Lowell, MA, United States
Lowell, MA, United States

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Mason J.A.,UMass Lowell | Allen G.,UMass Lowell | Podolskiy V.A.,UMass Lowell | Wasserman D.,UMass Lowell | Wasserman D.,University of Illinois at Urbana - Champaign
IEEE Photonics Technology Letters | Year: 2012

We demonstrate strong coupling between a mid-infrared perfect absorber and a molecular absorption resonance embedded in our absorber structure. Anti-crossing behavior is demonstrated as the perfect absorber resonance is tuned through the molecular absorption line, both experimentally and numerically, and is described analytically using a simple coupled harmonic oscillator model. Excellent agreement between experimental, numerical, and analytical results are shown. Such devices offer potential for mid-infrared sensing systems and actively tunable perfect absorber devices. © 2006 IEEE.


Grant
Agency: Department of Defense | Branch: Air Force | Program: STTR | Phase: Phase I | Award Amount: 149.89K | Year: 2014

ABSTRACT: Aurora will demonstrate on our automated fiber placement (AFP) machine that we are able to lay up composite panels that contain sacrificial fibers that can be thermally decomposed to create a microvascular network of small cavities that allow the panel to act as a heat exchanger. The AFP machine is capable of producing composite parts from mold tools as large as 9"high by 18"wide by 56"long. It lays down an 8"wide swath of composite material at 1500 inches per minute. UMass Lowell (UML) has developed a method to manufacture sacrificial fibers that is 10^5 times faster than prior demonstrations in the literature. These new fibers reduce removal times by 50% and do not require the expensive and toxic chemicals used in traditional manufacturing methods. The UML partners have produced fiber in a wide range of diameters with continuous lengths exceeding hundreds of feet. Aurora and UML will analyze various composite/fiber geometries for thermal and flow characteristics and then fabricate and test those geometries. BENEFIT: Aurora will ultimately have a unique automated capability to design and manufacture heat exchangers embedded within composite structure. Panels could be designed and fabricated as composite aircraft wing skins or fuselage panels that could be used to dissipate aircraft engine heat, payload sensor heat, or avionics heat. These heat exchangers will be lighter weight and lower cost than metallic heat exchangers traditionally used in aircraft. UMass Lowell will have the opportunity to further develop their manufacturing process to produce catalyst-impregnated polylactide filaments in production quantities, allowing their technology approach to be sold or licensed to the fiber extrusion industry.


Hendricks L.A.,University of California at Berkeley | Venugopalan S.,University of Texas at Austin | Rohrbach M.,University of California at Berkeley | Mooney R.,University of Texas at Austin | And 2 more authors.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2016

While recent deep neural network models have achieved promising results on the image captioning task, they rely largely on the availability of corpora with paired image and sentence captions to describe objects in context. In this work, we propose the Deep Compositional Captioner (DCC) to address the task of generating descriptions of novel objects which are not present in paired imagesentence datasets. Our method achieves this by leveraging large object recognition datasets and external text corpora and by transferring knowledge between semantically similar concepts. Current deep caption models can only describe objects contained in paired image-sentence corpora, despite the fact that they are pre-trained with large object recognition datasets, namely ImageNet. In contrast, our model can compose sentences that describe novel objects and their interactions with other objects. We demonstrate our model's ability to describe novel concepts by empirically evaluating its performance on MSCOCO and show qualitative results on ImageNet images of objects for which no paired image-sentence data exist. Further, we extend our approach to generate descriptions of objects in video clips. Our results show that DCC has distinct advantages over existing image and video captioning approaches for generating descriptions of new objects in context.


Donahue J.,University of California at Berkeley | Hendricks L.A.,University of California at Berkeley | Guadarrama S.,University of California at Berkeley | Rohrbach M.,University of California at Berkeley | And 3 more authors.
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2015

Models based on deep convolutional networks have dominated recent image interpretation tasks; we investigate whether models which are also recurrent, or 'temporally deep', are effective for tasks involving sequences, visual and otherwise. We develop a novel recurrent convolutional architecture suitable for large-scale visual learning which is end-to-end trainable, and demonstrate the value of these models on benchmark video recognition tasks, image description and retrieval problems, and video narration challenges. In contrast to current models which assume a fixed spatio-temporal receptive field or simple temporal averaging for sequential processing, recurrent convolutional models are 'doubly deep' in that they can be compositional in spatial and temporal 'layers'. Such models may have advantages when target concepts are complex and/or training data are limited. Learning long-term dependencies is possible when nonlinearities are incorporated into the network state updates. Long-term RNN models are appealing in that they directly can map variable-length inputs (e.g., video frames) to variable length outputs (e.g., natural language text) and can model complex temporal dynamics; yet they can be optimized with backpropagation. Our recurrent long-term models are directly connected to modern visual convnet models and can be jointly trained to simultaneously learn temporal dynamics and convolutional perceptual representations. Our results show such models have distinct advantages over state-of-the-art models for recognition or generation which are separately defined and/or optimized. © 2015 IEEE.


