Echolocation by Smartphone Possible

Submarines, bats, and even humans can echolocate, but they need high-end acoustic gear, brainpower, or training in order to do it. Now electrical engineer Ivan Dokmanić, of the École Polytechnique Fédérale de Lausanne (EPFL), in Switzerland, could bring that capability to smartphones. He has used echolocation combined with a simple algorithm and off-the-shelf microphones to map part of a complex structure—the Lausanne Cathedral. Used in reverse, this kind of technology could one day help smartphones find their location inside buildings.

Echolocation at its most basic consists of sending a sound toward an item of interest and timing its return. If you know the medium, you also know how fast it will carry the sound. Solve a simple equation and you have the distance to the item.

But mapping even the simplest room, let alone a cathedral, is more complex. The first sound reflects from all the room’s surfaces, flooding the listener with signals from many directions. Even after passing the microphone the first time, those first sound waves can reflect on opposing walls and return to the microphone a second time, adding secondary reflections to the already confusing signal. “You need somehow a way to tell, ‘This group of echoes corresponds to one wall, and another group of echoes corresponds to another wall,’ ” Dokmanić says.

Some solutions involve sending sound from multiple known locations at different times. Other solutions involve using multiple microphones. Dokmanić, who says he has a taste for simplicity, once tried to calculate a hypothetical room’s geometry using just one sound source and one microphone [PDF]. This system worked on paper for some kinds of rooms in noiseless environments, but in the real world, noise is everywhere. “Maybe you’ll have some spurious spikes in your signal,” Dokmanić says, “so you also need a way to discard these.”

Dokmanić’s method, published online this week in the Proceedings of the National Academy of Sciences, uses a mathematical tool called a Euclidean distance matrix, which helps sort the reflected sounds along a timeline. But he conceded a point to complexity and used multiple microphones—although only one sound source.

Electrical engineering researcher Flavio P. Ribeiro, of Microsoft’s Applied Sciences Group, in Redmond, Wash., calls this application of Euclidean distance matrices “useful” but notes that Dokmanić’s algorithm assumes tidier environments than exist in the real world, such as rooms with no furniture or other clutter that might complicate the sound signal. Such clutter creates “sound shadows” that would require more computing power to untangle.

Other algorithms, including one created by electrical engineer Sakari Tervo of Aalto University, in Finland, and a colleague, seek to reconstruct a room’s geometry even in the absence of some of the initial sound reflections, although these algorithms rely on multiple microphones. Dokmanić’s latest system assumes he has captured all the first reflections before he can filter out the secondary reflections and noise.

Tervo also worries that Dokmanić’s algorithm will not translate to more complex settings. In their paper, Dokmanić and his colleagues note that their map of the cathedral is imperfect due to reflections from figurines, columns, and curved surfaces. They were unable to distinguish between some of the smaller walls and the secondary reflections from bigger walls, he says. They achieved much better accuracy when they mapped a simple classroom with a fifth wall made of stacked tables.

Even so, the experiments inspired Dokmanić to explore hiring a developer who could help create smartphone applications using his algorithm. In a room with known dimensions, a pair of sound-emitting devices might be able to calculate their positions in the room, he suggests. The algorithm might also help improve teleconferencing sound quality. Electrical engineer Fabio Antonacci at Politecnico di Milano, in Italy, says he and others aim to improve teleconferencing too. They presented a paper last year in which they tried to identify sound sources at multiple locations in order to focus the listening devices on all of them at once, in much the same way that recent experimental cameras allow users to focus on light images at multiple depths.

Achieving those goals will take “smarter algorithms,” Dokmanić says, but after this experiment, he is optimistic: “It is kind of surprising that you can do it with so little infrastructure.”

Why WhatsApp’s Technology Is Worth $19 Billion to Facebook

Facebook’s purchase of WhatsApp for $19 billion may sound like a Silicon Valley tycoon’s ransom for a simple mobile messaging service. But the acquisition gives Facebook access to a mobile messaging service that can reach millions of people worldwide who access mobile Internet services through either smartphones or simpler feature phones.

