Novel Method For Discrimination Research Articles

Measurement-Based Wideband Space-Time Channel Models for 77GHz Automotive Radar in Underground Parking Lots

In recent years, with the rapid development of Advanced Driver Assistance Systems (ADAS), vehicular millimeter-wave (mmWave) radar plays an important role in urban intelligent transportation system. Self-interference caused by multipath in radar channel is a widely concerned problem. However, the research on radar channel propagation characteristics is very limited and there are few models for radar channel available. In this paper, we propose a pioneering research for radar channel model. Based on measurement data, channel characteristics are analysed using the space-alternating generalized expectation-maximization (SAGE) algorithm. An empirical statistical model is established for the 77GHz mmWave radar channel in the underground parking lot scenario, which is one of the most challenging environments because of time-variation and abundant multipaths, filling the gap in the field. A path-association algorithm is used to obtain multipath component (MPC) trajectories in delay, Doppler frequency and power domains. Additionally, some trajectory (in range domain) features are analysed. Moreover, based on geometric analysis of trajectories in range domain, the deterministic component of trajectories is modeled, and then a novel method for discrimination between actual targets and ghost images is proposed and verified by simulation and measurement data.

Novel Method for Discrimination of Transformers Faults from Magnetizing Inrush Currents Using Wavelet Transform

Discrimination of power transformer inrush from fault current is most important to proper operation of differential relays. Most differential relays discriminate the inrush current from short circuit fault according to high content of second harmonic of inrush. Unfortunately, in some power system operation, it has been seen that this algorithm is not accurate enough due to the presence of some kinds of loads. On the other hand, current transformer (CT) saturation due to large inrush or fault current also can cause the discrimination algorithms mal-operation. This paper presents a novel algorithm for power transformer differential protection which differentiates internal faults from those of magnetizing inrush currents in the presence of CT saturation. In order to eliminate the impact of CT saturation on the performance of the proposed technique, at first the currents are compensated by a CT saturation compensation method. Furthermore, a technique based on wavelet transform is used for discrimination of transformer internal fault from magnetizing inrush currents. Using discrete wavelet transform (DWT) features of primary and secondary differential currents and those of flux measured at transformer primary winding, an algorithm was proposed. Discrimination criterion is based on synchronization of maximum or minimum points between wavelet coefficient of differential current and flux of the transformer. The efficiency of the algorithm was evaluated using different scenarios of inrush and internal fault current of a power transformer simulated in MATLAB/Simulink software environment.

Geographical discrimination and adulteration analysis for edible oils using two-dimensional correlation spectroscopy and convolutional neural networks (CNNs).

Geographical discrimination and adulteration analysis play significant roles in edible oil analysis. A novel method for discrimination and adulteration analysis of edible oils were proposed in this study. The two-dimensional correlation spectra of edible oils were obtained by solvents perturbation and the convolutional neural networks (CNNs) were constructed to analyze the synchronous and asynchronous correlation spectra of the edible oils. The differences for geographical origins of oils or oil types could be amplificated through the networks. For different networks, the layer sequences and the filter number of convolutional layers may affect the analysis results. A group of sesame oils from different geographical origins and a group of olive oils adulterated by other vegetable oils were adopted to evaluate the proposed method. The results show that the proposed method may provide an alternative method for edible oil discrimination and adulteration analysis in practical applications. For the two datasets, the prediction accuracy could be 97.3% and 88.5%, respectively.

A novel LC\u2013MS method using collagen marker peptides for species identification of glue applicable to samples with multiple animal origins

Animal glues derived from collagen-rich tissue are widely used as adhesives and binders in artworks. Identification of the animal source of glue is important not only to understand the artistic technique and historical background, but also to conduct appropriate treatment for restoration and conservation of artworks. Recently, mass spectrometric approaches have been used for species identification of glue. However, it appears that the existing methods were not applied to samples containing multiple animal sources, and the range of target species has been limited because of incomplete sequence information of collagen in public databases. In the present study, we established a novel method for discrimination of glues from eight animals applicable to samples with multiple animal origins. Trypsin-digested glue samples were analyzed by LC–MS in multiple reaction monitoring mode, and the animal source was determined based on the detection patterns of 12 type I collagen-derived marker peptides. Using the database-independent method, we successfully identified the animal source of commercial products and found the declared species for some glues to be incorrect. In addition, some products were identified to contain two different animal origins. Relative abundance of the animal origins in several impure glues was estimated using the marker peptides, which helped to speculate the reason for the detection of multiple species. We analyzed a painting (The Harvest by Camille Pissarro, 1882) and clarified that glues used in the ground and size layers of the canvas were derived from cattle (~ 65%) and sheep (~ 35%).

A novel method for discrimination between innocent and pathological heart murmurs

This paper presents a novel method for discrimination between innocent and pathological murmurs using the growing time support vector machine (GTSVM). The proposed method is tailored for characterizing innocent murmurs (IM) by putting more emphasis on the early parts of the signal as IMs are often heard in early systolic phase. Individuals with mild to severe aortic stenosis (AS) and IM are the two groups subjected to analysis, taking the normal individuals with no murmur (NM) as the control group. The AS is selected due to the similarity of its murmur to IM, particularly in mild cases. To investigate the effect of the growing time windows, the performance of the GTSVM is compared to that of a conventional support vector machine (SVM), using repeated random sub-sampling method. The mean value of the classification rate/sensitivity is found to be 88%/86% for the GTSVM and 84%/83% for the SVM. The statistical evaluations show that the GTSVM significantly improves performance of the classification as compared to the SVM.

