- Fastly’s S3 Latency Monitor — The graph represents real-time response latency for Amazon S3 as seen by Fastly’s Ashburn, VA edge server. I’ve been watching #sandy’s effect on the Internet in real-time, while listening to its effect on people in real-time. Amazing.
- Button Upgrade (Gizmodo) — elegant piece of button design, for sale on Shapeways.
- Inside a Dozen USB Chargers — amazing differences in such seemingly identical products. I love the comparison between genuine and counterfeit Apple chargers. (via Hacker News)
- Why Products Fail (Wired) — researcher scours the stock market filings of publicly-listed companies to extract information about warranties. Before, even information like the size of the market—how much gets paid out each year in warranty claims—was a mystery. Nobody, not analysts, not the government, not the companies themselves, knew what it was. Now Arnum can tell you. In 2011, for example, basic warranties cost US manufacturers $24.7 billion. Because of the slow economy, this is actually down, Arnum says; in 2007 it was around $28 billion. Extended warranties—warranties that customers purchase from a manufacturer or a retailer like Best Buy—account for an estimated $30.2 billion in additional claims payments. Before Arnum, this $60 billion-a-year industry was virtually invisible. Another hidden economy revealed. (via BoingBoing)
NYC’s PLAN to alert citizens to danger during Hurricane Sandy
A mobile alert system put messages where and when they were needed: residents' palms.
Starting at around 8:36 PM ET last night, as Hurricane Sandy began to flood the streets of lower Manhattan, many New Yorkers began to receive an unexpected message: a text alert on their mobile phones that strongly urged them to seek shelter. It showed up on iPhones:
This Emergency Alert just popped up on my phone. Ten seconds later, the TV went out. Here we go. #sandy #ny1sandy twitter.com/mbchp/status/2…
— Mike Beauchamp (@mbchp) October 30, 2012
…and upon Android devices:
Emergency alert on my phone. instagr.am/p/RYvlmJxJec/
— Heidi N. Moore (@moorehn) October 30, 2012
While the message was clear enough, the way that these messages ended up on the screens may not have been clear to recipients or observers. And still other New Yorkers were left wondering why emergency alerts weren’t on their phones.
Here’s the explanation: the emergency alerts that went out last night came from New York’s Personal Localized Alerting Network, the “PLAN” the Big Apple launched in late 2011.
NYC chief digital officer Rachel Haot confirmed that the messages New Yorkers received last night were the result of a public-private partnership between the Federal Communications Commission, the Federal Emergency Management Agency, the New York City Office of Emergency Management (OEM), the CTIA and wireless carriers.
While the alerts may look quite similar to text messages, the messages themselves run in parallel, enabling them to get through txt traffic congestion. NYC’s PLAN is the local version of the Commercial Mobile Alert System (CMAS) that has been rolling out nation-wide over the last year.
“This new technology could make a tremendous difference during
disasters like the recent tornadoes in Alabama where minutes – or even seconds – of extra warning could make the difference between life and death,” said FCC chairman Julius Genachowski, speaking last May in New York City. “And we saw the difference alerting systems can make in Japan, where they have an earthquake early warning system that issued alerts that saved lives.”
NYC was the first city to have it up and running, last December, and less than a year later, the alerts showed up where and when they mattered.
Four short links: 30 October 2012
Sandy's Latency, Better Buttons, Inside Chargers, and Hidden Warranties
Four short links: 29 October 2012
Behaviour Modification, Personal Archives, Key Printing, and Key Copying
- Inside BJ Fogg’s Behavior Design Bootcamp — see also Day 2 and Day 3.
- Recollect — archive your social media existence. Very easy to use and I wish I’d been using it longer. (via Tom Cotes)
- Duplicating House Keys on a 3D Printer — never did a title say so precisely what the post was about. (via Jim Stogdill)
- Teleduplication via Optical Decoding (PDF) — duplicating a key via a photograph.
Listening for tired machinery
Cheap sensors and sophisticated software keep expensive machines running smoothly
Software is making its way into places where it hasn’t usually been before, like the cutting surfaces of very fast, ultra-precise machine tools.
A high-speed milling machine can run at 42,000 RPM as it fabricates high-quality machine components within tolerances of a few microns. Excessive wear in that environment can lead to a failure that ruins an expensive part, but it’s difficult to use physical means to detect wear on cutting surfaces: human operators can’t see it and detailed microscopic inspections are costly. The result is that many operators simply replace parts on a pre-determined schedule — every two months, perhaps — that ends up being overly conservative.
