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Visual correspondence is a crucial step in key computer vision tasks, including camera localization, image registration, and structure from motion. The most effective techniques for matching keypoints currently involve using learned sparse or dense matchers, which need pairs of images. These neural networks have a good general understanding of features from both images, but they often struggle to match points from different semantic areas. This paper presents a new method that uses semantic cues from foundation vision model features (like DINOv2) to enhance local feature matching by incorporating semantic reasoning into existing descriptors. Therefore, the learned descriptors do not require image pairs at inference time, allowing feature caching and fast matching using similarity search, unlike learned matchers. We present adapted versions of six existing descriptors, with an average increase in performance of 29% in camera localization, with comparable accuracy to existing matchers as LightGlue and LoFTR in two existing benchmarks. Both code and trained models are available at https://www.verlab.dcc.ufmg.br/descriptors/reasoning_accv24, Comment: Accepted in ACCV 2024

We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method, dubbed XFeat (Accelerated Features), revisits fundamental design choices in convolutional neural networks for detecting, extracting, and matching local features. Our new model satisfies a critical need for fast and robust algorithms suitable to resource-limited devices. In particular, accurate image matching requires sufficiently large image resolutions - for this reason, we keep the resolution as large as possible while limiting the number of channels in the network. Besides, our model is designed to offer the choice of matching at the sparse or semi-dense levels, each of which may be more suitable for different downstream applications, such as visual navigation and augmented reality. Our model is the first to offer semi-dense matching efficiently, leveraging a novel match refinement module that relies on coarse local descriptors. XFeat is versatile and hardware-independent, surpassing current deep learning-based local features in speed (up to 5x faster) with comparable or better accuracy, proven in pose estimation and visual localization. We showcase it running in real-time on an inexpensive laptop CPU without specialized hardware optimizations. Code and weights are available at www.verlab.dcc.ufmg.br/descriptors/xfeat_cvpr24., Comment: CVPR 2024; Source code available at www.verlab.dcc.ufmg.br/descriptors/xfeat_cvpr24

We propose a novel learned keypoint detection method to increase the number of correct matches for the task of non-rigid image correspondence. By leveraging true correspondences acquired by matching annotated image pairs with a specified descriptor extractor, we train an end-to-end convolutional neural network (CNN) to find keypoint locations that are more appropriate to the considered descriptor. For that, we apply geometric and photometric warpings to images to generate a supervisory signal, allowing the optimization of the detector. Experiments demonstrate that our method enhances the Mean Matching Accuracy of numerous descriptors when used in conjunction with our detection method, while outperforming the state-of-the-art keypoint detectors on real images of non-rigid objects by 20 p.p. We also apply our method on the complex real-world task of object retrieval where our detector performs on par with the finest keypoint detectors currently available for this task. The source code and trained models are publicly available at https://github.com/verlab/LearningToDetect_PRL_2023, Comment: This is the accepted version of the paper to appear at Pattern Recognition Letters (PRL). The final journal version will be available at https://doi.org/10.1016/j.patrec.2023.08.012

Local feature extraction is a standard approach in computer vision for tackling important tasks such as image matching and retrieval. The core assumption of most methods is that images undergo affine transformations, disregarding more complicated effects such as non-rigid deformations. Furthermore, incipient works tailored for non-rigid correspondence still rely on keypoint detectors designed for rigid transformations, hindering performance due to the limitations of the detector. We propose DALF (Deformation-Aware Local Features), a novel deformation-aware network for jointly detecting and describing keypoints, to handle the challenging problem of matching deformable surfaces. All network components work cooperatively through a feature fusion approach that enforces the descriptors' distinctiveness and invariance. Experiments using real deforming objects showcase the superiority of our method, where it delivers 8% improvement in matching scores compared to the previous best results. Our approach also enhances the performance of two real-world applications: deformable object retrieval and non-rigid 3D surface registration. Code for training, inference, and applications are publicly available at https://verlab.dcc.ufmg.br/descriptors/dalf_cvpr23., Comment: CVPR 2023; Source code available at https://verlab.dcc.ufmg.br/descriptors/dalf_cvpr23

