15699236
Facial Phoneme Extraction for Taiwanese Sign Language Recognition
We have developed a system that recognizes the facial expressions in Taiwanese Sign Language (TSL) using a phoneme-based strategy. A facial expression is decomposed into three facial phonemes of eyebrow, eye, and mouth. A fast method is proposed for locating facial phonemes. The shapes of the phonemes were then matched by the deformable template method, giving feature points representing the corresponding phonemes. The trajectories of the feature points were tracked along the video image sequence and combined to recognize the type of facial expression. The tracking techniques and the feature points have been tailored for facial features in TSL. For example, the template matching methods have been simplified for tracking eyebrows and eyes. The mouth was tracked using the optical flow method, taking lips as homogeneous patches. The experiment has been conducted on 70 image sequences covering seven facial expressions. The average recognition rate is 83.3%.
8586136
Emotion recognition using acoustic features and textual content
The paper presents an approach to emotion recognition from speech signals and textual content. In the analysis of speech signals, thirty-three acoustic features are extracted from the speech input. After principle component analysis (PCA), 14 principle components are selected for discriminative representation. In this representation, each principle component is the combination of the 33 original acoustic features and forms a feature subspace. Support vector machines (SVMs) are adopted to classify the emotional states. In text analysis, all emotional keywords and emotion modification words are manually defined. The emotion intensity levels of emotional keywords and emotion modification words are estimated from a collected emotion corpus. The final emotional state is determined based on the emotion outputs from the acoustic and textual approaches. The experimental result shows that the emotion recognition accuracy of the integrated system is better than each of the two individual approaches.
292216
Motif identification neural design for rapid and sensitive protein family search
A new method, the motif identification neural design (MOTIFIND), has been developed for rapid and sensitive protein family identification. The method is an extension of our previous gene classification artificial neural system and employs new designs to enhance the detection of distant relationships. The new designs include an n-gram term weighting algorithm for extracting local motif patterns, an enhanced n-gram method for extracting residues of long-range correlation, and integrated neural networks for combining global and motif sequence information. The system has been tested and compared with several existing methods using three protein families, the cytochrome c, cytochrome b and flavodoxin. Overall it achieves 100% sensitivity and > 99.6% specificity, an accuracy comparable to BLAST, but at a speed of approximately 20 times faster. The system is much more robust than the PROSITE search which is based on simple signature patterns. MOTIFIND also compares favorably with BLIMPS, the Hidden Markov Model and PROFILESEARCH in detecting fragmentary sequences lacking complete motif regions and in detecting distant relationships, especially for members of under-represented subgroups within a family. MOTIFIND may be generally applicable to other proteins and has the potential to become a full-scale database search and sequence analysis tool.
7845091
A Hybrid Number System Processor with Geometric and Complex Arithmetic Capabilities
The architecture, design, and performance of a hybrid number system processor are described. The processor performs multiplication, division, square root, and square in the logarithmic number system (LNS) domain. However, the input, output, addition, and subtraction are all executed in the 32-b IEEE standard floating-point number system. With the LNS multiplier and pipelined architecture, the processor is able to perform the geometric and complex arithmetic very effectively. The processor is also shown to compare well to an existing 32-b floating-point DSP (digital signal processor) chip. For the same level of CMOS technology, the performance ratios between the hybrid number system and the floating-point processor are shown to be 6.4:1 and 8:1 for division and square root, respectively; for the complex FFT (fast Fourier transform) algorithm, the ratio is around 2:1. >
11414897
Trace-based analysis and tuning for distributed parallel applications
We present an integrated approach to deal with timestamp consistency, and trace based performance analysis techniques for distributed parallel applications. Our trace generation facility captures message passing and system events such as process dispatch with minimal trace overhead. Trace driven analysis tools are developed for post execution analysis, reporting information such as the time stolen by other processes in each node, and the observed message passing time and local wait time for each message. We then present our techniques to reduce total elapsed times based on observed message passing times and local wait times.
7629632
Speech content authentication integrated with CELP speech decoders
A speech content authentication scheme, which is integrated with CELP speech coders to minimize the total computational cost is proposed in this research. Speech features relevant to semantic meaning are extracted, encrypted and attached as the header information. A low cost synchronization algorithm is used to resolve mis-synchronization caused by content preserving operations. Silent and tonal regions in the speech are identified with algorithms of low complexity to enhance the precision of integrity verification. This scheme is not only much faster than traditional cryptographic bitstream integrity algorithms, but also more compatible for a variety of applications. Experimental results are collected by using the GSM-AMR speech coder, and statistical analysis is performed to calculate the false positive rate of tamper detection.
21970380
Counter-Propagation Neural Networks for Molecular Sequence Classification: Supervised LVQ and Dynamic Node Allocation
A modified counter-propagation (CP) algorithm with supervised learning vector quantizer (LVQ) and dynamic node allocation has been developed for rapid classification of molecular sequences. The molecular sequences were encoded into neural input vectors using an n–gram hashing method for word extraction and a singular value decomposition (SVD) method for vector compression. The neural networks used were three-layered, forward-only CP networks that performed nearest neighbor classification. Several factors affecting the CP performance were evaluated, including weight initialization, Kohonen layer dimensioning, winner selection and weight update mechanisms. The performance of the modified CP network was compared with the back-propagation (BP) neural network and the k–nearest neighbor method. The major advantages of the CP network are its training and classification speed and its capability to extract statistical properties of the input data. The combined BP and CP networks can classify nucleic acid or protein sequences with a close to 100% accuracy at a rate of about one order of magnitude faster than other currently available methods.