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China Venkateswarlu, S.
- Automated Tool to Generate Optimized Control Flow Graph for C Language
Authors
1 Department of CSE, BITS PILANI, Hyderabad, IN
2 HITS COE, Hyderabad, IN
3 Department of CSE, HITS COE, Hyderabad, IN
4 Department of CSE, Bhoj Reddy College of Engineering, Hyderabad, IN
Source
Software Engineering, Vol 3, No 6 (2011), Pagination: 254-261Abstract
Path Testing is considered to be one of the best techniques since it ensures that every statement, predicate, branch is executed at least once. But the draw back is that, as the program size increases the test paths also increases and exhaustive testing is impossible. Hence we need techniques to identify the test paths so as to cover statements, predicates and branch. In our work we explore the graph theory approach and apply several operations so as to reduce the no of test paths. The main aim of this work is optimize Test paths for the given program. Consider a program that consists of loops, conditions etc., while writing test cases we will consider it as single test case but internally it contains more statements. The intent of path testing is to execute every statement at least once. To satisfy this control flow graph is generated and test cases are written. In our proposed system we try to explore the graph matrix and Euler graph methods to identify 1) Number of Test paths 2) Optimized test paths and many other operations to analyze the test paths. We automate the process of conversion of a structured program to a control flow graph represented in the form of adjacency matrix and determine minimum no of test cases needed for path testing. Manual process of determining test cases can be replaced with this automated system.Keywords
Path Testing, Graph Matrix, Node Reduction Algorithm, Control Flow Graph.- Audio Compression Using Perceptual and Huffman Coding
Authors
1 JNTUH, HITS COE-Hyderabad, IN
2 Department of ECE, Karimnagar, IN
3 IT Department, HIT, IN
4 CSE Department, UCE, IN
Source
Programmable Device Circuits and Systems, Vol 2, No 9 (2010), Pagination: 132-139Abstract
Digital Audio compression allows the efficient storage and transmission of audio data. The various audio compression techniques offer different levels of complexity, compressed audio quality and amount of data compression. It has become increasingly more important with the advent of fast and inexpensive microprocessors. Advances in digital audio technology are fueled by two sources: hardware
Developments and new signal processing techniques. When processors dissipated tens ofWattsof power and memory densities were on the order of kilobits per square inch, Portable playback devices like an MP3 player were not possible. Now, however, power Dissipation, memory densities, and processor speeds have improved by several orders of Magnitude. Advancements in signal processing are exemplified by Internet broadcast Applications: if the desired sound quality for an internet broadcast used 16-bit PCM Encoding at 44.1 kHz, such an application would require a 1.4 Mbps channel for a stereo Signal! Increasing hardware efficiency and an expanding array of digital audio Representation formats are giving rise to a wide variety of new digital audio applications. These applications include portable music playback devices, digital surround sound for Cinema, high-quality digital radio and television broadcast, Digital Versatile Disc (DVD), and many others. This paper concentrates on digital audio signal compression, a technique essential to the implementation of many digital audio applications. Digital audio signal compression is the removal of redundant or otherwise irrelevant information from a digital audio signal. A process that is useful for conserving both transmission bandwidth and storage space.
The standards of compression coding are very poorly described. It is defined of various methods of decoding but lacks details for encoding. This makes the implementation of a compression coder a greater task than might otherwise be expected. Normally, no information loss is acceptable when compressing digital audio such as programs, source code, and text documents. Entropy coding is the most commonly used method used for loss-less compression. It exploits the fact that all bit combinations are not likely to appear in the data, which is used in coding algorithms such as Huffman. This approach works for the data types mentioned above, however digital audio signals such as music and speech cannot be efficiently encoded with entropy coding. In this paper begin by defining some useful terminology.