The acquisition of WhatsApp, based in Mountain View, Calif., comes at a time when Internet-based mobile messaging services have become increasingly popular at the expense of standard SMS text messaging. That’s because WhatsApp and other mobile messaging services offer “free” Internet messaging if users have a mobile data plan, unlike many SMS packages sold by telecommunications firms that charge customers per text. The trend has left many telecoms worried that they will become sidelined by Internet-based services and reduced to providing the mobile Internet infrastructure while others profit, the Financial Times reports.

An Informa consultancy report estimated 50 billion messages would be sent from WhatsApp and other mobile messaging services in 2014, according to TheNextWeb. By comparison, Informa estimated that just 21 billion traditional text messages would be sent this year. Still, the usage of SMS text messaging continues to rise annually, even if it pales next to the rapid growth of mobile messaging services.

Future trends among both mobile data plan subscriptions and smartphone ownership seem to favor the continued rise of mobile messaging services. MobiThinking reports that there were an estimated 2.1 billion mobile Web users as of mid 2013—a figure that has grown by 40 percent over the last three years. On the hardware side, Gartner consultants announced that worldwide sales of smartphones surpassed sales of feature phones for the first time in 2013.

WhatsApp has attracted 450 million users worldwide in part by building upon Java 2 Mobile Edition (J2ME), according to TextIt Blog. That has made the mobile messaging app usable on the many Nokia or Samsung feature phones available at cheaper prices compared to Apple’s iPhones or Google’s Android smartphones. And that advantage has allowed WhatsApp to reach customers beyond smartphone users in the developing world.

But WhatsApp is not alone in the mobile messaging market. Its rivals include Japan’s Line with 350 million registered users, China’s WeChat with 272 million monthly active users, and South Korea’s Kakao with 133 million registered users, according to The Wall Street Journal. Such rival services differ from WhatsApp by offering many more services on top of messaging, including games and mobile payments.

The strength of “weak signals”

As information thunders through the digital economy, it’s easy to miss valuable “weak signals” often hidden amid the noise. Arising primarily from social media, they represent snippets—not streams—of information and can help companies to figure out what customers want and to spot looming industry and market disruptions before competitors do. Sometimes, companies notice them during data-analytics number-crunching exercises. Or employees who apply methods more akin to art than to science might spot them and then do some further number crunching to test anomalies they’re seeing or hypotheses the signals suggest. In any case, companies are just beginning to recognize and capture their value. Here are a few principles that companies can follow to grasp and harness the power of weak signals.

Engaging at the top

For starters, given the fluid nature of the insights that surface, it’s often useful to get senior leaders actively involved with the social-media sources that give rise to weak signals. Executives who are curious and attuned to the themes emerging from social media are more likely to spot such insights. For example, a global manufacturer whose high quality and low prices were the topic of one customer’s recent social-media post almost certainly would not have examined it but for a senior executive who was a sensitive social “listener” and found its implications intriguing. Did the company have an opportunity, the executive wondered, to increase prices or perhaps to seek market share more aggressively at the current prices?

To find out, the executive commissioned research to quantify what had started out as a qualitative hunch. Ultimately, the low-price perception turned out to be an anomaly, but the outsize perception of the product’s quality was widely held. In response, the company has started funneling marketing resources to the product in hopes of building its market share by capitalizing on its quality and differentiating it further from the offerings of competitors.

Listening and mapping

As the manufacturer’s example implies, spotting weak signals is more likely when companies can marshal dispersed networks of people who have a deep understanding of the business and act as listening posts. One global beverage company is considering including social-media awareness in its hiring criteria for some managers, to build its network and free its management team from “well-rehearsed habits.”