We present a novel learned keypoint detection method designed to maximize the number of correct matches for the task of non-rigid image correspondence. Our training framework uses true correspondences, obtained by matching annotated image pairs with a predefined descriptor extractor, as a ground-truth to train a convolutional neural network (CNN). We optimize the model architecture by applying known geometric transformations to images as the supervisory signal. Experiments show that our method outperforms the state-of-the-art keypoint detector on real images of non-rigid objects by 20 p.p. on Mean Matching Accuracy and also improves the matching performance of several descriptors when coupled with our detection method. We also employ the proposed method in one challenging realworld application: object retrieval, where our detector exhibits performance on par with the best available keypoint detectors. The source code and trained model are publicly available at https://github.com/verlab/LearningToDetect SIBGRAPI 2022

Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting and annotating training data under these conditions is expensive, time-consuming, error-prone, and not always practical. Usually, synthetic data is used as a feasible data source to increase the amount of training data. However, just directly using synthetic data may actually harm the model's performance under normal weather conditions while getting only small gains in adverse situations. Therefore, we present a novel architecture specifically designed for using synthetic training data for domain adaptation. We propose a simple yet powerful addition to DeepLabV3+ by using weather and time-of-the-day supervisors trained with multi-task learning, making it both weather and nighttime aware, which improves its mIoU accuracy by $14$ percentage points on the ACDC dataset while maintaining a score of $75\%$ mIoU on the Cityscapes dataset. Our code is available at https://github.com/lsmcolab/Semantic-Segmentation-under-Adverse-Conditions., Comment: This paper is accepted by BMVC 2022

Video stabilization plays a central role to improve videos quality. However, despite the substantial progress made by these methods, they were, mainly, tested under standard weather and lighting conditions, and may perform poorly under adverse conditions. In this paper, we propose a synthetic-aware adverse weather robust algorithm for video stabilization that does not require real data and can be trained only on synthetic data. We also present Silver, a novel rendering engine to generate the required training data with an automatic ground-truth extraction procedure. Our approach uses our specially generated synthetic data for training an affine transformation matrix estimator avoiding the feature extraction issues faced by current methods. Additionally, since no video stabilization datasets under adverse conditions are available, we propose the novel VSAC105Real dataset for evaluation. We compare our method to five state-of-the-art video stabilization algorithms using two benchmarks. Our results show that current approaches perform poorly in at least one weather condition, and that, even training in a small dataset with synthetic data only, we achieve the best performance in terms of stability average score, distortion score, success rate, and average cropping ratio when considering all weather conditions. Hence, our video stabilization model generalizes well on real-world videos and does not require large-scale synthetic training data to converge.

The growth of videos in our digital age and the users' limited time raise the demand for processing untrimmed videos to produce shorter versions conveying the same information. Despite the remarkable progress that summarization methods have made, most of them can only select a few frames or skims, creating visual gaps and breaking the video context. This paper presents a novel weakly-supervised methodology based on a reinforcement learning formulation to accelerate instructional videos using text. A novel joint reward function guides our agent to select which frames to remove and reduce the input video to a target length without creating gaps in the final video. We also propose the Extended Visually-guided Document Attention Network (VDAN+), which can generate a highly discriminative embedding space to represent both textual and visual data. Our experiments show that our method achieves the best performance in Precision, Recall, and F1 Score against the baselines while effectively controlling the video's output length. Visit https://www.verlab.dcc.ufmg.br/semantic-hyperlapse/tpami2022/ for code and extra results., Comment: Accepted to the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2022. arXiv admin note: text overlap with arXiv:2003.14229

