This study investigates carbon nanotube textiles as advanced personal protection equipment for firefighters and first responders. Carbon nanotubes are lightweight, flame resistant, and possess high mechanical and thermal properties. Carbon nanotubes are also thermally anisotropic, meaning they easily conduct heat along the axis of an individual tube, and are relatively insulating across the tube’s diameter. By recognizing this anisotropic behavior, heat transfer through a layer of aligned carbon nanotubes in a garment can be partially redirected to a cold reservoir thereby protecting the wearer from heat stress and exhaustion. Finite element models were developed to simulate a carbon nanotube layer embedded in a firefighting garment and thermally connected to a cold reservoir. Simulation showed that under heat stress conditions, firefighter skin temperature was considerably reduced by the cooling layer.

Water pollution by the discharge of dye residue has been polluting the planet for decades. Chitosan is one of the natural abundant biomasses that show excellent adsorption toward colorants. Nevertheless, sophisticated chemical modifications of chitosan are inevitable to enhance physical and chemical stabilities in practical applications. In the present study, simple freeze-drying has been adopted to chitosan (CHI) beads showing good stability at low levels of pH. The results show complete decolourisation of Reactive Blue 19 (RB19) within 2 hours at pH 3 and temperature of 37 ?C. The encapsulation of live bacterium Lactobacillus casei (L. casei) into chitosan beads for dyestuff removal is also evaluated. Kinetics studies show a faster initial rate of adsorption by L. casei-chitosan beads at pH 3 and temperature of 37 ?C. The adsorptions partially follow the intraparticle diffusion mechanism.

Antimicrobial Peptides (AMPs) were first known as a group of innate immune products that mainly targets on the invading pathogens among multiple species. The essential mechanisms of action of AMPs toward microbial cells have been reported as electrostatic attraction and hydrophobic interaction between AMPs (cationic AMPs) and microbial cell membranes. These effects also contribute to the potential mechanism of anticancer activities of AMPs as well. The membrane difference between cancer cells and normal cells are believed to play significant roles in AMPs orienting process. Membrane selective targeting properties make AMPs promising candidates for alternative approach to solve the problems from anticancer drug resistance.

The ECG plays a key role in the rapid diagnosis of heart diseases such as coronary heart disease, ischemic heart disease, myocardial infarction, arrhythmias, etc. Unfortunately, the adhesion of conventional electrodes to the skin sometimes is difficult if not impossible due to the wet skin caused by higher perspiration. Besides, for the application of long term ECG monitoring, the wet adhesive ECG electrodes are easy to cause infection of the wearers’ skin. Moreover, in certain situations it is difficult or simply too time-consuming to undress the patient for an ECG. Therefore, novel ECG monitoring techniques are urgently needed. In this paper, an easy-to-use noncontact ECG (NCECG) monitoring node for wireless body sensor network was designed and tested. The NCECG node introduced in this paper use the doubled shielded active ECG electrodes, compared with many other two electrodes noncontact ECG monitoring node, it also added a right-leg-drive circuit to reduce the common mode noise. The experiment result shows that it could accurately monitor the ECG signals while insulated by one layer of clothes. Furthermore, the NCECG monitoring node we proposed is high compact and easy to use. Besides ECG monitoring function, it also integrates with temperature, respiration and motion state monitoring function, while the size of the circuit board is just 93×40 mm. And the elastic belt architecture makes the ECG monitoring much more convenient and easy to use.

Drapeability is one of the most important visual properties affecting garment appearance. Even though there are many studies concerned with fabric drape, understanding about the drape behaviour of garments is very limited. This study analyzes the key properties affecting the drape behaviour of garments to provide prediction equations. Results are statistically analyzed. From multiple regression analysis, drape rank scores obtained from subjective analyses are predicted using weight, bending modulus and extensibility measured at 100 gf/cm with a correlation coefficient of 0.94. Ranking values obtained from subjective analyses can be more easily predicted using both circularity and wave length minimum. A new equation was derived to predict drape rank score values of garments (correlation coefficient r = 0.97) depending on circularity and wavelength minimum.

Biological enzyme is a kind of substance, which can catalyze specific reaction. In our work, sericin membranes are prepared under the catalysis of tyrosinase, which promoting protein molecules crosslinking through tape casting method. Taking the water solubility of sericin membranes as the evaluation index, the optimal preparation conditions are determined as follows: the dosage of tyrosinase 1000 U/g, the reaction temperature 45 ?C, for 90 min, 2% glycerol and drying temperature at 45 ?C. The results of infrared spectra indicate that the structure of amide I is changed in crosslinked sericin membranes. The XPS results indicate the O atom content is increased in crosslinked sericin membrane. This verifies the crosslinking of sericin protein by tyrosinase.

Traditionally woven fabric structure classification is based on manual work in textile industry. This paper proposes an automatic approach to classify the three woven fabrics: plain, twill and satin weave. Firstly 2-D wavelet transform is used to obtain low frequency sub-image in order to reduce the analysis of fabric images. Then graylevel co-occurrence matrix (GLCM) and Gabor wavelet are adopted to extract the texture features of pre-processing fabric images. Finally Probabilistic Neural Network (PNN) is applied to classify the three basic woven fabrics. The experimental results demonstrate that the proposed method can automatically, efficiently classify woven fabrics and obtain accurate classification results (93.33%).