A Novel Method for the Discrimination of Semen Arecae and Its Processed Products by Using Computer Vision, Electronic Nose, and Electronic Tongue.

Areca nut, commonly known locally as Semen Arecae (SA) in China, has been used as an important Chinese herbal medicine for thousands of years. The raw SA (RAW) is commonly processed by stir-baking to yellow (SBY), stir-baking to dark brown (SBD), and stir-baking to carbon dark (SBC) for different clinical uses. In our present investigation, intelligent sensory technologies consisting of computer vision (CV), electronic nose (E-nose), and electronic tongue (E-tongue) were employed in order to develop a novel and accurate method for discrimination of SA and its processed products. Firstly, the color parameters and electronic sensory responses of E-nose and E-tongue of the samples were determined, respectively. Then, indicative components including 5-hydroxymethyl furfural (5-HMF) and arecoline (ARE) were determined by HPLC. Finally, principal component analysis (PCA) and discriminant factor analysis (DFA) were performed. The results demonstrated that these three instruments can effectively discriminate SA and its processed products. 5-HMF and ARE can reflect the stir-baking degree of SA. Interestingly, the two components showed close correlations to the color parameters and sensory responses of E-nose and E-tongue. In conclusion, this novel method based on CV, E-nose, and E-tongue can be successfully used to discriminate SA and its processed products.

Stable isotope analysis of plant-derived nitrate – Novel method for discrimination between organically and conventionally grown vegetables

The lack of reliable markers for the discrimination between organic and conventional products makes the organic food market susceptible to attempted fraud. Robust analytical methodologies for organic food authentication are urgently needed. In this study a new approach, compound-specific nitrogen and oxygen isotope analysis of plant-derived nitrate, has been applied alongside bulk nitrogen isotope analysis for discrimination between organically and conventionally greenhouse-grown lettuce and retail potatoes and tomatoes. The method revealed significant differences between conventional and organic fertilisation. An intra-plant isotopic variation as well as significant impact of the fertiliser application rate on the nitrogen and oxygen isotope values of plant-derived nitrate has been observed. Nitrogen and oxygen isotope analysis of nitrate has a potential for differentiation between organic and conventional crops. Further analysis is needed to improve our understanding of the scope of application and robustness of this compound-specific approach.

A novel method for discrimination of beef and horsemeat using Raman spectroscopy

A new approach, based on the usage of Raman spectroscopy in combination with chemometrics, was developed for the rapid determination of beef adulteration with horsemeat. The data mining process of collected Raman spectra was performed with principal component analysis (PCA). Pure fat samples, extracted from forty-nine meat beef and horsemeat samples, were analysed using the Raman spectroscopy. All meat samples were classified successfully according to their origins. The presence of different concentrations (25%, 50%, 75%, w/w) of horsemeat in beef was also differentiated using the developed model system. This study offers a rapid assay for determination of meat adulteration by discriminating beef and horsemeat with high accuracy, a short analysis time (30s) and no requirement for time-consuming sample preparation procedures.

Discrimination of complex mixtures by a colorimetric sensor array: coffee aromas.

The analysis of complex mixtures presents a difficult challenge even for modern analytical techniques, and the ability to discriminate among closely similar such mixtures often remains problematic. Coffee provides a readily available archetype of such highly multicomponent systems. The use of a low-cost, sensitive colorimetric sensor array for the detection and identification of coffee aromas is reported. The color changes of the sensor array were used as a digital representation of the array response and analyzed with standard statistical methods, including principal component analysis (PCA) and hierarchical clustering analysis (HCA). PCA revealed that the sensor array has exceptionally high dimensionality with 18 dimensions required to define 90% of the total variance. In quintuplicate runs of 10 commercial coffees and controls, no confusions or errors in classification by HCA were observed in 55 trials. In addition, the effects of temperature and time in the roasting of green coffee beans were readily observed and distinguishable with a resolution better than 10 degrees C and 5 min, respectively. Colorimetric sensor arrays demonstrate excellent potential for complex systems analysis in real-world applications and provide a novel method for discrimination among closely similar complex mixtures.

Analysis of Dynamic Voluntary Muscle Contractions in Parkinson's Disease

A novel method for discrimination of dynamic muscle contractions between patients with Parkinson's disease (PD) and healthy controls on the basis of surface electromyography (EMG) and acceleration measurements is presented. In this method, dynamic EMG and acceleration measurements are analyzed using nonlinear methods and wavelets. Ten parameters capturing Parkinson's disease (PD) characteristic features in the measured signals are extracted. Each parameter is computed as time-varying, and for elbow flexion and extension movements separately. For discrimination between subjects, the dimensionality of the feature vectors formed from these parameters is reduced using a principal component approach. The cluster analysis of the low-dimensional feature vectors is then performed for flexion and extension movements separately. The EMG and acceleration data measured from 49 patients with PD and 59 healthy controls are used for analysis. According to clustering results, the method could discriminate 80 % of patient extension movements from 87 % of control extension movements, and 73 % of patient flexion movements from 82 % of control flexion movements. The results show that dynamic EMG and acceleration measurements can be informative for assessing neuromuscular dysfunction in PD, and furthermore, they may help in the objective clinical assessment of the disease.