Accelerating Real-Time, High-Resolution Depth Upsampling on FPGAs

While the popularity of high-resolution, computer-vision applications (e.g. mixed reality, autonomous vehicles) is increasing, there have been complementary advances in time-of-flight (ToF) depth-sensor resolution and quality. These advances in ToF sensors provide a platform that can enable real-time, depth-upsampling algorithms targeted for high-resolution video systems with low-latency requirements. This thesis demonstrates that filter-based upsampling algorithms are feasible for real-time, low-power scenarios, such as those on HMDs. Specifically, the author profiled, parallelized, and accelerated a filter-based depth-upsampling algorithm on an FPGA using high-level synthesis tools from Xilinx. We show that our accelerated algorithm can accurately upsample the resolution and reduce the noise of ToF sensors. We also demonstrate that this algorithm exceeds the real-time requirements of 90 frames-per-second (FPS) and 11 ms latency of mixed-reality hardware, achieving a lower-bound speedup of 40 times over the fastest CPU-only version and a 4.7 times speedup over the original GPU implementation

An Optimal Algorithm to Compute the Inverse Beacon Attraction Region

The beacon model is a recent paradigm for guiding the trajectory of messages or small robotic agents in complex environments. A beacon is a fixed point with an attraction pull that can move points within a given polygon. Points move greedily towards a beacon: if unobstructed, they move along a straight line to the beacon, and otherwise they slide on the edges of the polygon. The Euclidean distance from a moving point to a beacon is monotonically decreasing. A given beacon attracts a point if the point eventually reaches the beacon. The problem of attracting all points within a polygon with a set of beacons can be viewed as a variation of the art gallery problem. Unlike most variations, the beacon attraction has the intriguing property of being asymmetric, leading to separate definitions of attraction region and inverse attraction region. The attraction region of a beacon is the set of points that it attracts. It is connected and can be computed in linear time for simple polygons. By contrast, it is known that the inverse attraction region of a point - the set of beacon positions that attract it - could have Omega(n) disjoint connected components. In this paper, we prove that, in spite of this, the total complexity of the inverse attraction region of a point in a simple polygon is linear, and present a O(n log n) time algorithm to construct it. This improves upon the best previous algorithm which required O(n^3) time and O(n^2) space. Furthermore we prove a matching Omega(n log n) lower bound for this task in the algebraic computation tree model of computation, even if the polygon is monotone

Machine-Learning Space Applications on SmallSat Platforms with TensorFlow

Due to their attractive benefits, which include affordability, comparatively low development costs, shorter development cycles, and availability of launch opportunities, SmallSats have secured a growing commercial and educational interest for space development. However, despite these advantages, SmallSats, and especially CubeSats, suffer from high failure rates and (with few exceptions to date) have had low impact in providing entirely novel, market-redefining capabilities. To enable these more complex science and defense opportunities in the future, small-spacecraft computing capabilities must be flexible, robust, and intelligent. To provide more intelligent computing, we propose employing machine intelligence on space development platforms, which can contribute to more efficient communications, improve spacecraft reliability, and assist in coordination and management of single or multiple spacecraft autonomously. Using TensorFlow, a popular, open-source, machine-learning framework developed by Google, modern SmallSat computers can run TensorFlow graphs (principal component of TensorFlow applications) with both TensorFlow and TensorFlow Lite. The research showcased in this paper provides a flight-demonstration example, using terrestrial-scene image products collected in flight by our STP-H5/CSP system, currently deployed on the International Space Station, of various Convolutional Neural Networks (CNNs) to identify and characterize newly captured images. This paper compares CNN architectures including MobileNetV1, MobileNetV2, Inception-ResNetV2, and NASNet Mobile

A polynomial bound for untangling geometric planar graphs

To untangle a geometric graph means to move some of the vertices so that the resulting geometric graph has no crossings. Pach and Tardos [Discrete Comput. Geom., 2002] asked if every n-vertex geometric planar graph can be untangled while keeping at least n^\epsilon vertices fixed. We answer this question in the affirmative with \epsilon=1/4. The previous best known bound was \Omega((\log n / \log\log n)^{1/2}). We also consider untangling geometric trees. It is known that every n-vertex geometric tree can be untangled while keeping at least (n/3)^{1/2} vertices fixed, while the best upper bound was O(n\log n)^{2/3}. We answer a question of Spillner and Wolff [arXiv:0709.0170 2007] by closing this gap for untangling trees. In particular, we show that for infinitely many values of n, there is an n-vertex geometric tree that cannot be untangled while keeping more than 3(n^{1/2}-1) vertices fixed. Moreover, we improve the lower bound to (n/2)^{1/2}.Comment: 14 pages, 7 figure

Cauchy's Arm Lemma on a Growing Sphere

We propose a variant of Cauchy's Lemma, proving that when a convex chain on one sphere is redrawn (with the same lengths and angles) on a larger sphere, the distance between its endpoints increases. The main focus of this work is a comparison of three alternate proofs, to show the links between Toponogov's Comparison Theorem, Legendre's Theorem and Cauchy's Arm Lemma

Deterministic Sampling and Range Counting in Geometric Data Streams

We present memory-efficient deterministic algorithms for constructing epsilon-nets and epsilon-approximations of streams of geometric data. Unlike probabilistic approaches, these deterministic samples provide guaranteed bounds on their approximation factors. We show how our deterministic samples can be used to answer approximate online iceberg geometric queries on data streams. We use these techniques to approximate several robust statistics of geometric data streams, including Tukey depth, simplicial depth, regression depth, the Thiel-Sen estimator, and the least median of squares. Our algorithms use only a polylogarithmic amount of memory, provided the desired approximation factors are inverse-polylogarithmic. We also include a lower bound for non-iceberg geometric queries.Comment: 12 pages, 1 figur