The researchers’ milling machine, shown with sensors near the cutting device. (Source: X. Li, M.J. Er, H. Ge, O. P. Gan, S. Huang, L.Y. Zhai, S. Linn, Amin J. Torabi, “Adaptive Network Fuzzy Inference System and Support Vector Machine Learning for Tool Wear Estimation in High Speed Milling Processes,” Proceedings of the 38th Annual Conference of the IEEE Industrial Electronics Society, pp. 2809-2814, 2012.)
Enter software: in a paper delivered to the IEEE’s Industrial Electronics Society in Montreal last Thursday*, a group of researchers from Singapore propose a way to use low-cost sensors along with machine learning algorithms to accurately predict wear on machine parts — a technique that could cut costs for manufacturers by lengthening the lifespan of machine parts while avoiding failures.
The group’s demonstration is a promising illustration of the industrial Internet, which promises to bring more intelligence to machines by linking them to networks and integrating them with sophisticated software. Techniques from areas like machine learning, which can be computationally intensive, can thus be available in monitoring parts as small and common as cutting surfaces in milling machines.
Four short links: 26 October 2012
Windows 8 Web Theme, Taxing Mobile Payments, Digital Divide and Digital Service Delivery, and Consequences of Internet of Things
- BootMetro (github) — website templates with a Metro (Windows 8) look. (via Hacker News)
- Kenya’s Treasury to tax M-Pesa — 10% tax on mobile money-transfer systems. M-Pesa is the largest mobile money transfer service provider in Kenya, with more than 14 million subscribers. [...] It is estimated that M-Pesa reports some 2 million transactions per day. [...] the value of money transferred through mobile platforms jumped by 41 per cent in the first six months of 2012. Neer mind fighting you, you know you’re winning when they tax you! (via Evgeny Mozorov)
- Digital Divide and Fibre Rollout — As the group of non-users gets smaller, they are likely to become more seriously disadvantaged. The NBN – and high-speed broadband more generally – will drive a wave of new applications across most areas of life, transforming Australia’s service economy in fundamental ways. Those who are not connected in 2015 may be fewer, but they will be missing out on far more – in education, health, government, commerce, communication and entertainment. The costs will also fall on service providers forced to keep supplying expensive physical and face-to-face services to this declining number of people. This will be particularly significant in remote communities, where health consultations and evacuations by flying doctors, nurses and allied health professionals could potentially be reduced through e-health diagnostics, and where Centrelink still regularly sends teams out to communities. As gov2 expands and services move online, connectivity disadvantages are compounded. (via Ellen Strickland)
- Smart Body Smart World (Forrester) — take note of these two consequences of Internet of Things and Quantified Self: Verticals fuse: “Health and wellness” is not its own silo, but is connected to our finances, our shopping habits, our relationships. As bodies get connected, everyone is in the body business. Retail disperses: All retailers become computing retailers, and computing-specific retailers like Best Buy go the way of Blockbuster. You wouldn’t buy a smart toothbrush at a specialty CE store; you’d be more likely to buy it in the channel that solves the rest of your hygiene needs. (via Internet of Things)
Four short links: 25 October 2012
Big Data's Big Picture, Real-Time Queries, Real-Time Queries, Single-Process Real-Time Queries
- Big Data: the Big Picture (Vimeo) — Jim Stogdill’s excellent talk: although Big Data is presented as part of the Gartner Hype Cycle, it’s an epoch of the Information Age which will have significant effects on the structure of corporations and the economy.
- Impala (github) — Cloudera’s open source (Apache) implementation of Google’s F1 (PDF), for realtime queries across clusters. Impala is different from Hive and Pig because it uses its own daemons that are spread across the cluster for queries. Furthermore, Impala does not leverage MapReduce, allowing Impala to return result in real-time. (via Wired)
- druid (github) — open source (GPLv2) a distributed, column-oriented analytical datastore. It was originally created to resolve query latency issues seen with trying to use Hadoop to power an interactive service. See also the announcement of its open-sourcing.
- Supersonic (Google Code) — an ultra-fast, column oriented query engine library written in C++. It provides a set of data transformation primitives which make heavy use of cache-aware algorithms, SIMD instructions and vectorised execution, allowing it to exploit the capabilities and resources of modern, hyper pipelined CPUs. It is designed to work in a single process. Apache-licensed.
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