Since my discussion thread on the efficiency of the in-memory data structure of ZeroMQ with Martin Sustrik, I have been reading up a bit by bit on efficient data structures, primarily from the perspective of memory utilization. Data structures that provide constant lookup time with minimal memory utilization can give a significant performance boost since access to CPU cache is considerably faster than access to RAM. This post is a compendium of a few data structures I came across and salient aspects about them

Courier allows you to share files, images, photos, movies, and more with all your favourite online services - including Flickr and Facebook.

TELECOMS operators naturally prize mobile-phone subscribers who spend a lot, but some thriftier customers, it turns out, are actually more valuable. Known as “influencers”, these subscribers frequently persuade their friends, family and colleagues to follow them when they switch to a rival operator. The trick, then, is to identify such trendsetting subscribers and keep them on board with special discounts and promotions. People at the top of the office or social pecking order often receive quick callbacks, do not worry about calling other people late at night and tend to get more calls at times when social events are most often organised, such as Friday afternoons. Influential customers also reveal their clout by making long calls, while the calls they receive are generally short.

Programming in a startup is much different than programming at a big company. At a startup, not only are you the developer, but you are also the systems administrator for the most part. I’ve been startupping for three years now, and have had my ass kicked enough times to step back and think that maybe I should learn how to do things the right way rather than try to bludgeon my way through with raw intellect.

Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. The algorithm was published by David Lowe in 1999.[1] Applications include object recognition, robotic mapping and navigation, image stitching, 3D modeling, gesture recognition, video tracking, and match moving.

The task of finding point correspondences between two images of the same scene or object is an integral part of many machine vision or computer vision systems. The algorithm aims to find salient regions in images which can be found under a variety of image transformations. This allows it to form the basis of many vision based tasks; object recognition, video surveillance, medical imaging, augmented reality and image retrieval to name a few.

SURF (Speeded Up Robust Features) is a robust image detector & descriptor, first presented by Herbert Bay et al. in 2006, that can be used in computer vision tasks like object recognition or 3D reconstruction. It is partly inspired by the SIFT descriptor. The standard version of SURF is several times faster than SIFT and claimed by its authors to be more robust against different image transformations than SIFT. SURF is based on sums of approximated 2D Haar wavelet responses and makes an efficient use of integral images. As basic image features it uses a Haar wavelet approximation of the determinant of Hessian blob detector.

“SURF: Speeded Up Robust Features” is a performant scale- and rotation-invariant interest point detector and descriptor.

This android specific library exposes a native previewer, and image pool for easy access to live video frames in your own code. Currently the two core pieces of the library are the NativePreviewer and the GL2CameraViewer private NativePreviewer mPreview; private GL2CameraViewer glview;

Intent intent = new Intent(“some.custom.action”); intent.putExtra(“some_boolean”, true); sendStickyBroadcast(intent);

In the Facebook login dialog, the WebKit WebView password field misaligns text input and does not display correctly on Android 2.0 and 2.1. This is corrected in Android 2.2 (Froyo): see http://code.google.com/p/android/issues/detail?id=5596

I’m going to write a couple of tutorials on using OpenGL ES on Android phones. The theory of OpenGL ES is the same on different devices so it should be quite easy to convert them to another platform.

Here’s an example that tries to satisfy “Warrior’s” request on sending custom intents and grabbing them with broadcast receivers.

Base class for code that will receive intents sent by sendBroadcast(). You can either dynamically register an instance of this class with Context.registerReceiver() or statically publish an implementation through the tag in your AndroidManifest.xml. Note: If registering a receiver in your Activity.onResume() implementation, you should unregister it in Activity.onPause(). (You won’t receive intents when paused, and this will cut down on unnecessary system overhead). Do not unregister in Activity.onSaveInstanceState(), because this won’t be called if the user moves back in the history stack.