Free fbzx 1.8 software for linux

FBZX is a Sinclair Spectrum emulator, designed to work at full screen using the FrameBuffer.
Main features:
- emulates acurately the Original spectrum (both issue 2 and issue 3), the classic 128K, the Amstrad Plus 2 and the Amstrad Plus 2A. This include screen, keyboard and sound (both speaker and AY-3-8912 chip).
- Screen emulation is extremely acurate, so it can emulate border efects and even atribute efects.
- Supports Z80 snapshots, both loading and saving.
- Supports TAP (both read and write) and TZX (only read) tape files, supporting normal speed loading and fast speed loading.
- Emulates joysticks: kempston, cursor and sinclair.
- Emulates the Interface I and Microdrive.
- Is based in the great Z80 emulator created by Marat Fayzullin.
- Is distributed under GPL license (except the Z80 core, that cant be commercially distributed. See details in the homepage of Marat Fayzullin or in the documentation).

Blam is a tool that helps you keep track of the growing number of news feeds distributed as RSS. Blam lets you subscribe to any number of feeds and provides an easy to use and clean interface to stay up to date.
Enhancements:
- Support for RSS and RDF feeds
- Very easy to use interface
- Print the news entries you like
- Automatically update the feeds at regular intervals

gdbm is a database indexing library replacement for the traditional dbm and ndbm libraries.

gdm is useful for those who need to write C applications and need access to a simple and efficient database.

BPager is a lightweight pager designed to be used with NetWM-compliant window managers like OpenBox.

Unlike many other pagers out there, OBPager has very few dependencies, requiring only Xlib and glibc++ (no Gnome or KDE necessary).

Usage:

OBPager is pretty straightforward in terms of usage. It correctly displays the windows of a virtual desktop, indicates which is the active desktop, and allows you to switch desktops by clicking on the dockapp.

The command line syntax for obpager is:

obpager [--activefont < fontname >] [--inactivefont < fontname >] [desktop-number]

If no desktop number is specified, an instance of obpager for each virtual desktop is automatically created.

libsift is an algorithm to identify and locate interesting points within an image. For all such points, a descriptive signature is extracted.
The signatures can be stored and matched among multiple images, allowing for a large number of interesting applications, such as aligning overlapping images and identifying objects or motion within image sequences.
libsift is used by the autopano-sift program to create panorama images.
This library is a 100% C# implementation of the SIFT algorithm ("Scale-Invariant Feature Transform") and additional matching algorithms. The SIFT algorithm is an image feature location and extraction algorithm which provides the following key advantages over similar algorithms:
- Combined feature location and extraction algorithm.
- The keypoint locations are more precise and repeatable, because SIFT uses subpixel localization and multiple scale keypoint identification
- The descriptors are highly distinctive. For example, I tested with up to 300,000 keypoints while matching a large 60 picture panorama and it did not have a single invalid match.
- The feature vectors can be efficiently correlated using probabilistic algorithms like Best-Bin-First kd-tree search.
Enhancements:
- A serious bug in the Gaussian sigma computation and a bug in the DoG scale handling were fixed.

EMAN is a suite of scientific image processing tools aimed primarily at the transmission electron microscopy community, though it is beginning to be used in other fields as well. It has a particular focus on performing a task known as single particle reconstruction.
In this method, images of nanoscale molecules and molecular assemblies embedded in vitreous (glassy) ice are collected on a transmission electron microscope, then processed using EMAN to produce a complete 3-D recosntruction at resolutions now approaching atomic resolution. For low resolution structures (~2 nm), this may require ~8 hours of computer processing and a few thousand particles.
For structures aimed at ~0.5 nm or better resolution, hundreds of thousands of particles and hundreds of thousands of CPU-hours (on large computational clusters) may be required. Indeed, EMAN is often used in supercomputing facilities as a test application for large-scale computing.
Scientific image processing is distinguished from typical Photoshop image processing in that it is analyitical in nature. Images processed in EMAN are floating point greyscale images. That is, the pixel values in the images are represented as real numbers, not as small integers (typical GIF/JPG/PNG images are limited to integral values from 0-255 for each pixel).
Processing often includes complex image processing operations in Fourier or Wavelet space. EMAN was first released in 1999, and has been under continuous development since. It consists of a C++ library of hundreds of different image/volume processing algorithms with bindings into the popular Python scripting language. In new EMAN development, all user-level programs (of which there are over 200 in EMAN 1.8) are developed in Python, allowing the knowledgable end-user to make modifications without having to download or compile any of the C++ source code.
Enhancements:
- Substantial improvements were made in refine2d.py.
- Using refine2d.py on all new data sets is strongly suggested.
- Some programs use the EMAN2 style of arguments, i.e. "program < file > --option=value", rather than the old style, "program < file > option=value".
- There is a new HDF5 format compatible with EMAN2.
- New AIRS programs were added, such as "skeleton".
- New options were added to make refinement work better on large icosahedral objects.
- The parallelism infrastructure was improved, though network-related problems may still exist.
- Random model generation in makeinitialmodel.py was fixed.

ckjm project calculates Chidamber and Kemerer object-oriented metrics by processing the bytecode of compiled Java files.
For each class given, the program calculates the following six metrics proposed by Chidamber and Kemerer: WMC, weighted methods per class; DIT, depth of inheritance tree; NOC, number of children; CBO, coupling between object classes; RFC, response for a class; and LCOM, lack of cohesion in methods.
In addition, ckjm also calculates each classs afferent couplings (Ca) and its number of public methods (NPM).
Enhancements:
- The depth of inheritance tree (DIT) calculation now also takes into account classes that are not directly processed.
- The ckjm ant task has a corresponding structure.
- Contributed code contains Ruby scripts that plot ckjm metrics as charts.

PyBison is a Python binding to the Bison (yacc) and Flex (lex) parser-generator utilities.
It allows parsers to be quickly and easily developed as Python class declarations, and for these parsers to take advantage of the fast and powerful C-based Bison/Flex.
Users write a subclass of a basic Parser object, containing a set of methods and attributes specifying the grammar and lexical analysis rules, and taking callbacks for providing parser input, and receiving parser target events.
Presently, PyBison is only working on Linux (and possibly *BSD-based) systems. However, in time, (or if someone volunteers to help out with probably 2 hours coding for a small shim layer) its very possible PyBison will work on Windows as well.
Main features:
- Runs at near the speed of C-based parsers, due to direct hooks into bison-generated C code
- Full LALR(1) grammar support
- Includes a utility to convert your legacy grammar (.y) and scanner (.l) scripts into python modules compatible with PyBison
- Easy to understand - the walkthrough and the examples will have you writing your own parsers in minutes
- Comfortable and intuitive callback mechanisms
- Can export parse tree to XML with a simple method call (New!)
- Can reconstitute a parse tree from XML (New!)
- Examples include working parsers for the languages:
- - ANSI C
- - Java (1.4.2)