TDRS: Communicating Critical Data

As a vital information pipeline for space-based research and exploration ambitions, the TDRS constellation fulfills NASA's broadest communication demands. 

Now into it's fourth operational decade, the TDRS legacy continues to be communications excellence. 

The addition of the third generation of spacecraft will replenish the constellation and ensure that the critical lifeline of space-to-ground communication support will be available for many years to come. 

Parrot Parents Name Their Babies

Parrots, with their amazing abilities to mimic speech and talk to humans in addition to each other, are by far impressive communicators. But research shows that parrot conversations are even more complex. Each parrot has its own signature call that others use to address it, which is the parrot equivalent of having a name. But where do these “names” come from? New research has shown that just like with human babies, parrot parents name their offspring, even before the babies can communicate themselves.

The research, led by Karl Berg of Cornell University, used video cameras to record the communication process of green-rumped parrots (Forpus passerinus) in Venezuela. The wild parrot study showed that even before chicks begin to chirp back at their parents, adults give them a signature sound by which they are addressed. The babies will take this sound and in some cases tweak it before using it throughout their life.

Scientists have known for some time that parrots use these signature calls to refer to each other. Observing the process in captive birds led researchers to wonder how wild parrots dealt with naming, because it could show how names are given. The researchers felt there were two possibilities for how parrots get their names: it could be biologically innate (each bird names itself) or assigned by another older bird, which turned out to be the case.

For the study, the researchers placed video cameras in 16 green-rumped parrot nests in Venezula. These birds are part of a large wild population that has been living in nesting tubes rigged up by scientists in 1987. The researchers then moved around the parrot eggs so that half of the colony were raising babies that weren’t theirs genetically. Recordings of the calls made by the parents before the chicks were able to chirp , and of the calls once the chicks were individually vocal showed that parents started making the calls when the birds were very young. Additionally, the recordings showed that the parent’s calls provided a basis on which the baby would imitate and tweak their own name. The names bore more similarity to the parents that raised the offspring, than the biological parents, suggesting that the calls are in fact learned by the chicks rather than innate.

Parrots are not the only animals known to have names. In addition to humans, dolphins also use specific names for each individual. Researchers believe that the sophisticated social lives of these animals may be what drives the need to have names. For parrots, having a name is a valuable tool for knowing who is who when flocks change or shift members.

This discovery shows interesting corollaries between human communication and parrot communication that may be useful for subsequent studies of speech development. The studywas published in Proceedings of the Royal Society B.

Via: "GeekoSystem"

Quantum Networks

The quantum Internet is a term that has been bandied about a lot recently. And, for the moment it is utter nonsense. The Internet connects computers, so the quantum Internet pre-supposes the existence of useful quantum computers. The Internet also involves arbitrary on-the-fly routing through many intermediate stations, while current quantum communications protocols rely on point-to-point connections. I can't think of anything less Internet-like than that.

The nice thing about buzzwords, though, is that some people take them seriously while also recognizing the problems inherent to the idea. That leads to some fantastic research. A group of Japanese and British researchers have come up with a communications protocol that overcomes many of the fundamental problems associated with transferring quantum information over long distances. We still don't have a quantum computer, but when we do, these guys know how to connect them up.

Quantum Static On Your Phone Line

The issue boils down to the very nature of the quantum state. Usually, when we consider a quantum bit (qubits) of information, we are talking about a single photon. In any optical fiber, there is a certain probability the photon will be absorbed, which increases exponentially with distance. Generally, this limits point-to-point quantum data transfer to distances under 100km.

To expand that range, you need to entangle qubits at distant locations. The idea is that you send photon pairs over short distances and entangle them with neighboring photon pairs. That entangles the two most distance photons with each other. This can be repeated, transferring entanglement over large distances.

The process of entangling two photons is, however, not always successful. So, in a chain of photon pair sources, you might manage half the links on the first attempt. These should be stored in a memory, while the other half tries again. After several repetitions, the end-points are entangled with each other. Unfortunately, this requires a long-lasting quantum memory that can store a lot of different photons. This way, the right photon can be used once the entire entangled chain is established. Such a memory doesn't exist yet.

Spreading Your Bets On Photon Survival

This would all be so much easier if the quantum state was stored across multiple photons. This is what a team of Japanese and British researchers have been considering. Drawing on early results from optical quantum computing schemes, they realized the error correcting mechanisms proposed for those systems could be used to extend the distance over which a quantum state could be transported.

Their scheme relies on the fact that no one really wants to send a single qubit, instead we want to send lots. These individual qubits can then be blocked together to make one larger quantum state that is the superposition of the individual quantum states. In addition, we add more qubits whose values are determined by the results of mathematical operations on the data qubits. When we do this, a tiny bit of each qubit's state is held by several of the photons within the block, creating lots of redundancy.

