Rockville, MD, United States

Signal Processing, Inc.

www.signalpro.net
Rockville, MD, United States

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Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 1.50M | Year: 2016

Navy wants us to focus on contingency planning for MQ-4C. A software system is required that can perform the following: 1) Automatic generation of contingency plans; 2) Complete plan generation in less than 5 minutes; 3) Publish plan in a website; 4) Interface with JMPS.Our goal in Phase II.5 is to develop a high performance software to achieve the above requirements. This software will be an intelligent proxy for commanders. It is a decision aid that can operate under high pressure and short time constraint situations. It can effectively generate contingency plans when pop-up threats occur during the mission, assess the goodness of the flight plan, and dynamically optimize the Air Tasking Order (ATO) for the mission.The Advanced Automatic Mission Planning System (AAMPS) to be developed in Phase II.5 will further improve the Phase II prototype by adding functionalities that can deal with some practical issues such as replanning during a mission, low altitude approach, and windy conditions. Moreover, contingency plan generation for UCLASS will also be carried out in Phase II.5.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: STTR | Phase: Phase II | Award Amount: 750.00K | Year: 2012

Since Mars rovers have limited life span, NASA wants to maximize the exploration activities during this period. Rock sample analysis is one of the main tasks of rover missions. Traditionally, rock selection is decided by human operators. Due to long communication delay, manual selection process is time-consuming. There is a strong need to develop an automatic software system to automate the process.We propose a novel and high performance approach to enhancing rock selection process. We explicitly take advantage of the availability of LIBS instrument in the new generation of Mars rover. First, we use LIBS to quickly sample the neighborhood of the rover. LIBS can collect samples in seconds. Our software algorithms can quickly analyze the LIBS data and determine whether there are any interesting chemical elements. If yes, the APXS instrument will be activated. Otherwise, the rover will move to a new location and start the process again. In Phase I, we have demonstrated that our smart processing tools using actual Mars data and our results are more consistent than a current method. Moreover, our tools can implemented in a parallel processing system to achieve real-time performance. Our parallel processing system utilizes multi-core CPUs for distributed processing and we have used such processing architecture for speech and genomic processing.


Patent
Signal Processing, Inc. | Date: 2014-02-18

A method for removing noise from a noisy image includes a few important steps. The noise level of the noisy image is first estimated based on the smooth blocks of the image. The original image is divided into many small blocks. Each of these blocks is then converted from a two dimensional matrix to vector, which is a one dimensional array. The converted vectors are next grouped and then a matrix recovery process based on a principal component analysis (PCA) has been performed in order to remove the noise, wherein the key is to determine the number of principal components to retain during the denoising process. Next, the denoised vectors are converted back to the denoised blocks, and the denoised blocks are used to reconstruct a denoised image with a better image quality.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 750.00K | Year: 2011

We propose a novel, high performance, and standalone system for improving close air support (CAS) effectiveness. We have several important elements. First, we propose a standalone noise cancellation device (NCD) that can be inserted between the handset and the transceiver. That is, the handset output is connected to the NCD and the NCD output is connected to the transceiver. As a result, there is no change to the existing communication system. The NCD is self-powered from its own battery and equipped with one or two microphones and a digital signal processing (DSP) chip to process the signals from the handset mic and the mics in the NCD. Second, we have extensively evaluated numerous algorithms in Phase 1 for battlefield noise suppression. The best algorithms will be implemented in DSP in Phase 2. A hardware prototype with small size and low weight (


Grant
Agency: Department of Defense | Branch: Air Force | Program: SBIR | Phase: Phase I | Award Amount: 150.00K | Year: 2012

ABSTRACT: In this proposal, we first extend the notion of speech reception threshold (SRT) to aircraft sound reception threshold (ASRT). In speech recognition, SRT refers to the mixture signal to noise ratio (SNR) required to achieve a certain intelligibility score, typically 50%. SRT is used to quantify speech intelligibility under various SNR conditions. Here we define ASRT as the SNR required to achieve 90% correct detection of aircraft sounds. ASRT will be used to quantify the capability of aircraft sound detection under various SNR conditions. Second, we propose to model humans"capability on aircraft sound detection using ideal binary mask (IBM). An IBM is defined as a binary matrix within which 1 denotes that the target (aircraft sound) energy in the corresponding time-frequency (T-F) unit exceeds the interference (non-aircraft sound) energy by a predefined threshold and 0 denotes otherwise. It was found that the IBM plays an important role in human intelligibility. By the same token, the IBM will likely to play a substantial role in aircraft sound detection. Third, it is also important to quantify the listening model by connecting the IBM with AFRT under different noisy environments. Extensive experiments will be performed in Phase 1. BENEFIT: The proposed listener model has great potential in providing insights about human"s listening capability of aircraft sounds under various noisy conditions. We will produce a software tool for our model. It can also be used for other military and civilian applications. For example, detection of submarine using sonar, detection of acoustic events for perimeter defense, etc. are some other notable applications. The combined market for the above mentioned applications in this paragraph will easily exceed 10 million dollars. This is based on license cost of $2000 and a market size of 5,000 systems.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 489.01K | Year: 2012

