Three-step one-way model in terahertz biomedical detection

Terahertz technology has broad application prospects in biomedical detection. However, the mixed characteristics of actual samples make the terahertz spectrum complex and difficult to distinguish, and there is no practical terahertz detection method for clinical medicine. Here, we propose a three-step one-way terahertz model, presenting a detailed flow analysis of terahertz technology in the biomedical detection of renal fibrosis as an example: 1) biomarker determination: screening disease biomarkers and establishing the terahertz spectrum and concentration gradient; 2) mixture interference removal: clearing the interfering signals in the mixture for the biomarker in the animal model and evaluating and retaining the effective characteristic peaks; and 3) individual difference removal: excluding individual interference differences and confirming the final effective terahertz parameters in the human sample. The root mean square error of our model is three orders of magnitude lower than that of the gold standard, with profound implications for the rapid, accurate and early detection of diseases.

Three-Step One-Way Model in Terahertz Biomedical Detection

Terahertz technology has broad application prospects in biomedical detection. However, the mixed characteristics of actual samples make the terahertz spectrum complex and difficult to distinguish, and there is no practical terahertz detection method for medical clinics. Here, we propose a three-step one-way terahertz model, presenting a detailed flow analysis of terahertz technology in the biomedical detection of renal fibrosis as an example: 1) biomarker determination: screening disease biomarkers and establishing the terahertz spectrum and concentration gradient; 2) mixture interference removal: clearing the interfering signals in the mixture for the biomarker in the animal model and evaluating and retaining the effective characteristic peaks; and 3) individual difference removal: excluding individual interference differences and confirming the final effective terahertz parameters in the human sample. The root mean square error of our model is three orders of magnitude lower than that of the gold standard, with profound implications for the rapid, accurate and early detection of diseases.

Analysis of the effectiveness of constraining metrics of geometric simplification algorithms based on expert evaluation of line detail

The paper reveals dependencies between the character of the line shape and combination of constraining metrics that allows comparable reduction in detail by different geometric simplification algorithms. The study was conducted in a form of the expert survey. geometrically simplified versions of three coastline fragments were prepared using three different geometric simplification algorithms—Douglas-peucker, Visvalingam-Whyatt and Li-Openshaw. Simplification was constrained by similar value of modified hausdorff distance (linear offset) and similar reduction of number of line bends (compression of the number of detail elements). Respondents were asked to give a numerical estimate of the detail of each image, based on personal perception, using a scale from one to ten. The results of the survey showed that lines perceived by respondents as having similar detail can be obtained by different algorithms. however, the choice of the metric used as a constraint depends on the nature of the line. Simplification of lines that have a shallow hierarchy of small bends is most effectively constrained by linear offset. As the line complexity increases, the compression metric for the number of detail elements (bends) increases its influence in the perception of detail. For one of the three lines, the best result was consistently obtained with a weighted combination of the analyzed metrics as a constraint. None of the survey results showed that only reducing the number of bends can be used as an effective characteristic of similar reduction in detail. It was therefore found that the linear offset metric is more indicative when describing changes in line detail.

Statistical modeling of residential complex construction project

The article presents a statistical simulation of the deviation of the project duration from the planned value. Regression analysis was carried out - a method of statistical data processing that allows measuring the relationship between one or more causes (factor characteristics) and the consequence (effective characteristic). The end result is a curve and a correlation coefficient, which with a certain probability will allow us to predict the amount of pecuniary injury in this project.

Study on Characteristic Parameters of Speech Anti-Deliberate Imitation System

Deliberate imitation which is the reproduction of another speakers voice and speech behavior can pose a threat to the security of the voice authentication system. Therefore effective characteristic parameters are the key to the anti-deliberate imitation. The study chose speech database of anti-deliberate imitation and investigated some common feature parameters separating capacity and descriptive power against voice deliberate imitation. The study compared the ranking of subjective evaluation and feature parameters Euclidean distance of imitators. The comparison results indicate that Mel frequency cepstrum coefficient (MFCC) combined with its differential cepstrum parameters (WMFCC) have the best performance for anti-deliberate imitation. And the results were validated by the experiment based on VQ speaker verification system.

Speech Emotion Recognition Based on EMD in Noisy Environments

In the speech emotion recognition process, How to obtain effective characteristic parameters from the emotional data including the noise is one of the significant and difficult problem. This paper first removes the gauss white noise with the adaptive filter. Then the Mel Frequency Cepstrum Coefficients (MFCC) based on Empirical Mode Decomposition (EMD) is extracted and with its difference parameter to improve. At last we present an effective method for speech emotion recognition based on Fuzzy Least Squares Support Vector Machines (FLSSVM) so as to realize the speech recognition of four main emotions, i.e, anger, happy, surprise and natural. The experiment results show that this method has the better anti-noise effect when compared with traditional Support Vector Machines (SVM).