisted models significantly outperformed those relying solely on pre-trained knowledge. GPT-4 Turbo also consistently outperformed GPT-3.5 Turbo in accuracy and coherence, especially in short-answer responses. These findings demonstrate the potential of LLMs in automating exam generation while maintaining assessment quality. However, they also underscore the need for policy frameworks that promote fairness, transparency, and accessibility. Given regulatory considerations outlined in the European Union AI Act and the NIST AI Risk Management Framework, institutions using AI in education must establish governance protocols, bias mitigation strategies, and human oversight measures. The results of this study contribute to ongoing discussions on responsibly integrating AI in education, advocating for institutional policies that support AI-assisted assessment while preserving academic integrity. The empirical results suggest not only performance benefits but also actionable governance mechanisms, such as verifiable retrieval pipelines and oversight protocols, that can guide institutional policies