Decomposition of Multiple Coverings into More Parts

We prove that for every centrally symmetric convex polygon Q, there exists a constant alpha such that any alpha*k-fold covering of the plane by translates of Q can be decomposed into k coverings. This improves on a quadratic upper bound proved by Pach and Toth (SoCG'07). The question is motivated by a sensor network problem, in which a region has to be monitored by sensors with limited battery lifetime

Every Large Point Set contains Many Collinear Points or an Empty Pentagon

We prove the following generalised empty pentagon theorem: for every integer $\ell \geq 2$, every sufficiently large set of points in the plane contains $\ell$ collinear points or an empty pentagon. As an application, we settle the next open case of the "big line or big clique" conjecture of K\'ara, P\'or, and Wood [\emph{Discrete Comput. Geom.} 34(3):497--506, 2005]

Comparative Treatment Outcomes for Patients With Idiopathic Subglottic Stenosis.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadImportance: Surgical treatment comparisons in rare diseases are difficult secondary to the geographic distribution of patients. Fortunately, emerging technologies offer promise to reduce these barriers for research. Objective: To prospectively compare the outcomes of the 3 most common surgical approaches for idiopathic subglottic stenosis (iSGS), a rare airway disease. Design, setting, and participants: In this international, prospective, 3-year multicenter cohort study, 810 patients with untreated, newly diagnosed, or previously treated iSGS were enrolled after undergoing a surgical procedure (endoscopic dilation [ED], endoscopic resection with adjuvant medical therapy [ERMT], or cricotracheal resection [CTR]). Patients were recruited from clinician practices in the North American Airway Collaborative and an online iSGS community on Facebook. Main outcomes and measures: The primary end point was days from initial surgical procedure to recurrent surgical procedure. Secondary end points included quality of life using the Clinical COPD (chronic obstructive pulmonary disease) Questionnaire (CCQ), Voice Handicap Index-10 (VHI-10), Eating Assessment Test-10 (EAT-10), the 12-Item Short-Form Version 2 (SF-12v2), and postoperative complications. Results: Of 810 patients in this cohort, 798 (98.5%) were female and 787 (97.2%) were white, with a median age of 50 years (interquartile range, 43-58 years). Index surgical procedures were ED (n = 603; 74.4%), ERMT (n = 121; 14.9%), and CTR (n = 86; 10.6%). Overall, 185 patients (22.8%) had a recurrent surgical procedure during the 3-year study, but recurrence differed by modality (CTR, 1 patient [1.2%]; ERMT, 15 [12.4%]; and ED, 169 [28.0%]). Weighted, propensity score-matched, Cox proportional hazards regression models showed ED was inferior to ERMT (hazard ratio [HR], 3.16; 95% CI, 1.8-5.5). Among successfully treated patients without recurrence, those treated with CTR had the best CCQ (0.75 points) and SF-12v2 (54 points) scores and worst VHI-10 score (13 points) 360 days after enrollment as well as the greatest perioperative risk. Conclusions and relevance: In this cohort study of 810 patients with iSGS, endoscopic dilation, the most popular surgical approach for iSGS, was associated with a higher recurrence rate compared with other procedures. Cricotracheal resection offered the most durable results but showed the greatest perioperative risk and the worst long-term voice outcomes. Endoscopic resection with medical therapy was associated with better disease control compared with ED and had minimal association with vocal function. These results may be used to inform individual patient treatment decision-making.Patient-Centered Outcomes Research Institute - PCOR

In Pursuit of Graph Analysis for Neural-Network Performance Evaluation

High-level deep-learning frameworks such as TensorFlow and PyTorch abstract computation and data movement from neural network model designers, boosting productivity, and enabling deep-learning models to grow ever larger and more complex in pursuit of superhuman accuracies. Some of the largest models can even require multi-node clusters to efficiently train and deploy. When these models are published, often only the total floating-point operations (FLOPs) and the parameter count are given as proxies for performance compared to other architectures. The widespread use of GPUs to execute these network models calls into question the validity of using purely computational measures to gauge algorithms that are not compute-bound. While leveraging FLOPs has traditionally been the de facto method of evaluating computational cost, it ignores memory-access penalties, kernel-launch overheads, and data-movement costs. This dissertation chronicles the journey of identifying and addressing this issue, starting with a low-level hardware accelerator. Even though the FLOPs of the algorithm do not change, it was shown that the accelerator design alone can have a large impact on scalability and performance. From there, a foray into deep learning (DL) begins. An existing DL algorithm was augmented with a state-of-the-art backbone resulting in a model with fewer FLOPs than the original. The goal was to boost the original network's performance. Instead, performance was lost, puzzling the researchers and leading to a deeper analysis on the model itself. It was discovered that the diameter of the directed-acyclic-graph (the Critical Datapath Length) describing a neural-network model was highly correlated with execution time. This phenomenon was shown across a set of 48 popular models running on multiple devices. The suite of networks was expanded to include over 400 networks with a much wider variety of architectural features. These networks were analyzed with both graph- and compute-based metrics to form a dataset complete with a standard set of metrics including input Size, Parameter count, total Operations, and Critical Datapath Length. This suite of metrics was dubbed SPOC. When analyzed together, SPOC metrics can give actionable performance intuition and showcase how graph metrics can describe the initially perplexing benchmarks that were collected when this voyage began