Burning Terabyte CDs
A new device that tightly focuses
laser light could increase the density of optical data storage

Source: “Plasmonic Laser Antenna”
Ertugrul Cubukcu et al.
Applied Physics Letters 89: 093120

Results: By building a nano antenna directly onto a commercial semi­conductor laser, Ken Crozier and ­Federico Capasso of Harvard University were able to focus light with a wavelength of 830 nanometers to a spot 40 nanometers wide. The experimental work was done by graduate students ­Ertugrul Cubukcu and Eric Kort.

Why It Matters: Optical discs such as CDs and DVDs are written and read using laser light. A smaller wavelength produces a smaller spot size, which allows more data to be crammed onto a disc. For instance, CDs are written and read using light with a wavelength of 780 nanometers; for DVDs, the wavelength is 650 nanometers, and for Blu-ray discs, it’s 405 nanometers. That’s why Blu-ray discs store so much data–up to 50 gigabytes for dual-layer discs. Traditional optical techniques use mirrors and lenses to further shrink the spot, but at best they can shrink it to half the light’s wavelength. The researchers’ antenna sidesteps the limits of traditional optics to produce ultrasmall spots of light that could increase storage density to about three terabytes (3,000 gigabytes) on a disc the size of a CD. Moreover, the fabrication process they developed makes it easy and inexpensive to integrate the antenna into a commercial laser.

Methods: The antenna is made of two gold-coated nanosize rods separated by a 30-nanometer-wide gap. When light from the laser hits the nano rods, it applies a force to the electrons in the gold, nudging them out of place. The electrons oscillate back and forth, causing electrical charges to build up on both sides of the gap–­positive charges on one side and negative charges on the other. The rods and the gap act as a tiny capacitor, which effectively concentrates the energy from the laser light into a spot about the size of the gap.

Next Steps: The researchers are exploring fabrication techniques that can decrease the gap between the rods–and the spot size–to 20 nanometers. They are also exploring alternatives to the gold that coats the rods; silver, say, could focus light more efficiently than gold at the wavelengths used in the consumer electronics industry

Gadgets That Know Your Next Move
Researchers have developed a model that predicts people’s daily activities

Source: “Eigenbehaviors: Identifying Structure in Routine”
Nathan Eagle et al.
MIT Media Lab Vision and Modeling Technical Report 601

Results: Using location data, call logs, and other information collected from mobile phones, Nathan Eagle and Sandy Pentland of MIT’s Media Labo­ratory have developed a new data-analysis technique that, with only limited initial information, can predict the daily behavior and determine the social allegiances of study participants. By looking at a few early-morning activities and locations, the researchers can forecast a person’s remaining daily activities, associations, and locations with 79 percent accuracy. They can also identify group affiliations with 96 percent accuracy.

Why It Matters: As mobile devices generate increasingly immense amounts of behavioral data–about whom we call, where we go, and who is around us–they could learn to schedule meetings or recommend activities. But that will require new techniques to make sense of the data. Current computer models that predict behavior are complex and sometimes miss patterns that are simple for humans to see. The researchers’ approach can characterize and predict behavior more easily.

Methods: During the 2004-2005 school year, the researchers logged more than 350,000 hours of behavioral data collected from the mobile phones of 100 students and faculty members at MIT. The data included information on where participants were, whom they talked to on the phone, and which other participants were nearby. From this information, Eagle and ­Pentland extracted fundamental patterns–dubbed eigenbehaviors–that succinctly describe a person’s or a group’s daily activities. For example, sleeping late in the morning is part of the same eigenbehavior as going out that evening. Although the connection between these two behaviors may seem obvious to a person, it is difficult for a computer to spot using traditional behavior-­prediction models.

Next Steps: The researchers are looking beyond individual behaviors and group affiliations to explore people’s influences on one another. They will test how well they can determine the satisfaction of people working on projects in groups, with an eye toward predicting which groups will be more efficient.