Sharma D.,UMass Lowell
Journal of Thermal Analysis and Calorimetry | Year: 2012

This study explores a non-isothermal activated kinetics of the Crystalline to Smectic A (K-SmA) transition of the aligned octylcyanobiphenyl (8CB) liquid crystal. High resolution calorimetric technique was used to study the molecular motion and rearrangement of the 8CB molecules near the K-SmA transition as a function of temperature, rate, and time. In the presence of magnetic field, the transition peak was found to be shifted towards lower temperature by 0.5 K when compared with results of un-magnetized 8CB. The K-SmA transition showed a rate dependent kinetics following Arrhenius behavior where the increased shifting rate showing an increased thermal kinetics for the transition. The 8CB molecules get more aligned and more ordered that pushes the temperature of the transition towards lower temperature in the presence of magnetic field. Hence they show a temperature decrease in the peak of the transition temperature with a decrease in the enthalpy and hence needs more activation energy. This study may be useful to understand the liquid crystal behavior to upgrade liquid crystal devices (LCDs). © 2011 Akadémiai Kiadó, Budapest, Hungary.


Sharma D.,UMass Lowell
Journal of Thermal Analysis and Calorimetry | Year: 2012

Egg protein is an important part of our food to get protein in our daily diet, and makes this protein more important to researchers to understand its kinetic behavior to understand the energy involved in the digestion of the egg protein. Hence, the present study explores the denaturing kinetics of the protein obtained from the hen's egg white (EW) using high resolution calorimetric technique. Fresh EW was scanned for heating and cooling to see the thermodynamics from 10 to 100 °C at different heating ramp rates varying from 1 to 20 °C min -1. An endothermic peak was found on heating scan showing denaturing of protein which was found absent at the cooling indicating the absence of any residue after heating. The denature peak shifted towards higher temperature as ramp rate increases following Arrhenius behavior and shows an activated denaturing kinetics of the egg protein. This peak was also compared with the water to avoid water effects. Behavior of denaturing peak can be explained in terms of Arrhenius theory and further discussed to get the energy involved in digestion. © Akadémiai Kiadó, Budapest, Hungary 2012.


Kalkan-Savoy A.,UMass Lowell
Proceedings of Meetings on Acoustics | Year: 2013

Speckle tracking imaging is used as a method to estimate heart strain. An analysis of accuracy of speckle tracking and its potential to be utilized in quantification of myocardial stress through estimation of heart motion is examined. Multiple scattering effects are modeled using the Kirchoff integral formulation for the pressure field. The method of Pade approximants is used to accelerate convergence and to obtain temporal varying characteristics of the scattered field. Phantoms having varied acoustical contrast media and speckle density are used in this study. The effectiveness of inter-image frame of correlation methods for estimating speckle motion in high contrast media is considered. © 2013 Acoustical Society of America.


Hoffman J.,University of California at Berkeley | Darrell T.,University of California at Berkeley | Saenko K.,UMass Lowell
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2014

We pose the following question: what happens when test data not only differs from training data, but differs from it in a continually evolving way? The classic domain adaptation paradigm considers the world to be separated into stationary domains with clear boundaries between them. However, in many real-world applications, examples cannot be naturally separated into discrete domains, but arise from a continuously evolving underlying process. Examples include video with gradually changing lighting and spam email with evolving spammer tactics. We formulate a novel problem of adapting to such continuous domains, and present a solution based on smoothly varying embeddings. Recent work has shown the utility of considering discrete visual domains as fixed points embedded in a manifold of lower-dimensional subspaces. Adaptation can be achieved via transforms or kernels learned between such stationary source and target subspaces. We propose a method to consider non-stationary domains, which we refer to as Continuous Manifold Adaptation (CMA). We treat each target sample as potentially being drawn from a different subspace on the domain manifold, and present a novel technique for continuous transform-based adaptation. Our approach can learn to distinguish categories using training data collected at some point in the past, and continue to update its model of the categories for some time into the future, without receiving any additional labels. Experiments on two visual datasets demonstrate the value of our approach for several popular feature representations. © 2014 IEEE.