Most of the existing handcrafted and learning-based local descriptors are still at best approximately invariant to affine image transformations, often disregarding deformable surfaces. In this paper, we take one step further by proposing a new approach to compute descriptors from RGB-D images (where RGB refers to the pixel color brightness and D stands for depth information) that are invariant to isometric non-rigid deformations, as well as to scale changes and rotation. Our proposed description strategies are grounded on the key idea of learning feature representations on undistorted local image patches using surface geodesics. We design two complementary local descriptors strategies to compute geodesic-aware features efficiently: one efficient binary descriptor based on handcrafted binary tests (named GeoBit), and one learning-based descriptor (GeoPatch) with convolutional neural networks (CNNs) to compute features. In different experiments using real and publicly available RGB-D data benchmarks, they consistently outperforms state-of-the-art handcrafted and learning-based image and RGB-D descriptors in matching scores, as well as in object retrieval and non-rigid surface tracking experiments, with comparable processing times. We also provide to the community a new dataset with accurate matching annotations of RGB-D images of different objects (shirts, cloths, paintings, bags), subjected to strong non-rigid deformations, for evaluation benchmark of deformable surface correspondence algorithms., Comment: This is a preprint version of the paper to appear at Computer Vision and Image Understanding (CVIU). The final journal version will be available at https://doi.org/10.1016/j.cviu.2022.103409

Despite the advances in extracting local features achieved by handcrafted and learning-based descriptors, they are still limited by the lack of invariance to non-rigid transformations. In this paper, we present a new approach to compute features from still images that are robust to non-rigid deformations to circumvent the problem of matching deformable surfaces and objects. Our deformation-aware local descriptor, named DEAL, leverages a polar sampling and a spatial transformer warping to provide invariance to rotation, scale, and image deformations. We train the model architecture end-to-end by applying isometric non-rigid deformations to objects in a simulated environment as guidance to provide highly discriminative local features. The experiments show that our method outperforms state-of-the-art handcrafted, learning-based image, and RGB-D descriptors in different datasets with both real and realistic synthetic deformable objects in still images. The source code and trained model of the descriptor are publicly available at https://www.verlab.dcc.ufmg.br/descriptors/neurips2021., Comment: To appear in Proceedings of the Thirty-fifth Annual Conference on Neural Information Processing Systems (NeurIPS) 2021

This paper proposes a new end-to-end neural rendering architecture to transfer appearance and reenact human actors. Our method leverages a carefully designed graph convolutional network (GCN) to model the human body manifold structure, jointly with differentiable rendering, to synthesize new videos of people in different contexts from where they were initially recorded. Unlike recent appearance transferring methods, our approach can reconstruct a fully controllable 3D texture-mapped model of a person, while taking into account the manifold structure from body shape and texture appearance in the view synthesis. Specifically, our approach models mesh deformations with a three-stage GCN trained in a self-supervised manner on rendered silhouettes of the human body. It also infers texture appearance with a convolutional network in the texture domain, which is trained in an adversarial regime to reconstruct human texture from rendered images of actors in different poses. Experiments on different videos show that our method successfully infers specific body deformations and avoid creating texture artifacts while achieving the best values for appearance in terms of Structural Similarity (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), Mean Squared Error (MSE), and Fr\'echet Video Distance (FVD). By taking advantages of both differentiable rendering and the 3D parametric model, our method is fully controllable, which allows controlling the human synthesis from both pose and rendering parameters. The source code is available at https://www.verlab.dcc.ufmg.br/retargeting-motion/wacv2022., Comment: 10 pages, 6 figures, to appear in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) 2022

Transferring human motion and appearance between videos of human actors remains one of the key challenges in Computer Vision. Despite the advances from recent image-to-image translation approaches, there are several transferring contexts where most end-to-end learning-based retargeting methods still perform poorly. Transferring human appearance from one actor to another is only ensured when a strict setup has been complied, which is generally built considering their training regime's specificities. In this work, we propose a shape-aware approach based on a hybrid image-based rendering technique that exhibits competitive visual retargeting quality compared to state-of-the-art neural rendering approaches. The formulation leverages the user body shape into the retargeting while considering physical constraints of the motion in 3D and the 2D image domain. We also present a new video retargeting benchmark dataset composed of different videos with annotated human motions to evaluate the task of synthesizing people's videos, which can be used as a common base to improve tracking the progress in the field. The dataset and its evaluation protocols are designed to evaluate retargeting methods in more general and challenging conditions. Our method is validated in several experiments, comprising publicly available videos of actors with different shapes, motion types, and camera setups. The dataset and retargeting code are publicly available to the community at: https://www.verlab.dcc.ufmg.br/retargeting-motion., Comment: 19 pages, 13 figures