Inevitably there will be various types of fabric defect exists in textile production line. In order to distinguish and classify the types of defects more efficiently and accurately, an algorithm which combines Local Binary Patterns (LBP) and Gray-level Co-occurrence Matrix (GLCM) is proposed in this paper for fabric defect classification. The most pivotal step of the algorithm is to extract the local and global feature values of defect images. Firstly the local feature information of the image is extracted by adopting LBP algorithm. And then the overall texture information of the image is described via GLCM algorithm. In this way, the fabric image can be fully described from global and local. Finally, the two-part feature information are structured as a whole as the input of BP Neural Network. Thus the trained BP Neural Network can be used to classify the different types of defects. Experimental results show that the algorithm has higher classification accuracy.

This paper focus on the requiring of Made To Measure apparel production mode according to individual types and make calibration about body measurement and production in a line. In this paper, based on the measurement principle of structured light, a laser rotating body scanning of non-contact measurement system is introduced. Firstly, the key part of this system, such as digital motor, CCD, lens, etc, is introduced and discussed. Secondly, the principle of the whole system is brought up by analyzing the theory and realization of the structured light measurement, and a 3D reconstruction mathematical model is built based on the characteristic parameter of digital motor. Lastly, some important factors related to the accuracy of this system are discussed. The point cloud data of 3D body contour is acquired successfully via data processing and data piecing together. The further processing can be done according to the raw data. The validity of the principle of this system and the feasibility of arithmetic are verified by experiments.

Inductive transfer learning is a major research area in transfer learning which aims at achieving a high performance in the target domain by inducing the useful knowledge from the source domain. By combining decisions from individual classifiers, ensemble learning can usually reduce variance and achieve higher accuracy than a single classifier. In this paper, we propose a novel Ensemble Inductive Transfer Learning (EITL) method. EITL builds a set of classifiers by recording the iterative process of knowledge transfer. In each iteration, it uses the classifier of the source domain, the base classifier of the target domain built on the initial labeled data, and the most recent classifier built on the updated labeled data, to classify unlabeled instances, and add some self-labeled instances to the labeled data, and then trains a new classifier. At the end, all the classifiers built in this process are used for classification. We conduct experiments on synthetic data sets and six UCI data sets, which show that EITL is an effective algorithm in terms of classification accuracy.

Applying the original raw data to machine learning will bring in a poor performance, because so many features are not necessary and redundant. Extracting a small number of good features will be an important issue, and it can be solved by using dimensionality reduction techniques. However, the popular dimensionality reduction method will suffer from the eigen-decomposition of dense matrix problem which is expensive in memory and time. We adopt unsupervised (unlabeled) spectral regression method for dimensionality reduction, which well avoids the problem of dense matrix eigen-decomposition problem and can be applied on large scale data sets. Histograms of Oriented Gradients (HOG) are robust features which not only well characterize the local shape and appearance but also show a certain degree of local optical and geometry invariance. In order to characterize the local shape and appearance better, we extract a three-tier pyramid HOG descriptor vector for one sample. Then we adopt the unsupervised spectral regression method for dimensionality reduction on these descriptor vectors. Our algorithm can be applied in the library entrance guard system of university and other research fields. Several experiments on well-known face databases have shown good performance and good invariance against illumination, occlusion and local deformation, etc.

In order to improve the image reconstructed quality affected by soft filed feature and the speed of dynamic on-line data processing in Electrical Resistance Tomography, we propose a fast image reconstruction algorithm based on H∞ filtering theory. Mainly, on the H∞ filtering principle, a dynamic system is formulated firstly, whose inputs have unknown disturbances including noise errors and model errors, and the outputs have the estimation errors. Then, making the H∞ norm of this dynamic system as a cost function, a fast H∞ filtering algorithm is proposed whose criterion is to guarantee that the worst-cast effect of disturbance on estimation error is smaller than a given boundary. Experimental work was carried out for three typical flow distributions. Results showed that H∞ filter method improves the resolution of the reconstructed images and gains the strong robustness and anti-interference performance in unknown interference noise conditions. In addition, it dramatically reduces the computational time compared with the traditional Gauss-Newton iterative and Kalman filter methods. Therefore, the method is suitable for on-line multiphase flow measurement.

A novel nerve repairing material PRGD/PDLLA/β-TCP was synthesized and characterized with Scanning Electron Microscope (SEM), Fourier Transform Infrared (FTIR) spectroscopy, and mass loss ratio. The effects of PDLLA or PRGD/PDLLA/β-TCP on viability and growth of Schwann Cells (SCs) were investigated by MTT assay and SEM. After implantation of different materials, histological assessment was performed. The results showed that, compared with PDLLA, PRGD/PDLLA/β-TCP materials displayed better biocompatibility, degradation property and less inflammatory reaction. Moreover, PRGD/PDLLA/β-TCP materials promoted the adhesion and proliferation of Schwann cells and exhibited better degradation performance than pure PDLLA. These results indicated that PRGD/ PDLLA/β-TCP has a potential application in the fields of nerve regeneration.