News Article | February 15, 2017
Site: www.marketwired.com

Boston-based BrainSell, unbiased business consultancy and value added software reseller, proudly announce the hiring of Alexa Jackson, Marketing Coordinator TOPSFIELD, MA--(Marketwired - February 15, 2017) - BrainSell Technologies, a leader in business consulting and software solutions, are pleased to announce the expansion of their marketing team with new hire, Alexa Jackson. Alexa joined BrainSell in January 2017 as Marketing Coordinator. Her experience in customer service and problem solving are valuable assets to BrainSell's culture. As a former marketing team member for the UMass Lowell Campus Recreation Center, Alexa is skilled in advertising and marketing, particularly in regards to social media and inbound marketing. She has a passion for new technology, brand presence, and absorbing the latest industry trends. Consistently improving customer satisfaction and persistency are just a few of her many goals. "BrainSell is lucky to have found such a capable employee in Alexa," said Cat Stone, Marketing Director. "It can be overwhelming to provide partners with the attention their products deserve due to the changing requirements of digital advertising. Having another team member with fresh ideas and advanced social media experience will really elevate BrainSell in 2017". Alexa and Cat will work together closely on marketing campaigns, customer satisfaction initiatives, and exciting planned events. BrainSell is a business solutions company that is dedicated to helping businesses grow, create a delighted customer base and achieve grand success. BrainSell provides comprehensive ERP, CRM, and marketing automation solutions and services, including training, implementation and software development. Founded in 1994 and headquartered in Topsfield, Massachusetts, BrainSell continues to grow in product knowledge and offerings.


News Article | February 15, 2017
Site: www.marketwired.com

LOWELL, MA--(Marketwired - Feb 8, 2017) - Mass Innovation Nights (MIN), UMass Lowell's Innovation Hub (iHub) and the City of Lowell are collaborating on a startup showcase and networking event on Wednesday, Feb.15 from 6 to 8:30 p.m. The event will be held at the iHub at 110 Canal Street in Lowell. "We're bringing together the entire Lowell tech ecosystem to support these entrepreneurs," said Bobbie Carlton, the founder of Innovation Nights, and Innovation Women. "By working with both the UMass Lowell Innovation Hub and the City of Lowell, our community can help expand the impact of these product launches and the influence of innovation through the use of social media." "There's a great deal of cutting-edge, entrepreneurial work being done at UMass Lowell and in the region overall. Mass Innovation Nights gives us and our partners a great platform to show it off and to highlight the terrific workspace, equipment, program and collaboration resources available to entrepreneurs through the iHub," said Tom O'Donnell, director of the UMass Lowell Innovation Hub. "Here at the Innovation Hub, we are supporting a variety of early-stage companies that are pursuing truly innovative technologies, including machine learning, virtual reality, environmental monitoring, sustainable aquaculture and rehabilitative legwear for horses." "By working with Mass Innovation Nights, we are demonstrating why Lowell is the perfect place to be for business," said City Manager Kevin Murphy, from the City of Lowell. "From the easy commute, to affordable housing prices and excellent business resources, there's a lot to love about Lowell." Free-of-charge and open to the public, Mass Innovation Nights #95 features experts, networking, tabletop presentations with new local products and presentations from the winners of online voting. The companies whose products will be featured include: There will also be industry experts present from: Guests are encouraged to use hashtag #MIN95 and @MassInno to share their photos and commentary. The gatherings typically generate hundreds of tweets, Facebook posts, blogs, and videos, and are key visibility drivers for these companies. To attend, please RSVP at mass.innovationnights.com/events/mass-innovation-nights-95. About Mass Innovation Nights Mass Innovation Nights (MIN) offers an opportunity for people interested in innovative new products to connect live and online. Each month, different companies launch new products with Innovation Nights and the social media community helps spread the word. The popular product launch party and networking event draws attendees from the entire region. Over the past 7 years, it has launched almost 1000 new products which have collectively received more than $1.3 billion in funding. MIN is currently seeking hosts and sponsors for the latter half of the 2017 season. Contact the organization for additional information. Follow MIN on Twitter or visit the website at mass.innovationnights.com/. About the City of Lowell Innovation has been a cornerstone of Lowell since the City was founded. With a great cultural scene, easy commute, access to a talented workforce, attractive housing options, and business resources to get you started, come see why entrepreneurs say "there's a lot to like about Lowell." Learn more here: www.lowellma.gov/724/Economic-Development. About the UMass Lowell innovation Hub The UMass Lowell Innovation Hub is an 11,000 square-foot incubator and co-working facility in downtown Lowell supporting early-stage, technology-based ventures from idea to impact. Owned and operated by UMass Lowell, the iHub provides flexible, affordable workspace, program, event, community and support offerings to member companies. In addition, member companies have access to additional university resources including labs, testing and characterization equipment, prototyping gear, libraries and collaborations with faculty researchers and student interns. The iHub is open to both university spinoffs as well as startups from the broader greater Boston/Merrimack Valley region. Learn more at www.uml.edu/innovation-hub.

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