Weak signals are everywhere, of course, so deciding when and where to keep the antennae out is critical. One such situation involves a product, market, or service that doesn’t yet exist—but could. Consider the case of a global advertising company that was investigating (for one of its clients) a US growth opportunity related to child care. Because no one was offering the proposed service, keyword searches on social media (and on the web more broadly) wouldn’t work. Instead, the company looked to social-media platforms where it might find weak signals—finally discovering an online content service that allows users to create and share individualized newspapers.

In the child-care arena, digital-content channels are often curated by mothers and fathers, who invite conversations about their experiences and concerns, as well as assemble relevant articles by experts or government sources. Analysts used semantic clues to follow hundreds of fine-grained conversations on these sites. The exercise produced a wealth of relevant information about the types of services available in individual markets, the specific levels of service that parents sought, the prices they were willing to pay, the child-care options companies already sponsored, the strength of local providers (potential competitors), and the people in various communities who might become ambassadors for a new service. This wasn’t a number-crunching exercise; instead, it took an anthropological view of local child care—a mosaic formed from shards of information found only on social media. In the end, the weak signals helped the company to define the parameters of a not-yet-existing service.

Spotting visual clues

It’s also useful to search for weak signals when customers start engaging with products or services in new, tech-enabled ways, often simply by sharing perceptions about a company’s offerings and how they are using them. This can be hard for companies to relate to at first, as it’s quite removed from the usual practice of finding data patterns, clustering, and eliminating statistical noise. Spotting weak signals in such circumstances requires managers and employees to have the time and space to surf blogs or seek inspiration through services such as Tumblr or Instagram.

As intangible as these techniques may sound, they can deliver tangible results. US retailer Nordstrom, for example, took an early interest in the possibilities of Pinterest, the digital-scrapbooking site where users “pin” images they like on virtual boards and share them with a larger community. Displayed on Pinterest, the retailer’s products generate significant interest: the company currently has more than four million followers on the site.

Spotting an opportunity to share this online engagement with in-store shoppers, the company recently started displaying popular Pinterest items in two of its Seattle-area stores. When early results were encouraging, Nordstrom began rolling out the test more broadly to capitalize on the site’s appeal to customers as the “world’s largest ‘wish list,’” in the words of one executive. The retailer continues to look for more ways to match other customer interactions on Pinterest with its products. Local salespeople already use an in-store app to match items popular on Pinterest with items in the retailer’s inventory. As the “spotting” ability of companies in other industries matures, we expect visual tools such as Pinterest to be increasingly useful in detecting and capitalizing on weak signals.

Crossing functions

As the Nordstrom example demonstrates, listening for weak signals isn’t enough—companies must channel what’s been learned to the appropriate part of the organization so the findings can influence product development and other operational activities. Interestingly, TomTom, a company that offers products and services for navigation and traffic, found that the mechanism for spotting weak signals proved useful in enhancing its product-development process.

As part of normal operations, TomTom monitored social media closely, mining conversations to feed into performance metrics for marketing and customer-service executives. The normal process changed after an attentive company analyst noted that users posting on a UK forum were focused on connectivity problems. Rather than let the tenuous comments get lost in the company’s performance statistics, he channeled them to product-development teams. To resolve the issue, the teams worked directly—and in real time—with customers. That helped short-circuit an otherwise costly process, which would have required drivers using TomTom’s offerings to check out connectivity issues in a number of locales. The broader payoff came in the form of new R&D and product-development processes: TomTom now taps directly into its driving community for ideas on design and product features, as well as to troubleshoot new offerings quickly.

At most companies, weak signals will be unfamiliar territory for senior management, so an up-front investment in leadership time will be needed to clarify the strategic, organizational, and resource implications of new initiatives. The new roles will require people who are comfortable navigating diverse, less corporate sources of information.

Regardless of where companies observe weak signals, the authority to act on them should reside as close to the front lines as possible. Weak signals are strategic enough to demand top-management attention. They are sufficiently important to the day-to-day work of customer-service, technical-development, and marketing teams to make anything other than deep organizational engagement unwise.