Now, it turns out that on the receiving end, one can retrieve the entire quantum state of the data block provided a couple of conditions are met. At least one logical qubit within the block must make it through without loss. And, for each logical qubit, one physical qubit (a photon) survives.

So, if you have a block of a thousand photons—encoding 10 logical qubits in 10 photons with the remaining 990 photons being redundant—we require that one of those 10 photons makes it through cleanly. We also require the remaining photons that make it through contain information about the state of all the remaining qubits. If that occurs, you can retrieve the other 10 from the built-in redundancy.

The downside is, of course, that it takes many more physical qubits to encode a single logical qubit, so you might think that the data rate will be rather low. Not so fast, say the researchers. Information can be encoded on many different quantum states of the qubit, so one qubit could be stored in a superposition of polarization states, while another could be stored in phase, and another in spatial mode profiles, etc. (The nature of these states are unimportant save that we can play with one and not change the other.)

Those of you paying attention will be questioning this assumption though. Imagine in our previous example that we store three logical qubit states in a single photon. Well, if that photon is absorbed, we have lost three different qubits. Now, our encoding scheme relies on one logical qubit making it through in its entirety. Naively, we might think that this would reduce the chances of this occurring by a factor of three, which would be disastrous.

Luckily, the researchers are not as naive as me, and they have an answer. The encoding system works by blocking data and encoding it together into a single giant quantum state. The trick is to make sure the different quantum states of the same photon are used to encode qubits from different blocks of data. This way, the loss of a photon still results in losing three qubits, but they are from different blocks, making them the equivalent of losing three independent photons.

But Wait, It's A Router Too

The big advantage, though, is this scheme also allows something that looks like quantum routing. The basic process is that the photonic qubits are generated from matter qubits at one end and stored into matter qubits at a node. The storage process involves the emission of photons that can be used to determine the channel losses and appropriately decode the actual quantum data from the block. But, unlike point-to-point schemes, the decoding process doesn't involve measuring the quantum state, it only involves picking the right bits and performing operations on them. This is important, because a measurement would destroy the quantum state. Since the quantum state is preserved, it can be re-encoded in a new block and sent on to a new node.

One can then imagine blocks are chunked into super-blocks with the first block containing routing information, and the remaining blocks being data. The first block could be entirely classical and contain routing information, or it could be stored in qubits, which are read at a node (destroying the quantum nature of the state), and then re-encoded in new qubits. Either way would work.

Where Do I Order My Quantum Router?

All through reading this paper, I had my doubts. But it really is a solid bit of work. It is also, I think, going to be regarded as a key paper should quantum networks become ubiquitous and significant. Implementation, though, is going to be challenging. This is because the number of qubits per photon and the number of photons per qubit scale very fast. For instance, if we limit ourselves to losses of 50 percent—that is, the link destroys 50 percent of the photons sent to the receiver—and only store a single logical qubit per photon, then we require over 7000 photons (physical qubits) to transmit 10 logical qubits.

If we go the more complicated route and store multiple qubits per photon, then things get better: only 75 photons are required to transmit 15 photons. But, that assumes that 15 qubits are encoded on each photon, so, we actually need to send a minimum of 15 blocks of data, adding up to 1125 photons for 225 logical qubits.

Why do I care about these numbers? Well, that means we require 1125 matter qubits that can be set to the right value and retained until they are transferred to a photon. Furthermore, we require that each photon acquires 15 different qubits. To do that successfully 98 percent of the time, it will involve an encoding operation that works 99.8 percent of the time. At the moment, these operations have probabilities that are much lower, effectively limiting experiments to a couple of logical qubits per photon.

Nevertheless, these experimental difficulties are well-known and understood. Over time, the situation will improve and in a few years (less than 10), we will see encoding schemes similar to this that can encode more qubits per photon. The quantum Internet may yet lie ready, awaiting the arrival of a quantum computer.

Via: "ARS Technica"

The NASA - ESA InterPlanetary Internet

Astronaut On ISS Uses InterPlanetary Internet To Control Robot In Germany

NASA and the European Space Agency (ESA) used an experimental version of interplanetary Internet in late October to control an educational rover from the International Space Station, NASA says.

The experiment used NASA’s Disruption Tolerant Networking (DTN) protocol to transmit messages and demonstrate technology that one day may enable Internet-like communications with space vehicles and support habitats or infrastructure on another planet.

Space station Expedition 33 commander Sunita Williams in late October used a NASA-developed laptop to remotely drive a small LEGO robot at the European Space Operations Centre in Darmstadt, Germany. The European-led experiment used NASA’s DTN to simulate a scenario in which an astronaut in a vehicle orbiting a planetary body controls a robotic rover on the planet’s surface.