Synthesizing human motion through learning techniques is becoming an increasingly popular approach to alleviating the requirement of new data capture to produce animations. Learning to move naturally from music, i.e., to dance, is one of the more complex motions humans often perform effortlessly. Each dance movement is unique, yet such movements maintain the core characteristics of the dance style. Most approaches addressing this problem with classical convolutional and recursive neural models undergo training and variability issues due to the non-Euclidean geometry of the motion manifold structure.In this paper, we design a novel method based on graph convolutional networks to tackle the problem of automatic dance generation from audio information. Our method uses an adversarial learning scheme conditioned on the input music audios to create natural motions preserving the key movements of different music styles. We evaluate our method with three quantitative metrics of generative methods and a user study. The results suggest that the proposed GCN model outperforms the state-of-the-art dance generation method conditioned on music in different experiments. Moreover, our graph-convolutional approach is simpler, easier to be trained, and capable of generating more realistic motion styles regarding qualitative and different quantitative metrics. It also presented a visual movement perceptual quality comparable to real motion data., Comment: Accepted at the Elsevier Computers & Graphics (C&G) 2020

This paper addresses the problem of building augmented metric representations of scenes with semantic information from RGB-D images. We propose a complete framework to create an enhanced map representation of the environment with object-level information to be used in several applications such as human-robot interaction, assistive robotics, visual navigation, or in manipulation tasks. Our formulation leverages a CNN-based object detector (Yolo) with a 3D model-based segmentation technique to perform instance semantic segmentation, and to localize, identify, and track different classes of objects in the scene. The tracking and positioning of semantic classes is done with a dictionary of Kalman filters in order to combine sensor measurements over time and then providing more accurate maps. The formulation is designed to identify and to disregard dynamic objects in order to obtain a medium-term invariant map representation. The proposed method was evaluated with collected and publicly available RGB-D data sequences acquired in different indoor scenes. Experimental results show the potential of the technique to produce augmented semantic maps containing several objects (notably doors). We also provide to the community a dataset composed of annotated object classes (doors, fire extinguishers, benches, water fountains) and their positioning, as well as the source code as ROS packages., Comment: Preprint version of the article to appear at Journal of Intelligent & Robotic Systems (2020)

Creating plausible virtual actors from images of real actors remains one of the key challenges in computer vision and computer graphics. Marker-less human motion estimation and shape modeling from images in the wild bring this challenge to the fore. Although the recent advances on view synthesis and image-to-image translation, currently available formulations are limited to transfer solely style and do not take into account the character's motion and shape, which are by nature intermingled to produce plausible human forms. In this paper, we propose a unifying formulation for transferring appearance and retargeting human motion from monocular videos that regards all these aspects. Our method synthesizes new videos of people in a different context where they were initially recorded. Differently from recent appearance transferring methods, our approach takes into account body shape, appearance, and motion constraints. The evaluation is performed with several experiments using publicly available real videos containing hard conditions. Our method is able to transfer both human motion and appearance outperforming state-of-the-art methods, while preserving specific features of the motion that must be maintained (e.g., feet touching the floor, hands touching a particular object) and holding the best visual quality and appearance metrics such as Structural Similarity (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS)., Comment: 10 pages, 8 figures, to appear in Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) 2020

The growth of Social Networks has fueled the habit of people logging their day-to-day activities, and long First-Person Videos (FPVs) are one of the main tools in this new habit. Semantic-aware fast-forward methods are able to decrease the watch time and select meaningful moments, which is key to increase the chances of these videos being watched. However, these methods can not handle semantics in terms of personalization. In this work, we present a new approach to automatically creating personalized fast-forward videos for FPVs. Our approach explores the availability of text-centric data from the user's social networks such as status updates to infer her/his topics of interest and assigns scores to the input frames according to her/his preferences. Extensive experiments are conducted on three different datasets with simulated and real-world users as input, achieving an average F1 score of up to 12.8 percentage points higher than the best competitors. We also present a user study to demonstrate the effectiveness of our method.