Goal: The purpose of this paper is to investigate optimum compression to ventilation ratios in Cardiopulmonary Resuscitation (CPR). Methods: Mathematical modeling approach is used. Equations describing oxygen, carbon dioxide exchange and blood flow as functions of the compression to ventilation ratio during CPR are developed. The model is validated against normal physiology and animal studies of CPR. Then the model equations are solved to find the optimum compression to ventilation ratios for both professional and lay rescuers. As rescuer performance might vary greatly, Monte Carlo simulations with parameters of rescuer performance randomly chosen are performed to examine whether the optimum compression to ventilation ratios achieved above fit most cases. Results: Results show that the optimum compression to ventilation ratio is around 50:2 for professional rescuers, and is round 70:2 for lay rescuers. Conclusion: The 30:2 compression to ventilation ratio, which is specified in International Guideline, might not be optimum for professional rescuers, might be even worse for lay rescuers. It suggests the 50:2 and 70:2 compression to ventilation ratios might be optimum for professional and lay rescuers respectively. Significance: The 50:2 and 70:2 compression to ventilation ratios might maximize optimum oxygen delivery to body tissue during CPR, and thus lead to better survival rates.

This proposed method calculates the centroids of two registering images by applying the moments for acquiring the original displacement parameters, and then uses modified K-means clustering to classify the image coordinates. Before clustering, the medical image coordinates is centralized, the two-row coordinate matrix is created to construct the 2-D sample set to be partitioned into two classes, the slope of a straight line fitted to the two classes is computed, and the rotation angle is computed by solving the arc tangent of the slope. The edges are detected by the edge convolution kernel and the binary images covering the characteristic points are extracted. Experimental results from aligning experiments reveal that, this approach has lower computation costs and a higher registration precision.

Early detection of breast cancer, a significant public health problem in the world, is the key for improving breast cancer early prognosis. Mammography is considered the most reliable and widely used diagnostic technique for early detection of breast cancer. However, it is difficult for radiologists to perform both accurate and uniform evaluation for the enormous mammograms with widespread screening. Microcalcification clusters is one of the most important clue of the breast cancer, and their automated detection is very helpful for early breast cancer diagnosis. Because of the poor quality of the mammographic images and the small size of the microcalcifications, it is a very difficult task to perform detecting the early breast cancer. In this paper, we propose a novel approach based on hybrid subspace fusion for detection microcalcification clusters, and successfully apply it to detection task in digital mammograms. In such a system, subspace learning algorithms will be selectively fused according to the ability of preserving the classification information. Experimental results show that the proposed method improved the performance and stability of microcalcification cluster detection and could be adapt to the noise environments better. The proposed methods could get satisfactory results on sensitivity and reduce false positive rate, which provide some new ideas and methods for the research and development of computer-aided detection system in the breast cancer detection community.

It is very important to use the detection technology of float glass thickness in glass production. In the process of detecting glass thickness on line, we adopt laser trigonometry displacement detection theory. Directing at detection system theory in the high-temperature state, the paper does a further analysis. The paper analyzes the theory and experimental results by using the CDD detection method. The research improves the precision and stability of the detection, increases the glass production quality and cuts down the energy consumption and production cost.

A preliminary study is conducted to compare the feature distribution between normal and deceptive speech, and the results are reported in this paper. The objective of this research is to show that deceptive speech may be recognized through the acoustic parameters of general speech characteristics. Six speech parameters, i.e., Mel-frequency Cepstral Coefficients (MFCC), Relative Spectral Filter Perceptual Linear Prediction (RASTA-PLP), pitch frequency, time-domain samples, zero-crossing rate and fractal dimension are used in the statistics. The distributions of these parameters indicate clear differences between the two speech styles. The lowest average degree of difference for these features was 4.74%, and the highest degree was over 20%. Therefore, the distribution demonstrates that there is significant distinction between speech relating the truth and speech relating falsehoods. Linear Discriminant Analysis (LDA) and the Gaussian Mixture Model (GMM) are used to recognize the two psychological states of people’s pronunciation, with accuracy above 50%. The results show that there is in fact deceptive information in speech signals and that it can be detected by pattern recognition. These findings provide the theoretical basis for detecting deception in speech signals.

Two methods are proposed in this paper to inspect printed fabrics. One method is to apply a genetic algorithm to select parameters of optimal Gabor filter. Optimal Gabor filter can reduce the noise information of printed fabrics, which can achieve defect detection of printed fabrics. The other is in utilizing distance matching function to determine the unit of printed fabrics. Extracting features on a moving unit of printed fabrics can realize defect segmentation of printed fabrics. Two approaches of defect detection have their own advantages. Detecting method with Gabor filter using genetic algorithm has perfect detection results of random printed fabrics, the other method based on statistical rule can receive better defect detection results of regular printed fabrics. Both methods can be realized in practice and detection time of proposed methods can occupy little in total detection time.