“The demonstration showed the feasibility of using a new communications infrastructure to send commands to a surface robot from an orbiting spacecraft and receive images and data back from the robot,” said Badri Younes, deputy associate administrator for space communications and navigation at NASA Headquarters. “The experimental DTN we’ve tested from the space station may one day be used by humans on a spacecraft in orbit around Mars to operate robots on the surface, or from Earth using orbiting satellites as relay stations.”

The DTN architecture is a new communications technology that enables standardized communications similar to the Internet to function over long distances and through time delays associated with on-orbit or deep space spacecraft or robotic systems. The core of the DTN suite is the Bundle Protocol (BP), which is roughly equivalent to the Internet Protocol (IP) that serves as the core of the Internet on Earth.

While IP assumes a continuous end-to-end data path exists between the user and a remote space system, DTN accounts for disconnections and errors. In DTN, data move through the network “hop-by-hop.” While waiting for the next link to become connected, bundles are temporarily stored and then forwarded to the next node when the link becomes available.

NASA’s work on DTN is part of the agency’s Space Communication and Navigation (SCaN) Program. SCaN coordinates multiple space communications networks and network support functions to regulate, maintain and grow NASA’s space communications and navigation capabilities in support of the agency’s space missions.

Via: "Kurzweil AI"

Stanford Researchers Discover The 'Anternet'

The Behavior Of Harvester Ants As They Forage For Food Mirrors The Protocols That Control Traffic On The Internet.

Deborah Gordon, a biology professor at Stanford, has been studying ants for more than 20 years. When she figured out how the harvester ant colonies she had been observing in Arizona decided when to send out more ants to get food, she called across campus to Balaji Prabhakar, a professor of computer science at Stanford and an expert on how files are transferred on a computer network. At first he didn’t see any overlap between his and Gordon’s work, but inspiration would strike soon.

“The next day it occurred to me, ‘Oh wait, this is almost the same as how [Internet] protocols discover how much bandwidth is available for transferring a file!’” Prabhakar says. ”The algorithm the ants were using to discover how much food there is available is essentially the same as that used in the Transmission Control Protocol.”

Transmission Control Protocol, or TCP, is an algorithm that manages data congestion on the Internet, and as such was integral in allowing the early web to scale up from a few dozen nodes to the billions in use today. Here’s how it works: As a source, A, transfers a file to a destination, B, the file is broken into numbered packets. When B receives each packet, it sends an acknowledgment, or an ant, to A, that the packet arrived.

This feedback loop allows TCP to run congestion avoidance: If ants return at a slower rate than the data was sent out, that indicates that there is little bandwidth available, and the source throttles data transmission down accordingly. If ants return quickly, the source boosts its transmission speed. The process determines how much bandwidth is available and throttles data transmission accordingly.

It turns out that harvester ants (Pogonomyrmex barbatus) behave nearly the same way when searching for food. Gordon has found that the rate at which harvester ants—which forage for seeds as individuals—leave the nest to search for food corresponds to food availability.

A forager won’t return to the nest until it finds food. If seeds are plentiful, foragers return faster, and more ants leave the nest to forage. If, however, ants begin returning empty handed, the search is slowed, and perhaps called off.

Prabhakar wrote an ant algorithm to predict foraging behavior depending on the amount of food—i.e., bandwidth—available. Gordon’s experiments manipulate the rate of forager return. Working with Stanford student Katie Dektar, they found that the TCP-influenced algorithm almost exactly matched the ant behavior found in Gordon’s experiments.

“Ants have discovered an algorithm that we know well, and they’ve been doing it for millions of years,” Prabhakar says.

They also found that the ants followed two other phases of TCP. One phase is known as slow start, which describes how a source sends out a large wave of packets at the beginning of a transmission to gauge bandwidth; similarly, when the harvester ants begin foraging, they send out foragers to scope out food availability before scaling up or down the rate of outgoing foragers.

Another protocol, called time-out, occurs when a data transfer link breaks or is disrupted, and the source stops sending packets. Similarly, when foragers are prevented from returning to the nest for more than 20 minutes, no more foragers leave the nest.

Prabhakar says that had this discovery been made in the 1970s, before TCP was written, harvester ants very well could have influenced the design of the Internet.

Gordon thinks that scientists have just scratched the surface for how ant colony behavior could help us in the design of networked systems.

There are 11,000 species of ants, living in every habitat and dealing with every type of ecological problem, Gordon says. “Ants have evolved ways of doing things that we haven’t thought up, but could apply in computer systems. Computationally speaking, each ant has limited capabilities, but the collective can perform complex tasks.

“So ant algorithms have to be simple, distributed, and scalable—the very qualities that we need in large engineered distributed systems,” she says. “I think as we start understanding more about how species of ants regulate their behavior, we’ll find many more useful applications for network algorithms.”

The work is published in the Aug. 23 issue of PLoS Computational Biology.

Via: "Stanford University"