The detection of fiducial points on faces has significantly been favored by the rapid progress in the field of machine learning, in particular in the convolution networks. However, the accuracy of most of the detectors strongly depends on an enormous amount of annotated data. In this work, we present a domain adaptation approach based on a two-step learning to detect fiducial points on human and animal faces. We evaluate our method on three different datasets composed of different animal faces (cats, dogs, and horses). The experiments show that our method performs better than state of the art and can use few annotated data to leverage the detection of landmarks reducing the demand for large volume of annotated data., Comment: https://ieeexplore.ieee.org/document/8451026/

The remarkable technological advance in well-equipped wearable devices is pushing an increasing production of long first-person videos. However, since most of these videos have long and tedious parts, they are forgotten or never seen. Despite a large number of techniques proposed to fast-forward these videos by highlighting relevant moments, most of them are image based only. Most of these techniques disregard other relevant sensors present in the current devices such as high-definition microphones. In this work, we propose a new approach to fast-forward videos using psychoacoustic metrics extracted from the soundtrack. These metrics can be used to estimate the annoyance of a segment allowing our method to emphasize moments of sound pleasantness. The efficiency of our method is demonstrated through qualitative results and quantitative results as far as of speed-up and instability are concerned.

In this paper, we present a robust method for scene recognition, which leverages Convolutional Neural Networks (CNNs) features and Sparse Coding setting by creating a new representation of indoor scenes. Although CNNs highly benefited the fields of computer vision and pattern recognition, convolutional layers adjust weights on a global-approach, which might lead to losing important local details such as objects and small structures. Our proposed scene representation relies on both: global features that mostly refers to environment's structure, and local features that are sparsely combined to capture characteristics of common objects of a given scene. This new representation is based on fragments of the scene and leverages features extracted by CNNs. The experimental evaluation shows that the resulting representation outperforms previous scene recognition methods on Scene15 and MIT67 datasets, and performs competitively on SUN397, while being highly robust to perturbations in the input image such as noise and occlusion., Comment: CIARP 2017. To appear

Superconducting amplifiers are key components of modern quantum information circuits. To minimize information loss and reduce oscillations a tapered impedance transformer of new design is needed at the input/output for compliance with other 50 $\Omega$ components. We show that an optimal tapered transformer of length $\ell$, joining amplifier to input line, can be constructed using a variational principle applied to the linearized Riccati equation describing the voltage reflection coefficient of the taper. For an incident signal of frequency $\omega_o$ the variational solution results in an infinite set of equivalent optimal transformers, each with the same form for the reflection coefficient, each able to eliminate input-line reflections. For the special case of optimal lossless transformers, the group velocity $v_g$ is shown to be constant, with characteristic impedance dependent on frequency $\omega_c=\pi v_g / \ell$. While these solutions inhibit input-line reflections only for frequency $\omega_o$, a subset of optimal lossless transformers with $\omega_o$ significantly detuned from $\omega_c$ does exhibit a wide bandpass. Specifically, by choosing $\omega_o\rightarrow 0$ ($\omega_o\rightarrow\infty$), we obtain a subset of optimal low-pass (high-pass) lossless tapers with bandwidth $(0,\sim\omega_c)$ ($(\sim\omega_c,\infty)$). From the subset of solutions we derive both the wide-band low-pass and high-pass transformers, and we discuss the extent to which they can be realized given fabrication constraints. In particular, we demonstrate the superior reflection response of our high-pass transformer when compared to other taper designs. Our results have application to amplifier, transceiver, and other components sensitive to impedance mismatch., Comment: 35 preprint pages, 6 figures, submitted to Journal of Applied Physics

