Time-Domain Isolated Phoneme Classification Using Reconstructed Phase Spaces

This paper introduces a novel time-domain approach to modeling and classifying speech phoneme waveforms. The approach is based on statistical models of reconstructed phase spaces, which offer significant theoretical benefits as representations that are known to be topologically equivalent to the state dynamics of the underlying production system. The lag and dimension parameters of the reconstruction process for speech are examined in detail, comparing common estimation heuristics for these parameters with corresponding maximum likelihood recognition accuracy over the TIMIT data set. Overall accuracies are compared with a Mel-frequency cepstral baseline system across five different phonetic classes within TIMIT, and a composite classifier using both cepstral and phase space features is developed. Results indicate that although the accuracy of the phase space approach by itself is still currently below that of baseline cepstral methods, a combined approach is capable of increasing speaker independent phoneme accuracy

A Research on Maximum Symbolic Entropy from Intrinsic Mode Function and Its Application in Fault Diagnosis

Empirical mode decomposition (EMD) is a self-adaptive analysis method for nonlinear and nonstationary signals. It has been widely applied to machinery fault diagnosis and structural damage detection. A novel feature, maximum symbolic entropy of intrinsic mode function based on EMD, is proposed to enhance the ability of recognition of EMD in this paper. First, a signal is decomposed into a collection of intrinsic mode functions (IMFs) based on the local characteristic time scale of the signal, and then IMFs are transformed into a serious of symbolic sequence with different parameters. Second, it can be found that the entropies of symbolic IMFs are quite different. However, there is always a maximum value for a certain symbolic IMF. Third, take the maximum symbolic entropy as features to describe IMFs from a signal. Finally, the proposed features are applied to evaluate the effect of maximum symbolic entropy in fault diagnosis of rolling bearing, and then the maximum symbolic entropy is compared with other standard time analysis features in a contrast experiment. Although maximum symbolic entropy is only a time domain feature, it can reveal the signal characteristic information accurately. It can also be used in other fields related to EMD method

Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles.

Articulated wheel loaders used in the construction industry are heavy vehicles and have poor stability and a high rate of accidents because of the unpredictable changes of their body posture, mass and centroid position in complex operation environments. This paper presents a novel distributed multi-sensor system for real-time attitude estimation and stability measurement of articulated wheel loaders to improve their safety and stability. Four attitude and heading reference systems (AHRS) are constructed using micro-electro-mechanical system (MEMS) sensors, and installed on the front body, rear body, rear axis and boom of an articulated wheel loader to detect its attitude. A complementary filtering algorithm is deployed for sensor data fusion in the system so that steady state margin angle (SSMA) can be measured in real time and used as the judge index of rollover stability. Experiments are conducted on a prototype wheel loader, and results show that the proposed multi-sensor system is able to detect potential unstable states of an articulated wheel loader in real-time and with high accuracy

Protein kinase CK2α is overexpressed in colorectal cancer and modulates cell proliferation and invasion via regulating EMT-related genes

<p>Abstract</p> <p>Background</p> <p>Protein kinase CK2 is a highly conserved, ubiquitous protein serine/threonine kinase that phosphorylates many substrates and has a global role in numerous biological and pathological processes. Overexpression of the protein kinase CK2α subunit (CK2α) has been associated with the malignant transformation of several tissues, with not nearly as much focus on the role of CK2α in colorectal cancer (CRC). The aims of this study are to investigate the function and regulatory mechanism of CK2α in CRC development.</p> <p>Methods</p> <p>Expression levels of CK2α were analyzed in 144 patients (104 with CRC and 40 with colorectal adenoma) by immunohistochemistry. Proliferation, senescence, motility and invasion assays as well as immunofluorescence staining and western blots were performed to assess the effect of CK2α in CRC.</p> <p>Results</p> <p>The immunohistochemical expression of nuclear CK2α was stronger in tumor tissues than in adenomas and normal colorectal tissues. Suppression of CK2α by small-interfering RNA or the CK2α activity inhibitor emodin inhibited proliferation of CRC cells, caused G0/G1 phase arrest, induced cell senescence, elevated the expression of p53/p21 and decreased the expression of C-myc. We also found that knockdown of CK2α suppressed cell motility and invasion. Significantly, CK2α inhibition resulted in β-catenin transactivation, decreased the expression levels of vimentin and the transcription factors snail1 and smad2/3, and increased the expression of E-cadherin, suggesting that CK2α regulates the epithelial-mesenchymal transition (EMT) process in cancer cells.</p> <p>Conclusions</p> <p>Our results indicate that CK2α plays an essential role in the development of CRC, and inhibition of CK2α may serve as a promising therapeutic strategy for human CRC.</p