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Rockville, MD, United States

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Patent
Applied Research LLC | Date: 2016-07-11

The present invention provides a system to create a quiet zone by suppressing background noise near a users head. The present invention utilizes two microphones; one microphone receives environmental noise and the other one is located close to a persons head. A parabolic dish loudspeaker creates a uniform sound field near a users head. A high performance frequency-domain filtered-x least mean square with band selection (FD-FX-LMS-BS) algorithm is utilized to generate the anti-phase noise signals. The algorithm has high noise reduction performance and also allows selection of specific frequency bands for noise reduction. The FD-FX-LMS-BS algorithm is performed by a field programmable gate array (FPGA) chip, which has minimal delay in algorithm processing.


Grant
Agency: Department of Defense | Branch: Air Force | Program: STTR | Phase: Phase I | Award Amount: 150.00K | Year: 2015

ABSTRACT: Our main objective in this project is to propose a novel Structured Sparse Priors for Target Recognition (SSPTR) system and to demonstrate that discriminative applications such as data clustering or target detection, tracking, and classification can be solved effectively and directly on the compressed measurement domain without the need to recover the original data. Our proposed sparse-representation discriminative algorithms have, at worst, the same level of complexity as popular sparse recovery algorithms in CS signal reconstruction while yielding comparable clustering/detection/classification accuracy as state-of-the-art discriminative strategies applying on original data. Here the crucial observation is that a test sample can be reasonably approximated as a linear combination of training samples belonging to the same class, with no contributions from training samples of other classes. Therefore, the sparse code which is often recovered via either basis pursuit or matching pursuit naturally encodes discriminative information that is crucial to classification tasks. In other words, the semantic (label) information of the signal of interest is directly captured in and instantaneously available from the sparse representation. Moreover, we also propose to further improve our baseline sparse-representation-based classification approach by the development of a novel unifying robust discriminative framework based on sparse representations directly on the collected measurements via context-aware and observable data-adaptive dictionaries and available domain-knowledge priors.; BENEFIT: Target/pattern detection, classification, and recognition applications will benefit more by incorporating such class-specific discriminative information than merely by conventional sparse signal recovery followed by a conventional classification strategy. Hence, we focus on maximizing the discriminability within the sparse recovery process by enforcing meaningful adaptive class-specific priors/constraints directly in the data measurement domain along with adaptive sparse representations in the measurement space explicitly for the purpose of image understanding and classification. ???Our proposed system can be used for missile seekers and other military surveillance and reconnaissance applications. We expect our software will have a unit price of $300 per device. With an estimated sales of over 20,000 units in the next decade, the military market potential results in more than 6 million dollars in the next decade. ????Besides military applications, ARLLCs technology will have many users in the commercial world. For example, border patrol, security monitoring in buildings and parking lots, coastal patrol, urban development monitoring, vegetation monitoring, hurricane damage assessment, and many others can benefit from our technology. We expect the commercial market size will be at least 20 million dollars over the next decade. ?


A method and apparatus for real-time target recognition within a multispectral image includes generating radiance signatures from reflectance signatures, sensor information and environment information and detecting targets in the multispectral image with a sparsity-driven target recognition algorithm utilizing set of parameters tuned with a deep neural network.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: STTR | Phase: Phase I | Award Amount: 125.00K | Year: 2016

We propose high performance image processing algorithms that will support current and future Mastcam imagers. The algorithms fuses the acquired Mastcam stereo images at different wavelengths to generate multispectral image cubes which can then be used for both anomaly detection and rough composition estimation from relatively longer distances when compared to LIBS instrument. To address the challenge in the stereo image alignment, we propose a two-step image registration approach. The first step consists of using the well-known RANSAC (Random Sample Consensus) technique for an initial image registration. The second step uses this roughly aligned image with RANSAC and the left camera image and applies a Diffeomorphic registration process. Diffeomorphic registration is formulated as a constrained optimization problem which is solved with a step-then-correct strategy. This second step allows to reduce the registration errors to subpixel levels and makes it possible to conduct reliable anomaly detection and composition estimation analyses with the constructed multispectral image cubes. Finally, in this framework, we provide a set of both conventional and state-of-the-art anomaly detection and composition estimation techniques to be applied to the generated Mastcam multispectral image cubes for guiding the Mars rover to interesting locations.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2014