Many of the state-of-the-art algorithms for gesture recognition are based on Conditional Random Fields (CRFs). Successful approaches, such as the Latent-Dynamic CRFs, extend the CRF by incorporating latent variables, whose values are mapped to the values of the labels. In this paper we propose a novel methodology to set the latent values according to the gesture complexity. We use an heuristic that iterates through the samples associated with each label value, stimating their complexity. We then use it to assign the latent values to the label values. We evaluate our method on the task of recognizing human gestures from video streams. The experiments were performed in binary datasets, generated by grouping different labels. Our results demonstrate that our approach outperforms the arbitrary one in many cases, increasing the accuracy by up to 10%., Comment: Conference paper published at 2016 29th SIBGRAPI, Conference on Graphics, Patterns and Images (SIBGRAPI). 8 pages, 7 figures

This work is on the Physics of the B Factories. Part A of this book contains a brief description of the SLAC and KEK B Factories as well as their detectors, BaBar and Belle, and data taking related issues. Part B discusses tools and methods used by the experiments in order to obtain results. The results themselves can be found in Part C. Please note that version 3 on the archive is the auxiliary version of the Physics of the B Factories book. This uses the notation alpha, beta, gamma for the angles of the Unitarity Triangle. The nominal version uses the notation phi_1, phi_2 and phi_3. Please cite this work as Eur. Phys. J. C74 (2014) 3026., Comment: 928 pages, version 3 (arXiv:1406.6311v3) corresponds to the alpha, beta, gamma version of the book, the other versions use the phi1, phi2, phi3 notation

We have generated frequency combs spanning 0.5 to 20 GHz in superconducting half wave resonators at T=3 K. Thin films of niobium-titanium nitride enabled this development due to their low loss, high nonlinearity, low frequency dispersion, and high critical temperature. The combs nucleate as sidebands around multiples of the pump frequency. Selection rules for the allowed frequency emission are calculated using perturbation theory and the measured spectrum is shown to agree with the theory. The sideband spacing is measured to be accurate to 1 part in 10 million. The sidebands coalesce into a continuous comb structure that has been observed to cover at least 6 octaves in frequency.

The SLAC Linac can deliver damped bunches with ILC parameters for bunch charge and bunch length to End Station A. A 10Hz beam at 28.5 GeV energy can be delivered there, parasitic with PEP-II operation. We plan to use this facility to test prototype components of the Beam Delivery System and Interaction Region. We discuss our plans for this ILC Test Facility and preparations for carrying out experiments related to collimator wakefields and energy spectrometers. We also plan an interaction region mockup to investigate effects from backgrounds and beam-induced electromagnetic interference., Comment: 3 pages, 3 figures, contributed to Particle Accelerator Conference (PAC05), Knoxville, TN, May 16-20, 2005

A beam test of GLAST (Gamma-ray Large Area Space Telescope) components was performed at the Stanford Linear Accelerator Center in October, 1997. These beam test components were simple versions of the planned flight hardware. Results on the performance of the tracker, calorimeter, and anti-coincidence charged particle veto are presented., Comment: 39 pages, 21 figures

The fixed-wing, airplane configuration flight-test results of the Rotor System Research Aircraft (RSRA), NASA 740, at Ames/Dryden Flight Research Center are documented. Fourteen taxi and flight tests were performed from December 1983 to October 1984. This was the first time the RSRA was flown with the main rotor removed; the tail rotor was installed. These tests confirmed that the RSRA is operable as a fixed-wing aircraft. Data were obtained for various takeoff and landing distances, control sensitivity, trim and dynamics stability characteristics, performance rotor-hub drag, and acoustics signature. Stability data were obtained with the rotor hub both installed and removed. The speed envelope was developed to 261 knots true airspeed (KTAS), 226 knots calibrated airspeed (KCAS) at 10,000 ft density altitude. The airplane was configured at 5 deg. wing incidence with 5 deg. wing flaps as a normal configuration. Level-flight data were acquired at 167 KCAS for wing incidence from 0 to 10 deg. Step inputs and doublet inputs of various magnitudes were utilized to acquire dynamic stability and control sensitivity data. Sine-wave inputs of constantly increasing frequency were used to generate parameter identification data. The maximum load factor attained was 2.34 g at 206 KCAS.