In Phase I, we developed a novel matrix completion framework for recovering randomly missing pixels in images. Extensive simulations were performed using actual images from LADAR (18 images) and electro-optical images (23 images). We achieved high performance reconstruction even for missing rates as high as 99.9%. In Phase II, we will extend our algorithm to deal with missing data in hyperspectral images and missing links in social networks. Medium to high missing rates will be considered. The goal is to improve target detection and change detection performance by using hyperspectral images and to recover missing links in social network data. We will also address some theoretical issues in our algorithm such as how to choose the local window size. In addition, it should be noted that matrix completion algorithms are computationally intensive. So in Phase II, we will develop fast processing prototypes by using graphical processor unit (GPU), multi-core CPUs, and DSP. This will provide a number of fast processors for different applications. In the Phase II option, we will focus on a Navy application such as automatic target recognition (ATR) or social network. We will perform real-time or near real-time demonstrations and eventually integrate our prototype into a Navy program.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 750.00K | Year: 2012

In Phase 2, our Phase 1 version of the Automated Mission Planning System (AMPS) will be modified and expanded to generate contingency plans to handle unexpected problems such as popup threats or electrical and mechanical failures in UAV/UCAVs. The new software system must be adaptive and can react quickly to changes detected in the situation context. Conventional contingency plans are generated by human operators. The process is tedious, time consuming, and does not respond to unexpected situations quickly. In addition, plans generated by inexperienced operators under high pressure environments are not optimal. The new Advanced AMPS (called AAMPS) to be developed in Phase II will eventually need to interface with the Navy Joint Mission Planning System (JMPS) to obtain the required inputs for its reasoning algorithms, and its contingency plans will be published through Web Services to the concerned users (e.g., BAMS and Global Hawk).


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2013

We propose a portable data acquisition and prognostic system that contains both hardware and software with several innovative ideas. First, our hardware system consists of a high speed data acquisition card and a portable lunchbox PC. The portable lunchbox PC has advanced prognostics algorithms and user friendly Graphical User Interface (GUI) for displaying component fault status and trends. Second, the prognostics software has several innovative algorithms, which will be implemented in Labview. The first one is an adaptive physics based prognostic tool. The idea is motivated by damage mechanics, which associates the vibration amplitude and natural frequency of the vibration to the damage status. This idea has been experimentally proven to be very accurate in bearing failure prediction. The second prognostic tool is data driven and is a Hidden Markov Model (HMM) based approach that can predict the degraded state of the system. Tests using experimental data showed that the various degraded states can be correctly and unambiguously identified by the algorithm. The third tool is a hybrid one and was proven to achieve very high performance in the 2008 PHM Challenge. Finally, to further enhance the prognostic performance, we propose to apply Dempster Shafer algorithm to perform prognostic fusion.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2013

We propose a comprehensive and systematic contingency plan generation framework to deal with lost communication in UAVs. ATC regulations are explicitly incorporated into our system. Our proposed framework was motivated by our recent work for the Naval Air Station at Patuxent River. In should be noted that our earlier framework was very general, as we have designed a system which can deal with many types of pop-up threats such as enemy attacks, internal system faults, external interferences, etc. Currently, we have been focusing on generating contingency plans for engine out problems for the Navy. In this NASA Phase 1, we will focus on generating contingency plans for lost communication. Our proposed approach has one key component known as Risk Management Plan (RMP), which assesses mission risk of a given air task order (ATO) and provides solutions for known or unknown threats throughout the course of the mission. Four sub-plans are used to support RMP: Situation Analysis (SA), Preparedness & Prevention Plan (PPP), Incident Response Plan (IRP), and Rescue & Recovery Plan (RRP). We propose to apply case based reasoning (CBR) in two modules (PPP and IRP) to generate contingency flight paths, contingency points, safe points, and incident response rules. In CBR, we can easily incorporate ATC regulations, which can be formulated as used cases in the CBR.


Patent
Signal Processing, Inc. | Date: 2013-02-28

A noise cancellation device (NCD) comprises a microphone, an analog-to-digital converter, a digital-to-analog converter, a rechargeable battery, and a processor. The NCD acquires an audio input, from an external device such as a stethoscope or a cell phone, and passes the analog data into an ADC (analog-to-digital converter) for signal conversion. The digitized signals are then passed to the processor for further processing. The processor contains all the processing functions such as preprocessing (divide the input data into frames and apply shaping function to each frame), short term Fourier transform (STFT), adaptive filtering, inverse STFT, and signal synthesis.

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