Spacecraft cabin is noisy and uncomfortable. Sometimes, the noise level can exceed 80 dBA. There are 2 challenges to meet the above needs. One is to generate an anti-phase (180 deg out of phase) signal in real-time to reduce the loud cabin noise in a small area. Another one is to minimize the spillover of the anti-phase signal to other places. We propose a novel and high performance approach to suppressing background noise in noisy cabins. Our system utilizes a patent pending adaptive algorithm that can generate anti-phase signals in real-time to suppress background noise. Our system is portable and easy to set up. Most importantly, our system can suppress background noise in a small area and will not spill the anti-noise signal to other places.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2014

Some target signatures of interest in drought monitoring, flooding assessment, fire damage assessment, coastal changes, urban changes, etc. may need to be tracked periodically. In a typical change detection application, a hyperspectral image collected in an earlier visit may need to be compared with later images collected using different imagers with different viewing geometries, illumination, ground sampling distance (GSD), spectral sampling, signal-to-noise ratio (SNR), and atmospheric conditions. We propose a novel framework that can deal with all of the above challenges. We first propose to apply techniques such as flat-field to obtain the reflectance signature (fire damage signature, for example) from the target radiance signatures in a given hyperspectral image. The target reflectance signature is then saved in a target reflectance signature library for future use. After that, to detect targets (fire damage, for instance) in new images, we will expand a hyperspectral image processing system developed by the Johns Hopkins University/Applied Physics Lab (JHU/APL).


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2014

Researchers have been working on flare prediction for many decades. However, the best prediction result achieved by Falconer et al. for major flares, CMEs, and solar proton events (SPEs) is a probability of detection of 39%, meaning that only 39% of the events are correctly predicted. Existing flare prediction algorithms are mainly based on a combination of data, statistical analysis, and pattern recognition algorithms. A serious deficiency of these algorithms is that they do not include the constraints and predictive power of the basic equations of magnetohydrodynamics (MHD) that describe the dynamics of the plasma atmosphere. We propose a new approach to flare prediction based on combining a detailed data based description of the solar atmosphere with the equations of magnetohydrodynamics (MHD). In this approach, a subset of the MHD equations take data as input, and then predict physical quantities that are not measured but may be important for predicting flares. Since the MHD equations must be obeyed by the plasma, when combined with data they also provide new constraints on pattern recognition algorithms that search for correlations between the occurrence of a flare and the values of observed and MHD model predicted quantities that describe the pre-flare plasma.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 80.00K | Year: 2015

We propose a novel and high performance compression system that can meet all of the Navy's requirements. We filed an invention disclosure for our system. A patent will be filed soon. First, for non-image data such as 1-D time series data (sensor health, thermal, kinematic), we first propose to apply some preprocessing routines to convert the time series data into a 2-D format so that more efficient lossless compression can be achieved later. Second, we propose to jointly compress the same types of sensor data. This idea was motivated by data compression in sensor networks where the sensor data are correlated. We have applied the above scheme to wind tunnel data compression and observed very good compression performance. Actually, our algorithms outperformed all the existing commercial products in lossless compression. Third, for 2-D images/videos, we propose to directly apply X265, which is applicable to both images and videos. For text data, we propose to apply 7z and for voice data, we propose to apply Opus, which is a freeware that has been widely used in the market. Finally, since RF channel is unreliable and there is transmission errors, we propose to apply novel algorithms at the receiving end to recover corrupted data.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2014

Most sonar and camera images in practice are inherently sparse yet highly non-stationary. Optimal prediction, sampling, representation, and estimation of such signals require locally adaptive decomposition that can quickly capture the signal characteristics within a small neighborhood. In order to achieve the demanding required level of compression performance, we propose an adaptive block-based coding framework the strategy of choice of the most recent international image compression standard JPEG-XR as well as the intra-frame coding method in current international video compression standards H.264/H.265. In a nutshell, we propose to combine the best features of these well-established state-of-the-art coders and then add on top an unprecedented level of flexibility and adaptivity on a block-by-block basis. We will investigate a data-adaptive context-adaptive approach where each operator in the block-coding framework is optimized and selected based on the local statistics/characteristics of the given input image. The encoder will take into account the bit budget requirement for each block while minimizing its reconstructed mean-square error. Our compression is also robust and error resilient. It should be emphasized that our proposed framework is applicable to both lossless and lossy compression.


Trademark
Applied Research LLC | Date: 2012-10-16

Non-toxic natural insect control products, namely, Insect exterminating agents, Insect repellent agents; Non-toxic natural odor control products, namely, odor neutralizing preparations for general use on various surfaces.

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