The Rotor Systems Research Aircraft (RSRA) was flown in the airplane configuration. Eleven successful flights were made from May 8 to October 3, 1984. The takeoff and landing method was demonstrated and the resultant parameters were obtained. Control power data were obtained. In addition, main rotor hub drag, airplane configuration acoustics data, and aircraft stability data were obtained. Hub drag, acoustics, and stability will be separately reported in more detail. The tail rotor was installed for all tests.

The Rotor Systems Research Aircraft (RSRA) has been undergoing ground and flight tests by Ames Research Center since late 1979, primarily as a compound aircraft. The purpose was to train pilots and to check out and develop the design flight envelope established by the Sikorsky Aircraft Company. This paper reviews the preparation and flight test of the RSRA in the airplane, or fixed-wing, configuration and discusses the results of that test.

The flight test activities of the Rotor System Research Aircraft (RSRA), NASA 740, from June 30, 1981 to August 5, 1982 are reported. Tests were conducted in both the helicopter and compound configurations. Compound tests reconfirmed the Sikorsky flight envelope except that main rotor blade bending loads reached endurance at a speed about 10 knots lower than previously. Wing incidence changes were made from 0 to 10 deg.

A flight test program utilizing the Rotor Systems Research Aircraft (RSRA) main rotor balance system was conducted to obtain data for the helicopter configuration. The test program is discussed along with the employed data reduction methods, and the results. NASA 740, the RSRA used for the vertical drag test, was in the standard helicopter configuration. The 31-foot radius blades have a linear twist of eight degrees and NACA 0012 airfoil sections. Aspects of instrumentation are considered, taking into account the data recording system, the static calibration facility, and aspects of data calibration and processing. Attention is given to the test procedure, data analysis methods, balance measurements, the ground effect, and vertical drag. It is found that the RSRA rotor balance system is capable of providing high quality performance data. The vertical drag of the RSRA is 4%, compared to 2.9% predicted.

This is the final report summarizing the work completed under contract NAS8-26972. Concept selection, design, fabricating and testing of a prototype compact heat exchanger thermodynamic vent system are discussed. The system is designed to operate in a 2.7m (9 foot) spherical liquid oxygen tank with a heating rate of 32.2 - 35.2 watts (110-120 Btu/hr) and to control pressure to 310 + or - 13.8 kN/sq m (45 + or - 2.0 psia.) the design mission is of 2,590 ks (30 days) duration on board a space shuttle orbiter.

The application of synthetic aperture radar (SAR) in monitoring and managing earth resources is examined. Synthetic aperture radars form a class of side-looking airborne radar, often referred to as coherent SLAR, which permits fine-resolution radar imagery to be generated at long operating ranges by the use of signal processing techniques. By orienting the antenna beam orthogonal to the motion of the spacecraft carrying the radar, a one-dimensional imagery ray system is converted into a two-dimensional or terrain imaging system. The radar's ability to distinguish - or resolve - closely spaced transverse objects is determined by the length of the pulse. The transmitter components receivers, and the mixer are described in details.

A wicking investigation revealed that passive thermal conditioning was feasible and provided considerable weight advantage over active systems using throttled vent fluid in a Centaur D-1s launch vehicle. Experimental wicking correlations were obtained using empirical revisions to the analytical flow model. Thermal subcoolers were evaluated parametrically as a function of tank pressure and NPSP. Results showed that the RL10 category I engine was the best candidate for boost pump replacement and the option showing the lowest weight penalty employed passively cooled acquisition devices, thermal subcoolers, dry ducts between burns and pumping of subcooler coolant back into the tank. A mixing correlation was identified for sizing the thermodynamic vent system mixer. Worst case mixing requirements were determined by surveying Centaur D-1T, D-1S, IUS, and space tug vehicles. Vent system sizing was based upon worst case requirements. Thermodynamic vent system/mixer weights were determined for each vehicle.

