Ecotronics Ventures LLC

Bryans Road, MD, United States

Ecotronics Ventures LLC

Bryans Road, MD, United States

Time filter

Source Type

Herrero R.,Northeastern University | Cadirola M.,Ecotronics Ventures LLC
Proceedings of the Conference on Principles, Systems and Applications of IP Telecommunications, IPTComm 2014 | Year: 2014

In this paper we focus on Forward Error Correction (FEC) mechanisms based on media redundancy applied to various Low Bit Rate (LBR) speech codecs in the context of lossy mobile environments. Specifically, we deal with different state of the art linear prediction based codecs ranging from narrowband G.729AB and AMR-NB to wideband Opus and SILK, all of them enabled to support Discontinued Transmission (DTX) mode, and packetized over switched networks as RTP/UDP/IP traffic. We evaluate these speech codecs applied in conjunction with media redundancy techniques to a network scenario where packet loss bursts and latency are induced to mimic mobile environments that are normally mathematically modeled as a two-state Markov process. In addition to obtaining the application layer packet loss and comparing it to the mathematical model we also compute the Perceptual Evaluation of Speech Quality (PESQ) score in an attempt to correlate packet loss to real perceived speech quality.


Herrero R.,Ecotronics Ventures LLC | Ingle V.K.,Northeastern University
Signal, Image and Video Processing | Year: 2015

The sheer size of Atmospheric Infrared Sounder images, a type of ultraspectral cube that includes over two thousand spectral bands, is such that their compression is of critical importance. A traditional approach to this goal is by combining reversible preprocessing, where image redundancy is better exposed, with a pure prediction stage that performs compression at a cost of introducing some controlled distortion. In this paper we focus on the effect of using a prediction stage that integrates both, linear prediction (LP) and a search procedure, as a way to obtain better quality. Since it can be seen that this additional search stage does not affect the compression rate, its only drawback is from the computational point of view, making algorithm optimization a key factor. In addition, we introduce a mechanism to dynamically select the LP filter order such that when combined with two-stage prediction the overall rate distortion is greatly improved. © 2015 Springer-Verlag London


Herrero R.,Ecotronics Ventures LLC | Ingle V.K.,Northeastern University
Signal, Image and Video Processing | Year: 2015

Atmospheric infrared sounder images are ultraspectral data cubes that comprise over two thousand spectral bands accounting for well over 25 megapixels of information. In this paper, we focus on the analysis of backward and forward linear prediction (LP) applied in the context of ultraspectral image compression. We start by introducing a detailed analysis of the differences and similarities between them and proceed to present a mathematical model that integrates not only error signal but also LP coefficient encoding. In addition, to overcome some of the limitations of backward LP, we present a hybrid LP scheme where both, backward and forward LP, are put into consideration by dynamically interleaving them in order to minimize the mean square error of the error signal. The model is further extended to compare all three techniques, and both experimental and theoretical samples are contrasted to verify that hybrid LP provides most efficient compression method. © 2015 Springer-Verlag London


Herrero R.,Ecotronics Ventures LLC | Ingle V.K.,Northeastern University
Signal, Image and Video Processing | Year: 2015

Space-filling curves (SFCs) have been widely used in image compression as a way to assist prediction-based schemes by providing a method to map an $$m$$m-dimensional space into a highly correlated unidimensional space. In this paper, we not only analyze the effect of different state-of-the-art curves but also propose a novel scanning procedure applied to the preprocessing stage of an ultraspectral lossy compression architecture. Specifically, we focus on Atmospheric infrared sounder images that are good compression candidates as they account for well over 25 megapixels of information per cube. In addition, we introduce a metric to estimate the efficiency of the different SFCs and present a mathematical model that, based on the Laplacian nature of the pixels, it is used to derive bounds. Finally, to verify the accuracy of this metric, we obtain rate-distortion ratios calculated by applying the corresponding curve to the compression architecture. © 2013, Springer-Verlag London.


Herrero R.,Northeastern University | Cadirola M.,Ecotronics Ventures LLC
American Society for Photogrammetry and Remote Sensing Annual Conference, ASPRS 2013 | Year: 2013

In compression studies of ultraspectral data, AIRS (Atmospheric Infrared Sounder) images, which are used to perform weather forecast constitute good candidates as they include several thousand bands that account for well over 40MB of data per single cube. In this paper we present an analytical model of an improved architecture to accomplish lossy compression of AIRS images by integrating a new preprocessing stage with a linear prediction compression stage. We define a distortion measure based on AIRS level 2 products and approximate the ratedistortion of the architecture which is compared against the experimental performance of the algorithm. The analysis is based on vector quantization of the prediction error signal that assumes that the individual samples follow a Laplacian distribution as the only source of distortion. Copyright © (2013) by the American Society for Photogrammetry & Remote Sensing.


Herrero R.,Ecotronics Ventures LLC | Cadirola M.,Ecotronics Ventures LLC | Ingle V.K.,Northeastern University
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2015

Advancements in image sensors and signal processing have led to the successful development of lightweight hyperspectral imaging systems that are critical to the deployment of Photometry and Remote Sensing (PaRS) capabilities in unmanned aerial vehicles (UAVs). In general, hyperspectral data cubes include a few dozens of spectral bands that are extremely useful for remote sensing applications that range from detection of land vegetation to monitoring of atmospheric products derived from the processing of lower level radiance images. Because these data cubes are captured in the challenging environment of UAVs, where resources are limited, source encoding by means of compression is a fundamental mechanism that considerably improves the overall system performance and reliability. In this paper, we focus on the hyperspectral images captured by a state-of-the-art commercial hyperspectral camera by showing the results of applying ultraspectral data compression to the obtained data set. Specifically the compression scheme that we introduce integrates two stages; (1) preprocessing and (2) compression itself. The outcomes of this procedure are linear prediction coefficients and an error signal that, when encoded, results in a compressed version of the original image. Second, preprocessing and compression algorithms are optimized and have their time complexity analyzed to guarantee their successful deployment using low power ARM based embedded processors in the context of UAVs. Lastly, we compare the proposed architecture against other well known schemes and show how the compression scheme presented in this paper outperforms all of them by providing substantial improvement and delivering both lower compression rates and lower distortion. © 2015 SPIE.


Herrero R.,Ecotronics Ventures LLC | Ingle V.K.,Northeastern University
Signal, Image and Video Processing | Year: 2014

Atmospheric InfraRed Sounder images are a type of ultraspectral images that are good candidates to compression as they include several thousand bands that account for well over 40 MB of data per single cube. As part of most prediction based compression schemes, there is a preprocessing stage in which a reversible band ordering process is included to maximize spectral band correlation. In this paper, we analyze, compare, and propose novel improvements to different band ordering and correlation estimation techniques in the context of ultraspectral image compression. In order to measure the performance of these methods, we also introduce a new metric based on comparing a given sequence of spectral bands to the corresponding optimal one. Finally, we apply the techniques presented in this paper to a real compression algorithm in order to obtain compression rates as a function of band ordering and correlation factors while determining the best scenario. © 2013 Springer-Verlag London.

Loading Ecotronics Ventures LLC collaborators
Loading Ecotronics Ventures LLC collaborators