7 research outputs found
Recommended from our members
Dynamic daylight control system implementing thin cast arrays of polydimethylsiloxane-based millimeter-scale transparent louvers
The deep building layouts typical in the U.S. have led to a nearly complete reliance on artificial lighting in standard office buildings. The development of daylight control systems that maximize the penetration and optimize the distribution of natural daylight in buildings has the potential for saving a significant portion of the energy consumed by artificial lighting, but existing systems are either static, costly, or obstruct views towards the outside. We report the Dynamic Daylight Control System (DDCS) that integrates a thin cast transparent polydimethylsiloxane (PDMS)-based deformable array of louvers and waveguides within a millimeter-scale fluidic channel system. This system can be dynamically tuned to the different climates and sun positions to control daylight quality and distribution in the interior space. The series of qualitative and quantitative tests confirmed that DDCS exceeds conventional double glazing system in terms of reducing glare near the window and distributing light to the rear of the space. The system can also be converted to a visually transparent or a translucent glazing by filling the channels with an appropriate fluid. DDCS can be integrated or retrofitted to conventional glazing systems and allow for diffusivity and transmittance control.Chemistry and Chemical Biolog
Kalleidos (Spring 2011) IPRO 303
“Over the last few years there has been an explosion of different websites that allow average person to contribute different content such as pictures, comments, stories and reviews about places around them. While there are many companies that focus on aggregating such data into common stream - complexity of interpreting such data in a meaningful actionable way remains a challenge for many companies including NAVTEQ.” [Appendix B]  Identify new content from real time feeds which include 1. Points of Interest and their attribution (e.g. Hours of Operation, Languages Spoken at locale, etc…) 2. Any associated content (e.g. events, relationships to other Points of Interest (e.g. same owner, etc…)  Create visualizations based on this content - some basic examples could include 1. Hot/Not So Hot real estate areas (new construction, sales) based on agent and broker social media updates 2. High/Low crime areas based on social media stream 3. Hot/Not city zones based on number of people tweeting/updating Facebook/checking in/etc… in certain areas, parties, POIs.Sponsorship: NAVTEQDeliverable
Kalleidos (Spring 2011) IPRO 303: KalleidosIPRO303MidTermPresentationSp11
“Over the last few years there has been an explosion of different websites that allow average person to contribute different content such as pictures, comments, stories and reviews about places around them. While there are many companies that focus on aggregating such data into common stream - complexity of interpreting such data in a meaningful actionable way remains a challenge for many companies including NAVTEQ.” [Appendix B]  Identify new content from real time feeds which include 1. Points of Interest and their attribution (e.g. Hours of Operation, Languages Spoken at locale, etc…) 2. Any associated content (e.g. events, relationships to other Points of Interest (e.g. same owner, etc…)  Create visualizations based on this content - some basic examples could include 1. Hot/Not So Hot real estate areas (new construction, sales) based on agent and broker social media updates 2. High/Low crime areas based on social media stream 3. Hot/Not city zones based on number of people tweeting/updating Facebook/checking in/etc… in certain areas, parties, POIs.Sponsorship: NAVTEQDeliverable
Kalleidos (Spring 2011) IPRO 303: KalleidosIPRO303FinalReportSp11_redacted
“Over the last few years there has been an explosion of different websites that allow average person to contribute different content such as pictures, comments, stories and reviews about places around them. While there are many companies that focus on aggregating such data into common stream - complexity of interpreting such data in a meaningful actionable way remains a challenge for many companies including NAVTEQ.” [Appendix B]  Identify new content from real time feeds which include 1. Points of Interest and their attribution (e.g. Hours of Operation, Languages Spoken at locale, etc…) 2. Any associated content (e.g. events, relationships to other Points of Interest (e.g. same owner, etc…)  Create visualizations based on this content - some basic examples could include 1. Hot/Not So Hot real estate areas (new construction, sales) based on agent and broker social media updates 2. High/Low crime areas based on social media stream 3. Hot/Not city zones based on number of people tweeting/updating Facebook/checking in/etc… in certain areas, parties, POIs.Sponsorship: NAVTEQDeliverable
Kalleidos (Spring 2011) IPRO 303: KalleidosIPRO303BrochureSp11
“Over the last few years there has been an explosion of different websites that allow average person to contribute different content such as pictures, comments, stories and reviews about places around them. While there are many companies that focus on aggregating such data into common stream - complexity of interpreting such data in a meaningful actionable way remains a challenge for many companies including NAVTEQ.” [Appendix B]  Identify new content from real time feeds which include 1. Points of Interest and their attribution (e.g. Hours of Operation, Languages Spoken at locale, etc…) 2. Any associated content (e.g. events, relationships to other Points of Interest (e.g. same owner, etc…)  Create visualizations based on this content - some basic examples could include 1. Hot/Not So Hot real estate areas (new construction, sales) based on agent and broker social media updates 2. High/Low crime areas based on social media stream 3. Hot/Not city zones based on number of people tweeting/updating Facebook/checking in/etc… in certain areas, parties, POIs.Sponsorship: NAVTEQDeliverable
Kalleidos (Spring 2011) IPRO 303: KalleidosIPRO303ProjectPlanSp11_redacted
“Over the last few years there has been an explosion of different websites that allow average person to contribute different content such as pictures, comments, stories and reviews about places around them. While there are many companies that focus on aggregating such data into common stream - complexity of interpreting such data in a meaningful actionable way remains a challenge for many companies including NAVTEQ.” [Appendix B]  Identify new content from real time feeds which include 1. Points of Interest and their attribution (e.g. Hours of Operation, Languages Spoken at locale, etc…) 2. Any associated content (e.g. events, relationships to other Points of Interest (e.g. same owner, etc…)  Create visualizations based on this content - some basic examples could include 1. Hot/Not So Hot real estate areas (new construction, sales) based on agent and broker social media updates 2. High/Low crime areas based on social media stream 3. Hot/Not city zones based on number of people tweeting/updating Facebook/checking in/etc… in certain areas, parties, POIs.Sponsorship: NAVTEQDeliverable