Success is reported in the radar monitoring of such features of sea ice as concentration, floe size, leads and other water openings, drift, topographic features such as pressure ridges and hummocks, fractures, and a qualitative indication of age and thickness. Scatterometer measurements made north of Alaska show a good correlation with a scattering coefficient with apparent thickness as deduced from ice type analysis of stereo aerial photography. Indications are that frequencies from 9 GHz upward seem to be better for sea ice radar purposes than the information gathered at 0.4 GHz by a scatterometer. Some information indicates that 1 GHz is useful, but not as useful as higher frequencies. Either form of like-polarization can be used and it appears that cross-polarization may be more useful for thickness measurement. Resolution requirements have not been fully established, but most of the systems in use have had poorer resolution than 20 meters. The radar return from sea ice is found to be much different than that from lake ice. Methods to decrease side lobe levels of the Fresnel zone-plate processor and to decrease the memory requirements of a synthetic radar processor are discussed.

The use of a scanning antenna beam for a synthetic aperture system was examined. When the resolution required was modest, the radar did not use all the time the beam was passing a given point on the ground to build a synthetic aperture, so time was available to scan the beam to other positions and build several images at different ranges. The scanning synthetic-aperture radar (SCANSAR) could achieve swathwidths of well over 100 km with modest antenna size. Design considerations for a SCANSAR for hydrologic parameter observation are presented. Because of the high sensitivity to soil moisture at angles of incidence near vertical, a 7 to 22 deg swath was considered for that application. For snow and ice monitoring, a 22 to 37 deg scan was used. Frequencies from X-band to L-band were used in the design studies, but the proposed system operated in C-band at 4.75 GHz. It achieved an azimuth resolution of about 50 meters at all angles, with a range resolution varying from 150 meters at 7 deg to 31 meters at 37 deg. The antenna required an aperture of 3 x 4.16 meters, and the average transmitter power was under 2 watts.

The application of synthetic aperture radar (SAR) in monitoring and managing earth resources was examined. The function of spaceborne radar is to provide maps and map imagery to be used for earth resource and oceanographic applications. Spaceborne radar has the capability of mapping the entire United States regardless of inclement weather; however, the imagery must have a high degree of resolution to be meaningful. Attaining this resolution is possible with the SAR system. Imagery of the required quality must first meet mission parameters in the following areas: antenna patterns, azimuth and range ambiguities, coverage, and angle of incidence.

For abstract, see N77-28560.

Recent work was reviewed in the field of remote sensing relative to soil moisture. The target parameters were recognized that are necessary if optimum data retrieval is to be realized, and proper sensor instrumentation was recommended to achieve this goal.

The state of the art determination was made for radar measurement of: soil moisture, snow, standing and flowing water, lake and river ice, determination of required spacecraft radar parameters, study of synthetic-aperture radar systems to meet these parametric requirements, and study of techniques for on-board processing of the radar data. Significant new concepts developed include the following: scanning synthetic-aperture radar to achieve wide-swath coverage; single-sideband radar; and comb-filter range-sequential, range-offset SAR processing. The state of the art in radar measurement of water resources parameters is outlined. The feasibility for immediate development of a spacecraft water resources SAR was established. Numerous candidates for the on-board processor were examined.

Young open star cluster NGC 6823 membership based on relative proper motions data and color- magnitude diagram

The nuclear heating of the propellant in all of the four baseline RNS configurations studied was much lower than that of the nuclear flight module configuration with the 5000-MW NERVA analyzed previously. Although the nuclear heating has been reduced, the effect of nuclear heating on the propellant as well as the effect of nuclear heating on internal structures such as antivortex baffles, screens, and sump components cannot be neglected. In addition, it was found that the present analytical precedures were not able to predict boundary layer initiation and breakoff points with the accuracy necessary to predict propellant thermodynamic nonequilibrium (stratification) and/or mixing.