198169485
A robust Stroke Extraction method for Handwritten Chinese Characters
45865876
Graph theoretic characterization and reliability of the generalized Boolean n-cube network
In this paper, a general class of Boolean n-cube structures is investigated. The interconnection is based on a mixed radix number system which results in a variety of generalized Boolean n-cube structures for a given number of processors N = x·2y, where x and y are positive integers. A number of interesting properties of the network are revealed. By a constructive method, the node connectivity of this network is found. Finally, we show that the graph is super-λ and is an optimal reliable structure for interconnection networks.
21513395
VLSI architecture and implementation for FS1016 CELP decoder with reduced power and memory requirements
Abstract The code-excited linear predictive (CELP) coder is the most effective technique among various linear predictive coding methods for speech compression. Hence, designing a relatively low-cost and low-power CELP decoder chip for the portable systems and wireless digital communication environment becomes increasingly important. This paper presents the VLSI architecture and chip implementation for the FS1016 CELP decoder with reduced power and memory requirements. A single-chip implementation of the CELP decoder drastically reduces the cost and the size of many CELP vocoder systems. The decoder chip can achieve the following: (1) excellent accuracy results due to the accuracy studies for the finite word length, (2) power savings and high-speed operations owing to the combined advantages of pipeline and concurrent processing structures, (3) table size reducing by applying the memoryless realization for stochastic codebook and partial sums technique and (4) specification satisfying the FS1016 CELP coder. Fabricated with 0.8 μm double-metal CMOS technology, the chip contains approximately 13 000 transistors occupying a 6.1 × 6.2 mm2 area. It has been tested to be fully functional at IMS XL-60 tester and is the first working FS1016 CELP 4.8 K decoder chip. In particular, the fixed-point accuracy studies for the CELP decoder are also addressed herein.
11945508
Content-Based Audio Classification Using Support Vector Machines and Independent Component Analysis
In this paper, we present a new audio classification system. First, a frame-based multiclass support vector machine (SVM) for audio classification is proposed. The accuracy rate has significant improvements over conventional file-based SVM audio classifier. In feature selection, this study transforms the log powers of the critical-band filters based on independent component analysis (ICA). This new audio feature is combined with mel-frequency cepstral coefficients (MFCCs) and five perceptual features to form an audio feature set. The superiority of the proposed system has been demonstrated via a 15-class sound database with a 91.7% accuracy rate
19723482
A learning process to the identification of feature points on Chinese characters
The paper describes a new stroke extraction approach to identify the feature points of a character, using line-filtering and learning-based techniques. The line-filtering technique based on the convolution operations with a set of 1-D Gabor templates is efficient in extracting the stroke segments of the character and robust in noise tolerance. Furthermore, unlike conventional feature-point detection techniques where decision rules and thresholds have to be specified, our learning-based technique for feature-point identification implicitly represents the rules and thresholds without further parameter adjustments. Experimental results show that the learning-based technique is capable of generalizing the learning knowledge to identify feature points and can get an average identification rate of 95.27% for hand-printed test characters and 96.78% for machine-printed test characters.
17964420
A learning process to the identification of feature points on Chinese characters
The paper describes a novel stroke extraction approach to identify the feature points of a character, using line-filtering and learning-based techniques. The line-filtering technique based on convolution operations with a set of one-dimensional (1D) Gabor templates efficiently extracts the stroke segments from noisy and degraded characters. Furthermore, the relationship between endpoints of stroke segments is modeled as junction structure during a learning process. Finally, each endpoint is identified as a feature point to determine the junction structure by the learning-based technique, rather than rule-based techniques with manual rule creation. Experimental results indicate that the learning-based technique can generalize learning knowledge to identify 1200 feature points with an average identification rate of 93.58% for test set, using k-fold cross-validation testing.
43623999
A Partition-Based Parallel MPEG-2 Software Decoder
11808205
Mending broken handwriting with a macrostructure analysis method to improve recognition
Abstract Broken characters always create problems in handwriting recognition systems, especially those using boundary and/or skeleton information. This paper presents a macrostructure analysis (MSA) mending method based on skeleton and boundary information and an MSA that investigates the stroke tending direction and other properties of handwritings. A new skeleton end extension algorithm is introduced, which compensates the defectiveness of the skeletonization algorithm and obtains a satisfactory skeleton. When combined with suitable parameters, improved performance from a handwriting classifier is achieved. The experimental results from over 13 000 numerals show the efficiency and robustness of the proposed method, raising recognition rates by over 10% for broken handwritten digits, from 74.8% to 86.4%.
2694561
Identifying Approximately Common Substructures in Trees Based on a Restricted Edit Distance
In this paper we present a dynamic programming algorithm for identifying the largest approximately common substructures (LACSs) of two ordered labeled trees. We consider a substructure of a tree T to be a connected subgraph of T. Given two trees T1,T2 and an integer d, the LACS problem is to find a substructure U1 of T1 and a substructure U2 of T2 such that U1 is within distance d of U2 and where there does not exist any other substructure V1 of T1 and V2 of T2 such that V1 and V2 satisfy the distance restriction and the sum of the sizes of V1 and V2 is greater than the sum of the sizes of U1 and U2. The distance measure considered in the paper is a restricted edit distance originated from Selkow. The LACS problem is motivated by the studies of program and document comparison. The proposed algorithm solves the problem in time O(d2×|T1|×|T2|), which is as fast as the best known algorithm for calculating Selkow's distance of two trees when the distance allowed in the common substructures is a constant independent of the input trees.
11616193
Automatic Hierarchical Classification of Structured Deep Web Databases
We present a method that automatically classifies structured deep Web databases according to a pre-defined topic hierarchy. We assume that there are some manually classified databases, i.e., training databases, in every node of the topic hierarchy. Each training database is probed using queries constructed from the node titles of the topic hierarchy and the query result counts reported by the database are used to represent the content of the database. Hence, when adding a new database it can be probed by the same set of queries and classified to a node whose training databases are most similar to the new one. Specifically, a support vector machine classifier is trained on each internal node of the topic hierarchy with these training databases and the new database can be classified into the hierarchy top-down level by level. A feature extension method is proposed to create discriminant features. Experiments run on real structured Web databases collected from the Internet show that this classification method is quite accurate.
21940463
A hybrid method for unconstrained handwritten numeral recognition by combining structural and neural "gas" classifiers
Abstract This paper presents a hybrid method for handwritten numeral recognition that combines two compensatory recognition algorithms by analysing their performance for several aspects. The skeleton-based structural recognition algorithm employed in this method is robust under distortion but sensitive to noise and flaws. On the other hand, the neural network classifier, which uses scaled binary images as features and the neural “gas” model for classification, is relatively immune to noise and flaws but sensitive to distortion. The different performances of the two algorithms for broken, connected or slanted numerals, and the measurement-level decision provided by the neural network are detected and combined with different strategies to develop matching rules for each recognition method. Five combination methods based on performance analysis are developed to meet different requirements. As the two algorithms have fairly compensatory properties, the proposed method improves the recognition rate and reliability by exploiting the advantages and avoiding the weaknesses of each classifier. The experimental results from a large set of data show the efficiency and robustness of the proposed method.